{ "cells": [ { "cell_type": "markdown", "metadata": {}, "source": [ "# Day 3 Solutions" ] }, { "cell_type": "code", "execution_count": 63, "metadata": {}, "outputs": [], "source": [ "#loading the required packages\n", "%matplotlib inline\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "from scipy.spatial import cKDTree\n", "from astropy.cosmology import FlatLambdaCDM\n", "import glob\n" ] }, { "cell_type": "code", "execution_count": 74, "metadata": {}, "outputs": [], "source": [ "def get_xyz(ra, dec):\n", " ra = ra*np.pi/180.\n", " dec = dec*np.pi/180.\n", " x = np.cos(dec)*np.cos(ra)\n", " y = np.cos(dec)*np.sin(ra)\n", " z = np.sin(dec) \n", " return x, y, z\n" ] }, { "cell_type": "code", "execution_count": 75, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "(0.43301270189221946, 0.25, 0.8660254037844386)\n" ] } ], "source": [ "print(get_xyz(30,60))" ] }, { "cell_type": "code", "execution_count": 64, "metadata": {}, "outputs": [], "source": [ "# selection cut on the lens sample\n", "def lens_select(zmin=0.1, zmax=0.33, lammin=55, lammax=100):\n", " #please check the file path properly \n", " data = pd.read_csv('/home/idies/workspace/Storage/divyar/IAGRG_2022/DataStore/redmapper.dat', delim_whitespace=1)\n", " #sample selection cut\n", " idx = (data['lambda']>lammin) & (data['lambda']<=lammax)\n", " idx = idx & (data['zred']>zmin) & (data['zred']<=zmax)\n", " ra = data['ra'].values[idx]\n", " dec = data['dec'].values[idx]\n", " zred = data['zred'].values[idx]\n", " #as we have no weights to apply we set them to unity\n", " wgt = ra*1.0/ra\n", " print('number of lenses=%d'%len(ra))\n", " return ra, dec, zred, wgt\n" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] }, { "cell_type": "code", "execution_count": 65, "metadata": {}, "outputs": [], "source": [ "# sanity cheaks on the source files\n", "def read_sources(ifil):\n", " # various columns in sources \n", " # ragal, decgal, e1gal, e2gal, wgal, rms_egal, mgal, c1gal, c2gal, R2gal, zphotgal\n", " data = pd.read_csv(ifil, delim_whitespace=1).values\n", " zphotgal = data[:,-1]\n", " # sanity checks on the sources data\n", " idx = (np.sum(np.isnan(data), axis=1)==0) & (zphotgal>0)\n", " datagal = np.zeros((np.sum(idx),7))\n", " datagal[:,:6] = data[idx,:6]\n", " datagal[:,6] = data[idx,-1]\n", " # collects only - ragal, decgal, e1gal, e2gal, wgal, rms_egal, zphotgal\n", " return datagal" ] }, { "cell_type": "code", "execution_count": 66, "metadata": {}, "outputs": [], "source": [ "# following surhud's lectures\n", "def get_et(lra, ldec, sra, sdec, se1, se2):\n", " lra = lra*np.pi/180\n", " ldec = ldec*np.pi/180\n", " sra = sra*np.pi/180\n", " sdec = sdec*np.pi/180\n", "\n", " c_theta = np.cos(ldec)*np.cos(sdec)*np.cos(lra - sra) + np.sin(ldec)*np.sin(sdec)\n", " s_theta = np.sqrt(1-c_theta**2)\n", "\n", " # phi to get the compute the tangential shear\n", " c_phi = np.cos(ldec)*np.sin(sra - lra)*1.0/s_theta\n", " s_phi = (-np.sin(ldec)*np.cos(sdec) + np.cos(ldec)*np.cos(sra - lra)*np.sin(sdec))*1.0/s_theta\n", " # tangential shear\n", " e_t = - se1*(2*c_phi**2 -1) - se2*(2*c_phi * s_phi)\n", "\n", " return e_t" ] }, { "cell_type": "code", "execution_count": 67, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "-0.04534629189830734\n" ] } ], "source": [ "print(get_et(lra=0, ldec=0, sra=0.123, sdec=0.045, se1 = 4.5e-2, se2 = 1.7e-2))" ] }, { "cell_type": "code", "execution_count": 70, "metadata": {}, "outputs": [], "source": [ "def get_sigma_crit_inv(lzred, szred, cc):\n", " # some important constants for the sigma crit computations\n", " gee = 4.301e-9 #km^2 Mpc M_sun^-1 s^-2 gravitational constant\n", " cee = 3e5 #km s^-1\n", " # sigma_crit_calculations for a given lense-source pair\n", " sigm_crit_inv = cc.angular_diameter_distance(lzred).value * cc.angular_diameter_distance_z1z2(lzred, szred).value * (1.0 + lzred)**2 * 1.0/cc.angular_diameter_distance(szred).value\n", " sigm_crit_inv = sigm_crit_inv * 4*np.pi*gee*1.0/cee**2 \n", " sigm_crit_inv = 1e12*sigm_crit_inv #esd's are in pc not in Mpc\n", "\n", " return sigm_crit_inv" ] }, { "cell_type": "code", "execution_count": 71, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "0.00030436585298495184\n" ] } ], "source": [ "from astropy.cosmology import FlatLambdaCDM\n", "cc = FlatLambdaCDM(H0=100, Om0=0.999)\n", "print(get_sigma_crit_inv(lzred=0.33, szred=0.8, cc=cc))" ] }, { "cell_type": "code", "execution_count": 1, "metadata": {}, "outputs": [], "source": [ "\n", "def run_pipe(Omegam=0.315, rmin=0.2, rmax=2.0, nbins=10, zdiff=0.4, outputfile = 'iagrg_dsigma.dat'):\n", " #set the cosmology with omegaM parameter \n", " cc = FlatLambdaCDM(H0=100, Om0=Omegam) # fixing H0=100 to set units in Mpc h-1\n", " \n", " # set the projected radial binning \n", " rmin = rmin\n", " rmax = rmax\n", " nbins = nbins #10 radial bins for our case\n", " rbins = np.logspace(np.log10(rmin), np.log10(rmax), nbins + 1)\n", " rdiff = np.log10(rbins[1]*1.0/rbins[0])\n", " \n", " # initializing arrays for signal compuations\n", " sumdsig_num = np.zeros(len(rbins[:-1]))\n", " sumdsigsq_num = np.zeros(len(rbins[:-1]))\n", " sumwls = np.zeros(len(rbins[:-1]))\n", " sumwls_resp = np.zeros(len(rbins[:-1]))\n", "\n", " # getting the lenses data\n", " lra, ldec, lred, lwgt = lens_select(zmin=0.1, zmax=0.33, lammin=55, lammax=100)\n", "\n", " # convert lense ra and dec into x,y,z cartesian coordinates\n", " lx, ly, lz = get_xyz(lra, ldec)\n", " \n", " # putting kd tree around the lenses\n", " lens_tree = cKDTree(np.array([lx, ly, lz]).T)\n", " \n", " \n", " print('lenses tree is ready\\n')\n", " \n", " # setting maximum search radius\n", " dcommin = cc.comoving_distance(np.min(lred)).value\n", " dismax = (rmax*1.0/(dcommin)) \n", "\n", " # lets first catch the file list for sources\n", " sflist = np.sort(glob.glob('/home/idies/workspace/Storage/divyar/IAGRG_2022/DataStore/hsc/*.dat'))\n", "\n", " # Ready to pounce on the source data\n", " for ifil in sflist:\n", " # catching the source data matrix\n", " # please have a check for the columns names\n", " datagal = read_sources(ifil)\n", " Ngal = len(datagal[:,0]) # total number of galaxies in the source file\n", " # first two entries are ra and dec for the sources\n", " allragal = datagal[:,0]\n", " alldecgal = datagal[:,1]\n", " # ra and dec to x,y,z for sources\n", " allsx, allsy, allsz = get_xyz(allragal, alldecgal)\n", " # query in a ball around individual sources and collect the lenses ids with a maximum radius\n", " slidx = lens_tree.query_ball_point(np.transpose([allsx, allsy, allsz]), dismax) \n", " # various columns in sources \n", " # ragal, decgal, e1gal, e2gal, wgal, rms_egal, mgal, c1gal, c2gal, R2gal, zphotgal\n", " # looping over all the galaxies\n", " for igal in range(Ngal): \n", " ragal = datagal[igal,0]\n", " decgal = datagal[igal,1]\n", " e1gal = datagal[igal,2]\n", " e2gal = datagal[igal,3]\n", " wgal = datagal[igal,4]\n", " rms_egal = datagal[igal,5]\n", " zphotgal = datagal[igal,6]\n", " \n", " # array of lenses indices\n", " lidx = np.array(slidx[igal])\n", " # removing sources which doesn't have any lenses around them \n", " if len(lidx)==0:\n", " continue\n", " \n", " # selecting a cleaner background\n", " zcut = (lred[lidx] < (zphotgal - zdiff)) #only taking the foreground lenses\n", " # again skipping the onces which doesn't satisfy the above criteria\n", " if np.sum(zcut)==0.0:\n", " continue\n", " # collecting the data of lenses around individual source\n", " lidx = lidx[zcut] # this will catch the array indices for our lenses\n", " sra = ragal\n", " sdec = decgal\n", " \n", " l_ra = lra[lidx]\n", " l_dec = ldec[lidx]\n", " l_zred = lred[lidx] \n", " l_wgt = lwgt[lidx] \n", " \n", " sx, sy, sz = get_xyz(sra,sdec) # individual galaxy ra,dec-->x,y,z\n", " lx, ly, lz = get_xyz(l_ra,l_dec) # individual galaxy ra,dec-->x,y,z\n", " \n", " # getting the radial separations for a lense source pair \n", " sl_sep = np.sqrt((lx - sx)**2 + (ly - sy)**2 + (lz - sz)**2)\n", " sl_sep = sl_sep * cc.comoving_distance(l_zred).value\n", " for ll,sep in enumerate(sl_sep):\n", " if seprmax:\n", " continue\n", " rb = int(np.log10(sep*1.0/rmin)*1/rdiff)\n", " \n", " # get tangantial components given positions and shapes\n", " e_t = get_et(lra = l_ra[ll], ldec = l_dec[ll], sra = sra, sdec = sdec, se1 = e1gal, se2 = e2gal)\n", "\n", " # sigma_crit_calculations for a given lense-source pair\n", " sigm_crit_inv = get_sigma_crit_inv(l_zred[ll], zphotgal, cc)\n", "\n", " # following equations given in the surhud's lectures \n", " w_ls = l_wgt[ll] * wgal * (sigm_crit_inv)**2\n", " w_ls_by_av_sigc_inv = l_wgt[ll] * wgal * sigm_crit_inv\n", "\n", " # separate numerator and denominator computation \n", " sumdsig_num[rb] += w_ls_by_av_sigc_inv * e_t\n", " sumdsigsq_num[rb] += (w_ls_by_av_sigc_inv * e_t)**2\n", " sumwls[rb] += w_ls\n", " sumwls_resp[rb] += w_ls * (1-rms_egal**2)\n", "\n", " print(ifil)\n", " \n", " \n", " fout = open(outputfile, \"w\")\n", " fout.write(\"# 0:rmin/2+rmax/2 1:DeltaSigma 2:SN_ErrDeltaSigma\\n\")\n", " for i in range(len(rbins[:-1])):\n", " rrmin = rbins[i]\n", " rrmax = rbins[i+1]\n", " Resp = sumwls_resp[i]*1.0/sumwls[i]\n", " \n", " fout.write(\"%le\\t%le\\t%le\\n\"%(rrmin/2.0+rrmax/2.0, sumdsig_num[i]*1.0/sumwls[i]/2./Resp, np.sqrt(sumdsigsq_num[i])*1.0/sumwls[i]/2./Resp))\n", " fout.write(\"#OK\") \n", " fout.close()\n", " \n", " return 0" ] }, { "cell_type": "code", "execution_count": 61, "metadata": { "scrolled": false }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "number of lenses=560\n", "lenses tree is ready\n", "\n", "/home/idies/workspace/Storage/divyar/IAGRG_2022/DataStore/hsc/0000.dat\n", "/home/idies/workspace/Storage/divyar/IAGRG_2022/DataStore/hsc/0001.dat\n", 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\n", 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" ] }, "metadata": { "needs_background": "light" }, "output_type": "display_data" } ], "source": [ "dat = np.loadtxt('iagrg_dsigma.dat')\n", "\n", "plt.errorbar(dat[:,0], dat[:,1], yerr=dat[:,2], fmt='.', capsize=3, label='Data')\n", "plt.legend()\n", "\n", "plt.xlabel(r'$R[{\\rm h^{-1}Mpc}]$')\n", "plt.ylabel(r'$\\Delta\\Sigma [{\\rm h M_\\odot pc^{-2}}]$')\n", "plt.xscale('log')\n", "plt.yscale('log')" ] }, { "cell_type": "code", "execution_count": null, "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.7.3" } }, "nbformat": 4, "nbformat_minor": 4 }