{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Creating simulations using `deepszsim`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This tutorial provides instructions on how to create galaxy cluster simulations with `deepszsim`. For a step-by-step guide to the functions included in the simulation, see `demo_full_pipeline.ipynb`"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "id": "lOkPyRZEQWsm"
   },
   "source": [
    "## Imports"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "id": "lOkPyRZEQWsm"
   },
   "outputs": [],
   "source": [
    "from deepszsim import make_sz_cluster, dm_halo_dist, visualization, simclusters\n",
    "\n",
    "import time\n",
    "import h5py"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "## Creating simulations by hand"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Create a new flat redshift and virial mass distribution using the function `flastdist_halo` from `dm_halo_dist` which uses random uniform generation. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "#Generate a new flat z, Mvir distribution and save to file: \n",
    "nsources=10 #Number of halos to generate\n",
    "zdist,mdist=dm_halo_dist.flatdist_halo(0.01,1.1,1e14,2e15,nsources) #Generate a flat z, Mvir distribution for sims"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "scrolled": true
   },
   "source": [
    "Save our simulated data to a h5 file titled `massdist.h5`. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [],
   "source": [
    "sourceid=int(time.time()) #Create an initial ctime for the halo ID list to save catalog\n",
    "idlist=[sourceid+x for x in range(len(zdist))] #Create the halo ID list for catalog\n",
    "\n",
    "#Save this array to a h5 file\n",
    "with h5py.File('massdist.h5', 'w') as data:\n",
    "    data.create_dataset('Redshift', data=zdist)\n",
    "    data.create_dataset('Mass', data=mdist)\n",
    "    data.create_dataset('id', data=idlist)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now simulate submaps using our mass and redshift distribution using the `simulate_T_submaps` function from `make_sz_cluster`. This function returns an array of dicts with the following attributes (each dict contains the full information of each sim/cluster):\n",
    "- M200\n",
    "- R200\n",
    "- redshift_z\n",
    "- y_central\n",
    "- ID\n",
    "- cmb_map\n",
    "- noise_map\n",
    "- final_map"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "clusters = simclusters.simulate_clusters(mdist, zdist)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "10"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "len(clusters.id_list)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Creating simulations automatically"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Alternately, we can do everything from soup to nuts with the `simulate_clusters` class"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/sammcd/micromamba/envs/szsims/lib/python3.11/site-packages/deepszsim/filters.py:38: RuntimeWarning: Mean of empty slice.\n",
      "  ring_mean = dT_map[(r >= radmax_pixels) & (r < radius_out_pixels)].mean()\n",
      "/Users/sammcd/micromamba/envs/szsims/lib/python3.11/site-packages/numpy/core/_methods.py:129: RuntimeWarning: invalid value encountered in scalar divide\n",
      "  ret = ret.dtype.type(ret / rcount)\n"
     ]
    }
   ],
   "source": [
    "_ = clusters.get_T_maps()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This class instance populates an attribute named `clusters`, which is a dictionary. This dictionary is indexed by a run ID, and each of its values is itself a dictionary, which in turn contains two more dictionaries: `params` and `maps`"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['0822711_021_556314',\n",
       " '0552296_070_281581',\n",
       " '1372133_084_806690',\n",
       " '1371424_008_408584',\n",
       " '1998618_075_449070',\n",
       " '0198589_065_786397',\n",
       " '1525903_108_312178',\n",
       " '1124602_018_031953',\n",
       " '1846569_059_568057',\n",
       " '0517993_041_352332']"
      ]
     },
     "execution_count": 7,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "k10 = list(clusters.clusters.keys())\n",
    "k10"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "({'M200': 822711781329317.2,\n",
       "  'redshift_z': 0.2176700775626845,\n",
       "  'R200': 1.8181656839508509,\n",
       "  'angsize_arcmin': 5.5262467021573425,\n",
       "  'angsize500_arcmin': 5.5262467021573425,\n",
       "  'image_size_pixels': 41,\n",
       "  'dT_central': -242.36963689525484,\n",
       "  'ap': -45.38114063656835},\n",
       " dict_keys(['conv_map', 'CMB_map', 'signal_map', 'beamsig_map', 'final_map']))"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clusters.clusters[k10[0]]['params'], clusters.clusters[k10[0]]['maps'].keys()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "this class instance has a `save_map` method that can save individual maps as specified, or all clusters into a single nested file with `nest_h5 = True`, or all clusters individually"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "# c10.save_map()"
   ]
  }
 ],
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