def tau(meandens, line=tau1x, sigma=1.0, hightail=False,
hopkins=False, powertail=False, lowtail=False,
compressive=False, divide_by_col=False, **kwargs):
if compressive:
distr = turbulent_pdfs.compressive_distr(meandens,sigma,**kwargs)
elif lowtail:
distr = turbulent_pdfs.lowtail_distr(meandens,sigma,**kwargs)
elif powertail or hightail:
distr = turbulent_pdfs.hightail_distr(meandens,sigma,**kwargs)
elif hopkins:
T = hopkins_pdf.T_of_sigma(sigma, logform=True)
#distr = 10**dens * hopkins_pdf.hopkins(10**(dens), meanrho=10**(meandens), sigma=sigma, T=T) # T~0.05 M_C
distr = hopkins_pdf.hopkins_masspdf_ofmeandens(10**dens, 10**meandens, sigma_volume=sigma, T=T, normalize=True)
# Hopkins is integral-normalized, not sum-normalized
#distr /= distr.sum()
else:
#distr = lognormal(10**dens, 10**meandens, sigma) * dlndens
distr = lognormal_massweighted(10**dens, 10**meandens, sigma, normalize=True)
if divide_by_col:
return (distr*line/(10**col)).sum()
else:
return (distr*line).sum()
pl.plot(sigmas, lognormal_integrals)
pl.xlabel(r'$\sigma_s$')
pl.ylabel(r'$\int P_V(s)$')
pl.show()
pl.figure(2)
pl.clf()
pl.plot(sigmas, lognormalmass_integrals)
pl.xlabel(r'$\sigma_s$')
pl.ylabel(r'$\int P_M(s)$')
pl.figure(3)
pl.clf()
fits = []
for s in sigmas[::10]:
distr = lognormal_massweighted(rho, 1, s)
L, = pl.loglog(rho,
distr,
label=r'$\sigma_s=%f' % s,
linewidth=3,
alpha=0.5)
(fitted_rho0, fitted_s), cov = curve_fit(lognormal,
rho,
distr,
p0=[np.exp(s**2), s])
fitted_distr = lognormal(rho, fitted_rho0, fitted_s)
pl.loglog(rho, fitted_distr, color=L.get_color(), linestyle='--')
fits.append((fitted_rho0, fitted_s))
fits = np.array(fits)
pl.xlabel(r'$\rho$')
pl.plot(sigmas, lognormal_integrals)
pl.xlabel(r'$\sigma_s$')
pl.ylabel(r'$\int P_V(s)$')
pl.show()
pl.figure(2)
pl.clf()
pl.plot(sigmas, lognormalmass_integrals)
pl.xlabel(r'$\sigma_s$')
pl.ylabel(r'$\int P_M(s)$')
pl.figure(3)
pl.clf()
fits = []
for s in sigmas[::10]:
distr = lognormal_massweighted(rho, 1, s)
L, = pl.loglog(rho,distr, label=r'$\sigma_s=%f' % s, linewidth=3, alpha=0.5)
(fitted_rho0,fitted_s),cov = curve_fit(lognormal, rho, distr, p0=[np.exp(s**2),s])
fitted_distr = lognormal(rho, fitted_rho0, fitted_s)
pl.loglog(rho,fitted_distr,color=L.get_color(), linestyle='--')
fits.append((fitted_rho0,fitted_s))
fits = np.array(fits)
pl.xlabel(r'$\rho$')
pl.ylabel(r'$P_M(s)$')
pl.axis([1e-10,1e30,1e-10,1])
pl.figure(4)
pl.clf()
pl.plot(meanrhos, lognormal_integrals_rho)
pl.xlabel(r'$\rho_0$')
