def chi2_dir(cause, effect, unknown, n, p_cause, p_effect_given_cause):
cnt = count(zip(effect, unknown))
#print cnt
chi_indep = chi2_contingency(cnt)[1]
p_unknown_given_effect = [ float(cnt[0][1]) / sum(cnt[0]),
float(cnt[1][1]) / sum(cnt[1]) ]
#print 'p(bact|cd)=%s' % p_unknown_given_effect
exp=[[0,0],[0,0]]
for c in range(2):
for e in range(2):
for u in range(2):
exp[c][u] += (n *
p_of_val(p_cause, c) *
p_of_val(p_effect_given_cause[c], e) *
p_of_val(p_unknown_given_effect[e], u))
cnt = count(zip(cause, unknown))
#print "obs=%s" % cnt
#print 'cnt=%s' % cnt
#print 'expected if cd->bact=%s' % exp
chi_rev = chisquare(cnt, exp, axis=None, ddof=2)
chi_fwd = chi2_contingency(cnt)
#print 'expected if bact->cd=%s' % chi_fwd[3]
bayes_factor = chi2.pdf(chi_fwd[0],1) / chi2.pdf(chi_rev.statistic,1)
return struct(reject_indep=chi_indep,
bayes_fwd_rev=bayes_factor,
reject_fwd=chi_fwd[1],
reject_rev=chi_rev.pvalue)
def ej14(data_x, data_y, s):
a2, a1 = np.polyfit(data_x, data_y, 1)
chi2_t_list = []
chi2_f_list = []
for _ in range(1000):
new_y = np.random.normal(a1 + a2 * data_x, s)
p = np.polyfit(data_x, new_y, 1)
chi2_t_list.append(sum(((new_y - a1 - a2 * data_x) / s)**2))
chi2_f_list.append(sum(((new_y - p[1] - p[0] * data_x) / s)**2))
plt.figure(1)
my_hist(chi2_t_list, 20, label='Datos')
x = np.linspace(0, 30, 100)
chi211 = chi2.pdf(x, 11)
plt.plot(x, chi211, label='$\chi^2_{11}$', color=sns.color_palette()[2])
plt.legend()
plt.savefig('fig4.jpg')
plt.figure(2)
my_hist(chi2_f_list, 20, label='Datos')
x = np.linspace(0, 30, 100)
plt.plot(x, chi211, label='$\chi^2_{11}$', color=sns.color_palette()[2])
chi29 = chi2.pdf(x, 9)
plt.plot(x, chi29, label='$\chi^2_{9}$', color=sns.color_palette()[3])
plt.legend()
plt.savefig('fig5.jpg')
plt.show()
def prob1():
"""
using monte carlo methods
"""
#Part A using monte carlo methods
A = np.array([np.random.randn(10**i)**2 for i in [2, 4, 6]])
bin = np.linspace(0, 1, 50)
plt.subplot(131)
plt.hist(A[0], bins=bin, density=True)
plt.subplot(132)
plt.hist(A[1], bins=bin, density=True)
plt.subplot(133)
plt.hist(A[2], bins=bin, density=True)
#compute cdf
answers_A1 = []
answers_A2 = []
answers_A3 = []
for j in range(3):
for k in [.5, 1, 1.5]:
legth = len(A[j])
answers_A1.append((np.sum(A[j] <= k) / legth, k, (j + 1) * 2))
mean = np.average(A[j])
L = A[j] - mean
L = L**2
answers_A2.append(mean)
answers_A3.append(np.sum(L) / len(L))
#part B built-in functions
answers_B1 = []
answers_B2 = []
answers_B3 = []
plt.subplot(131)
plt.plot(bin, chi2.pdf(bin, 1))
plt.subplot(132)
plt.plot(bin, chi2.pdf(bin, 1))
plt.subplot(133)
plt.plot(bin, chi2.pdf(bin, 1))
answers_B1 = [chi2.cdf(i, df=1) for i in [0.5, 1, 1.5]]
plt.show()
answers_B2.append(chi2.mean(1))
answers_B3.append(chi2.var(1))
return "for k = 2, 4, 6, x= 0.5, 1.0, 1.5, the expected value is the first term, x is the second term, k is the third term" + str(
answers_A1
) + " These are the standard erros for each expected value " + str(
answers_A2) + str(answers_A3) + str(
answers_B1) + " mean for chi_squared " + str(answers_B2) + str(
answers_B3)
def calculate_bartlett_sphericity(x):
"""
Test the hypothesis that the correlation matrix
is equal to the identity matrix.identity
H0: The matrix of population correlations is equal to I.
H1: The matrix of population correlations is not equal to I.
The formula for Bartlett's Sphericity test is:
.. math:: -1 * (n - 1 - ((2p + 5) / 6)) * ln(det(R))
Where R det(R) is the determinant of the correlation matrix,
and p is the number of variables.
Parameters
----------
x : array-like
The array from which to calculate sphericity.
Returns
-------
statistic : float
The chi-square value.
p_value : float
The associated p-value for the test.
"""
n, p = x.shape
x_corr = corr(x)
corr_det = np.linalg.det(x_corr)
statistic = -np.log(corr_det) * (n - 1 - (2 * p + 5) / 6)
degrees_of_freedom = p * (p - 1) / 2
p_value = chi2.pdf(statistic, degrees_of_freedom)
return statistic, p_value
def plot_ts_vs_chi2(data, ext_list="ext1_ts", ndf_chi2=[1], subplot=[1, 2, 1], **kwargs):
ax = plt.subplot(subplot[0], subplot[1], subplot[2])
ext_data = column(data, "%s" % ext_list)
clean_data = [x for x in ext_data if not math.isnan(x)] # remove nan from data
n, bins, patches = plt.hist(
clean_data, int(math.ceil(max(column(data, "%s" % ext_list)))), normed=1, facecolor="green"
)
bincenters = 0.5 * (bins[1:] + bins[:-1])
chi2_vals = []
colors = ["r", "b", "g"]
for j in range(0, len(ndf_chi2)):
chi2_vals.append(chi2.pdf(bincenters, ndf_chi2[j]))
plt.plot(bincenters, chi2_vals[j], "%s--" % colors[j], linewidth=2.0, label="$\chi^2_%i$/2" % ndf_chi2[j])
legend = ax.legend(loc="upper right", frameon=False)
plt.ylabel("PDF")
plt.xlabel("TS$_{%s}$" % ext_list[0:4])
plt.yscale("log")
plt.ylim([0.00001, 2.0])
ax = plt.subplot(subplot[0], subplot[1], subplot[2] + 1)
n, bins, patches = plt.hist(
clean_data, int(math.ceil(max(column(data, "%s" % ext_list)))), normed=1, facecolor="green", cumulative=-1
)
chi2_sfvals = []
for j in range(0, len(ndf_chi2)):
chi2_sfvals.append(chi2.sf(bincenters, ndf_chi2[j]))
plt.plot(bincenters, chi2_sfvals[j], "%s--" % colors[j], linewidth=2.0, label="$\chi^2_%i$/2" % ndf_chi2[j])
legend = ax.legend(loc="upper right", frameon=False)
plt.ylabel("1-CDF")
plt.xlabel("TS$_{%s}$" % ext_list[0:4])
plt.yscale("log")
plt.ylim([0.00001, 2.0])
def Chi2Plots(instance, Nbins):
"""
This function plots the distribution of the likelihood function obtained by doing
the MC toy experiments. The distribution should follow a chi^2 distribution with
Nbins degrees of freedom.
params :
Nbins - int specifying the number of bins, i.e no. of dof.
returns :
"""
binning = np.linspace(0, 30, 20)
plt.hist(instance.lambda_true - instance.lambda_toys,
normed=True,
bins=binning,
label='toy MC')
x = np.linspace(0, 30, 1000)
plt.plot(x, chi2.pdf(x, df=8), label=r'$\chi^2$ dof$=8$', lw=3, color='r')
plt.legend(loc='best')
pathToChi2Toys = '/mnt/t3nfs01/data01/shome/jandrejk/higgs_model_dep/MoriondAnalysis/plots/chi2_DistFromToys/'
study_name = 'BSM1_compareDifferentResponseScenarios'
pathSave = pathToChi2Toys + '/' + instance.obs + '/' + instance.mode + '/'
ensure_dir(file_path=pathSave)
plt.savefig(pathSave + 'VHchi2.png')
plt.show()
def likelihoodRatio(idx, p, params1, params2, n=100):
try:
alpha1 = params1.alpha.mean()
beta1 = params1.beta.mean()
gamma1 = round(params1.gamma.mean()) + 1
except:
alpha1 = params1.alpha
beta1 = params1.beta
gamma1 = round(params1.gamma) + 1
try:
alpha2 = params2.alpha.mean()
beta2 = params2.beta.mean()
gamma2 = round(params2.gamma.mean()) + 1
except:
alpha2 = params2.alpha
beta2 = params2.beta
gamma2 = round(params2.gamma) + 1
sum1 = logLikelihood([int(x) for x in round(p)], params1, n, idx)
sum2 = logLikelihood([int(x) for x in round(p)], params2, n, idx)
ratio = sum1 - sum2
if ratio >= 0:
logStatus(Status(1, idx, "Could not perform likelihood ratio test."))
return float('nan')
pVal = chi2.pdf(-2 * ratio, 3)
return pVal
def calculate_daughter(self, x):
"""
Calculate daughter probability for amplitude
"""
# c = x*self.__emittance/(self.__parent_emittance**2)
c = x / self.__emittance
return chi2.pdf(c, self.__ndf)
def plot_A0(self):
sG=np.sqrt(np.var(self.gA0))
mG=np.mean(self.gA0)
amin, amax=plt.xlim()
alist=np.arange(3*amin, amax*3, amax/250.)
theoryGdist=norm.pdf(alist, loc=mG, scale=sG)
for i in range(len(self.fgnls)):
if (self.theoryplot==False):
lbl=self.TYPELABEL+"$=$"+NtoSTR(self.fgnls[i])
else:
lbl=None
neg=np.min(self.fgNLA0[i]-self.gA0)
const=9*6.*self.fgnls[i]*self.phisq
plot_hist(plt, (self.fgNLA0[i]-self.gA0+const), clr=self.clrs[i+1], alp=ALPHA, labl=lbl, ht='stepfilled')
#plot_hist(plt, (self.fgNLA0[i]-self.gA0), clr=self.clrs[i+1], alp=ALPHA, labl=lbl, ht='stepfilled')
amin, amax=plt.xlim()
alist=np.arange(3*amin, amax*3, 0.001)
if (self.theoryplot):
scl=6.0*self.A0const*self.Nconst*self.fgnls[i]
theorynGdist=np.sign(self.fgnls[i])*chi2.pdf(alist,1, scale=scl)
plt.plot(alist, theorynGdist, self.clrs[i+1], linestyle=self.ls[i+1], linewidth=LW, label=self.TYPELABEL+"="+NtoSTR(self.fgnls[i]))
plt.xlabel(r'$A_0$')
plt.ylabel(r'$p(A_0)$')
plt.yscale('log')
plt.ylim(0.01, 100)
plt.xlim(-0.02, 0.25)
plt.legend()
def chi2_2sample(s1, s2, bins=20, range=(0,1)):
"""
Chi square two sample test
see: http://www.itl.nist.gov/div898/software/dataplot/refman1/auxillar/chi2samp.htm
@return chi-square-statistic, degrees of freedom, p-value
"""
while True:
R, _e = numpy.histogram(s1, bins=bins, range=range)
S, _e = numpy.histogram(s2, bins=bins, range=range)
if min(R) >= 5 and min(S) >= 5:
break
bins -= 1
logging.debug('decreased bin count to %d for X2 test: less than 5 samples in a bin' % bins)
def _k1(s, r):
return math.sqrt(float(numpy.sum(s))/numpy.sum(r))
def _k2(s, r):
return math.sqrt(float(numpy.sum(r))/numpy.sum(s))
x2 = 0
K1 = _k1(R, S)
K2 = _k2(R, S)
for i, r in enumerate(R):
u = (K2 * R[i] - K2 * S[i])**2
d = R[i] + S[i]
x2 += u/d
df = bins - 1
pvalue = chi2.pdf(x2, df)
return x2, df, pvalue
def CombinedPvalue(self,output):
"""
Returns the p value of the combined pvalues based on the
method selected
"""
self.n = len(self.N)
if self.method == 'Tippett':
self.output = output
output = 1-(1-output)**self.n#beta.pdf(output,a = 1, b = self.n) #ST is Beta(1,n)
elif self.method == 'Stouffer':
self.output = output
output = norm.pdf(output,scale = self.n) #SS is N(0,n)
elif self.method == 'George':
self.output = output
output = t.pdf(output,self.n) #SG is Student t distribution (n)
elif self.method == 'Ed':
self.output = output
output = gamma.pdf(output,a = self.n) #SE is Gamma(x,n)
elif self.method == 'Pearson':
self.output = output
output = chi2.cdf(output,2*self.n) #SP is Chi-square df=2n
else:
self.output = output
output = chi2.pdf(output,2*self.n) #SF is Chi-square df=2n
return output
def plot_counts(data, xlabel=None, title=None, save=False):
fig, ax = plt.subplots(1, 1, tight_layout=True)
for p in data:
ax.errorbar(x=p['x'],
y=p['y'],
xerr=p['xerr'],
yerr=p['yerr'],
linestyle='',
color=p['color'],
marker=p['marker'],
label=p['label'],
alpha=0.8)
#ax.errorbar(x=data['bin_centres'], y=data['counts'], fmt='+')
ax.plot(p['x'],
chi2.pdf(p['x'], df=1),
label='Χ²',
linestyle='-.',
color='black',
alpha=0.6)
if title:
ax.set_title(title)
if xlabel:
ax.set_xlabel(xlabel)
ax.set_yscale('log')
ax.set_ylabel('counts (normalized)')
ax.legend(loc='best')
if save:
plt.savefig(save)
log.info(f'ts distribution plot saved in {save}')
else:
plt.show()
def get_calibration(distribution, realizations, b=10, plot=False):
"""
b : number of bins.
distribution of the variable and realizations of the variable. (mean, std), float list of length T.
"""
mean, std = distribution
# distribution will be divided into b bins delimited by the deciles of the disribution.
pdf = norm.pdf(np.linspace(-5, 5, 100))
rvf = norm.rvs(size=1000, loc=mean, scale=std)
deciles = set(qcut(rvf, b).to_numpy())
deciles = [x.right for x in deciles]
if plot == True:
plt.plot(np.linspace(-5, 5, 100), pdf)
x_axis = deciles
y_axis = [norm.pdf(x) for x in x_axis]
plt.scatter(x_axis, y_axis)
plt.show()
deciles = sorted([-5] + deciles[:-1] + [5])
bins = [[deciles[i], deciles[i + 1]] for i in range(len(deciles) - 1)]
p_content = [1 / b for i in range(b)]
counts, _ = np.histogram(realizations, deciles)
counts = counts / np.sum(counts)
# Now that we have the bins, we should get the
right_tail = 2 * len(realizations) * np.sum([
counts[i] * np.log((counts[i] / p_content[i] if counts[i] != 0 else 1))
for i in range(len(p_content))
])
C = 1 - chi2.pdf(right_tail, df=b - 1)
return C
def BackTesting(self, M):
ex = self.exceptions(M)
p = np.array(self.alphas)
x = ex.sum()[1:].values
m = ex.count()[1:].values
p_est = x / m
level = np.array([0.05] * len(self.alphas))
BackTesting = m * p
num = (p**x) * (1 - p)**(m - x)
den = (p_est**x) * (1 - p_est)**(m - x)
test_kupiec = -2 * np.log(list(num / den))
Pvalue = chi2.pdf(test_kupiec, 1)
zone = np.where(Pvalue <= level, "Rechazo H0", "No Rechazo H0")
#**********************************************************************
df = pd.DataFrame(
{
'x': x,
'm': m,
'p estimado': p_est,
'nivel de significancia': level,
'test de kupiec': test_kupiec,
'Valor P': Pvalue,
"Zona de rechazo": zone,
'BackTesting': BackTesting,
"Valor de eficiencia": 1 - p_est
},
index=["p=" + str(pp) for pp in p])
return df.T
def visualize_pruning(
w_norm,
n_retained,
title='Initial model weights vs theoretical for pruning'):
fig, ax1 = plt.subplots()
ax1.set_title(title)
ax1.hist(w_norm,
normed=True,
bins=200,
alpha=0.6,
histtype='stepfilled',
range=[0, n_retained * 5])
ax1.axvline(x=n_retained, linewidth=1, color='r')
ax1.set_ylabel('PDF', color='b')
ax2 = ax1.twinx()
ax2.set_ylabel('Survival Function', color='r')
ax1.set_xlabel('w_norm')
x = np.linspace(chi2.ppf(0.001, n_retained), chi2.ppf(0.999, n_retained),
100)
ax2.plot(x,
chi2.sf(x, n_retained),
'g-',
lw=1,
alpha=0.6,
label='chi2 pdf')
ax1.plot(x,
chi2.pdf(x, n_retained),
'r-',
lw=1,
alpha=0.6,
label='chi2 pdf')
def plot_A0(self):
sG=np.sqrt(np.var(self.gA0))
mG=np.mean(self.gA0)
amin, amax=plt.xlim()
alist=np.arange(3*amin, amax*3, amax/250.)
theoryGdist=norm.pdf(alist, loc=mG, scale=sG)
for i in range(len(self.fgnls)):
if (self.theoryplot==False):
lbl=self.TYPELABEL+"$=$"+NtoSTR(self.fgnls[i])
else:
lbl=None
neg=np.min(self.fgNLA0[i]-self.gA0)
const=9*6.*self.fgnls[i]*self.phisq
plot_hist(plt, (self.fgNLA0[i]-self.gA0+const)/(1-9.*3.*self.fgnls[i]*self.phisq), clr=self.clrs[i+1], alp=ALPHA, labl=lbl, ht='stepfilled')
amin, amax=plt.xlim()
alist=np.arange(3*amin, amax*3, 0.001)
if (self.theoryplot):
scl=6.0*self.A0const*self.Nconst*self.fgnls[i]
theorynGdist=np.sign(self.fgnls[i])*chi2.pdf(alist,1, scale=scl)
plt.plot(alist, theorynGdist, self.clrs[i+1], linestyle=self.ls[i+1], linewidth=LW, label=self.TYPELABEL+"="+NtoSTR(self.fgnls[i]))
plt.xlabel(r'$A_0$')
plt.ylabel(r'$p(A_0)$')
plt.yscale('log')
plt.ylim(0.01, 100)
plt.xlim(-0.02, 0.25)
plt.legend()
def get_chi2_text(self, a1):
k = 0
chi2_value = 0
best_value = a1.get_best_fit()['parameters']
r_ = len(best_value)
for spec in self.spectrumlist:
sp = spec.spectrum
e_add = spec.e_add
rate = self.model(e_add, best_value)
spec1 = self.get_A(rate, spec.e_lo, spec.e_hi, spec.e_add_num)
model_sp = spec.transform(spec1)
effinde = spec.effective_index
if effinde is not None:
model_sp = model_sp[effinde[0]:effinde[-1]]
sp = sp[effinde[0]:effinde[-1]]
index_list = self.chi2_check(model_sp)
new_model_sp = []
new_sp = []
for inde_i in index_list:
new_model_sp.append(np.sum(model_sp[inde_i]))
new_sp.append(np.sum(sp[inde_i]))
new_model_sp = np.array(new_model_sp)
new_sp = np.array(new_sp)
chi2_s = (new_sp - new_model_sp)**2 / new_model_sp
k = k + len(chi2_s)
chi2_value = chi2_value + chi2_s.sum()
df = k - r_ - 1
p = chi2.pdf(chi2_value, df)
return {'p': p, 'df': df, 'chi2': chi2_value}
def chisquareMC(df):
x = np.random.random() * 10
fx = np.random.random() * 1
if chi2.pdf(x, df) >= fx:
return x
else:
return chisquareMC(df)
def test_inv_chi2_pdf(self):
"""
Test to the Octave function.
>> format long
>> chi2pdf(1.5^-1, 4.0)
ans = 0.119421885095632
Inverse-chi-square distribution Calculator
http://keisan.casio.com/exec/system/1304908316 gives
gives (1.5, 4) -> 0.0530763933
R
> library(geoR)
> dinvchisq(1.5, df=4)
> ans=0.05307639
"""
octave_chi2pdf = 0.119421885095632
online_calculator = 0.0530763933
r_value = 0.05307639
v1 = 1.5
v2 = 4.0
my_func = inv_chi2(v1, v2)
using_inv_gamma = invgamma.pdf(v1, v2 / 2.0, 0.5)
print("... Value obtained using inv-gamma :", using_inv_gamma)
scipy_chi2pdf = chi2.pdf(v1**-1, v2)
print("... Value obtained using chi2 :", scipy_chi2pdf)
self.assertAlmostEqual(octave_chi2pdf, scipy_chi2pdf, places=5)
self.assertAlmostEqual(my_func, octave_chi2pdf, places=5)
def loglambda_plot(values, name):
thetas = values.keys()
nplots = len(thetas)
rcParams['xtick.major.pad'] = 12
rcParams['ytick.major.pad'] = 12
# For now plot only theta = 0.7
fig = plt.figure()
plt.hist(np.array(values[0.7]) * 2.0,
normed=True,
bins=75,
range=(0, 6.5),
color='#20A387')
xvals = np.linspace(0, 6.5, 300)
plt.plot(xvals,
chi2.pdf(xvals, df=1.0),
color='#440154',
alpha=0.8,
label=r'$\chi^{2}$, n.d.f = 1')
plt.xlabel(r'$-2 \ln \frac{L(\alpha = 0.7)}{L(\hat{\alpha})}$')
plt.ylabel('Probability density / %.2f' % (6.5 / 75))
fig.canvas.set_window_title(name)
plt.legend(loc='upper right')
plt.xlim([0, 6.5])
plt.subplots_adjust(left=0.15, right=0.97, top=0.97, bottom=0.17)
fig.show()
fig.savefig('loglambda_chisquare_mle_nn.pdf')
def calculate_daughter(self, x) :
"""
Calculate daughter probability for amplitude
"""
# c = x*self.__emittance/(self.__parent_emittance**2)
c = x/self.__emittance
return chi2.pdf(c, self.__ndf)
def sdist(x, sigma, n=10):
'''
Calculate PDF of the sampling distribution for standard deviations given the number
of samples obtained and the standard deviation of the original distribution.
The sampling distribution is given by:
(n*S**2)/sigma**2 ~ X2(10)
g'(S) ~ X2(10)
where S is the standard deviation. We can therefore not directly evaluate
the desired pdf. Instead we have to work with this transformed distribution.
Define:
g(S) = sqrt(S*sigma**2/n)
g'(S) = n*S**2/sigma**2
pdf(g'(S)) = chi2.pdf(g'(S), 10)
Then the pdf of the transformd variable is given by:
pfg(S) = chi2.pdf(g'(S), 10)* g'(S)/dy
See http://math.arizona.edu/~jwatkins/f-transform.pdf
'''
f = lambda x: chi2.pdf(x, n)
nb = n + 1
g_inv = lambda x: (nb * x**2) / (sigma**2)
dyg_inv = lambda x: (2 * nb * x) / (sigma**2)
y = f(g_inv(x)) * dyg_inv(x)
return y
def art_qi2(img, airmask, min_voxels=int(1e3), max_voxels=int(3e5), save_plot=True):
r"""
Calculates :math:`\text{QI}_2`, based on the goodness-of-fit of a centered
:math:`\chi^2` distribution onto the intensity distribution of
non-artifactual background (within the "hat" mask):
.. math ::
\chi^2_n = \frac{2}{(\sigma \sqrt{2})^{2n} \, (n - 1)!}x^{2n - 1}\, e^{-\frac{x}{2}}
where :math:`n` is the number of coil elements.
:param numpy.ndarray img: input data
:param numpy.ndarray airmask: input air mask without artifacts
"""
from sklearn.neighbors import KernelDensity
from scipy.stats import chi2
from mriqc.viz.misc import plot_qi2
# S. Ogawa was born
np.random.seed(1191935)
data = img[airmask > 0]
data = data[data > 0]
# Write out figure of the fitting
out_file = op.abspath('error.svg')
with open(out_file, 'w') as ofh:
ofh.write('<p>Background noise fitting could not be plotted.</p>')
if len(data) < min_voxels:
return 0.0, out_file
modelx = data if len(data) < max_voxels else np.random.choice(
data, size=max_voxels)
x_grid = np.linspace(0.0, np.percentile(data, 99), 1000)
# Estimate data pdf with KDE on a random subsample
kde_skl = KernelDensity(bandwidth=0.05 * np.percentile(data, 98),
kernel='gaussian').fit(modelx[:, np.newaxis])
kde = np.exp(kde_skl.score_samples(x_grid[:, np.newaxis]))
# Find cutoff
kdethi = np.argmax(kde[::-1] > kde.max() * 0.5)
# Fit X^2
param = chi2.fit(modelx[modelx < np.percentile(data, 95)], 32)
chi_pdf = chi2.pdf(x_grid, *param[:-2], loc=param[-2], scale=param[-1])
# Compute goodness-of-fit (gof)
gof = float(np.abs(kde[-kdethi:] - chi_pdf[-kdethi:]).mean())
if save_plot:
out_file = plot_qi2(x_grid, kde, chi_pdf, modelx, kdethi)
return gof, out_file
def differential_lrt(x, y, xmin=0):
from scipy.stats import chi2
lrtX = bimod_likelihood(x)
lrtY = bimod_likelihood(y)
lrtZ = bimod_likelihood(np.concatenate((x, y)))
lrt_diff = 2 * (lrtX + lrtY - lrtZ)
return chi2.pdf(x=lrt_diff, df=3)
def chi2(emission_index=0, t_horizon=100, tstep=.1):
"""
# use k paramater= t+2 as done in Cherubini (doi: 10.1111/j.1757-1707.2011.01156.x)
# """
yrs = np.linspace(0, t_horizon, (t_horizon / tstep + 1))
decay = chi2.pdf(yrs, emission_index + 2)
decay = decay * tstep
return decay
def art_qi2(img,
airmask,
min_voxels=int(1e3),
max_voxels=int(3e5),
save_plot=True):
r"""
Calculates :math:`\text{QI}_2`, based on the goodness-of-fit of a centered
:math:`\chi^2` distribution onto the intensity distribution of
non-artifactual background (within the "hat" mask):
.. math ::
\chi^2_n = \frac{2}{(\sigma \sqrt{2})^{2n} \, (n - 1)!}x^{2n - 1}\, e^{-\frac{x}{2}}
where :math:`n` is the number of coil elements.
:param numpy.ndarray img: input data
:param numpy.ndarray airmask: input air mask without artifacts
"""
from sklearn.neighbors import KernelDensity
from scipy.stats import chi2
from mriqc.viz.misc import plot_qi2
# S. Ogawa was born
np.random.seed(1191935)
data = img[airmask > 0]
data = data[data > 0]
# Write out figure of the fitting
out_file = op.abspath('error.svg')
with open(out_file, 'w') as ofh:
ofh.write('<p>Background noise fitting could not be plotted.</p>')
if len(data) < min_voxels:
return 0.0, out_file
modelx = data if len(data) < max_voxels else np.random.choice(
data, size=max_voxels)
x_grid = np.linspace(0.0, np.percentile(data, 99), 1000)
# Estimate data pdf with KDE on a random subsample
kde_skl = KernelDensity(bandwidth=0.05 * np.percentile(data, 98),
kernel='gaussian').fit(modelx[:, np.newaxis])
kde = np.exp(kde_skl.score_samples(x_grid[:, np.newaxis]))
# Find cutoff
kdethi = np.argmax(kde[::-1] > kde.max() * 0.5)
# Fit X^2
param = chi2.fit(modelx[modelx < np.percentile(data, 95)], 32)
chi_pdf = chi2.pdf(x_grid, *param[:-2], loc=param[-2], scale=param[-1])
# Compute goodness-of-fit (gof)
gof = float(np.abs(kde[-kdethi:] - chi_pdf[-kdethi:]).mean())
if save_plot:
out_file = plot_qi2(x_grid, kde, chi_pdf, modelx, kdethi)
return gof, out_file
def plot_chisq_hist(ax, chisqs, nu):
'''Plots a histogram of chisq values
Also overplots a chisq distribution for nu degrees of freedom.
'''
n, bins, patches = plt.hist(chisqs, density=False, facecolor='g')
plt.ylabel("Number of Occurrences")
plt.xlabel("Chisq Value")
x = np.linspace(0, 6, 100)
plt.plot(x, chi2.pdf(x, nu)* 100000,'r-', lw=5, alpha=0.6, label='chi2 pdf')
def pdf_random_rotation(self, x, v, mu, kappa, n):
"""
Gives back the probability of observing the vector x, such that its angle with v is coming from a Von Mises
distribution with k = self.kappa and its length coming form chi squared distribution with the parameter n.
"""
v = v/LA.norm(v,2)
x = x/LA.norm(x,2)
ang = sum(v*x)
return (.5/np.pi)*(chi2.pdf(n*LA.norm(x,2),n)*n)*(vonmises.pdf(ang, kappa))
def chi2_curve():
fig, ax = plt.subplots(1, 1)
fig.set_size_inches(4, 2)
x = np.linspace(0, 40, 1000)
ax.plot(x, chi2.pdf(x, 2), "r-", lw=2, alpha=0.6, label="chi2")
ax.vlines(32.977, 0, 0.5, linestyle="--")
ax.set_xlabel("Valeur du Chi2")
ax.set_ylabel("Probabilité")
return fig
def main():
fig, ax = plt.subplots(1, 1)
df = 5
loc = 20
scale = 8
mean, var, skew, kurt = chi2.stats(df, moments='mvsk')
#x = np.linspace(chi2.ppf(0.01, df, loc, scale),chi2.ppf(0.99, df, loc, scale), 20)
valmax = int(chi2.ppf(0.99, df, loc, scale)) + 1
if (valmax % 2 != 0): valmax = valmax + 1
#valmax= 46
x = np.linspace(0, valmax, valmax + 1)
#print (x)
proba = chi2.pdf(x, df, loc, scale)
vs = map(repr, proba.tolist())
repartition = list(zip(x.tolist(), proba.tolist()))
nbTranche = 5
valmin = loc
space = (valmax - valmin) / nbTranche
remain = 100
tranche0 = int(100 *
sum([pro for val, pro in repartition if (val < valmin)]))
remain -= tranche0
print("P(v < %d) = %d" % (valmin, tranche0))
for traidx in range(nbTranche):
deb = valmin + traidx * space
fin = valmin + (traidx + 1) * space
tranche = int(100 * sum(
[pro for val, pro in repartition if (val >= deb) and (val < fin)]))
remain -= tranche
print("P(%d <= v < %d) = %d" % (deb, fin, tranche))
trancheF = 100 * sum([
pro for val, pro in repartition if (val >= valmin + nbTranche * space)
])
print("P(v >= %d) = %d" % (valmin + nbTranche * space, remain))
for certitude in [0.6, 0.7, 0.75, 0.8, 0.85, 0.90, 0.95]:
print("certi = %d , val = %d" %
(certitude, int(chi2.ppf(certitude, df, loc, scale))))
#print (" ".join(list(vs)).replace (".",","))
#print (" ".join(proba.tolist()))
#print (" ".join().replace(".",","))
ax.plot(x, proba, 'r-', lw=5, alpha=0.6, label='chi2 pdf')
#rv = chi2(df, loc, scale)
#ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf')
#vals = chi2.ppf([0.001, 0.5, 0.999], df, loc, scale)
#np.allclose([0.001, 0.5, 0.999], chi2.cdf(vals, df, loc, scale))
#r = chi2.rvs(df, loc = loc, scale = scale , size=1000)
#ax.hist(r, density=True, histtype='stepfilled', alpha=0.2)
#ax.legend(loc='best', frameon=False)
plt.show()
def dchisq(x,df,ncp=0):
"""
Calculates the density/point estimate of the chi-square distribution
"""
from scipy.stats import chi2,ncx2
if ncp==0:
result=chi2.pdf(x=x,df=df,loc=0,scale=1)
else:
result=ncx2.pdf(x=x,df=df,nc=ncp,loc=0,scale=1)
return result
def chi_squared(df):
fig, ax = plt.subplots(1, 1)
# Calculate a few first moments:
mean, var, skew, kurt = chi2.stats(df, moments='mvsk')
# Display the probability density function (pdf):
x = np.linspace(chi2.ppf(0.01, df), chi2.ppf(0.99, df), 100)
ax.plot(x, chi2.pdf(x, df), 'r-', lw=5, alpha=0.6, label='chi2 pdf')
plt.show()
def posterior_sigma(x, mu0, nu0, sigma0, y, kappa0):
n = len(y)
bar_y = np.mean(y)
nu_n = nu0 + n
s = np.std(y)
sigma_n_squared = (nu0 * sigma0**2 + (n - 1.0) * s**2 + (kappa0 * n) *
(bar_y - mu0) / (kappa0 + n)) / nu_n
p_squared = chi2.pdf(nu_n**(-1.0), sigma_n_squared)
p = np.sqrt(p_squared)
return p
def report_lr(model_one_likelihood, model_two_likelihood):
x = -2*model_two_likelihood+2*model_one_likelihood
#p_value = -1
p_value = chi2.pdf(x,2)
r = "-------------------------\n"
r += "--Likelihood Ratio Test--\n"
r += "-------------------------\n"
r+="| The likelihood ratio of MODEL TWO/MODEL ONE (NULL/ALT) is %4.2f\n| This has a p_value of %s\n" % (x,p_value)
r += "-------------------------\n"
return [r,[x,p_value]]
def get_pdf(dist, x, df=1, loc=0, scale=1):
if dist == 'normal':
y = norm.pdf(x, loc=loc, scale=scale)
elif dist == 'uniform':
y = uniform.pdf(x, loc=loc, scale=scale)
elif dist == 'chi2':
y = chi2.pdf(x, df)
else:
print("No distribution found with name {:s}".format(dist))
y = np.zeros_like(x)
return y
def stat(samples, sets): S = [sum(map(pow, [random.gauss(1, random.random()) for x in range(0,samples)], [2 for x in range(0,samples)] )) for y in range(0,sets)] hist, bins = np.histogram(S, bins = 100) # plt.bar(np.mean(S), hist, align='center') rv = chi2.pdf(S) x = np.linspace(0, np.minimum(rv.dist.b, 3)) h = plt.plot(x, rv.pdf(x))
def confidence_update(self, update, betas): """ To estimate theta, we need to retrieve the appropriate P(E | beta), then optimize the gradient step with Newton Rapson. --- Params: update -- the full update step that will get weighted by confidence betas -- a vector of beta values for all features Returns: weights -- a vector of the new weights """ confidence = [1.0] * self.environment.num_features for i in range(self.environment.num_features): ### Compute update using P(r|beta) for the beta estimate we just computed ### # Compute P(r|beta) mus1 = self.P_beta[self.environment.feature_list[i]+"1"] mus0 = self.P_beta[self.environment.feature_list[i]+"0"] p_r0 = chi2.pdf(betas[i],mus0[0],mus0[1],mus0[2]) / (chi2.pdf(betas[i],mus0[0],mus0[1],mus0[2]) + chi2.pdf(betas[i],mus1[0],mus1[1],mus1[2])) p_r1 = chi2.pdf(betas[i],mus1[0],mus1[1],mus1[2]) / (chi2.pdf(betas[i],mus0[0],mus0[1],mus0[2]) + chi2.pdf(betas[i],mus1[0],mus1[1],mus1[2])) l = math.pi # Newton-Rapson setup; define function, derivative, and call optimization method. def f_theta(weights_p): num = p_r1 * np.exp(weights_p * update[i]) denom = p_r0 * (l/math.pi) ** (self.environment.num_features/2.0) * np.exp(-l*update[i]**2) + num return weights_p + self.step_size * num * update[i]/denom - self.environment.weights[i] def df_theta(weights_p): num = p_r0 * (l/math.pi) ** (self.environment.num_features/2.0) * np.exp(-l*update[i]**2) denom = p_r1 * np.exp(weights_p*update[i]) return 1 + self.step_size * num / denom weight_p = newton(f_theta,self.environment.weights[i],df_theta,tol=1e-04,maxiter=1000) num = p_r1 * np.exp(weight_p * update[i]) denom = p_r0 * (l/math.pi) ** (self.environment.num_features/2.0) * np.exp(-l*update[i]**2) + num confidence[i] = num/denom print "Here is weighted beta:", confidence weights = self.environment.weights - np.array(confidence) * self.step_size * update return weights
def chi2_distribution():
fig, ax = plt.subplots(1, 1)
#display the probability density function
df = 10
x=np.linspace(chi2.ppf(0.01, df), chi2.ppf(0.99, df), 100)
ax.plot(x, chi2.pdf(x,df))
#simulate the chi2 distribution
y = []
n=10
for i in range(1000):
chi2r=0.0
r = norm.rvs(size=n)
for j in range(n):
chi2r=chi2r+r[j]**2
y.append(chi2r)
ax.hist(y, normed=True, alpha=0.2)
plt.show()
fig, ax = plt.subplots(1, 1)
#display the probability density function
df = 10
x=np.linspace(-4, 4, 100)
ax.plot(x, t.pdf(x,df))
#simulate the t-distribution
y = []
for i in range(1000):
rx = norm.rvs()
ry = chi2.rvs(df)
rt = rx/np.sqrt(ry/df)
y.append(rt)
ax.hist(y, normed=True, alpha=0.2)
plt.show()
fig, ax = plt.subplots(1, 1)
#display the probability density function
dfn, dfm = 10, 5
x = np.linspace(f.ppf(0.01, dfn, dfm), f.ppf(0.99, dfn, dfm), 100)
ax.plot(x, f.pdf(x, dfn, dfm))
#simulate the F-distribution
y = []
for i in range(1000):
rx = chi2.rvs(dfn)
ry = chi2.rvs(dfm)
rf = np.sqrt(rx/dfn)/np.sqrt(ry/dfm)
y.append(rf)
ax.hist(y, normed=True, alpha=0.2)
plt.show()
def score_prior(self, ss, hps):
score = 0
for mu, var in zip(ss['mu'], ss['var']):
if mu <= self.EPSILON or mu >= (1-self.EPSILON):
return -np.inf
if (var / hps['var_scale']) < self.EPSILON:
return -np.inf
score += np.log(chi2.pdf(var/hps['var_scale'], self.CHI_VAL))
score += log_dirichlet_dens(ss['pi'], np.ones(hps['comp_k'])*hps['dir_alpha'])
return score
def plot_chi2(df):
x = list()
y = list()
for i in np.arange(0, 14, 0.1):
x.append(i)
# y.append(stats.chi2(i))
y.append(chi2.pdf(i, df))
plt.plot(x, y)
plt.axis([0, 14, 0, 0.5])
plt.grid()
plt.show()
def sampling_distribution():
fig, ax = plt.subplots(1, 1)
#display the probability density function
df = 10
x=np.linspace(chi2.ppf(0.01, df), chi2.ppf(0.99, df), 100)
ax.plot(x, chi2.pdf(x, df))
#simulate the sampling distribution
y = []
for i in range(1000):
r = norm.rvs(loc=5, scale=2, size=df+1)
rchi2 =(df)*np.var(r)/4
y.append(rchi2)
ax.hist(y, normed=True, alpha=0.2)
plt.savefig('sampling_distribution.png')
def rollQStat(n=25):
now = dt.datetime.now()
precalculatedDict = {}
iDate = dt.datetime(1996, 1, 31)
while iDate < now:
try:
nameArray, Qarray = Qstatistic(iDate)
p_values = [chi2.pdf(x, 6) for x in Qarray]
precalculatedDict[iDate]= [p_values, Qarray]
iDate = addMonths(iDate, 1)
print iDate.strftime('%Y/%m/%d %H:%M:%S')
except:
print "Unexpected error:", sys.exc_info()[0]
raise
Nameoutput = open('Qstatistic.pkl', 'wb')
pickle.dump(precalculatedDict, Nameoutput)
def chi2_distribution():
fig,ax = plt.subplots(1, 1)
df = 10
x=np.linspace(chi2.ppf(0.01, df),chi2.ppf(0.99, df), 100)
ax.plot(x, chi2.pdf(x,df))
y = []
n=10
for i in range(1000):
chi2r = 0.0
r = norm.rvs(size = 10)
for j in range(10):
chi2r = chi2r + r[j]**2
y.append(chi2r)
ax.hist(y, normed=True, alpha=0.2)
plt.show()
def make_ts_hist(tab,masks,cols,labels,cumulative=True):
if not isinstance(cols,list):
cols = [cols]*len(masks)
plt.figure()
for m,l,c in zip(masks,labels,cols):
h = Histogram(Axis.create(0.0,20,100))
h.fill(tab[m][c])
h = h.normalize()
if cumulative:
h = h.cumulative(lhs=False)
h.plot(hist_style='step',alpha=0.3,linewidth=2,label=l)
dof = 2
label = r"$\chi^2_{1} / 2$"
kwargs = dict( label = label, lw=1.5, c='k',dashes=(5,2))
if cumulative:
plt.gca().plot(h.axis(0).center,
0.5*(1-chi2.cdf(h.axis(0).edges[:-1],1)),**kwargs)
else:
plt.gca().plot(h.axis(0).center,
h.axis(0).width*chi2.pdf(h.axis(0).center,1),**kwargs)
label = r"$\chi^2_{2} / 2$"
kwargs = dict( label = label, lw=1.5, c='r',dashes=(5,2))
# plt.gca().plot(h.axis(0).center,
# 0.5*(1-chi2.cdf(h.axis(0).center,2)),**kwargs)
# if cumulative:
# plt.gca().plot(h.axis(0).center,
# 0.5*(1-chi2.cdf(h.axis(0).edges[:-1],2)),**kwargs)
# else:
# plt.gca().plot(h.axis(0).center,
# h.axis(0).width*chi2.pdf(h.axis(0).center,2),**kwargs)
plt.gca().set_yscale('log')
plt.gca().set_ylim(1E-4,1)
plt.gca().legend(frameon=False)
plt.gca().set_xlabel('TS$_\mathrm{ext}$')
plt.gca().set_ylabel('Cumulative Fraction')
def chi2_distribution():
fig, ax = plt.subplots(1, 1)
# display the probability density function
df = 10
x = np.linspace(chi2.ppf(0.01, df), chi2.ppf(0.99, df), 100)
ax.plot(x, chi2.pdf(x, df))
# simulate the chi2 distribution
y = []
n = 10
for i in range(1000):
chi2r = 0.0
r = norm.rvs(size=n)
for j in range(n):
chi2r = chi2r + r[j] ** 2
y.append(chi2r)
ax.hist(y, normed=True, alpha=0.2)
plt.savefig('chi2_distribution.png')
def stats(agg):
lags_ms = (agg.lags / agg.sample_rate) * 1e3
regions_to_use = ['L1', 'L2', 'L3', 'CM', 'NCM']
for reg in regions_to_use:
print '---------------- %s ----------------' % reg
i = (agg.df.cc > 0.20) & (agg.df.region == reg)
xi = agg.df[i].xindex.values
filts = agg.filters[xi, :]
cfreqs = compute_best_freq(filts, agg.sample_rate, lags_ms)
cfreqs = cfreqs[cfreqs > 0]
cfreqs = cfreqs.reshape([len(cfreqs), 1])
gmm1 = GMM(n_components=1)
gmm1.fit(cfreqs)
lk_null = gmm1.score(cfreqs).sum()
aic_null = gmm1.aic(cfreqs)
gmm2 = GMM(n_components=2)
gmm2.fit(cfreqs)
print 'Center frequencies of 2-component GMM:',gmm2.means_.squeeze()
print 'Covariances: ',np.sqrt(gmm2.covars_.squeeze())
lk_full = gmm2.score(cfreqs).sum()
aic_full = gmm2.aic(cfreqs)
lk_rat = -2*(lk_null - lk_full)
chi_df = 2
pval = chi2.pdf(lk_rat, chi_df)
print 'Null likelihood: %0.6f' % lk_null
print 'Full likelihood: %0.6f' % lk_full
print 'Likelihood Ratio: %0.6f' % lk_rat
print 'p-value: %0.6f' % pval
print 'Null AIC: %0.6f' % aic_null
print 'Full AIC: %0.6f' % aic_full
print 'Relative Likelihood (N=%d): %0.6f' % (i.sum(), np.exp((aic_full - aic_null) / 2.))
def PlotChi2(self):
img = gfh.ReadData(self.chi2)
hmin = 0#chi2.ppf(0.0001, df)
hmax = 1000#chi2.ppf(0.9999, df)
bins = 30000
x = np.linspace(hmin,hmax, bins)
chng = gfh.ReadData(self.chng)
nochng = (1 - chng).astype(np.uint8)
histr = cv2.calcHist([img],[0],nochng,[bins],[hmin,hmax])
histr /= sp.integrate.trapz(np.squeeze(histr),x = x)
plt.figure(4)
plt.plot(x, chi2.pdf(x, self.nvar),'r-', lw=5, alpha=0.6, label='chi2 pdf')
plt.plot(x,histr, label = 'chi2 histogram')
plt.show()
plt.legend()
def randomOfRandomness():
"""
Здесь содержится функция, которая создаёт агентов с исходными данными
"""
wealthOfAllDelta = []
number = 0
for i in np.linspace(0.00001, 0.061, 500):
randomNumbers = [] #Список для хранения случайных чисел
wealthDivided = [] # Богатство, поделенное случайным образом на случайное количество частей
wealthOfOneDeltaASRS = []
wealthOfOneDelta = 1000 * chi2.pdf(600 * i, 20)+0
numberOfAgents = random.randint(1, 1) # Какое количество агентов будет с одним дельта
for ii in range(numberOfAgents): # Добавление случайных чисел в список
randomNumbers.append(random.randint(1,100))
sumOfrandomAgents = sum(randomNumbers)
for x in range(numberOfAgents): # Богатство поделено между агентами в рамках одного дельта
wealthDivided.append(randomNumbers[x] * wealthOfOneDelta / sumOfrandomAgents)
for wealth in wealthDivided:
silver = random.uniform(0, wealth)
realEstateMoney = wealth - silver
number += 1
wealthOfOneDeltaASRS.append([realEstateMoney, silver, i, number])
wealthOfAllDelta.append(wealthOfOneDeltaASRS)
#wealthOfAllDelta = np.array(wealthOfAllDelta)
with open('simple_population', 'wb') as f:
pickle.dump(wealthOfAllDelta, f)
with open('numberOfagents', 'wb') as ff:
pickle.dump(number, ff)
simple_LL = -1*loss_function(simplified_results, func=simulate_simplified_dynamics) full_AIC = 2*3 - 2*full_LL simple_AIC = 2*2 - 2*simple_LL delta_AIC = simple_AIC - full_AIC print delta_AIC # The probability that the simplified model is better than the full model is given by its Akaike weight. # In[39]: 1 / (1 + np.exp(.5*delta_AIC)) # To put this in perspective, this probability is roughly the magnitude of flipping a fair coin eight hundred times and it only ever coming up heads. # In[40]: .5**800 # Since the full and simplified model are nested, we can also perform a Likelihood Ratio test, where the test statistic $D$ is $\chi^2$ distributed with one degree of freedom. We reject the null hypothesis that the simplified model is correct. # In[41]: from scipy.stats import chi2 D = 2*(full_LL - simple_LL) chi2_result = chi2.pdf(D,1) print chi2_result
@author: Flavio Lichtenstein @local: Unifesp DIS - Bioinformatica ''' import numpy as np from scipy.stats import chi2 import matplotlib.pyplot as plt fig, ax = plt.subplots(1, 1) # Calculate a few first moments: df = 2 mean, var, skew, kurt = chi2.stats(df, moments='mvsk') # Display the probability density function (pdf): x = np.linspace(chi2.ppf(0.01, df), chi2.ppf(0.99, df), 100) ax.plot(x, chi2.pdf(x, df), 'r-', lw=5, alpha=0.6, label='chi2 pdf') # Alternatively, the distribution object can be called (as a function) to fix the shape, location and scale parameters. This returns a “frozen” RV object holding the given parameters fixed. # Freeze the distribution and display the frozen pdf: rv = chi2(df) ax.plot(x, rv.pdf(x), 'k-', lw=2, label='frozen pdf') vals = chi2.ppf([0.001, 0.5, 0.999], df) print np.allclose([0.001, 0.5, 0.999], chi2.cdf(vals, df)) # Generate random numbers: r = chi2.rvs(df, size=10000) ax.hist(r, normed=True, histtype='stepfilled', alpha=0.2)
def run(self, dataSlice, slicePoint=None):
#Get the visit information
m5 = dataSlice[self.m5col]
#Number of visits
N = len(m5)
#magnitudes to be sampled
mag = np.arange(16,np.mean(m5),0.5)
#hold the distance between the completeness and contamination goals.
res = np.zeros(mag.shape)
#make them nans for now
res[:] = np.nan
#hold the measured noise-only variances
noiseonlyvar = np.zeros(self.numruns)
#Calculate the variance at a reference magnitude and scale from that
m0=20.
sigmaref = 0.2 * (10.**(-0.2*m5)) * (10.**(0.2*m0))
#run the simulations
#Simulate the measured noise-only variances at a reference magnitude
for i in np.arange(self.numruns):
# random realization of the Gaussian error distributions
scatter = np.random.randn(N)*sigmaref
noiseonlyvar[i] = np.var(scatter) # store the noise-only variance
#Since we are treating the underlying signal being representable by a
#fixed-width gaussian, its variance pdf is a Chi-squared distribution
#with the degrees of freedom = visits. Since variances add, the variance
#pdfs convolve. The cumulative distribution function of the sum of two
#random deviates is the convolution of one pdf with a cdf.
#We'll consider the cdf of the noise-only variances because it's easier
#to interpolate
noisesorted = np.sort(noiseonlyvar)
#linear interpolation
interpnoisecdf = UnivariateSpline(noisesorted,np.arange(self.numruns)/float(self.numruns),k=1,s=0)
#We need a binned, signal-only variance probability distribution function for numerical convolution
numsignalsamples = 100
xsig = np.linspace(chi2.ppf(0.001, N),chi2.ppf(0.999, N),numsignalsamples)
signalpdf = chi2.pdf(xsig, N)
#correct x to the proper variance scale
xsig = (self.signal**2.)*xsig/N
pdfstepsize = xsig[1]-xsig[0]
#Since everything is going to use this stepsize down the line,
#normalize so the pdf integrates to 1 when summed (no factor of stepsize needed)
signalpdf /= np.sum(signalpdf)
#run through the sample magnitudes, calculate distance between cont
#and comp thresholds.
#run until solution found.
solutionfound=False
for i,mref in enumerate(mag):
#i counts and mref is the currently sampled magnitude
#Scale factor from m0
scalefact = 10.**(0.4*(mref-m0))
#Calculate the desired contamination threshold
contthresh = np.percentile(noiseonlyvar,100.-100.*self.contamination)*scalefact
#Realize the noise CDF at the required stepsize
xnoise = np.arange(noisesorted[0]*scalefact,noisesorted[-1]*scalefact,pdfstepsize)
#Only do calculation if near the solution:
if (len(xnoise) > numsignalsamples/10) and (not solutionfound):
noisecdf = interpnoisecdf(xnoise/scalefact)
noisepdf = (noisecdf[1:]-noisecdf[:-1]) #turn into a noise pdf
noisepdf /= np.sum(noisepdf)
xnoise = (xnoise[1:]+xnoise[:-1])/2. #from cdf to pdf conversion
#calculate and plot the convolution = signal+noise variance dist.
convolution=0
if len(noisepdf) > len(signalpdf):
convolution = np.convolve(noisepdf,signalpdf)
else:
convolution = np.convolve(signalpdf,noisepdf)
xconvolved = xsig[0]+xnoise[0]+np.arange(len(convolution))*pdfstepsize
#calculate the completeness threshold
combinedcdf = np.cumsum(convolution)
findcompthresh = UnivariateSpline(combinedcdf,xconvolved,k=1,s=0)
compthresh = findcompthresh(1.-self.completeness)
res[i] = compthresh - contthresh
if res[i] < 0: solutionfound = True
#interpolate for where the thresholds coincide
#print res
if np.sum(np.isfinite(res)) > 1:
f1 = UnivariateSpline(mag[np.isfinite(res)],res[np.isfinite(res)],k=1,s=0)
#sample the magnitude range at given resolution
magsamples = np.arange(16,np.mean(m5),self.magres)
vardepth = magsamples[np.argmin(np.abs(f1(magsamples)))]
return vardepth
else:
return min(mag)-1
def errfunc(p,x,y):
"""produce goodness measures for fitfunc applied to independant
variable x compared with data y for chi2(2) statistics"""
chis = 2*y / fitfunc(p,x) #values relative to current model
probs = chi2.pdf(chis,2) #probability for each value
return log(probs).sum() #function goodness
ax.text(8.9, 60., '$\hat{\mu} = \\frac{1}{15}\sum x_i = %.1f$' % np.mean(data), fontsize=18,
bbox={'facecolor': 'none', 'pad':14, 'ec': 'r'})
fig.subplots_adjust(bottom=0.15)
pl.savefig('../chisquared_data.pdf')
pl.show()
fig.clf()
pl.close(fig)
# new figure
fig = pl.figure(figsize=(10,5), dpi=100)
# get the chi-squared value from the data
chisq = np.sum((data - np.mean(data))**2/(sigmad**2))
x = np.linspace(0., 50., 1000)
c2pdf = chi2.pdf(x, nu)
# plot chi-squared pdf
pl.plot(x, c2pdf)
pl.plot([chisq, chisq], [0, np.max(c2pdf)], 'k--')
pl.fill_between(x, np.zeros(len(x)), c2pdf, where=x>=chisq, alpha=0.6, facecolor='green', interpolate=True)
ax = pl.gca()
ax.set_xlabel('$\chi^2$', fontsize=14)
ax.set_ylabel('$p(\chi^2)$', fontsize=14)
ax.text(chisq+2, 0.07, '$p_{14}(\chi^2) = %.2f$' % chisq, fontsize=16)
# cumulative function
c2cdf = chi2.cdf(x, nu)
pvalue = 1.-c2cdf[x<=chisq][-1]
print pvalue
def plot_A0(self):
if self.TYPE != "gNL":
plot_hist(plt, self.gA0, clr="skyblue", alp=ALPHA, ht="stepfilled")
sG = np.sqrt(np.var(self.gA0))
mG = np.mean(self.gA0)
amin, amax = plt.xlim()
alist = np.arange(3 * amin, amax * 3, amax / 250.0)
theoryGdist = norm.pdf(alist, loc=mG, scale=sG)
if self.TYPE != "gNL":
plt.plot(
alist, theoryGdist, self.clrs[0], linestyle=self.ls[0], linewidth=LW, label=self.TYPELABEL + "$=0$"
)
for i in range(len(self.fgnls)):
if self.theoryplot == False:
lbl = self.TYPELABEL + "$=$" + NtoSTR(self.fgnls[i])
else:
lbl = None
if self.TYPE == "fNL":
plot_hist(plt, self.fgNLA0[i], clr=self.clrs[i + 1], alp=ALPHA, labl=lbl, ht="stepfilled")
if self.TYPE == "gNL":
# plot_hist(plt, self.fgNLA0[i]-self.gA0+6*self.fgnls[i]*self.efolds*self.A0const-6*self.fgnls[i]*self.phisq, clr=self.clrs[i+1], alp=ALPHA, labl=lbl, ht='stepfilled')
neg = np.min(self.fgNLA0[i] - self.gA0)
const = 9 * 6.0 * self.fgnls[i] * 1.5e-8
# scl=6.0*self.A0const*self.Nconst*self.fgnls[i]
plot_hist(
plt,
(self.fgNLA0[i] - self.gA0 + const) / (1 - 9.0 * 3.0 * self.fgnls[i] * 1.5e-8),
clr=self.clrs[i + 1],
alp=ALPHA,
labl=lbl,
ht="stepfilled",
)
amin, amax = plt.xlim()
alist = np.arange(3 * amin, amax * 3, 0.001)
if self.theoryplot:
if self.TYPE == "fNL":
theorynGdist = norm.pdf(
alist,
loc=mG,
scale=np.sqrt(16.0 * self.A0const * self.Nconst * self.fgnls[i] ** 2.0 + sG ** 2.0),
)
if self.TYPE == "gNL":
scl = 6.0 * self.A0const * self.Nconst * self.fgnls[i]
# scl=2.0*self.A0const*self.efolds*self.fgnls[i]
theorynGdist = np.sign(self.fgnls[i]) * chi2.pdf(alist, 1, scale=scl)
# new
"""
sigmaphi00=np.sqrt(self.A0const*self.efolds)
a0=24.*np.pi*self.fgnls[i]
theorynGdist=1./a0/np.sqrt(2.*np.pi)/sigmaphi00 * np.sqrt(a0/(alist+a0*sigmaphi00^2))*np.exp(-(alist+a0*sigmaphi00**2.0)/(2.*a0*sigmaphi00**2.0))
self.theorynGdist=theorynGdist
"""
LW1 = self.get_LW1(i)
plt.plot(
alist,
theorynGdist,
self.clrs[i + 1],
linestyle=self.ls[i + 1],
linewidth=LW1,
label=self.TYPELABEL + "$=$" + NtoSTR(self.fgnls[i]),
)
plt.xlabel(r"$A_0$")
plt.ylabel(r"$p(A_0)$")
plt.yscale("log")
if self.TYPE == "fNL":
plt.xlim(-1.0, 1.0)
plt.ylim(0.1, 100)
if self.TYPE == "gNL":
# plt.xscale('log')
plt.ylim(0.05, 1000)
plt.xlim(-0.02, 0.25)
plt.legend()
Q = inv(sqrtm(Sigma))
# == Generate observations of the normalized sample mean == #
error_obs = np.empty((2, replications))
for i in range(replications):
# == Generate one sequence of bivariate shocks == #
X = np.empty((2, n))
W = dw.rvs(n)
U = du.rvs(n)
# == Construct the n observations of the random vector == #
X[0, :] = W
X[1, :] = W + U
# == Construct the i-th observation of Y_n == #
error_obs[:, i] = np.sqrt(n) * X.mean(axis=1)
# == Premultiply by Q and then take the squared norm == #
temp = np.dot(Q, error_obs)
chisq_obs = np.sum(temp**2, axis=0)
# == Plot == #
fig, ax = plt.subplots()
xmax = 8
ax.set_xlim(0, 8)
xgrid = np.linspace(0, 8, 200)
lb = "Chi-squared with 2 degrees of freedom"
ax.plot(xgrid, chi2.pdf(xgrid, 2), 'k-', lw=2, label=lb)
ax.legend()
ax.hist(chisq_obs, bins=50, normed=True)
plt.show()
import matplotlib.pyplot as plt
import numpy as np
from numpy import linspace
from scipy.stats import chisquare, chi2
from scipy.stats.kde import gaussian_kde
m = 1000
n = 100
k = 10
y = []
for i in range(m):
x = [random.randint(1, k) for i in range(n)]
y.append(chisquare(x)[0])
mean_y, std_y = np.mean(y), np.std(y)
y = [(i - mean_y) / sqrt(np.std(y)) for i in y]
t = min(y)
y = [i - t for i in y]
pdf = gaussian_kde(y)
a = linspace(min(y), max(y), len(y) // 10)
fig, ax = plt.subplots(2, 1)
fig.subplots_adjust(wspace=0)
ax[0].plot(a, pdf(a))
ax[1].plot(a, chi2.pdf(a,k-1))
plt.show()
xu = np.linspace(chi2.ppf(0.01, dof), chi2.ppf(0.99, dof), 100)
ax.axvline(x=dof/df,color='k', linestyle='dashed',lw=4, label='UGC11680NED01 $\chi^2$')
ax.hist(chi_sfh/df,bins=30, normed=False,weights=weights, histtype='step', lw=3,label='All AGNs $\chi^2$ reduced distribution')
ax.legend(loc='best', frameon=False)
ax.set_ylabel('Probability density $\chi ^2$')
ax.set_xlabel('$x$')
plt.show()
1+ 1./2. + 1./3. + 1./4. +1./5. + 1./6. +1./7. +1./8. + 1./9. + 1./10. + 1./11. +1./12. +1./13. + 1./14. + 1./15. +1./16. +1./17. +1./18. +1./19. +1./20. +1./21.+ 1./22. +1./23. +1./24.
3.7759581777535067
2(1368) = 2736
n=1368.9986218003824*(chi2.pdf(x, df))
2736+3.7759581777535067
varianza= 52.34286921995921
media= 1368
mass 8, color 1
2736+2.45
varianza= 52.33020160480943
media= 1368
mass 9 color 1
def test(): x = linspace(0.1, 25, 200) for dof in [1, 2, 3, 5, 10, 50]: plot(x, chi2.pdf(x, dof))
def draw_figures():
pfile = '/auto/tdrive/mschachter/data/aggregate/decoders_pairwise_coherence_multi_freq.h5'
agg = AggregatePairwiseDecoder.load(pfile)
nbands = agg.df['band'].max()
sample_rate = 381.4697265625
freqs = get_freqs(sample_rate)
g = agg.df.groupby(['bird', 'block', 'segment', 'hemi'])
"""
# TODO: compute the average likelihood ratio between intecept-only and full model for all sites!
i = (agg.df['bird'] == 'GreBlu9508M') & (agg.df['block'] == 'Site4') & (agg.df['segment'] == 'Call1') & (agg.df['hemi'] == 'L') & (agg.df['band'] == 0)
assert i.sum() == 1
full_likelihood_for_null = agg.df['likelihood'][i].values[0]
null_likelihood = 1.63 # for GreBlu9508_Site4_Call1_L
null_likelihood_ratio = 2*(null_likelihood - full_likelihood_for_null)
print 'full_likelihood_for_null=',full_likelihood_for_null
print 'null_likelihood=',null_likelihood
print 'null_likelihood_ratio=',null_likelihood_ratio
"""
full_likelihoods = list()
likelihood_ratios = list()
pccs = list()
pcc_thresh = 0.25
single_band_likelihoods = list()
single_band_pccs = list()
for (bird,block,seg,hemi),gdf in g:
# get the likelihood of the fullmodel
i = gdf['band'] == 0
assert i.sum() == 1
num_samps = gdf[i]['num_samps'].values[0]
print 'num_samps=%d' % num_samps
full_likelihood = -gdf[i]['likelihood'].values[0] * num_samps
pcc = gdf[i]['pcc'].values[0]
if pcc < pcc_thresh:
continue
full_likelihoods.append(full_likelihood)
pccs.append(pcc)
# get the likelihood per frequency band
ratio_by_band = np.zeros(nbands)
single_likelihood_by_band = np.zeros(nbands)
single_pcc_band = np.zeros(nbands)
for k in range(nbands):
i = (gdf['band'] == k+1) & (gdf['exfreq'] == True)
assert i.sum() == 1
num_samps2 = gdf[i]['num_samps'].values[0]
assert num_samps2 == num_samps
leftout_likelihood = -gdf[i]['likelihood'].values[0] * num_samps
i = (gdf['band'] == k+1) & (gdf['exfreq'] == False)
assert i.sum() == 1
num_samps3 = gdf[i]['num_samps'].values[0]
assert num_samps3 == num_samps2
single_leftout_likelihood = -gdf[i]['likelihood'].values[0] * num_samps
pcc = gdf[i]['pcc'].values[0]
print '(%s,%s,%s,%s,%d) leftout=%0.6f, full=%0.6f, single=%0.6f, single_pcc=%0.6f, num_samps=%d' % \
(bird, block, seg, hemi, k, leftout_likelihood, full_likelihood, single_leftout_likelihood, pcc, num_samps)
# compute the likelihood ratio
lratio = -2*(leftout_likelihood - full_likelihood)
ratio_by_band[k] = lratio
single_likelihood_by_band[k] = single_leftout_likelihood
single_pcc_band[k] = pcc
likelihood_ratios.append(ratio_by_band)
single_band_likelihoods.append(single_likelihood_by_band)
single_band_pccs.append(single_pcc_band)
pccs = np.array(pccs)
likelihood_ratios = np.array(likelihood_ratios)
full_likelihoods = np.array(full_likelihoods)
single_band_likelihoods = np.array(single_band_likelihoods)
single_band_pccs = np.array(single_band_pccs)
# exclude segments whose likelihood ratio goes below zero
# i = np.array([np.any(lrat < 0) for lrat in likelihood_ratios])
i = np.ones(len(likelihood_ratios), dtype='bool')
print 'i.sum()=%d' % i.sum()
# compute significance threshold
x = np.linspace(1, 150, 1000)
df = 12
p = chi2.pdf(x, df)
sig_thresh = max(x[p > 0.01])
# compute mean and std
lrat_mean = likelihood_ratios[i, :].mean(axis=0)
lrat_std = likelihood_ratios[i, :].std(axis=0, ddof=1)
single_l_mean = single_band_likelihoods[i, :].mean(axis=0)
single_l_std = single_band_likelihoods[i, :].std(axis=0, ddof=1)
single_pcc_mean = single_band_pccs[i, :].mean(axis=0)
single_pcc_std = single_band_pccs[i, :].std(axis=0, ddof=1)
fig = plt.figure(figsize=(24, 16))
plt.subplots_adjust(top=0.95, bottom=0.05, left=0.05, right=0.99, hspace=0.40, wspace=0.20)
ax = plt.subplot(2, 3, 1)
plt.plot(full_likelihoods, pccs, 'go', linewidth=2.0)
plt.xlabel('log Likelihood')
plt.ylabel('PCC')
plt.axis('tight')
ax = plt.subplot(2, 3, 2)
for k,lrat in enumerate(likelihood_ratios[i, :]):
plt.plot(freqs, lrat, '-', linewidth=2.0, alpha=0.7)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Likelihood Ratio')
plt.axis('tight')
ax = plt.subplot(2, 3, 4)
nsamps = len(likelihood_ratios)
plt.errorbar(freqs, single_pcc_mean, yerr=single_pcc_std/np.sqrt(nsamps), ecolor='r', elinewidth=3.0, fmt='k-', linewidth=7.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('PCC')
plt.title('Mean Single Band Decoder PCC')
plt.axis('tight')
ax = plt.subplot(2, 3, 5)
plt.errorbar(freqs, single_l_mean, yerr=single_l_std/np.sqrt(nsamps), ecolor='r', elinewidth=3.0, fmt='k-', linewidth=7.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('log Likelihood')
plt.title('Mean Single Band Decoder Likelihood')
plt.axis('tight')
ax = plt.subplot(2, 3, 6)
plt.errorbar(freqs, lrat_mean, yerr=lrat_std/np.sqrt(nsamps), ecolor='r', elinewidth=3.0, fmt='k-', linewidth=7.0, alpha=0.75)
plt.plot(freqs, np.ones_like(freqs)*sig_thresh, 'k--', linewidth=7.0, alpha=0.75)
plt.xlabel('Frequency (Hz)')
plt.ylabel('Likelihood Ratio')
plt.title('Mean Likelihood Ratio')
plt.axis('tight')
plt.ylim(0, lrat_mean.max())
fname = os.path.join(get_this_dir(), 'figs.svg')
plt.savefig(fname, facecolor='w', edgecolor='none')
plt.show()