plot-all.py

from scipy import *
import numpy as np
from pylab import *
from scipy.interpolate import interp1d
import os
from astropy.io import fits
from matplotlib import cm

plot_design = 0
plot_pmatter = 0
plot_pmatterall = 0
plot_pmatter_evolve = 0
plot_sample_maps_include_reconstructed = 0
plot_sample_maps = 0
plot_hmf = 0
plot_merger_tree = 0
plot_mass_accretion = 0
test_pmatter_noise = 0
plot_kernels = 0
plot_pkappa = 0

h=0.7
k_arr = logspace(-2,1.5,100)

if plot_design:
    ims = 5
    ifs=16
    ilw=2
    imk = 'o'
    Muarr, Omarr, Asarr, S8arr=genfromtxt('cosmo_params_MuOmAsS8.txt').T
    
    f=figure(figsize=(8,8))
    ax1=f.add_subplot(2,2,1)
    ax2=f.add_subplot(2,2,2)
    ax3=f.add_subplot(2,2,3)
    ax4=f.add_subplot(2,2,4)
        
    ax1.scatter(Muarr, Asarr, marker=imk, s=ims, color='k')
    ax2.scatter(Muarr, Omarr, marker=imk, s=ims, color='k')
    ax3.scatter(Omarr, Asarr, marker=imk, s=ims, color='k')
    ax4.scatter(S8arr, Asarr, marker=imk, s=ims, color='k')

    ax1.scatter([0.1, 0],    [2.1, 2.1], marker=imk, s=ims+5, color='r')
    ax2.scatter([0.1, 0],    [0.3, 0.3], marker=imk, s=ims+5, color='r')
    ax3.scatter(0.3,    2.1, marker=imk, s=ims+5, color='r')
    ax4.scatter(0.8295, 2.1, marker=imk, s=ims+5, color='r')
    ax4.scatter(0.8523, 2.1, marker=imk, s=ims+5, color='r')
    
    ax1.set_xlabel(r'$M_\nu$',fontsize=ifs)
    ax1.set_ylabel(r'$10^9A_s$',fontsize=ifs)
    ax2.set_xlabel(r'$M_\nu$',fontsize=ifs)
    ax2.set_ylabel(r'$\Omega_m$',fontsize=ifs)
    ax3.set_xlabel(r'$\Omega_m$',fontsize=ifs)
    ax3.set_ylabel(r'$10^9A_s$',fontsize=ifs)
    ax4.set_ylabel(r'$10^9A_s$',fontsize=ifs)
    ax4.set_xlabel(r'$\sigma_8$',fontsize=ifs)
    ax1.set_xlim(-0.05, 0.7)
    ax2.set_xlim(-0.05, 0.7)
    ax2.set_ylim(0.15, 0.45)
    ax3.set_xlim(0.15, 0.45)
    ax1.set_xlim(-0.05, 0.7)
    for iax in (ax1,ax2,ax3,ax4):
        iax.grid(True)
        iax.locator_params(axis = 'both', nbins = 5)
    plt.tight_layout()
    #plt.subplots_adjust(wspace=0.35, left=0.07, right=0.97,bottom=0.18)
    savefig('plots/plot_design_test.pdf')
    close()



def savitzky_golay(y, window_size, order, deriv=0, rate=1):
    r"""Smooth (and optionally differentiate) data with a Savitzky-Golay filter.
    The Savitzky-Golay filter removes high frequency noise from data.
    It has the advantage of preserving the original shape and
    features of the signal better than other types of filtering
    approaches, such as moving averages techniques.
    Parameters
    ----------
    y : array_like, shape (N,)
        the values of the time history of the signal.
    window_size : int
        the length of the window. Must be an odd integer number.
    order : int
        the order of the polynomial used in the filtering.
        Must be less then `window_size` - 1.
    deriv: int
        the order of the derivative to compute (default = 0 means only smoothing)
    Returns
    -------
    ys : ndarray, shape (N)
        the smoothed signal (or it's n-th derivative).
    Notes
    -----
    The Savitzky-Golay is a type of low-pass filter, particularly
    suited for smoothing noisy data. The main idea behind this
    approach is to make for each point a least-square fit with a
    polynomial of high order over a odd-sized window centered at
    the point.
    Examples
    --------
    t = np.linspace(-4, 4, 500)
    y = np.exp( -t**2 ) + np.random.normal(0, 0.05, t.shape)
    ysg = savitzky_golay(y, window_size=31, order=4)
    import matplotlib.pyplot as plt
    plt.plot(t, y, label='Noisy signal')
    plt.plot(t, np.exp(-t**2), 'k', lw=1.5, label='Original signal')
    plt.plot(t, ysg, 'r', label='Filtered signal')
    plt.legend()
    plt.show()
    References
    ----------
    .. [1] A. Savitzky, M. J. E. Golay, Smoothing and Differentiation of
       Data by Simplified Least Squares Procedures. Analytical
       Chemistry, 1964, 36 (8), pp 1627-1639.
    .. [2] Numerical Recipes 3rd Edition: The Art of Scientific Computing
       W.H. Press, S.A. Teukolsky, W.T. Vetterling, B.P. Flannery
       Cambridge University Press ISBN-13: 9780521880688
    """
    import numpy as np
    from math import factorial
    
    try:
        window_size = np.abs(np.int(window_size))
        order = np.abs(np.int(order))
    except ValueError, msg:
        raise ValueError("window_size and order have to be of type int")
    if window_size % 2 != 1 or window_size < 1:
        raise TypeError("window_size size must be a positive odd number")
    if window_size < order + 2:
        raise TypeError("window_size is too small for the polynomials order")
    order_range = range(order+1)
    half_window = (window_size -1) // 2
    # precompute coefficients
    b = np.mat([[k**i for i in order_range] for k in range(-half_window, half_window+1)])
    m = np.linalg.pinv(b).A[deriv] * rate**deriv * factorial(deriv)
    # pad the signal at the extremes with
    # values taken from the signal itself
    firstvals = y[0] - np.abs( y[1:half_window+1][::-1] - y[0] )
    lastvals = y[-1] + np.abs(y[-half_window-1:-1][::-1] - y[-1])
    y = np.concatenate((firstvals, y, lastvals))
    return np.convolve( m[::-1], y, mode='valid')    
    
if plot_pmatter:
    
    kcamb1, Pcamb1 = np.loadtxt('camb/camb_mnv0.10000_om0.30000_As2.1000_matterpow_0.dat').T
    kcamb0, Pcamb0 = np.loadtxt('camb/camb_mnv0.00000_om0.30000_As2.1000_matterpow_0.dat').T

    ####### bird halofit
    kbird1, Pbird1 = np.loadtxt('camb-fidu/camb_mnv0.10000_om0.30000_As2.1000-bird_matterpow_0.dat').T
    kbird0, Pbird0 = np.loadtxt('camb-fidu/camb_mnv0.00000_om0.30000_As2.1000-bird_matterpow_0.dat').T

    ####### takahashi halofit
    ktaka1, Ptaka1 = np.loadtxt('camb-fidu/camb_mnv0.10000_om0.30000_As2.1000-halofit_matterpow_0.dat').T
    ktaka0, Ptaka0 = np.loadtxt('camb-fidu/camb_mnv0.00000_om0.30000_As2.1000-halofit_matterpow_0.dat').T
    
    ####### sims
    knb0_1024, Pnb0_1024 = np.loadtxt('matterpower/mnv0.00000_om0.30000_As2.1000/powerspec_tot_065.txt').T
    knb1_1024, Pnb1_1024 = np.loadtxt('matterpower/mnv0.10000_om0.30000_As2.1000/powerspec_tot_065.txt').T
    
    knb0_256, Pnb0_256 = np.loadtxt('matterpower/mnv0.00000_om0.30000_As2.1000/256Mpc/powerspec_tot_065.txt').T * array([1e3, 1e-9]).reshape(2,-1)
    knb1_256, Pnb1_256 = np.loadtxt('matterpower/mnv0.10000_om0.30000_As2.1000/256Mpc/powerspec_tot_065.txt').T * array([1e3, 1e-9]).reshape(2,-1)
    
    knb0_1024 *= 1e3
    knb1_1024 *= 1e3
    Pnb0_1024 *= 1e-9
    Pnb1_1024 *= 1e-9
    ####### interpolation
    Pcamb1_interp = interp1d(kcamb1, Pcamb1)(k_arr)
    Pcamb0_interp = interp1d(kcamb0, Pcamb0)(k_arr)
    Pbird1_interp = interp1d(kbird1, Pbird1)(k_arr)
    Pbird0_interp = interp1d(kbird0, Pbird0)(k_arr)
    Ptaka1_interp = interp1d(ktaka1, Ptaka1)(k_arr)
    Ptaka0_interp = interp1d(ktaka0, Ptaka0)(k_arr)

    ############  plots
    f=figure(figsize=(8,6))
    ax1=f.add_subplot(211)
    ax2=f.add_subplot(212)

    ax1.plot(kcamb0, Pcamb0, 'k--',lw=2.5, label=r'${\rm Linear\; Theory}\; (M_\nu = 0\; {\rm eV})$'  )
    ax1.plot(kcamb1, Pcamb1, 'k-', lw=1.5, label=r'${\rm Linear\; Theory}\; (M_\nu = 0.1\; {\rm eV})$')

    ax1.plot(knb0_1024, Pnb0_1024, 'g--', lw=2.5,label=r'${\rm Simulation}\; (M_\nu = 0\; {\rm eV})$'  )
    ax1.plot(knb1_1024, Pnb1_1024, 'g-',  lw=1.5,label=r'${\rm Simulation}\; (M_\nu = 0.1\; {\rm eV})$')
    
    #ax1.plot(knb0_256, Pnb0_256, 'r--', lw=2.5,label=r'${\rm Simulation}\; (M_\nu = 0\; {\rm eV})$'  )
    #ax1.plot(knb1_256, Pnb1_256, 'r-',  lw=1.5,label=r'${\rm Simulation}\; (M_\nu = 0.1\; {\rm eV})$')

    #ax.plot((0.2,0.2), (0.1,1e5),'k--')
    ax1.set_ylabel('$P_{m}(k)$',fontsize=18)
    ax1.set_xscale('log')
    ax1.set_yscale('log')
    ax1.set_xlim(1e-2, 10)
    ax1.set_ylim(0.1, 1e5)
    ax1.legend(frameon=0,loc=3,fontsize=12)
    #plt.yticks(fontsize=16)
    #ax1.set_xticklabels(fontsize=16)
    ax1.plot([0.2,0.2], [1e-2,1e5],'k--',lw=1)
    
    ax2.plot(k_arr,Pcamb1_interp/Pcamb0_interp-1,'k-',lw=2, label=r'${\rm Linear\; Theory}$')
    ax2.plot(k_arr,Pbird1_interp/Pbird0_interp-1,'--',color='orange',lw=2.0, label=r'${\rm Halofit\; (Bird2012)}$')
    ax2.plot(k_arr,Ptaka1_interp/Ptaka0_interp-1,'--',color='purple',lw=1.0, label=r'${\rm Halofit\; (Takahashi2012)}$')
    
    idx_stable = where(Pnb1_256/Pnb0_256-1 < -0.03)
    knb0_256,pnb_diff_256 = knb0_256 [idx_stable], (Pnb1_256/Pnb0_256-1) [idx_stable]
    
    ax2.plot(knb0_1024,Pnb1_1024/Pnb0_1024-1,'g-',lw=1, label=r'${\rm Simulation}$')
    ax2.plot(knb0_256,pnb_diff_256,'-',color='deeppink',lw=0.5, label=r'${\rm Simulation\; (higher\; res.)}$',alpha=0.6)
    
    ax2.legend(frameon=0,fontsize=12,ncol=1,loc=0)
    
    ax2.set_xscale('log')
    ax2.set_xlim(1e-2, 10)
    ax2.set_ylim(-0.09,0.0)
    ax2.set_xlabel(r'$k\; (h/{\rm Mpc})$',fontsize=18)
    ax2.set_ylabel(r'$P_{m}^{0.1eV} / P_{m}^{0 eV} - 1$',fontsize=18)
    ax2.plot([0.2,0.2], [-0.5,0],'k--',lw=1)
    
    plt.setp(ax1.get_xticklabels(), visible=False)
    plt.subplots_adjust(hspace=0.09,left=0.15)
    #ax1.locator_params(axis = 'y', nbins = 5)
    ax2.locator_params(axis = 'y', nbins = 5)
    plt.tight_layout()
    #ax1.grid(True)
    #ax2.grid(True)
    savefig('plots/plot_pmatter_fidu.pdf')
    close()

if plot_pmatterall:
    kcamb0, Pcamb0 = np.loadtxt('camb/camb_mnv0.00000_om0.30000_As2.1000_matterpow_0.dat').T
    Pcamb0_interp = interp1d(kcamb0, Pcamb0)(k_arr)    
    knb0_1024, Pnb0_1024 = np.loadtxt('matterpower/mnv0.00000_om0.30000_As2.1000/powerspec_tot_067.txt').T
    knb0_1024*=1e3
    
    
    def get_PS (cosmo,nsnap):
        ifn='matterpower/%s/powerspec_tot_%03d.txt'%(cosmo, nsnap)
        kcamb, Pcamb = np.loadtxt('camb/camb_%s_matterpow_0.dat'%(cosmo)).T
        Pcamb_interp = interp1d(kcamb,Pcamb)(k_arr)
        knb, Pnb = np.loadtxt(ifn).T
        #knb *= 1e3
        #Pnb *= 1e-9
        return Pcamb_interp/Pcamb0_interp-1, Pnb/Pnb0_1024-1

    params = loadtxt('params.txt')
    m_nu_arr = params.T[0]
    params[:-2]=params[:-2][argsort(m_nu_arr[:-2])]

    nsnaps_arr = genfromtxt('nsnaps.txt')#[notfailed==0]

    for i in arange(0, len(params)-1,25):
        f=figure(figsize=(8,5.5))
        ax2=f.add_subplot(111)
        seed(2046)
        ax2.plot([1e-2,20],[0,0],'k-',lw=1,alpha=0.5)
        j=0
        for iparam in params[i:i+25]:
            M_nu, omega_m, A_s9 = iparam
            #ilabel = r'$M_\nu=%.3f, \Omega_m=%.3f, 10^9A_s=%.3f$'%(M_nu, omega_m, A_s9)
            ilabel=r'$%.3f,\; %.2f,\; %.2f$'%(M_nu, omega_m, A_s9)
            icosmo = 'mnv%.5f_om%.5f_As%.4f'%(M_nu, omega_m, A_s9)
            dPcamb, dPnb = get_PS (icosmo, nsnaps_arr[i+j]-1)
            j+=1
            icolor=rand(3)
            ax2.plot(k_arr, dPcamb,'--',lw=1,color=icolor)
            ax2.plot(k_arr[:5], dPcamb[:5],'-',lw=1,color=icolor)
            ax2.plot(knb0_1024, dPnb,'-',lw=1,color=icolor,label=ilabel)
        ax2.set_xscale('log')
        ax2.set_ylim(-1,1.5)
        ax2.set_xlim(1e-2,10)
        ax2.set_xlabel(r'$k\; (h/{\rm Mpc})$',fontsize=18)
        ax2.set_ylabel(r'$P_{m} / P_{m}^{{\rm fidu}, 0eV} - 1$',fontsize=18)
        ax2.legend(frameon=0,loc=2,fontsize=10,ncol=4,labelspacing=0.11,columnspacing=0.3, 
                   handletextpad=0.1)
        ax2.set_title(r'${\rm Parameters}=[M_\nu, \Omega_m, 10^9A_s], \; {\rm Dashed: \; Linear\; Theory \;,\; Solid:\; Simulations}$',fontsize=13)
        plt.tight_layout()
        savefig('plots/plot_pmatter_all%i.pdf'%(i))
        
        close()

if plot_pmatter_evolve:
    def pmatter_evolve(nsnap):
        ell0,ps_0=loadtxt('matterpower/mnv0.00000_om0.30000_As2.1000/powerspec_tot_%03d.txt'%(nsnap)).T
        ell1,ps_1=loadtxt('matterpower/mnv0.10000_om0.30000_As2.1000/powerspec_tot_%03d.txt'%(nsnap)).T
        #diff_fidu = interp1d(ell1,ps_1,fill_value="extrapolate")(k_arr) /interp1d(ell0,ps_0,fill_value="extrapolate")(k_arr)-1
        diff_fidu = ps_1/ps_0-1.0
        return ell0,diff_fidu
    a_arr = loadtxt('params/outputs_mnv0.00000_om0.30000_As2.1000.txt')
    z_arr = 1.0/a_arr - 1.0
    f=figure(figsize=(8,5.5))
    ax=f.add_subplot(111)
    for nsnap in range(10,66):
        ell0, delta_P = pmatter_evolve(nsnap)
        ax.plot(ell0*1e3, delta_P, c=cm.jet(a_arr[nsnap]))
    ax.set_xscale('log')
    ax.set_ylim(-0.09,0.0)
    ax.set_xlim(1e-2,10)
    ax.set_xlabel(r'$k\; (h/{\rm Mpc})$',fontsize=18)
    ax.set_ylabel(r'$P_{m}^{0.1eV} / P_{m}^{0 eV} - 1$',fontsize=18)
    #ax.set_ylabel(r'$P_{m} / P_{m}^{{\rm fidu}, 0eV} - 1$',fontsize=18)
    cbaxes = f.add_axes([0.2, 0.3, 0.3, 0.03]) 

    cb1 = mpl.colorbar.ColorbarBase(cbaxes, cmap=cm.jet,#RdPu ,#
                                orientation='horizontal')
    tick_locator = matplotlib.ticker.MaxNLocator(nbins=6)
    cb1.locator = tick_locator
    cb1.update_ticks()
    cb1.set_label(r'${\rm Scale\; Factor\;} a$', fontsize=16)
    plt.tight_layout()
    savefig('plots/plot_pmatter_evolve_jet.pdf')
    close()
        
        
if plot_sample_maps_include_reconstructed:
    import WLanalysis
    conv_cmb0=fits.open('sample_maps/mnv0.00000_om0.30000_As2.1000/WLconv_z1100.00_0001r.fits')[0].data
    conv_cmb1=fits.open('sample_maps/mnv0.10000_om0.30000_As2.1000/WLconv_z1100.00_0001r.fits')[0].data
    conv_recon0=fits.open('sample_maps/mnv0.00000_om0.30000_As2.1000/jia_recon_massless_000000000.fits')[0].data
    conv_recon1=fits.open('sample_maps/mnv0.10000_om0.30000_As2.1000/jia_recon_massive_000000000.fits')[0].data
    
    conv_gal0=fits.open('sample_maps/mnv0.00000_om0.30000_As2.1000/12deg/WLconv_z2.00_0001r.fits')[0].data
    conv_gal1=fits.open('sample_maps/mnv0.10000_om0.30000_As2.1000/12deg/WLconv_z2.00_0001r.fits')[0].data
    
    widths=[3.5,]*4+[6.3,]*2
    fns = ['conv_cmb0', 'conv_cmb1', 'conv_recon0', 'conv_recon1', 'conv_gal012deg', 'conv_gal112deg']
    for ismooth in (1, 5):
        for i in range(4,6):
            img=[conv_cmb0, conv_cmb1, conv_recon0, conv_recon1, conv_gal0, conv_gal1][i]
            #if i>3:
             #   img = img[455:-455,455:-455]
            iw = 3.5#widths[i]
            img = WLanalysis.smooth(img, img.shape[0]/iw/60*ismooth)
            if i%2==0:
                istd = std(img)
                print i, istd
            f=figure(figsize=(8,6))
            imshow(img,origin='lower', extent=[0,iw,0,iw], vmin=-2*istd, vmax=3*istd)
            xlabel(r'${\rm deg}$', fontsize=16)
            ylabel(r'${\rm deg}$', fontsize=16)
            title(fns[i])
            colorbar()
            plt.tight_layout()
            savefig('plots/map_%s_%iarcmin.png'%(fns[i],ismooth))
            close()

if plot_sample_maps:
    import WLanalysis
    conv_cmb0=fits.open('sample_maps/mnv0.00000_om0.30000_As2.1000/WLconv_z1100.00_0001r.fits')[0].data
    conv_cmb1=fits.open('sample_maps/mnv0.10000_om0.30000_As2.1000/WLconv_z1100.00_0001r.fits')[0].data
   
    conv_gal0=fits.open('sample_maps/mnv0.00000_om0.30000_As2.1000/12deg/WLconv_z2.00_0001r.fits')[0].data
    conv_gal1=fits.open('sample_maps/mnv0.10000_om0.30000_As2.1000/12deg/WLconv_z2.00_0001r.fits')[0].data
    
    #conv_gal0, conv_gal1, conv_cmb0, conv_cmb1 = [WLanalysis.smooth(img, img.shape[0]/60/3.5) for img in [conv_gal0, conv_gal1, conv_cmb0, conv_cmb1]]
    
    titles=[r'$\kappa_{\rm galaxy}^{0\;eV}(z_s=2)$', 
            r'$\kappa_{\rm galaxy}^{0.1\;eV}-\kappa_{\rm galaxy}^{0\;eV}$',
            r'$\kappa_{\rm CMB}^{0\;eV}(z_s=1100)$', 
            r'$\kappa_{\rm CMB}^{0.1\;eV}-\kappa_{\rm CMB}^{0\;eV}$',]
    f=figure(figsize=(8,6))
    iii=0
    diff1=conv_gal1-conv_gal0
    diff2=conv_cmb1-conv_cmb0
    #diff1=conv_gal1/conv_gal0-1
    #diff2=conv_cmb1/conv_cmb0-1
    for img in [conv_gal0, diff1, conv_cmb0, diff2]:
        ax=f.add_subplot(2,2,iii+1)
        #imgs=img.copy()
        imgs=WLanalysis.smooth(img, img.shape[0]/60/3.5)
        istd = std(imgs)
        #if iii%2:
            #istd = 1.0
        #if iii%2:
            #imgs[imgs>=0]=1
            #imgs[imgs<0]=-1
        imshow(imgs,origin='lower', extent=[0,3.5,0,3.5], vmin=-3*istd, vmax=3*istd,cmap='jet',interpolation='nearest')
        ax.text(0.05,0.08,titles[iii],transform=ax.transAxes,color='k',bbox={'facecolor':'w','alpha':0.8, 'edgecolor':'k','pad':2, 'linewidth':1})
        icb=colorbar()
        tick_locator = matplotlib.ticker.MaxNLocator(nbins=6)
        icb.locator = tick_locator
        icb.update_ticks()
        plt.setp(ax.get_xticklabels(), visible=False)
        plt.setp(ax.get_yticklabels(), visible=False)
        iii+=1
    plt.tight_layout()
    savefig('plots/plot_sample_maps.pdf')
    #savefig('plots/plot_sample_maps_frac.pdf')
    close()

            
if plot_hmf:#[2,3,4,5,6,17,27,28]
    import hmf
    import astropy.units as u
    ##0ID 1DescID 2Mvir 3Vmax 4Vrms 5Rvir 6Rs 7Np 8X 9Y 10Z 11VX 12VY 13VZ 14JX 15JY 16JZ 17Spin 18rs_klypin 19Mvir_all 20M200b 21M200c 22M500c 23M2500c 24Xoff 25Voff 26spin_bullock 27b_to_a 28c_to_a 29A[x] 30A[y] 31A[z] 32b_to_a(500c) 33c_to_a(500c) 34A[x](500c) 35A[y](500c) 36A[z](500c) 37T/|U| 38M_pe_Behroozi 39M_pe_Diemer 40Halfmass_Radius
    Mvir0, Vmax0, Vrms0, Rvir0, Rs0, Spin0, b2a0, c2a0, ID0, X0, Y0, Z0 = load('sample_maps/mnv0.00000_om0.30000_As2.1000/out_66.list.npy').T
    Mvir1, Vmax1, Vrms1, Rvir1, Rs1, Spin1, b2a1, c2a1, ID1, X1, Y1, Z1 = load('sample_maps/mnv0.10000_om0.30000_As2.1000/out_66.list.npy').T
    
    hist0, edges0 = histogram(log10(Mvir0), bins=linspace(11.5, 15.5,31))
    hist1, edges1 = histogram(log10(Mvir1), bins=linspace(11.5, 15.5,31))
    bincenter = edges0[:-1]
    delta_logbin = edges0[1]-edges0[0]
    
    Hmassles = hmf.MassFunction(cosmo_params={"Ob0":0.04551, "H0":70.0, "Om0":0.29997, 
           "m_nu":array([0,0,0])*u.eV},z=0, Mmin=11, Mmax=15.5, dlog10m=0.1, sigma_8=0.8523, n=0.97,hmf_model=hmf.fitting_functions.Tinker10)
    Hmassive = hmf.MassFunction(cosmo_params={"Ob0":0.04551, "H0":70.0, "Om0":0.29780, 
           "m_nu":array([0.1,0,0])*u.eV},z=0, Mmin=11, Mmax=15.5, dlog10m=0.1, sigma_8=0.8295, n=0.97,hmf_model=hmf.fitting_functions.Tinker10)
    
    f=figure(figsize=(8,6))
    ax1=f.add_subplot(211)
    ax2=f.add_subplot(212)
    
    mbins = arange(11, 15.5, 0.1)
    ax1.plot(10**bincenter, hist0/delta_logbin/512**3, 'g--', lw=2.5,label=r'${\rm Simulation}\; (M_\nu = 0\; {\rm eV})$'  )
    ax1.plot(10**bincenter, hist1/delta_logbin/512**3, 'g-',  lw=1.5,label=r'${\rm Simulation}\; (M_\nu = 0.1\; {\rm eV})$')
    ax1.plot(10**mbins, Hmassles.dndlog10m, '--', color='orange',lw=2.5, label=r'${\rm Tinker\; et\; al.\; 2010 \; (M_\nu = 0\; {\rm eV})}$')
    ax1.plot(10**mbins, Hmassive.dndlog10m, '-', color='orange', label=r'${\rm Tinker\; et\; al.\; 2010 \; (M_\nu = 0.1\; {\rm eV})}$')
   
    ax1.set_ylabel(r'$n \; (h^3/{\rm Mpc}^3)$',fontsize=22)
    ax1.set_xscale('log')
    ax1.set_yscale('log')
    ax1.legend(frameon=0,loc=3,fontsize=12)    
    ax1.set_xlim(5e11, 1e15)
    ax2.plot(10**bincenter, hist1.astype(float)/hist0-1,'g-', lw=2.5, label=r'${\rm Simulation}$')  
    ax2.plot(10**mbins, Hmassive.dndlog10m/Hmassles.dndlog10m-1,'-', color='orange', lw=1.5, label=r'${\rm Tinker\; et\; al.\; 2010}$')  
    
    ax2.legend(frameon=0,fontsize=12,ncol=1,loc=0)
    ax2.set_xscale('log')
    ax2.set_xlim(5e11, 1e15)
    #ax2.set_ylim(-0.09,0.0)
    ax2.set_xlabel(r'$M_{vir}\; (M_\odot)$',fontsize=22)
    ax2.set_ylabel(r'$n^{0.1eV} / n^{0 eV} - 1$',fontsize=22)
    ax2.plot([5e11, 5e15], [0,0],'k--',lw=1)
    
    plt.setp(ax1.get_xticklabels(), visible=False)
    plt.subplots_adjust(hspace=0.09,left=0.15)
    ax2.locator_params(axis = 'y', nbins = 5)
    plt.tight_layout()
    savefig('plots/plot_hmf.pdf')
    close()

if plot_merger_tree:
    #data0 = load('sample_maps/mnv0.00000_om0.30000_As2.1000/out_66.list.npy')
    #data1 = load('sample_maps/mnv0.10000_om0.30000_As2.1000/out_66.list.npy')
    #Mvir0, Vmax0, Vrms0, Rvir0, Rs0, Spin0, b2a0, c2a0, ID0, X0, Y0, Z0 = data0.T
    #Mvir1, Vmax1, Vrms1, Rvir1, Rs1, Spin1, b2a1, c2a1, ID1, X1, Y1, Z1 = data1.T
    
    #mass145=amin(abs(log10(Mvir0)-14.5))
    #idx0 = where (abs(log10(Mvir0)-14.5)==mass145)
    #dist = amin( sqrt((X0[idx0]-X1)**2 + (Y0[idx0]-Y1)**2 + (Y0[idx0]-Y1)**2) )
    #idx1 = where(sqrt((X0[idx0]-X1)**2 + (Y0[idx0]-Y1)**2 + (Y0[idx0]-Y1)**2) == dist)
    
    ##forest0 = loadtxt('sample_maps/mnv0.00000_om0.30000_As2.1000/forests.list').T
    ##forest1 = loadtxt('sample_maps/mnv0.10000_om0.30000_As2.1000/forests.list').T
    #forest0 = load('sample_maps/mnv0.00000_om0.30000_As2.1000/forests.list.npy')
    #forest1 = load('sample_maps/mnv0.10000_om0.30000_As2.1000/forests.list.npy')
    ## scale(0) id(1) desc_scale(2) desc_id(3) num_prog(4) pid(5) upid(6) desc_pid(7) phantom(8) sam_Mmvir(9) Mmvir(10) Rmvir(11) rs(12) vrms(13) mmp?(14) scale_of_last_MM(15) vmax(16) x(17) y(18) z(19) vx(20) vy(21) vz(22) Jx(23) Jy(24) Jz(25) Spin(26) Breadth_first_ID(27) Depth_first_ID(28) Tree_root_ID(29) Orig_halo_ID(30) Snap_idx(31) Next_coprogenitor_depthfirst_ID(32) Last_progenitor_depthfirst_ID(33) Last_mainleaf_depthfirst_ID(34) Tidal_Force(35) Tidal_ID(36) Rs_Klypin Mmvir_all M200b M200c M500c M2500c Xoff Voff Spin_Bullock b_to_a c_to_a A[x] A[y] A[z] b_to_a(500c) c_to_a(500c) A[x](500c) A[y](500c) A[z](500c) T/|U| M_pe_Behroozi M_pe_Diemer Halfmass_Radius
    
    tree0 = loadtxt('sample_maps/tree_massless.txt')[:,1:]
    tree1 = loadtxt('sample_maps/tree_massive.txt')[:,1:]
    IDt0, IDt1=139120302,137345326
    ###### select all halos fall in the same tree root ID
    #tree0 = tree0[where(tree0[:,29]==IDt0)]
    #tree1 = tree1[where(tree1[:,29]==IDt1)]
    tree0 = tree0[where((tree0[:,29]==IDt0) & (tree0[:,10]>10**11.5))]
    tree1 = tree1[where((tree1[:,29]==IDt1) & (tree1[:,10]>10**11.5))]
    
    f=figure(figsize=(8,8))
    ax=f.add_subplot(111)
    jjj=0
    for itree in [tree0,tree1]:
        icolor=['orange','green',][jjj]
        icolor2=['darkorange','darkgreen'][jjj]
        a0, x0, mvir0 = itree[:, [0,19,10]].T
        x0-=itree[0,19]
        z0 = 1/a0
        
        #ax.scatter(x0,z0,edgecolor='none',facecolor='r',s=1.8**(log10(mvir0)-10))
        for iii in range(1,len(itree)):
            idx_proj = iii
            idx_desc = where (itree[:,1] ==itree[iii,3])[0][0]
            if iii==1:
                ax.plot( x0[[idx_proj, idx_desc]], z0[[idx_proj, idx_desc]], '-',color=icolor,lw=1,label=[r'$M_\nu = 0\; {\rm eV}$', r'$M_\nu = 0.1\; {\rm eV}$'][jjj] )
            else:
                ax.plot( x0[[idx_proj, idx_desc]], z0[[idx_proj, idx_desc]], '-',color=icolor,lw=0.5)
        idx_large = where(log10(mvir0)>13)#itree.T[14]
        ax.scatter(x0[idx_large],z0[idx_large],facecolor=icolor2,edgecolor='none',s=5**(log10(mvir0[idx_large])-12))#
        jjj+=1
    ax.set_yscale('log')
    ax.set_ylim(0.9,8)
    ax.set_xlim(-8,8)
    ax.set_yticks(arange(1,11))
    a=ax.get_yticks().tolist()
    anew = [str(int(ia-1)) for ia in a]
    ax.set_yticklabels(anew)
    [i.set_linewidth(1.5) for i in ax.spines.itervalues()]
    ax.tick_params(which='both', labelsize=18, width=1.5)
    ax.set_xlabel(r'$X - X_{\rm root}\; ({\rm Mpc}/h)$',fontsize=22)
    ax.set_ylabel(r'$z$',fontsize=22)
    ax.grid(True)#,axis='x')
    ax.legend(fontsize=18,loc=3,frameon=1)
    plt.tight_layout()
    savefig('plots/plot_tree_cut.pdf')
    
    close()
    
if plot_mass_accretion:
    tree0 = loadtxt('sample_maps/tree_massless.txt')[:,1:]
    tree1 = loadtxt('sample_maps/tree_massive.txt')[:,1:]
    IDt0, IDt1=139120302,137345326
    ###### select all halos fall in the same tree root ID
    tree0 = tree0[where(tree0[:,29]==IDt0)]
    tree1 = tree1[where(tree1[:,29]==IDt1)]
    f=figure(figsize=(8,4))
    ax=f.add_subplot(111)
    jjj=0
    for itree in [tree0,tree1]:
        icolor=['darkorange','green',][jjj]
        ilabel=[r'$M_\nu = 0\; {\rm eV}$', r'$M_\nu = 0.1\; {\rm eV}$'][jjj]
        idx_desc = 0
        idx_proj_all = where (itree[:,3] ==itree[idx_desc,1])[0]
        imassarr,iaarr  = [itree[idx_desc,10]],[itree[idx_desc,0]]
        halfmass = itree[idx_desc,10]/2.0
        while len(idx_proj_all):
            idx_proj_biggest = idx_proj_all[argmax(itree[idx_proj_all,10])]
            imassarr.append(itree[idx_proj_biggest,10])
            iaarr.append(itree[idx_proj_biggest,0])
            idx_desc = idx_proj_biggest
            idx_proj_all = where (itree[:,3] ==itree[idx_desc,1])[0]
        imassarr = array(imassarr)
        iaarr = array(iaarr)
        ax.plot(1/iaarr,imassarr,color=icolor,lw=3-jjj,label=ilabel,alpha=0.8)#drawstyle='steps',
        #idx_half = argmin( abs(imassarr-halfmass))
        #ax.plot([1/iaarr[idx_half],1/iaarr[idx_half]], [1e11,1e15],'--',lw=3-jjj,color=icolor)
        jjj+=1
    ax.set_yscale('log')
    ax.set_xscale('log')
    ax.set_xticks(arange(1,11))
    a=ax.get_xticks().tolist()
    anew = [str(int(ia-1)) for ia in a]
    ax.set_xticklabels(anew)
    [i.set_linewidth(1.5) for i in ax.spines.itervalues()]
    ax.tick_params(which='both', labelsize=18, width=1.5)
    ax.set_ylabel(r'$M_{vir}\; (M_\odot)$',fontsize=22)
    ax.set_xlabel(r'$z$',fontsize=22)
    ax.legend(fontsize=18,loc=0,frameon=0)
    ax.set_xlim(1,8)
    plt.tight_layout()
    
    savefig('plots/plot_most_massive_progenitor.pdf')
    close()
        
if test_pmatter_noise:
    kcamb1, Pcamb1 = np.loadtxt('camb/camb_mnv0.10000_om0.30000_As2.1000_matterpow_0.dat').T
    kcamb0, Pcamb0 = np.loadtxt('camb/camb_mnv0.00000_om0.30000_As2.1000_matterpow_0.dat').T

    ####### bird halofit
    kbird1, Pbird1 = np.loadtxt('camb-fidu/camb_mnv0.10000_om0.30000_As2.1000-bird_matterpow_0.dat').T
    kbird0, Pbird0 = np.loadtxt('camb-fidu/camb_mnv0.00000_om0.30000_As2.1000-bird_matterpow_0.dat').T

    ####### takahashi halofit
    ktaka1, Ptaka1 = np.loadtxt('camb-fidu/camb_mnv0.10000_om0.30000_As2.1000-halofit_matterpow_0.dat').T
    ktaka0, Ptaka0 = np.loadtxt('camb-fidu/camb_mnv0.00000_om0.30000_As2.1000-halofit_matterpow_0.dat').T
    
    ####### sims
    knb0_1024, Pnb0_1024 = np.loadtxt('matterpower/mnv0.00000_om0.30000_As2.1000/powerspec_tot_066.txt').T
    knb1_1024, Pnb1_1024 = np.loadtxt('matterpower/mnv0.10000_om0.30000_As2.1000/powerspec_tot_066.txt').T

    
    knb0_1024 *= 1e3
    knb1_1024 *= 1e3
    Pnb0_1024 *= 1e-9
    Pnb1_1024 *= 1e-9
    ####### interpolation
    Pcamb1_interp = interp1d(kcamb1, Pcamb1)(k_arr)
    Pcamb0_interp = interp1d(kcamb0, Pcamb0)(k_arr)
    Pbird1_interp = interp1d(kbird1, Pbird1)(k_arr)
    Pbird0_interp = interp1d(kbird0, Pbird0)(k_arr)
    Ptaka1_interp = interp1d(ktaka1, Ptaka1)(k_arr)
    Ptaka0_interp = interp1d(ktaka0, Ptaka0)(k_arr)
    Pnb0_interp =  interp1d(knb0_1024, Pnb0_1024,fill_value='extrapolate')(k_arr)
    Pnb1_interp =  interp1d(knb1_1024, Pnb1_1024,fill_value='extrapolate')(k_arr)
    
    f=figure()
    ax=f.add_subplot(111)
    ax.plot(k_arr, Pnb1_interp/Ptaka1_interp,label='massless')    
    ax.plot(k_arr, Pnb0_interp/Ptaka0_interp,label='massive')
    ax.plot(k_arr,ones(len(k_arr)),'k--')
    ax.set_xlabel('k (h/Mpc)')
    ax.set_ylabel('P_nbody / P_takahashi')
    ax.set_xlim(1e-2,10)
    ax.set_xscale('log')
    ax.set_ylim(0.5,1.2)
    ax.legend(loc=0)
    savefig('plots/test_pmatter_noise.jpg')
    close()

if plot_kernels:
    from scipy.integrate import quad
    zs_arr=[0.5, 1.0, 1.5, 2.0, 2.5, 1100]
    OmegaM=0.3
    OmegaV=1-OmegaM
    H0=100*h
    c = 299792.458#km/s
    H_inv = lambda z: 1.0/(H0*sqrt(OmegaM*(1+z)**3+OmegaV))
    DC = lambda z: c*quad(H_inv, 0, z)[0] 
    W_source = lambda z, z_ls: 1.5*OmegaM*H0**2*(1+z)*H_inv(z)*DC(z)/c*(1-DC(z)/DC(z_ls))
    z_arr = linspace(0,6, 1000)
    W_arr = array([[W_source(iz, iz_ls) for iz in z_arr] for iz_ls in zs_arr])
    #save('W_arr.npy',W_arr)
    
    seed(103)#seed(2018)
    f=figure(figsize=(8,4))
    ax=f.add_subplot(111)
    for iii in range(1,6):
        #rand(2)
        iW=W_arr[iii]
        izs=zs_arr[iii]
        iW/=amax(iW)
        idx=where(z_arr<=izs)
        ax.plot(z_arr[idx]+1, iW[idx], lw=4, alpha=0.8, color=rand(3), label=r'$z_s=%s$'%(zs_arr[iii]))
    ax.set_xscale('log')
    #ax.set_xticks(arange(1,7))
    ax.set_xticks([1,1.5, 2, 3, 4, 5, 6])

    a=ax.get_xticks().tolist()
    anew = [str((ia-1)) for ia in a]
    ax.set_xticklabels(anew)
    [i.set_linewidth(1.5) for i in ax.spines.itervalues()]
    ax.legend(fontsize=16,loc=0,frameon=0,title=r'${\rm Source\;\; Redshift}$')
    ax.tick_params(which='both', labelsize=18, width=1.5)
    ax.set_xlim(1,7)
    ax.set_ylim(0.01,1.05)
    ax.locator_params(axis = 'y', nbins = 6)
    ax.set_xlabel(r'$z$',fontsize=22)
    ax.set_ylabel(r'$W \; {\rm (rescaled)}$',fontsize=22)
    plt.tight_layout()
    savefig('plots/plot_kernels.pdf')
    close()

if plot_pkappa:
    zs_arr=[1.0, 1.5, 2.0, 2.5, 1100]
    def pkappa_gen (zs):
        ps0_all = load('/Users/jia/Documents/weaklensing/kspace_nu/neutrino-batch/sample_maps/jose/Om0.29997_As2.10000_mva0.00000_mvb0.00000_mvc0.00000_h0.70000_Ode0.69995/1024b512/Maps%02d/PS_50.npy'%(zs*10))
        ps1_all = load('/Users/jia/Documents/weaklensing/kspace_nu/neutrino-batch/sample_maps/jose/Om0.29780_As2.10000_mva0.02175_mvb0.02338_mvc0.05486_h0.70000_Ode0.69995/1024b512/Maps%02d/PS_50.npy'%(zs*10))
        ps0TH_all = load('/Users/jia/Documents/weaklensing/kspace_nu/neutrino-batch/sample_maps/jose/Om0.29997_As2.10000_mva0.00000_mvb0.00000_mvc0.00000_h0.70000_Ode0.69995/1024b512/Maps%02d/PS_50_TH.npy'%(zs*10))
        ps1TH_all = load('/Users/jia/Documents/weaklensing/kspace_nu/neutrino-batch/sample_maps/jose/Om0.29780_As2.10000_mva0.02175_mvb0.02338_mvc0.05486_h0.70000_Ode0.69995/1024b512/Maps%02d/PS_50_TH.npy'%(zs*10))
        ##[None,'original','bird','peacock','takahashi','mead','halomodel','casarini']
        ps0 = mean(ps0_all[1:],axis=0)
        idx=where(ps0>0)[0]
        ell = ps0_all[0][idx]
        ps0 = mean(ps0_all[1:],axis=0)[idx]
        ps1 = mean(ps1_all[1:],axis=0)[idx]
        pstd0 = std(ps0_all[1:],axis=0)[idx]
        pstd1 = std(ps1_all[1:],axis=0)[idx]
        pcamb0 = ps0TH_all[[1,3,5],:][:,idx]
        pcamb1 = ps1TH_all[[1,3,5],:][:,idx]
        return ell, ps0, ps1, pstd0, pstd1, pcamb0, pcamb1
    
    ############  plots
    f=figure(figsize=(8,8))
    ax1=f.add_subplot(211)
    ax2=f.add_subplot(212)
    
    
    seed(103)
    for izs in zs_arr:
        icolor=rand(3)
        #colors.pop(0)
        ell, ps0, ps1, pstd0, pstd1, pcamb0, pcamb1 = pkappa_gen(izs)
        ie=ell*(1+ell)/2/pi
        ax1.plot(ell, ie*pcamb0[2], '--',color=icolor,lw=2, alpha=0.8, label=r'$z_s=%s$'%(izs)  )
        ax1.plot(ell,ie*ps0,'-',color=icolor, lw=3)#ie*pstd0/sqrt(1000.)
        
        ax2.plot(ell,pcamb1[2]/pcamb0[2]-1,'--',color=icolor,lw=2)
        ax2.plot(ell,ps1/ps0-1,'-',color=icolor,lw=3, alpha=0.8, label=r'$z_s=%s$'%(izs))


    ax1.set_ylabel(r'$\frac{\ell(\ell+1)}{2\pi}P_{\kappa}^{0 eV}(\ell)$',fontsize=22)
    ax1.set_xscale('log')
    ax1.set_yscale('log')
    ax1.set_xlim(100, 1e4)
    ax1.set_ylim(1e-5, 1e-2)
    
    ax2.legend(frameon=0,loc=0,fontsize=16,ncol=1,labelspacing=0.11,columnspacing=0.3)
    #ax1.set_yticklabels(fontsize=16)
    ax2.set_xscale('log')
    ax2.set_xlim(100, 1e4)
    ax2.set_ylim(-0.08,-0.02)
    ax2.set_xlabel(r'$\ell$',fontsize=22)
    ax2.set_ylabel(r'$P_{\kappa}^{0.1eV} / P_{\kappa}^{0 eV} - 1$',fontsize=22)
    
    plt.setp(ax1.get_xticklabels(), visible=False)
    plt.subplots_adjust(hspace=0.09,left=0.15)
    ax2.locator_params(axis = 'y', nbins = 5)
    plt.tight_layout()
    savefig('plots/plot_pkappa_fidu.pdf')
    close()