Gen_inp_Periodic_box-size.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "declared-woman",
   "metadata": {},
   "outputs": [],
   "source": [
    "import os\n",
    "from astropy.table import Table\n",
    "import pickle"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "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_kappa'  #prefix to be appended to the files being saved. Should be different for different datasets.\n",
    "\n",
    "os.makedirs(file_dir,exist_ok=True)\n",
    "\n",
    "# from within corr_pc directory, you can run corr_pc as \n",
    "# ./corr_pc file_dir/file_name.inp\n",
    "# For example:\n",
    "# ./corr_pc ./temp/ill_test/ill_test_kappa.inp"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "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=3,n_shape=0,n_density=0,njk=100,do_jk=1):\n",
    "    s=\"\"\n",
    "    s=s+\"which_corr     \"+str(corr)+\"\\n\" #0: density-density, 1: ia: shape-density, 2 ia:shape-shape, 3:kappa-density\n",
    "    s=s+\"coordinates    6\"+\"\\n\"  #0: rp-pi\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     0\"+\"\\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+'_kappa.dat'+\"\\n\"    # e1, e2 or kappa\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         20\"+\"\\n\"  #number of pi bins to use\n",
    "    s=s+\"pmin         -25\"+\"\\n\"  # pi_min (line of sight or z separation)\n",
    "    s=s+\"pmax         25\"+\"\\n\"   # pi_max (line of sight or z separation)\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     75\"+\"\\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": 5,
   "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": "code",
   "execution_count": 6,
   "id": "focal-concert",
   "metadata": {},
   "outputs": [],
   "source": [
    "ill_dir='/hildafs/datasets/IllustrisTNG/TNG100-1/' #path to the illustris data"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "environmental-least",
   "metadata": {},
   "outputs": [],
   "source": [
    "S_dat_name='TNG100-1_99_galaxy_component_shapes_3I_subfind_mass-cut9' #filenames within illustris from which to read data\n",
    "D_dat_name='TNG100-1_99_galaxy_component_shapes_3I_subfind_mass-cut9'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "7dd7fe91",
   "metadata": {},
   "outputs": [],
   "source": [
    "#read in shape sample\n",
    "fname=ill_dir+S_dat_name+'.p'\n",
    "with open(fname, \"rb\") as input_file:\n",
    "    S_dat =pickle.load(input_file)\n",
    "S_dat=Table(S_dat) #astropy table format, see to add new columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "c73b3097",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define x,y,z columns for cordinates. We will use the names x,y,z, below, instead of gal_pos_x etc. Different versions of the catalog have different naming conventions, please check.\n",
    "# We will also use units of Mpc instead of Kpc, hence division by 1000\n",
    "S_dat['x']=float64(S_dat['gal_pos_x']/1000)\n",
    "S_dat['y']=float64(S_dat['gal_pos_y']/1000)\n",
    "S_dat['z']=float64(S_dat['gal_pos_z']/1000)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "agricultural-commissioner",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "jk_box using Lbox= 75.0\n"
     ]
    }
   ],
   "source": [
    "#get jackknife regions for all the galaxies. \n",
    "S_dat['jk'] = jk_box(data=S_dat)\n",
    "S_dat.sort('z')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "ffdec25e",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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+AAAAAAAAgEfo8AMAAAAAAAA8QocfAAAAAAAA4BE6/AAAAAAAAACP0OEHAAAAAAAAeOT/BzCo5CRaHfteAAAAAElFTkSuQmCC\n",
      "text/plain": [
       "<Figure size 1500x1125 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=hist2d(S_dat['y'],S_dat['z'],1000,norm=LogNorm())"
   ]
  },
  {
   "cell_type": "raw",
   "id": "d637dd82",
   "metadata": {},
   "source": [
    "# read in density sample, if different from shape sample. Do this if density and shape sample have different mass cuts etc.\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": "69b054ee",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we will assume density and shape sample to be same, hence following can be done.\n",
    "D_dat=S_dat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "atlantic-italic",
   "metadata": {},
   "outputs": [],
   "source": [
    "#define the column that will be used for the cross correlation quantity, e.g. size, mass, etc. \n",
    "S_weight_column='mass'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "7af25bd2",
   "metadata": {},
   "outputs": [],
   "source": [
    "#we define a new column kappa, which will contain the \"processed\" version of weight column defined above. The \"processed\" here could be mean subtraction, renormalization or taking log of the quantity.\n",
    "#Remeber to do this on the final version of catalog, after all the cuts have been applied since that can affect the mean, etc.\n",
    "# For mass, we will take log to prevent few massive galaxies from dominating the measurements.\n",
    "S_dat['kappa']=np.log10(S_dat[S_weight_column])\n",
    "S_dat['kappa']-=S_dat['kappa'].mean()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "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 'kappa' in data.columns: #ellipticities. corr_pc takes e1, e2\n",
    "        np.savetxt(file_name+'_kappa.dat',data['kappa'])\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": "code",
   "execution_count": 16,
   "id": "infectious-highlight",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Write shape data to files\n",
    "file_nameS=file_name+'_shape'\n",
    "write_data(data=S_dat,file_name=file_nameS,file_dir=file_dir,)#wt_col='mass')\n",
    "\n",
    "# Write density data to files. Only necessary if the two catalogs are different\n",
    "# file_nameD=file_name+'_density'\n",
    "# write_data(data=D_dat,file_name=file_nameD,file_dir=file_dir,wt_col='mass')\n",
    "file_nameD=file_nameS"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "efficient-anniversary",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Generate the input file. Corr_pac will be called using this file. \n",
    "inp_gen(file_dir=file_dir,file_name=file_name,file_nameS=file_nameS,file_nameD=file_nameD,corr=3,n_shape=len(S_dat),n_density=len(D_dat),\n",
    "        njk=int(S_dat['jk'].max()+1),do_jk=1) \n",
    "#corr=0 will only do wgg like correlation, no shapes. set corr=1 for wg+ and wgg"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "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": 19,
   "id": "3fc726ce",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_mean(xvar='',yvar='',data=[],data_cuts=None,rms=0,bins=None,N_bins=20,x_min=None,x_max=None,xlog=False,**kwargs):\n",
    "    \"\"\"\n",
    "    Compute the mean of y in bins of x.\n",
    "    \n",
    "    Arguments\n",
    "    ----------------\n",
    "    xvar: name of the x variable column\n",
    "    yvar: name of the y variable column\n",
    "    data: Dataset, can be a dict, astropy or pandas table or numpy named array\n",
    "    data_cuts: Any cuts to be applied on data. Boolean array. Useful for jackknife calculations\n",
    "    rms: if non-zero, compute rms instead of mean. quantity returned is <y**(rms+1)>^{1./(rms+1)}. rms can be any number.\n",
    "    bins: bin edges for the x axis to use. If None, we can compute it here using following arguments\n",
    "    N_bins: Number of bins\n",
    "    x_min: minimum value of x to use. If None, minimum from data[xvar] will be used\n",
    "    x_max: maximum value of x to use. If None, maximum from data[xvar] will be used\n",
    "    xlog: If True, use log bins in x. Otherwise linear bins are used.\n",
    "    \n",
    "    Returns:\n",
    "    bins: bin edges\n",
    "    bc: Bin centers\n",
    "    mean_y: mean value of y within bins\n",
    "    \"\"\"\n",
    "    datx=data[xvar]\n",
    "    daty=data[yvar]\n",
    "    if data_cuts is not None:\n",
    "        datx=datx[data_cuts]\n",
    "        daty=daty[data_cuts]\n",
    "    if bins is None:\n",
    "        if x_min is None:\n",
    "            x_min=min(datx)\n",
    "        if x_max is None:\n",
    "            x_max=max(datx)\n",
    "        if xlog:\n",
    "            bins=np.logspace(np.log10(x_min),np.log10(x_max),N_bins+1)\n",
    "        else:\n",
    "            bins=np.linspace(x_min,x_max,N_bins+1)\n",
    "    x_c,_=np.histogram(datx,bins=bins)\n",
    "    x_c=np.float64(x_c)\n",
    "    y_c,_=np.histogram(datx,weights=(daty**(rms+1.))/(rms+1),bins=bins)\n",
    "    mean_y=y_c/x_c\n",
    "    mean_y=mean_y**(1./(rms+1.))\n",
    "    bc=0.5*(bins[1:]+bins[:-1])\n",
    "    return bins,bc,mean_y"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "c84cd0d8",
   "metadata": {},
   "outputs": [],
   "source": [
    "def calc_mean_jk(mean_func=calc_mean,jk_col='jk',njk=None,xvar='',yvar='',data=[],data_cuts=None,rms=0,bins=None,N_bins=20,x_min=None,x_max=None,xlog=False,**kwargs):\n",
    "    \"\"\"\n",
    "    Compute the mean of y in bins of x, for every jackknife region. Returns a dict with calclations for all jackknife regions as well as the jackknife mean and covariance.\n",
    "    Arguments\n",
    "    ----------------\n",
    "    mean_func=calc_mean: function to use for computing mean values. \n",
    "    jk_col: jackknife column name\n",
    "    remaining arguments are same as calc_mean function above.\n",
    "    \n",
    "    returns:\n",
    "    \n",
    "    \"\"\"\n",
    "    if njk is None:\n",
    "        njk=max(data[jk_col])+1\n",
    "    njk=int(njk)\n",
    "    jkr=np.arange(njk)\n",
    "    means={}\n",
    "    bins,bins_center,means['full']=mean_func(xvar=xvar,yvar=yvar,data=data,data_cuts=data_cuts,rms=rms,bins=bins,N_bins=N_bins,x_min=x_min,x_max=x_max,xlog=xlog,**kwargs)\n",
    "    mean=np.zeros((njk,len(bins_center)))\n",
    "    dat_jk=data[jk_col]\n",
    "    for i in jkr:\n",
    "        x=dat_jk!=i\n",
    "        if data_cuts is not None:\n",
    "            x=np.logical_and(x,data_cuts)\n",
    "        bins,bins_center,mean[i]=mean_func(xvar=xvar,yvar=yvar,data=data,data_cuts=x,rms=rms,bins=bins,N_bins=N_bins,x_min=x_min,x_max=x_max,xlog=xlog,**kwargs)\n",
    "    means['regions']=mean\n",
    "    means['jk_mean']=np.mean(mean,axis=0)\n",
    "    means['jk_var']=np.var(mean,axis=0)*(njk-1)*(njk-1)/njk\n",
    "    means['jk_err']=np.sqrt(means['jk_var'])\n",
    "    return bins,bins_center,means"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "15f70724",
   "metadata": {},
   "outputs": [],
   "source": [
    "bins,bc,m=calc_mean_jk(jk_col='jk',data=S_dat,xvar='mass',yvar='f_disk',xlog=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "9c976452",
   "metadata": {
    "scrolled": false
   },
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x7fb14be064c0>"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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      "text/plain": [
       "<Figure size 1500x1125 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "_=hist2d(np.float64(S_dat['mass']),S_dat['f_disk'],bins=[np.float64(bins),np.linspace(0,0.7,15)],norm=LogNorm(),cmap='Reds')\n",
    "errorbar(bc,m['jk_mean'],m['jk_err'])\n",
    "xscale('log')\n",
    "xlabel('mass')\n",
    "ylabel('f-disk')\n",
    "colorbar()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bf23d9eb",
   "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
}