Gen_inp_Periodic_box_ED.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "declared-woman",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from astropy.table import Table,join\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "earned-confidentiality",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_dir='./temp/ill_test/' #directory where files for corr_pc will be placed. I recommend using full path rather than relative path as here.\n",
    "file_name='ill_test_ED'  #prefix to be appended to the files being saved. Should be different for different datasets.\n",
    "\n",
    "os.makedirs(file_dir,exist_ok=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "plain-ecuador",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\" this function generates the input file for corr_pc\n",
    " corr_pc takes two samples to compute cross correlations. \n",
    " Shape sample: Sample of galaxies with shapes. For density correlations it is not necessary to pass shapes. \n",
    "               Prefix of filenames for this sample should be passed vias file_nameS\n",
    " Density sample: Sample of galaxies/particles that trace density field. For density correlations it is not necessary to pass shapes. \n",
    "                 If shapes are passed for this sample, w++ can be computed. Prefix of filenames for this sample should be passed vias file_nameD.\n",
    "                 If auto correlation is to be computed, simply set file_nameD=file_nameS.\n",
    " file_name: This is the prefix corr_pc will use to save the output files containing two point correlations. Input file to be passed to corr_pc is \n",
    "            also saved with this name. input file is saved in file_dir and the outputs are saved in new directory, file_dir+corr_data_out.\n",
    " corr: two point correlation to be computed. This should be density-density (wgg,wmm), shape-density (wg+) or shape-shape(w++). \n",
    "        When computing wg+, corr_pc will also compute and save wgg by default since pair counting is done anyways. \n",
    "        When doing w++, wg+ and wgg are also computed and saved.\n",
    " n_shape, n_density: number of galaxies in the shape sample and density sample respectively.\n",
    " do_jk: Whether to do jackknifing or not.\n",
    " njk: number of jackknife regions. jackknife regions are assumed to be labelled from 0...njk-1.\n",
    "\"\"\"\n",
    "def inp_gen(file_dir,file_name,file_nameS,file_nameD,corr=5,n_shape=0,n_density=0,njk=100,do_jk=1,Lbox=75):\n",
    "    s=\"\"\n",
    "    s=s+\"which_corr     \"+str(corr)+\"\\n\" #0: density-density, 1: ia: shape-density, 2 ia:shape-shape, 5: ED\n",
    "    s=s+\"coordinates    7\"+\"\\n\"  #6: rp-pi, 7:r-mu, \n",
    "    s=s+\"estimator      0\"+\"\\n\"  #0:landy-szalay (LS): cross. works for both auto and cross correlations. For auto, pass same filenames for shape and density samples \n",
    "                                 # 1: LS:auto, for auto correlation only\n",
    "    s=s+\"data_sorted     1\"+\"\\n\"  #0: no sorting, 1:sorted by z, 2:sorted by dec (y)\n",
    "    s=s+\"use_comoving    1\"+\"\\n\"\n",
    "    s=s+\"do_jk           \"+str(do_jk)+\"\\n\" #0: no jackknife, 1: do jackknife. jk regions must be passed in a file. are assumed to be from 0..njk-1\n",
    "    s=s+\"sig_crit        0\"+\"\\n\" #for weak lensing, not important for ia, gg corrs.\n",
    "\n",
    "    #following passes information about shape sample\n",
    "    s=s+\"shape_pos       \"+file_dir+file_nameS+'_pos.dat'+\"\\n\"  # x, y coordinates\n",
    "    s=s+\"shape_z         \"+file_dir+file_nameS+'_z.dat'+\"\\n\"    # z coordinate\n",
    "    s=s+\"shape_e         \"+file_dir+file_nameS+'_e.dat'+\"\\n\"    # e1, e2\n",
    "#     s=s+\"shape_wt         \"+file_dir+file_nameS+'_wt.dat'+\"\\n\"  # weights on galaxies. If no weighting, pass 0\n",
    "    s=s+\"shape_wt         0\"+\"\\n\"\n",
    "    s=s+\"shape_jk        \"+file_dir+file_nameS+'_jk.dat'+\"\\n\"   # jackknife regions\n",
    "\n",
    "    # following passes information about density sample\n",
    "    s=s+\"density_patch   0\"+\"\\n\" #for weak lensing, not important for ia, gg corrs.\n",
    "    s=s+\"density_pos     \"+file_dir+file_nameD+'_pos.dat'+\"\\n\"  # x, y coordinates\n",
    "    s=s+\"density_z       \"+file_dir+file_nameD+'_z.dat'+\"\\n\"    # z coordinate\n",
    "    s=s+\"density_wt       0\"+\"\\n\"  # weights on galaxies/particles. If no weighting, pass 0\n",
    "#     s=s+\"density_wt       \"+file_dir+file_nameD+'_wt.dat'+\"\\n\"  # weights on galaxies/particles. If no weighting, pass 0\n",
    "    s=s+\"density_jk      \"+file_dir+file_nameD+'_jk.dat'+\"\\n\"   # jackknife regions    \n",
    "    s=s+\"density_e      0\"+\"\\n\"   #if doing shape-shape, this file should be passed. # e1, e2\n",
    "\n",
    "    # following passes information about randoms corresponding to shape sample\n",
    "    s=s+\"Srandom_pos      0\"+\"\\n\"\n",
    "    s=s+\"Srandom_z        0\"+\"\\n\"\n",
    "    s=s+\"Srandoms_wt      0\"+\"\\n\"\n",
    "    s=s+\"Srand_jk         0\"+\"\\n\"\n",
    "\n",
    "    # following passes information about randoms corresponding to density sample\n",
    "    s=s+\"Drandom_patch    0\"+\"\\n\"\n",
    "    s=s+\"Drandom_pos      0\"+\"\\n\"\n",
    "    s=s+\"Drandom_z        0\"+\"\\n\"\n",
    "    s=s+\"Drandoms_wt      0\"+\"\\n\"\n",
    "    s=s+\"drand_jk         0\"+\"\\n\"\n",
    "\n",
    "    s=s+\"distances        0\"+\"\\n\"  #not needed for PB\n",
    "    s=s+\"patch_file       0\"+\"\\n\"  #not needed for PB\n",
    "    s=s+\"out_file         \"+file_dir+\"corr_data_out/\"+file_name+\"_\"+\"\\n\"  # prefix for the output files.\n",
    "    \n",
    "    os.makedirs(file_dir+\"corr_data_out/\",exist_ok=True) #directory where outputs are saved.\n",
    "    \n",
    "    s=s+\"n_threads       0\"+\"\\n\"   # number of openmp threads to use. 0== use maximum available.\n",
    "    s=s+\"n_shape         \"+str(n_shape)+\"\\n\"  #number of galaxies in shape sample\n",
    "    s=s+\"n_density       \"+str(n_density)+\"\\n\"  #number of galaxies in density sample\n",
    "    s=s+\"n_Srand         0\"+\"\\n\"\n",
    "    s=s+\"n_Drand         0\"+\"\\n\"\n",
    "    s=s+\"rand_subsample  0\"+\"\\n\"  #subsample randoms to speed up calculations.\n",
    "    s=s+\"n_jk          \"+str(njk)+\"\\n\"  #number of jk regions. regions are assumed to be labelled from 0....njk-1\n",
    "    s=s+\"n_patch         0 \"+\"\\n\"#not needed for PB\n",
    "\n",
    "    s=s+\"binR_min        0.1\"+\"\\n\"  # rp_min for the bins\n",
    "    s=s+\"binR_max       10\"+\"\\n\" # rp_max for the bins\n",
    "    s=s+\"n_bins         10\"+\"\\n\" #number of rp bins to use\n",
    "    s=s+\"lin_bin         0\"+\"\\n\"  #linear or log bins. 0: log, 1:linear\n",
    "    s=s+\"n_p_bin         1\"+\"\\n\"  #number of pi bins to use. When using r-mu, pi==mu. \n",
    "    s=s+\"pmin         -1\"+\"\\n\"  # pi_min (when mu, mu_min=-1)\n",
    "    s=s+\"pmax         1\"+\"\\n\"   # pi_max (mu_max=1)\n",
    "    s=s+\"z_min         0\"+\"\\n\"    \n",
    "    s=s+\"z_max         300\"+\"\\n\" \n",
    "    s=s+\"dz            0.0001\"+\"\\n\" #not needed for PB\n",
    "    s=s+\"z_sep_min     -100\"+\"\\n\"  \n",
    "    s=s+\"z_sep_max     100\"+\"\\n\"   \n",
    "    s=s+\"periodic_box  1\"+\"\\n\"\n",
    "    s=s+\"box_size     \"+str(Lbox)+\"\\n\"  #size of the box.\n",
    "\n",
    "    fil=open(file_dir+file_name+'.inp','w')\n",
    "    fil.write(s)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "willing-billion",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "This function splits the sim box into a grid in x-y plane. Number of grid splits along each axis are given by njk1, for a total of njk1^2 sub-sample regions.\n",
    "After this, each point in data (galaxy or particle) is assigned to its sub-sample.\n",
    "\"\"\"\n",
    "def jk_box(data=[],Lbox=None,njk1=7):\n",
    "    if Lbox is None:\n",
    "        Lbox=np.ceil(max(data['x']))\n",
    "        print('jk_box using Lbox=',Lbox)\n",
    "    xjk=np.linspace(0,Lbox,njk1+1)\n",
    "    yjk=np.linspace(0,Lbox,njk1+1)\n",
    "    jk=np.zeros(len(data))\n",
    "    jjk=0\n",
    "    for i in np.arange(njk1):\n",
    "        xil=xjk[i]\n",
    "        xih=xjk[i+1]\n",
    "        x1=data['x']>xil\n",
    "        x2=data['x']<xih\n",
    "        for j in np.arange(njk1):\n",
    "            yil=yjk[j]\n",
    "            yih=yjk[j+1]\n",
    "            y1=data['y']>yil\n",
    "            y2=data['y']<=yih\n",
    "            jk[x1*x2*y1*y2]=jjk\n",
    "            jjk+=1\n",
    "#     jk=np.array(jk,dtype=[('jk','int32')])\n",
    "    return jk\n"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3a3e9016",
   "metadata": {},
   "source": [
    "# ill_dir='/hildafs/datasets/IllustrisTNG/TNG300-1/TNG300-1_particle_data/'\n",
    "ill_dir='/hildafs/datasets/IllustrisTNG/TNG300-1/shape_catalogues/'\n",
    "# S_dat_name='TNG300-1_50_non-reduced_galaxy_shapes' \n",
    "S_dat_name='TNG300-1_99_non-reduced_galaxy_shapes'\n",
    "S_dat_name2='TNG300-1_50_vagc'\n",
    "D_dat_name=S_dat_name\n",
    "\n",
    "#read in shape sample\n",
    "fname=ill_dir+S_dat_name+'.dat'\n",
    "fname2=ill_dir+S_dat_name2+'.dat'\n",
    "S_dat=Table(np.genfromtxt(fname,names=True))\n",
    "S_dat2=Table(np.genfromtxt(fname2,names=True))\n",
    "\n",
    "S_dat3=join(S_dat,S_dat2,keys='gal_id')\n",
    "\n",
    "len(S_dat3),len(S_dat2),len(S_dat)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d3e19391",
   "metadata": {},
   "source": [
    "S_dat=S_dat3\n",
    "\n",
    "S_dat['x']/=1000.\n",
    "S_dat['y']/=1000.\n",
    "S_dat['z']/=1000."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "a6e1691b",
   "metadata": {},
   "outputs": [],
   "source": [
    "fname='/hildafs/datasets/IllustrisTNG/TNG100-1/TNG100-1_99_non-reduced_galaxy_shapes_multi_scale_1024_MLP_only_cent.pkl'\n",
    "with open(fname, \"rb\") as f:\n",
    "    S_dat=pickle.load(f)\n",
    "S_dat['x']=S_dat['gal_pos_x']\n",
    "S_dat['y']=S_dat['gal_pos_y']\n",
    "S_dat['z']=S_dat['gal_pos_z']"
   ]
  },
  {
   "cell_type": "raw",
   "id": "3f62bc62",
   "metadata": {},
   "source": [
    "S_dat=S_dat[:2]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "3d305192",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0038732943243422443"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_dat['a'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "agricultural-commissioner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jk_box using Lbox= 75.0\n"
     ]
    }
   ],
   "source": [
    "\n",
    "# S_dat = Table.read(fname)\n",
    "S_dat['jk'] = jk_box(data=S_dat)\n",
    "S_dat.sort('z')\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "5bc0c572",
   "metadata": {},
   "outputs": [],
   "source": [
    "S_dat['e1']=S_dat['dm_av_x']\n",
    "S_dat['e2']=S_dat['dm_av_y']\n",
    "S_dat['e3']=S_dat['dm_av_z']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "fdaf4cdb",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "&lt;Column name=&apos;e1&apos; dtype=&apos;float64&apos; length=50737&gt;\n",
       "<table>\n",
       "<tr><td>0.9999999999999997</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
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       "<tr><td>1.0</td></tr>\n",
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       "<tr><td>1.0000000000000002</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>1.0000000000000009</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>0.9999999999999999</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>0.9999999999999998</td></tr>\n",
       "<tr><td>0.9999999999999998</td></tr>\n",
       "<tr><td>1.0000000000000002</td></tr>\n",
       "<tr><td>1.0000000000000007</td></tr>\n",
       "<tr><td>1.0000000000000009</td></tr>\n",
       "<tr><td>0.9999999999999998</td></tr>\n",
       "<tr><td>0.9999999999999999</td></tr>\n",
       "<tr><td>...</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
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       "<tr><td>1.0000000000000004</td></tr>\n",
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       "<tr><td>1.0</td></tr>\n",
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       "<tr><td>0.9999999999999998</td></tr>\n",
       "<tr><td>1.0000000000000002</td></tr>\n",
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       "<tr><td>1.0000000000000002</td></tr>\n",
       "<tr><td>1.0000000000000007</td></tr>\n",
       "<tr><td>1.0000000000000007</td></tr>\n",
       "<tr><td>1.0000000000000002</td></tr>\n",
       "<tr><td>1.0000000000000002</td></tr>\n",
       "<tr><td>1.0000000000000002</td></tr>\n",
       "<tr><td>1.0</td></tr>\n",
       "<tr><td>1.0000000000000004</td></tr>\n",
       "<tr><td>1.0000000000000007</td></tr>\n",
       "<tr><td>0.9999999999999998</td></tr>\n",
       "<tr><td>1.0000000000000004</td></tr>\n",
       "<tr><td>1.0000000000000004</td></tr>\n",
       "</table>"
      ],
      "text/plain": [
       "<Column name='e1' dtype='float64' length=50737>\n",
       "0.9999999999999997\n",
       "               1.0\n",
       "1.0000000000000004\n",
       "               1.0\n",
       "0.9999999999999996\n",
       "               1.0\n",
       "0.9999999999999999\n",
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       "               1.0\n",
       "1.0000000000000009\n",
       "               1.0\n",
       "0.9999999999999999\n",
       "               1.0\n",
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       "               ...\n",
       "               1.0\n",
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       "0.9999999999999996\n",
       "0.9999999999999999\n",
       "0.9999999999999998\n",
       "1.0000000000000004\n",
       " 0.999999999999999\n",
       "               1.0\n",
       "1.0000000000000007\n",
       "0.9999999999999998\n",
       "1.0000000000000002\n",
       "0.9999999999999996\n",
       "1.0000000000000002\n",
       "1.0000000000000007\n",
       "1.0000000000000007\n",
       "1.0000000000000002\n",
       "1.0000000000000002\n",
       "1.0000000000000002\n",
       "               1.0\n",
       "1.0000000000000004\n",
       "1.0000000000000007\n",
       "0.9999999999999998\n",
       "1.0000000000000004\n",
       "1.0000000000000004"
      ]
     },
     "execution_count": 9,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_dat['e1']**2+S_dat['e2']**2+S_dat['e3']**2"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b5103dbe",
   "metadata": {},
   "source": [
    "S_dat=S_dat[:1000]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "c87b6e0f",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lbox=S_dat['x'].max()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "05144736",
   "metadata": {},
   "outputs": [],
   "source": [
    "Lbox=75"
   ]
  },
  {
   "cell_type": "raw",
   "id": "a08f2d67",
   "metadata": {},
   "source": [
    "# read in density sample.\n",
    "fname=ill_dir+D_dat_name+'_coords.p'\n",
    "with open(fname, \"rb\") as input_file:\n",
    "    D_dat = Table(pickle.load(input_file)/1000,names=('x','y','z'))\n",
    "fname=ill_dir+D_dat_name+'_masses.p'\n",
    "with open(fname, \"rb\") as input_file:\n",
    "    D_dat['mass'] = pickle.load(input_file)\n",
    "D_dat['jk'] = jk_box(data=D_dat)\n",
    "D_dat.sort('z')\n",
    "\n",
    "D_dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "d6e2ff6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "D_dat=S_dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "atlantic-italic",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "True"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "'e1' in S_dat.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "another-reunion",
   "metadata": {},
   "outputs": [],
   "source": [
    "\"\"\"\n",
    "Function to write data files in format that can be read by corr_pc\n",
    "\"\"\"\n",
    "def write_data(data,file_name,file_dir,wt_col=None):\n",
    "    file_name=file_dir+file_name\n",
    "    np.savetxt(file_name+'_pos.dat',np.column_stack((data['x'],data['y']))) #x,y position of galaxies\n",
    "    np.savetxt(file_name+'_z.dat',data['z']) # z position of galaxies\n",
    "    if 'jk' in data.columns:\n",
    "        np.savetxt(file_name+'_jk.dat',data['jk'],fmt='%i') #jackknife is read as integer\n",
    "    if 'e1' in data.columns: #ellipticities. corr_pc takes e1, e2\n",
    "        np.savetxt(file_name+'_e.dat',np.column_stack((data['e1'],data['e2'],data['e3'])))\n",
    "    if wt_col is not None: #wt to be applied to each galaxy/particle.\n",
    "        wt=data[wt_col]*1.\n",
    "        wt/=wt.mean()\n",
    "        np.savetxt(file_name+'_wt.dat',wt)"
   ]
  },
  {
   "cell_type": "raw",
   "id": "b2473987",
   "metadata": {},
   "source": [
    "D_dat=Table()\n",
    "D_dat['x']=np.random.uniform(0,Lbox,size=len(S_dat))\n",
    "D_dat['y']=np.random.uniform(0,Lbox,size=len(S_dat))\n",
    "D_dat['z']=np.random.uniform(0,Lbox,size=len(S_dat))\n",
    "D_dat['jk'] = jk_box(data=D_dat)\n",
    "D_dat.sort('z')\n",
    "file_nameD=file_name+'_density_rand'\n",
    "write_data(data=D_dat,file_name=file_nameD,file_dir=file_dir)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "infectious-highlight",
   "metadata": {},
   "outputs": [],
   "source": [
    "file_nameS=file_name+'_shape'\n",
    "write_data(data=S_dat,file_name=file_nameS,file_dir=file_dir,)#wt_col='mass')\n",
    "\n",
    "# file_nameD=file_name+'_density_rand'\n",
    "# write_data(data=D_dat,file_name=file_nameD,file_dir=file_dir)\n",
    "file_nameD=file_nameS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "double-speaker",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "S_dat['jk'].min()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "37cde861",
   "metadata": {},
   "outputs": [],
   "source": [
    "d={}\n",
    "sep=0\n",
    "cos1=0\n",
    "cos2=0\n",
    "j=1\n",
    "for i in ['x','y','z']:\n",
    "    d[i]=(S_dat[i][1]-S_dat[i][0])\n",
    "    sep+=d[i]**2\n",
    "    cos1+=d[i]*S_dat['e'+str(j)][0]\n",
    "    cos2-=d[i]*S_dat['e'+str(j)][1]\n",
    "    j+=1\n",
    "sep=np.sqrt(sep)\n",
    "cos1/=sep\n",
    "cos2/=sep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "8a7cc9bc",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "40.588123285268615"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "sep"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "2f0ab1b4",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'x': -11.429977416992188, 'y': 38.94549226760864, 'z': 0.0003175664460286498}"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "d"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "9570655e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.7741269309884793"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "522ee5bd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.022733816938238196"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cos2"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6ddae91e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "-0.03343866747622176"
      ]
     },
     "execution_count": 22,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "(cos1**2+cos2**2)/2-1./3"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "afa1d28e",
   "metadata": {},
   "outputs": [],
   "source": [
    "do_jk=1"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "efficient-anniversary",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp_gen(file_dir=file_dir,file_name=file_name,file_nameS=file_nameS,file_nameD=file_nameD,corr=5,n_shape=len(S_dat),n_density=len(D_dat),\n",
    "        njk=int(S_dat['jk'].max()+1),do_jk=do_jk,Lbox=Lbox) \n",
    "#corr=0 will only do wgg like correlation, no shapes. set corr=1 for wg+ and wgg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "atmospheric-proxy",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1125 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=hist(S_dat['jk'],int(S_dat['jk'].max()+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "5d81ff15",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 1500x1125 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=hist(D_dat['jk'],int(D_dat['jk'].max()+1))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "9dff0b22",
   "metadata": {},
   "outputs": [],
   "source": [
    "x=np.arange(10)\n",
    "y=np.tile(x,3).reshape(3,10).T"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "1b1c101f",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([[0, 0, 0],\n",
       "       [1, 1, 1],\n",
       "       [2, 2, 2],\n",
       "       [3, 3, 3],\n",
       "       [4, 4, 4],\n",
       "       [5, 5, 5],\n",
       "       [6, 6, 6],\n",
       "       [7, 7, 7],\n",
       "       [8, 8, 8],\n",
       "       [9, 9, 9]])"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "bacbe517",
   "metadata": {},
   "outputs": [],
   "source": [
    "import random"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "fdc47c1a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "[array([3, 3, 3]), array([7, 7, 7]), array([5, 5, 5])]"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "random.sample(list(y),3)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "3fc726ce",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "514338a8",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "intel",
   "language": "python",
   "name": "intel"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.9.7"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}