Commit 756a790c by NUNEZ Arturo

Automatic commit mercredi 6 décembre 2017, 16:30:01 (UTC+0100)

parent 90734f5a
 { "cells": [ { "cell_type": "code", "execution_count": 3, "metadata": { "collapsed": true }, "outputs": [], "source": [ "%matplotlib notebook\n", "from scipy.stats import rv_continuous\n", "from scipy.special import gamma\n", "import numpy as np\n", "import emcee\n", "from mpl_toolkits.axes_grid1 import make_axes_locatable\n", "from numpy import exp, sqrt\n", "from scipy.integrate import quad, dblquad\n", "import matplotlib.patches as patches\n", "from scipy.integrate import quad\n", "import scipy.optimize as optimize\n", "import matplotlib.pyplot as plt\n", "import matplotlib as mpl\n", "from sklearn.neighbors import KDTree\n", "import sys\n", "import lmfit\n", "from py_unsio import *\n", "import pymc\n", "import os\n", "from pymodelfit import FunctionModel1DAuto\n", "import wkbl\n", "from mpl_toolkits.mplot3d import axes3d\n", "from matplotlib import cm\n", "import wkbl.astro.nbody_essentials as nbe\n", "import cfalcon\n", "CF =cfalcon.CFalcon()\n", "import iminuit\n", "from iminuit import Minuit, describe, Struct\n", "import probfit\n", "import warnings\n", "warnings.filterwarnings('ignore')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [ { "ename": "TypeError", "evalue": "__init__() got multiple values for keyword argument 'getcen'", "output_type": "error", "traceback": [ "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", "\u001b[0;31mTypeError\u001b[0m Traceback (most recent call last)", "\u001b[0;32m\u001b[0m in \u001b[0;36m\u001b[0;34m()\u001b[0m\n\u001b[1;32m 2\u001b[0m \u001b[0;31m#path = \"/data/POL/HALOA/output_01274\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m 3\u001b[0m \u001b[0;31m#path = \"/data/MANU/anunez/output_00690\"\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 4\u001b[0;31m \u001b[0mmyhalo\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mwkbl\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mGalaxy_Hound\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mpath\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\"halo,gas,stars\"\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0mgetcen\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mFalse\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m", "\u001b[0;31mTypeError\u001b[0m: __init__() got multiple values for keyword argument 'getcen'" ] } ], "source": [ "path = \"/data/POL/HALOB/output_00417\"\n", "#path = \"/data/POL/HALOA/output_01274\"\n", "#path = \"/data/MANU/anunez/output_00690\"\n", "myhalo = wkbl.Galaxy_Hound(path,\"halo,gas,stars\",getcen=False)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ok,rho,_= CF.getDensity(np.array(myhalo.st.pos3d.reshape(len(myhalo.st.pos3d)*3),dtype=np.float32), myhalo.st.mass)\n", "centro_rho = myhalo.st.pos3d[np.where(rho == rho.max())][0]\n", "print \"density\",centro_rho\n", "myhalo.center_shift(centro_rho)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "myhalo.r_virial(600,n=4)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "pos_dm = np.array(myhalo.dm.pos3d.reshape(len(myhalo.dm.pos3d)*3),dtype=np.float32)\n", "pos_gs = np.array(myhalo.gs.pos3d.reshape(len(myhalo.gs.pos3d)*3),dtype=np.float32)\n", "pos_st = np.array(myhalo.st.pos3d.reshape(len(myhalo.st.pos3d)*3),dtype=np.float32)\n", "pos = np.concatenate((pos_dm, pos_st, pos_gs))\n", "mass = np.concatenate((myhalo.dm.mass,myhalo.st.mass,myhalo.gs.mass))\n", "v = np.concatenate((myhalo.dm.v,myhalo.st.v,myhalo.gs.v))\n", "print len(mass)*3, len(pos)\n", "pos3d = pos.reshape(len(pos)/3,3)\n", "r2 = pos3d[:,0]**2 + pos3d[:,1]**2 +pos3d[:,2]**2" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "myhalo.gs.hsml.min()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "ok, acc, Phy = CF.getGravity(pos,mass,0.15,G=myhalo.p.G)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "#r_array = np.logspace(np.log10(min_R),2*np.log10(4*myhalo.r200),100)" ] }, { "cell_type": "markdown", "metadata": { "collapsed": true }, "source": [ "# Potential and Rmax " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "\n", "bin_num = 400\n", "\n", "pot_sph, bins_pot = np.histogram(r2,bins=bin_num,\n", " weights=Phy)\n", "n, _ = np.histogram(r2,bins=bin_num)\n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.set_title(\"spherical shells\",fontsize=30)\n", "#ax.set_ylim([pot_sph.min(),np.abs(pot_sph.min())/6.])\n", "ax.set_ylabel(r'$\\Phi$ [kpc $^2$ M$_{\\odot}$ s$^{-2}$ ] ', fontsize=15)\n", "#ax.set_xscale('log')\n", "ax.set_xlabel(r'r [kpc] ', fontsize=15)\n", "ax.plot(np.sqrt(bins_pot[1:]),pot_sph/n,'bo-')\n", "\n", "ax.add_patch(\n", " patches.Rectangle((450, -1.4e-29),\n", " 100,\n", " 0.9e-29,\n", " fill=False # remove background\n", " )\n", ")\n", "\n", "\n", "left, bottom, width, height = [0.35, 0.23, 0.4, 0.4]\n", "ax2 = fig.add_axes([left, bottom, width, height])\n", "ax2.set_xlim([450,550])\n", "ax2.set_ylim([-1.2e-29,-0.6e-29])\n", "\n", "ax2.set_ylabel(r'$\\Phi$ [kpc $^2$ M$_{\\odot}$ s$^{-2}$ ] ', fontsize=15)\n", "ax2.set_xlabel(r'r [kpc] ', fontsize=15)\n", "ax2.plot(np.sqrt(bins_pot[1:]),pot_sph/n,'bo-')" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "pot_sph, bins_pot = np.histogram(r2,bins=np.logspace(np.log10(2),2*np.log10(4*myhalo.r200),1000),\n", " weights=Phy)\n", "n, _ = np.histogram(r2,bins=np.logspace(np.log10(2),2*np.log10(4*myhalo.r200),1000))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "hide_input": true }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.set_title(\"spherical shells\",fontsize=30)\n", "#ax.set_ylim([pot_sph.min(),np.abs(pot_sph.min())/6.])\n", "ax.set_ylabel(r'$\\Phi$ [kpc $^2$ M$_{\\odot}$ s$^{-2}$ ] ', fontsize=15)\n", "#ax.set_xscale('log')\n", "ax.set_xlabel(r'r [kpc] ', fontsize=15)\n", "ax.plot(np.sqrt(bins_pot[1:]),pot_sph/n,'bo-')\n", "\n", "ax.add_patch(\n", " patches.Rectangle((450, -1.4e-29),\n", " 100,\n", " 0.9e-29,\n", " fill=False # remove background\n", " )\n", ")\n", "\n", "\n", "left, bottom, width, height = [0.35, 0.23, 0.4, 0.4]\n", "ax2 = fig.add_axes([left, bottom, width, height])\n", "ax2.set_xlim([450,550])\n", "ax2.set_ylim([-1.2e-29,-0.6e-29])\n", "\n", "ax2.set_ylabel(r'$\\Phi$ [kpc $^2$ M$_{\\odot}$ s$^{-2}$ ] ', fontsize=15)\n", "ax2.set_xlabel(r'r [kpc] ', fontsize=15)\n", "ax2.plot(np.sqrt(bins_pot[1:]),pot_sph/n,'bo-')" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# l.o.s story " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "ok,rho_dm,_= CF.getDensity(np.array(myhalo.dm.pos3d.reshape(len(myhalo.dm.pos3d)*3),dtype=np.float32), myhalo.dm.mass)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# center of big neighbour\n", "neighbourg = rho_dm[np.where((myhalo.dm.r<520)&(myhalo.dm.r>480))].max()\n", "myneighbourg = myhalo.dm.pos3d[np.where(rho_dm==neighbourg)][0]\n", "# its radius squared\n", "r_nei2 = myneighbourg[0]**2 + myneighbourg[1]**2 + myneighbourg[2]**2\n", "# distance to every point proyected to the line conecting center to neigblourg \n", "adyacent = (pos3d[:,0]*myneighbourg[0] + pos3d[:,1]*myneighbourg[1] + pos3d[:,2]*myneighbourg[2]) / np.sqrt(r_nei2)\n", "# distance to each point\n", "hipoteneuse = np.sqrt(pos3d[:,0]**2 + pos3d[:,1]**2 + pos3d[:,2]**2)\n", "# angle of cone\n", "alpha = np.radians(5)\n", "cos_alpha = np.cos(alpha)\n", "# cosine of all particles respective to their angle to the l.o.s\n", "cos_all = adyacent / hipoteneuse \n", "# final selection of cone\n", "my_cone = pos3d[np.where((cos_all)>cos_alpha)]" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "hide_input": true }, "outputs": [], "source": [ "fig, ax = plt.subplots(figsize=[6,6])\n", "ax.set_xlim([-600,0])\n", "ax.set_ylim([-320,300])\n", "\n", "ax.scatter(my_cone[:,0], my_cone[:,2],c='r',lw=0,s=0.1,alpha=0.5)\n", "ax.scatter(myneighbourg[0],myneighbourg[2],s=20)" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# Virial radius and mass of neighbour \n", "\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "# radii from center of neighbour\n", "r_neig = np.sqrt((pos3d[:,0] - myneighbourg[0])**2 +(pos3d[:,1] - myneighbourg[1])**2 +(pos3d[:,2] - myneighbourg[2])**2 )\n", "# calculating the R200 of neighbout\n", "mhist, rhist = np.histogram(r_neig,range=(0.0,np.sqrt(r_nei2)),bins=512, weights=mass )\n", "vol_bin = (4./3.)*np.pi*(rhist[:-1]**3)\n", "r_bin = rhist[:-1]+ 0.5*(rhist[2]-rhist[1])\n", "rho_s = np.cumsum(mhist) / vol_bin\n", "r200_neigh = r_bin[np.argmin(np.abs(rho_s - (200 * myhalo.p.rho_crit)))]\n", "# mass of neighbour\n", "mass_neigh = np.sum(mass[np.where(r2cos_alpha)\n", "cone_pos = my_cone\n", "cone_mass = mass[los_condition]\n", "cone_Phy = Phy[los_condition]\n", "cone_r2 = my_cone[:,0]**2 + my_cone[:,1]**2 + my_cone[:,2]**2\n", "# now the histogram\n", "bin_num = 1000\n", "pot_los, bins_pot_los = np.histogram(cone_r2,bins=bin_num,\n", " weights=cone_Phy)\n", "n_los, _ = np.histogram(cone_r2,bins=bin_num)\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false, "hide_input": true }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.set_title(\"l.o.s\",fontsize=30)\n", "#ax.set_ylim([pot_sph.min(),np.abs(pot_sph.min())/6.])\n", "ax.set_ylabel(r'$\\Phi$ [kpc $^2$ M$_{\\odot}$ s$^{-2}$ ] ', fontsize=15)\n", "#ax.set_xscale('log')\n", "ax.set_xlabel(r'r [kpc] ', fontsize=15)\n", "ax.plot(np.sqrt(bins_pot_los[1:]),pot_los/n_los,'bo-')\n", "\n", "ax.add_patch(\n", " patches.Rectangle((450, -1.4e-29),\n", " 100,\n", " 0.9e-29,\n", " fill=False # remove background\n", " )\n", ")\n", "\n", "\n", "left, bottom, width, height = [0.35, 0.23, 0.4, 0.4]\n", "ax2 = fig.add_axes([left, bottom, width, height])\n", "ax2.set_xlim([480,545])\n", "ax2.set_ylim([-1e-29,-0.8e-29])\n", "\n", "ax2.set_ylabel(r'$\\Phi$ [kpc $^2$ M$_{\\odot}$ s$^{-2}$ ] ', fontsize=15)\n", "ax2.set_xlabel(r'r [kpc] ', fontsize=15)\n", "ax2.plot(np.sqrt(bins_pot_los[1:]),pot_los/n_los,'bo-')\n" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# virial ratio q\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "inside_halo = np.where(r2<(myhalo.r200)**2)\n", "kmtokpc = 1 / 3.08567758128e+16 \n", "q = (np.sum(mass[inside_halo]*(v[inside_halo]*kmtokpc)**2) / np.sum(mass[inside_halo]*Phy[inside_halo])) + 1 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print \"q = \",q,r\", numerator = \",np.sum(mass[inside_halo]*(v[inside_halo]*kmtokpc)**2),\", denominator = \",np.sum(mass[inside_halo]*Phy[inside_halo])\n", "print \"ratio = \",(np.sum(mass[inside_halo]*(v[inside_halo]*kmtokpc)**2) / np.sum(mass[inside_halo]*Phy[inside_halo]))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "dm = len(myhalo.dm.mass)+len(myhalo.st.mass)\n", "inside_halo = np.where(r2[:dm]<(myhalo.r200)**2)\n", "kmtokpc = 1 / 3.08567758128e+16 \n", "q = (np.sum(mass[inside_halo]*(v[inside_halo]*kmtokpc)**2) / np.sum(mass[inside_halo]*Phy[inside_halo])) + 1 " ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print \"q = \",q,r\", numerator = \",np.sum(mass[inside_halo]*(v[inside_halo]*kmtokpc)**2),\", denominator = \",np.sum(mass[inside_halo]*Phy[inside_halo])\n", "print \"ratio = \",(np.sum(mass[inside_halo]*(v[inside_halo]*kmtokpc)**2) / np.sum(mass[inside_halo]*Phy[inside_halo]))" ] }, { "cell_type": "markdown", "metadata": {}, "source": [ "# $\\beta$ parameter\n", "\n", "$$\\beta = 1 - \\frac{ \\sigma_{\\phi}^2 + \\sigma_{\\theta}^2}{2 \\sigma_{r}^2}$$" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [ "point_num = 300\n", "r_beta = np.logspace(-1,np.log10(4*myhalo.r200),point_num)\n", "vphi = np.concatenate((myhalo.dm.vphi,myhalo.st.vphi,myhalo.gs.vphi))\n", "vtheta = np.concatenate((myhalo.dm.vtheta,myhalo.st.vtheta,myhalo.gs.vtheta))\n", "vr = np.concatenate((myhalo.dm.vr,myhalo.st.vr,myhalo.gs.vr))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "def beta_param(i):\n", " condition = np.where((r2>r_beta[i]**2)&(r2<=r_beta[i+1]**2))\n", " v_r = vr[condition]\n", " v_phi = vphi[condition]\n", " v_theta = vtheta[condition]\n", " #print (np.std(v_phi))**2 ,(np.std(v_theta))**2 , (np.std(v_r))**2 \n", " return 1 - ((np.std(v_phi))**2 +(np.std(v_theta))**2) / 2. / (np.std(v_r))**2 \n", "get_beta = np.vectorize(beta_param)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "beta_r = get_beta(range(point_num-1))" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "print len(r_beta[:-1]), len(beta_r)" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": false }, "outputs": [], "source": [ "fig, ax = plt.subplots()\n", "ax.set_xscale('log')\n", "ax.set_xlabel(\"r [kpc]\",fontsize=15)\n", "ax.set_ylabel(r\"$\\beta(r)$\",fontsize=18)\n", "ax.plot((r_beta[1:]+r_beta[:-1])/2/10**0.8,beta_r)\n", "#ax.axvline(x=myhalo.r200, color='k',linestyle='--',label=r'r$_{200}$')\n", "#ax.axvline(x=myhalo.r97, color='gray',linestyle='--',label=r'r$_{97}$')\n", "#ax.axvline(x=np.sqrt(r_nei2), color='g',linestyle='--',label=r'first neighbourg')\n", "#legend = ax.legend(loc='lower right', ncol=1, shadow=False, fontsize=14)\n", "#frame = legend.get_frame()" ] }, { "cell_type": "code", "execution_count": null, "metadata": { "collapsed": true }, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 2", "language": "python", "name": "python2" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 2 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython2", "version": "2.7.12" }, "latex_envs": { "bibliofile": "biblio.bib", "cite_by": "apalike", "current_citInitial": 1, "eqLabelWithNumbers": true, "eqNumInitial": 0 } }, "nbformat": 4, "nbformat_minor": 1 }
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 { "cells": [], "metadata": {}, "nbformat": 4, "nbformat_minor": 1 }