User_selected_params.ipynb

{
 "cells": [
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import os",
   "execution_count": 1,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "survey_file = os.path.join('/Users/rbiswas/data/ztf/ObsTables/', 'test_schedule_v6.db')",
   "execution_count": 2,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "sfd98_dir = os.path.join('/Users/rbiswas/data/MWDUST')\n\n# This does not give the program access  but is the dir which contains \n!ls /Users/rbiswas/data/MWDUST/",
   "execution_count": 3,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Fitzpatrick99.README     SFD_dust_4096_ngp.fits   SFD_mask_4096_sgp.fits\r\nFitzpatrick99_xt_3.1.txt SFD_dust_4096_sgp.fits   SFD_temp_ngp.fits\r\nFitzpatrick99_xt_3.2.txt SFD_i100_4096_ngp.fits   SFD_temp_sgp.fits\r\nFitzpatrick99_xt_3.3.txt SFD_i100_4096_sgp.fits   SFD_xmap_ngp.fits\r\nNOTES                    SFD_mask_4096_ngp.fits   SFD_xmap_sgp.fits\r\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import numpy as np\nimport matplotlib.pyplot as plt\n%matplotlib inline\n\nimport simsurvey\nimport sncosmo\nfrom astropy.cosmology import Planck15\n\nimport simsurvey_tools as sst ",
   "execution_count": 4,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "# Load the ZTF CCD corners and filters\nccds = sst.load_ztf_ccds()\nsst.load_ztf_filters()",
   "execution_count": 5,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "# Load simulated survey from file (download from ftp://ftp.astro.caltech.edu/users/ebellm/one_year_sim_incomplete.db)\n# Currently DES filters are used as proxies for ZTF filters\nplan = simsurvey.SurveyPlan(load_opsim=survey_file, band_dict={'g': 'ztfg', 'r': 'ztfr', 'i': 'desi'}, ccds=ccds)\n\nmjd_range = (plan.cadence['time'].min() - 30, plan.cadence['time'].max() + 30)",
   "execution_count": 6,
   "outputs": []
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "RB: Can't we use ztfi now?"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "mjd_range",
   "execution_count": 7,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 7,
     "data": {
      "text/plain": "(57403.16732874228, 57736.41649200727)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "plan.pointings.to_pandas().head()",
   "execution_count": 8,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 8,
     "data": {
      "text/plain": "           time  band  zp     skynoise        RA    Dec  field  ccd  comment\n0  57433.167329  ztfr  30  1235.458255  97.27032 -24.25    258  NaN  all_sky\n1  57433.167792  ztfr  30  1032.415561  93.69218 -17.05    307  NaN  all_sky\n2  57433.168255  ztfr  30   831.160655  96.84022  -9.85    358  NaN  all_sky\n3  57433.168749  ztfr  30   768.014326  89.36968   4.55    459  NaN  all_sky\n4  57433.169220  ztfr  30   754.615141  85.58463  11.75    511  NaN  all_sky",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>time</th>\n      <th>band</th>\n      <th>zp</th>\n      <th>skynoise</th>\n      <th>RA</th>\n      <th>Dec</th>\n      <th>field</th>\n      <th>ccd</th>\n      <th>comment</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>57433.167329</td>\n      <td>ztfr</td>\n      <td>30</td>\n      <td>1235.458255</td>\n      <td>97.27032</td>\n      <td>-24.25</td>\n      <td>258</td>\n      <td>NaN</td>\n      <td>all_sky</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>57433.167792</td>\n      <td>ztfr</td>\n      <td>30</td>\n      <td>1032.415561</td>\n      <td>93.69218</td>\n      <td>-17.05</td>\n      <td>307</td>\n      <td>NaN</td>\n      <td>all_sky</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>57433.168255</td>\n      <td>ztfr</td>\n      <td>30</td>\n      <td>831.160655</td>\n      <td>96.84022</td>\n      <td>-9.85</td>\n      <td>358</td>\n      <td>NaN</td>\n      <td>all_sky</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>57433.168749</td>\n      <td>ztfr</td>\n      <td>30</td>\n      <td>768.014326</td>\n      <td>89.36968</td>\n      <td>4.55</td>\n      <td>459</td>\n      <td>NaN</td>\n      <td>all_sky</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>57433.169220</td>\n      <td>ztfr</td>\n      <td>30</td>\n      <td>754.615141</td>\n      <td>85.58463</td>\n      <td>11.75</td>\n      <td>511</td>\n      <td>NaN</td>\n      <td>all_sky</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "sncosmo.get_source('hsiao', version='3.0')\np, w, f = sncosmo.read_griddata_fits(\n    os.path.join(sncosmo.builtins.get_cache_dir(),\n    'sncosmo/models/hsiao/Hsiao_SED_V3.fits')\n)\nmodel = sncosmo.Model(\n    source=sncosmo.StretchSource(p, w, f, name='hsiao-stretch'),\n    effects=[sncosmo.CCM89Dust()],\n    effect_names=['host'],\n    effect_frames=['rest']\n)",
   "execution_count": 9,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "def random_parameters(redshifts, model,\n                      mag=(-18., 1.),\n                      r_v=2., ebv_rate=0.11,\n                      alpha=1.3,\n                      cosmo=Planck15,\n                      **kwargs):\n    \"\"\"\n    \"\"\"\n    out = {}\n\n    amp = []\n    for z in redshifts:\n        mabs = np.random.normal(mag[0], mag[1])\n        model.set(z=z)\n        model.set_source_peakabsmag(mabs, 'bessellb', 'vega', cosmo=cosmo)\n        amp.append(model.get('amplitude'))\n\n    out['amplitude'] = np.array(amp)\n    out['hostr_v'] = r_v * np.ones(len(redshifts))\n    out['hostebv'] =  np.random.exponential(ebv_rate, len(redshifts))\n\n    out['s'] = np.random.normal(1., 0.1, len(redshifts))\n    out['amplitude'] *= 10 ** (0.4 * alpha * (out['s'] - 1))\n\n    return out\n",
   "execution_count": 10,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "transientprop = {\n    'lcmodel': model,\n    'lcsimul_func': random_parameters# ,\n    # 'lcsimul_prop': {'mag': (-19.3, 0.1)}\n}\n",
   "execution_count": 11,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "model.param_names",
   "execution_count": 12,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 12,
     "data": {
      "text/plain": "['z', 't0', 'amplitude', 's', 'hostebv', 'hostr_v']"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "transientprop['lcsimul_func'](redshifts=[0.01, 0.02, 0.03, 0.005], model=model)",
   "execution_count": 13,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 13,
     "data": {
      "text/plain": "{'amplitude': array([1.10223455e-06, 6.49040077e-07, 1.79149521e-07, 5.97228390e-06]),\n 'hostr_v': array([2., 2., 2., 2.]),\n 'hostebv': array([0.00047189, 0.10848363, 0.26401109, 0.00564114]),\n 's': array([1.12073349, 0.6662376 , 1.04175303, 0.8443051 ])}"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "tr = simsurvey.get_transient_generator((0.0, 0.05),\n                                       ratefunc=lambda z: 3e-5,\n                                       ra_range=(0,360),\n                                       dec_range=(-30,90),\n                                       mjd_range=mjd_range, #(58178, 58543),\n                                       transientprop=transientprop)",
   "execution_count": 14,
   "outputs": [
    {
     "output_type": "stream",
     "text": "/Users/rbiswas/soft/mypython3/lib/python3.7/site-packages/simsurvey/simultarget.py:641: UserWarning: SFD98 dust map files not found. MW E(B-V) will be set to zero.\n  warnings.warn(\"SFD98 dust map files not found. \"\n",
     "name": "stderr"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "tr.ntransient",
   "execution_count": 15,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 15,
     "data": {
      "text/plain": "839"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "##  Now we provide user supplied parameters"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "tr._derived_properties['simul_parameters']['zcmb'] = [0.01, 0.02, 0.03, 0.005, 0.015, 0.007, 0.012]\nprint(tr._derived_properties['simul_parameters']['zcmb'])\nprint(tr.ntransient)\n# If you provided a different number of redshift then there was before, \n# make sure to create matching-length arrays for RA, Dec etc\nr_, d_ = simsurvey.utils.random.radec(tr.ntransient,\n                                      ra_range=tr.ra_range,\n                                      dec_range=tr.dec_range)\ntr._derived_properties['simul_parameters']['ra'] = r_\ntr._derived_properties['simul_parameters']['dec'] = d_\n\nmjd = np.random.uniform(mjd_range[0], mjd_range[1], len(r_))\ntr._derived_properties['simul_parameters']['mjd'] = mjd\n# MW E(B-V) can just be reset becaus it will be reread from the maps when needed\ntr._reset_mwebv_()\n\n# Now regenerate the lightcurve model parameters\nlc = tr.transient_coverage[\"lightcurve_prop\"]\nparam = lc[\"param_func\"](tr.zcmb, tr.model,\n                         **lc[\"param_func_kwargs\"])\ntr._derived_properties[\"simul_parameters\"][\"lightcurve\"] = param",
   "execution_count": 16,
   "outputs": [
    {
     "output_type": "stream",
     "text": "[0.01, 0.02, 0.03, 0.005, 0.015, 0.007, 0.012]\n7\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "tr._derived_properties['simul_parameters']",
   "execution_count": 17,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 17,
     "data": {
      "text/plain": "{'zcmb': [0.01, 0.02, 0.03, 0.005, 0.015, 0.007, 0.012],\n 'mjd': array([57554.27941731, 57647.84724408, 57735.48113532, 57622.89937408,\n        57648.61679466, 57632.27902035, 57550.83824948]),\n 'ra': array([143.43288522, 215.27404104,  33.5439331 ,  20.17263192,\n        151.35366132,  58.8902822 , 218.41012146]),\n 'dec': array([71.65844246, 67.53425265, -2.87574217, -7.61688718, 56.0857594 ,\n         2.06490795, 19.55507412]),\n 'lightcurve': {'amplitude': array([5.45137631e-07, 2.19420617e-07, 4.46723913e-08, 1.83801743e-06,\n         2.12243437e-07, 8.44310384e-07, 1.24054699e-06]),\n  'hostr_v': array([2., 2., 2., 2., 2., 2., 2.]),\n  'hostebv': array([0.03715887, 0.00647934, 0.10455452, 0.10576463, 0.01179971,\n         0.12394003, 0.08348758]),\n  's': array([1.04311862, 0.93838798, 0.86791909, 1.09220414, 0.93188908,\n         1.11233478, 1.16909086])}}"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "mjd_range",
   "execution_count": 18,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 18,
     "data": {
      "text/plain": "(57403.16732874228, 57736.41649200727)"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "tr.zcmb",
   "execution_count": 19,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 19,
     "data": {
      "text/plain": "[0.01, 0.02, 0.03, 0.005, 0.015, 0.007, 0.012]"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {},
   "cell_type": "markdown",
   "source": "RB: OK, so now we have 7 objects inside the mjd range and we are not doing MW exclusions yet (because of a problem with my dustmaps and not by design)"
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "survey = simsurvey.SimulSurvey(generator=tr, plan=plan)\n    \nlcs = survey.get_lightcurves(progress_bar=True, notebook=True # If you get an error because of the progress_bar, delete this line.\n)",
   "execution_count": 20,
   "outputs": [
    {
     "output_type": "stream",
     "text": "Determining field IDs for all objects\n",
     "name": "stdout"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "FloatProgress(value=0.0)",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "abb2259b37d74cc69461f779c104062d"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "text": "\nGenerating lightcurves\n",
     "name": "stdout"
    },
    {
     "output_type": "display_data",
     "data": {
      "text/plain": "FloatProgress(value=0.0)",
      "application/vnd.jupyter.widget-view+json": {
       "version_major": 2,
       "version_minor": 0,
       "model_id": "d8de039359244f14b1a7f5805ba93184"
      }
     },
     "metadata": {}
    },
    {
     "output_type": "stream",
     "text": "\n",
     "name": "stdout"
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "import pandas as pd",
   "execution_count": 21,
   "outputs": []
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "pd.DataFrame(lcs.meta_full)",
   "execution_count": 22,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 22,
     "data": {
      "text/plain": "       z            t0     amplitude  hostr_v   hostebv         s  mwebv  \\\n0  0.010  57554.279417  5.451376e-07      2.0  0.037159  1.043119      0   \n1  0.020  57647.847244  2.194206e-07      2.0  0.006479  0.938388      0   \n2  0.015  57648.616795  2.122434e-07      2.0  0.011800  0.931889      0   \n3  0.007  57632.279020  8.443104e-07      2.0  0.123940  1.112335      0   \n4  0.012  57550.838249  1.240547e-06      2.0  0.083488  1.169091      0   \n5  0.030  57735.481135  4.467239e-08      2.0  0.104555  0.867919      0   \n6  0.005  57622.899374  1.838017e-06      2.0  0.105765  1.092204      0   \n\n           ra        dec  mwebv_sfd98  idx_orig  \n0  143.432885  71.658442            0         0  \n1  215.274041  67.534253            0         1  \n2  151.353661  56.085759            0         4  \n3   58.890282   2.064908            0         5  \n4  218.410121  19.555074            0         6  \n5   33.543933  -2.875742            0         2  \n6   20.172632  -7.616887            0         3  ",
      "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>z</th>\n      <th>t0</th>\n      <th>amplitude</th>\n      <th>hostr_v</th>\n      <th>hostebv</th>\n      <th>s</th>\n      <th>mwebv</th>\n      <th>ra</th>\n      <th>dec</th>\n      <th>mwebv_sfd98</th>\n      <th>idx_orig</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>0.010</td>\n      <td>57554.279417</td>\n      <td>5.451376e-07</td>\n      <td>2.0</td>\n      <td>0.037159</td>\n      <td>1.043119</td>\n      <td>0</td>\n      <td>143.432885</td>\n      <td>71.658442</td>\n      <td>0</td>\n      <td>0</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>0.020</td>\n      <td>57647.847244</td>\n      <td>2.194206e-07</td>\n      <td>2.0</td>\n      <td>0.006479</td>\n      <td>0.938388</td>\n      <td>0</td>\n      <td>215.274041</td>\n      <td>67.534253</td>\n      <td>0</td>\n      <td>1</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>0.015</td>\n      <td>57648.616795</td>\n      <td>2.122434e-07</td>\n      <td>2.0</td>\n      <td>0.011800</td>\n      <td>0.931889</td>\n      <td>0</td>\n      <td>151.353661</td>\n      <td>56.085759</td>\n      <td>0</td>\n      <td>4</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>0.007</td>\n      <td>57632.279020</td>\n      <td>8.443104e-07</td>\n      <td>2.0</td>\n      <td>0.123940</td>\n      <td>1.112335</td>\n      <td>0</td>\n      <td>58.890282</td>\n      <td>2.064908</td>\n      <td>0</td>\n      <td>5</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>0.012</td>\n      <td>57550.838249</td>\n      <td>1.240547e-06</td>\n      <td>2.0</td>\n      <td>0.083488</td>\n      <td>1.169091</td>\n      <td>0</td>\n      <td>218.410121</td>\n      <td>19.555074</td>\n      <td>0</td>\n      <td>6</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>0.030</td>\n      <td>57735.481135</td>\n      <td>4.467239e-08</td>\n      <td>2.0</td>\n      <td>0.104555</td>\n      <td>0.867919</td>\n      <td>0</td>\n      <td>33.543933</td>\n      <td>-2.875742</td>\n      <td>0</td>\n      <td>2</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>0.005</td>\n      <td>57622.899374</td>\n      <td>1.838017e-06</td>\n      <td>2.0</td>\n      <td>0.105765</td>\n      <td>1.092204</td>\n      <td>0</td>\n      <td>20.172632</td>\n      <td>-7.616887</td>\n      <td>0</td>\n      <td>3</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "len(lcs.meta_full)",
   "execution_count": 23,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 23,
     "data": {
      "text/plain": "7"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "len(lcs.lcs)",
   "execution_count": 24,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 24,
     "data": {
      "text/plain": "5"
     },
     "metadata": {}
    }
   ]
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  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "param",
   "execution_count": 25,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 25,
     "data": {
      "text/plain": "{'amplitude': array([5.45137631e-07, 2.19420617e-07, 4.46723913e-08, 1.83801743e-06,\n        2.12243437e-07, 8.44310384e-07, 1.24054699e-06]),\n 'hostr_v': array([2., 2., 2., 2., 2., 2., 2.]),\n 'hostebv': array([0.03715887, 0.00647934, 0.10455452, 0.10576463, 0.01179971,\n        0.12394003, 0.08348758]),\n 's': array([1.04311862, 0.93838798, 0.86791909, 1.09220414, 0.93188908,\n        1.11233478, 1.16909086])}"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "tr.model.param_names",
   "execution_count": 26,
   "outputs": [
    {
     "output_type": "execute_result",
     "execution_count": 26,
     "data": {
      "text/plain": "['z', 't0', 'amplitude', 's', 'hostebv', 'hostr_v', 'mwebv', 'mwr_v']"
     },
     "metadata": {}
    }
   ]
  },
  {
   "metadata": {
    "trusted": true
   },
   "cell_type": "code",
   "source": "",
   "execution_count": null,
   "outputs": []
  }
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