def histo(denlist):
for d in denlist:
h,bin_edges = N.histogram(N.log10(d.flatten()),normed=True,range=(-2,2),bins=50)
M.semilogx(10.**(bin_edges),h)
M.show()
def plot_curve(x,
y,
xlog=0,
ylog=0,
xname='x',
yname='y',
oplot=0,
color='k',
lineStyle='-',
linewidth=1,
xrange=None,
yrange=None):
if oplot == 0:
win = Window()
else:
win = None
marker = '%s%s' % (color, lineStyle)
if xlog and ylog:
pylab.loglog(x, y, marker, markersize=3, linewidth=linewidth)
elif xlog:
pylab.semilogx(x, y, marker, markersize=3, linewidth=linewidth)
elif ylog:
pylab.semilogy(x, y, marker, markersize=3, linewidth=linewidth)
else:
pylab.plot(x, y, marker, markersize=3, linewidth=linewidth)
if not oplot:
pylab.xlabel(xname)
pylab.ylabel(yname)
setAxis(xrange, yrange)
return win
def plot_ex(ion, line):
''' Plot the collisions cross-section with electrons
'''
global cst, iplot
iplot += 1
ofname = format(iplot, '0>4')+'_ex.pdf'
T = 5000.
x0 = cst / line.lbd * Cst.Q / Cst.K / T
x_vec_log = pl.linspace(-3, 4, 100)
x_vec = 10**x_vec_log
q0 = [sigma_weak_seaton(i, ion, line, T) for i in x_vec]
q1 = [sigma_strong_seaton(i, ion, line, T) for i in x_vec]
q = [sigma_seaton(i, ion, line, T) for i in x_vec]
ll = line.lower
ul = line.upper
#title='Na I: '+str(ll.cfg)+' '+str(ll.term)+' [g='+str(ll.g)+'] <=> '+str(ul.cfg)+' '+str(ul.term)+' [g='+str(ul.g)+'], Ro = '+format(orbital_radius(ion, line.lower), '4.2f')+' a$_0$, f = '+str(line.f)
title = SPE+' '+DEG+': '+str(ll.cfg)+' '+str(ll.term)+' [g='+str(ll.g)+'] '+str(ul.cfg)+' '+str(ul.term)+' [g='+str(ul.g)+'] Ro = '+str(orbital_radius(ion, line.lower))+' f = '+str(line.f)
pl.figure(figsize=(12, 6), dpi=100, facecolor='w', edgecolor='k')
pl.plot(x_vec*Cst.K*T/Cst.Q, q0, 'k', lw=1, label='Weak coupling')
pl.plot(x_vec*Cst.K*T/Cst.Q, q1, 'k', lw=2, label='Strong coupling')
pl.plot(x_vec*Cst.K*T/Cst.Q, q, 'r+', label='IPM (Seaton 1962)')
pl.semilogx()
#pl.semilogy()
pl.xlim([1e-2, 1e4])
pl.xlabel('E [eV]')
pl.ylabel('Cross-section [$\pi a_0^2$]')
pl.legend(frameon=False, loc=0)
pl.title(title)
#bbox_inches='tight'
pl.savefig(ofname, dpi=100, format='pdf', orientation='landscape', papertype='a4')
pl.close()
def plot_cf(xx, sname=''):
max_y = 0
n = 1
plt.figure()
try:
for x in xx:
plt.fill_between(x[:, 0],
x[:, 1] - x[:, 2],
x[:, 1] + x[:, 2],
alpha=0.5)
plt.semilogx(x[:, 0], x[:, 1], '-', label='q ' + str(n))
#plt.semilogx(x[:,0],x[:,1],'o',label='q '+str(n))
#plt.errorbar(x[:,0],x[:,1],yerr=x[:,2])
if max_y < x[1, 1]:
max_y = x[1, 1] * 1
n += 1
except:
plt.fill_between(xx[:, 0],
xx[:, 1] - xx[:, 2],
xx[:, 1] + xx[:, 2],
alpha=0.5)
plt.semilogx(xx[:, 0], xx[:, 1], '-', label='q ' + str(0))
#plt.semilogx(x[:,0],x[:,1],'o',label='q '+str(n))
if max_y < xx[1, 1]:
max_y = xx[1, 1] * 1
plt.xlabel(r"lag time $\tau$ (s)")
plt.ylabel(r"g$^{(2)}$(q,$\tau$)")
#plt.ylim((1,1+(max_y-1)*1.3))
try:
plt.legend(loc=1, fontsize=10, ncol=n // 6)
except:
plt.legend(loc=1, fontsize=10)
plt.title(sname)
plt.tight_layout()
plt.show()
def main():
print("[INFO] Loading channel data from graphviz file")
xvec, yvec = [], []
runningMean, runningVar = 0.0, 0.0
inFile = sys.argv[1]
csvFile = "%s.csv" % inFile
f = open(csvFile, "w")
f.write("time,n,mutual information\n")
f.close()
for n in np.logspace(1, 5, 20):
print("Computing for sequence of length %s " % n)
ch = Channel(inFile)
t1 = time.time()
inputSeq = np.random.choice(list(ch.inputAlphabets), n)
outputSeq = ch.simulate(inputSeq)
ixy = mutual_info(inputSeq, outputSeq)
t = time.time() - t1
xvec.append(n)
yvec.append(ixy)
print("|- Mutual info %s, time taken %s" % (ixy, t))
print(abs(np.mean(yvec) - ixy), np.std(yvec))
with open(csvFile, "a") as f:
f.write("%s,%s,%s\n" % (t, n, ixy))
pylab.semilogx(xvec, yvec)
pylab.savefig('%s_mutual_info.png' % inFile)
def PLOT_DATA(arr, Xplot, Yplot, lb, Ef, Log, Plot_Label):
p.xlabel(Plot_Label[Xplot][1] + Plot_Label[Xplot][3])
p.ylabel(Plot_Label[Yplot][1] + Plot_Label[Yplot][3])
p.title(Plot_Label[Yplot][1] + " as a function of " + Plot_Label[Xplot][1])
if Xplot == "E":
x = (arr[:, Plot_Label[Xplot][0]] - Ef) * float(Plot_Label[Xplot][4])
else:
x = arr[:, Plot_Label[Xplot][0]] * float(Plot_Label[Xplot][4])
if Yplot == "E":
y = (arr[:, Plot_Label[Yplot][0]] - Ef) * float(Plot_Label[Yplot][4])
else:
y = arr[:, Plot_Label[Yplot][0]] * float(Plot_Label[Yplot][4])
if Xplot == "N" and Log == "y":
indn = p.where(x < 0)
indp = p.where(x > 0)
pp = raw_input("Plot electron or hole ? > ")
if pp == "electron":
p.semilogx(abs(x[indn]), y[indn], label=lb + "$ ; n-Type$")
elif pp == "hole":
p.semilogx(x[indp], y[indp], label=lb + "$ ; p-Type $")
elif Yplot == "N" and Log == "y":
indn = p.where(y < 0)
indp = p.where(y > 0)
pp = raw_input("Plot electron or hole ?? > ")
if pp == "electron":
p.semilogy(x[indn], abs(y[indn]), label=lb + "$ ; n-Type$")
elif pp == "hole":
p.semilogy(x[indp], y[indp], label=lb + "$ ; p-Type$")
else:
p.plot(x, y, label=lb)
def sums2nloop(model_input, images_input, iterations):
"""Run through a loop of calculating the summed S/N for a set of data.
This is useful if you'd like to see how well we'd do as we keep adding
more and more data together in order to attempt to extract a signal.
"""
if images_input == "simdata":
header = model_input.simdata()
else:
header = getheaderinfo(images_input)
# iterations = header['numexp']
snvec = numpy.zeros((iterations, 2))
for i in range(0, iterations):
snvec[i] = models2n(i + 1, model_input, images_input)
print snvec
# GRAB EXPOSURE TIMES
timearr = header["tstart"][0:iterations] # should be error bar from tstart to tstop
# Plot the results
pylab.semilogx(timearr, snvec[:, 1], linestyle="", marker="o")
pylab.semilogx(timearr, snvec[:, 0], linestyle="", marker="x")
pylab.xlim(50, 200000)
pylab.xlabel("Time")
pylab.ylabel("Summed S/N (not a lightcurve!)")
plottitle = "Model: " + model_input.name + ", Data: " + images_input
pylab.title(plottitle)
def flipPlot(minExp, maxExp):
ratios, diffs, xAxis = [], [], []
for exp in range(minExp, maxExp + 1):
xAxis.append(2**exp)
for numFlips in xAxis:
numHeads = 0
for n in range(numFlips):
if random.choice(('H', 'T')) == 'H':
numHeads += 1
numTails = numFlips - numHeads
try:
ratios.append(numHeads / numTails)
diffs.append(abs(numHeads - numTails))
except ZeroDivisionError:
continue
print("xAxis:", xAxis, "ratios:", ratios, "diffs:", diffs)
pylab.title('Difference Between Heads and Tails')
pylab.xlabel('Number of FLips')
pylab.ylabel('Abs(#Heads - #Tails)')
pylab.semilogx()
pylab.semilogy()
pylab.plot(xAxis, diffs, 'ko')
pylab.figure()
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips')
pylab.ylabel('#Heads/#Tails')
pylab.semilogx()
pylab.semilogy()
pylab.plot(xAxis, ratios, 'ko')
def plotevol(dir='test',
var='radius',
ymax=500.0,
col='k',
lab='test',
fig=1,
fhold=False,
lloc='lower left'):
[t, rs, fs] = dataset(dir=dir + '/', var=var)
#if not fhold: pylab.close(fig)
#figure = matplotlib.pyplot.figure(num=fig)
pylab.semilogx(t, fs, col, linewidth=lw, label=lab)
pylab.hold(fhold)
#if var != 'Mach':
# pylab.semilogx(t,rs,label='RS')
#if var == 'velocity':
# pylab.ylim([1.e1, 1.e4])
pylab.xlim([10.0, 5.0e3])
pylab.xlabel('t, years')
ylab = var
ylab = ylab.replace('_', '_{')
if '_{' in ylab: ylab = ylab + '}'
pylab.ylim([0.0, ymax])
pylab.ylabel('$\mathrm{' + ylab + '}$')
pylab.legend(loc=lloc)
var = var.replace('/', '_')
print var
if not fhold:
matplotlib.pyplot.savefig(basedir + dir + '/' + var + '_t.eps')
def plot():
for time_filename, size_filename, f_label, f_style in lines_to_plot:
time_file = open('parser_results/' + time_filename)
size_file = open('parser_results/' + size_filename)
times = {}
for mark, _, _ in swarmsize_lines:
times[mark] = []
for line in time_file:
time = float(line.split()[0])
size = int(size_file.next().split()[0])
for mark, _, _ in swarmsize_lines:
if size <= mark:
times[mark].append(time)
break
for mark, m_label, m_style in reversed(swarmsize_lines):
cum_values = cdf.cdf(times[mark])
x = [cum_value[1] for cum_value in cum_values]
y = [cum_value[0] for cum_value in cum_values]
pylab.semilogx(x, y, f_style+m_style,
label=f_label+' '+m_label,
markevery=markevery)
pylab.legend(loc='lower right')
pylab.savefig(output_filename)
# pylab.close()
print 'Output saved to:', output_filename
def _cumdist(sample, annotation='', color='k'):
order = np.argsort(sample['giso_insol'].values)[::-1]
w = np.cumsum(sample['weight'].values[order]) / num_stars
n = np.array(range(len(sample['weight'].values))) / float(len(sample))
f = sample['giso_insol'].values[order]
pl.step(f,
n,
'k-',
lw=2,
linestyle='dashed',
alpha=0.25,
color=color,
label=None)
pl.step(f, w / np.max(w), 'k-', lw=2, color=color)
aloc = (0.1, 0.85)
#pl.annotate(annotation, xy=aloc, xycoords='axes fraction', fontsize=20,
# horizontalalignment='left')
pl.semilogx()
pl.xlim(2000, 30)
pl.ylim(0, 1)
ax = pl.gca()
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_major_formatter(
matplotlib.ticker.FormatStrFormatter('%0.0f'))
ax.xaxis.set_ticks([30, 100, 300, 1000, 3000])
pl.xlabel('Stellar light intensity relative to Earth (S)')
# pl.ylabel('cumulative fraction of planet detections')
pl.ylabel(r'Fraction of planets with S$_{\rm inc} < S$')
def filterresponse(b,a,fs=44100,scale='log',**kwargs):
w, h = freqz(b,a)
pl.subplot(2,1,1)
pl.title('Digital filter frequency response')
pl.plot(w/max(w)*fs/2, 20 * np.log10(abs(h)),**kwargs)
pl.xscale(scale)
# if scale=='log':
# pl.semilogx(w/max(w)*fs/2, 20*np.log10(np.abs(h)), 'k')
# else:
# pl.plot(w/max(w)*fs/2, 20*np.log10(np.abs(h)), 'k')
pl.ylabel('Gain (dB)')
pl.xlabel('Frequency (rad/sample)')
pl.axis('tight')
pl.grid()
pl.subplot(2,1,2)
angles = np.unwrap(np.angle(h))
if scale=='log':
pl.semilogx(w/max(w)*fs/2, angles, **kwargs)
else:
pl.plot(w/max(w)*fs/2, angles, **kwargs)
pl.ylabel('Angle (radians)')
pl.grid()
pl.axis('tight')
pl.xlabel('Frequency (rad/sample)')
def noise_data(self, chan=0, m=1, plot=False):
NFFT = self.NFFT
N = self.N
lendata = self.lendata
Fs = self.Fs
pst = np.zeros(NFFT)
cnt = 0
while cnt < m:
data, addr = self.r.get_data_udp(N*lendata, demod=True)
if self.use_r2 and ((not np.all(np.diff(addr)==2**21/N)) or data.shape[0]!=256*lendata):
print "bad"
else:
ps, f = mlab.psd(data[:,chan], NFFT=NFFT, Fs=Fs/self.r.nfft)
pst += ps
cnt += 1
pst /= cnt
pst = 10.*np.log10(pst)
if plot:
ind = f > 0
pl.semilogx(f[ind], pst[ind])
pl.xlabel('Hz')
pl.ylabel('dB/Hz')
pl.title('chan data psd')
pl.grid()
pl.show()
return f, pst
def test_cross_validation():
np.random.seed(1)
n_features = 3
func = get_test_model(n_features=n_features)
dataset_train, labels_train = get_random_dataset(func,
n_datapoints=5e3, n_features=n_features, noise_level=1.)
alphas = 10**np.linspace(-3, 2, 10)
# Fit scikit lasso
lasso_scikit = lasso.SKLasso(alpha=0.001, max_iter=1000)
lasso_scikit.fit_CV(dataset_train, labels_train[:,0], alphas=alphas,
n_folds=5)
# Fit tf lasso
gen_lasso = gl.GeneralizedLasso(alpha=0.001, max_iter=1000,
link_function=None)
gen_lasso.fit_CV(dataset_train, labels_train[:,0], alphas=alphas,
n_folds=5)
pl.figure()
pl.title('CV test. TF will have higher errors, since it is not exact')
pl.semilogx(alphas, gen_lasso.alpha_mse, 'o-', label='tf')
pl.semilogx(alphas, lasso_scikit.alpha_mse, '*-', label='scikit')
pl.legend(loc='best')
pl.xlabel('alpha')
pl.ylabel('cost')
pl.show()
def plot(self, marker='o', color='red', m=0, M=6000):
"""Plots the position / height of the peaks in the well
.. plot::
:include-source:
from fragment_analyser import Line, fa_data
l = Line(fa_data("alternate/peaktable.csv"))
well = l.wells[0]
well.plot()
"""
import pylab
if len(self.df) == 0:
print("Nothing to plot (no peaks)")
return
x = self.df['Size (bp)'].astype(float).values
y = self.df['RFU'].astype(float).values
pylab.stem(x, y, marker=marker, color=color)
pylab.semilogx()
pylab.xlim([1, M])
pylab.ylim([0, max(y) * 1.2])
pylab.grid(True)
pylab.xlabel("size (bp)")
pylab.ylabel("RFU")
return x, y
def PlotScaledPathLength_vs_pZL(LZarray, numtries=2,
pZLarray=10.**numpy.arange(-1., 2.001, 0.25)):
"""
PlotScaledPathLength_vs_pZL(((L1,Z1),(L2,Z2),...), numtries,
[pZLa,pZLb,pZLc...])
will plot the scaled path length for small world networks of size Li and
neighbors Zi, at scaled rewiring probabilities pZLa, pZLb, ...
Uses either MultiPlot.py to do the scaling, or rescales by hand, depending
on the implementation chosen.
To rescale, p is multiplied by Z*L and the mean path length ell is
multiplied by 2*Z/L.
"""
pathlengthBar = {}
pathlengthSigma = {}
pdata = {}
for L,Z in LZarray:
# Shift evaluated points to good, scaled range
pdata[L,Z] = pZLarray/(Z*L)
pathlengthBar[L,Z], pathlengthSigma[L,Z] = \
GetPathLength_vs_p(L, Z, numtries, pdata[L,Z])
pylab.figure(1)
MultiPlot.MultiPlot(pdata, pathlengthBar,
xform='p->p', yform='ell->ell',
yerrdata = pathlengthSigma, showIt=False)
pylab.semilogx()
pylab.figure(2)
MultiPlot.MultiPlot(pdata, pathlengthBar,
xform='p->p*Z*L', yform='ell->(2*ell*Z)/L',
yerrdata=pathlengthSigma,
yerrform='sig->(2*sig*Z)/L',
keyNames=('L','Z'),
loc = 3, showIt=False)
pylab.semilogx()
pylab.show()
def get_opt_theta(n, T, steps_range, which, label = 'label'):
parameters = get_parameters(which)
prob = Problem_FSI_1D(n = n, **parameters)
dx = prob.dx
N_steps = np.array([int(2**i) for i in np.linspace(0, 50, 1000)])
dts = T/N_steps
CFL = [dt/(dx**2) for dt in dts]
theta = [prob.DNWR_theta_opt(dt, dt) for dt in dts]
lim_zero = parameters['alpha_2']/(parameters['alpha_1'] + parameters['alpha_2'])
lim_inf = parameters['lambda_2']/(parameters['lambda_1'] + parameters['lambda_2'])
pl.figure()
pl.semilogx(CFL, theta, label = label)
pl.axhline(lim_zero, ls = '--', color = 'k', label = r'$\theta_{c \rightarrow 0}$')
pl.axhline(lim_inf, ls = ':', color = 'k', label = r'$\theta_{c \rightarrow \infty}$')
pl.legend()
pl.xlabel('c', labelpad = -30, position = (1.05, -1), fontsize = 20)
lp = -50 if label == 'Air-Steel' else -20
pl.ylabel(r'$\theta_{opt}$', rotation = 0, labelpad = lp, position = (2., 1.), fontsize = 20)
pl.title(label)
pl.savefig('plots/CFL_vs_opt_theta_{}.png'.format(which), dpi = 100)
return
def showFreqComplex(f, vector, title='Magnitude/Phase Frequency Plot'):
"""
@showFreqComplex
showFreqComplex(f,vector,title)
Linear frequency magnitude and phase plot
Required parameters:
f : Frequency vector (Hz)
vector : Complex vector
Optional parameters:
title : Plot title
Returns nothing
"""
fig = _plotStart()
ax = _subplotStart(fig, 211, title, '', 'Magnitude')
mag = np.absolute(vector)
pl.semilogx(f, mag)
_subplotEnd(ax)
ax = _subplotStart(fig, 212, '', 'Frequency (Hz)', 'Phase')
phase = np.angle(vector, deg=True)
pl.semilogx(f, phase)
_subplotEnd(ax)
_plotEnd()
def array_plot(request):
halo = halo_from_request(request)
name = decode_property_name(request.matchdict['nameid'])
val, property_info = halo.calculate(name, True)
if len(val.shape) > 1:
return image_plot(request, val, property_info)
with _matplotlib_lock:
start(request)
p.plot(property_info.plot_x_values(val), val)
if property_info.plot_xlog() and property_info.plot_ylog():
p.loglog()
elif property_info.plot_xlog():
p.semilogx()
elif property_info.plot_ylog():
p.semilogy()
if property_info.plot_yrange():
p.ylim(*property_info.plot_yrange())
add_xy_labels(property_info, request)
return finish(request)
def clust(Nclust=25,loadfile='/media/HD1_/Documents/AG3C/Results/formated_data.p',writefile='/media/HD1_/Documents/AG3C/Results/cluster_kmeans.p',plots=True):
#load the formated data
(avout,stdout,av_reference)=pickle.load(open(loadfile,'rb'))
TFav=[]
TFref=[]
TFstd=[]
#do k-means clustering
(o,n)=kmeans2(np.array(avout),Nclust)
#save the output
pickle.dump((o,n),open(writefile,'wb'))
if plots==True:
#make the plots
t=[3.,4.,5.,6.,8.,24.,48,7*24.,14*24.]
plt.figure()
for i in range(Nclust):
idx=np.where(n==i)[0]
plt.subplot(np.ceil(np.sqrt(Nclust)),np.ceil(np.sqrt(Nclust)),i+1)
plt.hold(True)
for j in range(len(idx)):
plt.semilogx(t,avout[idx[j]])
plt.ylim([0,1])
plt.show()
def plotResults(self, titlestr="", ylimits=[0.5,1.05], plotfunc = pl.semilogx, ylimitsB=[0,101],
legend_loc=3, show=True ):
pl.figure(num=None, figsize=(15,5))
xvals = range(1, (1+len(self.removed)) )
#Two subplots. One the left is the test accuracy vs. iteration
pl.subplot(1,2,1)
plotfunc(xvals, self.test_acc_list, "b", label="Test Accuracy")
pl.hold(True)
plotfunc(xvals, self.getRollingAvgTestAcc(window_size=10), "r", label="Test Acc (rolling avg)")
plotfunc(xvals, self.getRollingAvgTrainAcc(window_size=10), "g--", label="Train Acc (rolling avg)")
pl.ylim(ylimits)
if titlestr == "":
pl.title("Iterative Feature Removal")
else:
pl.title(titlestr)
pl.ylabel("Test Accuracy")
pl.xlabel("Iteration")
pl.legend(loc=legend_loc) #3=lower left
pl.hold(False)
#second subplot. On the right is the number of features removed per iteration
pl.subplot(1,2,2)
Ns = [ len(lst) for lst in self.removed ]
pl.semilogx(xvals, Ns, "bo", label="#Features per Iteration")
pl.xlabel("Iteration")
pl.ylabel("Number of Features Selected")
pl.title("Number of Features Removed per Iteration")
pl.ylim(ylimitsB)
pl.subplots_adjust(left=0.05, bottom=0.15, right=0.95, top=0.90, wspace=0.20, hspace=0.20)
if show: pl.show()
def flipPlot(minExp,maxExp):
'''假定minExp和maxExp是正整数，并且minExp＜maxExp，
绘制出2**minExp到2**maxExp次抛硬币的结果'''
ratios=[]
diffs=[]
xAxis=[]
for exp in range(minExp,maxExp+1):
xAxis.append(2**exp)
for numFlips in xAxis:
numHeads=0
for n in range(numFlips):
if random.random()<0.5:
numHeads+=1
numTails=numFlips-numHeads
ratios.append(numHeads/float(numTails))
diffs.append(abs(numHeads-numTails))
pylab.title('Difference Between Heads and Tails')
pylab.xlabel('Number of Flips')
pylab.semilogx()
pylab.semilogy()
pylab.ylabel('Abs(#Heads-#Tails)')
pylab.plot(xAxis,diffs,'bo')
pylab.figure()
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips')
pylab.semilogx()
pylab.ylabel('#Heads/#Tails')
pylab.plot(xAxis,ratios,'bo')
def plot_passing_rate(depth):
import pylab
ax = pylab.gca()
enu = numpy.logspace(3, 7, 101)
for zenith, color in zip(reversed((0, 41.4, 69.5, 81.4)), colors):
cos_theta = numpy.cos(numpy.radians(zenith))
emu = minimum_muon_energy(overburden(cos_theta, depth))
correlated = correlated_passing_rate(enu, emu, cos_theta)
uncorr_numu = uncorrelated_passing_rate(enu,
emu,
cos_theta,
kind='numu')
uncorr_nue = uncorrelated_passing_rate(enu, emu, cos_theta, kind='nue')
pylab.plot(enu, correlated, color=color, label='%d' % zenith)
pylab.plot(enu, correlated * uncorr_numu, color=color, ls=':')
pylab.plot(enu, uncorr_nue, color=color, ls='--')
pylab.semilogx()
pylab.ylim((0, 1))
pylab.ylabel('Passing fraction')
pylab.xlabel('Neutrino energy [GeV]')
olegend = pylab.legend(loc='lower left', title='Zenith [deg]')
pylab.legend(
ax.lines[:3],
(r'$\nu_{\mu}$ (Correlated)', r'$\nu_{\mu}$ (Total)', r'$\nu_{e}$'),
loc='center right',
prop=dict(size='small'))
ax.add_artist(olegend)
pylab.title('Atmospheric neutrino self-veto at %d m depth' % (opts.depth))
pylab.show()
def plot():
(n, P, eP, A, eA, ALines) = numbers()
pylab.subplot(2, 1, 1)
pylab.errorbar(n, P, eP, ecolor='r', linewidth=3, fmt=None)
pylab.semilogx()
pylab.xticks([2, 4, 8, 16, 32, 64, 128],
['2', '4', '8', '16', '32', '64', '128'])
pylab.axis([1, 256, 4, 18])
pylab.ylabel('Precision of 95% Confidence')
pylab.title(
'Estimating Time Constant of RC circuit, Gaussian Noise, 1000 trials')
pylab.subplot(2, 1, 2)
pylab.plot(n, A, '*', color='r', markersize=12)
#pylab.axhspan(ALines[0],ALines[0],color='b')
pylab.axhspan(ALines[1], ALines[1], color='b')
pylab.axhspan(ALines[2], ALines[2], color='b')
pylab.semilogx()
pylab.xticks([2, 4, 8, 16, 32, 64, 128],
['2', '4', '8', '16', '32', '64', '128'])
pylab.axis([1, 256, 0.9, 1.0])
pylab.text(1.5, 0.95, 'Accurate')
pylab.text(1.5, 0.99, 'Under-Confident')
pylab.text(1.5, 0.91, 'Over-Confident')
pylab.xlabel('Number of Data Points')
pylab.ylabel('Accuracy of 95% Confidence')
pylab.ion()
def prad_err_hist():
old = cksgaia.io.load_table('j17')
new = cksgaia.io.load_table(full_sample)
old['iso_prad_frac_err'] = cksgaia.errors.frac_err(old['iso_prad'],
old['iso_prad_err1'],
old['iso_prad_err2'])
new['gdir_prad_frac_err'] = cksgaia.errors.frac_err(
new['gdir_prad'], new['gdir_prad_err1'], new['gdir_prad_err2'])
old['iso_prad_frac_err'] *= 100
new['gdir_prad_frac_err'] *= 100
print(len(old), len(new))
med_old = np.nanmedian(old['iso_prad_frac_err'])
med_new = np.nanmedian(new['gdir_prad_frac_err'])
xbins = np.logspace(np.log10(0.5), np.log10(50.0), 50)
old['iso_prad_frac_err'].hist(bins=xbins,
histtype='step',
lw=2,
color='0.7')
new['gdir_prad_frac_err'].hist(bins=xbins,
histtype='step',
lw=2,
color='k')
pl.axvline(med_old, color='0.7', linestyle='dashed', lw=2)
pl.axvline(med_new, color='k', linestyle='dashed', lw=2)
pl.annotate("median = {:.0f}%".format(np.round(med_old)),
xy=(med_old, 200),
xycoords='data',
xytext=(-15, 0),
textcoords='offset points',
rotation=90,
verticalalignment='left')
pl.annotate("median = {:.0f}%".format(np.round(med_new)),
xy=(med_new, 200),
xycoords='data',
xytext=(-15, 0),
textcoords='offset points',
rotation=90,
verticalalignment='left')
pl.xlim(0.5, 40.0)
# pl.ylim(0, 130)
pl.ylabel('Number of Planets')
pl.xlabel('Fractional Planet Radius Uncertainty [%]')
# pl.title('NEA')
pl.semilogx()
ax = pl.gca()
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_major_formatter(matplotlib.ticker.FormatStrFormatter('%0.1f'))
pl.xticks([1.0, 2.0, 3.0, 5.0, 10.0, 15.0, 30.0])
pl.grid(False)
pl.legend(['Johnson+17', 'this work'], loc='upper left')
def plot_mass_magnitude(self, mag, savefile=None):
"""
Make a standard mass-luminosity relation plot for this isochrone.
Parameters
----------
mag : string
The name of the magnitude column to be plotted.
savefile : string (default None)
If a savefile is specified, then the plot will be saved to that file.
"""
plt.clf()
plt.semilogx(self.points['mass'], self.points[mag], 'k.')
plt.gca().invert_yaxis()
plt.xlabel(r'Mass (M$_\odot$)')
plt.ylabel(mag + ' (mag)')
fmt_title = 'logAge={0:.2f}, d={1:.2f} kpc, AKs={2:.2f}'
plt.title(fmt_title.format(self.points.meta['LOGAGE'],
self.points.meta['DISTANCE']/1e3,
self.points.meta['AKS']))
if savefile != None:
plt.savefig(savefile)
return
def PlotScaledPathLength_vs_pZL(LZarray, numtries=2,
pZLarray=10.**numpy.arange(-1., 2.001, 0.25)):
"""
PlotScaledPathLength_vs_pZL(((L1,Z1),(L2,Z2),...), numtries,
[pZLa,pZLb,pZLc...])
will plot the scaled path length for small world networks of size Li and
neighbors Zi, at scaled rewiring probabilities pZLa, pZLb, ...
Uses either MultiPlot.py to do the scaling, or rescales by hand, depending
on the implementation chosen.
To rescale, p is multiplied by Z*L and the mean path length ell is
multiplied by 2*Z/L.
"""
pathlengthBar = {}
pathlengthSigma = {}
pdata = {}
for L,Z in LZarray:
# Shift evaluated points to good, scaled range
pdata[L,Z] = pZLarray/(Z*L)
pathlengthBar[L,Z], pathlengthSigma[L,Z] = \
GetPathLength_vs_p(L, Z, numtries, pdata[L,Z])
pylab.figure(1)
MultiPlot.MultiPlot(pdata, pathlengthBar,
xform='p->p', yform='ell->ell',
yerrdata = pathlengthSigma, showIt=False)
pylab.semilogx()
pylab.figure(2)
MultiPlot.MultiPlot(pdata, pathlengthBar,
xform='p->p*Z*L', yform='ell->(2*ell*Z)/L',
yerrdata=pathlengthSigma,
yerrform='sig->(2*sig*Z)/L',
keyNames=('L','Z'),
loc = 3, showIt=False)
pylab.semilogx()
pylab.show()
def check_recovered_positions(self,
toleranceArcsec=12.0,
SNRKey='SNR',
SNRMax=10.0,
plotLabel=None,
plotsDir="plots"):
"""Blah
"""
inTab = self.inTab
outTab = self.outTab
rDeg = self.rDeg
SNRs = outTab[SNRKey]
medOffsetArcmin = np.median(rDeg) * 60
SNRMask = np.less(SNRs, SNRMax)
medOffsetArcmin_SNR10 = np.median(rDeg[SNRMask]) * 60 #[SNRMask])*60
print('... median recovered position offset = %.2f" (full sample)' %
(medOffsetArcmin * 60))
label = 'median recovered position offset = %.2f" (SNR < 10)' % (
medOffsetArcmin_SNR10 * 60)
print("... %s" % (label))
if plotLabel is not None:
plt.figure(figsize=(10, 8))
plt.plot(SNRs, rDeg * 3600., 'r.')
plt.semilogx()
plt.xlabel("SNR")
plt.ylabel('Position offset (")')
plt.title(label, fontdict={'size': plotTitleSize})
plt.savefig(plotsDir + os.path.sep + plotLabel + "_posRec.png")
plt.close()
if medOffsetArcmin_SNR10 * 60 > toleranceArcsec:
self._status = "FAILED"
else:
self._status = "SUCCESS"
def flipPlot(minExp, maxExp):
"""Assumes minExp and maxExp positive integers; minExp < maxExp
Plots results of 2**minExp to 2**maxExp coin flips"""
ratios = []
diffs = []
xAxis = []
for exp in range(minExp, maxExp + 1):
xAxis.append(2 ** exp)
for numFlips in xAxis:
numHeads = 0
for n in range(numFlips):
if random.random() < 0.5:
numHeads += 1
numTails = numFlips - numHeads
ratios.append(numHeads / float(numTails))
diffs.append(abs(numHeads - numTails))
pylab.title('Difference Between Heads and Tails')
pylab.xlabel('Number of Flips')
pylab.ylabel('Abs(#Heads - #Tails)')
pylab.rcParams['lines.markersize'] = 10
pylab.semilogx()
pylab.semilogy()
pylab.plot(xAxis, diffs, 'bo')
pylab.figure()
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips')
pylab.ylabel('Heads/Tails')
pylab.plot(xAxis, ratios)
def compare(self, x, precision, target, linear=False, diff="relative"):
r"""
Compare the different computation methods using the given precision.
"""
if precision == 'single':
#n=11; plotdiff(x, target, self.call_mpmath(x, n), 'mp %d bits'%n, diff=diff)
#n=23; plotdiff(x, target, self.call_mpmath(x, n), 'mp %d bits'%n, diff=diff)
pass
elif precision == 'double':
#n=53; plotdiff(x, target, self.call_mpmath(x, n), 'mp %d bits'%n, diff=diff)
#n=83; plotdiff(x, target, self.call_mpmath(x, n), 'mp %d bits'%n, diff=diff)
pass
plotdiff(x,
target,
self.call_numpy(x, precision),
'numpy ' + precision,
diff=diff)
plotdiff(x,
target,
self.call_ocl(x, precision, 0),
'OpenCL ' + precision,
diff=diff)
pylab.xlabel(self.xaxis)
if diff == "relative":
pylab.ylabel("relative error")
elif diff == "absolute":
pylab.ylabel("absolute error")
else:
pylab.ylabel(self.name)
pylab.semilogx(x, target, '-', label="true value")
if linear:
pylab.xscale('linear')
def plotBode(f*g, lowerFreq, higherFreq = None):
"""' plot Bode diagram using matplotlib
Default frequency width is 3 decades
'"""
import pylab as py
#import pdb; pdb.set_trace()
if ( higherFreq == None):
rangeAt = 1000.0 # 3 decade
else:
assert higherFreq > lowerFreq
rangeAt = float(higherFreq)/lowerFreq
N = 128
lstScannAngFreqAt = [2*sc.pi*1j*lowerFreq*sc.exp(
sc.log(rangeAt)*float(k)/N)
for k in range(N)]
t = [ lowerFreq*sc.exp(sc.log(rangeAt)*float(k)/N)
for k in range(N)]
py.subplot(211)
py.loglog( t, [(abs(f*g(x))) for x in lstScannAngFreqAt] )
py.ylabel('gain')
py.grid(True)
py.subplot(212)
py.semilogx( t, [sc.arctan2(f*g(zz).imag, f*g(zz).real)
for zz in lstScannAngFreqAt])
py.ylabel('phase')
py.grid(True)
py.gca().xaxis.grid(True, which='minor') # minor grid on too
py.show()
def flipPlot(minExp, maxExp):
""" Assumes minExp and maxExp positive integers; minExp < maxExp
Plots results of 2**minExp to 2**maxExp coin flips"""
ratios = []
diffs = []
xAxis = []
for exp in range(minExp, maxExp + 1):
xAxis.append(2**exp)
for numFlips in xAxis:
numHeads = 0
for n in range(numFlips):
if random.random() < 0.5:
numHeads += 1
numTails = numFlips - numHeads
ratios.append(numHeads / float(numTails))
diffs.append(abs(numHeads - numTails))
pylab.title('Difference between heads and tails')
pylab.xlabel('Number of flips')
pylab.ylabel('Abs(#Heads - #Tails)')
pylab.plot(xAxis, diffs, 'bo')
pylab.semilogx()
pylab.semilogy()
pylab.figure()
pylab.title('Heads/Tails Ratio')
pylab.xlabel('Number of flips')
pylab.ylabel('#Heads/#Tails')
pylab.plot(xAxis, ratios, 'bo')
pylab.semilogx()
pylab.show()
def print_ROC(multi_result, n_imgs, save_filename = None, show_curve = True,
xmin = None, ymin = None, xmax = None, ymax = None,
grid_major = False, grid_minor = False):
points = []
n_imps = float(n_imgs)
for result in multi_result:
tp = float(result.n_true_positives())
fp = float(result.n_false_positives())
fn = float(result.n_false_negatives())
#n_imgs = float(len(result.images))
points.append((fp / n_imgs, tp / (tp + fn)))
points.sort()
if save_filename != None:
f = open(save_filename, "w")
cPickle.dump(points, f)
f.close()
if show_curve:
pylab.semilogx([a[0] for a in points], [a[1] for a in points])
if xmin != None:
pylab.axis(xmin = xmin)
if xmax != None:
pylab.axis(xmax = xmax)
if ymin != None:
pylab.axis(ymin = ymin)
if ymax != None:
pylab.axis(ymax = ymax)
pylab.xlabel("False positives per image (FPPI)")
pylab.ylabel("Detection rate")
pylab.show()
def plotResistance():
dic = makeListResistanceSims(3)
pl.close("all")
colors = []
for k,key in enumerate(np.sort(dic.keys())):
pl.figure(k,(11,7))
pl.title("starting property violation: %s"%key)
low = 0
high = 0
if rootDir == '/Users/maithoma/work/compute/pgames_resistance/':
range = np.arange(key,0.11,0.005)
iter = 2000
elif rootDir == '/Users/maithoma/work/compute/pgames_resistance2/':
range = np.arange(0.043,0.062,0.002)
iter = 4000
color = np.linspace(0.7,0.3,len(range))
for i,s in enumerate(range):
for j,jx in enumerate(dic[key]):
if j==0:
label = str(s)
elif j>0:
label = '_nolegend_'
filename = 'iter_%s_l_100_h_100_d_0.500_cl_0.500_ns_4_il_1.000_q_0.000_M_5_m_0.000_s_%.4f_%s.csv'%(iter,s,jx)
print filename
#if s < 0.04 or s > 0.065:
# continue
try:
x,y = coopLevel(filename)
#print key,i,jx,s,y[-1],filename
if float(y[-1]) < 0.7:
pl.semilogx(x,y,alpha=1,lw= 1.,label=label,color=[color[i],color[i],1])
#pl.semilogx(x,y,alpha=1,lw= 1.,label=label,color='c')
low +=1
elif float(y[-1]) > 0.7:
pl.semilogx(x,y,alpha=1,lw= 1.,label=label,color=[color[i],1,color[i]])
#pl.semilogx(x,y,alpha=1,lw= 1.,label=label,color='m')
high +=1
except:
continue
#pl.text(100000,0.85,"high:%s \n low:%s"%(high,low))
pl.legend(loc=0)
pl.xlabel("Iterations (log10)")
pl.ylabel("Cooperation Level")
pl.xlim(xmax=5*10**8)
pl.ylim(0.,1)
def flip_plot(min_exp, max_exp):
"""
由min_exp,max_exp生成 一系列 2 ** min_exp, 2**(min_exp+1) .... 2 ** max_exp。
这些序列将作为 num_flips的实际参数
"""
ratios, diffs, x_axis = [], [], []
for exp in range(min_exp, max_exp + 1):
x_axis.append(2**exp)
for num_flip in x_axis:
num_heads = 0
for flip in range(num_flip):
if 'H' == random.choice(["H", "T"]):
num_heads += 1
num_tails = num_flip - num_heads
try:
ratios.append(num_heads / num_tails)
diffs.append(abs(num_heads - num_tails))
except ZeroDivisionError:
continue
pylab.figure(1)
pylab.semilogx(base=2) # 修改代码
pylab.semilogy() # 修改代码
pylab.title(" Difference Between Heads and Tails")
pylab.xlabel('Number of Flips')
pylab.ylabel('Abs(#Heads - #Tails)')
pylab.plot(x_axis, diffs, 'ko') # 修改代码
pylab.figure(2)
pylab.semilogx(base=2) # 修改代码
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips ')
pylab.ylabel(' Heads/ #Tails')
pylab.plot(x_axis, ratios, 'ko') # 修改代码
pylab.show()
def test_regularization():
np.random.seed(1)
n_features = 5
func = get_test_model(n_features=n_features)
dataset_train, labels_train = get_random_dataset(func, n_datapoints=1e3,
n_features=n_features,
noise_level=1.e-10)
alphas = 10**np.linspace(-1, 3, 10)
alpha_coeffs = np.zeros([n_features, len(alphas)])
for i, alpha in enumerate(alphas):
gen_lasso = gl.GeneralizedLasso(alpha=alpha, max_iter=2000,
link_function=None)
gen_lasso.fit(dataset_train, labels_train[:,0])
alpha_coeffs[:,i] = gen_lasso.coeffs[:,0]
# Plot results
for i in range(n_features):
pl.semilogx(alphas, alpha_coeffs[i,:], label='coeff no %d' % i)
pl.semilogx(alphas, np.ones_like(alphas)*func.coeffs[i], ':')
pl.legend(loc='best')
pl.title('Test regularization')
pl.ylabel('Coeff value')
pl.xlabel('alpha')
pl.show()
def logscale_rad(axis='y'):
if axis == 'x':
pl.semilogx()
pl.xlabel('Planet Size [Earth radii]')
pl.xticks([.7, 1.0, 1.3, 1.8, 2.4, 3.5, 4.5, 6, 8.0])
# pl.xticks(np.logspace(np.log10(0.7), np.log10(8), 6))
ax = pl.gca()
ax.xaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.xaxis.set_major_formatter(
matplotlib.ticker.FormatStrFormatter('%0.1f'))
pl.xlim(0.7, 6.0)
elif axis == 'y':
pl.semilogy()
pl.ylabel('Planet Size [Earth radii]')
pl.yticks([.7, 1.0, 1.3, 1.8, 2.4, 3.5, 4.5, 6, 8.0])
# pl.xticks(np.logspace(np.log10(0.7), np.log10(8), 6))
ax = pl.gca()
ax.yaxis.set_major_formatter(matplotlib.ticker.ScalarFormatter())
ax.yaxis.set_major_formatter(
matplotlib.ticker.FormatStrFormatter('%0.1f'))
pl.ylim(0.7, 6.0)
else:
print("Please specify x or y axis (default: x)")
return ax
def flipPlot(minExp, maxExp):
"""minExpとmaxExpは minExp < maxExp を満たす正の整数とする
2**minExp から 2**maxExp 回のコイン投げの結果をプロットする"""
ratios = []
diffs = []
xAxis = []
for exp in range(minExp, maxExp + 1):
xAxis.append(2**exp)
for numFlips in xAxis:
numHeads = 0
for n in range(numFlips):
if random.random() < 0.5:
numHeads += 1
numTails = numFlips - numHeads
ratios.append(numHeads / float(numTails))
diffs.append(abs(numHeads - numTails))
pylab.title('Difference Between Heads and Tails ')
pylab.xlabel('Number of Flips')
pylab.ylabel('Abs(#Heads - #Tails)')
pylab.semilogx()
pylab.semilogy()
pylab.plot(xAxis, diffs, 'bo')
pylab.figure()
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips')
pylab.ylabel('#Heads/#Tails')
pylab.semilogx()
pylab.semilogy()
pylab.plot(xAxis, ratios, 'bo')
def drunkTest(walkLengths, numTrials, dClass):
"""walkLength: 0 以上の整数シークエンス
numTrials: 正の整数
dClass: Drunk型のサブクラス
walkLength の各要素を酔歩の移動回数として, numTrials 回の酔歩を
シミュレートする simWalks を実行し, 結果を出力する. """
means = []
for numSteps in walkLengths:
distances = simWalks(numSteps, numTrials, dClass)
print(dClass.__name__, 'random walk of', numSteps, 'steps')
print(' Mean =',
sum(distances) / len(distances), 'CV =', CV(distances))
print(' Max =', max(distances), 'Min =', min(distances))
means.append(sum(distances) / len(distances))
styleChoice = styleIterator(('b-', 'r:', 'm-.'))
curStyle = styleChoice.nextStyle()
pylab.plot(walkLengths, means, curStyle, label='Distance form origin')
pylab.plot(walkLengths, [pow(x, 0.5) for x in walkLengths],
styleChoice.nextStyle(),
label='Square root of steps')
pylab.title('Average Distance from Origine ({} trials)'.format(numTrials))
pylab.xlabel('Number of Steps')
pylab.ylabel('Distance from Origin')
pylab.legend(loc='best')
pylab.semilogx()
pylab.semilogy()
pylab.show()
def chunked_timing(X, Y, axis=1, metric="euclidean", **kwargs):
sizes = [20, 50, 100,
200, 500, 1000,
2000, 5000, 10000,
20000, 50000, 100000,
200000]
t0 = time.time()
original(X, Y, axis=axis, metric=metric, **kwargs)
t1 = time.time()
original_timing = t1 - t0
chunked_timings = []
for batch_size in sizes:
print("batch_size: %d" % batch_size)
t0 = time.time()
chunked(X, Y, axis=axis, metric=metric, batch_size=batch_size,
**kwargs)
t1 = time.time()
chunked_timings.append(t1 - t0)
import pylab as pl
pl.semilogx(sizes, chunked_timings, '-+', label="chunked")
pl.hlines(original_timing, sizes[0], sizes[-1],
color='k', label="original")
pl.grid()
pl.xlabel("batch size")
pl.ylabel("execution time (wall clock)")
pl.title("%s %d / %d (axis %d)" % (
str(metric), X.shape[0], Y.shape[0], axis))
pl.legend()
pl.savefig("%s_%d_%d_%d" % (str(metric), X.shape[0], Y.shape[0], axis))
pl.show()
def flipPlot(minExp, maxExp):
ratios = []
diffs = []
xAxis = []
for exp in range(minExp, maxExp + 1):
xAxis.append(2**exp)
print "xAxis: ", xAxis
for numFlips in xAxis:
numHeads = 0
for n in range(numFlips):
if random.random() < 0.5:
numHeads += 1
numTails = numFlips - numHeads
ratios.append(numHeads / float(numTails))
diffs.append(abs(numHeads - numTails))
pylab.figure()
pylab.title('Difference Between Heads and Tails')
pylab.xlabel('Number of Flips')
pylab.ylabel('Abs(#Heads - #Tails')
pylab.plot(xAxis, diffs, 'bo') #do not connect, show dot
pylab.semilogx()
pylab.semilogy()
pylab.figure()
pylab.plot(xAxis, ratios, 'bo') #do not connect, show dot
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips')
pylab.ylabel('Heads/Tails')
pylab.semilogx()
def tossPlot(minTrials, maxTrials):
ratios, diffs, xaxis = [], [], []
#diffs = []
#xaxis = []
for n in range(minTrials, maxTrials + 1):
xaxis.append(2**n)
#print(xaxis) #the number of times coin is tossed
for numFlips in xaxis:
numHeads = 0
for n in range(numFlips):
if random.random() < 0.5:
numHeads += 1
numTails = numFlips - numHeads
#print(numHeads, numTails)
ratios.append(numHeads / float(numTails))
diffs.append(abs(numHeads - numTails))
#print(ratios, diffs)
pylab.title('Difference between Heads and Tails')
pylab.semilogx('Number of tosses')
pylab.ylabel('abs(#ofHeads - #ofTails)')
pylab.plot(xaxis, diffs, 'bo')
pylab.figure()
pylab.title('Heads/Tails ratios')
pylab.semilogx('Number of tosses')
pylab.ylabel('#ofHeads/#ofTails')
pylab.plot(xaxis, ratios, 'bo')
def flipPlot(minExp,maxExp):
ratios = []
diffs = []
xAxis = []
for exp in range(minExp,maxExp+1):
xAxis.append(2 ** exp)
print "xAxis: ", xAxis
for numFlips in xAxis:
numHeads = 0
for n in range(numFlips):
if random.random() < 0.5:
numHeads += 1
numTails = numFlips - numHeads
ratios.append(numHeads/float(numTails))
diffs.append(abs(numHeads - numTails))
pylab.figure()
pylab.title('Difference Between Heads and Tails')
pylab.xlabel('Number of Flips')
pylab.ylabel('Abs(#Heads - #Tails')
pylab.plot(xAxis, diffs, 'bo') #do not connect, show dot
pylab.semilogx()
pylab.semilogy()
pylab.figure()
pylab.plot(xAxis, ratios, 'bo') #do not connect, show dot
pylab.title('Heads/Tails Ratios')
pylab.xlabel('Number of Flips')
pylab.ylabel('Heads/Tails')
pylab.semilogx()
def figure(self, err=1e5):
now_error, Minb, Maxa = self.error()
if float(now_error) <= err:
self.freq = np.linspace(100, 100 / np.sqrt(self.C2 * self.L2), 1E6)
a = self.R1 + self.freq * complex("j") * self.L1
b = self.R2 + self.freq * complex("j") * self.L2 + 1 / (
self.freq * complex("j") * self.C2)
self.impedance = abs(a * b / (a + b))
pylab.plot(self.freq, self.impedance)
pylab.semilogx(self.freq, self.impedance)
pylab.xlabel("Freq", color="red")
pylab.ylabel("Impedance", color="red")
pylab.text(1000, self.impendenceMin(max(Minb, Maxa)),
'Combination=' + self.Name)
pylab.text(Minb, self.impendenceMin(Minb), 'Min')
pylab.text(Maxa, self.impendenceMin(Maxa), 'Max')
pylab.text(
(Maxa + Minb) / 1000, 10 * self.impendenceMin(max(Minb, Maxa)),
'wave propagation = %s' %
str(abs(self.impendenceMin(Maxa) - self.impendenceMin(Minb))))
pylab.title("Error: %s%%" % now_error, color="black")
pylab.gca().spines['right'].set_color('none')
pylab.gca().spines['top'].set_color('none')
#pylab.savefig('figure_test\Nums-%s and Name-(%s).png' % (self.Num, self.Name))
pylab.show()
#pylab.clf()
return float(now_error)
else:
return False
def sums2nloop(model_input,images_input,iterations):
'''Run through a loop of calculating the summed S/N for a set of data.
This is useful if you'd like to see how well we'd do as we keep adding
more and more data together in order to attempt to extract a signal.
'''
if images_input == "simdata":
header = model_input.simdata()
else: header = getheaderinfo(images_input)
# iterations = header['numexp']
snvec = numpy.zeros((iterations,2))
for i in range(0,iterations):
snvec[i] = models2n(i+1,model_input,images_input)
print snvec
# GRAB EXPOSURE TIMES
timearr = header['tstart'][0:iterations] # should be error bar from tstart to tstop
# Plot the results
pylab.semilogx(timearr,snvec[:,1],linestyle='',marker='o')
pylab.semilogx(timearr,snvec[:,0],linestyle='',marker='x')
pylab.xlim(50,200000)
pylab.xlabel('Time')
pylab.ylabel('Summed S/N (not a lightcurve!)')
plottitle = 'Model: ' + model_input.name + ', Data: ' + images_input
pylab.title(plottitle)
def flipPlot(minExp, maxExp, numTrials):
meanRatios = []
meanDiffs = []
ratiosSDs = []
diffsSDs = []
xAxis = []
for exp in range(minExp, maxExp + 1):
xAxis.append(2**exp)
for numFlips in xAxis:
ratios = []
diffs = []
for t in range(numTrials):
numHeads, numTails = runTrial(numFlips)
ratios.append(numHeads/float(numTails))
diffs.append(abs(numHeads - numTails))
meanRatios.append(sum(ratios)/numTrials)
meanDiffs.append(sum(diffs)/numTrials)
ratiosSDs.append(stdDev(ratios))
diffsSDs.append(stdDev(diffs))
pylab.plot(xAxis, meanRatios, 'bo')
pylab.title('Mean Heads/Tails Ratios ('
+ str(numTrials) + ' Trials)')
pylab.xlabel('Number of Flips')
pylab.ylabel('Mean Heads/Tails')
pylab.semilogx()
pylab.figure()
pylab.plot(xAxis, ratiosSDs, 'bo')
pylab.title('SD Heads/Tails Ratios ('
+ str(numTrials) + ' Trials)')
pylab.xlabel('Number of Flips')
pylab.ylabel('Standard Deviation')
pylab.semilogx()
pylab.semilogy()
def plot(self, marker='o', color='red', m=0, M=6000):
"""Plots the position / height of the peaks in the well
.. plot::
:include-source:
from fragment_analyser import Line, fa_data
l = Line(fa_data("alternate/peaktable.csv"))
well = l.wells[0]
well.plot()
"""
import pylab
if len(self.df) == 0:
print("Nothing to plot (no peaks)")
return
x = self.df['Size (bp)'].astype(float).values
y = self.df['RFU'].astype(float).values
pylab.stem(x, y, marker=marker, color=color)
pylab.semilogx()
pylab.xlim([1, M])
pylab.ylim([0, max(y)*1.2])
pylab.grid(True)
pylab.xlabel("size (bp)")
pylab.ylabel("RFU")
return x, y
def testScalingCollapse():
"""Simple test of MakeScalingPlot and ScaleIt"""
p = {}
ell = {}
sig = {}
p[(100, 2)] = numpy.array([4., 8., 12.])
ell[(100, 2)] = numpy.array([10., 20., 30.])
sig[(100, 2)] = numpy.array([0.5, 0.5, 0.5])
p[(200, 4)] = numpy.array([1., 2., 3.])
ell[(200, 4)] = numpy.array([11., 18., 33.])
sig[(200, 4)] = numpy.array([1.0, 2.0, 3.0])
pylab.figure(1)
MakeScalingPlot(p, ell, yerrdata=sig)
pylab.figure(2)
sig = {}
sig[(100, 2)] = numpy.array([0.5, 0.5, 0.5])
sig[(200, 4)] = numpy.array([1.0, 2.0, 3.0])
p_sc, ell_sc, ellerr_sc = MakeScalingPlot(p,
ell,
yerrdata=sig,
xform='p->p*Z*L',
yform='ell->(2*ell*Z)/L',
yerrform='sig->(2*sig*Z)/L',
keyNames=('L', 'Z'),
showIt=False,
loc=3)
pylab.semilogx()
pylab.show()
return p, ell
def extrapolate(n, y, tolerance=1e-15, plot=False, call_show=True):
"Extrapolate functional value Y from sequence of values (n, y)."
# Make sure we have NumPy arrays
n = array(n)
y = array(y)
# Create initial "bound"
Y0 = 0.99*y[-1]
Y1 = 1.01*y[-1]
# Compute initial interior points
phi = (sqrt(5.0) + 1.0) / 2.0
Y2 = Y1 - (Y1 - Y0) / phi
Y3 = Y0 + (Y1 - Y0) / phi
# Compute initial values
F0, e, nn, ee, yy = _eval(n, y, Y0)
F1, e, nn, ee, yy = _eval(n, y, Y1)
F2, e, nn, ee, yy = _eval(n, y, Y2)
F3, e, nn, ee, yy = _eval(n, y, Y3)
# Solve using direct search (golden ratio fraction)
while Y1 - Y0 > tolerance:
if F2 < F3:
Y1, F1 = Y3, F3
Y3, F3 = Y2, F2
Y2 = Y1 - (Y1 - Y0) / phi
F2, e, nn, ee, yy = _eval(n, y, Y2)
else:
Y0, F0 = Y2, F2
Y2, F2 = Y3, F3
Y3 = Y0 + (Y1 - Y0) / phi
F3, e, nn, ee, yy = _eval(n, y, Y3)
print Y0, Y1
# Compute reference value
Y = 0.5*(Y0 + Y1)
# Print results
print
print "Reference value:", Y
# Plot result
if plot:
pylab.figure()
pylab.subplot(2, 1, 1)
pylab.title("Reference value: %g" % Y)
pylab.semilogx(n, y, 'b-o')
pylab.semilogx(nn, yy, 'g--')
pylab.subplot(2, 1, 2)
pylab.loglog(n, e, 'b-o')
pylab.loglog(nn, ee, 'g--')
pylab.grid(True)
if call_show:
pylab.show()
return Y
def _plotBodeTrimmed(fvector, gvector):
# Draw plot
plt.figure(facecolor="white") # White border
# Magnitude
ax1 = pl.subplot(2, 1, 1)
pl.semilogx(fvector, dB(np.absolute(gvector)))
pl.xlabel('Frequency (Hz)') # Set X label
pl.ylabel('Magnitude (dB)') # Set Y label
pl.title('Bode response plot') # Set title
pl.grid(True)
# Remove high frequency phase
hfreq = 1.0 / (min_sample * 20.0)
fv = []
pv = []
for f, g in zip(fvector, gvector):
if f < hfreq:
fv.append(f)
pv.append(np.angle(g, deg=True))
# Phase plot
ax2 = pl.subplot(2, 1, 2, sharex=ax1)
if fv != []:
pl.semilogx(fv, pv)
pl.xlabel('Frequency (Hz)') # Set X label
pl.ylabel('Phase (deg)') # Set Y label
pl.grid(True)
pl.show()
pl.close()
def show_blocking(self, plotfile=''):
'''Print out the blocking data and show a graph of the behaviour of the standard error with block size.
If plotfile is given, then the graph is saved to the specifed file rather than being shown on screen.'''
# print blocking output
# header...
print '%-11s' % ('# of blocks'),
fmt = '%-14s %-15s %-18s '
header = ('mean (X_%i)', 'std.err. (X_%i)', 'std.err.err. (X_%i)')
for data in self.data:
data_header = tuple(x % (data.data_col) for x in header)
print fmt % data_header,
for key in self.covariance:
str = 'cov(X_%s,X_%s)' % tuple(key.split(','))
print '%-14s' % (str),
for key in self.combination_stats:
fmt = ['mean (X_%s'+self.combination+'X_%s)', 'std.err. (X_%s'+self.combination+'X_%s)']
strs = tuple([s % tuple(key.split(',')) for s in fmt])
print '%-16s %-18s' % strs,
print
# data
block_fmt = '%-11i'
fmt = '%-#14.12g %-#12.8e %-#18.8e '
for s in range(len(self.data[0].stats)):
print block_fmt % (self.data[0].stats[s].block_size),
for data in self.data:
print fmt % (data.stats[s].mean, data.stats[s].se, data.stats[s].se_error),
for cov in self.covariance.itervalues():
print '%+-#14.5e' % (cov[s]),
for comb in self.combination_stats.itervalues():
print '%-#16.12f %-#18.12e' % (comb[s].mean, comb[s].se),
print
# plot standard error
if PYLAB:
# one sub plot per data set.
nplots = len(self.data)
for (i, data) in enumerate(self.data):
pylab.subplot(nplots, 1, i+1)
blocks = [stat.block_size for stat in data.stats]
se = [stat.se for stat in data.stats]
se_error = [stat.se_error for stat in data.stats]
pylab.semilogx(blocks, se, 'g-', basex=2, label=r'$\sigma(X_{%s})$' % (data.data_col))
pylab.errorbar(blocks, se, yerr=se_error, fmt=None, ecolor='g')
xmax = 2**pylab.ceil(pylab.log2(blocks[0]+1))
pylab.xlim(xmax, 1)
pylab.ylabel('Standard error')
pylab.legend(loc=2)
if i != nplots - 1:
# Don't label x axis points.
ax = pylab.gca()
ax.set_xticklabels([])
pylab.xlabel('# of blocks')
if plotfile:
pylab.savefig(plotfile)
else:
pylab.draw()
pylab.show()
def check_C_testset(mss_id):
import pylab
import expenv
import numpy
from helper import Options
from method_hierarchy_svm_new import Method
#from method_augmented_svm_new import Method
#costs = 10000 #[float(c) for c in numpy.exp(numpy.linspace(numpy.log(10), numpy.log(20000), 6))]
costs = [float(c) for c in numpy.exp(numpy.linspace(numpy.log(0.4), numpy.log(10), 6))]
print costs
mss = expenv.MultiSplitSet.get(mss_id)
train = mss.get_train_data(-1)
test = mss.get_eval_data(-1)
au_roc = []
au_prc = []
for cost in costs:
#create mock param object by freezable struct
param = Options()
param.kernel = "WeightedDegreeStringKernel"
param.wdk_degree = 10
param.transform = cost
param.base_similarity = 1.0
param.taxonomy = mss.taxonomy
param.id = 666
#param.cost = cost
param.cost = 10000
param.freeze()
# train
mymethod = Method(param)
mymethod.train(train)
assessment = mymethod.evaluate(test)
au_roc.append(assessment.auROC)
au_prc.append(assessment.auPRC)
print assessment
assessment.destroySelf()
pylab.title("auROC")
pylab.semilogx(costs, au_roc, "-o")
pylab.show()
pylab.figure()
pylab.title("auPRC")
pylab.semilogx(costs, au_prc, "-o")
pylab.show()
return (costs, au_roc, au_prc)
def plotLitSersic(outSB=None,plot=True):
# plots a variety of Sersic profiles with values from the literature
# can return the profiles in an array, can specify central SB (mag/arcsec^2)
# can turn off plotting if desired
# literature values
# [KB99, Courteau2011_E, Courteau2011_M, Dorman2013]
# Sersic index
n_b = np.array([2.19, 2.18, 1.83, 1.917])
# half-light radius, in kpc
R_e_kpc = np.array([1.0, 0.82, 0.74, 0.778])
# in pc
R_e = R_e_kpc*1000.
# central surface brightness, in mag arcsec^-2
# [V, 3.6 um, I, I] (last two are same data set)
if outSB is None:
mu_e = np.array([17.55, 15.77, 17.73, 17.849])
else:
mu_e = np.ones(4)*outSB
# convert to central intensity
I_e = 10.**(mu_e/(-2.5))
# radius to calculate for (radius of data is 2" - going out slightly further)
radarcsec = 60.
radpc = radarcsec*3.73
R = np.arange(radpc)
#R = np.arange(radarcsec)
I_b = np.zeros((len(n_b),len(R)),dtype=float)
mu_b = np.zeros((len(n_b),len(R)),dtype=float)
if plot:
fig=py.figure(2)
py.clf()
for i in np.arange(len(n_b)):
# from Cappaccioli 1989
b_n = 1.9992*n_b[i] - 0.3271
negb_n = -1. * b_n
I_b[i,:] = I_e[i]*np.exp(negb_n*( ((R/R_e[i])**(1./n_b[i])) - 1.))
mu_b[i,:] = -2.5*np.log10(I_b[i,:])
if plot:
py.semilogx(R[1:-1],mu_b[i,1:-1])
if plot:
py.legend(('KB99 (V band)','C11_E (3.6 $\mu$m)','C11_M (I band)','D13 (I band)'))
py.ylim(mu_b.max()+0.5,mu_b.min()-0.5)
py.ylabel('$\mu$ (mag arcsec$^{-2}$)')
py.xlabel('Radius (pc)')
ax1 = fig.add_subplot(111)
ax2 = ax1.twiny()
ax2.set_xlim(ax1.get_xlim())
ax2.set_xscale('log')
ax2.set_xticks(np.array([3.73,37.3,223.]))
ax2.set_xticklabels(['1','10','60'])
ax2.set_xlabel('Radius (arcsec)')
return mu_b, I_b, R
def plot_precision(self):
x = self.n_list
y = [i - np.pi for i in self.pi_list]
plt.figure()
plt.semilogx(x,y,'o')
plt.xlabel('n')
plt.ylabel('pi estimate - pi')
plt.show()
def Intensity(self,PhotonE=3e3, Rho=10.28, Distance=TestRange,Plot=True):
MuRho = self.muValue(PhotonE)
IAtten = exp(-MuRho*Rho*Distance*100.0)
if Plot==True:
# pl.loglog(Distance,IAtten)
pl.semilogx(Distance,IAtten)
pl.xlabel('Distance [m]')
pl.ylabel('Relative Intensity [I/Io]')
return Distance,IAtten
def main():
# Setup parameters for simulation and plotting
simdt= 1e-2
plotDt= 1
# Factors to change in the dose concentration in log scale
factorExponent = 10 ## Base: ten raised to some power.
factorBegin = -20
factorEnd = 21
factorStepsize = 1
factorScale = 10.0 ## To scale up or down the factors
# Load Model and set up the steady state solver.
# model = sys.argv[1] # To load model from a file.
model = './19085.cspace'
modelPath, modelName, modelType = parseModelName(model)
outputDir = modelPath
modelId = moose.loadModel(model, 'model', 'ee')
dosePath = '/model/kinetics/b/DabX' # The dose entity
ksolve, state = setupSteadyState( simdt, plotDt)
vol = moose.element( '/model/kinetics' ).volume
iterInit = 1000
solutionVector = []
factorArr = []
enz = moose.element(dosePath)
init = enz.kcat # Dose parameter
# Change Dose here to .
for factor in range(factorBegin, factorEnd, factorStepsize ):
scale = factorExponent ** (factor/factorScale)
enz.kcat = init * scale
print(factor)
for num in range(iterInit):
stateType, solStatus, vector = getState( ksolve, state, vol)
if solStatus == 0:
solutionVector.append(vector[0]/sum(vector))
factorArr.append(scale)
joint = np.array([factorArr, solutionVector])
joint = joint[:,joint[1,:].argsort()]
# Plot dose response.
fig0 = plt.figure()
pylab.semilogx(joint[0,:],joint[1,:],marker="o",label = 'concA')
pylab.xlabel('Dose')
pylab.ylabel('Response')
pylab.suptitle('Dose-Reponse Curve for a bistable system')
pylab.legend(loc=3)
#plt.savefig(outputDir + "/" + modelName +"_doseResponse" + ".png")
plt.show()
#plt.close(fig0)
quit()
def plotResults(results):
numFlips, avgLongestRuns = tuple(zip(*results))
pylab.figure(1)
pylab.clf()
pylab.title("Longest heads run")
pylab.xlabel("Number of flips")
pylab.ylabel("Average longest heads run")
pylab.semilogx()
pylab.plot(numFlips, avgLongestRuns, 'ro-')