def test_pearson(self):
p_x = np.array([0.0, 0.9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])
p_y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])
p_sx = np.array([0.03, 0.03, 0.04, 0.035, 0.07, 0.11, 0.13, 0.22, 0.74, 1.0])
p_sy = np.array([1.0, 0.74, 0.5, 0.35, 0.22, 0.22, 0.12, 0.12, 0.1, 0.04])
p_dat = RealData(p_x, p_y, sx=p_sx, sy=p_sy)
# Reverse the data to test invariance of results
pr_dat = RealData(p_y, p_x, sx=p_sy, sy=p_sx)
p_mod = Model(self.pearson_fcn, meta=dict(name="Uni-linear Fit"))
p_odr = ODR(p_dat, p_mod, beta0=[1.0, 1.0])
pr_odr = ODR(pr_dat, p_mod, beta0=[1.0, 1.0])
out = p_odr.run()
assert_array_almost_equal(out.beta, np.array([5.4767400299231674, -0.4796082367610305]))
assert_array_almost_equal(out.sd_beta, np.array([0.3590121690702467, 0.0706291186037444]))
assert_array_almost_equal(
out.cov_beta,
np.array([[0.0854275622946333, -0.0161807025443155], [-0.0161807025443155, 0.003306337993922]]),
)
rout = pr_odr.run()
assert_array_almost_equal(rout.beta, np.array([11.4192022410781231, -2.0850374506165474]))
assert_array_almost_equal(rout.sd_beta, np.array([0.9820231665657161, 0.3070515616198911]))
assert_array_almost_equal(
rout.cov_beta,
np.array([[0.6391799462548782, -0.1955657291119177], [-0.1955657291119177, 0.0624888159223392]]),
)
def graph(a,toc): #a:odr with xerrors or curvefit without? toc: number of the figure
plt.figure(toc,figsize=(15,10))
for tickLabel in plt.gca().get_xticklabels()+plt.gca().get_yticklabels():
tickLabel.set_fontsize(15)
plt.title("Number of coincidence counts in the peak of $Na^{22}$ wrt angle",fontsize=17)
plt.xlabel(r'Angle $\theta$ (°)',fontsize=17)
plt.ylabel('# coincidence events',fontsize=17)
plt.scatter(Ld,res,label='\n Data with parameters'+\
' \n $16$ $cm$ source-detectors'+\
' \n $150$ $s/points$')
if a==False:
par, par_va = curve_fit(simplegaussian, Ld, res, p0=[70000, 180, 10,1000],sigma=err_res,absolute_sigma=True)
chi2=round(sum(((res - simplegaussian(Ld,*par) )/ err_res) ** 2)/(len(Ld)-3),2)
plt.plot(Lc,simplegaussian(Lc, *par),color='gold',label='Fit with '+r'$A\exp\{\frac{-(\theta-\mu)^2}{2\sigma^2}\}+Cst$'+\
' \n $A =$%s'%int(par[0])+' $ \pm$ %s'%int(np.sqrt(np.diag(par_va)[0]))+' #'+\
' \n $\mu =$ %s'%round(par[1],1)+' $\pm$ %s'%round(np.sqrt(np.diag(par_va)[1]),1)+'°'+\
' \n $\sigma =$ %s'%round(par[2],1)+ '$\pm$ %s'%round(np.sqrt(np.diag(par_va)[2]),1)+'°'+\
' \n $Cst=$ %s'%int(par[3])+' $\pm$ %s'%int(np.sqrt(np.diag(par_va)[3]))+' #'+\
' \n $\chi^2/dof = $ %s'%chi2)
plt.errorbar(Ld, res, err_res,fmt='.',label=r'$y=\sqrt{counts}$ '+\
'\n $x=0°$', color='black',ecolor='lightgray', elinewidth=3, capsize=0)
else:
data = RealData(Ld,res,err_resx,err_res)
model = Model(simplegaussianl)
odr = ODR(data, model, [73021, 183, 11,1208])
odr.set_job(fit_type=2)
output = odr.run()
xn = Lc
yn = simplegaussianl(output.beta, xn)
#pl.hold(True)
#plot(Ld,res,'ro')
#print(x,y)
plt.plot(xn,yn,'k-',label=' ODR leastsq fit'+\
' \n $\chi^2/dof = $ %s'%round(output.sum_square/(len(Ld)-3),2)+'\n')
odr.set_job(fit_type=0)
output = odr.run()
par,par_va=output.beta,output.cov_beta
yn = simplegaussianl(output.beta, xn)
plt.plot(xn,yn,color='gold',label='ODR fit '+r'$A\exp\{\frac{-(\theta-\mu)^2}{2\sigma^2}\}+Cst$'+\
' \n $A =$%s'%int(par[0])+' $ \pm$ %s'%int(np.sqrt(np.diag(par_va)[0]))+' #'+\
' \n $\mu =$ %s'%round(par[1],1)+' $\pm$ %s'%round(np.sqrt(np.diag(par_va)[1]),1)+'°'+\
' \n $\sigma =$ %s'%round(par[2],1)+ '$\pm$ %s'%round(np.sqrt(np.diag(par_va)[2]),1)+'°'+\
' \n $Cst=$ %s'%int(par[3])+' $\pm$ %s'%int(np.sqrt(np.diag(par_va)[3]))+' #'+\
' \n Sum of squares/dof $= $ %s'%round(output.sum_square/(len(Ld)-3),2))
plt.legend(loc=0)
plt.errorbar(Ld, res, err_res,err_resx,label=r'$y=\sqrt{counts}$ '+\
'\n $x=$%s'%errx+'°',fmt='.', color='black',ecolor='lightgray', elinewidth=3, capsize=0)
plt.gca().set_xlim(140,220)
plt.legend(bbox_to_anchor=(0.68, 0.58), loc=1, borderaxespad=0.,prop={'size':14})
plt.yscale('log')
plt.ylim(1e2,1e5)
def test_ifixx(self):
x1 = [-2.01, -0.99, -0.001, 1.02, 1.98]
x2 = [3.98, 1.01, 0.001, 0.998, 4.01]
fix = np.vstack((np.zeros_like(x1, dtype=int), np.ones_like(x2, dtype=int)))
data = Data(np.vstack((x1, x2)), y=1, fix=fix)
model = Model(lambda beta, x: x[1, :] - beta[0] * x[0, :]**2., implicit=True)
odr1 = ODR(data, model, beta0=np.array([1.]))
sol1 = odr1.run()
odr2 = ODR(data, model, beta0=np.array([1.]), ifixx=fix)
sol2 = odr2.run()
assert_equal(sol1.beta, sol2.beta)
def test_ifixx(self):
x1 = [-2.01, -0.99, -0.001, 1.02, 1.98]
x2 = [3.98, 1.01, 0.001, 0.998, 4.01]
fix = np.vstack((np.zeros_like(x1, dtype=int), np.ones_like(x2, dtype=int)))
data = Data(np.vstack((x1, x2)), y=1, fix=fix)
model = Model(lambda beta, x: x[1, :] - beta[0] * x[0, :]**2., implicit=True)
odr1 = ODR(data, model, beta0=np.array([1.]))
sol1 = odr1.run()
odr2 = ODR(data, model, beta0=np.array([1.]), ifixx=fix)
sol2 = odr2.run()
assert_equal(sol1.beta, sol2.beta)
def chi2_iterative(k, k_nn, Foward=True):
"""
Esta funcion agarra un eje X (k), un eje Y (k_nn) y busca los parametros para
ajustar la mejor recta, buscando el regimen lineal de la curva. Esto lo hace
sacando puntos de la curva, ajustando la curva resultante, y luego comparando
los parametros de los distintos ajustes, seleccionando el de menor chi2.
Si Foward=True entonces la funcion va a ir sacando puntos del final para
encontrar kmax. Si Foward=False, la funcion va a sacar puntos del principio para
calcular kmin. El punto va a estar dado por k[index].
Returns: m, b, chi2_stat, index
m: pendiente de la recta resultante
b: ordenada de la recta resultante
chi2: estadistico de chi2 de la recta resultante
index: indice del elemento donde empieza/termina el regimen lineal.
.
.
"""
chi2_list = []
m_list = []
b_list = []
if Foward == True:
for j in range(0, len(k) - 3):
k_nn_temp = k_nn[:len(k_nn) - j]
k_temp = k[:len(k) - j]
linear_model = Model(linear)
data = RealData(k_temp, k_nn_temp)
odr = ODR(data, linear_model, beta0=[0., 1.])
out = odr.run()
chi2_list.append(out.res_var)
m_list.append(out.beta[0])
b_list.append(out.beta[1])
else:
for j in range(0, len(k) - 3):
k_nn_temp = k_nn[j:]
k_temp = k[j:]
linear_model = Model(linear)
data = RealData(k_temp, k_nn_temp)
odr = ODR(data, linear_model, beta0=[0., 1.])
out = odr.run()
chi2_list.append(out.res_var)
m_list.append(out.beta[0])
b_list.append(out.beta[1])
#index = ClosestToOne(chi2_list)
index = chi2_list.index(min(chi2_list))
m = m_list[index]
b = b_list[index]
chi2 = chi2_list[index]
return m, b, chi2, index
def do_bestfit(self):
"""
do bestfit using scipy.odr
"""
self.check_important_variables()
x = np.array(self.args["x"])
y = np.array(self.args["y"])
if self.args.get("use_RealData", True):
realdata_kwargs = self.args.get("RealData_kwargs", {})
data = RealData(x, y, **realdata_kwargs)
else:
data_kwargs = self.args.get("Data_kwargs", {})
data = Data(x, y, **data_kwargs)
model = self.args.get("Model", None)
if model is None:
if "func" not in self.args.keys():
raise KeyError("Need fitting function")
model_kwargs = self.args.get("Model_kwargs", {})
model = Model(self.args["func"], **model_kwargs)
odr_kwargs = self.args.get("ODR_kwargs", {})
odr = ODR(data, model, **odr_kwargs)
self.output = odr.run()
if self.args.get("pprint", False):
self.output.pprint()
self.fit_args = self.output.beta
return self.fit_args
def roda(self, dados):
x, ux = Variáveis[self.x], Variáveis[self.ux]
y, uy = Variáveis[self.y], Variáveis[self.uy]
if self.tipo == 'odr':
data = RealData(x, y, ux, uy)
odr = ODR(data, linear, beta0=[1, 0], ndigit=16, maxit=100)
DadosAjuste = odr.run()
p = DadosAjuste.beta
u = sqrt(diag(DadosAjuste.cov_beta))
elif self.tipo == 'cfit':
p, cov = curve_fit(flinear,
x,
y,
sigma=uy,
absolute_sigma=True,
method='trf')
u = sqrt(diag(cov))
elif self.tipo == 'cfitse':
p, cov = curve_fit(flinear, x, y, method='trf')
u = sqrt(diag(cov))
print(p, u)
Ajustes[self.ref] = ({
'a': p[0],
'ua': u[0],
'b': p[1],
'ub': u[1]
}, x, ux, y, uy)
def fit_slope(X, Y, eX=None, eY=None):
"""Will fit a Y vs X with errors using the scipy
ODR functionalities and a linear fitting function
Args:
X:
Y: input arrays
eX:
eY: input uncertaintites [None]
Returns:
odr output
"""
# First filtering the Nan
good = ~np.isnan(X)
if eX is None: eX_good = None
else: eX_good = eX[good]
if eY is None: eY_good = None
else: eY_good = eY[good]
linear = Model(linear_fit)
odr_data = RealData(X[good], Y[good], sx=eX_good, sy=eY_good)
odr_run = ODR(odr_data, linear, beta0=[2., 1.])
return odr_run.run()
def test_explicit(self):
explicit_mod = Model(
self.explicit_fcn,
fjacb=self.explicit_fjb,
fjacd=self.explicit_fjd,
meta=dict(name='Sample Explicit Model',
ref='ODRPACK UG, pg. 39'),
)
explicit_dat = Data([0.,0.,5.,7.,7.5,10.,16.,26.,30.,34.,34.5,100.],
[1265.,1263.6,1258.,1254.,1253.,1249.8,1237.,1218.,1220.6,
1213.8,1215.5,1212.])
explicit_odr = ODR(explicit_dat, explicit_mod, beta0=[1500.0, -50.0, -0.1],
ifixx=[0,0,1,1,1,1,1,1,1,1,1,0])
explicit_odr.set_job(deriv=2)
explicit_odr.set_iprint(init=0, iter=0, final=0)
out = explicit_odr.run()
assert_array_almost_equal(
out.beta,
np.array([1.2646548050648876e+03, -5.4018409956678255e+01,
-8.7849712165253724e-02]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([1.0349270280543437, 1.583997785262061, 0.0063321988657267]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[4.4949592379003039e-01, -3.7421976890364739e-01,
-8.0978217468468912e-04],
[-3.7421976890364739e-01, 1.0529686462751804e+00,
-1.9453521827942002e-03],
[-8.0978217468468912e-04, -1.9453521827942002e-03,
1.6827336938454476e-05]]),
)
def ajuste_odr_u(x, y):
data = RealData(unp.nominal_values(x), unp.nominal_values(y), sx=unp.std_devs(x), sy=unp.std_devs(y))
odr = ODR(data, mlinear, beta0=[1., 1.], ndigit=20)
ajuste = odr.run()
a, b = ajuste.beta
ua, ub = np.sqrt(np.diag(ajuste.cov_beta))
return a, ua, b, ub
def regress_odr(x, y, sx, sy, beta0=[0., 1.]):
"""Return an ODR linear fit
"""
linear = Model(my_linear)
mydata = RealData(x.ravel(), y.ravel(), sx=sx.ravel(), sy=sy.ravel())
myodr = ODR(mydata, linear, beta0=beta0)
return myodr.run()
def voltage_current_regression():
# read data from datafile
df = pd.read_table(os.path.join(os.path.dirname(__file__), DATA_PATH,
'1_a_soneloid.dat'),
delim_whitespace=True,
names=['current', 'voltage_min', 'voltage_max'],
decimal=',',
comment='#')
# add columns for voltage mean and error
df['voltage_mean'] = 0.5 * (df['voltage_min'] + df['voltage_max'])
df['voltage_error'] = df['voltage_max'] - df['voltage_min']
# Create a model for fitting.
linear_model = Model(regression_func)
x = np.array(df['current'])
y = np.array(df['voltage_mean'])
sy = np.array(df['voltage_error'])
data = RealData(x, y, sy=sy)
odr = ODR(data, linear_model, beta0=[0., 1.])
out = odr.run()
return out
def orthoregress(x, y):
"""Perform an Orthogonal Distance Regression on the given data,
using the same interface as the standard scipy.stats.linregress function.
Adapted from https://gist.github.com/robintw/d94eb527c44966fbc8b9#file-orthoregress-py
Arguments:
x: x data
y: y data
Returns:
[slope, intercept, residual]
Uses standard ordinary least squares to estimate the starting parameters
then uses the scipy.odr interface to the ODRPACK Fortran code to do the
orthogonal distance calculations.
"""
def f(p, x):
"""Basic linear regression 'model' for use with ODR"""
return (p[0] * x) + p[1]
linreg = stats.linregress(x, y)
mod = Model(f)
dat = Data(x, y)
od = ODR(dat, mod, beta0=linreg[0:2])
out = od.run()
return list(out.beta) + [out.res_var]
def meanResiduals(pts):
f = lambda B, x: B[0] * x + B[1]
model = Model(f)
data = RealData(list(p.x() for p in pts), list(p.y() for p in pts))
odr = ODR(data, model, beta0=[0., 1.])
out = odr.run()
return out.sum_square / len(pts)
def test_quadratic_model(self):
x = np.linspace(0.0, 5.0)
y = 1.0 * x**2 + 2.0 * x + 3.0
data = Data(x, y)
odr_obj = ODR(data, quadratic)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [1.0, 2.0, 3.0])
def test_explicit(self):
explicit_mod = Model(
self.explicit_fcn,
fjacb=self.explicit_fjb,
fjacd=self.explicit_fjd,
meta=dict(name='Sample Explicit Model',
ref='ODRPACK UG, pg. 39'),
)
explicit_dat = Data([0.,0.,5.,7.,7.5,10.,16.,26.,30.,34.,34.5,100.],
[1265.,1263.6,1258.,1254.,1253.,1249.8,1237.,1218.,1220.6,
1213.8,1215.5,1212.])
explicit_odr = ODR(explicit_dat, explicit_mod, beta0=[1500.0, -50.0, -0.1],
ifixx=[0,0,1,1,1,1,1,1,1,1,1,0])
explicit_odr.set_job(deriv=2)
explicit_odr.set_iprint(init=0, iter=0, final=0)
out = explicit_odr.run()
assert_array_almost_equal(
out.beta,
np.array([1.2646548050648876e+03, -5.4018409956678255e+01,
-8.7849712165253724e-02]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([1.0349270280543437, 1.583997785262061, 0.0063321988657267]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[4.4949592379003039e-01, -3.7421976890364739e-01,
-8.0978217468468912e-04],
[-3.7421976890364739e-01, 1.0529686462751804e+00,
-1.9453521827942002e-03],
[-8.0978217468468912e-04, -1.9453521827942002e-03,
1.6827336938454476e-05]]),
)
def test_multilinear_model(self):
x = np.linspace(0.0, 5.0)
y = 10.0 + 5.0 * x
data = Data(x, y)
odr_obj = ODR(data, multilinear)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [10.0, 5.0])
def test_exponential_model(self):
x = np.linspace(0.0, 5.0)
y = -10.0 + np.exp(0.5 * x)
data = Data(x, y)
odr_obj = ODR(data, exponential)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [-10.0, 0.5])
def find_c13(self):
'''
Find the c13 parameter by fitting a curve to
the measured group velocities using the scipy
ODR library. Since c13 is par tof the equation
for the curve, the c13 value corresponding to
the best fit curve is the result of the inversion.
'''
self.c13_max = np.sqrt(
self.c33 *
(self.c11)) #The most physically reasonable maximum for c13
self.c13_min = np.sqrt(self.c33 * (self.c11 - 2 * self.c66) +
self.c66**2) - self.c66
self.c13_0 = self.c13_min * 1.2
model = Model(self.forward_model,
estimate=[self.c13_0, self.theta0, self.c11, self.c33])
data = RealData(self.group_angles,
self.group_vels,
sy=self.group_vel_err)
fit = ODR(data,
model,
beta0=[self.c13_0, self.theta0, self.c11, self.c33],
ifixb=[1, 1, 1, 1])
output = fit.run()
best_fit = output.beta
errors = output.sd_beta
self.c13 = best_fit[0]
self.c13_err = errors[0]
self.theta0 = best_fit[1]
def test_unilinear_model(self):
x = np.linspace(0.0, 5.0)
y = 1.0 * x + 2.0
data = Data(x, y)
odr_obj = ODR(data, unilinear)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [1.0, 2.0])
def do_the_fit(obs, **kwargs):
global print_output, beta0
func = kwargs.get('function')
yerr = kwargs.get('yerr')
length = len(yerr)
xerr = kwargs.get('xerr')
if length == len(obs):
assert 'x_constants' in kwargs
data = RealData(kwargs.get('x_constants'), obs, sy=yerr)
fit_type = 2
elif length == len(obs) // 2:
data = RealData(obs[:length], obs[length:], sx=xerr, sy=yerr)
fit_type = 0
else:
raise Exception('x and y do not fit together.')
model = Model(func)
odr = ODR(data, model, beta0, partol=np.finfo(np.float64).eps)
odr.set_job(fit_type=fit_type, deriv=1)
output = odr.run()
if print_output and not silent:
print(*output.stopreason)
print('chisquare/d.o.f.:', output.res_var)
print_output = 0
beta0 = output.beta
return output.beta[kwargs.get('n')]
def test_polynomial_model(self):
x = np.linspace(0.0, 5.0)
y = 1.0 + 2.0 * x + 3.0 * x**2 + 4.0 * x**3
poly_model = polynomial(3)
data = Data(x, y)
odr_obj = ODR(data, poly_model)
output = odr_obj.run()
assert_array_almost_equal(output.beta, [1.0, 2.0, 3.0, 4.0])
def ajuste_odr(x, y, ux, uy):
data = RealData(x, y, sx=ux, sy=uy)
odr = ODR(data, mlinear, beta0=[-1000., 1.], ndigit=20)
ajuste = odr.run()
a, b = ajuste.beta
ua, ub = np.sqrt(np.diag(ajuste.cov_beta))
ajuste.pprint()
return a, ua, b, ub
def test_pearson(self):
p_x = np.array([0., .9, 1.8, 2.6, 3.3, 4.4, 5.2, 6.1, 6.5, 7.4])
p_y = np.array([5.9, 5.4, 4.4, 4.6, 3.5, 3.7, 2.8, 2.8, 2.4, 1.5])
p_sx = np.array([.03, .03, .04, .035, .07, .11, .13, .22, .74, 1.])
p_sy = np.array([1., .74, .5, .35, .22, .22, .12, .12, .1, .04])
p_dat = RealData(p_x, p_y, sx=p_sx, sy=p_sy)
# Reverse the data to test invariance of results
pr_dat = RealData(p_y, p_x, sx=p_sy, sy=p_sx)
p_mod = Model(self.pearson_fcn, meta=dict(name='Uni-linear Fit'))
p_odr = ODR(p_dat, p_mod, beta0=[1., 1.])
pr_odr = ODR(pr_dat, p_mod, beta0=[1., 1.])
out = p_odr.run()
assert_array_almost_equal(
out.beta,
np.array([5.4767400299231674, -0.4796082367610305]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([0.3590121690702467, 0.0706291186037444]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[0.0854275622946333, -0.0161807025443155],
[-0.0161807025443155, 0.003306337993922]]),
)
rout = pr_odr.run()
assert_array_almost_equal(
rout.beta,
np.array([11.4192022410781231, -2.0850374506165474]),
)
assert_array_almost_equal(
rout.sd_beta,
np.array([0.9820231665657161, 0.3070515616198911]),
)
assert_array_almost_equal(
rout.cov_beta,
np.array([[0.6391799462548782, -0.1955657291119177],
[-0.1955657291119177, 0.0624888159223392]]),
)
def ortho_regress(x, y):
linreg = linregress(x, y)
mod = Model(f)
dat = Data(x, y)
od = ODR(dat, mod, beta0=linreg[0:2])
out = od.run()
#print(list(out.beta))
#return list(out.beta) + [np.nan, np.nan, np.nan]
return(list(out.beta))
def orthoregress(x, y, xerr, yerr):
linreg = linregress(x, y)
mod = Model(f)
dat = RealData(x, y, sx=xerr, sy=yerr)
od = ODR(dat, mod, beta0=linreg[0:2])
out = od.run()
return list(out.beta)
def sigma(X, Y, X_err, Y_err):
linear_model = Model(Linear)
data = RealData(X, Y, sx=X_err, sy=Y_err)
odr = ODR(data, linear_model, beta0=[0., 1.])
out = odr.run()
m = out.beta[0]
b = out.beta[1]
recta = [(I[i] - (V[i] * m + b)) for i in range(len(I))]
return len(recta), f.dispersion(recta)
def fit(self, x, y):
# Initial estimate of betas
linreg = linregress(x, y)
linear = Model(self.model)
mydata = Data(x, y)
myodr = ODR(mydata, linear, beta0=linreg[0:2])
myoutput = myodr.run()
self.betas = myoutput.beta
def orthogonal_distance_regression(func, data, err, beta0=None):
"""Fit the function using scipy.odr and return fit output."""
if not beta0:
beta0 = _start_values(data)
model = Model(func)
rdata = _real_data(data, err)
fit = ODR(rdata, model, beta0=beta0)
output = fit.run()
print(output.beta)
return output
def orth_regression(model,obs):
linear = Model(f)
mydata = RealData(obs, model)
myodr = ODR(mydata, linear, beta0=[1., 0.])
myoutput = myodr.run()
params = myoutput.beta
gradient = params[0]
y_intercept = params[1]
res_var = myoutput.res_var
return np.around(gradient,2), np.around(y_intercept,2), np.around(res_var,2)
def test_lorentz(self):
l_sy = np.array([.29] * 18)
l_sx = np.array([
.000972971, .000948268, .000707632, .000706679, .000706074,
.000703918, .000698955, .000456856, .000455207, .000662717,
.000654619, .000652694, .000000859202, .00106589, .00106378,
.00125483, .00140818, .00241839
])
l_dat = RealData(
[
3.9094, 3.85945, 3.84976, 3.84716, 3.84551, 3.83964, 3.82608,
3.78847, 3.78163, 3.72558, 3.70274, 3.6973, 3.67373, 3.65982,
3.6562, 3.62498, 3.55525, 3.41886
],
[
652, 910.5, 984, 1000, 1007.5, 1053, 1160.5, 1409.5, 1430,
1122, 957.5, 920, 777.5, 709.5, 698, 578.5, 418.5, 275.5
],
sx=l_sx,
sy=l_sy,
)
l_mod = Model(self.lorentz, meta=dict(name='Lorentz Peak'))
l_odr = ODR(l_dat, l_mod, beta0=(1000., .1, 3.8))
out = l_odr.run()
assert_array_almost_equal(
out.beta,
np.array([
1.4306780846149925e+03, 1.3390509034538309e-01,
3.7798193600109009e+00
]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([
7.3621186811330963e-01, 3.5068899941471650e-04,
2.4451209281408992e-04
]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[
2.4714409064597873e-01, -6.9067261911110836e-05,
-3.1236953270424990e-05
],
[
-6.9067261911110836e-05, 5.6077531517333009e-08,
3.6133261832722601e-08
],
[
-3.1236953270424990e-05, 3.6133261832722601e-08,
2.7261220025171730e-08
]]),
)
def ODR_fitting(xdata, ydata, fitfunction, beta, fix):
bpl_all = Model(fitfunction)
data_all = RealData(xdata,
ydata,
sx=np.cov([xdata, ydata])[0][1],
sy=np.cov([xdata, ydata])[0][1])
odr_all = ODR(data_all, bpl_all, beta0=beta, ifixb=fix)
odr_all.set_job(fit_type=0)
output_all = odr_all.run()
#output_all.pprint()
return (output_all.beta, output_all.sd_beta)
def orth_regression(obs, model):
linear = Model(f)
mydata = RealData(obs, model)
myodr = ODR(mydata, linear, beta0=[1., 0.])
myoutput = myodr.run()
params = myoutput.beta
gradient = params[0]
y_intercept = params[1]
res_var = myoutput.res_var
return np.around(gradient, 2), np.around(y_intercept,
2), np.around(res_var, 2)
def _run_odr(self):
"""Run an ODR regression"""
linear = Model(self._modelODR)
mydata = Data(ravel(self._datax), ravel(self._datay), 1)
myodr = ODR(mydata, linear, beta0=self._guess, maxit=10000)
myoutput = myodr.run()
self._result = myoutput.beta
self._stdev = myoutput.sd_beta
self._covar = myoutput.cov_beta
self._odr = myoutput
def odrlin(x, y, sx, sy):
"""
Linear fit of 2-D data set made with Orthogonal Distance Regression
@params x, y: data to fit
@param sx, sy: respective errors of data to fit
"""
model = models.unilinear # defines model as beta[0]*x + beta[1]
data = RealData(x, y, sx=sx, sy=sy)
kinit = (y[-1] - y[0]) / (x[-1] - x[0])
init = (kinit, y[0] - kinit * x[0])
linodr = ODR(data, model, init)
return linodr.run()
def _run_odr(self):
"""Run an ODR regression"""
linear = Model(self._modelODR)
mydata = Data(ravel(self._datax), ravel(self._datay), 1)
myodr = ODR(mydata, linear, beta0=self._guess, maxit=10000)
myoutput = myodr.run()
self._result = myoutput.beta
self._stdev = myoutput.sd_beta
self._covar = myoutput.cov_beta
self._odr = myoutput
def odrlin(x,y, sx, sy):
"""
Linear fit of 2-D data set made with Orthogonal Distance Regression
@params x, y: data to fit
@param sx, sy: respective errors of data to fit
"""
model = models.unilinear # defines model as beta[0]*x + beta[1]
data = RealData(x,y,sx=sx,sy=sy)
kinit = (y[-1]-y[0])/(x[-1]-x[0])
init = (kinit, y[0]-kinit*x[0])
linodr = ODR(data, model, init)
return linodr.run()
def test_ticket_1253(self):
def linear(c, x):
return c[0]*x+c[1]
c = [2.0, 3.0]
x = np.linspace(0, 10)
y = linear(c, x)
model = Model(linear)
data = Data(x, y, wd=1.0, we=1.0)
job = ODR(data, model, beta0=[1.0, 1.0])
result = job.run()
assert_equal(result.info, 2)
def slope(x, y, xerr, yerr, verbose=True, null=np.double(-9.99999488e+08)):
"""
Calculates the slope of a color trajectory.
Uses the scipy ODRpack and total least squares algorithm.
Parameters
----------
x, y : array-like
Colors or magnitudes to calculate slopes of.
xerr, yerr : array-like
Corresponding errors (stdev) on `x` and `y`.
Be sure to make sure that the HMKPNT errors are properly adjusted!
verbose : bool. optional
Whether to print a verbose output. Default true.
Returns
-------
slope : float
Slope (in rise/run) of the linear fit.
intercept : float
Y-value where the linear fit intercepts the Y-axis.
slope_error : float
The standard error on the fitted slope: an indication of fit quality.
Notes
-----
Much of this code is borrowed from the dosctring example found
at https://github.com/scipy/scipy/blob/master/scipy/odr/odrpack.py#L27 .
See http://docs.scipy.org/doc/scipy/reference/odr.html and
http://stackoverflow.com/questions/9376886/orthogonal-regression-fitting-in-scipy-least-squares-method
for further discussion.
"""
if (len(x) != len(y)) or len(x) == 0 or len(y) == 0:
return null, null, null
mydata = RealData(x, y, sx=xerr, sy=yerr)
# Someday, we may want to improve the "initial guess" with
# a leastsq first-pass.
myodr = ODR(mydata, linear, beta0=[1.,2.])
myoutput = myodr.run()
if verbose:
print myoutput.pprint()
return myoutput.beta[0], myoutput.beta[1], myoutput.sd_beta[0]
def fit_odr(self, t_scale, xmin=-sys.maxint, xmax=sys.maxint):
# initial guess for the parameters
params_initial = [1e-4, 1.0] #np.array([1e-4, 1.0]) #a, b
# function to fit
def f(B, x):
return B[0]*pow(x/t_scale, -B[1])
powerlaw = Model(f)
def powerlaw_err(x, a, b, aerr, berr, cov):
val = f([a,b], x)#f(x, a, b)
err = np.sqrt(pow(aerr/a, 2)+pow(np.log(x/t_scale), 2)*cov)#*val
return err
xdata = []
ydata = []
yerr = []
for ipoint, xpoint in enumerate(self.get_xdata()):
if xpoint>=xmin and xpoint<xmax:
xdata.append(xpoint)
ydata.append(self.get_ydata()[ipoint])
yerr.append(np.sqrt(self.get_yerr()[0][ipoint]*self.get_yerr()[1][ipoint]))
xdata = np.array(xdata)
ydata = np.array(ydata)
yerr = np.array(yerr)
if len(xdata)<3:
logging.info('Only {0} data points. Fitting is skipped.'.format(len(xdata)))
return 1
mydata = RealData(x=xdata, y=ydata, sy=yerr)
myodr = ODR(mydata, powerlaw, beta0=params_initial)
myoutput = myodr.run()
myoutput.pprint()
params_optimal = myoutput.beta
cov = myoutput.cov_beta
params_err = myoutput.sd_beta
#logging.info("""Opimized parameters: {0}
#Error: {1}""".format(params_optimal, params_err))
#params_err = np.sqrt(np.diag(cov))
for iparam, param, in enumerate(params_optimal):
logging.info("""Parameter {0}: {1} +/- {2}""".format(iparam, params_optimal[iparam], params_err[iparam]))
x_draw = np.linspace(min(xdata), max(xdata), 100)
y_draw = np.zeros_like(x_draw)
yerr_draw = np.zeros_like(y_draw)
for ix, x in enumerate(x_draw):
y_draw[ix] = f(params_optimal, x) #f(x, params_optimal[0], params_optimal[1])
yerr_draw[ix] = powerlaw_err(x, params_optimal[0], params_optimal[1], params_err[0], params_err[1], cov[1][1])
return ((params_optimal, params_err), (x_draw, y_draw, yerr_draw))
def calculate_ortho_regression(x, y, samples):
sx = numpy.cov(x, y)[0][0]
sy = numpy.cov(x, y)[1][1]
linear = Model(func)
# data = Data(x, y, wd=1./pow(sx, 2), we=1./pow(sy, 2))
data = Data(x, y)
odr = ODR(data, linear, beta0=[1., 2.])
out = odr.run()
print '\n'
out.pprint()
return (out.beta[0], out.beta[1], out.res_var)
def scipyODR(recipe, *args, **kwargs):
from scipy.odr import Data, Model, ODR, RealData, odr_stop
# FIXME
# temporarily change _weights to _weights**2 to fit the ODR fits
a = [w ** 2 for w in recipe._weights]
recipe._weights = a
model = Model(recipe.evaluateODR,
# implicit=1,
meta=dict(name='ODR fit'),
)
x = [recipe._contributions.values()[0].profile.x]
y = [recipe._contributions.values()[0].profile.y]
dy = [recipe._contributions.values()[0].profile.dy]
cont = recipe._contributions.values()
for i in range(1, len(cont)):
xplus = x[-1][-1] - x[-1][-2] + x[-1][-1]
x.append(cont[i].profile.x + x[-1][-1] + xplus)
y.append(cont[i].profile.y)
dy.append(cont[i].profile.dy)
x.append(np.arange(len(recipe._restraintlist))
* (x[-1][-1] - x[-1][-2]) + x[-1][-1])
y.append(np.zeros_like(recipe._restraintlist))
dy = np.concatenate(dy)
dy = np.concatenate(
[dy, np.ones_like(recipe._restraintlist) + np.average(dy)])
data = RealData(x=np.concatenate(x), y=np.concatenate(y), sy=dy)
odr_kwargs = {}
if kwargs.has_key('maxiter'):
odr_kwargs['maxit'] = kwargs['maxiter']
odr = ODR(data, model, beta0=recipe.getValues(), **odr_kwargs)
odr.set_job(deriv=1)
out = odr.run()
# out.pprint()
# FIXME
# revert back
a = [np.sqrt(w) for w in recipe._weights]
recipe._weights = a
return {'x': out.beta,
'esd': out.sd_beta,
'cov': out.cov_beta,
'raw': out,
}
def test_implicit(self):
implicit_mod = Model(
self.implicit_fcn,
implicit=1,
meta=dict(name='Sample Implicit Model',
ref='ODRPACK UG, pg. 49'),
)
implicit_dat = Data([
[0.5,1.2,1.6,1.86,2.12,2.36,2.44,2.36,2.06,1.74,1.34,0.9,-0.28,
-0.78,-1.36,-1.9,-2.5,-2.88,-3.18,-3.44],
[-0.12,-0.6,-1.,-1.4,-2.54,-3.36,-4.,-4.75,-5.25,-5.64,-5.97,-6.32,
-6.44,-6.44,-6.41,-6.25,-5.88,-5.5,-5.24,-4.86]],
1,
)
implicit_odr = ODR(implicit_dat, implicit_mod,
beta0=[-1.0, -3.0, 0.09, 0.02, 0.08])
out = implicit_odr.run()
assert_array_almost_equal(
out.beta,
np.array([-0.9993809167281279, -2.9310484652026476, 0.0875730502693354,
0.0162299708984738, 0.0797537982976416]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([0.1113840353364371, 0.1097673310686467, 0.0041060738314314,
0.0027500347539902, 0.0034962501532468]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[2.1089274602333052e+00, -1.9437686411979040e+00,
7.0263550868344446e-02, -4.7175267373474862e-02,
5.2515575927380355e-02],
[-1.9437686411979040e+00, 2.0481509222414456e+00,
-6.1600515853057307e-02, 4.6268827806232933e-02,
-5.8822307501391467e-02],
[7.0263550868344446e-02, -6.1600515853057307e-02,
2.8659542561579308e-03, -1.4628662260014491e-03,
1.4528860663055824e-03],
[-4.7175267373474862e-02, 4.6268827806232933e-02,
-1.4628662260014491e-03, 1.2855592885514335e-03,
-1.2692942951415293e-03],
[5.2515575927380355e-02, -5.8822307501391467e-02,
1.4528860663055824e-03, -1.2692942951415293e-03,
2.0778813389755596e-03]]),
)
def test_lorentz(self):
l_sy = np.array([.29]*18)
l_sx = np.array([.000972971,.000948268,.000707632,.000706679,
.000706074, .000703918,.000698955,.000456856,
.000455207,.000662717,.000654619,.000652694,
.000000859202,.00106589,.00106378,.00125483, .00140818,.00241839])
l_dat = RealData(
[3.9094, 3.85945, 3.84976, 3.84716, 3.84551, 3.83964, 3.82608,
3.78847, 3.78163, 3.72558, 3.70274, 3.6973, 3.67373, 3.65982,
3.6562, 3.62498, 3.55525, 3.41886],
[652, 910.5, 984, 1000, 1007.5, 1053, 1160.5, 1409.5, 1430, 1122,
957.5, 920, 777.5, 709.5, 698, 578.5, 418.5, 275.5],
sx=l_sx,
sy=l_sy,
)
l_mod = Model(self.lorentz, meta=dict(name='Lorentz Peak'))
l_odr = ODR(l_dat, l_mod, beta0=(1000., .1, 3.8))
out = l_odr.run()
assert_array_almost_equal(
out.beta,
np.array([1.4306780846149925e+03, 1.3390509034538309e-01,
3.7798193600109009e+00]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([7.3621186811330963e-01, 3.5068899941471650e-04,
2.4451209281408992e-04]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[2.4714409064597873e-01, -6.9067261911110836e-05,
-3.1236953270424990e-05],
[-6.9067261911110836e-05, 5.6077531517333009e-08,
3.6133261832722601e-08],
[-3.1236953270424990e-05, 3.6133261832722601e-08,
2.7261220025171730e-08]]),
)
def size_linewidth_slope(catalog):
"""
Measures a catalog's slope of sizes and linewidths using scipy.odr.
ODR means "orthogonal distance regression", and is a way of fitting
models to data where both the x and y values have scatter.
Parameters
----------
catalog : astropy.table.table.Table
Table containing columns for 'size' and 'v_rms'.
Returns
-------
odr_output : scipy.odr.odrpack.Output
Output of the scipy ODR routine. The fit parameters
are stored in odr_output.beta .
"""
if 'size' not in catalog.colnames or 'v_rms' not in catalog.colnames:
raise ValueError("'size' and 'v_rms' must be columns in `catalog`!")
if 'error_size_plus' not in catalog.colnames or 'error_size_minus' not in catalog.colnames:
mean_size_error = None
else:
mean_size_error = (catalog['error_size_plus']*catalog['error_size_minus'])**(1/2)
# if any clouds have no error in their size, the procedure will puke unless we do this:
mean_size_error[mean_size_error==0] = 0.01
size_linewidth_data = RealData(catalog['size'], catalog['v_rms'], sx=mean_size_error)
powerlaw_model = Model(powerlaw_function)
# The provided `beta0` is a throwaway guess that shouldn't change the outcome.
odr_object = ODR(size_linewidth_data, powerlaw_model, beta0=[1., 1.])
odr_output = odr_object.run()
return odr_output
def test_multi(self):
multi_mod = Model(
self.multi_fcn,
meta=dict(name='Sample Multi-Response Model',
ref='ODRPACK UG, pg. 56'),
)
multi_x = np.array([30.0, 50.0, 70.0, 100.0, 150.0, 200.0, 300.0, 500.0,
700.0, 1000.0, 1500.0, 2000.0, 3000.0, 5000.0, 7000.0, 10000.0,
15000.0, 20000.0, 30000.0, 50000.0, 70000.0, 100000.0, 150000.0])
multi_y = np.array([
[4.22, 4.167, 4.132, 4.038, 4.019, 3.956, 3.884, 3.784, 3.713,
3.633, 3.54, 3.433, 3.358, 3.258, 3.193, 3.128, 3.059, 2.984,
2.934, 2.876, 2.838, 2.798, 2.759],
[0.136, 0.167, 0.188, 0.212, 0.236, 0.257, 0.276, 0.297, 0.309,
0.311, 0.314, 0.311, 0.305, 0.289, 0.277, 0.255, 0.24, 0.218,
0.202, 0.182, 0.168, 0.153, 0.139],
])
n = len(multi_x)
multi_we = np.zeros((2, 2, n), dtype=float)
multi_ifixx = np.ones(n, dtype=int)
multi_delta = np.zeros(n, dtype=float)
multi_we[0,0,:] = 559.6
multi_we[1,0,:] = multi_we[0,1,:] = -1634.0
multi_we[1,1,:] = 8397.0
for i in range(n):
if multi_x[i] < 100.0:
multi_ifixx[i] = 0
elif multi_x[i] <= 150.0:
pass # defaults are fine
elif multi_x[i] <= 1000.0:
multi_delta[i] = 25.0
elif multi_x[i] <= 10000.0:
multi_delta[i] = 560.0
elif multi_x[i] <= 100000.0:
multi_delta[i] = 9500.0
else:
multi_delta[i] = 144000.0
if multi_x[i] == 100.0 or multi_x[i] == 150.0:
multi_we[:,:,i] = 0.0
multi_dat = Data(multi_x, multi_y, wd=1e-4/np.power(multi_x, 2),
we=multi_we)
multi_odr = ODR(multi_dat, multi_mod, beta0=[4.,2.,7.,.4,.5],
delta0=multi_delta, ifixx=multi_ifixx)
multi_odr.set_job(deriv=1, del_init=1)
out = multi_odr.run()
assert_array_almost_equal(
out.beta,
np.array([4.3799880305938963, 2.4333057577497703, 8.0028845899503978,
0.5101147161764654, 0.5173902330489161]),
)
assert_array_almost_equal(
out.sd_beta,
np.array([0.0130625231081944, 0.0130499785273277, 0.1167085962217757,
0.0132642749596149, 0.0288529201353984]),
)
assert_array_almost_equal(
out.cov_beta,
np.array([[0.0064918418231375, 0.0036159705923791, 0.0438637051470406,
-0.0058700836512467, 0.011281212888768],
[0.0036159705923791, 0.0064793789429006, 0.0517610978353126,
-0.0051181304940204, 0.0130726943624117],
[0.0438637051470406, 0.0517610978353126, 0.5182263323095322,
-0.0563083340093696, 0.1269490939468611],
[-0.0058700836512467, -0.0051181304940204, -0.0563083340093696,
0.0066939246261263, -0.0140184391377962],
[0.011281212888768, 0.0130726943624117, 0.1269490939468611,
-0.0140184391377962, 0.0316733013820852]]),
)
def total_least_squares(data1, data2, data1err=None, data2err=None,
print_results=False, ignore_nans=True, intercept=True,
return_error=False, inf=1e10):
"""
Use Singular Value Decomposition to determine the Total Least Squares linear fit to the data.
(e.g. http://en.wikipedia.org/wiki/Total_least_squares)
data1 - x array
data2 - y array
if intercept:
returns m,b in the equation y = m x + b
else:
returns m
print tells you some information about what fraction of the variance is accounted for
ignore_nans will remove NAN values from BOTH arrays before computing
Parameters
----------
data1,data2 : np.ndarray
Vectors of the same length indicating the 'x' and 'y' vectors to fit
data1err,data2err : np.ndarray or None
Vectors of the same length as data1,data2 holding the 1-sigma error values
"""
if ignore_nans:
badvals = numpy.isnan(data1) + numpy.isnan(data2)
if data1err is not None:
badvals += numpy.isnan(data1err)
if data2err is not None:
badvals += numpy.isnan(data2err)
goodvals = True-badvals
if goodvals.sum() < 2:
if intercept:
return 0,0
else:
return 0
if badvals.sum():
data1 = data1[goodvals]
data2 = data2[goodvals]
if intercept:
dm1 = data1.mean()
dm2 = data2.mean()
else:
dm1,dm2 = 0,0
arr = numpy.array([data1-dm1,data2-dm2]).T
U,S,V = numpy.linalg.svd(arr, full_matrices=False)
# v should be sorted.
# this solution should be equivalent to v[1,0] / -v[1,1]
# but I'm using this: http://stackoverflow.com/questions/5879986/pseudo-inverse-of-sparse-matrix-in-python
M = V[-1,0]/-V[-1,-1]
varfrac = S[0]/S.sum()*100
if varfrac < 50:
raise ValueError("ERROR: SVD/TLS Linear Fit accounts for less than half the variance; this is impossible by definition.")
# this is performed after so that TLS gives a "guess"
if data1err is not None or data2err is not None:
try:
from scipy.odr import RealData,Model,ODR
except ImportError:
raise ImportError("Could not import scipy; cannot run Total Least Squares")
def linmodel(B,x):
if intercept:
return B[0]*x + B[1]
else:
return B[0]*x
if data1err is not None:
data1err = data1err[goodvals]
data1err[data1err<=0] = inf
if data2err is not None:
data2err = data2err[goodvals]
data2err[data2err<=0] = inf
if any([data1.shape != other.shape for other in (data2,data1err,data2err)]):
raise ValueError("Data shapes do not match")
linear = Model(linmodel)
data = RealData(data1,data2,sx=data1err,sy=data2err)
B = data2.mean() - M*data1.mean()
beta0 = [M,B] if intercept else [M]
myodr = ODR(data,linear,beta0=beta0)
output = myodr.run()
if print_results:
output.pprint()
if return_error:
return numpy.concatenate([output.beta,output.sd_beta])
else:
return output.beta
if intercept:
B = data2.mean() - M*data1.mean()
if print_results:
print "TLS Best fit y = %g x + %g" % (M,B)
print "The fit accounts for %0.3g%% of the variance." % (varfrac)
print "Chi^2 = %g, N = %i" % (((data2-(data1*M+B))**2).sum(),data1.shape[0]-2)
return M,B
else:
if print_results:
print "TLS Best fit y = %g x" % (M)
print "The fit accounts for %0.3g%% of the variance." % (varfrac)
print "Chi^2 = %g, N = %i" % (((data2-(data1*M))**2).sum(),data1.shape[0]-1)
return M
def show():
data_filename = 'number.txt'
data = np.loadtxt(data_filename,skiprows=0)
number = np.reshape(data,(41,3))
#emperical error
xerr = np.sqrt(number[:,0])/3
yerr = number[:,2]
data = Data(number[:,0].T, number[:,1].T, we = 1/(np.power(xerr.T,2)+np.spacing(1)), wd = 1/(np.power(yerr.T,2)+np.spacing(1)))
model = Model(ord_function)
odr = ODR(data, model, beta0=[0, 0])
odr.set_job(fit_type=2)
output = odr.run()
popt = output.beta
perr = output.sd_beta
output.pprint()
fitting_error = np.mean(np.sqrt(np.power(popt[0]*number[:,0]+popt[1] - number[:,1],2)))
labels = np.array([[1,1,1,1,1,1,\
2,2,2,2,2,2,\
3,3,3,3,3,3,\
4,4,4,4,4,4,\
5,5,5,5,5,5,\
6,6,6,6,6,\
7,7,7,7,7,7],
[0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,
0,1,2,3,4,5]])
fig, ax = plt.subplots(ncols = 1)
ax.errorbar(number[:,0], number[:,1], xerr = xerr, yerr = yerr, fmt='o')
ax.plot(number[:,0], popt[0]*number[:,0]+popt[1], '-r')
bbox_props = dict(boxstyle="square,pad=0.3", fc="white", ec="black", lw=2)
annotation_text = "function: y = kx + b \n" \
"k = %.2f" % popt[0] + " +/- %.2f" % perr[0] + '\n' \
"b = %.2f" % popt[1] + " +/- %.2f" % perr[1] + '\n' \
"Error: %.2f" % fitting_error
ax.text(10, np.amax(number[:,1])+10, annotation_text, ha="left", va="top", rotation=0,
size=15, bbox=bbox_props)
for i in range(0, len(number[:,0])): # <--
ax.annotate('(%s, %s)' % (labels[0,i], labels[1,i]),\
(number[i,0]+1, number[i,1]-1)) #
ax.set_title('Algorithom Performance')
ax.set_xlabel('Bubble Number Counted Manually')
ax.set_ylabel('Bubbble Number Counted by Algorithom')
plt.grid()
plt.xlim((np.amin(number[:,0])-5,np.amax(number[:,0])+5))
plt.ylim((0,np.amax(number[:,1])+20))
plt.show()
def main():
try:
if (len(sys.argv) > 1 and sys.argv[1] == '1'):
data_filename = 'number.txt'
start_time = time.time()
number = np.zeros((41,3))
index = -1
for det in range(1,8):
if (det!=6):
num_n = 6
else:
num_n = 5
for n in range(0,num_n):
index = index + 1
temp = np.zeros(3)
for angle in range(1,4):
filename = 'detector_' + str(det) + '_no_' + str(n) \
+ '_angle_' + str(angle) + '.jpg'
refname = 'detector_' + str(det) + '_no_' + str(n) \
+ '_background.jpg'
elapse_time = (time.time()-start_time)
if(elapse_time >= 1):
remain_time = elapse_time/(index*3+angle-1)*41*3-elapse_time
print 'Processing .. ' + filename \
+ time.strftime(" %H:%M:%S", time.gmtime(elapse_time)) \
+ ' has past. ' + 'Remaining time: ' \
+ time.strftime(" %H:%M:%S", time.gmtime(remain_time))
else:
print 'Processing .. ' + filename \
+ time.strftime(" %H:%M:%S", time.gmtime(elapse_time)) \
+ ' has past'
image = rgb2gray(io.imread(filename))
ref = rgb2gray(io.imread(refname))
temp[angle-1] = ellipse.count_bubble(image,ref)
#temp[angle-1] = ellipse.count_bubble(image)
number[index,1] = np.mean(temp)
number[index,2] = np.std(temp)
manual_count = np.array([1,27,40,79,122,160,1,18,28,42,121,223,0,11,24,46,\
142,173,3,19,23,76,191,197,0,15,24,45,91,152,0,\
16,27,34,88,0,9,12,69,104,123])
number[:,0] = manual_count.T
number.tofile(data_filename,sep=" ")
elif(len(sys.argv) > 1):
data_filename = sys.argv[1]
data = np.loadtxt(data_filename,skiprows=0)
number = np.reshape(data,(41,3))
else:
data_filename = 'number.txt'
data = np.loadtxt(data_filename,skiprows=0)
number = np.reshape(data,(41,3))
#emperical error
xerr = np.sqrt(number[:,0])/3
yerr = number[:,2]
data = Data(number[:,0].T, number[:,1].T, we = 1/(np.power(xerr.T,2)+np.spacing(1)), wd = 1/(np.power(yerr.T,2)+np.spacing(1)))
model = Model(ord_function)
odr = ODR(data, model, beta0=[0, 0])
odr.set_job(fit_type=2)
output = odr.run()
popt = output.beta
perr = output.sd_beta
output.pprint()
# popt, pcov = curve_fit(linear_fit_function, number[:,0], number[:,1], [0, 0], number[:, 2])
# perr = np.sqrt(np.diag(pcov))
fitting_error = np.mean(np.sqrt(np.power(popt[0]*number[:,0]+popt[1] - number[:,1],2)))
labels = np.array([[1,1,1,1,1,1,\
2,2,2,2,2,2,\
3,3,3,3,3,3,\
4,4,4,4,4,4,\
5,5,5,5,5,5,\
6,6,6,6,6,\
7,7,7,7,7,7],
[0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,5,\
0,1,2,3,4,
0,1,2,3,4,5]])
fig, ax = plt.subplots(ncols = 1)
ax.errorbar(number[:,0], number[:,1], xerr = xerr, yerr = yerr, fmt='o')
ax.plot(number[:,0], popt[0]*number[:,0]+popt[1], '-r')
bbox_props = dict(boxstyle="square,pad=0.3", fc="white", ec="black", lw=2)
annotation_text = "function: y = kx + b \n" \
"k = %.2f" % popt[0] + " +/- %.2f" % perr[0] + '\n' \
"b = %.2f" % popt[1] + " +/- %.2f" % perr[1] + '\n' \
"Error: %.2f" % fitting_error
ax.text(10, np.amax(number[:,1])+10, annotation_text, ha="left", va="top", rotation=0,
size=15, bbox=bbox_props)
for i in range(0, len(number[:,0])): # <--
ax.annotate('(%s, %s)' % (labels[0,i], labels[1,i]),\
(number[i,0]+1, number[i,1]-1)) #
ax.set_title('Algorithom Performance')
ax.set_xlabel('Bubble Number Counted Manually')
ax.set_ylabel('Bubbble Number Counted by Algorithom')
plt.grid()
plt.xlim((np.amin(number[:,0])-5,np.amax(number[:,0])+5))
plt.ylim((0,np.amax(number[:,1])+20))
plt.show()
except KeyboardInterrupt:
print "Shutdown requested... exiting"
except Exception:
traceback.print_exc(file=sys.stdout)
sys.exit(0)
d_orion, catalog_orion, header, metadata, template_cube, template_header, new_selection_dictionary, selection_ID_dictionary = orion_output
# the fit
selection = new_selection_dictionary['Mon R2'] + new_selection_dictionary['Orion A'] + new_selection_dictionary['Orion B']
selection_ids = [struct.idx for struct in selection]
s_size = catalog_orion[selection_ids]['size']
s_linewidth = catalog_orion[selection_ids]['v_rms']
mydata = RealData(s_size, s_linewidth)
def powerlaw(B, x):
return B[0] * (x**B[1])
powerlaw_model = Model(powerlaw)
myodr = ODR(mydata, powerlaw_model, beta0=[1.,1.])
myoutput = myodr.run()
orion_fit_constant = myoutput.beta[0]
orion_fit_power = myoutput.beta[1]
# the plot
xs = np.logspace(-1,4,20)
ys = orion_fit_constant * xs**orion_fit_power
dv_orion = d_orion.viewer()
dv_orion.hub.select(1, selection)
dsl_orion = Scatter(d_orion, dv_orion.hub, catalog_orion, 'size', 'v_rms')
dsl_orion.set_loglog()
def main():
try:
legend = ['curvature', 'neural-network', 'hough transform', 'SVM-Preliminary']
data_filename = ['number.txt', 'skflow.txt', 'curvature.txt', 'SVM.txt']
colors = ['blue', 'red', 'black', 'green']
annotation_text = "function: y = kx + b";
fig, ax = plt.subplots(ncols = 1)
for i in range(len(data_filename)):
temp = np.loadtxt(data_filename[i],skiprows=0)
data = np.reshape(temp,(41,len(temp)/41))
#print data
data[:,1:3] = np.true_divide(data[:,1:3], np.amax(data[:,1]))
data[:,1] = data[:,1]+i
#print data
#emperical error
xerr = np.sqrt(data[:,0])/3
yerr = data[:,2]
data_fit = Data(data[:,0].T, data[:,1].T, \
we = 1/(np.power(xerr.T,2)+np.spacing(1)),\
wd = 1/(np.power(yerr.T,2)+np.spacing(1)))
model = Model(ord_function)
odr = ODR(data_fit, model, beta0=[0, 0])
odr.set_job(fit_type=2)
output = odr.run()
popt = output.beta
perr= output.sd_beta
popt, pcov = curve_fit(linear_fit_function,
data[:,0], data[:,1], [1, 0], data[:, 2])
perr = np.sqrt(np.diag(pcov))
A = popt[0]/np.sqrt(popt[0]*popt[0]+1)
B = -1/np.sqrt(popt[0]*popt[0]+1)
C = popt[1]/np.sqrt(popt[0]*popt[0]+1)
fitting_error= np.mean(np.abs(A*data[:,0]+B*data[:,1]+C))
ax.errorbar(data[:,0], data[:,1], xerr = xerr,\
yerr = yerr, fmt='o',color=colors[i])
### not using error bar in fitting #########
popt[0],popt[1] = np.polyfit(data[:,0], data[:,1], 1)
###
ax.plot(data[:,0], popt[0]*data[:,0]+popt[1], colors[i], linewidth = 2)
annotation_text = annotation_text + "\n" +\
legend[i] + "(" + \
colors[i] + ") \n" + \
"k = %.2f b = %.2f Error = %.2f"%( \
popt[0], popt[1], fitting_error)
bbox_props = dict(boxstyle="square,pad=0.3", fc="white", ec="black", lw=2)
#ax.text(0, 4.15, annotation_text, ha="left", va="top", \
# rotation=0, size=14, bbox=bbox_props)
ax.set_title('Algorithom Performance')
ax.set_xlabel('Bubble Number Counted Manually')
ax.set_ylabel('Normalized Bubbble Number Counted by Algorithom')
plt.grid()
plt.xlim((np.amin(data[:,0])-5,np.amax(data[:,0])+5))
plt.ylim((0,np.amax(data[:,1])+0.2))
plt.show()
except KeyboardInterrupt:
print "Shutdown requested... exiting"
except Exception:
traceback.print_exc(file=sys.stdout)
sys.exit(0)
def linear_beta(x, beta_0, beta_1):
return beta_0 + beta_1 * x
intensity = np.array(As).reshape(9, 5).T[[0, 3, 4, 1, 2]].T # move peak 2 & 3 to the correct position
A_lambda = intensity[:-1].T # entries of each peak put together
peaks = ['558.3', '563.5', '564.7', '570.8', '576.7']
intersects = []
x = np.array(concentration[:-1])
sx = 4
for i, peak, A in enum(peaks, A_lambda):
y = un.nominal_values(A)
sy = un.std_devs(A)
data = RealData(x, y, sx=sx, sy=sy)
model = Model(func)
odr = ODR(data, model, [6, 0])
odr.set_job(fit_type=0)
output = odr.run()
beta = uc.correlated_values(output.beta, output.cov_beta)
fit = linear_beta(x_fit, *beta)
#Intersects
y0 = intensity[-1][i] # peak intensities of the original sample
x0 = (y0 - beta[0]) / beta[1]
intersects.append(x0)
lamb_all = np.array(x0s).reshape(9, 5)
d_lin = np.load(npy_dir + 'ccd_calibration.npy')
lamb_all = linear(lamb_all, *d_lin) # Integrate error on wavelength of CCD
lamb_mean = np.mean(lamb_all, 0)
lamb_laser = np.load(npy_dir + 'ccd_lamb_laser.npy')[0]
dnu = (1 / lamb_mean - 1 / lamb_laser) * 10**7
lamb_mean = np.sort(lamb_mean)
lits = np.array([888, 1028, 1091, 1274, 1464])
for i, x0, dnu_cm, lit,intersect in enum(lamb_mean, lamb_to_cm(lamb_mean), lits, intersects):
def quad_func(p, x):
m, c = p
return m*x + c
# Create a model for fitting.
quad_model = Model(quad_func)
# Create a RealData object using our initiated data from above.
data = RealData(x_obs, y_obs, sx=x_error, sy=y_error)
# Set up ODR with the model and data.
odr = ODR(data, quad_model, beta0=[0.15, 1.0])
# Run the regression.
out = odr.run()
# define the model/function to be fitted.
def model(x_obs, y_obs):
m = pm.Uniform('m', 0, 10, value= 0.15)
n = pm.Uniform('n', -10, 10, value= 1.0)
x_pred = pm.Normal('x_true', mu=x_obs, tau=(x_error)**-2) # this allows error in x_obs
#Theoretical values
@pm.deterministic(plot=False)
def linearD(x_true=x_pred, m=m, n=n):
return m * x_true + n
@pm.deterministic
def random_operation_on_observable(obs_values=y_obs, m=m):
return obs_values + 0 * m
def zeroth_fit_2var(Nml,Eth,nu_des_ini,t_start,t_stop):
deca=0
sizeOfFont = 16
fontProperties = {'family':'sans-serif',
'weight' : 'normal', 'size' : sizeOfFont}
#rc('text', usetex=True)
rc('font',**fontProperties)
ticks_font = font_manager.FontProperties(family='cm', style='normal',
size=sizeOfFont, weight='normal', stretch='normal')
filenm='12CO_13CO_1_1_fit.txt'
#if molec =='n2':
# m_mol=30.0e-3
# xtex=0.042
#if molec =='co':
m_mol=28.0e-3
xtex=0.0375
#if molec =='co2':
# m_mol=44.0e-3
#if molec =='h2o':
# m_mol=18.0e-3
errort=0.1
T_tot, N_tot,N_13co = np.loadtxt(filenm,unpack=True,skiprows=1)
N_tot=N_13co
T_ini=T_tot
T_tot=T_tot
T_tot_err=T_tot*0.0+errort
N_tot=np.clip(N_tot,1e-12,1e30)
N_tot_err=1e12+N_tot*0
h_rate=1/60.0 #Kmin-1
T=T_tot[np.where((T_tot>t_start) & (T_tot<t_stop))]
N=N_tot[np.where((T_tot>t_start) & (T_tot<t_stop))]
N_err=1e10+N*0
T_err=T*0.0+errort
x=1.0/T
x_err=T_err/(T**2)
#print x_err
y=np.log(N/(1e15))
y_err=N_err/N
#y=np.log(N)
p=[0.,0.]
#nu_des_ini=7e11#np.sqrt(2.0*1e19*8.31*Eth/(np.pi*np.pi*m_mol))
#slope, intercept, r_value, p_value, std_err=scipy.stats.linregress(x, y)
def func(p, t):
return p[1] +t*p[0]
#def func (p,t):
# return np.log(1e12/h_rate)+t*p[0]
#def func(p, t):
# return np.log(np.sqrt(2.0*1e19*8.31*-1*p[0]/(np.pi*np.pi*m_mol))/h_rate) +t*p[0]
#p[1]=np.log(1e12/h_rate)
#p,pcov=curve_fit(func, x, y)
#err = np.sqrt(np.diag(pcov))
data = RealData(x, y, sx=x_err,sy=y_err)#, sy=np.log(0.001))
model = Model(func)
#odr = ODR(data, model, beta0=[-800,1e12])
odr = ODR(data, model, beta0=[-750,7e11])
odr.set_job(fit_type=2)
output = odr.run()
p=output.beta
err=output.sd_beta
E_des=-p[0]
nu_des=h_rate*np.exp(p[1])
nu_theo=h_rate*np.exp(np.log(np.sqrt(2.0*1e19*8.31*-1*p[0]/(np.pi*np.pi*m_mol))/h_rate))
#p[1]=np.log(np.sqrt(2.0*1e19*8.31*-1*p[0]/(np.pi*np.pi*m_mol))/h_rate)
#err[1]=0.0
#nu_theo=np.sqrt(2.0*1e19*8.31*Eth/(np.pi*np.pi*m_mol))
print 'E_des fit', p[0]*-1, '+-',err[0]
print 'E_des litt', Eth
print 'nu fit e12', 1e-12*h_rate*np.exp(p[1]), '+-',err[1]*1e-12*h_rate*np.exp(p[1])
print 'nu litt e12', 1e-12*nu_des_ini
print 'nu theo e12', 1e-12*nu_theo
#nu_des=2e11
#print "nu e12n", nu_des*1e-12
# def funct(x,t):
# if x[0]>0.0:
# f_ml = -(nu_des/h_rate)*np.exp(-E_des/t)
# f_g= f_ml-x[1]
# if x[0]<0.0:
# f_ml=0
# f_g= f_ml-x[1]
# return [f_ml,f_g]
# F_pure = odeint(funct,[Nml,0.00],T_tot)
# N_ml = np.clip(F_pure[:,0],0.0,10)
# N_g = -F_pure[:,1]
# k_d_ml = (nu_des/h_rate)*np.exp(-E_des/T_tot)
# k_d_mode=(nu_des_ini/h_rate)*np.exp(-Eth/T_tot)
#print "Ns",N_ml
#if order==0:
# k_d_ml[np.where(k_d_ml>np.max(N))]=0.0
#A[:,i] = k_d_ml
#A[:,i] = np.clip(k_d_ml,-1e15,1e15)
#x,resid = optimize.nnls(A,b)
#slope, intercept, r_value, p_value, std_err=scipy.stats.linregress(x, y)
plot_tpd='12CO_reglinfit_2var.pdf'
plt.errorbar(x,y,xerr=x_err,yerr=y_err,color='k')
#plt.errorbar(a,b,yerr=c, linestyle="None")
plt.plot(x,p[1]+p[0]*x,color='r',linestyle='--',linewidth=3.0)
#plt.plot(x,p[1]+p[0]*x,color='r',linestyle='--',linewidth=3.0)
plt.text(xtex,0.0,'-Slope: '+str(round(E_des))+' +- '+str(round(err[0])))
plt.text(xtex,-0.5,'exp(Intercept)*h_rate: '+ str(round(1e-12*h_rate*np.exp(p[1]),2))+ '+/-'+str(round(err[1]*1e-12*h_rate*np.exp(p[1]),2))+ 'e12')
#plt.plot(x,p[0]+(p[1]-err[1])*x,color='r',linestyle='--',linewidth=3.0)
#plt.plot(x,p[0]+(p[1]+err[1])*x,color='r',linestyle='--',linewidth=3.0)
#plt.plot(x,np.log(1e12/h_rate)-Eth*x,color='b',linestyle='--',linewidth=3.0)
#plt.xlim(1/(t_stop+5),1/(t_start-5))
plt.savefig(plot_tpd)
plt.close('all')
#ind=np.where(N_tot<np.max(N_tot))
#plot_tpd='12CO_tpdfit_2var.pdf'
#plt.errorbar(T_tot[ind],N_tot[ind]/(1e15),xerr=T_tot_err[ind],yerr=N_tot_err[ind]/1e15,color='k')
#plt.plot([t_start-deca,t_start-deca],[0,np.max(N_tot)/1e15],linestyle='--',color='g')
#plt.plot([t_stop-deca,t_stop-deca],[0,np.max(N_tot)/1e15],linestyle='--',color='g')
#plt.plot(T,-np.exp(intercept)*np.exp(-1*slope/T),color='r',linestyle='--',linewidth=3.0)
#plt.plot(T,(60*1.5e12/5.3e15)*np.exp(-950.0/T),color='b',linestyle='--',linewidth=3.0)
#plt.plot(T_tot,np.clip(k_d_ml,0,2*np.max(N_tot/1e15)),color='r')
#plt.plot(T_tot,np.clip(k_d_mode,0,2*np.max(N_tot/1e15)),color='r')
#plt.xlim(15,50)
#plt.ylim(-0.005,np.max(N_tot)/1e15+0.1)
#plt.savefig(plot_tpd)
plt.close('all')
x = numpy.linspace(-4, 3.0, N)
y = model((a0,b0,c0),x) + normal(0.0, 0.5, N) # Mean 0, sigma 1
errx = normal(0.3, 0.3, N)
erry = normal(0.2, 0.4, N)
print("\nTrue model values [a0,b0,c0]:", [a0,b0,c0])
beta0 = [1,1,1]
#beta0 = [1.8,-0.5,0.1]
print("\nODR:")
print("==========")
from scipy.odr import Data, Model, ODR, RealData, odr_stop
linear = Model(model)
mydata = RealData(x, y, sx=errx, sy=erry)
myodr = ODR(mydata, linear, beta0=beta0, maxit=5000)
#myodr.set_job(2)
myoutput = myodr.run()
print("Fitted parameters: ", myoutput.beta)
print("Covariance errors: ", numpy.sqrt(myoutput.cov_beta.diagonal()))
print("Standard errors: ", myoutput.sd_beta)
print("Minimum chi^2: ", myoutput.sum_square)
print("Minimum (reduced)chi^2: ", myoutput.res_var)
beta = myoutput.beta
# Prepare fit routine
fitobj = kmpfit.Fitter(residuals=residuals, data=(x, y, errx, erry),
xtol=1e-12, gtol=1e-12)
fitobj.fit(params0=beta0)
print("\n\n======== Results kmpfit with effective variance =========")
print("Fitted parameters: ", fitobj.params)
print("Covariance errors: ", fitobj.xerror)
def fitDoubleGaussians(charges, charge_err, data, data_err):
# some simple peakfinding to find the first and second peaks
peak1_value = np.max(data)
peak1_bin = [x for x in range(len(data)) if data[x]==peak1_value][0]
peak1_charge = charges[peak1_bin]
# Preliminary fits to get better estimates of parameters...
first_peak_data = RealData(charges[peak1_bin-30:peak1_bin+30],
data[peak1_bin-30:peak1_bin+30],
charge_err[peak1_bin-30:peak1_bin+30],
data_err[peak1_bin-30:peak1_bin+30],)
first_peak_model = Model(gauss_fit)
first_peak_odr = ODR(first_peak_data, first_peak_model,
[peak1_value, peak1_charge, 0.1*peak1_charge])
first_peak_odr.set_job(fit_type=2)
first_peak_output = first_peak_odr.run()
first_peak_params = first_peak_output.beta
#second_peak_params, covariance = curve_fit(gauss_fit2,
# data[peak1_bin-30:peak1_bin+30],
# data[peak1_bin-30:peak1_bin+30],
# p0 = [peak1_value, peak1_charge, 0.1*peak1_charge])
# subtract the largest peak so we can search for the other one
updated_data = data-gauss_fit(first_peak_params, charges)
#updated_data = data[:int(len(data)*2.0/3.0)]
peak2_value = np.max(updated_data)
peak2_bin = [x for x in range(len(updated_data)) if updated_data[x]==peak2_value][0]
peak2_charge = charges[peak2_bin]
#first_peak_params, covariance = curve_fit(gauss_fit2,
# updated_data[peak2_bin-30:peak2_bin+30],
# updated_data[peak2_bin-30:peak2_bin+30],
# p0 = [peak2_value, peak2_charge, 0.1*peak2_charge])
# and the second peak...
second_peak_data = RealData(charges[peak2_bin-30:peak2_bin+30],
data[peak2_bin-30:peak2_bin+30],
charge_err[peak2_bin-30:peak2_bin+30],
data_err[peak2_bin-30:peak2_bin+30],)
second_peak_model = Model(gauss_fit)
second_peak_odr = ODR(second_peak_data, second_peak_model,
[peak2_value, peak2_charge, first_peak_params[2]])
second_peak_odr.set_job(fit_type=2)
second_peak_output = second_peak_odr.run()
second_peak_params = second_peak_output.beta
# Now fit both gaussians simultaneously
double_peak_data = RealData(charges, data,
charge_err, data_err)
double_peak_model = Model(double_gauss_fit)
double_peak_odr = ODR(double_peak_data, double_peak_model,
[first_peak_params[0], first_peak_params[1], first_peak_params[2],
second_peak_params[0], second_peak_params[1], second_peak_params[2]])
double_peak_odr.set_job(fit_type=0)
double_peak_output = double_peak_odr.run()
double_peak_params = double_peak_output.beta
double_peak_sigmas = double_peak_output.sd_beta
#double_peak_params = [first_peak_params[0], first_peak_params[1], first_peak_params[2],
# second_peak_params[0], second_peak_params[1], second_peak_params[2]]
return double_peak_params, double_peak_sigmas