def plot_example_psds(example,rate):
"""
This function creates a figure with 4 lines to show the overall psd for
the four sleep examples. (Recall row 0 is REM, rows 1-3 are NREM stages 1,
2 and 3/4)
"""
sleep_stages = ['REM sleep', 'Stage 1 NREM sleep', 'Stage 2 NREM sleep', 'Stage 3 and 4 NREM sleep'];
plt.figure()
##YOUR CODE HERE
for i in range( len( example[:,0]) ):
# Apply power spectral density using a Fast Fourier Transform
# to generate blocks of data
psd, frequency = m.psd(example[i, :], NFFT = 512, Fs = rate )
# normalize frequency
psd = psd / np.sum(psd)
# plot sleep stages
plt.plot(frequency, psd, label = sleep_stages[i])
# add legend
plt.ylabel('Normalized Power Spectral Density')
plt.xlabel('Frequency (Hz)')
plt.xlim(0,20)
plt.legend(loc=0)
plt.title('Overall ower Spectral Density for Sleep Stages')
return
def make_corr1d_fig(dosave=False):
corr = make_corr_both_hemi()
lw=2; fs=16
pl.figure(1)#, figsize=(8, 7))
pl.clf()
pl.xlim(4,300)
pl.ylim(-400,+500)
lambda_titles = [r'$20 < \lambda < 30$',
r'$30 < \lambda < 40$',
r'$\lambda > 40$']
colors = ['blue','green','red']
for i in range(3):
corr1d, rcen = corr_1d_from_2d(corr[i])
ipdb.set_trace()
pl.semilogx(rcen, corr1d*rcen**2, lw=lw, color=colors[i])
#pl.semilogx(rcen, corr1d*rcen**2, 'o', lw=lw, color=colors[i])
pl.xlabel(r'$s (Mpc)$',fontsize=fs)
pl.ylabel(r'$s^2 \xi_0(s)$', fontsize=fs)
pl.legend(lambda_titles, 'lower left', fontsize=fs+3)
pl.plot([.1,10000],[0,0],'k--')
s_bao = 149.28
pl.plot([s_bao, s_bao],[-9e9,+9e9],'k--')
pl.text(s_bao*1.03, 420, 'BAO scale')
pl.text(s_bao*1.03, 370, '%0.1f Mpc'%s_bao)
if dosave: pl.savefig('xi1d_3bin.pdf')
def plot_svc(X, y, mysvc, bounds=None, grid=50):
if bounds is None:
xmin = np.min(X[:, 0], 0)
xmax = np.max(X[:, 0], 0)
ymin = np.min(X[:, 1], 0)
ymax = np.max(X[:, 1], 0)
else:
xmin, ymin = bounds[0], bounds[0]
xmax, ymax = bounds[1], bounds[1]
aspect_ratio = (xmax - xmin) / (ymax - ymin)
xgrid, ygrid = np.meshgrid(np.linspace(xmin, xmax, grid),
np.linspace(ymin, ymax, grid))
plt.gca(aspect=aspect_ratio)
plt.xlim(xmin, xmax)
plt.ylim(ymin, ymax)
plt.xticks([])
plt.yticks([])
plt.hold(True)
plt.plot(X[y == 1, 0], X[y == 1, 1], 'bo')
plt.plot(X[y == -1, 0], X[y == -1, 1], 'ro')
box_xy = np.append(xgrid.reshape(xgrid.size, 1), ygrid.reshape(ygrid.size, 1), 1)
if mysvc is not None:
scores = mysvc.decision_function(box_xy)
else:
print 'You must have a valid SVC object.'
return None;
CS=plt.contourf(xgrid, ygrid, scores.reshape(xgrid.shape), alpha=0.5, cmap='jet_r')
plt.contour(xgrid, ygrid, scores.reshape(xgrid.shape), levels=[0], colors='k', linestyles='solid', linewidths=1.5)
plt.contour(xgrid, ygrid, scores.reshape(xgrid.shape), levels=[-1,1], colors='k', linestyles='dashed', linewidths=1)
plt.plot(mysvc.support_vectors_[:,0], mysvc.support_vectors_[:,1], 'ko', markerfacecolor='none', markersize=10)
CB = plt.colorbar(CS)
def plot_example_spectrograms(example,rate):
"""
This function creates a figure with spectrogram sublpots to of the four
sleep examples. (Recall row 0 is REM, rows 1-3 are NREM stages 1,
2 and 3/4)
"""
sleep_stages = ['REM sleep', 'Stage 1 NREM sleep', 'Stage 2 NREM sleep', 'Stage 3 and 4 NREM sleep'];
plt.figure()
###YOUR CODE HERE
for i in range( len(example[:,0]) ):
# plot every sleep stage in a separate plot
plt.subplot(2,2,i+1)
# plot spectogram
plt.specgram(example[i, :],NFFT=512,Fs=rate)
# add legend
plt.xlabel('Time (Seconds)')
plt.ylabel('Frequency (Hz)')
plt.title( 'Spectogram ' + sleep_stages[i] )
plt.ylim(0,60)
plt.xlim(0,290)
return
def test1():
x = [0.5]*3
xbounds = [(-5, 5) for y in x]
GA = GenAlg(fitcalc1, x, xbounds, popMult=100, bitsPerGene=9, mutation=(1./9.), crossover=0.65, crossN=2, direction='min', maxGens=60, hammingDist=False)
results = GA.run()
print "*** DONE ***"
#print results
plt.ioff()
#generate pareto frontier numerically
x1_ = np.arange(-5., 0., 0.05)
x2_ = np.arange(-5., 0., 0.05)
x3_ = np.arange(-5., 0., 0.05)
pfn = []
for x1 in x1_:
for x2 in x2_:
for x3 in x3_:
pfn.append(fitcalc1([x1,x2,x3]))
pfn.sort(key=lambda x:x[0])
plt.figure()
i = 0
for x in results:
plt.scatter(x[1][0], x[1][1], 20, c='r')
plt.scatter([x[0] for x in pfn], [x[1] for x in pfn], 1.0, c='b', alpha=0.1)
plt.xlim([-20,-1])
plt.ylim([-12, 2])
plt.draw()
def visualization2(self, sp_to_vis=None):
if sp_to_vis:
species_ready = list(set(sp_to_vis).intersection(self.all_sp_signatures.keys()))
else:
raise Exception('list of driver species must be defined')
if not species_ready:
raise Exception('None of the input species is a driver')
for sp in species_ready:
# Setting up figure
plt.figure()
plt.subplot(313)
mon_val = OrderedDict()
signature = self.all_sp_signatures[sp]
for idx, mon in enumerate(list(set(signature))):
if mon[0] == 'C':
mon_val[self.all_comb[sp][mon] + (-1,)] = idx
else:
mon_val[self.all_comb[sp][mon]] = idx
mon_rep = [0] * len(signature)
for i, m in enumerate(signature):
if m[0] == 'C':
mon_rep[i] = mon_val[self.all_comb[sp][m] + (-1,)]
else:
mon_rep[i] = mon_val[self.all_comb[sp][m]]
# mon_rep = [mon_val[self.all_comb[sp][m]] for m in signature]
y_pos = numpy.arange(len(mon_val.keys()))
plt.scatter(self.tspan[1:], mon_rep)
plt.yticks(y_pos, mon_val.keys())
plt.ylabel('Monomials', fontsize=16)
plt.xlabel('Time(s)', fontsize=16)
plt.xlim(0, self.tspan[-1])
plt.ylim(0, max(y_pos))
plt.subplot(312)
for name in self.model.odes[sp].as_coefficients_dict():
mon = name
mon = mon.subs(self.param_values)
var_to_study = [atom for atom in mon.atoms(sympy.Symbol)]
arg_f1 = [numpy.maximum(self.mach_eps, self.y[str(va)][1:]) for va in var_to_study]
f1 = sympy.lambdify(var_to_study, mon)
mon_values = f1(*arg_f1)
mon_name = str(name).partition('__')[2]
plt.plot(self.tspan[1:], mon_values, label=mon_name)
plt.ylabel('Rate(m/sec)', fontsize=16)
plt.legend(bbox_to_anchor=(-0.1, 0.85), loc='upper right', ncol=1)
plt.subplot(311)
plt.plot(self.tspan[1:], self.y['__s%d' % sp][1:], label=parse_name(self.model.species[sp]))
plt.ylabel('Molecules', fontsize=16)
plt.legend(bbox_to_anchor=(-0.15, 0.85), loc='upper right', ncol=1)
plt.suptitle('Tropicalization' + ' ' + str(self.model.species[sp]))
# plt.show()
plt.savefig('s%d' % sp + '.png', bbox_inches='tight', dpi=400)
def plot(self, bit_stream):
if self.previous_bit_stream != bit_stream.to_list():
self.previous_bit_stream = bit_stream
x = []
y = []
bit = None
for bit_time in bit_stream.to_list():
if bit is None:
x.append(bit_time)
y.append(0)
bit = 0
elif bit == 0:
x.extend([bit_time, bit_time])
y.extend([0, 1])
bit = 1
elif bit == 1:
x.extend([bit_time, bit_time])
y.extend([1, 0])
bit = 0
plt.clf()
plt.plot(x, y)
plt.xlim([0, 10000])
plt.ylim([-0.1, 1.1])
plt.show()
plt.pause(0.005)
def plot_q(frame,file_prefix='claw',file_format='petsc',path='./_output/',plot_pcolor=True,plot_slices=True,slices_xlimits=None):
import sys
sys.path.append('.')
import gaussian_1d
sol=Solution(frame,file_format=file_format,read_aux=False,path=path,file_prefix=file_prefix)
x=sol.state.grid.x.centers
mx=len(x)
bathymetry = 0.5
eta=sol.state.q[0,:] + bathymetry
if frame < 10:
str_frame = "00"+str(frame)
elif frame < 100:
str_frame = "0"+str(frame)
else:
str_frame = str(frame)
fig = pl.figure(figsize=(40,10))
ax = fig.add_subplot(111)
ax.set_aspect(aspect=1)
ax.plot(x,eta)
#pl.title("t= "+str(sol.state.t),fontsize=20)
#pl.xticks(size=20); pl.yticks(size=20)
#pl.xlim([0, gaussian_1d.Lx])
pl.ylim([0.5, 1.0])
pl.xlim([0., 4.0])
#pl.axis('equal')
pl.savefig('./_plots/eta_'+str_frame+'_slices.png')
pl.close()
def plot_q(model='cem', r_min=0.0, r_max=6371.0, dr=1.0):
"""
Plot a radiallysymmetric Q model.
plot_q(model='cem', r_min=0.0, r_max=6371.0, dr=1.0):
r_min=minimum radius [km], r_max=maximum radius [km], dr=radius
increment [km]
Currently available models (model): cem, prem, ql6
"""
import matplotlib.pylab as plt
r = np.arange(r_min, r_max + dr, dr)
q = np.zeros(len(r))
for k in range(len(r)):
if model == 'cem':
q[k] = q_cem(r[k])
elif model == 'ql6':
q[k] = q_ql6(r[k])
elif model == 'prem':
q[k] = q_prem(r[k])
plt.plot(r, q, 'k')
plt.xlim((0.0, r_max))
plt.xlabel('radius [km]')
plt.ylabel('Q')
plt.show()
def plotFirstTacROC(dataset):
import matplotlib.pylab as plt
from os.path import join
from src.utils import PROJECT_DIR
plt.figure(figsize=(6, 6))
time_sampler = TimeSerieSampler(n_time_points=12)
evaluator = Evaluator()
time_series_idx = 0
methods = {
"cross_correlation": "Cross corr. ",
"kendall": "Kendall ",
"symbol_mutual": "Symbol MI ",
"symbol_similarity": "Symbol sim.",
}
for method in methods:
print method
predictor = SingleSeriesPredictor(good_methods[method], time_sampler)
prediction = predictor.predictAllInstancesCombined(dataset, time_series_idx)
roc_auc, fpr, tpr = evaluator.evaluate(prediction)
plt.plot(fpr, tpr, label=methods[method] + " (auc = %0.3f)" % roc_auc)
plt.legend(loc="lower right")
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.05])
plt.xlabel("False Positive Rate")
plt.ylabel("True Positive Rate")
plt.grid()
plt.savefig(join(PROJECT_DIR, "output", "firstTACROC.pdf"))
def fit_plot_unlabeled_data(unlabeled_data_x, labeled_data_x, labeled_data_y, fit_order, data_type, other_data_list, other_data_name):
output = open('predictions.csv','wb')
coeffs = np.polyfit(labeled_data_x, labeled_data_y, fit_order) #does poly git to nth deg on labeled data
fit_eq = np.poly1d(coeffs) #Eqn from fit
predicted_y = fit_eq(unlabeled_data_x)
i = 0
writer = csv.writer(output,delimiter=',')
header = [str(data_type),str(other_data_name),'Predicted_Num_Inc']
writer.writerow(header)
while i < len(predicted_y):
output_data = [unlabeled_data_x[i],other_data_list[i],predicted_y[i]]
writer.writerow(output_data)
print 'For '+str(data_type)+' of: '+str(unlabeled_data_x[i])+', Predicted Number of Incidents is: '+str(predicted_y[i])
i = i + 1
plt.scatter(unlabeled_data_x, predicted_y, color='blue', label='Predicted Number of Incidents')
fit_line_x = np.arange(min(unlabeled_data_x), max(unlabeled_data_x), 1)
plt.plot(fit_line_x, fit_eq(fit_line_x), color='red',linestyle='dashed',label=' Order '+str(fit_order)+' Polynomial Fit')
#____Use below line to plot actual data also!!
#plt.scatter(labeled_data_x, labeled_data_y, color='green', label='Actual Incident Report Data')
plt.title('Predicted Number of 311 Incidents by '+str(data_type))
plt.xlabel(str(data_type))
plt.ylabel('Number of 311 Incidents')
plt.grid()
plt.xlim([min(unlabeled_data_x)-1500, max(unlabeled_data_x)+1500])
plt.legend(loc='upper left')
plt.show()
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 draw_lineplot(x, y, title="title", xlab="x", ylab="y", odir="", xlim=None, ylim=None, outfmt='eps'):
if len(x) == 0 or len(y) == 0:
return;
#fi
plt.cla();
plt.plot(x, y, marker='x');
plt.xlabel(xlab);
plt.ylabel(ylab);
plt.title(title);
if xlim == None:
xmin = min(x);
xmax = max(x);
xlim = [xmin, xmax];
#fi
if ylim == None:
ymin = min(y);
ymax = max(y);
ylim = [ymin, ymax];
#fi
plt.xlim(xlim);
plt.ylim(ylim);
plt.savefig('%s%s.%s' % (odir + ('/' if odir else ""), '_'.join(title.split(None)), outfmt), format=outfmt);
return '%s.%s' % ('_'.join(title.split(None)), outfmt), title;
def plot_behavior_count(agent_id, behavior_count):
"""Create a plot for the behavior count.
Args:
agent_id: The identifier of the agent.
behavior_count: The count of the agent behaviors.
"""
fig = plt.figure()
ax = fig.add_subplot(111)
plt.xlabel(u'Número de jogos')
plt.ylabel(u'Probabilidades de selecionar comportamento')
plt.title(u'Probabilidades do agente %d selecionar comportamento'
% agent_id)
plt.xlim([0, 115])
plt.ylim([-0.1, 1.1])
data = np.array([b for b in behavior_count.values()])
prob = data/np.sum(data, axis=0)
for i, behavior in enumerate(behavior_count):
coeff = calculate_regression_coefficients(prob[i], degree=4)
regression = [calculate_regression_y(x, coeff)
for x in range(len(prob[i]))]
ax.plot(regression, label=T[behavior],
c=COLOR_TABLE[COLOR_LIST[i]], linewidth=2.0)
ax.scatter(range(len(prob[i])), prob[i], c=COLOR_TABLE[COLOR_LIST[i]],
alpha=1)
ax.legend()
def plot_average(filenames, save_plot=True, show_plot=False, dpi=100):
''' Plot Signal average from a list of averaged files. '''
fname = get_files_from_list(filenames)
# plot averages
pl.ioff() # switch off (interactive) plot visualisation
factor = 1e15
for fnavg in fname:
name = fnavg[0:len(fnavg) - 4]
basename = os.path.splitext(os.path.basename(name))[0]
print fnavg
# mne.read_evokeds provides a list or a single evoked based on condition.
# here we assume only one evoked is returned (requires further handling)
avg = mne.read_evokeds(fnavg)[0]
ymin, ymax = avg.data.min(), avg.data.max()
ymin *= factor * 1.1
ymax *= factor * 1.1
fig = pl.figure(basename, figsize=(10, 8), dpi=100)
pl.clf()
pl.ylim([ymin, ymax])
pl.xlim([avg.times.min(), avg.times.max()])
pl.plot(avg.times, avg.data.T * factor, color='black')
pl.title(basename)
# save figure
fnfig = os.path.splitext(fnavg)[0] + '.png'
pl.savefig(fnfig, dpi=dpi)
pl.ion() # switch on (interactive) plot visualisation
def plot_df(self,show=False):
from matplotlib import pylab as plt
if self.afp is None:
print 'afp not initilized. call update afp'
return -1
linecords,td,df,rtn,minmaxy = self.afp
formatter = PlotDateFormatter(df.index)
#fig = plt.figure()
#ax = plt.addsubplot()
fig, ax = plt.subplots()
ax.xaxis.set_major_formatter(formatter)
ax.plot(np.arange(len(df)), df['p'])
for cord in linecords:
plt.plot(cord[0],cord[1],color='red')
fig.autofmt_xdate()
plt.xlim(-10,len(df.index) + 10)
plt.ylim(df.p.min() - 10,df.p.max() + 10)
plt.grid(ax)
#if show:
# plt.show()
#"{0}{1}.png".format("./data/",datetime.datetime.strftime(datetime.datetime.now(),'%Y%M%m%S'))
if self.plot_file:
save_path = self.plot_file.format(self.symbol)
if os.path.exists(os.path.dirname(save_path)):
plt.savefig(save_path)
plt.clf()
plt.close()
def nova_plot():
erg2mev=624151.
fig=plot.figure()
yrange = [1e-6,2e-4]
xrange = [1e-1,1e5]
plot.fill_between([0.2,10e3],[yrange[1],yrange[1]],[yrange[0],yrange[0]],facecolor='yellow',interpolate=True,color='yellow',alpha=0.5)
plot.annotate('AMEGO',xy=(3,9e-5),xycoords='data',fontsize=26,color='black')
lat=ascii.read("data/NMon2012.LAT.dat",names=['energy','en_low','en_high','flux','flux_err','tmp'])
plot.scatter(lat['energy'],lat['flux']*erg2mev,color='red')
plot.errorbar(lat['energy'],lat['flux']*erg2mev,xerr=[lat['en_low'],lat['en_high']],yerr=lat['flux_err']*erg2mev,ecolor='red',capsize=0,fmt='none')
latul=ascii.read("data/NMon2012.LAT.limits.dat",names=['energy','en_low','en_high','flux','tmp1','tmp2','tmp3','tmp4'])
plot.errorbar(latul['energy'],latul['flux']*erg2mev,xerr=[latul['en_low'],latul['en_high']],yerr=0.5*latul['flux']*erg2mev,uplims=True,ecolor='red',capsize=0,fmt='none')
plot.scatter(latul['energy'],latul['flux']*erg2mev,color='red')
leptonic=ascii.read("data/sp-NMon12-IC-best-fit-1MeV-30GeV.txt",names=['energy','flux'],data_start=1)
hadronic=ascii.read("data/sp-NMon12-pi0-and-secondaries.txt",names=['energy','flux1','flux2'],data_start=1)
plot.plot(leptonic['energy'],leptonic['flux']*erg2mev,'r--',color='black',lw=2,label='Leptonic')
plot.plot(hadronic['energy'],hadronic['flux2']*erg2mev,color='black',lw=2,label='Hadronic+Secondary Leptons')
plot.legend(loc='upper right',fontsize='small',frameon=False,framealpha=0.5)
plot.xscale('log')
plot.yscale('log')
plot.ylim(yrange)
plot.xlim(xrange)
plot.xlabel(r'Energy (MeV)')
plot.ylabel(r'Energy$^2 \times $ Flux (Energy) (erg cm$^{-2}$ s$^{-1}$)')
plot.title('Nova V339 Del 2013')
plot.savefig('Nova_SED.png', bbox_inches='tight')
plot.savefig('Nova_SED.eps', bbox_inches='tight')
plot.show()
plot.close()
def chooseDegree(npts, mindegree=0, maxdegree=20, filename=None):
"""Gets noisy data, uses cross validation to estimate error, and fits new data with best model."""
x, y = bv.noisyData(npts)
degrees = numpy.arange(mindegree,maxdegree+1)
errs = numpy.zeros_like(degrees,dtype=numpy.float)
for i,d in enumerate(degrees):
errs[i] = estimateError(x, y, d)
plt.subplot(1,2,1)
plt.plot(degrees,errs,'bo-')
plt.xlabel("Degree")
plt.ylabel("CV Error")
besti = numpy.argmin(errs)
bestdegree = degrees[besti]
plt.subplot(1,2,2)
x2, y2 = bv.noisyData(npts)
plt.plot(x2,y2,'ro')
xs = numpy.linspace(min(x),max(x),150)
fitf = numpy.poly1d(numpy.polyfit(x2,y2,bestdegree))
plt.plot(xs,fitf(xs),'g-',lw=2)
plt.xlim((bv.MIN,bv.MAX))
plt.ylim((-2.,2.))
plt.suptitle('Selected Degree '+str(bestdegree))
bv.outputPlot(filename)
def _gaussian_test():
import matplotlib.pyplot as plt
n = 10000
mu_x = 0.0
mu_y = 0.0
#sig_x, sig_y = 1.5, 1.5
tau = 0.0
seeing = 1.5
sigma = seeing / (2. * np.sqrt(2. * np.e))
slit_width = 0.2
slit_height = 10.0
slit_x = np.empty(n, dtype=np.float64)
slit_y = np.empty(n, dtype=np.float64)
slit_x, slit_y = slit_gaussian_psf(n, mu_x, mu_y, sigma, sigma, tau, slit_width, slit_height)
log.info("x range: [%s, %s]", slit_x.min(), slit_x.max())
log.info("y range: [%s, %s]", slit_y.min(), slit_y.max())
plt.scatter(slit_x, slit_y, alpha=0.8)
plt.fill([-slit_width/2, slit_width/2, slit_width/2, -slit_width/2],
[-slit_height/2, -slit_height/2, slit_height/2, slit_height/2],
'r',
alpha=0.10,
edgecolor='k')
plt.gca().set_aspect("equal")
plt.title("Gaussian distribution")
plt.xlim([-slit_height/2., slit_height/2])
plt.show()
def plot(x,y,field,filename,c=200):
plt.figure()
# define grid.
xi = np.linspace(min(x),max(x),100)
yi = np.linspace(min(y),max(y),100)
# grid the data.
si_lin = griddata((x, y), field, (xi[None,:], yi[:,None]), method='linear')
si_cub = griddata((x, y), field, (xi[None,:], yi[:,None]), method='linear')
print np.min(field)
print np.max(field)
plt.subplot(211)
# contour the gridded data, plotting dots at the randomly spaced data points.
CS = plt.contour(xi,yi,si_lin,c,linewidths=0.5,colors='k')
CS = plt.contourf(xi,yi,si_lin,c,cmap=plt.cm.jet)
plt.colorbar() # draw colorbar
# plot data points.
# plt.scatter(x,y,marker='o',c='b',s=5)
plt.xlim(min(x),max(x))
plt.ylim(min(y),max(y))
plt.title('Lineaarinen interpolointi')
#plt.tight_layout()
plt.subplot(212)
# contour the gridded data, plotting dots at the randomly spaced data points.
CS = plt.contour(xi,yi,si_cub,c,linewidths=0.5,colors='k')
CS = plt.contourf(xi,yi,si_cub,c,cmap=plt.cm.jet)
plt.colorbar() # draw colorbar
# plot data points.
# plt.scatter(x,y,marker='o',c='b',s=5)
plt.xlim(min(x),max(x))
plt.ylim(min(y),max(y))
plt.title('Kuubinen interpolointi')
plt.savefig(filename)
def plot_roc(test_cat, plot_data, savefig=False):
# Plot ROC curve
plt.clf()
results = []
# calcualte and sort labels by roc_auc
for method, method_results in plot_data.items():
fpr, tpr, roc_auc = compute_roc(test_cat, method_results, method)
label = "[%s] area = %0.2f" % (method, roc_auc)
res = {"label": label, "fpr": fpr, "tpr": tpr, "roc_auc": roc_auc}
results.append(res)
results = sorted(results, key=lambda k: k['roc_auc'], reverse=True)
# plot according to roc_auc ranking
for r in results:
plt.plot(r["fpr"], r["tpr"], label=r["label"])
plt.title('Receiver Operating Characteristic Curve (ROC)')
plt.legend(loc="lower right")
plt.plot([0, 1], [0, 1], 'k--')
plt.xlim([0.0, 1.0])
plt.ylim([0.0, 1.0])
plt.xlabel('False Positive Rate')
plt.ylabel('True Positive Rate')
if savefig:
plt.savefig("classifiers_roc.png")
plt.show()
def plot(self,file):
cds = CaseDataset(file, 'bson')
data = cds.data.driver('driver').by_variable().fetch()
cds2 = CaseDataset('../output/therm_mc_20141110173851.bson', 'bson')
data2 = cds2.data.driver('driver').by_variable().fetch()
#temp
temp_boundary_k = data['hyperloop.temp_boundary']
temp_boundary_k.extend(data2['hyperloop.temp_boundary'])
temp_boundary = [((x-273.15)*1.8 + 32) for x in temp_boundary_k]
#histogram
n, bins, patches = plt.hist(temp_boundary, 100, normed=1, histtype='stepfilled')
plt.setp(patches, 'facecolor', 'b', 'alpha', 0.75)
#stats
mean = np.average(temp_boundary)
std = np.std(temp_boundary)
percentile = np.percentile(temp_boundary,99.5)
print "mean: ", mean, " std: ", std, " 99.5percentile: ", percentile
x = np.linspace(50,170,150)
plt.plot(x,mlab.normpdf(x,mean,std), color='black', lw=2)
plt.xlim([60,160])
plt.ylabel('Probability', fontsize=18)
plt.xlabel(u'Equilibrium Temperature, \N{DEGREE SIGN}F', fontsize=18)
#plt.show()
plt.tight_layout()
plt.savefig('../output/histo.pdf', dpi=300)
def errbar_labels(labels,data,error,bar_opts={},ticks_opts={}):
from matplotlib.pylab import plot,xlim
bar_opts.update(opts(width=0,linewidth=0, fill=0))
xlocations=barplot(labels,data,error,bar_opts,ticks_opts)
plot(xlocations,data,"or")
xlim(0, xlocations[-1]+0.5)
return xlocations
def plot_eye(Nodes,axes = None):
"""
Create a movie of eye growth. To be used with EyeGrowthFunc
:param Nodes: structure containing nodes
:type Nodes: struct
:param INTSTEP: time step used for integration
:type INTSTEP: int
:returns: plot handle for Node plot. Used to update during for loop.
.. note::
Called in EyeGrowthFunc
"""
#set plotting parameters:
if axes == None:
fig = plt.figure(figsize=(10, 8))
axes = fig.add_subplot(111,aspect='equal')
plt.xlim([-13, 13])
plt.ylim([-13, 13])
axes.plot(np.r_[ Nodes['x'][0],Nodes['x'][0,0] ] * Nodes['radius'],
np.r_[ Nodes['y'][0], Nodes['y'][0,0] ] * Nodes['radius'],
'-ok', markerfacecolor = 'k',linewidth = 4, markersize = 10)
axes = pf.TufteAxis(axes,['left','bottom'])
#axes.set_axis_bgcolor('w')
return axes
def plot_tuning_curves(direction_rates, title):
"""
This function takes the x-values and the y-values in units of spikes/s
(found in the two columns of direction_rates) and plots a histogram and
polar representation of the tuning curve. It adds the given title.
"""
x = direction_rates[:,0]
y = direction_rates[:,1]
plt.figure()
plt.subplot(2,2,1)
plt.bar(x,y,width=45,align='center')
plt.xlim(-22.5,337.5)
plt.xticks(x)
plt.xlabel('Direction of Motion (degrees)')
plt.ylabel('Firing Rate (spikes/s)')
plt.title(title)
plt.subplot(2,2,2,polar=True)
r = np.append(y,y[0])
theta = np.deg2rad(np.append(x, x[0]))
plt.polar(theta,r,label='Firing Rate (spikes/s)')
plt.legend(loc=8)
plt.title(title)
def unteraufgabe_g():
# Sampling punkte
x = np.linspace(0.0,1.0,1000)
N = np.arange(2,16)
LU = np.ones_like(N,dtype=np.floating)
LT = np.ones_like(N,dtype=np.floating)
# Approximiere Lebesgue-Konstante
for i,n in enumerate(N):
################################################################
#
# xU = np.linspace(0.0,1.0,n)
#
# LU[i] = ...
#
# j = np.arange(n+1)
# xT = 0.5*(np.cos((2.0*j+1.0)/(2.0*(n+1.0))*np.pi) + 1.0)
#
# LT[i] = ...
#
################################################################
continue
# Plot
plt.figure()
plt.semilogy(N,LU,"-ob",label=r"Aequidistante Punkte")
plt.semilogy(N,LT,"-og",label=r"Chebyshev Punkte")
plt.grid(True)
plt.xlim(N.min(),N.max())
plt.xlabel(r"$n$")
plt.ylabel(r"$\Lambda^{(n)}$")
plt.legend(loc="upper left")
plt.savefig("lebesgue.eps")
def plot_lift_data(lift_data, with_ellipses=True):
np.random.seed(42113)
fig = plt.figure()
ax = fig.add_subplot(111)
alpha = [l['fit']['alpha'] for l in lift_data.values()]
alpha_error = [l['fit']['alpha_error'] for l in lift_data.values()]
beta = [l['fit']['beta'] for l in lift_data.values()]
beta_error = [l['fit']['beta_error'] for l in lift_data.values()]
message_class = lift_data.keys()
num = len(beta)
beta_jitter = np.random.randn(num)
np.random.seed(None)
beta = np.array(beta) + beta_jitter*0.0
ax.plot(beta, alpha, color='red', linestyle='', marker='o', markersize=10)
if not with_ellipses:
ax.errorbar(beta, alpha, xerr=beta_error, yerr=alpha_error, linestyle='')
else:
for x, y, xerr, yerr, in zip(beta, alpha, beta_error, alpha_error):
width = 2*xerr
height = 2*yerr
ellipse = patches.Ellipse((x, y), width, height,
angle=0.0, linewidth=2,
fill=True, alpha=0.15, color='gray')
ax.add_patch(ellipse)
for a, b, c in zip(alpha, beta, message_class):
ax.annotate(c, xy=(b, a), xytext=(b+2, a+.01), fontsize=17)
plt.xlim(0, max(beta)+30)
plt.ylim(0, 0.9)
plt.xlabel('Duration (days)')
plt.ylabel('Initial Lift')
plt.show()
def plot_prediction_accuracy(x, y):
plt.scatter(x, y, c='g', alpha=0.5)
plt.title('Logistic Regression')
plt.xlabel('r')
plt.ylabel('Prediction Accuracy')
plt.xlim(0,200)
plt.show()
def plot(all_models):
import matplotlib.pylab as plt
import numpy.random
plt.close("all")
plt.figure()
plt.subplot(211)
alt = np.arange(0., 500., 2.)
sza = 0.
for m in all_models:
d = m(alt, sza)
plt.plot(ne_to_fp(d)/1E6, alt,lw=2)
# plt.plot(m(alt, sza),alt,lw=2)
plt.ylim(0., 400.)
plt.ylabel('Altitude / km')
# plt.xlabel(r'$n_e / cm^{-3}$')
plt.xlabel(r'$f / MHz$')
plt.subplot(212)
for m in all_models:
delay, freq = m.ais_response()
plt.plot(freq/1E6, delay*1E3, lw=2.)
plt.hlines(-2 * np.amax(alt) / speed_of_light_kms * 1E3, *plt.xlim(), linestyle='dashed')
# plt.vlines(ne_to_fp(1E5)/1E6, *plt.ylim())
# plt.hlines( -(500-150) * 2 / speed_of_light_kms * 1E3, *plt.xlim())
plt.ylim(-10,0)
plt.ylabel('Delay / ms')
plt.xlim(0, 7)
plt.xlabel('f / MHz')
plt.show()
def plot_fullstack( binning = np.linspace(0,10,1), myquery='', plotvar = default_plot_variable, \
scalefactor = 1., user_ylim = None):
fig = plt.figure(figsize=(10,6))
plt.grid(True)
lasthist = 0
myhistos = gen_histos(binning=binning,myquery=myquery,plotvar=plotvar,scalefactor=scalefactor)
for key, (hist, bins) in myhistos.iteritems():
plt.bar(bins[:-1],hist,
width=bins[1]-bins[0],
color=colors[key],
bottom = lasthist,
edgecolor = 'k',
label='%s: %d Events'%(labels[key],sum(hist)))
lasthist += hist
plt.title('CCSingleE Stacked Backgrounds',fontsize=25)
plt.ylabel('Events',fontsize=20)
if plotvar == '_e_nuReco' or plotvar == '_e_nuReco_better':
xstring = 'Reconstructed Neutrino Energy [GeV]'
elif plotvar == '_e_CCQE':
xstring = 'CCQE Energy [GeV]'
else:
xstring = plotvar
plt.xlabel(xstring,fontsize=20)
plt.legend()
plt.xticks(list(plt.xticks()[0]) + [binning[0]])
plt.xlim([binning[0],binning[-1]])
kva = w/va
fig=plt.figure(num=1,figsize=(3.5,3.5),dpi=300,facecolor='w',edgecolor='k')
left = 0.15 # the left side of the subplots of the figure
right = 0.95 # the right side of the subplots of the figure
bottom = 0.23 # the bottom of the subplots of the figure
top = 0.96 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots
hspace = 0.1 # the amount of height reserved for white space between subplots
plt.subplots_adjust(left=left, bottom=bottom, right=right, top=top, wspace=wspace, hspace=hspace)
ax = plt.subplot(1,1,1)
plt.plot(k,w/1000000,label='Whistler')
plt.plot(kva,w/1000000,label='Alfven')
plt.xlim(0,100)
plt.ylim(0,4)
plt.hlines(wci/1000000,0,100,color='green',linestyles='dotted')
plt.hlines(300000*2*np.pi/1000000,0,100,color='grey',linestyles='dashed',linewidth=0.5)
plt.xlabel(r'k [rad/m]',fontsize=10)
plt.ylabel(r'$\omega \times 10^{6}$ [Rad/Sec]',fontsize=10)
plt.text(10,3.65,r'$\omega_{ci}$',fontsize=6,color='green')
plt.text(10,2,r'$f\approx 300kHz$',fontsize=5)
plt.text(0,-1,'B='+str(B)+'G',fontsize=8)
leg=plt.legend(loc='lower right',fontsize=5,frameon=False)
plt.figure(2)
plt.semilogy(k,w/1000000,label='Whistler')
plt.semilogy(kva,w/1000000,label='Alfven')
plt.xlim(0,100)
plt.ylim(5e-1,4e0)
n = 20
t = np.linspace(0, n - 1, n)
fv_1 = np.ones(len(t)) * pv
fv_2 = pv * (1 + r * t) #simple interest
fv_3 = pv * (1 + r)**t
fv_1plot = plt.plot(t, fv_1, 'b-', label='Under your pillow')
fv_2plot = plt.plot(t, fv_2, 'g-', label='Simple Interest')
fv_3plot = plt.plot(t, fv_3, 'r-', label='Compound Interest')
#plt.plot(t,examp4,'m-')
plt.title(
"One time investment of $1000 \n Simple vs. Compounded Interest Rate, 8%")
plt.xlabel("Number of Years, t")
plt.ylabel("US Dollars")
plt.xlim(0, 21)
plt.ylim(0, 5000)
plt.legend()
#plt.show()
plt.savefig('finance_int_compound.png', dpi=600)
pmt = -1000
fv = -1000
pv = 1000
r = 0.08
n = 20
rate1 = 0.001
rate2 = 0.08
nper = np.arange(0, n)
examp4 = np.fv(0, nper, pmt, fv, when='begin')
examp5 = np.fv(rate1, nper, pmt, fv, when='begin')
def plot(self,
title,
xlim=None,
ylim=None,
plot_type='amplitude',
show_limits=False,
show_ssa_limits=False,
beam_on=False,
annotate=False,
beam_start=20,
beam_end=22,
hide_fwd=False):
""" Plot signals of interest in RF Station time-series simulation.
Supports amplitude, phase and cartesian plots with a number of options.
"""
fund_k_probe = self.fund_mode_dict['k_probe']
fund_k_drive = self.fund_mode_dict['k_drive']
fund_k_em = self.fund_mode_dict['k_em']
fund_k_beam = self.fund_mode_dict['k_beam']
# Amplitude
if plot_type == 'amplitude':
if hide_fwd == False:
plt.plot(self.trang * 1e3,
np.abs(self.E_fwd) * fund_k_drive,
'-',
label=r'Forward $\left(\vec E_{\rm fwd}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.abs(self.E_reverse / fund_k_em),
'-',
label=r'Reverse $\left(\vec E_{\rm reverse}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.abs(self.cav_v),
'-',
label=r'Cavity Field',
linewidth=2,
color='r')
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe),
'-',
label=r'Set-point $\left(\vec E_{\rm sp}\right)$',
linewidth=2,
color='c')
# Y label
plt.ylabel('Amplitude [V]', fontsize=30)
if show_limits == True:
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) * (1 + 1e-4),
label='Upper limit',
linewidth=2,
color='m')
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) * (1 - 1e-4),
label='Lower limit',
linewidth=2,
color='y')
plt.axhspan(16e6 / 1.00005,
16e6 * 1.00005,
color='blue',
alpha=0.2)
if show_ssa_limits == True:
# If SSA noise were a sine wave, this would be the amplitude limit
ssa_limit = 4e-2 * fund_k_drive * np.sqrt(3.8e3)
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) +
ssa_limit / np.sqrt(2) / 2,
label='SSA Upper (RMS) limit',
linewidth=2,
color='m')
plt.plot(self.trang * 1e3,
np.abs(self.set_point / fund_k_probe) -
ssa_limit / np.sqrt(2) / 2,
label='SSA Lower (RMS) limit',
linewidth=2,
color='y')
low_index = np.where(self.trang == 20e-3)[0][0]
high_index = np.where(self.trang == 49.9e-3)[0][0]
ssa_std = np.std(self.E_fwd[low_index:high_index] *
fund_k_drive)
ssa_std_percent = 100 * ssa_std / (fund_k_drive *
np.sqrt(3.8e3))
ssa_std_text = r'$\sigma_{\rm SSA}\,=\,$' + '%.2f %% RMS' % (
np.abs(ssa_std_percent))
plt.text(35,
13e6,
ssa_std_text,
verticalalignment='top',
fontsize=30)
# Phase
if plot_type == 'phase':
plt.plot(self.trang * 1e3,
np.angle(self.cav_v, deg=True),
'-',
label=r'Cavity Field',
linewidth=2,
color='r')
plt.plot(self.trang * 1e3,
np.angle(self.set_point / fund_k_probe, deg=True),
label=r'Set-point $\left(\vec E_{\rm sp}\right)$',
linewidth=2,
color='c')
if show_limits:
plt.plot(self.trang * 1e3,
1e-2 * np.ones(len(self.trang)),
label='Upper limit',
linewidth=2,
color='m')
plt.plot(self.trang * 1e3,
-1e-2 * np.ones(len(self.trang)),
label='Lower limit',
linewidth=2,
color='y')
plt.axhspan(-4e-3, 4e-3, color='blue', alpha=0.2)
# Y label
plt.ylabel('Phase [degrees]', fontsize=30)
# Cartesian coordinates
if plot_type == 'cartesian':
plt.plot(self.trang * 1e3,
np.real(self.E_fwd) * fund_k_drive,
'-',
label=r'Forward $\Re \left(\vec E_{\rm fwd}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.imag(self.E_fwd) * fund_k_drive,
'-',
label=r'Forward $\Im \left(\vec E_{\rm fwd}\right)$',
linewidth=2)
plt.plot(self.trang * 1e3,
np.real(self.cav_v),
'-',
label=r'$\Re$ Cavity Field',
linewidth=2)
plt.plot(self.trang * 1e3,
np.imag(self.cav_v),
'-',
label=r'$\Im$ Cavity Field',
linewidth=2)
# Add annotation for a very specific plot
if annotate:
delta_E_fwd_text = r'$\Delta \left| \vec E_{\rm fwd} \right| \approx \,$' + \
'%.d MV' % (round(np.abs(fund_k_beam)*100e-6*1e-6))
plt.annotate(delta_E_fwd_text, xy=(21.05, 18e6), fontsize=25)
index_of_21 = np.where(self.trang == 21e-3)
plt.annotate(s='',
xy=(21,
np.abs(self.E_fwd[index_of_21]) * fund_k_drive),
xytext=(21, np.abs(self.cav_v[index_of_21])),
arrowprops=dict(arrowstyle='<->'))
# Add clear red shade if beam current is on to highlight the location of the beam train
if beam_on:
plt.axvspan(beam_start, beam_end, color='red', alpha=0.2)
plt.xticks(fontsize=20)
plt.yticks(fontsize=20)
plt.title(title, fontsize=40, y=1.02)
plt.xlabel('Time [ms]', fontsize=30)
if xlim:
plt.xlim(xlim)
if ylim:
plt.ylim(ylim)
plt.rc('font', **{'size': 20})
plt.legend(loc='upper right')
plt.show()
def main():
name_to_labels = load_labels()
for k,v in name_to_labels.items():
name_to_labels[k] = [0 if i==0 else 1 for i in v]
name_to_feats = {}
for k,v in name_to_labels.items():
name_to_feats[k] = []
for i,_ in enumerate(v):
# draws 1 index
frame = load_raw(k, i+1)
if frame is not None:
feat = features(frame, draw_num=i)
else:
feat = None
name_to_feats[k].append(feat)
#max_feats = max([len(x) for x in name_to_feats.values()[0] if x is not None])
#for k,v in name_to_feats.items():
#for i in range(len(v)):
#v[i] = v[i] + [0]*(max_feats - len(v[i]))
# fix the features where can't make with zeros
accum = []
for x in trange(10000):
patients = name_to_labels.keys()
n_train = int(len(patients) * .8)
train_pat = np.random.choice(patients, size=n_train).tolist()
test_pat = list(set(patients) - set(train_pat))
trX, trY = gather_feats_labels(train_pat, name_to_feats, name_to_labels)
teX, teY = gather_feats_labels(test_pat, name_to_feats, name_to_labels)
if len(np.unique(teY)) == 1:
continue
if len(np.unique(trY)) == 1:
continue
X, Y = gather_feats_labels(patients, name_to_feats, name_to_labels)
idx = np.arange(len(X))
np.random.shuffle(idx)
spt = int(len(X)*.8)
trX = X[idx[:spt]]
teX = X[idx[spt:]]
trY = Y[idx[:spt]]
teY = Y[idx[spt:]]
trPY, tePY = shuffle_train(trX, trY, teX)
heldout_auc = roc_auc_score(teY, tePY)
train_auc = roc_auc_score(trY, trPY)
fpr, tpr, _ = roc_curve(teY, tePY)
#plt.plot(fpr, tpr)
accum.append([heldout_auc, train_auc])
print np.mean(accum, axis=0), np.var(accum, axis=0)
accum = np.asarray(accum)
plt.subplot(2,1,1)
plt.title("Histogram of AUC from ML based model")
plt.hist(accum[:, 0], bins=100, normed=True, range=[0,1])
plt.xlabel("Validation AUC")
plt.subplot(2,1,2)
plt.hist(accum[:, 1], bins=100, normed=True, range=[0,1])
plt.xlim([0, 1])
plt.xlabel("Train AUC")
plt.show()
def plot_2d_dist2(x,
y,
xlim,
ylim,
nxbins,
nybins,
cmin=1.e-4,
cmax=1.0,
log=False,
weights=None,
xlabel='x',
ylabel='y',
clevs=None,
smooth=None,
fig_setup=None,
savefig=None):
"""
log = specifies whether logged quantities are passed to be plotted on log-scale outside this routine
"""
if fig_setup is None:
fig, ax = plt.subplots(figsize=(2.5, 2.5))
plt.ylabel(ylabel)
plt.xlabel(xlabel)
plt.xlim(xlim[0], xlim[1])
plt.ylim(ylim[0], ylim[1])
else:
ax = fig_setup
if xlim[1] < 0.: ax.invert_xaxis()
if weights is None: weights = np.ones_like(x)
H, xbins, ybins = np.histogram2d(x,
y,
weights=weights,
bins=(np.linspace(xlim[0], xlim[1],
nxbins),
np.linspace(ylim[0], ylim[1],
nybins)))
H = np.rot90(H)
H = np.flipud(H)
#X,Y = np.meshgrid(xbins,ybins)
X, Y = np.meshgrid(xbins[:-1], ybins[:-1])
if smooth is not None:
if (np.size(smooth) < 2):
raise Exception(
"smooth needs to be an array of size 2 containing 0=SG window size, 1=SG poly order"
)
H = sgolay2d(H, window_size=smooth[0], order=smooth[1])
H = H / np.sum(H)
Hmask = np.ma.masked_where(H == 0, H)
if log:
X = np.power(10., X)
Y = np.power(10., Y)
pcol = ax.pcolormesh(X,
Y, (Hmask),
vmin=cmin * np.max(Hmask),
vmax=cmax * np.max(Hmask),
cmap=plt.cm.BuPu,
norm=LogNorm(),
linewidth=0.,
rasterized=True)
pcol.set_edgecolor('face')
if clevs is not None:
lvls = []
for cld in clevs:
sig = opt.brentq(conf_interval, 0., 1., args=(H, cld))
lvls.append(sig)
ax.contour(X,
Y,
H,
linewidths=(1.0, 0.75, 0.5, 0.25),
colors='black',
levels=sorted(lvls),
norm=LogNorm(),
extent=[xbins[0], xbins[-1], ybins[0], ybins[-1]])
if savefig:
plt.savefig(savefig, bbox_inches='tight')
if fig_setup is None:
plt.show()
return
transit_duration,
[planet, firstmoon, secondmoon])
# Output information
print 'TTV amplitude =', numpy.amax(ttv_array), \
'[min] = ', numpy.amax(ttv_array) * 60, '[sec]'
print 'TDV amplitude =', numpy.amax(tdv_array), \
'[min] = ', numpy.amax(tdv_array) * 60, '[sec]'
ax = plt.axes()
plt.plot(ttv_array, tdv_array, color = 'k')
plt.rc('font', **{'family': 'serif', 'serif': ['Computer Modern']})
plt.rc('text', usetex=True)
plt.tick_params(axis='both', which='major', labelsize = 16)
plt.xlabel('transit timing variation [minutes]', fontsize = 16)
plt.ylabel('transit duration variation [minutes]', fontsize = 16)
ax.tick_params(direction='out')
plt.ylim([numpy.amin(tdv_array) * 1.2, numpy.amax(tdv_array) * 1.2])
plt.xlim([numpy.amin(ttv_array) * 1.2, numpy.amax(ttv_array) * 1.2])
plt.plot((0, 0), (numpy.amax(tdv_array) * 10., numpy.amin(tdv_array) * 10.), 'k', linewidth=0.5)
plt.plot((numpy.amin(ttv_array) * 10., numpy.amax(ttv_array) * 10.), (0, 0), 'k', linewidth=0.5)
# Fix axes for comparison with eccentric moon
plt.xlim(-0.4, +0.4)
plt.ylim(-1, +1)
plt.savefig("fig_system_ix.eps", bbox_inches = 'tight')
x = inti_x
x_history = []
for i in range(step_num):
x_history.append(x.copy())
grad = numerical_gradient(f, x) #数値微分
x -= lr * grad
return x, np.array(x_history)
def function_2(x):
return x[0]**2 + x[1]**2
init_x = np.array([-3.0, 4.0])
lr = 0.1
step_num = 20
x, x_history = gradient_descent(function_2, init_x, lr=lr, step_num=step_num)
plt.plot([-5, 5], [0, 0], '--b')
plt.plot([0, 0], [-5, 5], '--b')
plt.plot(x_history[:, 0], x_history[:, 1], 'o')
plt.xlim(-3.5, 3.5)
plt.ylim(-4.5, 4.5)
plt.xlabel("x0")
plt.ylabel("x1")
plt.show()
def plot_cluster_expression(out,data1,data2,donor,gene, image):
# function for setting the colors of the box plots pairs
def setBoxColors(bp):
setp(bp['boxes'][0], color='blue')
setp(bp['caps'][0], color='blue')
setp(bp['caps'][1], color='blue')
setp(bp['whiskers'][0], color='blue')
setp(bp['whiskers'][1], color='blue')
setp(bp['fliers'][0], color='blue')
setp(bp['fliers'][1], color='blue')
setp(bp['medians'][0], color='blue')
setp(bp['boxes'][1], color='red')
setp(bp['caps'][2], color='red')
setp(bp['caps'][3], color='red')
setp(bp['whiskers'][2], color='red')
setp(bp['whiskers'][3], color='red')
setp(bp['fliers'][2], color='red')
setp(bp['fliers'][3], color='red')
setp(bp['medians'][1], color='red')
N_probes=data1.shape[0]
fig = figure()
ax = axes()
hold(True)
s=1
f=2
p_value=[]
t_stat=[]
ticks=[]
for i in range(N_probes):
t,p=stats.ttest_ind(data1[i,:], data2[i,:])
bp = boxplot([data1[i,:],data2[i,:]], positions = [s, f], widths = 0.6)
setBoxColors(bp)
ticks.append( (s+f)/2. )
s+=3
f+=3
p_value.append(p)
t_stat.append(t)
hB, = plot([1,1],'b-')
hR, = plot([1,1],'r-')
xlim(0,f+2)
ylim(2,20)
legend((hB, hR),('Inside', 'Outside'))
for i in range(N_probes):
text(f+3,10-i,'Probe #{}: p-value={}'.format(i+1,np.round(p_value[i],3) ) )
ax.set_xticklabels(['probe #{}'.format(j) for j in range(1,N_probes+1)])
ax.set_xticks(ticks)
title('Donor {}, Allen Brain expression of gene {} inside/outside clusters formed in {} image'.format(donor, gene,image))
hB.set_visible(False)
hR.set_visible(False)
try:
savefig(os.path.join(out,donor+"_"+gene+".png") )
except:
savefig(os.path.join(out,donor+"_"+gene+".svg") )
scores = np.zeros(len(densities))
trials = 10
for i, density in enumerate(densities):
for n in xrange(trials):
window = np.empty(sh)
for f in xrange(sh[-1]):
#window = (np.random.random(sh) < density).astype(np.float64)
window[...,f] = (np.random.random(sh[:-1]) < back[0,0,f] * density).astype(np.float64)
#print "Density", density, window.sum()/np.prod(window.shape)
#contribution_map = np.zeros(sh[:-1])
data = \
window * np.log(kernels[mixcomp]) + \
(1.0-window) * np.log(1.0 - kernels[mixcomp]) + \
(-1) * (1.0-window) * np.log(1.0 - back[0,0]) + \
(-1) * window * np.log(back[0,0])
#contribution_map += data
scores[i] += data.sum()#contribution_map.sum()
scores /= trials
plt.plot(densities, scores)
plt.xlim((0, 1))
plt.xlabel('Feature density')
plt.ylabel('Log likelihood ratio')
plt.title('Scores for random feature activity')
plt.show()
def plot(experiment_path,
save_dir="/tmp/",
save_name="results",
limit_steps=None):
fig = plt.figure(figsize=(20, 10))
if len(glob(os.path.join(experiment_path, "train/*monitor*"))) != 0:
exps = glob(experiment_path)
print(exps)
# Get data
df = load_results(os.path.join(experiment_path, "train"))
roll = 5
rdf = df.rolling(5)
df['steps'] = df['l'].cumsum() / 1000000
# Plots
ax = plt.subplot(1, 1, 1)
df.rolling(roll).mean().plot('steps',
'r',
style='-',
ax=ax,
legend=False)
rdf.max().plot('steps',
'r',
style='-',
ax=ax,
legend=False,
color="#28B463",
alpha=0.65)
rdf.min().plot('steps',
'r',
style='-',
ax=ax,
legend=False,
color="#F39C12",
alpha=0.65)
# X axis
gap = 0.5
ax.set_xticks(
np.arange(0, ((df['steps'].iloc[-1] // gap) + 1) * gap, gap))
ax.set_xlabel('Num steps (M)')
if limit_steps: plt.xlim(0, limit_steps)
# Y axis
gap = 25
ax.set_yticks(
np.arange(((df['r'].min() // gap) - 1) * gap,
((df['r'].max() // gap) + 1) * gap, gap))
ax.set_ylabel('Reward')
ax.grid(True)
fig.subplots_adjust(left=0.1,
bottom=0.1,
right=0.9,
top=0.9,
wspace=0.1,
hspace=0.2)
# Save figure
ax.get_figure().savefig(os.path.join(save_dir, save_name) + ".jpg")
plt.clf()
import numpy as np
import matplotlib.pylab as plt
dis = np.loadtxt('dis.txt')
time = dis[:1900, 0]
disp = dis[:1900, 1]
plt.figure()
plt.plot(time, disp)
plt.grid()
plt.xlabel("Time (second) ")
plt.xlim([0, 20])
plt.ylabel("Displacements (meter) ")
plt.savefig('input_motion.pdf', bbox_inches='tight')
import numpy as np
for fileID in [2]:
with open( dirIn + allfiles[fileID]) as f:
lines = (line for line in f if not line.startswith('#'))
allCl = np.loadtxt(lines, skiprows=1)
l = allCl[:, lID[fileID] ]
Cl = allCl[:, ClID[fileID]]
emax = allCl[:, emaxID[fileID]]
emin = allCl[:, eminID[fileID]]
print l.shape
plt.figure(99)
plt.errorbar(l, Cl, yerr=[emax, emin], fmt='x', label = allfiles[fileID][2:-4], alpha = 0.8, ms=1)
#plt.yscale('log')
#plt.xscale('log')
plt.xlim([0,3500])
plt.ylim([-1000,10000])
plt.legend()
#plt.show()
#print allCl.shape
def longStats(af = 4, filter=None):
dataDirectory = "/data/routeviews/archive.routeviews.org/route-views.linx/bgpdata/"
space = 1
yearRange = range(2004, 2017)
#monthRange = range(1,13)
monthRange = [6] #range(1,13)
day = 15
dateRange = []
tier1 = {"3356":[], "1299":[], "174":[] ,"2914":[],"3257":[]}#, "6453":[], "3491":[], "701":[], "1239":[], "6762":[]}
# Find the first RIB files for each year
ribFiles = []
for ye in yearRange:
for month in monthRange:
ribs = glob.glob(dataDirectory+"{ye}.{mo:02d}/RIBS/rib.{ye}{mo:02d}{da:02d}.*.bz2".format(ye=ye, mo=month, da=day))
ribs.sort()
if len(ribs) < 1:
continue
ribFiles.append(((ye,month,day),ribs[0]))
dateRange.append(datetime.datetime(ye,month,day))
outDir = "../results/longStats_space%s_ipv%s/" % (space, af)
try:
os.makedirs(os.path.dirname(outDir))
except OSError as exc: # Guard against race condition
if exc.errno != errno.EEXIST:
raise
plt.figure()
ccmap = mpl.cm.get_cmap('copper_r')
# Using contourf to provide my colorbar info, then clearing the figure
Z = [[0,0],[0,0]]
CS3 = plt.contourf(Z, yearRange, cmap=ccmap)
plt.clf()
for i, (date, ribFile) in enumerate(ribFiles):
print date
if filter is None:
centralityFile = outDir+"/%s%02d%02d_af%s.pickle" % (date[0], date[1], date[2], af)
fList = None
else:
centralityFile = outDir+"/%s%02d%02d_AS%s_af%s.pickle" % (date[0], date[1], date[2],filter, af)
fList = [filter]
if not os.path.exists(centralityFile):
rtree, _ = ashash.readrib(ribFile, space, af, filterAS=fList)
asAggProb, asProb, _ = ashash.computeCentrality(rtree.search_exact("0.0.0.0/0").data, space, filterAS=filter)
pickle.dump((asAggProb, asProb), open(centralityFile, "wb"))
else:
asAggProb, asProb = pickle.load(open(centralityFile,"rb"))
if asAggProb is None or len(asAggProb) < 1:
continue
if filter is None and af==4:
for k,v in tier1.iteritems():
v.append(asAggProb[str(k)])
if not filter is None:
del asAggProb[str(filter)]
sortedAS = sorted(asAggProb, key=lambda k: asAggProb[k], reverse=True)
i=0
while i<len(sortedAS) and asAggProb[sortedAS[i]]>.05:
print "%s: %s" % (sortedAS[i], asAggProb[sortedAS[i]])
i+=1
eccdf(asAggProb.values(), lw=1.3, label=date[0],c=ccmap(i/float(len(ribFiles)) ) )
# print date
# maxKey = max(asAggProb, key=asAggProb.get)
# print "AS%s = %s" % (maxKey, asAggProb[maxKey])
plt.grid(True)
#plt.legend(loc="right")
plt.colorbar(CS3)
plt.xscale("log")
plt.yscale("log")
#plt.yscale("log")
# plt.xlim([10**-8, 10**-2])
if filter is None:
plt.xlim([10**-7, 1.1])
else:
plt.xlim([10**-7, 1.1])
# plt.ylim([10**-3, 1.1])
plt.xlabel("AS hegemony")
plt.ylabel("CCDF")
plt.tight_layout()
if filter is None:
plt.title("IPv%s" % af)
plt.savefig(centralityFile.rpartition("/")[0]+"/hegemonyLongitudinal_af%s.eps" % af)
else:
plt.title("AS%s (IPv%s)" % (filter, af))
plt.savefig(centralityFile.rpartition("/")[0]+"/hegemonyLongitudinal_AS%s_af%s.eps" % (filter, af))
if filter is None and af==4:
fig = plt.figure(figsize=(10,3))
for k,v in tier1.iteritems():
plt.plot(dateRange, v, label="AS"+k)
plt.ylim([0,0.3])
plt.grid(True)
plt.ylabel("AS hegemony")
plt.xlabel("Time")
plt.legend(loc="center", bbox_to_anchor=(0.5, 1), ncol=len(tier1))
plt.tight_layout()
plt.savefig(centralityFile.rpartition("/")[0]+"/tier1.eps")
right = 0.97 # the right side of the subplots of the figure
bottom = 0.1 # the bottom of the subplots of the figure
top = 0.96 # the top of the subplots of the figure
wspace = 0.2 # the amount of width reserved for blank space between subplots
hspace = 0.15 # the amount of height reserved for white space between subplots
plt.subplots_adjust(left=left,
bottom=bottom,
right=right,
top=top,
wspace=wspace,
hspace=hspace)
ax1 = plt.subplot(1, 1, 1)
plt.plot(time_ms, disI_2p2 / 1000.0, color='red', label='2.2kV')
plt.plot(time_ms, disI_1p8 / 1000.0, color='orange', label='1.8kV')
plt.xticks(fontsize=12)
plt.xlabel(r't [$\mu$s]', fontsize=16)
plt.xlim(0, 198)
plt.yticks(fontsize=12)
plt.ylabel('Discharge Current kA', fontsize=16)
plt.ylim(0, 80)
#plt.vlines(0,0,80,color='gray',linestyle='dotted',linewidth=3.5)
leg = plt.legend(loc='upper right', fontsize=12, frameon=False, handlelength=5)
savefilename = 'Discharge_current_compare_forAPSDPP18.png'
#savefilename='Discharge_current_'+date+'_shot'+str(shot)+'_forAPSDPP18.png'
savefile = os.path.normpath(savefilename)
#plt.savefig(savefile,dpi=600,facecolor='w',edgecolor='k')
#plt.clf()
#plt.close()
def peerSensitivity():
space = 1
af = 4
allasCount = {}
resultsFile = "../results/peerSensitivity/KLdiv_%s.pickle"
collectorsDataFile = "../results/peerSensitivity/collectorData.pickle"
allPeersFile = "../results/peerSensitivity/allPeersDist.pickle"
collectorsDist = []
results = defaultdict(list)
if not os.path.exists(resultsFile % "Betweenness"):
ribFiles = glob.glob("/data/routeviews/archive.routeviews.org/*/*/RIBS/rib.20160601.0000.bz2")
ribFiles.extend(glob.glob("/data/routeviews/archive.routeviews.org/*/*/*/RIBS/rib.20160601.0000.bz2"))
ribFiles.extend(glob.glob("/data/ris/*/*/bview.20160601.0000.gz"))
ribFiles.append("/data/bgpmon/ribs/201606/ribs")
for i, ribFile in enumerate(ribFiles):
words = ribFile.split("/")
if "routeviews" in ribFile:
if words[-4] == "route-views3":
label = "rv3"
elif words[-5] == "archive.routeviews.org" and words[-4] == "bgpdata":
label = "rv2"
elif not "." in words[-5] and words[-5].startswith("route-views"):
label = "rv"+words[-5][-1]
else:
label = words[-5].split(".")[-1]
elif "ris" in ribFile:
label = words[-3]
else:
label = "bgpmon"
asCountFile = "../results/peerSensitivity/20160601.0000_asCount%s.pickle" % (i)
if not os.path.exists(asCountFile):
rtree, _ = ashash.readrib(ribFile, space, af)
asCount = rtree.search_exact("0.0.0.0/0").data
asHegemony, _, nbPeers = ashash.computeCentrality(asCount, space)
# asBetweenness = ashash.computeBetweenness(asCount, space)
pickle.dump((asCount, asHegemony, nbPeers), open(asCountFile, "wb"))
else:
asCount, asHegemony, nbPeers = pickle.load(open(asCountFile,"rb"))
collectorsDist.append( (label, nbPeers, asHegemony) )
print "%s: %s peers" % (label, len(asCount))
for peer, count in asCount.iteritems():
if count["totalCount"]>2000000000:
if not peer in allasCount:
allasCount[peer] = count
else:
print "Warning: peer %s is observed multiple times (%s)" % (peer, ribFile)
asHegemonyRef, _, nbPeers = ashash.computeCentrality(allasCount, space)
asBetweennessRef, _, _ = ashash.computeBetweenness(allasCount, space)
pickle.dump((asHegemonyRef, asBetweennessRef, nbPeers), open(allPeersFile,"wb"))
for metricLabel, ref, computeMetric in [("Hegemony", asHegemonyRef, ashash.computeCentrality),
("Betweenness", asBetweennessRef, ashash.computeBetweenness)]:
if not os.path.exists(resultsFile % metricLabel):
# Remove AS with a score of 0.0
toremove = [asn for asn, score in ref.iteritems() if score==0.0]
for asn in toremove:
del ref[asn]
minVal = min(ref.values())
nbPeersList = range(0, len(allasCount), 10)
nbPeersList[0] = 1
for nbPeers in nbPeersList:
tmp = []
for _ in range(10):
# Randomly select peers
peersIndex = random.sample(range(len(allasCount)), nbPeers)
asCount = {}
for p in peersIndex:
asCount[allasCount.keys()[p]] = allasCount.values()[p]
asMetric, _, _ = computeMetric(asCount, space)
# Remove AS with a score == 0.0
toremove = [asn for asn, score in asMetric.iteritems() if score==0.0]
if not toremove is None:
for asn in toremove:
del asMetric[asn]
# Set the same number of ASN for both distributions
missingAS = set(ref.keys()).difference(asMetric.keys())
if not missingAS is None:
for asn in missingAS:
asMetric[asn] = minVal
# Compute the KL-divergence
dist = [asMetric[asn] for asn in ref.keys()]
kldiv = sps.entropy(dist, ref.values())
tmp.append(kldiv)
results[metricLabel].append(tmp)
print tmp
# save final results
pickle.dump((nbPeersList, results[metricLabel]),open(resultsFile % metricLabel,"wb"))
pickle.dump(collectorsDist, open(collectorsDataFile,"wb"))
else:
(nbPeersList, results[metricLabel]) = pickle.load(open(resultsFile % metricLabel,"rb"))
collectorsDist = pickle.load(open(collectorsDataFile,"rb"))
else:
for metricLabel in ["Hegemony", "Betweenness"]:
nbPeersList, results[metricLabel] = pickle.load(open(resultsFile % metricLabel,"rb"))
collectorsDist = pickle.load(open(collectorsDataFile,"rb"))
asHegemonyRef, asBetweennessRef, allFullFeedPeers = pickle.load(open(allPeersFile,"rb"))
#return asHegemonyRef
def plotRef(references, legendFmt="%s", alpha=1.0):
for asDistRef, metricLabel, color in references:
m = np.mean(results[metricLabel][1:], axis=1)
s = np.std(results[metricLabel][1:], axis=1)
# mi = m-np.min(results[metricLabel][1:], axis=1)
# ma = np.max(results[metricLabel][1:], axis=1)-m
# mi, ma = sms.DescrStatsW(results[metricLabel][1:]).tconfint_mean(alpha=0.1)
mi = np.min(results[metricLabel][1:], axis=1)
ma = np.max(results[metricLabel][1:], axis=1)
x = nbPeersList[1:]
plt.fill_between(x, mi, ma, facecolor=color, alpha=alpha*0.5)
# plt.plot(x, m,"-+", ms=4, color="0.6", label="Randomly selected")
# plt.plot(x, m,"-+", ms=4, color="0.6", label="Random peers (%s)" % metricLabel)
try:
plt.plot(x, m,"-o", ms=3, label=legendFmt % metricLabel, color=color, alpha=alpha)
except TypeError:
plt.plot(x, m,"-o", ms=3, label=legendFmt, color=color, alpha=alpha)
# plt.errorbar(x,m, [mi, ma], fmt="C3.", ms=4)
plt.xlabel("Number of peers")
plt.ylabel("KL divergence")
# plt.yscale("log")
plt.legend()
plt.tight_layout()
# Compare betweenness and hegemony
plt.figure()
plotRef([(asHegemonyRef, "Hegemony", "C0"), (asBetweennessRef, "Betweenness", "C5")])
plt.ylim([0.00001, 1])
plt.xscale("log")
plt.savefig("../results/peerSensitivity/meanKL_%s.pdf" % metricLabel)
# Compare collectors and random sample (hegemony):
# Remove AS with a score of 0.0
toremove = [asn for asn, score in asHegemonyRef.iteritems() if score==0.0]
for asn in toremove:
del asHegemonyRef[asn]
minVal = min(asHegemonyRef.values())
plt.figure()
plotRef([(asHegemonyRef, "Hegemony", "C0")], legendFmt="Random peers", alpha=0.5)
plt.xlabel("Number of peers")
plt.ylabel("KL divergence")
for collectorLabel, nbPeers, asHegemony in collectorsDist:
if asHegemony is None :
print "warning: ignore collector %s" % collectorLabel
continue
# Remove AS with a score == 0.0
toremove = [asn for asn, score in asHegemony.iteritems() if score==0.0]
if not toremove is None:
for asn in toremove:
del asHegemony[asn]
# Set the same number of ASN for both distributions
missingAS = set(asHegemonyRef.keys()).difference(asHegemony.keys())
if not missingAS is None:
for asn in missingAS:
asHegemony[asn] = minVal
# Compute the KL-divergence
dist = [asHegemony[asn] for asn in asHegemonyRef.keys()]
kldiv = sps.entropy(dist, asHegemonyRef.values())
print "%s:\t %s peers \t %s " % (collectorLabel, nbPeers, kldiv)
if kldiv>0.4 :
continue
if collectorLabel.startswith("rrc"):
plt.plot(nbPeers, kldiv,"C1x", label="RIS")
collectorLabel = collectorLabel.replace("rrc","")
elif collectorLabel == "bgpmon":
plt.plot(nbPeers, kldiv,"C3^")
else:
plt.plot(nbPeers, kldiv,"C3+", label="Route Views")
if kldiv<1 or nbPeers>10:
plt.text(nbPeers+0.005, kldiv+0.005, collectorLabel, fontsize=8)
# plt.yscale("log")
# plt.xscale("log")
handles, labels = plt.gca().get_legend_handles_labels()
by_label = OrderedDict(zip(labels, handles))
plt.legend(by_label.values(), by_label.keys())
plt.ylim([0.00001, 0.4])
plt.xlim([9, 50])
plt.tight_layout()
plt.savefig("../results/peerSensitivity/collectorDiversity.pdf")
return (nbPeersList, results)
zh = float(line[3])
f.close()
x = x - xh
y = y - yh
z = z - zh
#plt.figure(figsize=(20,6))
plt.subplot(131)
#plt.scatter(x,y,c=colors[j], marker=shapes[j])
plt.hist2d(x, y, bins=(xbins, xbins), norm=LogNorm())
plt.xlabel(r'$x$ (kpc/$h$)', fontsize=18)
plt.ylabel(r'$y$ (kpc/$h$)', fontsize=18)
plt.xlim(-200, 200)
plt.ylim(-200, 200)
plt.subplot(132)
#plt.scatter(x,z,c=colors[j], marker=shapes[j])
plt.hist2d(x, z, bins=(xbins, xbins), norm=LogNorm())
plt.xlabel(r'$x$ (kpc/$h$)', fontsize=18)
plt.ylabel(r'$z$ (kpc/$h$)', fontsize=18)
plt.xlim(-200, 200)
plt.ylim(-200, 200)
plt.subplot(133)
#plt.scatter(y,z,c=colors[j], marker=shapes[j])
plt.hist2d(y, z, bins=(xbins, xbins), norm=LogNorm())
plt.xlabel(r'$y$ (kpc/$h$)', fontsize=18)
plt.ylabel(r'$z$ (kpc/$h$)', fontsize=18)
plt.subplots(figsize=(20, 8))
#make 5000 noise and 1000 of each x sample
N_samples = 1000
noise = np.random.randn(5 * N_samples, noise_dim).astype('float32')
x_gen = np.repeat(xgen, 1000)
x_gen = x_gen.reshape(5000, 1)
#plug into posterior
z_samples = posterior(x_gen, noise)
z_samples = tf.reshape(z_samples, [xgen.shape[0], N_samples, 2]).eval()
for i in range(5):
plt.subplot(2, 5, i + 1)
sns.kdeplot(z_samples[i, :, 0], z_samples[i, :, 1], cmap='Greens')
plt.axis('square')
plt.title('q(z|x={})'.format(y[i]))
plt.xlim([xmin, xmax])
plt.ylim([xmin, xmax])
plt.xticks([])
plt.yticks([])
plt.subplot(2, 5, 5 + i + 1)
plt.contour(xrange,
xrange,
np.exp(logprior + llh[i]).reshape(300, 300).T,
cmap='Greens')
plt.axis('square')
plt.title('p(z|x={})'.format(y[i]))
plt.xlim([xmin, xmax])
plt.ylim([xmin, xmax])
plt.xticks([])
plt.yticks([])
plt.savefig('JCADVgudlog Final Fig')
def pathDerivativeDist():
space = 1
af = 4
outDir = "../results/longStats_space%s_ipv%s/" % (space, af)
dataDirectory = "/data/routeviews/archive.routeviews.org/route-views.linx/bgpdata/"
filename = dataDirectory+"2016.06/UPDATES/updates.20160601.0000.bz2"
centralityFile = outDir+"/%s%02d%02d_af%s.pickle" % (2016, 6, 15, af)
asAggProb, asProb = pickle.load(open(centralityFile,"rb"))
allDiff = []
linDiff = []
if filename.startswith("@bgpstream:"):
p1 = Popen(["bgpreader", "-m", "-w", filename.rpartition(":")[2], "-p", "routeviews", "-c", "route-views.linx", "-t", "updates"], stdout=PIPE)
else:
p1 = Popen(["bgpdump", "-m", "-v", filename], stdout=PIPE, bufsize=-1)
for line in p1.stdout:
res = line[:-1].split('|',15)
if res[5] == "0.0.0.0/0":
continue
if af != 0:
if af == 4 and ":" in res[5]:
continue
elif af == 6 and "." in res[5]:
continue
if res[2] == "W":
continue
else:
zTd, zDt, zS, zOrig, zAS, zPfx, sPath, zPro, zOr, z0, z1, z2, z3, z4, z5 = res
path = list(unique_justseen(sPath.split(" ")))
try :
# hegeAll = map(lambda x: round(asAggProb[x],3), path[1:-1])
hegeAll = map(lambda x: asAggProb[x], path)
hegeDiff = np.diff(hegeAll)
allDiff.extend(list(hegeDiff))
N = 11
dim = np.linspace(0,1,N)
interpData = np.interp(dim, np.linspace(0,1,len(hegeAll)), hegeAll)
linDiff.append(interpData)
except Exception:
# print path
# print "New AS"
continue
plt.figure()
ecdf(allDiff)
plt.xlim([-0.1, 0.1])
plt.xlabel("Hegemony derivative")
plt.ylabel("CDF")
plt.tight_layout()
plt.savefig("../results/pathDerivative/derivativeDist.pdf")
lda = np.array(linDiff)
plt.figure()
# plt.plot(dim, lda.mean(axis=0),"o-")
plt.errorbar(dim, lda.mean(axis=0), lda.std(axis=0)/np.sqrt(N), fmt="o-")
plt.xlabel("Relative position in the path")
plt.ylabel("AS hegemony")
plt.xlim([-0.03, 1.03])
plt.tight_layout()
plt.savefig("../results/pathDerivative/meanHegemony.pdf")
return lda
def plot_options_greedy(self, sess, coord, saver):
eigenvectors_path = os.path.join(
os.path.join(self.config.stage_logdir, "models"),
"eigenvectors.npy")
eigenvalues_path = os.path.join(
os.path.join(self.config.stage_logdir, "models"),
"eigenvalues.npy")
eigenvectors = np.load(eigenvectors_path)
eigenvalues = np.load(eigenvalues_path)
for k in ["poz", "neg"]:
for option in range(len(eigenvalues)):
# eigenvalue = eigenvalues[option]
eigenvector = eigenvectors[
option] if k == "poz" else -eigenvectors[option]
prefix = str(option) + '_' + k + "_"
plt.clf()
with sess.as_default(), sess.graph.as_default():
for idx in range(self.nb_states):
dx = 0
dy = 0
d = False
s, i, j = self.env.get_state(idx)
if not self.env.not_wall(i, j):
plt.gca().add_patch(
patches.Rectangle(
(j, self.config.input_size[0] - i -
1), # (x,y)
1.0, # width
1.0, # height
facecolor="gray"))
continue
# Image.fromarray(np.asarray(scipy.misc.imresize(s, [512, 512], interp='nearest'), np.uint8)).show()
feed_dict = {self.orig_net.observation: np.stack([s])}
fi = sess.run(self.orig_net.fi, feed_dict=feed_dict)[0]
transitions = []
terminations = []
for a in range(self.action_size):
s1, r, d, _ = self.env.fake_step(a)
feed_dict = {
self.orig_net.observation: np.stack([s1])
}
fi1 = sess.run(self.orig_net.fi,
feed_dict=feed_dict)[0]
transitions.append(
self.cosine_similarity((fi1 - fi),
eigenvector))
terminations.append(d)
transitions.append(
self.cosine_similarity(np.zeros_like(fi),
eigenvector))
terminations.append(True)
a = np.argmax(transitions)
# if a == 4:
# d = True
if a == 0: # up
dy = 0.35
elif a == 1: # right
dx = 0.35
elif a == 2: # down
dy = -0.35
elif a == 3: # left
dx = -0.35
if terminations[a] or np.all(
transitions[a] == np.zeros_like(
fi)): # termination
circle = plt.Circle(
(j + 0.5,
self.config.input_size[0] - i + 0.5 - 1),
0.025,
color='k')
plt.gca().add_artist(circle)
continue
plt.arrow(j + 0.5,
self.config.input_size[0] - i + 0.5 - 1,
dx,
dy,
head_width=0.05,
head_length=0.05,
fc='k',
ec='k')
plt.xlim([0, self.config.input_size[1]])
plt.ylim([0, self.config.input_size[0]])
for i in range(self.config.input_size[1]):
plt.axvline(i, color='k', linestyle=':')
plt.axvline(self.config.input_size[1],
color='k',
linestyle=':')
for j in range(self.config.input_size[0]):
plt.axhline(j, color='k', linestyle=':')
plt.axhline(self.config.input_size[0],
color='k',
linestyle=':')
plt.savefig(
os.path.join(
self.summary_path,
"SuccessorFeatures_" + prefix + 'policy.png'))
plt.close()
# Create segment CD.
C = ga.Point2D(5, 1, name='C')
D = ga.Point2D(5, 4, name='D')
CD = ga.Segment2D(C, D)
# Plot the points and the segments.
A.plot()
B.plot()
C.plot(offset=(0.2, 0))
D.plot(offset=(0.2, 0))
AB.plot()
CD.plot()
# Compute first intersection point.
X, _, coords = AB.intersect_segment(CD, return_coords=True)
X.name = 'X'
X.plot(color='red', offset=(0.2, 0.2))
print('Intersection point is on AB at parametric coordinate: {}'.format(
coords[0]))
print('Intersection point is on CD at parametric coordinate: {}'.format(
coords[1]))
# Adjust the plot.
plt.axis('scaled')
plt.xlim(1, 7)
plt.ylim(0, 5)
plt.grid()
plt.show()
def plot_rssi(self, port):
self.count = 0
self.dt = 0.5
self.tlen = 120
# Generate mesh for plotting
Y, X = np.mgrid[slice(0 - .5, 26 + 1.5, 1),
slice(0 - self.dt / 2, self.tlen + 1 -
self.dt / 2, self.dt)]
Z = np.zeros_like(X)
# X and Y are bounds, so Z should be the value *inside* those bounds.
# Therefore, remove the last value from the Z array.
Z = Z[:, :-1]
logging.debug("Creating figure")
fig = plt.figure()
pcm = plt.pcolormesh(X,
Y,
Z,
vmin=-128,
vmax=0,
cmap=plt.cm.get_cmap('jet'))
plt.xlim([0, self.tlen])
plt.ylim([0, 26])
plt.colorbar(label="Measured signal level [dB]")
plt.ylabel("Channel number")
plt.xlabel("Time [s]")
plt.ion()
logging.debug("Show plot window")
plt.show()
ch_min = 26
ch_max = 0
last_update = time.time()
logging.info("Begin collecting data from serial port")
while True:
line = port.readline().rstrip()
pkt_data = re.match(
r"\[([-+]?\d+),\s*([-+]?\d+),\s*([-+]?\d+)\]\s*(.*)",
line.decode(errors='replace'))
if pkt_data:
now = time.time()
try:
iface_id = int(pkt_data.group(1))
timestamp = int(pkt_data.group(2))
count = int(pkt_data.group(3))
tidx = int(timestamp / (self.dt * 1000000)) % (Z.shape[1])
except ValueError:
continue
logging.debug("data: tidx=%d if=%d t=%d", tidx, iface_id,
timestamp)
raw = pkt_data.group(4)
resize = False
for ch_ed in raw.split(","):
try:
pair = ch_ed.split(":")
ch = int(pair[0])
ed = float(pair[1])
if ch < ch_min:
ch_min = ch
resize = True
if ch > ch_max:
ch_max = ch
resize = True
Z[ch, tidx] = ed
except (ValueError, IndexError):
continue
#print("ch: %d ed: %d" % (ch, ed))
if resize:
logging.debug("resize: %d %d" % (ch_min, ch_max))
plt.ylim([ch_min - .5, ch_max + .5])
if now > last_update + 1:
last_update = now
#pcm = plt.pcolormesh(X, Y, Z)
pcm.set_array(Z.ravel())
pcm.autoscale()
pcm.changed()
plt.pause(0.01)
return lambda t: d*t + y
if __name__ == '__main__':
x0 = np.arange(-2, 2.5, 0.25)
x1 = np.arange(-2, 2.5, 0.25)
X, Y = np.meshgrid(x0, x1)
X = X.flatten()
Y = Y.flatten()
grad = numerical_gradient(function_2, np.array([X, Y]).T).T
plt.figure()
plt.quiver(X, Y, -grad[0], -grad[1], angles="xy",color="#666666")
plt.xlim([-2, 2])
plt.ylim([-2, 2])
plt.xlabel('x0')
plt.ylabel('x1')
plt.grid()
plt.draw()
plt.show()
## Sequence Model
- The following graph is taken from [Christopher Olah's blog post: Understanding LSTM Networks](http://colah.github.io/posts/2015-08-Understanding-LSTMs/)
numbers = min_ind / 2.
return numbers, min_val
high_num, err_h = get_best_match(higher_img, numbers_base)
low_num, err_l = get_best_match(lower_img, numbers_base)
video_num = np.floor(high_num) * 10 + low_num
video_num[25::25] = np.nan
#%%
plt.figure()
lab_t, = plt.plot(np.diff(stamp), label='timestamp')
dd = np.diff(video_num)
dd[dd < -50] = np.nan
lab_v, = plt.plot(dd * 20, label='video number')
plt.xlim((0, 12000))
plt.ylim((0, 550))
plt.legend(handles=[lab_t, lab_v])
plt.xlabel('frame number')
plt.ylabel('time difference (ms)')
plt.xlim((1800, 2000))
#%%
#plt.figure()
#plt.imshow(lower_img[1328]-numbers_base[-1])
#plt.figure()
#plt.imshow(higher_img[1328]-numbers_base[1])
tr = np.transpose
import pandas as pd
import json
from wpdy.dvr import ExpDVR
j = json.load(open("out/params.json"))
dvr = ExpDVR(j["dvr_n"], j["dvr_x0"], j["dvr_xN"])
mass = j["mass"]
nstate = j["nstate"]
xs = np.array(pd.read_csv("out/xs.csv")["val"])
nx = len(xs)
ws = pd.read_csv("out/ws.csv")["val"]
ts = pd.read_csv("out/ts.csv")["val"]
its = [0,20,40,60,80]
for it in its:
df = pd.read_csv("out/{0}/coef.csv".format(it))
tmp = np.array(df["re"] + 1j * df["im"])
coef = tmp.reshape((len(xs), nstate))
psi1 = coef[:,0] / np.sqrt(ws)
psi2 = coef[:,1] / np.sqrt(ws)
# pl, = plt.plot(xs, abs(psi1)**2, label="state 1")
pl, = plt.plot(xs, psi1.real, label="state 1")
plt.plot( xs, psi2.real, color=pl.get_color(), linestyle="--", label="state 2")
plt.xlim(-11,11)
plt.savefig("psi.pdf")
def cov_plot(self, matrix, station="", hour="", date="", averaged=""):
""" Basic plot for the correlation matrix """
var = self.var_dics[self.var]['name']
fig, ax = plt.subplots()
date = self.date_prettyfier(date)
hour = str(hour).replace('0', '00:00').replace('1', '12:00')
if not averaged:
title = "Stat: " + station + ', H: ' + hour + ', Date: ' + date + ', ' + var
filename = 'Cov_' + station + '_hour_' + hour.replace(
':', '') + '_date_' + str(date).replace('/', '') + '_' + var
elif averaged:
title = var.replace(
'temp', 'Temp.') + " , Stat: " + station + ', H: ' + str(
hour) + ', Date: ' + str(date)
filename = 'Cov_' + station + '_hour_' + str(hour).replace(
':', '') + '_averaged_' + str(date).replace('/',
'') + '_' + var
plt.title(title.replace('_', ' '), y=1.03, fontsize=self.font - 2)
num = len(matrix[0, :])
Num = range(num)
vmin, vmax = -3, 3
if self.var == 'direction':
vmin, vmax = -10, 10
color_map = plt.imshow(
matrix,
interpolation='nearest',
cmap='RdYlBu',
vmin=vmin,
vmax=vmax
) # nearest serves for discreete grid # cmaps blue, seismic
plt.ylim(-0.5, 15.5)
plt.xlim(-0.5, 15.5)
plt.xticks(Num, Num)
plt.xlabel('Pressure level an_dep [hPa]', fontsize=self.font - 2)
plt.yticks(Num, Num)
plt.ylabel('Pressure level fg_dep [hPa]', fontsize=self.font - 2)
ax.set_xticklabels(labels=self.pretty_pressure,
fontsize=self.font - 4,
rotation=45)
ax.set_yticklabels(labels=self.pretty_pressure, fontsize=self.font - 4)
bar = plt.colorbar()
bar.ax.set_ylabel("Covariance", fontsize=self.font)
for i in Num: # creating text labels
for j in Num:
value = '{0:.2f}'.format(matrix[i, j])
text = ax.text(j,
i,
value,
ha='center',
va='center',
color='black',
fontsize=5)
if not os.path.isdir('plots/covariances/' + station):
os.mkdir('plots/covariances/' + station)
plt.savefig('plots/covariances/' + station + '/' + filename + '.png',
bbox_inches='tight',
dpi=200)
plt.close()
**{
'family': 'sans-serif',
'sans-serif': ['Helvetica']
})
plt.rc('xtick.major', pad=10)
plt.rc('xtick.minor', pad=10)
plt.rc('ytick.major', pad=10)
plt.rc('ytick.minor', pad=10)
#rfull,xifull = np.loadtxt('../s2_out.dat',usecols=(0,1),unpack=True)
plt.plot(lr,
np.log10(np.abs(xir)),
linewidth=1.5,
c='b',
label=r'$\sigma_2^2(r)$ Gaussian (spline)')
plt.plot(lr,
np.log10(np.abs(xir2)),
linewidth=1.5,
c='m',
label=r'$\sigma_2^2(r)$ TH (Romberg)')
plt.ylim(-10.0, 8.0)
plt.xlim(-0.99, 2.5)
#plt.xlim(8.0,16.0);
plt.xlabel(r'\textbf{$\log_{10}(R/\rm Mpc)$}', labelpad=5)
plt.ylabel(r'$\log_{10}(\sigma^2_2(R))$', labelpad=10)
plt.title('power spectrum moment', fontsize=16)
plt.legend(loc='upper right')
plt.show()
io.quit()
# Calculate measures of interest from collected data
txtimes = results[:, 0]
expe2edelay = results[:, 1]
rxtimes = results[:, 2]
e2edelays = rxtimes - txtimes
msgintervals = txtimes[1:] - txtimes[:-1]
mintime = min(txtimes.min(), rxtimes.min())
maxtime = max(txtimes.max(), rxtimes.max())
statstr = "Min: %.3f, Max: %.3f, Mean: %.3f, Std: %.3f"
rtdstats = statstr % (e2edelays.min(), e2edelays.max(), e2edelays.mean(),
e2edelays.std())
expstats = statstr % (expe2edelay.min(), expe2edelay.max(), expe2edelay.mean(),
expe2edelay.std())
# Plot the results.
from matplotlib import pylab as pl
pl.xlim(mintime - 1.0, maxtime + 1.0)
pl.plot(txtimes, e2edelays, label="ioHub")
pl.plot(txtimes, expe2edelay, label="Experiment")
pl.xlabel("Time (msec)")
pl.ylabel("Round Trip Delay (msec)")
pl.legend()
pl.title("ioHub-ioSync Delay:: %s\nPsychoPy-ioSync Delay:: %s" %
(rtdstats, expstats))
pl.show()
def outliers_example(self,
corr='',
out='',
date='',
N='',
lower='',
upper='',
median='',
flag='',
upper_s='',
lower_s='',
station='',
what=''):
pressure = self.pretty_pressure_dic[str(self.an_p)]
var = self.var_dics[self.var]['name']
hour = str(self.hour).replace('0', '00:00').replace('1', '12:00')
plt.title(var + ' ' + what + ' Outliers - Stat: ' + station + ', H: ' +
hour + ', P: ' + pressure + ' [hPa]',
y=1.03)
corr_ = [n for n in corr if not np.isnan(n)]
out_ = [n for n in out if not np.isnan(n)]
num_a = '{:.1f}'.format(len(corr_) / len(out_ + corr_) * 100)
num_o = '{:.1f}'.format(len(out_) / len(out_ + corr_) * 100)
plt.scatter(date,
corr,
label='Accepted [' + num_a + '%]',
color='cyan',
s=3)
plt.scatter(date,
out,
label='Outliers [' + num_o + '%]',
color='black',
s=3)
X = [min(date), max(date)]
plt.plot(X, [lower, lower], label='Lower', color='blue', ls='--')
plt.plot(X, [upper, upper], label='Upper', color='red', ls='--')
# adding the upper and lower values for skewed distributions
plt.plot(X, [lower_s, lower_s],
label='Lower Skewed',
color='blue',
ls='-')
plt.plot(X, [upper_s, upper_s],
label='Upper Skewed',
color='red',
ls='-')
plt.plot(X, [median, median],
label='Median [' + '{:.1f}'.format(median) + ']',
color='black',
ls='--')
plt.legend(fontsize=self.font - 6, loc='upper right', ncol=2)
plt.grid(linestyle=':', color='lightgray', lw=1.2)
plt.ylabel('Departure ' + self.var_dics[self.var]['units'],
fontsize=self.font)
plt.xlabel('Date', fontsize=self.font)
plt.xticks(rotation=45)
out_c = [n for n in out if not np.isnan(n)]
corr_c = [n for n in corr if not np.isnan(n)]
plt.xlim(min(date) - 1 / 365, max(date) + 1 / 365)
plt.ylim(-10, 10)
plt.savefig('plots/outliers/outliers_' + flag + '_' + str(N) +
'_date_' + str(min(date)) + '_hour_' + self.hour + '_' +
self.var + '_anp_' + str(self.an_p) + '_fgp_' +
str(self.fg_p) + '.png',
bbox_inches='tight')
plt.close()
# Save the frequencies and smoothened spectral heat currents to text file
np.savetxt('frequencies_and_ITC.txt', np.column_stack(
(x_Frequency, y_ITC)))
np.savetxt('frequencies_accumulated_ITC.txt',
np.column_stack((x_Frequency, accumulated_ITC)))
# *************************** Plotting ************************* (It can be reduced to a graph function)
# Phonon transmission T(w)
plt.plot(x_Frequency,
T_w,
'-',
linewidth=3,
label="Interfacial Phonon Transmission")
plt.xlabel('w/(2*pi) (THz)')
plt.ylabel('Transmission')
plt.xlim(0, max(x_Frequency)) # Frequency range
plt.ylim(0, max(T_w) + 5) # It depends on your case
plt.legend(fontsize=15, loc='best')
plt.show()
plt.savefig(fileprefix + '_Tw.eps')
# Spectral thermal conductance
plt.plot(x_Frequency,
y_ITC,
'-',
linewidth=3,
label="Spectral thermal conductance")
plt.xlabel('w/(2*pi) (THz)')
plt.ylabel('G(w) (GW/m^2/K/THz)')
plt.xlim(0, max(x_Frequency)) # Frequency range
plt.ylim(0, max(y_ITC) + (max(y_ITC) / 2)) # It depends on your case
xx = np.zeros(nrun)
yy = np.zeros(nrun)
xx[0] = 0.5
yy[0] = 0.0
for irun in range(1, nrun):
if np.mod(irun, 10000) == 0:
print(irun)
rand = random.random()
if rand < 0.02:
xx[irun] = 0.5
yy[irun] = 0.27 * yy[irun - 1]
elif rand >= 0.02 and rand < 0.17:
xx[irun] = -0.139 * xx[irun - 1] + 0.263 * yy[irun - 1] + 0.57
yy[irun] = 0.246 * xx[irun - 1] + 0.224 * yy[irun - 1] - 0.036
elif rand >= 0.17 and rand < 0.3:
xx[irun] = 0.17 * xx[irun - 1] - 0.215 * yy[irun - 1] + 0.408
yy[irun] = 0.222 * xx[irun - 1] + 0.176 * yy[irun - 1] - 0.0893
else:
xx[irun] = 0.781 * xx[irun - 1] + 0.034 * yy[irun - 1] + 0.1075
yy[irun] = -0.032 * xx[irun - 1] + 0.739 * yy[irun - 1] + 0.27
pl.plot(xx, yy, 's', markersize=1)
pl.xlim(0, 1)
pl.ylim(0, 1)
pl.xlabel(r'X', fontsize=20)
pl.ylabel(r'Y', fontsize=20)
pl.show()