diff --git a/.github/workflows/python-package.yml b/.github/workflows/python-package.yml new file mode 100644 index 0000000..b35f1a8 --- /dev/null +++ b/.github/workflows/python-package.yml @@ -0,0 +1,40 @@ +# This workflow will install Python dependencies, run tests and lint with a variety of Python versions +# For more information see: https://docs.github.com/en/actions/automating-builds-and-tests/building-and-testing-python + +name: Python package + +on: + push: + branches: [ "dev_2" ] + pull_request: + branches: [ "dev_2" ] + +jobs: + build: + + runs-on: ubuntu-latest + strategy: + fail-fast: false + matrix: + python-version: ["3.9", "3.10", "3.11"] + + steps: + - uses: actions/checkout@v3 + - name: Set up Python ${{ matrix.python-version }} + uses: actions/setup-python@v3 + with: + python-version: ${{ matrix.python-version }} + - name: Install dependencies + run: | + python -m pip install --upgrade pip + python -m pip install flake8 pytest + if [ -f requirements.txt ]; then pip install -r requirements.txt; fi + - name: Lint with flake8 + run: | + # stop the build if there are Python syntax errors or undefined names + flake8 . --count --select=E9,F63,F7,F82 --show-source --statistics + # exit-zero treats all errors as warnings. The GitHub editor is 127 chars wide + flake8 . --count --exit-zero --max-complexity=10 --max-line-length=127 --statistics + - name: Test with pytest + run: | + pytest diff --git a/.gitignore b/.gitignore index 411b7f5..7b25264 100644 --- a/.gitignore +++ b/.gitignore @@ -1,6 +1,7 @@ # Created by https://www.toptal.com/developers/gitignore/api/python # Edit at https://www.toptal.com/developers/gitignore?templates=python +.vscode/ ### Python ### # Byte-compiled / optimized / DLL files __pycache__/ diff --git a/.vscode/settings.json b/.vscode/settings.json new file mode 100644 index 0000000..7dd5076 --- /dev/null +++ b/.vscode/settings.json @@ -0,0 +1,7 @@ +{ + "python.testing.pytestArgs": [ + "snsim" + ], + "python.testing.unittestEnabled": false, + "python.testing.pytestEnabled": true +} \ No newline at end of file diff --git a/Examples/Gen_SN_Parameters.ipynb b/Examples/Gen_SN_Parameters.ipynb index f4987e4..c609341 100644 --- a/Examples/Gen_SN_Parameters.ipynb +++ b/Examples/Gen_SN_Parameters.ipynb @@ -18,15 +18,7 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: The update_default_config function is deprecated and may be removed in a future version. [sncosmo]\n" - ] - } - ], + "outputs": [], "source": [ "import sys\n", "import snsim\n", @@ -61,12 +53,12 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ "randseed = 1234 #random seed for generation\n", - "n_obj = 50000 #number of SN to generate\n", + "n_obj = 1000 #number of SN to generate\n", "z_range = [0.01, 0.2] #redshift range to extract SN redshift\n", "\n", "#time range of SNIa observation to extract t0 for each object\n", @@ -76,15 +68,16 @@ "\n", "snia_gen = {'M0': 'jla', #'jla' is a default value, you can just put a number\n", " 'sigM': 0.12,# intrinsic scatter of SNIA in Hubble Diagram\n", - " 'sct_model': 'G10', #'G10' and 'C11' default values for scattering model of SNIa\n", + " #'sct_model': 'G10', #'G10' and 'C11' default values for scattering model of SNIa\n", " 'rate': 'ztf20', #default rate value, you can use a general lambda function for rate \n", - " 'model_config': {'model_name': 'salt2',\n", - " 'alpha': 0.14,\n", - " 'beta': 2.9,\n", - " 'dist_x1': 'N21',#default value, you can use gauss distribution [mean,sig] or\n", - " # asymmetric gaussian [mean,sig1,sig2]\n", - " 'dist_c': [-0.055, 0.023, 0.150]}} #asym gaussian for generate c parameter of salt model\n", - " \n", + " 'model_name': 'salt2',\n", + " 'model_version': '2.0',\n", + " 'alpha': 0.14,\n", + " 'beta': 2.9,\n", + " 'dist_x1': 'N21',#default value, you can use gauss distribution [mean,sig] or\n", + " # asymmetric gaussian [mean,sig1,sig2]\n", + " 'dist_c': [-0.055, 0.023, 0.150]} #asym gaussian for generate c parameter of salt model\n", + " \n", "\n", "cosmology = {'name':'planck18'} #cosmology, in astropy format\n", "\n", @@ -112,7 +105,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -121,19 +114,29 @@ "\n", "# Give the input configuration\n", "SNgenerator = gen_class(snia_gen,\n", - " cmb,\n", " snsim.utils.set_cosmo(cosmology),\n", " z_range=z_range,\n", " time_range=time_range,\n", " vpec_dist=vpec_dist,\n", " mw_dust=mw_dust,\n", + " cmb=cmb\n", " )# there is also a parameter called geometry to give the fields where extract ra, dec of SNe\n", " # in this case we generate SNe full sky\n", "\n", "\n", "\n", + "\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [], + "source": [ "# Gen basic parameters\n", - "params = SNgenerator.gen_astrobj_par(n_obj, randseed)\n" + "params = SNgenerator.gen_basic_par(n_obj, randseed)" ] }, { @@ -145,12 +148,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 5, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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\n", 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" ] @@ -169,12 +172,12 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 6, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -201,14 +204,37 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ "# Generate SN obj\n", - "SNs = SNgenerator(n_obj, randseed, astrobj_par=params)" + "SNs = SNgenerator(n_obj, randseed, basic_par=params)" ] }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "{'mw_dust': {'model': 'CCM89', 'rv': 3.1},\n", + " 'mod_fcov': False,\n", + " 'M0': -19.123830232811475,\n", + " 'sigM': 0.12,\n", + " 'alpha': 0.14,\n", + " 'beta': 2.9}" + ] + }, + "execution_count": 13, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [] + }, { "cell_type": "markdown", "metadata": {}, @@ -223,7 +249,7 @@ "outputs": [ { "data": { - "image/png": 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AFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMJq2dQLAIBtpdtFU5t6CQA0MXd4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFVbLgGTBgQFq0aLHZP9OmTWt03o033pj+/funoqIiVVVVGTJkSB5++OEtvtbMmTMzZMiQVFVVpaKiIv37989NN91Uqq0AAAAF0bLUFzzppJNSUVHRYHyvvfZqMHb++ednwoQJadu2bY499tjU1NRk+vTpueeeezJp0qSccMIJDebcfvvtOfXUU/Pmm2/mqKOOSqdOnXLvvfdm5MiRmTt3bq688spSbwkAAGimSh48V155Zbp16/a2582YMSMTJkxIx44dM2vWrOy3335JklmzZmXAgAEZNWpUBgwYkMrKyro5S5cuzZlnnpmNGzfm9ttvz/Dhw5Mkr7zySo444oiMGzcun/rUpzJgwIBSbwsAAGiGmuwzPOPHj0+SXHzxxXWxkySHHnpozjnnnFRXV+e6666rN+faa6/NihUrMmzYsLrYSZLddtst3/3ud5Mk48aN2w6rBwAAmoMmCZ61a9fmvvvuS5KMGDGiwfFNY1OmTKk3PnXq1M3OGTp0aMrKyjJjxozU1NSUeskAAEAzVPK3tF133XV5/fXXs9NOO6Vnz5454YQTsvfee9c7Z968eVm3bl06d+6cLl26NLhGv379kiRz586tNz5nzpx6x9+qdevWOfDAA/PYY4/l2WefTe/evUu1JQAAoJkqefCMHTu23l9feOGFGTNmTMaMGVM3tmjRoiRpNHaSpLy8PJWVlVm2bFlWrlyZ9u3bZ8WKFVm+fPkW53Xp0iWPPfZYFi5c+LbBc8ABBzQ6vmDBgnTv3n2LcwEAgOahZG9pO+qoo3LzzTdnwYIFWbNmTebNm5fvfOc7admyZb71rW9lwoQJdeeuWrUqSdKuXbvNXq+8vDxJsnLlynpztjTvn+cAAADvbyW7w3PppZfW++uePXvmG9/4Rj7ykY/kE5/4RC655JJ84QtfSNu2bUv1klvlqaeeanR8c3d+AACA5mebP7Tg2GOPzUc+8pFUV1fnkUceSZK67+lZs2bNZuetXr06SdK+fft6c7Y075/nAAAA72/b5Sltmx47/fLLLydJ3UMMXnrppUbPX716daqrq7PrrrvWxUuHDh2yyy67bHHepvGuXbuWbvEAAECztV2CZ9myZUn+9zM2vXr1Sps2bbJkyZIsXry4wflPPPFEkjR48ECfPn3qHX+rDRs25Mknn0xZWVl69uxZ0vUDAADN0zYPniVLluT3v/99kv99nHTbtm0zcODAJMltt93WYM6kSZOSJMcdd1y98aFDh9Y7/lZ33XVXampqMmjQoJSVlZVuAwAAQLNVkuB5+OGHc8cdd2Tjxo31xl988cWceOKJWb16dY4//vh6j5MePXp0kn88xnr+/Pl147Nmzco111yTysrKnHXWWfWud/bZZ6dDhw658847M3ny5LrxV199NV//+teTJBdccEEptgQAABRASZ7S9uyzz2bUqFHZfffd069fv1RWVmbhwoV5/PHHU1NTkwMOOCA/+clP6s0ZNGhQzjvvvEyYMCF9+/bNMccck/Xr12f69Ompra3NDTfckMrKynpzqqqqcv311+eUU07JiBEjMmDAgHTs2DEzZsxIdXV1Ro8enQEDBpRiSwAAQAGUJHg+9rGP5d///d/zyCOP5I9//GOWLVuW8vLy9O3bNyeffHL+/d//vdHHUV911VXp27dvJk6cmOnTp6d169YZNGhQxowZk8MOO6zR1zrppJPy0EMPZezYsZk9e3bWr1+f/fffP+eee25GjhxZiu0AAAAF0aK2tra2qRexI9n0PTyb+54eAJqPbhdNbeolsAN78fKhTb0E4B3Y2t/Pt8tT2gAAAJqC4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIXVsqkXAADQFLpdNLWpl5AkefHyoU29BCg0d3gAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwWjb1AgAopm4XTW3qJQCAOzwAAEBxCR4AAKCwtknwvP766/nABz6QFi1apEePHls898Ybb0z//v1TUVGRqqqqDBkyJA8//PAW58ycOTNDhgxJVVVVKioq0r9//9x0002l3AIAAFAA2yR4Lrjggrz22mtve97555+fUaNG5cknn8ygQYPSv3//TJ8+PUcddVTuuOOORufcfvvtOfroozNt2rT07t07gwcPzvz58zNy5MhceOGFJd4JAADQnJU8eO6999789Kc/zec///ktnjdjxoxMmDAhHTt2zJw5c3LHHXdk2rRpeeihh7Lzzjtn1KhRqa6urjdn6dKlOfPMM7Nx48ZMmjQpDzzwQCZNmpRnnnkmPXr0yLhx4/LAAw+UeksAAEAzVdLgWbt2bf7t3/4t+++//9vebRk/fnyS5OKLL85+++1XN37ooYfmnHPOSXV1da677rp6c6699tqsWLEiw4YNy/Dhw+vGd9ttt3z3u99NkowbN65U2wEAAJq5kgbPf/7nf+b555/P1VdfnVatWm32vLVr1+a+++5LkowYMaLB8U1jU6ZMqTc+derUzc4ZOnRoysrKMmPGjNTU1LznPQAAAMVRsuCZO3duxo0bl1GjRuXII4/c4rnz5s3LunXr0rlz53Tp0qXB8X79+tVd863mzJlT7/hbtW7dOgceeGBqamry7LPPvtdtAAAABVKSLx598803c/bZZ6eysrLurWVbsmjRoiRpNHaSpLy8PJWVlVm2bFlWrlyZ9u3bZ8WKFVm+fPkW53Xp0iWPPfZYFi5cmN69e29xDQcccECj4wsWLEj37t3fdg8AAMCOryR3eL7//e/nj3/8Y6644op07Njxbc9ftWpVkqRdu3abPae8vDxJsnLlynpztjTvn+cAAADvb1t9h2fRokW5+OKLc/TRR+eMM84owZK2j6eeeqrR8c3d+QEAAJqfrb7D86UvfSnr16/P1Vdf/Y7nVFRUJEnWrFmz2XNWr16dJGnfvn29OVua989zAACA97etvsNz1113pbKyMuecc0698U1PSlu8eHEGDBiQJPnFL36R3XffPXvvvXeS5KWXXmr0mqtXr051dXV23XXXunjp0KFDdtlllyxfvjwvvfRS9t9//wbzNl2va9euW7stAACgAEry0ILq6uo8+OCDjR6rqampO7Ypgnr16pU2bdpkyZIlWbx4cfbaa696c5544okkafDggT59+uShhx7KE0880SB4NmzYkCeffDJlZWXp2bNnKbYFAAA0c1v9lrba2tpG/7zwwgtJku7du9eNdevWLUnStm3bDBw4MEly2223NbjmpEmTkiTHHXdcvfGhQ4fWO/5Wd911V2pqajJo0KCUlZVt7bYAAIACKOkXj74bo0ePTpKMHTs28+fPrxufNWtWrrnmmlRWVuass86qN+fss89Ohw4dcuedd2by5Ml146+++mq+/vWvJ0kuuOCC7bB6AACgOWiy4Bk0aFDOO++8vP766+nbt29OOOGEDBkyJEcddVTeeOON3HDDDamsrKw3p6qqKtdff3122mmnjBgxIgMHDszJJ5+cXr165bnnnsvo0aPrPi8EAABQks/wvFdXXXVV+vbtm4kTJ2b69Olp3bp1Bg0alDFjxuSwww5rdM5JJ52Uhx56KGPHjs3s2bOzfv367L///jn33HMzcuTI7bwDAABgR9aitra2tqkXsSPZ9D08m/ueHgDemW4XTW3qJUCz8OLlQ5t6CbBD29rfz5vsLW0AAADbmuABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABRWyYJn/PjxGT58ePbbb7/ssssuadOmTbp27ZrPfe5z+ctf/rLZeTfeeGP69++fioqKVFVVZciQIXn44Ye3+FozZ87MkCFDUlVVlYqKivTv3z833XRTqbYCAAAURMmC5//9v/+X3/72t6mqqsq//uu/ZujQoSkrK8vNN9+cgw8+OHfddVeDOeeff35GjRqVJ598MoMGDUr//v0zffr0HHXUUbnjjjsafZ3bb789Rx99dKZNm5bevXtn8ODBmT9/fkaOHJkLL7ywVNsBAAAKoEVtbW1tKS40c+bMHHzwwSkrK6s3/sMf/jBf+tKXsttuu+Wll15Ky5YtkyQzZszIMccck44dO2bWrFnZb7/9kiSzZs3KgAED0q5du7zwwguprKysu9bSpUuzzz77ZMWKFbn99tszfPjwJMkrr7ySI444Is8991zuv//+DBgw4D3v44ADDkiSPPXUU+/5GgAk3S6a2tRLgGbhxcuHNvUSYIe2tb+fl+wOz+GHH94gdpLki1/8Yrp3755XXnklTz/9dN34+PHjkyQXX3xxXewkyaGHHppzzjkn1dXVue666+pd69prr82KFSsybNiwuthJkt122y3f/e53kyTjxo0r1ZYAAIBmbrs8tKBVq1ZJktatWydJ1q5dm/vuuy9JMmLEiAbnbxqbMmVKvfGpU6duds6mt9DNmDEjNTU1pVs8AADQbG3z4Ln55pszb9687LfffnV3cubNm5d169alc+fO6dKlS4M5/fr1S5LMnTu33vicOXPqHX+r1q1b58ADD0xNTU2effbZUm8DAABohlqW+oJXXHFFnnrqqaxevTp//etf89RTT2XPPffMrbfemp133jlJsmjRoiRpNHaSpLy8PJWVlVm2bFlWrlyZ9u3bZ8WKFVm+fPkW53Xp0iWPPfZYFi5cmN69e29xnZveC/jPFixYkO7du7+jvQLsiHx2BgD+V8mD53e/+13uvffeur/u2rVrbrrpphx88MF1Y6tWrUqStGvXbrPXKS8vT3V1dV3wbJqzpXnl5eVJkpUrV27VHgAAgGIoefDMmDEjSVJdXZ2//OUvufTSS3P00Udn7Nix+eY3v1nql3vPNveUh83d+QEAAJqfbfYZnsrKyhx55JG5++67c/DBB2fMmDH54x//mCSpqKhIkqxZs2az81evXp0kad++fb05W5r3z3MAAID3t23+0IJWrVrl1FNPTW1tbd1T1/bee+8kyUsvvdTonNWrV6e6ujq77rprXbx06NAhu+yyyxbnbRrv2rVrSfcAAAA0T9vlsdSdOnVKkixZsiRJ0qtXr7Rp0yZLlizJ4sWLG5z/xBNPJEmDBw/06dOn3vG32rBhQ5588smUlZWlZ8+eJV0/AADQPG2X4HnwwQeTpO7pZ23bts3AgQOTJLfddluD8ydNmpQkOe644+qNDx06tN7xt7rrrrtSU1OTQYMGNfoFqAAAwPtPSYJn5syZmTZtWt5888164xs2bMj3v//93HzzzWnbtm1OPfXUumOjR49OkowdOzbz58+vG581a1auueaaVFZW5qyzzqp3vbPPPjsdOnTInXfemcmTJ9eNv/rqq/n617+eJLngggtKsSUAAKAASvKUtvnz52fUqFHp1KlTDj744HTs2DGvvfZa/vKXv+Tll19OWVlZbrzxxnzwgx+smzNo0KCcd955mTBhQvr27Ztjjjkm69evz/Tp01NbW5sbbrghlZWV9V6nqqoq119/fU455ZSMGDEiAwYMSMeOHTNjxoxUV1dn9OjRGTBgQCm2BAAAFEBJgufoo4/ON77xjTz44IOZO3duXnvttbRu3TrdunXLiBEj8pWvfCU9evRoMO+qq65K3759M3HixEyfPj2tW7fOoEGDMmbMmBx22GGNvtZJJ52Uhx56KGPHjs3s2bOzfv367L///jn33HMzcuTIUmwHAAAoiBa1tbW1Tb2IHcmm7+HZ3Pf0AOzoul00tamXALwLL14+tKmXADu0rf39fLs8tAAAAKApCB4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsFo29QIAiqLbRVObeglAM7Qj/H/Hi5cPbeolwDbjDg8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhdWyqRcAsLW6XTS1qZcAAOyg3OEBAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKqyTBs2bNmtxxxx0566yz0qtXr5SVlaW8vDx9+vTJpZdemlWrVm127o033pj+/funoqIiVVVVGTJkSB5++OEtvt7MmTMzZMiQVFVVpaKiIv37989NN91Uiq0AAAAFUpLg+fnPf54TTzwx119/fXbeeeccf/zxOfLII/PCCy/k29/+dj760Y/m1VdfbTDv/PPPz6hRo/Lkk09m0KBB6d+/f6ZPn56jjjoqd9xxR6Ovdfvtt+foo4/OtGnT0rt37wwePDjz58/PyJEjc+GFF5ZiOwAAQEGUJHhatWqVL3zhC3n66afz9NNP51e/+lWmTZuWefPm5aCDDsozzzyT888/v96cGTNmZMKECenYsWPmzJmTO+64I9OmTctDDz2UnXfeOaNGjUp1dXW9OUuXLs2ZZ56ZjRs3ZtKkSXnggQcyadKkPPPMM+nRo0fGjRuXBx54oBRbAgAACqAkwTNy5Mhcc801+fCHP1xvfI899sgPfvCDJMnkyZOzfv36umPjx49Pklx88cXZb7/96sYPPfTQnHPOOamurs51111X73rXXnttVqxYkWHDhmX48OF147vttlu++93vJknGjRtXii0BAAAFsM0fWtCnT58kybp16/L6668nSdauXZv77rsvSTJixIgGczaNTZkypd741KlTNztn6NChKSsry4wZM1JTU1O6DQAAAM3WNg+e559/Psk/3vZWVVWVJJk3b17WrVuXzp07p0uXLg3m9OvXL0kyd+7ceuNz5sypd/ytWrdunQMPPDA1NTV59tlnS7oHAACgeWq5rV9gwoQJSZLBgwenTZs2SZJFixYlSaOxkyTl5eWprKzMsmXLsnLlyrRv3z4rVqzI8uXLtzivS5cueeyxx7Jw4cL07t17i+s64IADGh1fsGBBunfv/vYbAwAAdnjb9A7P3Xffneuuuy6tWrXKZZddVje+6THV7dq12+zc8vLyJMnKlSvrzdnSvH+eAwAAvL9tszs8zzzzTE4//fTU1tbmiiuuqPssz47iqaeeanR8c3d+AACA5meb3OFZvHhxBg8enGXLlmX06NE577zz6h2vqKhI8o8vLN2c1atXJ0nat29fb86W5v3zHAAA4P2t5MGzdOnSHHvssVm4cGFGjRqVK6+8ssE5e++9d5LkpZdeavQaq1evTnV1dXbddde6eOnQoUN22WWXLc7bNN61a9et3gcAAND8lTR4Vq1alU9+8pN5+umnM3z48PzkJz9JixYtGpzXq1evtGnTJkuWLMnixYsbHH/iiSeSpMGDBza9LW7T8bfasGFDnnzyyZSVlaVnz56l2A4AANDMlSx41q1bl2HDhuXRRx/NJz7xidx6663ZeeedGz23bdu2GThwYJLktttua3B80qRJSZLjjjuu3vjQoUPrHX+ru+66KzU1NRk0aFDKysq2ai8AAEAxlCR4Nm7cmNNOOy333XdfjjzyyEyePDmtW7fe4pzRo0cnScaOHZv58+fXjc+aNSvXXHNNKisrc9ZZZ9Wbc/bZZ6dDhw658847M3ny5LrxV199NV//+teTJBdccEEptgQAABRASZ7SNnHixPz6179OknTq1Clf/OIXGz3vyiuvTKdOnZIkgwYNynnnnZcJEyakb9++OeaYY7J+/fpMnz49tbW1ueGGG1JZWVlvflVVVa6//vqccsopGTFiRAYMGJCOHTtmxowZqa6uzujRozNgwIBSbAkAACiAkgTPsmXL6v7zpvBpzCWXXFIXPEly1VVXpW/fvpk4cWKmT5+e1q1bZ9CgQRkzZkwOO+ywRq9x0kkn5aGHHsrYsWMze/bsrF+/Pvvvv3/OPffcjBw5shTbAQAACqJFbW1tbVMvYkey6Xt4Nvc9PcCOp9tFU5t6CQDN2ouXD23qJcBmbe3v59vke3gAAAB2BIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFFbLpl4A0Hx1u2hqUy8BAGCL3OEBAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLBaNvUCAABoWt0umtrUS0iSvHj50KZeAgVUkjs8jz/+eC6//PIMHz48Xbp0SYsWLdKiRYu3nXfjjTemf//+qaioSFVVVYYMGZKHH354i3NmzpyZIUOGpKqqKhUVFenfv39uuummUmwDAAAomJLc4bnsssty5513vqs5559/fiZMmJC2bdvm2GOPTU1NTaZPn5577rknkyZNygknnNBgzu23355TTz01b775Zo466qh06tQp9957b0aOHJm5c+fmyiuvLMV2AACAgihJ8Bx66KHp3bt3PvrRj+ajH/1ounXrlnXr1m32/BkzZmTChAnp2LFjZs2alf322y9JMmvWrAwYMCCjRo3KgAEDUllZWTdn6dKlOfPMM7Nx48bcfvvtGT58eJLklVdeyRFHHJFx48blU5/6VAYMGFCKLQEAAAVQkre0/d//+39z6aWX5rjjjsvuu+/+tuePHz8+SXLxxRfXxU7yj3A655xzUl1dneuuu67enGuvvTYrVqzIsGHD6mInSXbbbbd897vfTZKMGzeuFNsBAAAKYrs/pW3t2rW57777kiQjRoxocHzT2JQpU+qNT506dbNzhg4dmrKyssyYMSM1NTWlXjIAANBMbffgmTdvXtatW5fOnTunS5cuDY7369cvSTJ37tx643PmzKl3/K1at26dAw88MDU1NXn22We3waoBAIDmaLs/lnrRokVJ0mjsJEl5eXkqKyuzbNmyrFy5Mu3bt8+KFSuyfPnyLc7r0qVLHnvssSxcuDC9e/d+23UccMABjY4vWLAg3bt3fydbAQAAdnDb/Q7PqlWrkiTt2rXb7Dnl5eVJkpUrV9abs6V5/zwHAADgffvFo0899VSj45u78wMAADQ/2/0OT0VFRZJkzZo1mz1n9erVSZL27dvXm7Olef88BwAAYLsHz957750keemllxo9vnr16lRXV2fXXXeti5cOHTpkl1122eK8TeNdu3Yt9ZIBAIBmarsHT69evdKmTZssWbIkixcvbnD8iSeeSJIGDx7o06dPveNvtWHDhjz55JMpKytLz549t8GqAQCA5mi7B0/btm0zcODAJMltt93W4PikSZOSJMcdd1y98aFDh9Y7/lZ33XVXampqMmjQoJSVlZV6yQAAQDPVora2trbUFy0rK8u6deuyuUvPmDEjxxxzTDp27JhZs2Zlv/32S5LMmjUrH//4x9O2bdu88MILqaysrJuzdOnS7LPPPlmxYkVuv/32DB8+PEny6quv5vDDD89zzz2X+++/PwMGDNiqtW96aMHmHmoAO4puF01t6iUAQEm9ePnQpl4CO6Ct/f28JHd4pk6dmkMOOaTuz/r165Ok3tjUqf/7y9mgQYNy3nnn5fXXX0/fvn1zwgknZMiQITnqqKPyxhtv5IYbbqgXO0lSVVWV66+/PjvttFNGjBiRgQMH5uSTT06vXr3y3HPPZfTo0VsdOwAAQLGU5LHUS5YsySOPPNJg/K1jS5YsqXfsqquuSt++fTNx4sRMnz49rVu3zqBBgzJmzJgcdthhjb7OSSedlIceeihjx47N7Nmzs379+uy///4599xzM3LkyFJsBQAAKJBt8pa25sxb2mguvKUNgKLxljYas0O8pQ0AAGBHJHgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAABQWIIHAAAoLMEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4AAKCwBA8AAFBYLZt6AdDcdLtoalMvAQCAd8gdHgAAoLAEDwAAUFiCBwAAKCyf4QEAYIewo3xO9sXLhzb1Eighd3gAAIDCEjwAAEBhCR4AAKCwBA8AAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACqtlUy8A3qluF01t6iUAANDMuMMDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBheUobAAC8xY7yZNgXLx/a1EsoBHd4AACAwmqWwbN27dp861vfSs+ePVNWVpY999wzZ555ZhYvXtzUSwMAAHYgzS54ampqMnDgwFx22WVZtWpVhg0blg9+8IO54YYbctBBB+X5559v6iUCAAA7iGb3GZ6xY8dm9uzZOfTQQ3PPPfekoqIiSTJ+/PhccMEFOfPMM/PAAw807SILZkd5HysAALxbzeoOz/r16zNx4sQkyQ9+8IO62EmS0aNHp3fv3nnwwQfz+OOPN9USAQCAHUizusMzc+bMLF++PN27d89BBx3U4PiIESMyd+7cTJkyJQcffHATrBAAAEpjR3mXTXN/WlyzusMzZ86cJEm/fv0aPb5pfO7cudttTQAAwI6rWd3hWbRoUZKkS5cujR7fNL5w4cK3vdYBBxzQ6PgzzzyTVq1abfb49jT/lVVNvQQAAN7nDphS8fYnbUMLFixIq1at3vP8ZhU8q1b9IwDatWvX6PHy8vIkycqVK9/za7Ro0WKr/gstpf122zb/41qwYEGSpHv37tvk+jQNP9fi8TMtJj/X4vEzLSY/1x1Hq1at6n7Pfy+aVfCU0lNPPdXUS2gym+5evZ//OygiP9fi8TMtJj/X4vEzLSY/1+JoVp/h2fRUtjVr1jR6fPXq1UmS9u3bb7c1AQAAO65mFTx77713kuSll15q9Pim8a5du263NQEAADuuZhU8ffr0SZI88cQTjR7fNN67d+/ttiYAAGDH1ayC5/DDD88uu+ySBQsW5M9//nOD45MmTUqSHHfccdt5ZQAAwI6oWQVP69atc+655yZJvvSlL9V9ZidJxo8fn7lz5+boo4/2paMAAECSpEVtbW1tUy/i3aipqcmAAQPyyCOPZI899siRRx6ZhQsX5pFHHknnzp0ze/bs7Lvvvk29TAAAYAfQ7IInSdauXZv/+q//ys9//vP87W9/S1VVVQYPHpzLLrtss19KCgAAvP80y+ABAAB4J5rVZ3gAAADeDcEDAAAUluABAAAKS/AAAACFJXgAAIDCEjwAAEBhCR4yd+7cnHvuuTnkkEOy5557pk2bNtlll11y6KGH5vvf/342bNjQ1EvkXXrmmWfy3//93/n4xz+eTp06pVWrVtl9990zfPjw/P73v2/q5fEerV69OjfffHO+/OUv52Mf+1jatGmTFi1a5JJLLmnqpfE21q5dm29961vp2bNnysrKsueee+bMM8/M4sWLm3ppvEePP/54Lr/88gwfPjxdunRJixYt0qJFi6ZeFu/RmjVrcscdd+Sss85Kr169UlZWlvLy8vTp0yeXXnppVq1a1dRLZCv4Hh4yceLEfPnLX07Xrl3To0ePdO7cOUuWLMnMmTNTU1OTo48+Ovfcc09at27d1EvlHerSpUsWL16cioqKHHLIIamqqsrTTz+dJ598Mi1atMj48eNz/vnnN/UyeZf+/Oc/56CDDmow/u1vf1v07MBqamry8Y9/PLNnz84ee+yRI488Mi+++GIeffTRdO7cObNnz86+++7b1MvkXTrhhBNy5513Nhj3a1XzdO211+bzn/98kuTDH/5wDjzwwKxYsSIPP/xwVq5cmQ996EN58MEH84EPfKCJV8p74Q4PGTJkSBYsWJAXX3wxM2bMyK233poZM2bkxRdfzIEHHpgHH3wwP/7xj5t6mbwLH/rQh3LTTTdlyZIlmT59en75y1/mL3/5S66++urU1tbmwgsvzNNPP93Uy+Rdat++fc4666xcffXVefzxx3PppZc29ZJ4B8aOHZvZs2fn0EMPzbPPPptf/vKXeeSRRzJu3LgsWbIkZ555ZlMvkffg0EMPzZgxY/Kb3/wmL7/8ctq0adPUS2IrtGrVKl/4whfy9NNP5+mnn86vfvWrTJs2LfPmzctBBx2UZ555xr8obMbc4WGLbrnllnz2s5/NiSeemMmTJzf1ciiBT3ziE7nnnntyySWX5Nvf/nZTL4etcPnll+c//uM/3OHZga1fvz4f+MAHsnz58jzxxBMN7tD16dMnc+fOzWOPPZaDDz64iVZJKZSVlWXdunXu8BTQrFmzcthhh6VNmzZZsWKFd7w0Q+7wsEWtWrVKEn9zF0ifPn2SJP/zP//TxCuB4ps5c2aWL1+e7t27N/p2xBEjRiRJpkyZsr2XBrxDm/65uW7durz++utNvBreC8HDZi1btizjxo1LkgwdOrSJV0OpPP/880mS3XffvYlXAsU3Z86cJEm/fv0aPb5pfO7cudttTcC7s+mfm61atUpVVVUTr4b3omVTL4Adx/z58/Od73wnb775Zl555ZU8/PDDWbVqVc4555x85jOfaerlUQILFizIXXfdlSQ5/vjjm3g1UHyLFi1K8o8HiTRm0/jChQu325qAd2fChAlJksGDB/usVjMleKjzyiuv5Kc//Wm9sa985Su57LLLstNObgY2d2+88UbOOOOMrFu3LqeeeqrPC8B2sOlRtu3atWv0eHl5eZJk5cqV221NwDt3991357rrrkurVq1y2WWXNfVyeI8ETwGceOKJ+etf//qu5tx0003p379/vbEjjjgitbW12bhxYxYtWpRf//rX+c///M/89re/zT333JNu3bqVcNVsSal+pm/1la98JX/4wx+y77775oc//OHWLpH3YFv8XAHYNp555pmcfvrpqa2tzRVXXFH3WR6aH8FTAC+88ELmzZv3ruasWbNms8d23nnn7LPPPhk9enS6deuWk046KV/+8pd9qHY7KvXP9Dvf+U5+9KMfZbfddsvvfvc770FuIqX+ubLjq6ioSLL5n+Pq1auT/OOR48COY/HixRk8eHCWLVuW0aNH57zzzmvqJbEVBE8B/PnPf95m1z7xxBNTUVGRadOmZf369Z7Wtp2U8md69dVX5+KLL84uu+ySadOmpUePHiW7Nu/Otvx7lR3T3nvvnSR56aWXGj2+abxr167bbU3Ali1dujTHHntsFi5cmFGjRuXKK69s6iWxlXwwgy1q0aJFqqqq8sYbb2TZsmVNvRzepV/84hf50pe+lHbt2mXq1Knp27dvUy8J3lc2vQXmiSeeaPT4pvHevXtvtzUBm7dq1ap88pOfzNNPP53hw4fnJz/5SVq0aNHUy2IrCR626Pnnn8/f/va3dOjQIZ06dWrq5fAu3H333fnc5z6Xli1b5te//nUOP/zwpl4SvO8cfvjh2WWXXbJgwYJG7/BNmjQpSXLcccdt55UB/2zdunUZNmxYHn300XziE5/Irbfemp133rmpl0UJCB7y/e9/P3//+98bjM+bNy+f/vSnU1tbm8997nP+pm9GZs6cmREjRqS2tja//OUvc+yxxzb1kuB9qXXr1jn33HOTJF/60pfqPrOTJOPHj8/cuXNz9NFHe2oiNLGNGzfmtNNOy3333ZcjjzwykydP9jb+AmlRW1tb29SLoGl169Ytf/vb39KnT5/06NEjtbW1WbhwYR5//PG8+eabOeqoozJ16tS6D9+y49t1111TXV2dffbZJ0cddVSj5xxxxBE5++yzt/PK2FonnnhiXn755STJ//zP/+Rvf/tb9tprr7rvc9ljjz3y61//uimXyD+pqanJgAED8sgjj2SPPfbIkUcemYULF+aRRx5J586dM3v27Oy7775NvUzepalTp9Z7TPGjjz6a2trafOxjH6sbGzNmjC/ubiYmTJiQ888/P8k//n+2Q4cOjZ535ZVXesdLM+ShBeQ73/lO7r777jz22GP53e9+l7Vr16aqqirHHHNMTjvttHz2s5/1PTzNTHV1dZJ/PBXshRde2Ox5gqf5+dOf/tTgSyoXL16cxYsXJ/Hh9x1RWVlZ7r///vzXf/1Xfv7zn+eOO+5IVVVVzjjjjFx22WWb/VJSdmxLlizJI4880mD8rWNLlizZnktiK7z1c8pb+pdGl1xyieBphtzhAQAACsu/tgcAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsAQPAO/K/fffn5NOOil77bVXWrdunV133TW9evXKySefnIkTJ2b58uWbnfvoo4+mRYsWadGiRS699NItvs6m897OpvPe6Z9u3bplzZo1ueOOO3LWWWelV69eKSsrS3l5efr06ZNLL700q1atetf/vQCwY2pRW1tb29SLAKB5uPTSS/Ptb387SfLhD384H/rQh9KqVavMmzcvf/nLX/Lmm29m1qxZOeSQQxqd/+UvfzkTJ05MkvTs2TPz5s3b7Gttip23+8fUGWec0WDsD3/4QxYsWJA+ffqkb9++9Y516tQpH/rQh/L5z3++bh8HHnhgVqxYkYcffjgrV67Mhz70oTz44IP5wAc+sMXXBmDHJ3gAeEcef/zxfPSjH03Lli3zq1/9KieccEK943//+99zyy235FOf+lQ+9KEPNZi/YcOG7Lnnnnnttdey++675+9//3tmz56dj33sY42+3jsNnsacccYZ+elPf5pvf/vbueSSSxoc/+lPf5qHH344559/fj784Q/Xjb/88ssZOnRo/vSnP+W0007Lz3/+83f92gDsWLylDYB3ZPLkyamtrc0pp5zSIHaSZPfdd8+FF17YaOwkybRp0/Laa6/l8MMPzxe/+MUkyc0337wtl7xZI0eOzDXXXFMvdpJkjz32yA9+8IMk/9jv+vXrm2J5AJSQ4AHgHVmyZEmSpHPnzu9p/i233JIkOf3003P66acnSX75y19mw4YNpVlgifTp0ydJsm7durz++uvveN5nPvOZtGjRImPHjm1wbNasWWnXrl06duyYZ555pmRrBeDtCR4A3pEPfvCDSZLbb789r7766ruau3z58vzmN79J69atc8opp2SfffbJYYcdltdeey3Tpk3bFst9z55//vkkSatWrVJVVfWO51166aVp1apVxo8fX+/BDfPnz8/xxx+fJJkyZcpm74ABsG0IHgDekc985jNp27Zt/va3v6VHjx4544wzcu211+ZPf/pTNm7cuMW5kyZNSk1NTT75yU/WRcSmuzxN9ba2zZkwYUKSZPDgwWnTps07nte9e/ecddZZWbZsWb73ve8l+cddsU9+8pNZtmxZbr311hx22GHbZM0AbJ7gAeAd2XfffTNlypR88IMfzMqVK/PTn/40n//859OvX7906tQpX/ziF/Pyyy83OndT1GyKnCQ55ZRT0qpVq0yZMmWLj7Lenu6+++5cd911adWqVS677LJ3PX/MmDFp27ZtrrrqqixevDjHHXdcFixYkB/+8IcZNmxYvXMfe+yxfO5zn0uPHj3SokWLXHzxxaXaBgBvIXgAeMf+9V//Nc8991wmT56cc845J/369UvLli1TXV2dH/3oR+nbt2+DR00vWrQoDz30UCorK3PcccfVjXfs2DFDhgxJTU1Nbrvttu29lQaeeeaZnH766amtrc0VV1xR91med2PPPffMueeem+XLl6dv37555JFHMmbMmHzhC19ocO7MmTMze/bsHHHEEdlll11KsQUAGiF4AHhXWrdunRNPPDE/+tGP8vjjj2fJkiX50Y9+lF133TWvvvpqzj333Hrn/+xnP0ttbW1GjBjR4C1im+74bHqgQVNZvHhxBg8enGXLlmX06NE577zz3vO1vvrVr2annXbKa6+9ljPOOGOzX7D65S9/Oc8++2xuvPHGVFZWvufXA2DLWjb1AgBo3iorK3POOedkzz33zLBhw3L//fdnzZo1adeuXZL/fTvbAw88kCOOOKLe3E2PfX7ooYeycOHCdO3adfsuPsnSpUtz7LHHZuHChRk1alSuvPLK93yt2trajB49Om+++WaSpGXLzf9jdqed/DtHgO3B/9sCUBIDBw5MkmzcuDHV1dVJ/vFlpX/961+TJM8991xmzpxZ788f//jHJP8IhZ/97Gfbfc2rVq3KJz/5yTz99NMZPnx4fvKTn9R94el78bWvfS2/+MUvMmTIkOyxxx658cYbM3/+/BKuGIB3S/AA8I7U1tZu8fhzzz2X5B9veevUqVOS/32r2oUXXpja2tpG/zzwwAP1zt1e1q1bl2HDhuXRRx/NJz7xidx6663Zeeed3/P1JkyYkHHjxqV///657bbbctFFF+WNN97ImDFjSrhqAN4twQPAOzJmzJh87Wtfy4IFCxocW7x4cf7t3/4tSXL88cendevW2bhxY2699dYkyWmnnbbZ6x555JHZa6+98te//jWPP/74tln8P9m4cWNOO+203HfffTnyyCMzefLktG7d+j1f77bbbstXv/rVdO/ePXfddVfatWuXL3zhC9lrr73yq1/9Kn/+859Lt3gA3hWf4QHgHVm1alUmTJiQK6+8Mj179sz++++fsrKyvPTSS3nkkUeyYcOG9OjRI1dddVWS5J577skrr7ySnj17pl+/fpu97k477ZRTTz0148ePz80335yDDz643vFDDjlks3PPPvvsnH322e96LxMnTsyvf/3rJKl7pHZjrrzyyrq7VZvz0EMP5bOf/Ww6deqUadOmpXPnzkmSsrKy/Md//EfOPffcfPOb38zUqVPf9ToB2HqCB4B35OKLL85HPvKR/O53v8ucOXPy+9//PsuXL0+HDh3Sv3//DBs2LF/84hdTXl6e5H8fVrCluzubnHbaaRk/fnxuvfXWXHnllfU+7P/II49sdt7gwYPf016WLVtW9583hU9jLrnkki0Gz9NPP51hw4Zl5513zpQpU9KjR496xz//+c/nv//7v3P33XfnD3/4Q4OHNgCw7bWofbs3ZQMA21S3bt1y+umnZ+zYsU29FIDCcYcHAJrAkiVL8uCDDyZJ1qxZk2eeeSaTJk1KeXl5PvnJTzbx6gCKwx0eAGgCDzzwQD7+8Y83GO/atWtefPHF7b8ggIISPAAAQGF5LDUAAFBYggcAACgswQMAABSW4AEAAApL8AAAAIUleAAAgMISPAAAQGEJHgAAoLAEDwAAUFiCBwAAKCzBAwAAFJbgAQAACkvwAAAAhSV4AACAwhI8AABAYQkeAACgsP4/IpJl88tZ/z8AAAAASUVORK5CYII=\n", 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", "text/plain": [ "
" ] @@ -235,7 +261,7 @@ "source": [ "# Plot x1 dist\n", "plt.figure(dpi=150)\n", - "plt.hist([SN.sim_x1 for SN in SNs], bins=20)\n", + "plt.hist([SN.x1 for SN in SNs], bins=20)\n", "plt.xlabel('SALT2 $x_1$')\n", "plt.show()" ] @@ -247,7 +273,7 @@ "outputs": [ { "data": { - "image/png": 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q1q2bvvjiC3366ae65pprtGTJEj300EPlvkb37t31zTffaNCgQdq2bZs++OADBQcHa8qUKXr33Xfl5eVl13IAAAAAGMDLsizL00XUJqXHUld0bDU8p834NE+XgFpq5/Pxni4BAABUk6r+fm7bHR4AAAAAqG0IPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADCWbYEnJiZGXl5eFX4tW7as3HFz585VdHS0AgICFBwcrLi4OK1evbrS11q1apXi4uIUHBysgIAARUdHa968eXYtBQAAAIAhvO2ecNCgQQoICCjTfvHFF5dpGzNmjFJSUuTn56c+ffqouLhY6enp+uSTT5SamqoBAwaUGbNgwQLddtttOnnypHr27KkWLVro008/VWJiojZu3KjJkyfbvSQAAAAAdZTtgWfy5Mlq06bNGa/LyMhQSkqKmjdvrszMTLVr106SlJmZqZiYGA0dOlQxMTFyOByuMQcPHtSwYcN04sQJLViwQAMHDpQk7d27V9dff72Sk5N18803KyYmxu5lAQAAAKiDbA88Z2vKlCmSpAkTJrjCjiR169ZNI0eO1EsvvaRZs2Zp7Nixrr6ZM2cqPz9f/fv3d4UdSWrZsqVeeOEFDRw4UMnJyQQeoJ5pMz7N0yVIknY+H+/pEgAAwGk8cmhBUVGRPvvsM0lSQkJCmf7StiVLlri1p6WlVTgmPj5evr6+ysjIUHFxsd0lAwAAAKiDbL/DM2vWLB04cEANGjRQ+/btNWDAALVu3drtms2bN+vo0aMKCQlRaGhomTk6d+4sSdq4caNb+4YNG9z6T+Xj46Mrr7xSa9eu1ZYtW9SpUye7lgQAAACgjrI98EyaNMntz48++qgmTpyoiRMnutp27dolSeWGHUny9/eXw+FQXl6eCgoKFBgYqPz8fB06dKjScaGhoVq7dq2ys7PPGHg6duxYbvv27dsVHh5e6VgAAAAAdYNtj7T17NlTb7zxhrZv364jR45o8+bNeu655+Tt7a2//e1vSklJcV17+PBhSVKTJk0qnM/f31+SVFBQ4DamsnGnjwEAAABQv9l2h+eZZ55x+3P79u31+OOP65prrtFNN92kp556Svfee6/8/Pzseskq2bRpU7ntFd35AQAAAFD3VPuhBX369NE111wjp9Opr776SpJcn9Nz5MiRCscVFhZKkgIDA93GVDbu9DEAAAAA6rcaOaWt9NjpPXv2SJLrEIOcnJxyry8sLJTT6VSzZs1c4SUoKEhNmzatdFxpe1hYmH3FAwAAAKizaiTw5OXlSfrve2wiIiLUuHFj5ebmavfu3WWuX79+vSSVOXggMjLSrf9UJSUlysrKkq+vr9q3b29r/QAAAADqpmoPPLm5ufriiy8k/fc4aT8/P/Xu3VuSNH/+/DJjUlNTJUn9+vVza4+Pj3frP9XSpUtVXFys2NhY+fr62rcAAAAAAHWWLYFn9erVWrRokU6cOOHWvnPnTt16660qLCzULbfc4nacdFJSkqTfj7HeunWrqz0zM1MzZsyQw+HQ8OHD3eYbMWKEgoKCtHjxYi1cuNDVvm/fPo0bN06SNHbsWDuWBAAAAMAAtpzStmXLFg0dOlStWrVS586d5XA4lJ2drXXr1qm4uFgdO3bU66+/7jYmNjZWo0ePVkpKiqKionTjjTfq2LFjSk9Pl2VZmjNnjhwOh9uY4OBgzZ49W0OGDFFCQoJiYmLUvHlzZWRkyOl0KikpSTExMXYsCQAAAIABbAk8f/jDH3T//ffrq6++0jfffKO8vDz5+/srKipKgwcP1v3331/ucdRTp05VVFSUpk2bpvT0dPn4+Cg2NlYTJ05U9+7dy32tQYMGaeXKlZo0aZLWrFmjY8eOqUOHDnrwwQeVmJhox3IAAAAAGMLLsizL00XUJqWfw1PR5/TAc9qMT/N0CUCldj4f7+kSAAAwTlV/P6+RU9oAAAAAwBMIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABjL29MFAIAp2oxP83QJkqSdz8d7ugQAAGoN7vAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwVrUEngMHDuiCCy6Ql5eX2rZtW+m1c+fOVXR0tAICAhQcHKy4uDitXr260jGrVq1SXFycgoODFRAQoOjoaM2bN8/OJQAAAAAwQLUEnrFjx2r//v1nvG7MmDEaOnSosrKyFBsbq+joaKWnp6tnz55atGhRuWMWLFigXr16admyZerUqZP69u2rrVu3KjExUY8++qjNKwEAAABQl9keeD799FP9+9//1j333FPpdRkZGUpJSVHz5s21YcMGLVq0SMuWLdPKlSvVsGFDDR06VE6n023MwYMHNWzYMJ04cUKpqalasWKFUlNT9dNPP6lt27ZKTk7WihUr7F4SAAAAgDrK1sBTVFSk++67Tx06dDjj3ZYpU6ZIkiZMmKB27dq52rt166aRI0fK6XRq1qxZbmNmzpyp/Px89e/fXwMHDnS1t2zZUi+88IIkKTk52a7lAAAAAKjjbA08Tz/9tH7++WdNnz5djRo1qvC6oqIiffbZZ5KkhISEMv2lbUuWLHFrT0tLq3BMfHy8fH19lZGRoeLi4vNeAwAAAABz2BZ4Nm7cqOTkZA0dOlQ9evSo9NrNmzfr6NGjCgkJUWhoaJn+zp07u+Y81YYNG9z6T+Xj46Mrr7xSxcXF2rJly/kuAwAAAIBBbAk8J0+e1IgRI+RwOFyPllVm165dklRu2JEkf39/ORwO5eXlqaCgQJKUn5+vQ4cOVTqutD07O/uc1wAAAADAPN52TPLyyy/rm2++0Zw5c9S8efMzXn/48GFJUpMmTSq8xt/fX06nUwUFBQoMDHSNqWycv7+/JLlCUmU6duxYbvv27dsVHh5+xvEAAAAAar8q3+HZtWuXJkyYoF69eunuu++2oSQAAAAAsEeV7/A88MADOnbsmKZPn37WYwICAiRJR44cqfCawsJCSVJgYKDbmNJxQUFBZxxTmU2bNpXbXtGdHwAAAAB1T5UDz9KlS+VwODRy5Ei39tKT0nbv3q2YmBhJ0jvvvKNWrVqpdevWkqScnJxy5ywsLJTT6VSzZs1c4SUoKEhNmzbVoUOHlJOTow4dOpQZVzpfWFhYVZcFAAAAwAC2vIfH6XTq888/L7evuLjY1VcagiIiItS4cWPl5uZq9+7duvjii93GrF+/XpLUqVMnt/bIyEitXLlS69evLxN4SkpKlJWVJV9fX7Vv396OZQEAAACo46r8Hh7Lssr92rFjhyQpPDzc1damTRtJkp+fn3r37i1Jmj9/fpk5U1NTJUn9+vVza4+Pj3frP9XSpUtVXFys2NhY+fr6VnVZAAAAAAxg6wePnoukpCRJ0qRJk7R161ZXe2ZmpmbMmCGHw6Hhw4e7jRkxYoSCgoK0ePFiLVy40NW+b98+jRs3TpI0duzYGqgeAAAAQF3gscATGxur0aNH68CBA4qKitKAAQMUFxennj176vjx45ozZ44cDofbmODgYM2ePVsNGjRQQkKCevfurcGDBysiIkLbtm1TUlKS6/1CAAAAAGDLe3jO19SpUxUVFaVp06YpPT1dPj4+io2N1cSJE9W9e/dyxwwaNEgrV67UpEmTtGbNGh07dkwdOnTQgw8+qMTExBpeAQAAAIDazMuyLMvTRdQmpcdSV3RsNTynzfg0T5cA1Ak7n4/3dAkAANimqr+fe+yRNgAAAACobgQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBY3p4uAABgrzbj0zxdgiRp5/Pxni4BAADu8AAAAAAwF4EHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABjLtsAzZcoUDRw4UO3atVPTpk3VuHFjhYWF6a677tL3339f4bi5c+cqOjpaAQEBCg4OVlxcnFavXl3pa61atUpxcXEKDg5WQECAoqOjNW/ePLuWAgAAAMAQtgWev//97/roo48UHBysG264QfHx8fL19dUbb7yhLl26aOnSpWXGjBkzRkOHDlVWVpZiY2MVHR2t9PR09ezZU4sWLSr3dRYsWKBevXpp2bJl6tSpk/r27autW7cqMTFRjz76qF3LAQAAAGAAL8uyLDsmWrVqlbp06SJfX1+39ldffVUPPPCAWrZsqZycHHl7e0uSMjIydOONN6p58+bKzMxUu3btJEmZmZmKiYlRkyZNtGPHDjkcDtdcBw8e1KWXXqr8/HwtWLBAAwcOlCTt3btX119/vbZt26bly5crJibmvNfRsWNHSdKmTZvOew5Ujzbj0zxdAoBzsPP5eE+XAAAwQFV/P7ftDs91111XJuxI0qhRoxQeHq69e/fqhx9+cLVPmTJFkjRhwgRX2JGkbt26aeTIkXI6nZo1a5bbXDNnzlR+fr769+/vCjuS1LJlS73wwguSpOTkZLuWBAAAAKCOq5FDCxo1aiRJ8vHxkSQVFRXps88+kyQlJCSUub60bcmSJW7taWlpFY4pfYQuIyNDxcXF9hUPAAAAoM6q9sDzxhtvaPPmzWrXrp3rTs7mzZt19OhRhYSEKDQ0tMyYzp07S5I2btzo1r5hwwa3/lP5+PjoyiuvVHFxsbZs2WL3MgAAAADUQd52T/jiiy9q06ZNKiws1I8//qhNmzbpoosu0n/+8x81bNhQkrRr1y5JKjfsSJK/v78cDofy8vJUUFCgwMBA5efn69ChQ5WOCw0N1dq1a5Wdna1OnTrZvTQAAAAAdYztgefjjz/Wp59+6vpzWFiY5s2bpy5durjaDh8+LElq0qRJhfP4+/vL6XS6Ak/pmMrG+fv7S5IKCgrOWGfpm59Ot337doWHh59xPAAAAIDaz/ZH2jIyMmRZlvLy8rRy5Uq1a9dOvXr10nPPPWf3SwEAAABApWy/w1PK4XCoR48e+vDDD9WtWzdNnDhRffr00bXXXquAgABJ0pEjRyocX1hYKEkKDAyUJNeY0nFBQUFnHFOZio61q+jODwAAAIC6p9oPLWjUqJFuu+02WZblOnWtdevWkqScnJxyxxQWFsrpdKpZs2au8BIUFKSmTZtWOq60PSwszNY1AAAAAKibauRY6hYtWkiScnNzJUkRERFq3LixcnNztXv37jLXr1+/XpLKHDwQGRnp1n+qkpISZWVlydfXV+3bt7e1fgAAAAB1U40Ens8//1ySXIcB+Pn5qXfv3pKk+fPnl7k+NTVVktSvXz+39vj4eLf+Uy1dulTFxcWKjY0t9wNQAQAAANQ/tgSeVatWadmyZTp58qRbe0lJiV5++WW98cYb8vPz02233ebqS0pKkiRNmjRJW7dudbVnZmZqxowZcjgcGj58uNt8I0aMUFBQkBYvXqyFCxe62vft26dx48ZJksaOHWvHkgAAAAAYwJZDC7Zu3aqhQ4eqRYsW6tKli5o3b679+/fr+++/1549e+Tr66u5c+fqkksucY2JjY3V6NGjlZKSoqioKN144406duyY0tPTZVmW5syZI4fD4fY6wcHBmj17toYMGaKEhATFxMSoefPmysjIkNPpVFJSkmJiYuxYEgAAAAAD2BJ4evXqpccff1yff/65Nm7cqP3798vHx0dt2rRRQkKCHn74YbVt27bMuKlTpyoqKkrTpk1Tenq6fHx8FBsbq4kTJ6p79+7lvtagQYO0cuVKTZo0SWvWrNGxY8fUoUMHPfjgg0pMTLRjOQAAAAAM4WVZluXpImqT0mOpKzq2Gp7TZnyap0sAcA52Ph/v6RIAAAao6u/nNXJoAQAAAAB4AoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxvL2dAEAADO1GZ/m6RK08/l4T5cAAPAw7vAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIzl7ekCUPu1GZ/m6RIAAACA88IdHgAAAADGIvAAAAAAMBaBBwAAAICxbAk8R44c0aJFizR8+HBFRETI19dX/v7+ioyM1DPPPKPDhw9XOHbu3LmKjo5WQECAgoODFRcXp9WrV1f6eqtWrVJcXJyCg4MVEBCg6OhozZs3z46lAAAAADCILYHn7bff1q233qrZs2erYcOGuuWWW9SjRw/t2LFDTz75pK699lrt27evzLgxY8Zo6NChysrKUmxsrKKjo5Wenq6ePXtq0aJF5b7WggUL1KtXLy1btkydOnVS3759tXXrViUmJurRRx+1YzkAAAAADGFL4GnUqJHuvfde/fDDD/rhhx/03nvvadmyZdq8ebOuvvpq/fTTTxozZozbmIyMDKWkpKh58+basGGDFi1apGXLlmnlypVq2LChhg4dKqfT6Tbm4MGDGjZsmE6cOKHU1FStWLFCqamp+umnn9S2bVslJydrxYoVdiwJAAAAgAFsCTyJiYmaMWOGrrjiCrf2Cy+8UK+88ookaeHChTp27Jirb8qUKZKkCRMmqF27dq72bt26aeTIkXI6nZo1a5bbfDNnzlR+fr769++vgQMHutpbtmypF154QZKUnJxsx5IAAAAAGKDaDy2IjIyUJB09elQHDhyQJBUVFemzzz6TJCUkJJQZU9q2ZMkSt/a0tLQKx8THx8vX11cZGRkqLi62bwEAAAAA6qxqDzw///yzpN8fewsODpYkbd68WUePHlVISIhCQ0PLjOncubMkaePGjW7tGzZscOs/lY+Pj6688koVFxdry5Yttq4BAAAAQN1U7YEnJSVFktS3b181btxYkrRr1y5JKjfsSJK/v78cDofy8vJUUFAgScrPz9ehQ4cqHVfanp2dbd8CAAAAANRZ3tU5+YcffqhZs2apUaNGevbZZ13tpcdUN2nSpMKx/v7+cjqdKigoUGBgoNvR1hWN8/f3lyRXSKpMx44dy23fvn27wsPDzzgeAAAAQO1XbXd4fvrpJ915552yLEsvvvii6708AAAAAFBTquUOz+7du9W3b1/l5eUpKSlJo0ePdusPCAiQ9PsHllaksLBQkhQYGOg2pnRcUFDQGcdUZtOmTeW2V3TnBwAAAEDdY/sdnoMHD6pPnz7Kzs7W0KFDNXny5DLXtG7dWpKUk5NT7hyFhYVyOp1q1qyZK7wEBQWpadOmlY4rbQ8LC6vyOgAAAADUfbYGnsOHD+t//ud/9MMPP2jgwIF6/fXX5eXlVea6iIgINW7cWLm5udq9e3eZ/vXr10uSOnXq5NZe+lhcaf+pSkpKlJWVJV9fX7Vv396O5QAAAACo42x7pO3o0aPq37+/vv76a9100036z3/+o4YNG5Z7rZ+fn3r37q2PPvpI8+fP15gxY9z6U1NTJUn9+vVza4+Pj9fKlSuVmpqqO++8061v6dKlKi4u1s033yxfX1+7lgUAqMPajE/zdAmSpJ3Px3u6BACot2y5w3PixAndfvvt+uyzz9SjRw8tXLhQPj4+lY5JSkqSJE2aNElbt251tWdmZmrGjBlyOBwaPny425gRI0YoKChIixcv1sKFC13t+/bt07hx4yRJY8eOtWNJAAAAAAxgyx2eadOm6f3335cktWjRQqNGjSr3usmTJ6tFixaSpNjYWI0ePVopKSmKiorSjTfeqGPHjik9PV2WZWnOnDlyOBxu44ODgzV79mwNGTJECQkJiomJUfPmzZWRkSGn06mkpCTFxMTYsSQAAAAABrAl8OTl5bn+uzT4lOepp55yBR5Jmjp1qqKiojRt2jSlp6fLx8dHsbGxmjhxorp3717uHIMGDdLKlSs1adIkrVmzRseOHVOHDh304IMPKjEx0Y7lAAAAADCEl2VZlqeLqE1Kj6Wu6Njq+qi2PAMPAHUV7+EBgPNX1d/Pq+2DRwEAAADA0wg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBY3p4uAAAA07UZn+bpEiRJO5+P93QJAFDjuMMDAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWLYEnnXr1un555/XwIEDFRoaKi8vL3l5eZ1x3Ny5cxUdHa2AgAAFBwcrLi5Oq1evrnTMqlWrFBcXp+DgYAUEBCg6Olrz5s2zYxkAAAAADONtxyTPPvusFi9efE5jxowZo5SUFPn5+alPnz4qLi5Wenq6PvnkE6WmpmrAgAFlxixYsEC33XabTp48qZ49e6pFixb69NNPlZiYqI0bN2ry5Ml2LAcAAACAIWwJPN26dVOnTp107bXX6tprr1WbNm109OjRCq/PyMhQSkqKmjdvrszMTLVr106SlJmZqZiYGA0dOlQxMTFyOByuMQcPHtSwYcN04sQJLViwQAMHDpQk7d27V9dff72Sk5N18803KyYmxo4lAQAAADCALY+0PfbYY3rmmWfUr18/tWrV6ozXT5kyRZI0YcIEV9iRfg9OI0eOlNPp1KxZs9zGzJw5U/n5+erfv78r7EhSy5Yt9cILL0iSkpOT7VgOAAAAAEPU+KEFRUVF+uyzzyRJCQkJZfpL25YsWeLWnpaWVuGY+Ph4+fr6KiMjQ8XFxXaXDAAAAKCOqvHAs3nzZh09elQhISEKDQ0t09+5c2dJ0saNG93aN2zY4NZ/Kh8fH1155ZUqLi7Wli1bqqFqAAAAAHWRLe/hORe7du2SpHLDjiT5+/vL4XAoLy9PBQUFCgwMVH5+vg4dOlTpuNDQUK1du1bZ2dnq1KnTGevo2LFjue3bt29XeHj42SwFAAAAQC1X43d4Dh8+LElq0qRJhdf4+/tLkgoKCtzGVDbu9DEAAAAAUON3eGqLTZs2ldte0Z0fAAAAAHVPjd/hCQgIkCQdOXKkwmsKCwslSYGBgW5jKht3+hgAAAAAqPHA07p1a0lSTk5Ouf2FhYVyOp1q1qyZK7wEBQWpadOmlY4rbQ8LC7O7ZAAAAAB1VI0HnoiICDVu3Fi5ubnavXt3mf7169dLUpmDByIjI936T1VSUqKsrCz5+vqqffv21VA1AAAAgLqoxgOPn5+fevfuLUmaP39+mf7U1FRJUr9+/dza4+Pj3fpPtXTpUhUXFys2Nla+vr52lwwAAACgjqrxwCNJSUlJkqRJkyZp69atrvbMzEzNmDFDDodDw4cPdxszYsQIBQUFafHixVq4cKGrfd++fRo3bpwkaezYsTVQPQAAAIC6wpbAk5aWpq5du7q+jh07JklubWlpaa7rY2NjNXr0aB04cEBRUVEaMGCA4uLi1LNnTx0/flxz5syRw+Fwe43g4GDNnj1bDRo0UEJCgnr37q3BgwcrIiJC27ZtU1JSkmJiYuxYDgAAAABD2HIsdW5urr766qsy7ae25ebmuvVNnTpVUVFRmjZtmtLT0+Xj46PY2FhNnDhR3bt3L/d1Bg0apJUrV2rSpElas2aNjh07pg4dOujBBx9UYmKiHUsBAAAAYBAvy7IsTxdRm5R+Dk9Fn9NTH7UZn3bmiwAAtd7O5+M9XQIAnLOq/n7ukffwAAAAAEBNIPAAAAAAMBaBBwAAAICxbDm0AAAA1H615T2ZvJcIQE3iDg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWN6eLgAAANQvbcaneboESdLO5+M9XQKAGsAdHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACM5e3pAgAAADyhzfg0T5cgSdr5fLynSwCMxh0eAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjcSx1LVZbjssEAAAA6iru8AAAAAAwFoEHAAAAgLF4pA0AAMCDasMj7Dufj/d0CUC1qZN3eIqKivS3v/1N7du3l6+vry666CINGzZMu3fv9nRpAAAAAGqROneHp7i4WL1799aaNWt04YUXqn///tq5c6fmzJmjpUuXas2aNbrssss8XSYAAECdURvuMkncaUL1qHN3eCZNmqQ1a9aoW7du2rJli95991199dVXSk5OVm5uroYNG+bpEgEAAADUEnUq8Bw7dkzTpk2TJL3yyisKCAhw9SUlJalTp076/PPPtW7dOk+VCAAAAKAWqVOBZ9WqVTp06JDCw8N19dVXl+lPSEiQJC1ZsqSmSwMAAABQC9Wp9/Bs2LBBktS5c+dy+0vbN27cWGM1AQAAwB68lwjVoU4Fnl27dkmSQkNDy+0vbc/Ozq6xmgAAAGCW2hK8aou6HgDrVOA5fPiwJKlJkybl9vv7+0uSCgoKzjhXx44dy23/6aef1KhRowr7a9Kvew97ugQAAADUcx2XBJz5omq0fft2NWrU6LzH16nAUxO8vLyq9A21U7uWnv0f19nYvn27JCk8PNzDleBM2Ku6g72qG9inuoO9qjvYq7qjJveqUaNGrhsb56NOBZ7SU9mOHDlSbn9hYaEkKTAw8Ixzbdq0yb7C6rHSO2F8P2s/9qruYK/qBvap7mCv6g72qu6oS3tVp05pa926tSQpJyen3P7S9rCwsBqrCQAAAEDtVacCT2RkpCRp/fr15faXtnfq1KnGagIAAABQe9WpwHPdddepadOm2r59u7777rsy/ampqZKkfv361XBlAAAAAGqjOhV4fHx89OCDD0qSHnjgAdd7diRpypQp2rhxo3r16qUuXbp4qkQAAAAAtUidOrRAkiZMmKCMjAytXr1a7dq1U48ePZSdna2vvvpKISEhmj17tqdLBAAAAFBLeFmWZXm6iHNVVFSk//u//9Pbb7+tX375RcHBwerbt6+effbZCj+UFAAAAED9UycDDwAAAACcjTr1Hh4AAAAAOBcEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg/OyapVqxQXF6fg4GAFBAQoOjpa8+bNO+d59u/fr1mzZunee+9VVFSUvL295eXlpblz59pftKGKior0t7/9Te3bt5evr68uuugiDRs2TLt37z7nufLy8jR69GiFhYWpcePGCgsL05gxY+R0Ou0vvJ6xa58+//xzPf3004qPj1dISIi8vLzUpk2b6im6nrJjr5xOp95++23dfvvtuvTSS+Xj46PAwED94Q9/UEpKikpKSqpxBfWHHXt1/PhxPfXUU4qPj9dll12mwMBA+fr6ql27dho1apSys7OrcQX1h53/Vp1q69at8vPzk5eXl2JjY22qtn6za6/atGkjLy+vCr9++umnalpBJSzgLKWmploNGza0vLy8rF69elmDBg2yHA6HJckaO3bsOc31/vvvW5LKfM2ZM6d6ijdMUVGR1bVrV0uSdeGFF1pDhgyxoqOjLUlWSEiItX379rOeKzc312rbtq0lybrsssusIUOGWB07drQkWe3bt7cOHDhQjSsxm537FBkZWebnJSwsrPqKr2fs2qsnnnjCkmR5eXlZV199tXXbbbdZvXv3tho3bmxJsq6//nqrsLCwmldjNrv2qqCgwJJkBQQEWN27d7cSEhKsW265xWrdurUlyQoKCrK++eabal6N2ez8O/B0MTExlpeXlyXJuuGGG2ysun6yc6/CwsIsSVZiYmK5X7/++ms1rqR8BB6clQMHDlhBQUGWJGvBggWu9t9++831y/Ly5cvPer7Vq1dbo0aNsmbPnm19//331j333EPgOQelv1R169bNKigocLUnJydbkqxevXqd9Vx33HGHJckaOHCgVVJS4mp/6KGHXH9h4fzYuU9//etfrUmTJlkff/yxtWnTJgKPzezaq7///e/WuHHjrOzsbLf2LVu2uH6R/t///V87S6937NqrkpIS68svv3T7e8+yLOv48ePW+PHjLUlWly5d7Cy93rHz78BTzZw505Jk3XvvvQQem9i5V6WBpzapXdWg1vrHP/5hSbL69+9fpm/hwoWWJOvmm28+7/nvu+8+As9ZOnr0qNW0aVNLkrV+/foy/Z06dbIkWWvXrj3jXL/++qvVoEEDy8fHx/rtt9/c+oqLi62QkBCrYcOG1t69e22rv76wc59Ot2fPHgKPjapzr0719ttvW5KsNm3aVGme+qym9qqkpMTy9fW1JFlOp7NKc9VX1bVXv/32m9WsWTPrxhtvtJYvX07gsYHde1UbAw/v4cFZSUtLkyQlJCSU6YuPj5evr68yMjJUXFxc06XVO6tWrdKhQ4cUHh6uq6++ukx/6R4tWbLkjHMtW7ZMJ0+eVI8ePdSyZUu3vsaNG6tfv346ceKEPvzwQ3uKr0fs3CdUr5raq8jISEnSr7/+WqV56rOa2isvLy81bNhQXl5e8vHxqdJc9VV17dXo0aNVVFSkV1991ZY6UT/+vSLw4Kxs2LBBktS5c+cyfT4+PrryyitVXFysLVu21HRp9U5le3Fq+8aNG2t0Lrjje1t31NRe/fzzz5KkVq1aVWme+qwm9sqyLP3jH/9QYWGh/vjHP8rPz++856rPqmOvPvzwQ7377rt6/PHH1bZt26oXCUnV93P14osvauTIkRo9erT+9a9/KTc3t2qFVoG3x14ZdUZ+fr4OHTokSQoNDS33mtDQUK1du1bZ2dnq1KlTTZZX7+zatUtS5Xsh6axOGLJzLrjje1t31NRepaSkSJL69+9fpXnqs+raq8cee0x79+5Vfn6+Nm7cqO3bt+uKK67QzJkzq1ZwPWb3XhUWFmrUqFGKiIjQY489Zk+RkFR9P1fjxo1z+/Mjjzyil19+WcOGDTuPKquGwIMzOnz4sOu/mzRpUu41/v7+kqSCgoIaqak+K90PO/bCzrngju9t3VETezV9+nRlZGTI4XBo/Pjx5z1PfVdde7VgwQJt377d9edOnTrpzTff1KWXXnqelcLuvZowYYKys7O1fPlyHjO0md17dcstt+iPf/yjunTpopCQEP3888+aPXu2UlJSNGLECDVv3rzG/48fAk89ceutt+rHH388pzHz5s1TdHR0NVUEAPXDF198odGjR8vLy0uzZ8/WRRdd5OmScJpt27ZJ+v0z4tatW6cnnnhCXbp00euvv67ExEQPV4e1a9fqpZde0l133aWYmBhPl4MzeOmll9z+3LFjRyUnJ+vyyy/Xvffeq8cee4zAg+qxY8cObd68+ZzGHDlyRJIUEBDg1hYUFFTm2sLCQklSYGBgFarE2Sjdj9L9Od257IWdc8Ed39u6ozr3KisrS/3799exY8f00ksv6dZbbz3/QlHtP1ctWrTQTTfdpK5du+qqq67S/fffr969e+uSSy45v4LrMbv26vjx47rnnnvkcDg0efJke4uEpJr792r48OGaMGGCNm/erJ07d9boh2cTeOqJ77777rzHBgUFqWnTpjp06JBycnLUoUOHMtfk5ORIksLCws77dXB2WrduLem/3/PTncte2DkX3PG9rTuqa6927NihPn36KC8vT0899ZQeeuihqhWKGvu5atq0qfr166dXX31V6enpHnnPQV1n117l5OTou+++U6tWrTR48GC3PqfTKUlat26d687PihUrzr/oeqqmfq4aNGig8PBw7du3T3v27CHwoPaJjIzUypUrtX79+jKBp6SkRFlZWfL19VX79u09VGH9UXq07fr168vtL20/m8Mj7JwL7vje1h3VsVd79uzRjTfeqD179mj06NF68sknq14oavTnqkWLFpLk0ZOl6jK79+q3337Tb7/9Vm6f0+nU559/fh5VQqrZn6u8vDxJ/31fUI3x9AcBoW7gg0drj1M/IOzbb78t03++Hzx6+oeL8sGjVWPnPp2ODx61l917dfDgQeuqq66yJFlDhw61Tp48aXPF9Vd1/lydrlevXpYka/78+VWeqz6qib3ig0ftUVM/V1lZWZaXl5fVpEkT6+jRo1Wa61wReHBWDhw4YAUFBVmSrAULFrja9+7da7Vt29aSZC1fvrzMuIiICCsiIsLKycmpdH4Cz7l54oknLElW9+7drcOHD7vak5OTLUlWr1693K5/+eWXrYiICGv8+PFl5rrjjjssSdagQYOskpISV/vDDz9sSbISExOraxnGs3OfTkXgsZ9de1VYWGh169bNkmQNGTLEOn78eE2UX6/YtVdLly61Vq1aVWb+wsJC6/HHH7ckWa1atbIKCgqqZR31QXX9HViKwGMfu/YqLS3N+vTTT8vMv2HDBuuKK66wJFkPP/xwtayhMjzShrMSHBys2bNna8iQIUpISFBMTIyaN2+ujIwMOZ1OJSUllXtySulBCSUlJWX6unbt6vrvHTt2SJKeffZZTZ8+XdLvH3TFJymXb8KECcrIyNDq1avVrl079ejRQ9nZ2frqq68UEhKi2bNnu12/f/9+bd68WXv27Ckz19SpU7VmzRotWLBAl19+ua655hpt2rRJWVlZateunaZMmVJTyzKOnfs0c+ZM12eClP487dmzx+3n6NVXX63wg+NQObv26oknnlBmZqYaNmwob29vDR8+vNzXmzt3bnUtxXh27dU333yjp59+WhdffLGioqLUtGlT/fbbb/ruu+908OBBNW3aVO+9957bwT04N3b+HYjqZddeff3113r66acVFhamyMhINWnSRD///LPWr1+v48ePKyYmRs8//3xNLu13NR6xUKd9+eWXVt++fS2Hw2E1adLEuuaaa6y5c+dWeL0kS5K1Y8eOCvsq+jr9/02AuyNHjlgTJ060wsPDLR8fH6tVq1bW3Xffbf3yyy9lrn3yyScrvVtz4MAB66GHHrIuueQSy8fHx7rkkkushx9+2MrLy6veRdQDdu1TaV9lX+XdZcXZs2OvEhMTz7hP/NNbdXbs1YYNG6ykpCTr2muvtS644ALL29vbCgwMtK6++mrrf//3f61ff/21hlZjNjv/rTodd3jsZcderV692ho2bJh11VVXWc2bN7e8vb2t4OBgKyYmxnr99dc9dtfby7Isq7pDFQAAAAB4QgNPFwAAAAAA1YXAAwAAAMBYBB4AAAAAxiLwAAAAADAWgQcAAACAsQg8AAAAAIxF4AEAAABgLAIPAAAAAGMReAAAAAAYi8ADAAAAwFgEHgAAAADGIvAAAGyxfPlyDRo0SBdffLF8fHzUrFkzRUREaPDgwZo2bZoOHTpU6fhRo0bJy8tLDRo0UHZ2doXX7dy5U15eXmrTpk2Zvrlz58rLy+ucvp566ilJ0k8//aR//OMf+uMf/6gWLVqoUaNGatWqlQYOHKgvvviiKt8aAIAHeVmWZXm6CABA3fbMM8/oySeflCRdccUVuvzyy9WoUSNt3rxZ33//vU6ePKnMzEx17dq13PHHjh3ThRdeqIMHD0qSnnvuOT3++OPlXrtz505deumlCgsL086dO936vvzyS82cObPMmH//+9+SpEGDBikgIMCtb8CAARowYIBCQ0O1e/duBQQEqGvXrgoODtYPP/ygrKwseXl5acqUKRozZsy5fFsAALUAgQcAUCXr1q3TtddeK29vb7333nsaMGCAW/9vv/2mN998UzfffLMuv/zycudYtGiRbr31Vl144YXas2ePLr/8cv3444/lXltZ4KmIl5eXJGnHjh3l3hmSpNjYWCUmJmrw4MHy9fV1tc+YMUMjR45Uw4YNtXHjRnXo0OGsXhMAUDvwSBsAoEoWLlwoy7I0ZMiQMmFHklq1aqVHH320wrAjSW+88YYk6emnn1Z4eLh++uknrV27trpKLldGRob+8pe/uIUdSbrvvvvUp08fnThxQvPnz6/RmgAAVUfgAQBUSW5uriQpJCTkvMY7nU6lpaWpcePGGjJkiO644w5J/w1BtUFkZKQk6ddffz2ncSUlJZo+fbquv/56ORwO+fn5qW3btho6dKjWrVtXHaUCAE5D4AEAVMkll1wiSVqwYIH27dt3zuPfe+89HT16VDfffLOaNm2qO++8U5L0zjvv6Pjx47bWer5+/vlnSb/frTpbhYWFio2N1f3336/vvvtOXbt2Vf/+/dWiRQu99dZbtSrQAYDJCDwAgCq544475Ofnp19++UVt27bV3XffrZkzZ+rbb7/ViRMnzji+9Bf/0qDTrl07RUdHa9++ffrkk0+qtfazsX37di1dulSSdMstt5z1uNGjR2vlypXq2bOnduzYoWXLlumdd97RmjVr9Msvv+j222+vrpIBAKcg8AAAquSyyy7TkiVLdMkll6igoED//ve/dc8996hz585q0aKFRo0apT179pQ7dseOHVq1apWCg4MVFxfnai8NP56+C3L8+HHdfffdOnr0qG677TZ16dLlrMb9+uuvmjt3rho3bqx58+aVedyvZcuW+sMf/lAdJQMATkPgAQBU2Q033KBt27Zp4cKFGjlypDp37ixvb285nU699tprioqK0ubNm8uMe+utt1wHHvj4+Lja//SnP8nb21uLFy9WQUFBTS7FzcMPP6wvv/xSl112mV599dWzHrdixQqdOHFCffv2VVhYWDVWCAA4EwIPAMAWPj4+uvXWW/Xaa69p3bp1ys3N1WuvvaZmzZpp3759evDBB8uMOf1xtlIhISG66aabVFRUpAULFtRI/ad77rnn9Nprr6lly5b6+OOPFRwcfNZjf/nlF0lSeHh4dZUHADhL3p4uAABgJofDoZEjR+qiiy5S//79tXz5ch05ckRNmjSRJH399dfasmWLJOmxxx4rMz4nJ0fS76Ho7rvvrrG6JWn69OmaMGGCmjZtqmXLlqlt27Y1+voAAPsQeAAA1ap3796SpBMnTsjpdLoCz6nvz1m1alWF41esWKGcnByFhoZWb6H/3zvvvKMHHnhATZo0UVpamqKios55jtKT67Zv325zdQCAc8UjbQCAKrEsq9L+bdu2Sfr9kbcWLVpI+v0wgHfffVeSlJWVJcuyyv26++67dfLkSb311lvVu4j/78MPP9Rdd90lb29vvf/++7ruuuvOa56YmBg1bNhQH3/8sevxNgCAZxB4AABVMnHiRP31r38t927G7t27dd9990n6/Ujn0oMJli1bptzcXF111VXq2LFjhXOXHt385ptvVkPl7latWqWEhARZlqV3331Xffr0Oe+5LrroIt11110qLi5WYmKiDhw44Na/b98+ffXVV1UtGQBwFnikDQBQJYcPH1ZKSoomT56s9u3bq0OHDvL19VVOTo6++uorlZSUqG3btpo6daprTOnjbGf6LJobbrhBF1xwgbKysvTdd9+5PV62Z88ede3atcKxEydOVHx8/Fmv4+abb1ZRUZEuvfRSLVq0SIsWLSpzzfXXX68RI0ac1XwpKSnavHmzli9frrCwMPXs2VNBQUHKzs7W+vXrdf/993M0NQDUAAIPAKBKJkyYoGuuuUYff/yxNmzYoC+++EKHDh1SUFCQoqOj1b9/f40aNUr+/v6SpPz8fH3wwQeSfj9+ujINGzbU4MGD9corr+iNN95wCzzHjh2r9C5Jbm7uOa3D6XRK+v2zgXbs2FHhdWcbeAIDA7V8+XJNnz5db731lr744gudOHFCF110ke644w7ddddd51QfAOD8eFlnevgaAAAAAOoo3sMDAAAAwFgEHgAAAADGIvAAAAAAMBaBBwAAAICxCDwAAAAAjEXgAQAAAGAsAg8AAAAAYxF4AAAAABiLwAMAAADAWAQeAAAAAMYi8AAAAAAwFoEHAAAAgLEIPAAAAACMReABAAAAYCwCDwAAAABjEXgAAAAAGIvAAwAAAMBYBB4AAAAAxvp/4J7FvvdzBWAAAAAASUVORK5CYII=\n", + "image/png": 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", "text/plain": [ "
" ] @@ -259,7 +285,7 @@ "source": [ "# Plot c dist\n", "plt.figure(dpi=150)\n", - "plt.hist([SN.sim_c for SN in SNs], bins=20)\n", + "plt.hist([SN.c for SN in SNs], bins=20)\n", "plt.xlabel('SALT2 c')\n", "plt.show()" ] @@ -271,7 +297,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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", "text/plain": [ "
" ] @@ -283,7 +309,7 @@ "source": [ "# Plot mb dist\n", "plt.figure(dpi=150)\n", - "plt.hist([SN.sim_mb for SN in SNs], bins=20)\n", + "plt.hist([SN.mb for SN in SNs], bins=20)\n", "plt.xlabel('SALT2 mb')\n", "plt.show()" ] @@ -295,7 +321,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] @@ -307,7 +333,7 @@ "source": [ "# Plot HD (with peculiar velocities effect on z)\n", "plt.figure(dpi=150)\n", - "plt.scatter([SN.zobs for SN in SNs], [SN.sim_mu for SN in SNs], s=1)\n", + "plt.scatter([SN.zobs for SN in SNs], [SN.mu for SN in SNs], s=1)\n", "plt.xlabel('Observed redshift $z_{obs}$')\n", "plt.ylabel('Distance modulus $\\mu$')\n", "plt.show()" @@ -329,12 +355,12 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ "randseed = 5678 #random seed for generation\n", - "n_obj = 6000 #number of SN to generate\n", + "n_obj = 1000 #number of SN to generate\n", "z_range = [0.05, 0.25] #redshift range to extract SN redshift\n", "\n", "#time range of SNIa observation to extract t0 for each object\n", @@ -350,12 +376,12 @@ " #you can just put a list with [sig,sig] for gaussian scatter or\n", " #[sig1,sig2] for asymmetric gaussian scatter\n", " 'rate': 'ptf19', #default rate value, you can use a general lambda function for rate\n", - " 'model_config': {'model_name': ['v19-2006t','v19-2008aq','v19-2008ax','v19-2008bo']\n", - " }}# here we pass a list of sncosmo template as model, the generator ramndomly extract the\n", - " #the template to generate each object. you can pass just 1 template or the default which\n", - " # are: 'all', 'vinc_corr' and vinc_nocorr'\n", + " 'model_name': ['v19-2006t','v19-2008aq','v19-2008ax','v19-2008bo']}\n", + " # here we pass a list of sncosmo template as model, the generator ramndomly extract the\n", + " # the template to generate each object. you can pass just 1 template or the default which\n", + " # are: 'all', 'vinc_corr' and vinc_nocorr'\n", "\n", - "#Cosmology, CMB dipole, peculiar velocity and dust same as previous example\n" + "# Cosmology, CMB dipole, peculiar velocity and dust same as previous example\n" ] }, { @@ -367,7 +393,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -376,18 +402,23 @@ "\n", "# Give the input configuration\n", "SNgenerator = gen_class(sniib_gen,\n", - " cmb,\n", " snsim.utils.set_cosmo(cosmology),\n", + " time_range,\n", " z_range=z_range,\n", - " time_range=time_range,\n", " vpec_dist=vpec_dist,\n", - " mw_dust=mw_dust, \n", - " )\n", - "\n", - "\n", - "\n", + " mw_dust=mw_dust,\n", + " cmb=cmb\n", + " )" + ] + }, + { + "cell_type": "code", + "execution_count": 23, + "metadata": {}, + "outputs": [], + "source": [ "# Gen basic parameters\n", - "params = SNgenerator.gen_astrobj_par(n_obj, randseed)\n" + "params = SNgenerator.gen_basic_par(n_obj, randseed)" ] }, { @@ -399,12 +430,14 @@ }, { "cell_type": "code", - "execution_count": 36, - "metadata": {}, + "execution_count": 24, + "metadata": { + "scrolled": true + }, "outputs": [], "source": [ "# Generate SN obj\n", - "SNs = SNgenerator(n_obj, randseed, astrobj_par=params)" + "SNs = SNgenerator(n_obj, randseed, basic_par=params)" ] }, { @@ -416,12 +449,12 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 25, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -433,7 +466,7 @@ "source": [ "# Plot HD (with peculiar velocities effect on z)\n", "plt.figure(dpi=150)\n", - "plt.scatter([SN.zobs for SN in SNs], [SN.sim_mu for SN in SNs], s=1)\n", + "plt.scatter([SN.zobs for SN in SNs], [SN.mu for SN in SNs], s=1)\n", "plt.xlabel('Observed redshift $z_{obs}$')\n", "plt.ylabel('Distance modulus $\\mu$')\n", "plt.show()" @@ -441,12 +474,12 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 26, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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", "text/plain": [ "
" ] @@ -458,19 +491,19 @@ "source": [ "# Plot mb dist\n", "plt.figure(dpi=150)\n", - "plt.hist([SN.sim_mb for SN in SNs], bins=20)\n", + "plt.hist([SN.mb for SN in SNs], bins=20)\n", "plt.xlabel('mb')\n", "plt.show()" ] }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 27, "metadata": {}, "outputs": [ { "data": { - "image/png": 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oX79+GjFihMLDw/XNN98oOTlZMTExrl4SAAAAAA/i0t3hysrKHG9Vu//++y94/KxZsxQZGanZs2crIyNDPj4+io2N1bRp09S9e/dKa4YPH661a9dq+vTp2rhxo06fPq2OHTtq/PjxSkxMdOVyAAAAAHggL8uyLHc3UZfYt8iuagttAMDFafN4urtbwC/sfy7e3S0AwCWrifvzGvuyVAAAAACoiwhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYxaVbZAMA3I9d2QAAqB5PggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEbxdncDAACgdrR5PN3dLUiS9j8X7+4WABiOJ0EAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAo7g0BB09elSTJ09WeHi4/P39FRISoqioKP3f//1fpccvX75cffr0UXBwsIKDgxUTE6P09PRqX2Pnzp0aMWKEWrRoIX9/f910002aNWuWzp0758qlAAAAAPBQLgtBn3/+uW688UalpKSoUaNGGjx4sG677TadOHFCL774YoXjZ82apTvvvFMbNmxQjx491K9fP23evFl33HGHZs+eXelrZGVl6dZbb1VaWppuuOEG3XnnnTp27Jgee+wx3X333bIsy1XLAQAAAOChvF1xkqNHj2rgwIEqKSnRsmXLdOeddzrNb9682enPe/bs0eTJk+Xr66tPPvlE3bp1kyTt3btX3bt312OPPaaBAweqXbt2jpry8nLdd999Kikp0cyZM/XYY49JkoqKijRgwAAtWrRIcXFxGjVqlCuWBAAAAMBDueRJ0JNPPqljx47p+eefrxCAJCk6Otrpz6mpqTp79qzGjRvnCECS1KFDB/35z3/WmTNnlJqa6lTz/vvv67vvvlNERIQjAElSYGCg48lRSkqKK5YDAAAAwINdcQgqKSnRm2++qYCAACUlJV1Ujf1zPwkJCRXm7GPLly+/6JqoqCjdcMMN2rFjh/bv338p7QMAAAAwzBW/HW7r1q0qLCxUz5495e/vr//+97/KyMhQaWmpOnTooJEjR+qaa65xHJ+fn68DBw5Ikrp27VrhfNddd52aN2+u3NxcFRQUKDg4WJK0fft2ST8HnspERUXp22+/VXZ2ttq0aXOlywIAAADgoa44BO3atUuSdNVVV2nIkCFatmyZ0/wTTzyhuXPn6p577pEkRwBq2rSpAgICKj1naGiojh07ptzcXN10001OdaGhoVXWSFJubu4VrggAAACAJ7viEJSXlydJ+uCDD9SwYUO9/PLLGjFihE6dOqXZs2frhRdeUGJiom688UZFRkaqqKhIktS4ceMqz2kPR4WFhY6xC9VVVlOdTp06VTq+b98+tW3b9qLOAQAAAKD+ueLPBNm/n+fMmTN6+umn9dBDD6lFixYKCwvT888/rxEjRqi8vFzPP//8FTcLAAAAAFfqip8EBQYGOv53ZRsjJCUladGiRVqzZo3T8adOnarynMXFxZKkoKAgp9fJy8ursq6ymurs3Lmz0vGqnhABAAAA8AxX/CQoLCxM0s9vU2vRokWFefsmBUeOHJEktW7dWtLPb6OzB5dfOnjwoNO5z6+zz11MDQAAAAD80hWHIPsObyUlJSorK6swf+LECUn/ewJks9kcgeaLL76ocPz333+vY8eOKSwszLEznCRFRERIkrZt21ZpH/bxLl26XO5SAAAAABjgikNQ69atFRERIcuyHG95O5997PztsOPj4yVJaWlpFY63jw0aNMhpvLqaL774Qt9++606d+7M9tgAAAAAqnXFIUiSpkyZIkmaPHmyfvzxR8f4l19+qZSUFEnSuHHjHOMTJkxQw4YN9eqrr2rjxo2O8ZycHD377LPy9vbWhAkTnF5j6NChuv7667V9+3a9+OKLjvHi4mI9/PDDkqRJkya5YjkAAAAAPJhLQtC9996rxMREffXVV+rYsaPi4+PVr18/3XbbbTpx4oQefPBBjRgxwnF8eHi4nn/+eZWVlalXr16Ki4vTkCFDFBERoePHj2vmzJlq166d02s0atRIb775pvz9/ZWcnKzbbrtNd911l9q3b6+srCwlJCQoMTHRFcsBAAAA4MFcEoIkaf78+frXv/6ltm3b6tNPP9XmzZsVFRWlBQsW6F//+leF4x977DF98MEH6tatm9atW6dVq1bplltu0fLly/XII49U+hrdu3fXli1bNHz4cH3zzTf64IMPFBISopkzZ+q9996Tl5eXq5YDAAAAwEN5WZZlubuJusS+RXZVW2gDQF3X5vF0d7cAVGv/c/HubgFAPVIT9+cuexIEAAAAAPUBIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjuCwExcTEyMvLq8qflStXVlq3YMECRUdHKzAwUCEhIYqLi9OGDRuqfa3169crLi5OISEhCgwMVHR0tBYuXOiqpQAAAADwYN6uPuHw4cMVGBhYYfzaa6+tMDZx4kSlpqbK399fAwYMUGlpqTIyMvTxxx8rLS1NQ4YMqVCzePFi3XXXXTp37px69+6t5s2ba9WqVUpMTFR2drZeeOEFVy8JAAAAgAdxeQh64YUX1KZNmwsel5mZqdTUVDVr1kxZWVlq3769JCkrK0sxMTFKSkpSTEyMbDabo+bEiRMaPXq0zp49q8WLF2vYsGGSpMOHD6tnz55KSUnRHXfcoZiYGFcvCwAAAICHcNtngmbOnClJmjp1qiMASVK3bt00btw45efna+7cuU41r732mgoKCjR48GBHAJKkli1basaMGZKklJSUWugeAAAAQH3llhBUUlKi1atXS5ISEhIqzNvHli9f7jSenp5eZU18fLz8/PyUmZmp0tJSV7cMAAAAwEO4/O1wc+fO1fHjx9WgQQN16NBBQ4YMUevWrZ2O2bNnj8rKytSiRQuFhoZWOEdUVJQkKTs722l8+/btTvPn8/HxUefOnbV161bt3btXXbp0cdWSAAAAAHgQl4eg6dOnO/158uTJmjZtmqZNm+YYO3DggCRVGoAkKSAgQDabTXl5eSosLFRQUJAKCgp08uTJautCQ0O1detW5ebmEoIAAAAAVMplIah3794aO3asunfvrquvvlrff/+90tLSNH36dP3lL39RcHCwJkyYIEkqKiqSJDVu3LjK8wUEBCg/P98Rguw11dUFBARIkgoLCy/Yb6dOnSod37dvn9q2bXvBegAAAAD1k8s+E/T000/r/vvv1w033CB/f3916NBBTzzxhJYuXSpJeuqpp1RSUuKqlwMAAACAy+Lyt8P90oABA3TLLbdo69at2rRpk2JiYhzfI3Tq1Kkq64qLiyVJQUFBkuT03UOnTp1ScHDwBWuqs3PnzkrHq3pCBAAAAMAz1MrucPYtsH/88UdJcmyUcPDgwUqPLy4uVn5+vpo2beoINMHBwWrSpEm1dfbxsLAw1zUPAAAAwKPUSgjKy8uT9L/P7ISHh8vX11dHjx7VoUOHKhy/bds2SaqwuUFERITT/PnKy8u1Y8cO+fn5qUOHDi7tHwAAAIDnqPEQdPToUa1bt07S/7a29vf3V79+/SRJixYtqlCTlpYmSRo0aJDTeHx8vNP8+VasWKHS0lLFxsbKz8/PdQsAAAAA4FFcEoI2bNigpUuX6uzZs07j+/fv19ChQ1VcXKw777zTaWvr5ORkST9vqZ2Tk+MYz8rK0pw5c2Sz2TRmzBin840dO1bBwcFatmyZlixZ4hg/cuSIpkyZIkmaNGmSK5YEAAAAwEO5ZGOEvXv3KikpSa1atVJUVJRsNptyc3P1+eefq7S0VJ06ddK///1vp5rY2FhNmDBBqampioyMVP/+/XX69GllZGTIsizNnz9fNpvNqSYkJETz5s3TyJEjlZCQoJiYGDVr1kyZmZnKz89XcnKyYmJiXLEkAAAAAB7KJSHo17/+tf74xz9q06ZN2rJli/Ly8hQQEKDIyEiNGDFCf/zjH+Xv71+hbtasWYqMjNTs2bOVkZEhHx8fxcbGatq0aerevXulrzV8+HCtXbtW06dP18aNG3X69Gl17NhR48ePV2JioiuWAwAAAMCDeVmWZbm7ibrEvkV2VVtoA0Bd1+bxdHe3AFRr/3Px7m4BQD1SE/fntbI7HAAAAADUFYQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBSXfE8QAICtqQEAqC94EgQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFG93NwAAAMzS5vF0d7cgSdr/XLy7WwDgJjwJAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRaiQEHT9+XFdddZW8vLzUrl27ao9dsGCBoqOjFRgYqJCQEMXFxWnDhg3V1qxfv15xcXEKCQlRYGCgoqOjtXDhQlcuAQAAAICHqpEQNGnSJB07duyCx02cOFFJSUnasWOHYmNjFR0drYyMDPXu3VtLly6ttGbx4sXq06ePVq5cqS5dumjgwIHKyclRYmKiJk+e7OKVAAAAAPA0Lg9Bq1at0uuvv64HH3yw2uMyMzOVmpqqZs2aafv27Vq6dKlWrlyptWvXqmHDhkpKSlJ+fr5TzYkTJzR69GidPXtWaWlp+vTTT5WWlqavv/5a7dq1U0pKij799FNXLwkAAACAB3FpCCopKdEf/vAHdezY8YJPZWbOnClJmjp1qtq3b+8Y79atm8aNG6f8/HzNnTvXqea1115TQUGBBg8erGHDhjnGW7ZsqRkzZkiSUlJSXLUcAAAAAB7IpSHor3/9q7799lu9+uqratSoUZXHlZSUaPXq1ZKkhISECvP2seXLlzuNp6enV1kTHx8vPz8/ZWZmqrS09LLXAAAAAMCzuSwEZWdnKyUlRUlJSerVq1e1x+7Zs0dlZWVq0aKFQkNDK8xHRUU5znm+7du3O82fz8fHR507d1Zpaan27t17ucsAAAAA4OFcEoLOnTunsWPHymazOd6WVp0DBw5IUqUBSJICAgJks9mUl5enwsJCSVJBQYFOnjxZbZ19PDc395LXAAAAAMAM3q44yUsvvaQtW7Zo/vz5atas2QWPLyoqkiQ1bty4ymMCAgKUn5+vwsJCBQUFOWqqqwsICJAkR3CqTqdOnSod37dvn9q2bXvBegAAAAD10xU/CTpw4ICmTp2qPn36aNSoUS5oCQAAAABqzhU/CXr44Yd1+vRpvfrqqxddExgYKEk6depUlccUFxdLkoKCgpxq7HXBwcEXrKnOzp07Kx2v6gkRAAAAAM9wxSFoxYoVstlsGjdunNO4fYe2Q4cOKSYmRpL07rvvqlWrVmrdurUk6eDBg5Wes7i4WPn5+WratKkj0AQHB6tJkyY6efKkDh48qI4dO1aos58vLCzsSpcFAAAAwEO55DNB+fn5WrNmTaVzpaWljjl7MAoPD5evr6+OHj2qQ4cO6dprr3Wq2bZtmySpS5cuTuMRERFau3attm3bViEElZeXa8eOHfLz81OHDh1csSwAAAAAHuiKPxNkWValP999950kqW3bto6xNm3aSJL8/f3Vr18/SdKiRYsqnDMtLU2SNGjQIKfx+Ph4p/nzrVixQqWlpYqNjZWfn9+VLgsAAACAh3Lpl6VeiuTkZEnS9OnTlZOT4xjPysrSnDlzZLPZNGbMGKeasWPHKjg4WMuWLdOSJUsc40eOHNGUKVMkSZMmTaqF7gEAAADUV24LQbGxsZowYYKOHz+uyMhIDRkyRHFxcerdu7fOnDmj+fPny2azOdWEhIRo3rx5atCggRISEtSvXz+NGDFC4eHh+uabb5ScnOz4/BEAAAAAVMYlnwm6XLNmzVJkZKRmz56tjIwM+fj4KDY2VtOmTVP37t0rrRk+fLjWrl2r6dOna+PGjTp9+rQ6duyo8ePHKzExsZZXAAAAAKC+8bIsy3J3E3WJfYvsqrbQBoCqtHk83d0tALgE+5+Ld3cLAC5CTdyfu+3tcAAAAADgDoQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEbxdncDAHCl2jye7u4WAABAPcKTIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADCKy0LQzJkzNWzYMLVv315NmjSRr6+vwsLC9Lvf/U5fffVVlXULFixQdHS0AgMDFRISori4OG3YsKHa11q/fr3i4uIUEhKiwMBARUdHa+HCha5aCgAAAAAP5rIQ9Le//U3//e9/FRISottvv13x8fHy8/PTG2+8oZtvvlkrVqyoUDNx4kQlJSVpx44dio2NVXR0tDIyMtS7d28tXbq00tdZvHix+vTpo5UrV6pLly4aOHCgcnJylJiYqMmTJ7tqOQAAAAA8lJdlWZYrTrR+/XrdfPPN8vPzcxr/5z//qYcfflgtW7bUwYMH5e3tLUnKzMxU//791axZM2VlZal9+/aSpKysLMXExKhx48b67rvvZLPZHOc6ceKErr/+ehUUFGjx4sUaNmyYJOnw4cPq2bOnvvnmG33yySeKiYm57HV06tRJkrRz587LPgeA2tXm8XR3twCgHtr/XLy7WwBwEWri/txlT4J69OhRIQBJ0kMPPaS2bdvq8OHD2rVrl2N85syZkqSpU6c6ApAkdevWTePGjVN+fr7mzp3rdK7XXntNBQUFGjx4sCMASVLLli01Y8YMSVJKSoqrlgQAAADAA9XKxgiNGjWSJPn4+EiSSkpKtHr1aklSQkJChePtY8uXL3caT09Pr7LG/va7zMxMlZaWuq55AAAAAB6lxkPQG2+8oT179qh9+/aOJz579uxRWVmZWrRoodDQ0Ao1UVFRkqTs7Gyn8e3btzvNn8/Hx0edO3dWaWmp9u7d6+plAAAAAPAQ3q4+4fPPP6+dO3equLhYu3fv1s6dO3XNNdfonXfeUcOGDSVJBw4ckKRKA5AkBQQEyGazKS8vT4WFhQoKClJBQYFOnjxZbV1oaKi2bt2q3NxcdenSxdVLAwAAAOABXB6CPvroI61atcrx57CwMC1cuFA333yzY6yoqEiS1Lhx4yrPExAQoPz8fEcIstdUVxcQECBJKiwsvGCf9g9Y/dK+ffvUtm3bC9YDAAAAqJ9c/na4zMxMWZalvLw8rV27Vu3bt1efPn307LPPuvqlAAAAAOCSufxJkJ3NZlOvXr304Ycfqlu3bpo2bZoGDBigW2+9VYGBgZKkU6dOVVlfXFwsSQoKCpIkR429Ljg4+II11alqi72qnhABAADPUle212erbqD21fjGCI0aNdJdd90ly7Icu721bt1aknTw4MFKa4qLi5Wfn6+mTZs6Ak1wcLCaNGlSbZ19PCwszKVrAAAAAOA5amWL7ObNm0uSjh49KkkKDw+Xr6+vjh49qkOHDlU4ftu2bZJUYXODiIgIp/nzlZeXa8eOHfLz81OHDh1c2j8AAAAAz1ErIWjNmjWS5NhwwN/fX/369ZMkLVq0qMLxaWlpkqRBgwY5jcfHxzvNn2/FihUqLS1VbGxspV/aCgAAAACSi0LQ+vXrtXLlSp07d85pvLy8XC+99JLeeOMN+fv766677nLMJScnS5KmT5+unJwcx3hWVpbmzJkjm82mMWPGOJ1v7NixCg4O1rJly7RkyRLH+JEjRzRlyhRJ0qRJk1yxJAAAAAAeyiUbI+Tk5CgpKUnNmzfXzTffrGbNmunYsWP66quv9OOPP8rPz08LFizQdddd56iJjY3VhAkTlJqaqsjISPXv31+nT59WRkaGLMvS/PnzZbPZnF4nJCRE8+bN08iRI5WQkKCYmBg1a9ZMmZmZys/PV3JysmJiYlyxJAAAAAAeyiUhqE+fPnriiSe0Zs0aZWdn69ixY/Lx8VGbNm2UkJCgRx99VO3atatQN2vWLEVGRmr27NnKyMiQj4+PYmNjNW3aNHXv3r3S1xo+fLjWrl2r6dOna+PGjTp9+rQ6duyo8ePHKzEx0RXLAQAAAODBvCzLstzdRF1i3yK7qi20AdQ9dWWbWwC4HGyRDVSvJu7Pa2VjBAAAAACoKwhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGMUlIejUqVNaunSpxowZo/DwcPn5+SkgIEARERF6+umnVVRUVGXtggULFB0drcDAQIWEhCguLk4bNmyo9vXWr1+vuLg4hYSEKDAwUNHR0Vq4cKErlgIAAADAw7kkBL399tsaOnSo5s2bp4YNG+rOO+9Ur1699N133+nJJ5/UrbfeqiNHjlSomzhxopKSkrRjxw7FxsYqOjpaGRkZ6t27t5YuXVrpay1evFh9+vTRypUr1aVLFw0cOFA5OTlKTEzU5MmTXbEcAAAAAB7MJSGoUaNG+v3vf69du3Zp165d+s9//qOVK1dqz5496tq1q77++mtNnDjRqSYzM1Opqalq1qyZtm/frqVLl2rlypVau3atGjZsqKSkJOXn5zvVnDhxQqNHj9bZs2eVlpamTz/9VGlpafr666/Vrl07paSk6NNPP3XFkgAAAAB4KJeEoMTERM2ZM0c33nij0/jVV1+tl19+WZK0ZMkSnT592jE3c+ZMSdLUqVPVvn17x3i3bt00btw45efna+7cuU7ne+2111RQUKDBgwdr2LBhjvGWLVtqxowZkqSUlBRXLAkAAACAh6rxjREiIiIkSWVlZTp+/LgkqaSkRKtXr5YkJSQkVKixjy1fvtxpPD09vcqa+Ph4+fn5KTMzU6Wlpa5bAAAAAACPUuMh6Ntvv5X081vmQkJCJEl79uxRWVmZWrRoodDQ0Ao1UVFRkqTs7Gyn8e3btzvNn8/Hx0edO3dWaWmp9u7d69I1AAAAAPAcNR6CUlNTJUkDBw6Ur6+vJOnAgQOSVGkAkqSAgADZbDbl5eWpsLBQklRQUKCTJ09WW2cfz83Ndd0CAAAAAHgU75o8+Ycffqi5c+eqUaNGeuaZZxzj9i2zGzduXGVtQECA8vPzVVhYqKCgIKdttquqCwgIkCRHcKpOp06dKh3ft2+f2rZte8F6AAAAAPVTjT0J+vrrr3X//ffLsiw9//zzjs8GAQAAAIA71ciToEOHDmngwIHKy8tTcnKyJkyY4DQfGBgo6ecvWa1KcXGxJCkoKMipxl4XHBx8wZrq7Ny5s9Lxqp4QAQAAAPAMLn8SdOLECQ0YMEC5ublKSkrSCy+8UOGY1q1bS5IOHjxY6TmKi4uVn5+vpk2bOgJNcHCwmjRpUm2dfTwsLOyK1wEAAADAM7k0BBUVFem3v/2tdu3apWHDhunf//63vLy8KhwXHh4uX19fHT16VIcOHaowv23bNklSly5dnMbtb6mzz5+vvLxcO3bskJ+fnzp06OCK5QAAAADwQC4LQWVlZRo8eLA2b96s3/zmN3rnnXfUsGHDSo/19/dXv379JEmLFi2qMJ+WliZJGjRokNN4fHy80/z5VqxYodLSUsXGxsrPz++K1gIAAADAc7kkBJ09e1b33HOPVq9erV69emnJkiXy8fGptiY5OVmSNH36dOXk5DjGs7KyNGfOHNlsNo0ZM8apZuzYsQoODtayZcu0ZMkSx/iRI0c0ZcoUSdKkSZNcsSQAAAAAHsolGyPMnj1b77//viSpefPmeuihhyo97oUXXlDz5s0lSbGxsZowYYJSU1MVGRmp/v376/Tp08rIyJBlWZo/f75sNptTfUhIiObNm6eRI0cqISFBMTExatasmTIzM5Wfn6/k5GTFxMS4YkkALkKbx9Pd3QIAAMAlc0kIysvLc/xvexiqzFNPPeUIQZI0a9YsRUZGavbs2crIyJCPj49iY2M1bdo0de/evdJzDB8+XGvXrtX06dO1ceNGnT59Wh07dtT48eOVmJjoiuUAAAAA8GBelmVZ7m6iLrFvkV3VFtoA/ocnQQBw5fY/F+/uFoA6rSbuz2vsy1IBAAAAoC4iBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBSXbJENAACAy1MXdtpkhzqYhidBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwiktC0Oeff67nnntOw4YNU2hoqLy8vOTl5XXBugULFig6OlqBgYEKCQlRXFycNmzYUG3N+vXrFRcXp5CQEAUGBio6OloLFy50xTIAAAAAGMDbFSd55plntGzZskuqmThxolJTU+Xv768BAwaotLRUGRkZ+vjjj5WWlqYhQ4ZUqFm8eLHuuusunTt3Tr1791bz5s21atUqJSYmKjs7Wy+88IIrlgPUC20eT3d3CwAAAPWSS0JQt27d1KVLF91666269dZb1aZNG5WVlVV5fGZmplJTU9WsWTNlZWWpffv2kqSsrCzFxMQoKSlJMTExstlsjpoTJ05o9OjROnv2rBYvXqxhw4ZJkg4fPqyePXsqJSVFd9xxh2JiYlyxJAAAAAAeyiVvh/vTn/6kp59+WoMGDVKrVq0uePzMmTMlSVOnTnUEIOnnMDVu3Djl5+dr7ty5TjWvvfaaCgoKNHjwYEcAkqSWLVtqxowZkqSUlBRXLAcAAACAB6v1jRFKSkq0evVqSVJCQkKFefvY8uXLncbT09OrrImPj5efn58yMzNVWlrq6pYBAAAAeJBaD0F79uxRWVmZWrRoodDQ0ArzUVFRkqTs7Gyn8e3btzvNn8/Hx0edO3dWaWmp9u7dWwNdAwAAAPAUtR6CDhw4IEmVBiBJCggIkM1mU15engoLCyVJBQUFOnnyZLV19vHc3FxXtwwAAADAg7hkY4RLUVRUJElq3LhxlccEBAQoPz9fhYWFCgoKctRUVxcQECBJjuB0IZ06dap0fN++fWrbtu1FnQMAAABA/cOXpQIAAAAwSq0/CQoMDJQknTp1qspjiouLJUlBQUFONfa64ODgC9ZcyM6dOysdr+oJEQAAAADPUOtPglq3bi1JOnjwYKXzxcXFys/PV9OmTR2BJjg4WE2aNKm2zj4eFhbm6pYBAAAAeJBaD0Hh4eHy9fXV0aNHdejQoQrz27ZtkyR16dLFaTwiIsJp/nzl5eXasWOH/Pz81KFDhxroGgAAAICnqPUQ5O/vr379+kmSFi1aVGE+LS1NkjRo0CCn8fj4eKf5861YsUKlpaWKjY2Vn5+fq1sGAAAA4EHcsjFCcnKyJGn69OnKyclxjGdlZWnOnDmy2WwaM2aMU83YsWMVHBysZcuWacmSJY7xI0eOaMqUKZKkSZMm1UL3AAAAAOozl4Sg9PR03XbbbY6f06dPS5LTWHp6uuP42NhYTZgwQcePH1dkZKSGDBmiuLg49e7dW2fOnNH8+fNls9mcXiMkJETz5s1TgwYNlJCQoH79+mnEiBEKDw/XN998o+TkZMXExLhiOQAAAAA8mEt2hzt69Kg2bdpUYfz8saNHjzrNzZo1S5GRkZo9e7YyMjLk4+Oj2NhYTZs2Td27d6/0dYYPH661a9dq+vTp2rhxo06fPq2OHTtq/PjxSkxMdMVSAAAAAHg4L8uyLHc3UZfYt8iuagttoK5o83j6hQ8CAOAi7H8u3t0tAFWqifvzWv+eIAAAANQtdeUXa4Qx1Ba3bIwAAAAAAO5CCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUb3c3ANQ3bR5Pd3cLAAAAuAI8CQIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAAAAAjEIIAgAAAGAUQhAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGMXb3Q0AAAAAktTm8XR3tyBJ2v9cvLtbQA3jSRAAAAAAoxCCAAAAABiFEAQAAADAKIQgAAAAAEYhBAEAAAAwCiEIAAAAgFEIQQAAAACMQggCAAAAYBRCEAAAAACjEIIAAAAAGIUQBAAAAMAohCAAAAAARvF2dwPAxWrzeLq7WwAAAIAHqJdPgkpKSvSXv/xFHTp0kJ+fn6655hqNHj1ahw4dcndrAAAAAOq4evckqLS0VP369dPGjRt19dVXa/Dgwdq/f7/mz5+vFStWaOPGjbrhhhvc3SYAAABwRerKu2D2Pxfv7hZcrt49CZo+fbo2btyobt26ae/evXrvvfe0adMmpaSk6OjRoxo9erS7WwQAAABQh9WrJ0GnT5/W7NmzJUkvv/yyAgMDHXPJycl6/fXXtWbNGn3++ee6+eab3dWmx6krv4UAAAAAXKFehaD169fr5MmTatu2rbp27VphPiEhQdnZ2Vq+fDkhCAAAAJeFXwB7vnr1drjt27dLkqKioiqdt49nZ2fXWk8AAAAA6pd6FYIOHDggSQoNDa103j6em5tbaz0BAAAAqF/q1dvhioqKJEmNGzeudD4gIECSVFhYeMFzderUqdLxr7/+Wo0aNapyvjblHC5ydwsAAAAwXKflgRc+qAbt27dPjRo1cuk561UIqg1eXl4u/z9Z+vkvT5Latm170TXtW7r3goN7Xc41A7NxzeBScc3gUnHN4FK54ppp1KiR42GHq9SrEGTfDe7UqVOVzhcXF0uSgoKCLniunTt3uq6xi2B/slTbr4v6i2sGl4prBpeKawaXimsGl6quXjP16jNBrVu3liQdPHiw0nn7eFhYWK31BAAAAKB+qVchKCIiQpK0bdu2Suft4126dKm1ngAAAADUL/UqBPXo0UNNmjTRvn379OWXX1aYT0tLkyQNGjSoljsDAAAAUF/UqxDk4+Oj8ePHS5Iefvhhx2eAJGnmzJnKzs5Wnz59+KJUAAAAAFWqVxsjSNLUqVOVmZmpDRs2qH379urVq5dyc3O1adMmtWjRQvPmzXN3iwAAAADqMC/Lsix3N3GpSkpK9Pe//11vv/22vv/+e4WEhGjgwIF65plnqvwiVQAAAACQ6mkIAgAAAIDLVa8+EwQAAAAAV4oQBAAAAMAohCAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCKpBX3/9tf7xj3+ob9++at68uRo1aqRWrVpp2LBhWrduXaU1hw8f1ty5czV06FCFhobKx8dHNptNffr00euvvy52NPdsl3PN2B08eFBJSUm65ppr5Ofnpw4dOujJJ59UaWlpLXUPdykuLtYbb7yhRx55RL/+9a/l6+srLy8vPfXUUxes/c9//qN+/fqpadOmatSokVq2bKnBgwfr008/rfG+4R5Xcr1I0vbt2/XAAw8oNDRUvr6+atmypWJiYjR//vyabRxuc6XXjN3atWvVoEEDeXl5aezYsTXTLOqEy7lmav0e2EKNufbaay1JVmBgoBUbG2uNHDnS6ty5syXJ8vLysl588cUKNffdd58lyfL29rZuu+0266677rJ69uxpNWjQwJJkJSQkWGfOnKn9xaBWXM41Y1mWlZOTYzVv3tySZHXu3NkaOXKkdcMNN1iSrB49elilpaW1uxDUqi+++MKSVOHnySefrLZu4sSJjn9v+vbta40cOdKKiopy1M+ZM6d2FoBadbnXi2VZ1r///W/L29vbatiwodWjRw/r7rvvtvr27Ws1bdrUuv3222u+ebjFlVwzdqWlpVZ4eLjl5eVlSbLGjBlTcw3D7S7nmqnte2BCUA26/fbbrYULF1olJSVO46+++qolyWrYsKG1c+dOp7lHH33UevbZZ60jR444jW/evNkKDg7mxsTDXc41Y1mW1aNHD0uS9eijjzrGysvLraFDh17yf6hQ/3zzzTfWmDFjrFdffdX6/PPPraeffvqCf+/bt2+3JFk2m63CNfXOO+9YXl5eVkBAgFVYWFjD3aO2Xc71YlmWtWrVKsvLy8tq166dtWvXLqe5srIya9u2bTXYNdzpcq+Z802dOtXy8vKyxo4dSwgywOVcM7V9D0wIcpMBAwZYkqynnnrqomv+9re/WZKsmJiYGuwMdVVV18ymTZssSdZVV11V4YnPTz/9ZDVq1Mhq2rSpVV5eXpvtwo3+/ve/X/A/Ni+99JIlyfrDH/5Q6XyXLl0sSdamTZtqqEvUFRdzvViWZXXs2NFq0KCBlZ2dXTuNoc662GvGbseOHZaPj481duxYa/78+YQgA13qNfNLNXEPzGeC3CQiIkKS9MMPP9RoDTxHVX//6enpkqRBgwbJ19fXaa5ly5bq1auX8vLy9Nlnn9VOo6gXfnmtVKVZs2Y13Anqg/Xr12vXrl2KiYnRTTfd5O52UI9YlqXf//73atKkif7xj3+4ux3UUzVxD0wIcpNvv/1WktSqVasarYHnqOrvf/v27ZKkqKioSuvs49nZ2TXYHeqbvn37ytvbW++995527drlNPfuu+/qq6++Up8+fdS2bVs3dYi6ZPXq1ZKk7t27q6SkRPPnz9f48eM1YcIELVy4UCUlJW7uEHXVK6+8og0bNiglJUUhISHubgf1VE3cA3u77Ey4aPv27dOKFSskSXfeeedF1ZSXl+uf//ynJGnw4ME11hvqpuqumQMHDkiSQkNDK621j+fm5tZgh6hv2rVrpxdffFETJkxQRESEevXqpauuuko5OTn64osvNGjQIM2bN8/dbaKOsAflc+fOqWvXrtqzZ4/T/LRp07RixQqeEsHJoUOH9P/+3/9T37599cADD7i7HdRTNXUPzJOgWnbmzBmNGjVKZWVluuuuu3TzzTdfVN20adO0e/duXX/99Ro3blwNd4m65ELXTFFRkSSpcePGldYHBARIkgoLC2u2UdQ748eP19tvvy0fHx998skneu+997Rt2za1atVK/fv357e2cMjLy5MkzZgxQ8XFxfrwww918uRJffXVV+rfv78OHDigQYMG6dSpU27uFHXJ+PHjVVpaqldeecXdraAeq6l7YJ4EVWPo0KHavXv3JdUsXLhQ0dHRVc4/+uij+uyzz3TDDTc4Uu2FvPvuu5oxY4b8/Pz09ttvV3mzC/erK9cM6peauG4uxLIsPfbYY0pNTdW4ceOUnJysa665Rjt37tTkyZP1yCOPaPfu3Xr55Zcv+zVQM9xxvZw7d07Sz7+UWbx4seNcnTt31vLly9WuXTvl5ubqrbfe0oMPPnjZr4Oa4Y5rZsmSJVq6dKn+8pe/KDw8/LLPA/dwxzVTmZq8ByYEVeO7776r8Mj/Qqr7Ldizzz6rV155RS1bttRHH310Ub9lXb16tUaNGqUGDRronXfe0W233XZJ/aB2ueOaCQwMrPY8xcXFkqSgoKBL6gu1x9XXzcV4/fXXlZqaqsGDBzv9ljY6Olrp6en61a9+pVdeeUUPPfSQOnXqdEWvBddyx/Vi/3emY8eOFW5yfH19de+992rGjBlas2YNIagOqu1rpqCgQI888ojat2+vJ5544rLPA/dxx78zv1TT98CEoGp8+eWXLjvXq6++qqlTp6pJkyZauXKl2rVrd8GaLVu2aPDgwTp9+rTmzp2rIUOGuKwf1Ax3XDOtW7fWF198oYMHD1Y6bx8PCwtzWW9wLVdeNxfrjTfekCQlJCRUmAsKCtLAgQM1b948ffbZZ4SgOsYd14v93482bdpUOm8fP3LkSC11hEtR29fMtm3b9MMPP6hNmzb6zW9+4zT3008/Sfp5Z9OYmBi1atVK7777bq32hwtzx78z56uNe2BCUC1499139fDDD6tx48ZKT09XZGTkBWt27dql3/72tyoqKtKLL76opKSkmm8UdcalXDMRERFatmyZtm3bVum8fbxLly410SrqKXs4btKkSaXz9nH7Z0Fgtq5du0qq+no4ceKEpP89MQIkaf/+/dq/f3+lcz/99JN++uknfkGHCmrrHpiNEWrYhx9+qN/97nfy9vbW+++/rx49elywZv/+/RowYICOHz+up556ShMnTqz5RlFnXOo1Ex8fL0lavny5ysrKnOYOHz6sdevWqWnTphd17cEc9m1Gt27dWum8fbyq3/zDLHFxcfL29tZXX33lCDznW7NmjaT/hSWYLSYmRpZlVfozf/58SdKYMWNkWVaVIQlmqs17YEJQDVq/fr0SEhJkWZbee+89DRgw4II1R44c0YABA3To0CFNmjRJTz75ZC10irricq6Z6Oho9ejRQ0eOHNGf/vQnx/iZM2f00EMPqby8XI8++qgaNWpUk62jnrG/tWDmzJnavHmz09zs2bO1bt06BQUFXdQ1CM/XvHlzJSUlqaioSI8++qhOnz7tmHv99deVkZEhPz8/jRo1yn1NAqjXavse2MuyLKtGX8FgTZs2VX5+vq6//nr17t270mN69uypsWPHOv48dOhQLV26VI0bN9aIESMqrWnevLleeOGFGukZ7nU514wk5eTkqFu3bjp+/LhuuukmdezYUVu2bNG3336r7t27a/Xq1fL19a2NJcBNhg4dqh9//FHSz9+o/f333+vaa691fE/U1Vdfrffff99xfGlpqfr376/PPvtMDRo0ULdu3Ry7w+3atUsNGzbU/Pnz+W4PD3Wp14sknTx5Ur169dJXX32l1q1b65ZbbtGBAwe0detWrhcDXM41U5kFCxYoKSlJY8aM0WuvvVajPcO9LvWaqfV7YAs1RtIFfxITE51q+vTpc8GasLAwt6wHNe9yrhm7AwcOWKNGjbJatWpl+fj4WO3atbOmTZtmlZSU1O4i4BZhYWGX/O9GWVmZlZKSYkVHR1tBQUGWt7e3dfXVV1sJCQlWVlZW7S8CteZyrhfLsqyioiLriSeesNq1a2f5+PhYISEh1h133GF99tlntbsA1LrLvWZ+af78+ZYka8yYMTXbMNzuUq+Z2r4H5kkQAAAAAKPwmSAAAAAARiEEAQAAADAKIQgAAACAUQhBAAAAAIxCCAIAAABgFEIQAAAAAKMQggAAAAAYhRAEAAAAwCiEIAAAAABGIQQBAAAAMAohCAAAAIBRCEEAgHrBy8vL8ZOVlVXlcf/5z38cx7Vp08Zp7tNPP5WXl5dGjRpVs80CAOo0QhAAoN556623qpx78803a7ETAEB9RAgCANQbDRs21E033aT33ntPZ86cqTB//PhxrVy5UlFRUW7oDgBQXxCCAAD1yn333adjx47po48+qjD33nvvqby8XPfff78bOgMA1BeEIABAvXLvvffKy8ur0re9vfnmmwoMDNTgwYMveJ4ff/xRo0aNUsuWLeXv76+oqCgtXLiwJloGANQx3u5uAACAS3Hdddepd+/e+uCDD1RUVKTAwEBJ0rfffqusrCw98MADaty4cbXnOHHihG677TaVlZUpJiZGeXl5+uSTT5SYmKhvv/1WTz31VC2sBADgLjwJAgDUO/fff79OnTqlJUuWOMbsmyVczFvhli9frvDwcO3bt0/vvfeePv74Y23YsEGBgYF65plntG3bthrrHQDgfoQgAEC9k5CQIF9fX6dd4t566y1dffXVuv322y9Y36BBA7300ksKCAhwjN166616+OGHde7cOf3zn/+skb4BAHUDIQgAUO/YbDbFx8dr1apV+umnn7Rlyxbt2bNHd999txo2bHjB+sjISIWHh1cYv+eeeyRJ69atc3nPAIC6gxAEAKiX7r//fp09e1bvvvuuY5OEi90VLiwsrNJx+5er/vDDDy7pEQBQN7ExAgCgXoqLi5PNZtPChQv1ww8/6MYbb+T7gQAAF4UnQQCAesnX11cjRozQF198ocOHD1/SdwPl5uZWO37NNde4pEcAQN1ECAIA1FsPPPCAmjVrpubNm+u+++676Lovv/xSOTk5FcbfffddSVLPnj1d1iMAoO4hBAEA6q1evXrp2LFjOnr0aJWf86nMuXPn9Mgjj+jUqVOOsc8//1yzZ8+Wl5eX/vjHP9ZEuwCAOoLPBAEAjHPHHXdo+/btatu2rXr37q2TJ09q9erVKi8v19SpU3XLLbe4u0UAQA0iBAEAjNOsWTNt3LhRf/rTn/TRRx+poKBAHTt21MSJEzVq1Ch3twcAqGFelmVZ7m4CAAAAAGoLnwkCAAAAYBRCEAAAAAC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", 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" ] @@ -487,30 +520,6 @@ "plt.show()" ] }, - { - "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "outputs": [ - { - "data": { - "image/png": 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eHq6srCx9++23KioqUmxsrN544w2nPomJiRo7dqxSU1PVrl079ejRQ8XFxVq+fLmMMZo7d67Cw8Od+kRERGjOnDkaMmSIBg0apISEBEVGRiojI0O5ubkaP368EhISPHFIAAAAACzgkcBz880368EHH9RXX32lDRs2KCcnR8HBwWrXrp0GDx6sBx98UIGBgS79XnrpJbVr106zZ8/W8uXL5efnp8TERE2dOlWdO3d2u6+BAwdq9erVmjFjhtavX6/i4mK1atVKY8aMUXJysicOBwAAAIAlPBJ4rr/+ev3973+/oL7Dhw/X8OHDK9Xn1ltv1ccff3xB+wMAAABw5bgsT2kDAAAAAG8g8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWMsj7+EBAABA9RUzOd3bQwAuGa7wAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLVqeXsAAAAA3hAzOd3bQwBwGXCFBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtS5J4Dl69Kiuuuoq+fj4qFmzZuW2nTdvnuLi4hQSEqKIiAj17t1ba9euLbfPmjVr1Lt3b0VERCgkJERxcXF6++23PXkIAAAAACxwSQLPhAkTdOTIkfO2GzdunEaMGKEtW7YoMTFRcXFxWr58ubp166ZFixa57bNgwQLFx8dr2bJlatOmjXr16qVdu3YpOTlZEydO9PCRAAAAAKjOPB54PvvsM/3v//6vfve735XbLiMjQ6mpqYqMjNSmTZu0aNEiLVu2TKtXr1bNmjU1YsQI5ebmOvU5duyYRo4cqZKSEqWlpWnVqlVKS0vT9u3b1axZM6WkpGjVqlWePiQAAAAA1ZRHA09hYaF+//vfq1WrVue92jJr1ixJ0pNPPqnmzZs7yjt16qQHHnhAubm5euutt5z6vPnmm8rLy1O/fv00YMAAR3m9evU0c+ZMSVJKSoqnDgcAAABANefRwPOnP/1Ju3fv1quvvipfX98y2xUWFmrFihWSpEGDBrnUl5YtWbLEqTw9Pb3MPklJSQoICFBGRoaKioou+BgAAAAA2MNjgWfz5s1KSUnRiBEj1LVr13Lb7tixQ6dOnVJUVJQaNmzoUt+hQwfHNs+1adMmp/pz+fn5qXXr1ioqKtLOnTsv9DAAAAAAWMQjgefs2bMaPXq0wsPDHV8tK8++ffskyW3YkaTg4GCFh4crJydH+fn5kqS8vDwdP3683H6l5VlZWZU+BgAAAAD2qeWJjbz88svasGGD5s6dq8jIyPO2P3HihCQpKCiozDbBwcHKzc1Vfn6+QkNDHX3K6xccHCxJjpBUntjYWLflmZmZatq06Xn7AwAAAKj6LvoKz759+/Tkk08qPj5ew4cP98CQAAAAAMAzLvoKz8MPP6zi4mK9+uqrFe4TEhIiSTp58mSZbQoKCiRJoaGhTn1K+4WFhZ23T3m2bt3qtrysKz8AAAAAqp+LDjxLly5VeHi4HnjgAafy0ielHThwQAkJCZKk//u//1P9+vXVuHFjSdL+/fvdbrOgoEC5ubmqU6eOI7yEhYWpdu3aOn78uPbv369WrVq59CvdXnR09MUeFgAAAAALeOQentzcXH3++edu64qKihx1pSGoZcuW8vf3V3Z2tg4cOKBrrrnGqc/GjRslSW3atHEqb9u2rVavXq2NGze6BJ7Tp09ry5YtCggIUIsWLTxxWAAAAACquYu+h8cY4/Znz549kqSmTZs6ymJiYiRJgYGB6t69uyRp/vz5LttMS0uTJPXp08epPCkpyan+XEuXLlVRUZESExMVEBBwsYcFAAAAwAIeffFoZYwfP16SNGPGDO3atctRvm7dOr322msKDw/XqFGjnPqMHj1aYWFhWrx4sRYuXOgoP3z4sCZNmiRJmjBhwmUYPQAAAIDqwCNfabsQiYmJGjt2rFJTU9WuXTv16NFDxcXFWr58uYwxmjt3rsLDw536REREaM6cORoyZIgGDRqkhIQERUZGKiMjQ7m5uRo/frzjfiEAAFA1xUxO9/YQAFxBvBZ4JOmll15Su3btNHv2bC1fvlx+fn5KTEzU1KlT1blzZ7d9Bg4cqNWrV2vGjBlav369iouL1apVK40ZM0bJycmX+QgAAAAAVGU+xhjj7UFUJaWPpS7rsdUAAODicIUHqF72Ppfk1f1f7Odzr93DAwAAAACXGoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1anl7AAAA4PKImZzu7SEAwGXHFR4AAAAA1iLwAAAAALAWX2kDAOAS46tkAOA9XOEBAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLU8FnhmzZqlAQMGqHnz5qpdu7b8/f0VHR2t3/72t/rhhx/K7Ddv3jzFxcUpJCREERER6t27t9auXVvuvtasWaPevXsrIiJCISEhiouL09tvv+2pQwEAAABgCY8FnmeffVYff/yxIiIidNtttykpKUkBAQH6xz/+oY4dO2rp0qUufcaNG6cRI0Zoy5YtSkxMVFxcnJYvX65u3bpp0aJFbvezYMECxcfHa9myZWrTpo169eqlXbt2KTk5WRMnTvTU4QAAAACwgI8xxnhiQ2vWrFHHjh0VEBDgVP73v/9dDz/8sOrVq6f9+/erVq1akqSMjAz16NFDkZGRWrdunZo3by5JWrdunRISEhQUFKQ9e/YoPDzcsa1jx46pSZMmysvL04IFCzRgwABJ0qFDh9SlSxf9+9//1sqVK5WQkHDBxxEbGytJ2rp16wVvAwCAc8VMTvf2EADggu19Lsmr+7/Yz+ceu8Jz6623uoQdSXrooYfUtGlTHTp0SD/++KOjfNasWZKkJ5980hF2JKlTp0564IEHlJubq7feestpW2+++aby8vLUr18/R9iRpHr16mnmzJmSpJSUFE8dEgAAAIBq7rI8tMDX11eS5OfnJ0kqLCzUihUrJEmDBg1yaV9atmTJEqfy9PT0MvuUfoUuIyNDRUVFnhs8AAAAgGrrkgeef/zjH9qxY4eaN2/uuJKzY8cOnTp1SlFRUWrYsKFLnw4dOkiSNm/e7FS+adMmp/pz+fn5qXXr1ioqKtLOnTs9fRgAAAAAqqFant7g888/r61bt6qgoEDbtm3T1q1bdfXVV+v9999XzZo1JUn79u2TJLdhR5KCg4MVHh6unJwc5efnKzQ0VHl5eTp+/Hi5/Ro2bKhvvvlGWVlZatOmTbnjLP0u4K9lZmaqadOmFTpWAAAAAFWbxwPPJ598os8++8zxe3R0tN5++2117NjRUXbixAlJUlBQUJnbCQ4OVm5uriPwlPYpr19wcLAkKT8//6KOAQAAAIAdPB54MjIyJEm5ubn64Ycf9PTTTys+Pl4zZszQlClTPL27C1bWUx7KuvIDAAAAoPq5ZPfwhIeHq2vXrvroo4/UsWNHTZ06VRs2bJAkhYSESJJOnjxZZv+CggJJUmhoqFOf8vr9ug8AAACAK9slf2iBr6+vhg4dKmOM46lrjRs3liTt37/fbZ+CggLl5uaqTp06jvASFham2rVrl9uvtDw6OtqjxwAAAACgerosj6WuW7euJCk7O1uS1LJlS/n7+ys7O1sHDhxwab9x40ZJcnnwQNu2bZ3qz3X69Glt2bJFAQEBatGihUfHDwAAAKB6uiyB5/PPP5ckx9PPAgMD1b17d0nS/PnzXdqnpaVJkvr06eNUnpSU5FR/rqVLl6qoqEiJiYluX4AKAAAA4MrjkcCzZs0aLVu2TGfPnnUqP336tF5++WX94x//UGBgoIYOHeqoGz9+vCRpxowZ2rVrl6N83bp1eu211xQeHq5Ro0Y5bW/06NEKCwvT4sWLtXDhQkf54cOHNWnSJEnShAkTPHFIAAAAACzgkae07dq1SyNGjFDdunXVsWNHRUZG6siRI/rhhx908OBBBQQEaN68eWrUqJGjT2JiosaOHavU1FS1a9dOPXr0UHFxsZYvXy5jjObOnavw8HCn/URERGjOnDkaMmSIBg0apISEBEVGRiojI0O5ubkaP368EhISPHFIAAAAACzgkcATHx+vJ554Qp9//rk2b96sI0eOyM/PTzExMRo0aJAeffRRNWvWzKXfSy+9pHbt2mn27Nlavny5/Pz8lJiYqKlTp6pz585u9zVw4ECtXr1aM2bM0Pr161VcXKxWrVppzJgxSk5O9sThAAAAALCEjzHGeHsQVUnpe3jKek8PAKD6iJmc7u0hAEC1t/e5JK/u/2I/n1+WhxYAAAAAgDcQeAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxVy9sDAADYKWZyureHAAAAV3gAAAAA2IvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwlkcCz8mTJ7Vo0SKNGjVKLVu2VEBAgIKDg9W2bVs9/fTTOnHiRJl9582bp7i4OIWEhCgiIkK9e/fW2rVry93fmjVr1Lt3b0VERCgkJERxcXF6++23PXEoAAAAACzikcDz3nvv6a677tKcOXNUs2ZN9e3bV127dtWePXs0bdo03XTTTTp8+LBLv3HjxmnEiBHasmWLEhMTFRcXp+XLl6tbt25atGiR230tWLBA8fHxWrZsmdq0aaNevXpp165dSk5O1sSJEz1xOAAAAAAs4ZHA4+vrq//3//6ffvzxR/3444/617/+pWXLlmnHjh1q3769tm/frnHjxjn1ycjIUGpqqiIjI7Vp0yYtWrRIy5Yt0+rVq1WzZk2NGDFCubm5Tn2OHTumkSNHqqSkRGlpaVq1apXS0tK0fft2NWvWTCkpKVq1apUnDgkAAACABWp5YiPJyclKTk52KW/QoIFeeeUVde7cWQsXLlRxcbH8/PwkSbNmzZIkPfnkk2revLmjT6dOnfTAAw/ob3/7m9566y1NmDDBUffmm28qLy9P/fr104ABAxzl9erV08yZMzVgwAClpKQoISHBE4cFANVSzOR0bw8BAIAq45I/tKBt27aSpFOnTuno0aOSpMLCQq1YsUKSNGjQIJc+pWVLlixxKk9PTy+zT1JSkgICApSRkaGioiLPHQAAAACAauuSB57du3dL+uVrbxEREZKkHTt26NSpU4qKilLDhg1d+nTo0EGStHnzZqfyTZs2OdWfy8/PT61bt1ZRUZF27tzp0WMAAAAAUD155Ctt5UlNTZUk9erVS/7+/pKkffv2SZLbsCNJwcHBCg8PV05OjvLz8xUaGqq8vDwdP3683H4NGzbUN998o6ysLLVp06bcccXGxrotz8zMVNOmTc9/YADwK3yVDACAqueSXuH56KOP9NZbb8nX11fPPPOMo7z0MdVBQUFl9g0ODpYk5efnO/Upr9+v+wAAAAC4sl2yKzzbt2/XfffdJ2OMnn/+ece9PFXF1q1b3ZaXdeUHAAAAQPVzSa7wHDhwQL169VJOTo7Gjx+vsWPHOtWHhIRI+uWFpWUpKCiQJIWGhjr1Ka/fr/sAAAAAuLJ5PPAcO3ZMPXv2VFZWlkaMGKEXXnjBpU3jxo0lSfv373e7jYKCAuXm5qpOnTqO8BIWFqbatWuX26+0PDo6+qKPAwAAAED159HAc+LECd1xxx368ccfNWDAAL3xxhvy8fFxadeyZUv5+/srOztbBw4ccKnfuHGjJLk8eKD0a3Gl9ec6ffq0tmzZooCAALVo0cIThwMAAACgmvNY4Dl16pT69eunr7/+Wrfffrvef/991axZ023bwMBAde/eXZI0f/58l/q0tDRJUp8+fZzKk5KSnOrPtXTpUhUVFSkxMVEBAQEXdSwAAAAA7OCRwFNSUqK7775bK1asUNeuXbVw4UL5+fmV22f8+PGSpBkzZmjXrl2O8nXr1um1115TeHi4Ro0a5dRn9OjRCgsL0+LFi7Vw4UJH+eHDhzVp0iRJ0oQJEzxxSAAAAAAs4JGntM2ePVsffPCBJKlu3bp66KGH3LZ74YUXVLduXUlSYmKixo4dq9TUVLVr1049evRQcXGxli9fLmOM5s6dq/DwcKf+ERERmjNnjoYMGaJBgwYpISFBkZGRysjIUG5ursaPH6+EhARPHBIAAAAAC3gk8OTk5Dj+d2nwcWf69OmOwCNJL730ktq1a6fZs2dr+fLl8vPzU2JioqZOnarOnTu73cbAgQO1evVqzZgxQ+vXr1dxcbFatWqlMWPGKDk52ROHAwAAAMASPsYY4+1BVCWl7+Ep6z09AFCWmMnp3h4CAAAet/e5JK/u/2I/n1+S9/AAAAAAQFVA4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArFXL2wMAgIsVMznd20MAAABVFFd4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFirlrcHAKD6ipmc7u0hAAAAlIsrPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWquXtAQC4MDGT0709BAAAgCqPKzwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKzlkcDz7bff6rnnntOAAQPUsGFD+fj4yMfH57z95s2bp7i4OIWEhCgiIkK9e/fW2rVry+2zZs0a9e7dWxEREQoJCVFcXJzefvttTxwGAAAAAMvU8sRGnnnmGS1evLhSfcaNG6fU1FQFBgaqZ8+eKioq0vLly/Xpp58qLS1N/fv3d+mzYMECDR06VGfPnlW3bt1Ut25dffbZZ0pOTtbmzZv1wgsveOJwAAAAAFjCI4GnU6dOatOmjW666SbddNNNiomJ0alTp8psn5GRodTUVEVGRmrdunVq3ry5JGndunVKSEjQiBEjlJCQoPDwcEefY8eOaeTIkSopKdGCBQs0YMAASdKhQ4fUpUsXpaSk6M4771RCQoInDgkAAACABTzylbY//OEPevrpp9WnTx/Vr1//vO1nzZolSXryyScdYUf6JTg98MADys3N1VtvveXU580331ReXp769evnCDuSVK9ePc2cOVOSlJKS4onDAQAAAGCJy/7QgsLCQq1YsUKSNGjQIJf60rIlS5Y4laenp5fZJykpSQEBAcrIyFBRUZGnhwwAAACgmrrsgWfHjh06deqUoqKi1LBhQ5f6Dh06SJI2b97sVL5p0yan+nP5+fmpdevWKioq0s6dOy/BqAEAAABUR5c98Ozbt0+S3IYdSQoODlZ4eLhycnKUn58vScrLy9Px48fL7VdanpWV5ekhAwAAAKimPPLQgso4ceKEJCkoKKjMNsHBwcrNzVV+fr5CQ0MdfcrrFxwcLEmOkHQ+sbGxbsszMzPVtGnTCm0DAAAAQNXGi0cBAAAAWOuyX+EJCQmRJJ08ebLMNgUFBZKk0NBQpz6l/cLCws7b53y2bt3qtrysKz8AAAAAqp/LfoWncePGkqT9+/e7rS8oKFBubq7q1KnjCC9hYWGqXbt2uf1Ky6Ojoz09ZAAAAADV1GUPPC1btpS/v7+ys7N14MABl/qNGzdKktq0aeNU3rZtW6f6c50+fVpbtmxRQECAWrRocQlGDQAAAKA6uuyBJzAwUN27d5ckzZ8/36U+LS1NktSnTx+n8qSkJKf6cy1dulRFRUVKTExUQECAp4cMAAAAoJryykMLxo8fL0maMWOGdu3a5Shft26dXnvtNYWHh2vUqFFOfUaPHq2wsDAtXrxYCxcudJQfPnxYkyZNkiRNmDDhMoweAAAAQHXhkcCTnp6uW265xfFTXFwsSU5l6enpjvaJiYkaO3asjh49qnbt2ql///7q3bu3unXrpjNnzmju3LkKDw932kdERITmzJmjGjVqaNCgQerevbsGDx6sli1b6t///rfGjx+vhIQETxwOAAAAAEt45Clt2dnZ+uqrr1zKzy3Lzs52qnvppZfUrl07zZ49W8uXL5efn58SExM1depUde7c2e1+Bg4cqNWrV2vGjBlav369iouL1apVK40ZM0bJycmeOBQAAAAAFvExxhhvD6IqKX0sdVmPrQaqipjJ6edvBAAAcJH2Ppfk1f1f7Ofzy/4eHqC6I2gAAABUH155aAEAAAAAXA4EHgAAAADWIvAAAAAAsBaBBwAAAIC1eGgBqg0eFgAAAIDK4goPAAAAAGtxhQfnxZUVAAAAVFdc4QEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAa9Xy9gBQtpjJ6d4eAgAAAFCtcYUHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeAAAAANYi8AAAAACwFoEHAAAAgLWqZeApLCzUU089pRYtWiggIEBXX321Ro4cqQMHDnh7aAAAAACqkGoXeIqKitS9e3c988wzOnHihPr166dGjRpp7ty5at++vXbv3u3tIQIAAACoIqpd4JkxY4bWr1+vTp06aefOnfrnP/+pr776SikpKcrOztbIkSO9PUQAAAAAVUS1CjzFxcWaPXu2JOmVV15RSEiIo278+PFq06aNPv/8c3377bfeGiIAAACAKqRaBZ41a9bo+PHjatq0qdq3b+9SP2jQIEnSkiVLLvfQAAAAAFRB1SrwbNq0SZLUoUMHt/Wl5Zs3b75sYwIAAABQdVWrwLNv3z5JUsOGDd3Wl5ZnZWVdtjEBAAAAqLpqeXsAlXHixAlJUlBQkNv64OBgSVJ+fv55txUbG+u2fPv27fL19S2z/nL6+dAJbw8BAAAAV7jYJSHnb3QJZWZmytfX94L7V6vAczn4+Phc1IR6QmZmpiSpedOmXh2HTUrntClz6jHMqWcxn57HnHoec+pZzKfnMaeel5mZqcwTh7w6p76+vo4LGxeiWgWe0qeynTx50m19QUGBJCk0NPS829q6davnBuZhpVeXqvIYqxvm1POYU89iPj2POfU85tSzmE/PY049z4Y5rVb38DRu3FiStH//frf1peXR0dGXbUwAAAAAqq5qFXjatm0rSdq4caPb+tLyNm3aXLYxAQAAAKi6qlXgufXWW1W7dm1lZmbq+++/d6lPS0uTJPXp0+cyjwwAAABAVVStAo+fn5/GjBkjSXr44Ycd9+xI0qxZs7R582bFx8erY8eO3hoiAAAAgCqkWj20QJKefPJJZWRkaO3atWrevLm6du2qrKwsffXVV4qKitKcOXO8PUQAAAAAVYSPMcZ4exCVVVhYqL/85S9677339NNPPykiIkK9evXSM888U+ZLSQEAAABceapl4AEAAACAiqhW9/AAAAAAQGUQeAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDyVVFhYqKeeekotWrRQQECArr76ao0cOVIHDhyo9LZycnI0duxYRUdHy9/fX9HR0Ro3bpxyc3Pdth8+fLh8fHzK/Hn11VfL3NeSJUsUHx+vsLAwhYWFKSEhQenp6ZUe86XgrTndu3dvufNZ+jNy5EinfvPmzSu3/bBhwy50KjzCU/P5+eef609/+pOSkpIUFRUlHx8fxcTEnLdfSUmJXnzxRd1www0KDAxUVFSUhgwZom3btpXb70o4Rys7p6dPn9ann36qMWPGqHXr1goKClJgYKCuv/56TZw4UdnZ2W77VfVzVPLueWrjWuqt+bR1HZU8M6e5ubl67733dPfdd6tJkyby8/NTaGiobr75ZqWmpur06dNl9mUtde9C5tTWtdSb52i1W0cNKqywsNDccsstRpJp0KCBGTJkiImLizOSTFRUlMnMzKzwtrKzs02zZs2MJHPttdeaIUOGmNjYWCPJtGjRwhw9etSlT3JyspFkbr/9dpOcnOzys2LFCrf7evHFF40kU6tWLdOrVy/Tr18/ExgYaCSZl19++YLnwxO8OafZ2dlu57H0JyAgwEgyc+bMceo3d+5cI8m0bdvWbb+///3vHpmbC+HJ+Wzbtq2R5PQTHR1dbp+SkhJz1113GUkmPDzcDBw40MTHxxsfHx8TFBRkvvrqK7f9rpRztLJzunz5cke7mJgYM2DAANOnTx9Tt25dI8nUr1/fbN++3aVfVT5HjfH+eWrbWurN+bRxHTXGc3M6ZcoUI8n4+PiY9u3bm6FDh5ru3bsbf39/I8l06dLFFBQUuPRjLS3bhcypjWupt8/R6raOEngqofSk6NSpk8nPz3eUp6SkGEkmPj6+wtu69957jSQzYMAAc/r0aUf5I488YiSZ5ORklz6lJ9fKlSsrvJ/t27ebmjVrGn9/f7N27VpH+Y4dO0xkZKSpVauW2bVrV4W352nentOy/Pjjj0aSCQwMNMePH3eqK10Ap02bVuHtXS6enM/HH3/czJgxw3zyySdm69atFfog+cYbbxhJpnnz5uY///mPozwtLc1IMs2aNXP62xhzZZ2jlZ3Tzz77zAwZMsTlw01ubq65/fbbHeP6tap8jhrj/fPUtrXU2/NZluq6jhrjuTl99tlnzaRJk0xWVpZT+c6dO03jxo2NJPPHP/7RpR9radkuZE5tXEu9fY5Wt3WUwFNBp06dMrVr1zaSzMaNG13q27RpYySZb7755rzb+vnnn02NGjWMn5+f00JmjDFFRUUmKirK1KxZ0xw6dMip7kJOrgcffNBIMmPHjnWpmzVrlpFkxowZU+HteVJVmNOyPPHEE0aSGTZsmEtdVV0APTmfv3bw4MEKffC5/vrrjSTzwQcfuNT17dvXSDJpaWlO5VfKOfprFZ3Tshw4cMDxXyz37t3rVFdVz1Fjqsac2rSWVoX5LEt1XEeNubRzeq733nvPccXh11hLL0x5c1qW6riWVoX5rG7rKPfwVNCaNWt0/PhxNW3aVO3bt3epHzRokKRfvpd4PsuWLdPZs2fVtWtX1atXz6nO399fffr0UUlJiT766KOLHnfpdyJLx3ehY74UquqcGmP03nvvSZLuv//+ihxKleDJ+bwQe/bs0bZt2xQYGKikpKQK7/9KOUc97eqrr1ZUVJQk6eeff77s+79QVXlOy1NVz9OqOp/VdR2VLt+ctm3bVpLrv1/W0gtX1pyWpzqupVV5PsvjzXO01iXZqoU2bdokSerQoYPb+tLyzZs3e2Rbc+bMKXNbCxcu1IIFC1RSUqImTZqoT58+uu6661za5ebmat++fZLk9h9Eo0aNVLduXWVlZSkvL09hYWHnHbsnVaU5PdeXX36pvXv36qqrrlLPnj3LbPftt9/q8ccfV15enurXr6/u3bsrPj7+vNu/VDw5nxez/9atW8vX17dC+7+SzlFPy83NVU5OjiSpfv36bttUtXNUqlpzasNaWpXm81zVdR2VLt+c7t69W5Lrv1/W0gtX1pyWpzqupVVpPqvLOkrgqaDSP1LDhg3d1peWZ2VlXfJtvfzyy06//+EPf9CDDz6o1NRU1ar13z9p6X7q1Kmj4ODgMvd15MgRZWVl6YYbbjjv2D2pKs3pud555x1J0rBhw5zm89eWLl2qpUuXOn5/+umnFR8fr3/+858uV5kuB0/OweXa/5V0jnraK6+8ojNnzuiGG25QkyZN3LapaueoVLXm1Ia1tCrN57mq6zoqXb45TU1NlST169fvovdflc9RyftzWp7quJZWpfmsLusoX2mroBMnTkiSgoKC3NaX/vHy8/Mv2bbat2+vV199VTt37tTJkye1e/duvfLKKwoPD9ff//53Pf7445XaT2XH7WlVYU5/7dSpU5o/f76ksr+G0aBBA02fPl3fffedjh8/rv/85z/68MMPdd111+nzzz/XnXfeqZKSkvOO2dM8OZ+Xa/9X0jnqSd99951mzJghSfrrX//qUl9Vz1GpasypTWtpVZjPX6vO66h0eeb01VdfVUZGhsLDwzV58uSL3n9VPkcl789pWarrWloV5rO6raNc4alGxo4d6/R7kyZN9NBDDyk+Pl4dOnTQ7NmzNX78eDVq1MhLI6z+0tPTlZOTo+uuu0433nij2za33367br/9dsfvYWFh6tOnj37zm9+oY8eO+uabb/Svf/1Ld9999+UaNq4ghw4d0oABA1RUVKRx48bpjjvucGnDOVo+1tJLi3W0fF988YXGjh0rHx8fzZkzR1dffbW3h1TtXcicspaWrSLzWd3WUa7wVFBISIgk6eTJk27rCwoKJEmhoaGXdVuSFBsbq759++rMmTP67LPPKryfC9mXJ1XFOS39GsaF3GQbEhKiRx99VJL0ySefVLr/xfL0eXU59n8lnaOekJ+fr969e2vv3r0aPHiwUlJSKtXf2+do6RikqjOn56qOa2lVnM/qvI6WjkG6NHO6ZcsW9evXT8XFxUpNTdVdd93lkf1X5XNU8v6c/lp1X0ur2nyeq6quowSeCmrcuLEkaf/+/W7rS8ujo6Mv67ZKNW/eXJJ08OBBl/3k5OQ4TiJP7MtTqtqc5ubm6qOPPpKPj4/uvffe8+7THXd/h8vlUpxXl3r/V9I5erGKiorUt29fbdy4UT179tQ777yjGjUqv4R78xyVqtaculPd1tKqNp/VfR2VLt2c7tmzRz179lROTo6mT5+uRx55xGP7r8rnqOT9OT2XDWtpVZpPd6riOkrgqaDSR/Nt3LjRbX1peZs2bS7rtkqVPmHk3BvBwsPDHSfYd99959Lnp59+0pEjRxQdHX3Zn9giVb05/de//qVTp06pa9euF/yPzd3f4XK5FOfVhex/y5YtOn36dIX2fyWdoxfjzJkzGjp0qFatWqXOnTtr4cKF8vPzu6BtefMclarOnJaluq2lVW0+q/s6Kl2aOT148KB69OihgwcPauzYsZo2bdp5989aWr7KzGkpW9bSqjKfZamS6+glebuPhc59ydN3333nUn+hL8n89YswL+QlmUVFRaZRo0ZGkvniiy+c6qrLi8iqwpx27drVSDJvvPFGpY+l1ODBg40k88wzz1zwNi6UJ+fz13jxqPfm9OzZs+a+++4zkky7du1MTk5Opfd1Lm+eo8ZUjTktS3VcS6vafFb3ddQYz8/psWPHzA033GAkmREjRpizZ8+etw9rafkuZE5tWkurwnyWpaquowSeSpgyZYqRZDp37mxOnDjhKE9JSTGSTHx8vFP7l19+2bRs2dJMnjzZZVv33nuvkWQGDhxoTp8+7Sh/9NFHjSSTnJzs1H7btm3m7bffNkVFRU7lhw8fNv379zeSTNu2bV1O0u3bt5uaNWsaf39/s27dOkf5zp07TWRkpKlVq5bZtWtXZafCY7w5p+fau3ev8fHxMQEBASY3N7fcMT/77LMmOzvbqay4uNhMnz7dSDKBgYFm//795W7jUvHkfJ6roh983njjDSPJNG/e3ClcLliwwEgyzZo1c/rbGHNlnaPnquiclp6/1113nTl8+HCFxlyVz1FjvDunNq6l3j5HS9myjhrjuTktKCgwnTp1MpLMkCFDzJkzZyq0f9ZSz8+pbWupN+ezOq6jBJ5KKCwsNDfffLORZBo0aGCGDBni+D0qKspkZmY6tZ82bVqZH7Szs7NN06ZNjSTTtGlTM3ToUNO6dWvHAnf06FGn9itXrjSSTJ06dUyPHj3MPffcYxISEkxoaKiRZBo2bGh27NjhdtylqblWrVrmjjvuMP369TOBgYFGkvnb3/7msfm5EN6c03P9+c9/NpLM4MGDzztmScbf39/ceuutZtiwYaZ3797m6quvNpJMQECAWbBgQaXnwVM8OZ9vvPGGufnmm83NN99sOnToYCQZPz8/R9nNN99svv32W6c+JSUl5q677nKcq4MGDTIJCQnGx8fHBAYGmvXr17sd95VyjlZ2ThctWmQkGUmmR48eJjk52e3Ptm3bnPZTlc9RY7w7pzaupd7+d1/KlnXUGM/N6bhx44wkU7NmTXPPPfeU+W/411hLPTunNq6l3pzP6riOEngq6eTJk2bq1KmmadOmxs/Pz9SvX98MHz7c/PTTTy5ty/s/FWOMOXr0qHnkkUdMo0aNjJ+fn2nUqJF59NFH3V5mPXDggBk3bpy55ZZbTP369Y2vr68JCQkxHTp0MNOmTTPHjh0rd9wffvih6dq1qwkJCTEhISGma9euZsmSJRcyBR7nrTk9V6tWrYwks3jx4vOO96mnnjI9evQwjRs3NoGBgSYgIMA0a9bM/P73vzfbt2+vyCFfUp6az9K68n5Wrlzp0u/MmTMmJSXFxMbGmoCAABMZGWkGDRpktm7dWu64r4RztLJzOnfu3PO2d/d3qOrnqDHem1Nb11Jv/7s3xq511BjPzGlycnKF/g27w1rquTm1dS311nxWx3XUxxhjBAAAAAAW4iltAAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFoEHgAAAADWIvAAAAAAsBaBBwAAAIC1CDwAAAAArEXgAQAAAGAtAg8AAAAAaxF4AAAAAFiLwAMAAADAWgQeANYqKCjQrFmz9Jvf/Eb16tWTn5+f6tSpo06dOumpp57Svn37vD3Ey87Hx0cxMTFX3P69fdwVMX36dPn4+GjevHkX1H/v3r3y8fFRQkJCpfqVNzd/+9vfFBsbK39//wvaNgBUBbW8PQAAuBTWrl2rgQMH6j//+Y+CgoJ0yy23qF69ejp+/Lg2bNig9evXa+bMmVq6dKkSExO9PVygylm4cKHGjh2rOnXqqG/fvgoODtZ1112nvXv3qkmTJoqPj9eqVau8PUwAOC8CDwDrfP/997rttttUVFSkP/zhD5o6daqCg4Md9WfPntWiRYs0adIk7d+/34sjvfJs27ZNvr6+3h4GzlHW32TRokWSpLS0NHXv3t1Rvnfv3ss0MgDwDAIPAKsYY3T//ferqKhI06dP17Rp01za1KhRQwMGDNBtt92mn376yQujvHJdd9113h4CfqWsv0npfwy49tprL+dwAMDjuIcHgFWWLVumLVu2qGHDhpoyZUq5bWvXrq3WrVs7la1bt079+vVTVFSU/P39FRMTo4ceekg///yz221s2bJF9913n6699loFBAQoKipK7dq107hx43Tw4EFHu3PvrygoKND48ePVqFEjBQYGqkOHDlqyZImj7fz583XzzTcrODhY9erV06OPPqrCwkK3+6/seMtT2W0tXLhQt9xyi4KCglS3bl0NHjxY//73v8u9F6Ws+0V++uknPfroo2rRooUCAwMVERGhG2+8UX/605+Ul5fn1DY9PV0jR47U9ddfr7CwMAUHB6tt27Z69tlnderUqUof96+d+7fKy8vT+PHj1aRJE/n6+mrcuHEu4x4zZoyaNm2qgIAARURE6M4779TatWvL3P6HH36oTp06KSgoSJGRkRo4cKB27txZZvuKnmPnKiws1OTJkxUdHS1/f381a9ZMf/3rX2WMcWn7679J6d9v5cqVkqQmTZrIx8fHMSdNmjSRJH3++eeOch8fHw0fPrzMYzjX4MGDnfq5+/nyyy8rtC0AqAiu8ACwSnp6uqRfPlTVqlW5Je6dd97R8OHDVVJSoltvvVWNGjXSxo0b9T//8z9auHChVq1a5fRfw7/99lt16dJFRUVFatOmjfr166eTJ09q9+7dSk1NVf/+/dWgQQOnfRQXF+u2227Tnj171K1bNx05ckSrV6/WXXfdpWXLlumHH37QpEmTFB8fr9tvv12rV6/Wyy+/rKNHj+rdd9+9qPF68thTU1M1btw41ahRQ926dVP9+vX11VdfKS4uTn369KnUvH/xxRfq27evcnNzFRMToz59+qiwsFDbt2/X9OnT1a9fP7Vr187RftSoUSosLFTr1q3Vpk0bHT9+XF9//bWmTJmizz77TJ9++qlq1qxZqTG4U1hYqPj4eGVlZSk+Pl4dOnRQnTp1HPXr1q1TUlKScnJy1LJlSyUlJSk7O1uffPKJli1bpnfffVdDhw512uarr76qBx98UD4+PuratasaNGig9evXlzlvF3qO9ezZUz/++KMjYH/++eeaPHmy8vPzNWPGjHKPu127dkpOTtayZct06NAhDRw4UCEhIZJ+uRpUt25dLViwQPXq1VOvXr0c/bp06VKheb3++uuVnJzsUr5v3z6tXLlSvr6+atOmTYW2BQAVYgDAIrfeequRZP7xj39Uqt++fftMYGCgqVmzplm8eLGjvKSkxIwbN85IMjfeeKNTn9/+9rdGknnhhRdctrdt2zbz888/O37fs2ePkWQkme7du5sTJ0446ubOnWskmWbNmpk6deqYDRs2OOoOHDhgrrrqKiPJZGZmXtR4jTFGkomOjr6oY8/MzDR+fn7Gz8/PrFixwlF++vRpM2LECMdxzp0797z7P3r0qImKijKSzPPPP29KSkqc2q9du9YcOnTIqWzRokXm5MmTTmV5eXnmzjvvNJLM//7v/1bouMty7t+qU6dOJicnx6XN8ePHTYMGDUzNmjXNO++841S3YcMGU6dOHRMSEmIOHz7sKN+7d68JCAgwvr6+ZtmyZY7y4uJic++997qdtws9x+Lj483x48edxlSzZk0TFBRk8vPzKzQ38fHxRpLZs2eP2/mJj4936XOh9uzZY6Kjo42vr6/54IMPPLZdADDGGAIPAKtcd911RpLTB8qKeOqpp4wkc/fdd7vUFRUVmauvvtpIMl9++aWj/I477jCSzPfff3/e7Zd+SKxRo4bZsWOHU11JSYmpW7eukWSefPJJl76PPfaYywfhCxmvMe4/3FZ2W1OmTDGSzKhRo1za5+TkmJCQkAoHnr/+9a9GkunVq5dL28ratWuXkWQGDBhw3v2W59zgcG74PNeLL75oJJkJEya4rZ81a5aRZGbNmuUoK53n3/72ty7tjxw5YoKCglzm7ULPse3bt7vUlwbClStXOpV7O/AQdgBcatzDAwD65WtVknTvvfe61Pn7+2vw4MFO7SSpY8eOkqSHH35Yq1at0pkzZ867n5iYGLVo0cKprEaNGoqOjpYk9ezZ06VP6U3j596vcSHjLUtlt7VmzRpJcpSfKzw83O0xlCUjI0OS9Pvf/77CfSRp165dSk1N1SOPPKKRI0dq+PDheuaZZxx1ntCgQQPdeOONbus+/fRTSdKAAQPc1nft2lWS9PXXXzvKSudv2LBhLu0jIyPdztuFnGPR0dFq2bKlS3npeVfWfT/esHfvXiUkJOjnn3/W/Pnz1b9/f28PCYCFuIcHgFUiIyMlSdnZ2ZXqV3pjflkvYCwtP3DggKPs8ccf15dffqlVq1bpN7/5jUJCQtSpUyclJSVp+PDhql27tst2rrnmGrfbL71Hwl19ad25N+RfyHjLUtltlX5gbtSokdv2jRs3Pu8+S5U+Ja9p06YVam+M0cSJE/Xiiy+6vQFfkvLz8yu8//KUdxylj2a+9dZby93GkSNHHP+7dJ5Lw+2vuZv/CznHGjZs6Hb7oaGhkuSRBzt4wq/DTr9+/bw9JACW4goPAKuU3ty+ceNGj27Xx8fHpSwsLEwrVqzQF198oUmTJqlVq1ZasWKFxo0bp5YtW7q90lCjRvnL7vnqL2a8VWFbF+uf//ynZs2apYYNGyotLU0HDhxQcXGxjDGOD/JlBaHKCggIKLPu7NmzkqRBgwYpOTm5zJ/bbrvtosZwKc6xqqCiYae4uFjPPPOMWrRo4Xhy4J/+9CdH/fHjxzVmzBg1aNBA4eHhuuuuu1yeKrhw4ULFxcUpNDRUERER6tatm3bs2HFJjw9A1cIVHgBWSUpK0iuvvKL58+dr5syZFX5S29VXX60dO3YoKytLsbGxLvWl/0X/11dgfHx81KVLF8cTqg4fPqxx48bp/fff15QpU/Svf/3r4g7Iw+P1xLYaNGigHTt26KefflKrVq1c2lfm3UaNGjXS9u3blZmZqRtuuOG87T/44ANJ0v/8z/8oKSnJqW737t0V3u/FatiwoXbs2KHJkyc7vnZ2PqXzlpWV5XbesrKy3Pbz1jl2qZwbdtLS0tS3b1+37c6cOaPevXtry5YtmjJlilq1aqUff/xR+/btk/TLlbwuXbooICBAr7zyivz9/TVlyhQNHDhQa9eulY+Pjz766CPdf//9mj59um666Sbl5ubq448/dnoRMQD7Vf3/DAQAldCrVy/FxsZq//79+vOf/1xu27y8PG3dulXSf++5eP/9913aFRcXa/78+U7tynLVVVdp+vTpkn55f8ql4qnxXsi2Sr/GtWDBApf2x48fd9zfUhGJiYmSpNdff71C7XNyciS5/9rW5fzg36NHD0n/DWAVUTp/7sZ57NixCs/b5TrHyuLn5ydJFbqf6NdKw87BgwfLDTuS9Oyzz+r777/Xhg0b9Mgjj+i2227TI488oueff16S9MQTT6ioqEirVq3SgAEDlJSUpNmzZ2v9+vWO9xq99957GjZsmB5//HElJCSof//+eu2118r82h8AOxF4AFjFx8dH77zzjgICAjR9+nT98Y9/VEFBgVMbY4w+/PBD3XjjjdqwYYOkX97tEhgYqP/7v/9zvMtH+uWrS0888YQOHDigjh07Ot2z8eqrr2rPnj0uY/joo48klX2PiydcyHg9ta0RI0bIz89Pb7/9tlavXu1oX1JSogkTJlTqHprRo0erbt26+vjjj/XSSy+5fB1t/fr1Onz4sOP30hvvX3/9dae2X3zxheOD8OXw+9//XldddZVmzpyp119/3fEVt1JnzpzRJ5984hRIRowYIX9/f7377ruOhzVI0unTp/XYY4+5nKeSd8+xstStW1e+vr7KzMxUSUlJhfudG3bmz59fbtg5c+aMXnzxRU2ePNntMRYVFWnevHl67LHHnK7WlN4HVfq1tsDAQH3wwQd6/fXXHWEZwBXIi0+IA4BL5ssvvzT16tUzkkxQUJC57bbbzD333GOSkpIc5QEBASYjI8PR5+233zY1atQwPj4+pkuXLubuu+82LVu2NJJMvXr1zLZt25z20bZtWyPJtGrVygwcONAMHTrUURYQEOD0SOjzPcq3rEcAG/Pf9/RMmzbNqbyy4zWm7EcQV3ZbL730kuMRyL/5zW/MsGHDzLXXXmvCw8PNfffdZySZd999t0L7X7lypQkNDTWSTJMmTcyQIUNMnz59TLNmzYwk89133zna7tixwwQHBzvmfdiwYaZr167Gx8fHTJw4sczjK6vcnYo+dnndunWOx4k3atTI3HHHHeaee+4x3bt3N+Hh4UaSy2OWZ8+e7Zi3hIQEM2zYMBMTE2Nq167teBfPuY+l9uQ5Nm3aNLePCy9rbso7J/v06WMkmdjYWHP//febUaNGmTlz5pQ7X926dTOSTIsWLUxycrLbn08++cQYY8zXX39tJJmdO3e63dbatWuNJJfHb3/11VdGkvnxxx+NMb+852nUqFEmNDTU+Pn5mcGDB5sDBw6UO04A9iHwALBWfn6+eeGFF0x8fLyJiooytWrVMuHh4ebmm28206ZNMz/99JNLnzVr1pg+ffqYyMhI4+vraxo3bmwefPBBs3//fpe2H374oRk5cqSJjY014eHhJigoyLRo0cKMHj3a5YPYpQg8lR2vMeV/8K/sttLS0kxcXJwJDAw0derUMQMGDDA7duwwo0ePLvNdSGXtf/fu3eaBBx4wMTExxs/Pz0RERJiOHTuap59+2uTl5Tm13bZtm+nTp4+56qqrTFBQkGnfvr15/fXXy93+pQg8xhhz8OBBM2nSJBMbG2uCgoJMUFCQadq0qenXr5+ZN2+ey0s+jTHmgw8+MDfffLNj3vr162e2bdvmNpB48hzzZOA5dOiQuf/++039+vVNzZo1jSSTnJxc5jyVlJQ4gmp5P0uXLnUctySnl6eeq7Q+NzfXqXzmzJkmKirK5QW2p06dMvPnzzd16tRx+/4oAHbzMcZDj7MBAFzxSkpK1KZNG23btk0///yz6tev7+0hoRrasGGD4uLi9NVXXykuLs6l/rvvvlOHDh20ceNGtW/fXpKUm5urVq1aafjw4Xr22WfdbrdHjx665pprNG/evEs5fABVDIEHAFBpmZmZioyMVHh4uKPs1KlTeuKJJzRr1iwlJiZq+fLl3hsgqrXi4mLdcMMN8vX11VNPPaWIiAh9++23CgwM1KOPPqqSkhK1a9dOwcHBmjZtmgoKCjRjxgzVrFlTX375pQIDA/W73/1OQUFBio+PV+3atZWenq6XX35ZK1eudDzxDsCVgcADAKi05557TtOmTVPHjh3VqFEj5eXladOmTTp48KDq1q2rL7/8Ui1btvT2MFGN7dq1S4899pjWrVvnCEAzZ850hJXMzEw99NBDWrt2rUJCQjRw4ED95S9/cbxgNSUlRe+//7527dqlGjVqqH379nr66acJO8AViMADAKi0DRs2aNasWVq/fr2ys7N15swZXXPNNbr99tv1xz/+0StPDwMAwB0CDwAAAABr8R4eAAAAANYi8AAAAACwFoEHAAAAgLUIPAAAAACsReABAAAAYC0CDwAAAABrEXgAAAAAWIvAAwAAAMBaBB4AAAAA1iLwAAAAALAWgQcAAACAtQg8AAAAAKxF4AEAAABgLQIPAAAAAGsReAAAAABYi8ADAAAAwFr/H3VZPRKbijovAAAAAElFTkSuQmCC\n", - "text/plain": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "# Plot redshift hist (scale with cosmological volume)\n", - "plt.figure(dpi=150)\n", - "plt.hist(params['zcos'], bins=20)\n", - "plt.xlabel('Cosmological redshift $z_{cos}$')\n", - "plt.show()" - ] - }, { "cell_type": "code", "execution_count": null, @@ -521,9 +530,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "snsim_dev", "language": "python", - "name": "python3" + "name": "snsim_dev" }, "language_info": { "codemirror_mode": { diff --git a/Examples/SNSim_one_SN_LC_simulation.ipynb b/Examples/SNSim_one_SN_LC_simulation.ipynb index a6ab11b..8853dfd 100644 --- a/Examples/SNSim_one_SN_LC_simulation.ipynb +++ b/Examples/SNSim_one_SN_LC_simulation.ipynb @@ -18,19 +18,12 @@ "cell_type": "code", "execution_count": 1, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: The update_default_config function is deprecated and may be removed in a future version. [sncosmo]\n" - ] - } - ], + "outputs": [], "source": [ "import snsim\n", "import numpy as np\n", "import pandas as pd\n", + "import sncosmo\n", "from snsim.constants import C_LIGHT_KMS" ] }, @@ -86,7 +79,7 @@ "outputs": [ { "data": { - "image/png": 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\n", + "image/png": 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PN74FAASE1/Hq2LGj1qxZU2396tWr1b59e58MBQBATbx+ndfTTz+t+++/X0eOHNHdd98tSdq4caNee+01vf766z4fEACAX/I6XoMHD9batWv17LPP6o033lBYWJg6deqkjz76SH379vXHjAAAVFGrd9gYNGiQBg0a5OtZAADwSK1e53X27Fm98sormjVrlk6fPi1J2rVrlwoLC306HAAAl+P1kdfevXuVmJgoh8Oho0ePauLEiWrUqJHeeust5efn69VXX/XHnAAAuHl95DVt2jQlJyfr0KFDCg0Nda+/9957eZ0XACAgvI5XTk6OHnnkkWrrf/e73+n48eM+GQoAgJp4HS+73X7ZFyMfPHhQTZo08clQAADUxOt4DRkyRP/zP/+jCxcuSJJsNpvy8/M1c+ZMDR8+3OcDAgDwS17H64UXXlBZWZmaNm2qH3/8UX379lVcXJwiIiL0zDPP+GNGAACq8PpqQ4fDoezsbG3btk179+5VWVmZunbtqsTERH/MBwBANbV6kbIk9enTR3369PHlLAAAeMSreFVWViorK0tvvfWWjh49KpvNplatWumBBx7QmDFjZLPZ/DUnAABuHj/n5XK5NGTIEE2cOFGFhYXq2LGjbr31Vn377bdKTk7WsGHD/DknAABuHh95ZWVlaevWrdq4caMSEhKq3Ldp0yYNHTpUr776qsaOHevzIQEA+DmPj7xee+01zZo1q1q4JOnuu+9WWlqaVq5c6dPhAAC4HI/jtXfvXiUlJV3x/oEDB2rPnj0+GQoAgJp4HK/Tp08rKirqivdHRUXpzJkzPhkKAICaeByviooKhYRc+Smy4OBgXbx40SdDAQBQE48v2HC5XEpOTpbdbr/s/U6n02dDAQBQE4/jNW7cuF/dhisNAQCB4HG8li9f7s85AADwmNdvzAsAgNWIFwDAOMQLAGAc4gUAMI4x8XrmmWfUu3dvhYeH67rrrrN6HACAhYyJ1/nz5zVixAg9+uijVo8CALBYrT+MMtD+/Oc/S7r07vYAgGubMfGqDafTWeWdP0pKSiycBgDgK8acNqyNjIwMORwO9xIdHW31SAAAH7A0XmlpabLZbDUueXl5td5/enq6iouL3UtBQYEPpwcAWMXS04bTp09XcnJyjdvExsbWev92u/2KbyQMADCXpfFq0qSJmjRpYuUIAAADGXPBRn5+vk6fPq38/HxVVFQoNzdXkhQXF6eGDRtaOxwAIKCMidfs2bO1YsUK9+3bbrtNkvTxxx8rPj7eoqkAAFYw5mrDrKwsuVyuagvhAoBrjzHxAgDgJ8QLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxiFeAADjEC8AgHGIFwDAOMQLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxiFeAADjEC8AgHGIFwDAOMQLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxiFeAADjEC8AgHGIFwDAOMQLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxiFeAADjEC8AgHGIFwDAOEbE6+jRo5owYYJatWqlsLAwtW7dWnPmzNH58+etHg0AYIEQqwfwRF5eniorK7VkyRLFxcVp3759mjRpksrLyzVv3jyrxwMABJgR8UpKSlJSUpL7dmxsrA4cOKDMzMwa4+V0OuV0Ot23S0pK/DonACAwjDhteDnFxcVq1KhRjdtkZGTI4XC4l+jo6ABNBwDwJyPjdfjwYS1cuFCPPPJIjdulp6eruLjYvRQUFARoQgCAP1kar7S0NNlsthqXvLy8Kl9TWFiopKQkjRgxQpMmTapx/3a7XZGRkVUWAID5LH3Oa/r06UpOTq5xm9jYWPd/FxUVKSEhQb1799bLL7/s5+kAAFcrS+PVpEkTNWnSxKNtCwsLlZCQoG7dumn58uUKCjLyjCcAwAeMuNqwsLBQ8fHxuummmzRv3jydPHnSfV+zZs0snAwAYAUj4pWdna3Dhw/r8OHDatGiRZX7XC6XRVMBAKxixLm35ORkuVyuyy4AgGuPEfECAODniBcAwDjECwBgHOIFADAO8QIAGId4AQCMQ7wAAMYhXgAA4xAvAIBxiBcAwDjECwBgHOIFADAO8QIAGId4AQCMQ7wAAMYhXgAA4xAvAIBxiBcAwDjECwBgHOIFADAO8QIAGId4AQCMQ7wAAMYhXgAA4xAvAIBxiBcAwDjECwBgHOIFADAO8QIAGId4AQCMQ7wAAMYhXgAA4xgTryFDhigmJkahoaG68cYbNWbMGBUVFVk9FgDAAsbEKyEhQf/85z914MABvfnmmzpy5IgeeOABq8cCAFggxOoBPPXEE0+4//umm25SWlqahg4dqgsXLqhevXoWTgYACDRj4vVzp0+f1sqVK9W7d+8aw+V0OuV0Ot23S0pKAjEeAMDPjDltKEkzZ85UgwYNdMMNNyg/P1/r1q2rcfuMjAw5HA73Eh0dHaBJAQD+ZGm80tLSZLPZalzy8vLc2z/55JPavXu3PvzwQwUHB2vs2LFyuVxX3H96erqKi4vdS0FBQSD+WAAAP7P0tOH06dOVnJxc4zaxsbHu/27cuLEaN26sW265Re3atVN0dLS2b9+uXr16XfZr7Xa77Ha7L0cGAFwFLI1XkyZN1KRJk1p9bWVlpSRVeU4LAHBtMOKCjc8++0w5OTnq06ePrr/+eh05ckRPP/20WrdufcWjLgBA3WXEBRvh4eF666231K9fP7Vp00YTJkxQp06dtGXLFk4LAsA1yIgjr44dO2rTpk1WjwEAuEoYceQFAMDPES8AgHGIFwDAOMQLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxiFeAADjEC8AgHGIFwDAOMQLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxiFeAADjEC8AgHGIFwDAOMQLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxgmxeoA6Zf9+qycAgMCw+Pcd8fKFxo2l8HBp9GirJwGAwAkPv/T7zwLEyxdiYi79K+TUKasnAYDAadz40u8/CxAvX4mJsex/IgBca7hgAwBgHOIFADAO8QIAGId4AQCMQ7wAAMYxLl5Op1NdunSRzWZTbm6u1eMAACxgXLxmzJih5s2bWz0GAMBCRsVr/fr1+vDDDzVv3jyrRwEAWMiYFyl///33mjRpktauXavw8HCPvsbpdMrpdLpvl5SU+Gs8AEAAGXHk5XK5lJycrMmTJ6t79+4ef11GRoYcDod7iY6O9uOUAIBAsTReaWlpstlsNS55eXlauHChSktLlZ6e7tX+09PTVVxc7F4KCgr89CcBAASSzeVyuaz65idPntQPP/xQ4zaxsbF68MEH9e6778pms7nXV1RUKDg4WKNGjdKKFSs8+n4lJSVyOBwqLi5WZGTkb5odAOB7nv6etjRensrPz6/yfFVRUZEGDBigN954Qz179lSLFi082g/xAoCrm6e/p424YCPmF+/W3rBhQ0lS69atPQ4XAKDuMOKCDQAAfs6II69fatmypQw42wkA8BOOvAAAxiFeAADjGHnasLZ+OtXIO20AwNXpp9/Pv/bU0DUVr9LSUkninTYA4CpXWloqh8NxxfuNeJ2Xr1RWVqqoqEgRERFVXvBcUlKi6OhoFRQU8PovP+Dx9R8eW//hsfWvKz2+LpdLpaWlat68uYKCrvzM1jV15BUUFFTj68IiIyP5S+pHPL7+w2PrPzy2/nW5x7emI66fcMEGAMA4xAsAYBziJclut2vOnDmy2+1Wj1In8fj6D4+t//DY+tdvfXyvqQs2AAB1A0deAADjEC8AgHGIFwDAOMQLAGAc4nUFTqdTXbp0kc1mU25urtXj1AlHjx7VhAkT1KpVK4WFhal169aaM2eOzp8/b/VoRlq0aJFatmyp0NBQ9ezZUzt27LB6pDohIyNDt99+uyIiItS0aVMNHTpUBw4csHqsOum5556TzWbT1KlTvf5a4nUFM2bMUPPmza0eo07Jy8tTZWWllixZoi+//FLz58/X4sWLNWvWLKtHM86aNWs0bdo0zZkzR7t27VLnzp01YMAAnThxwurRjLdlyxalpKRo+/btys7O1oULF9S/f3+Vl5dbPVqdkpOToyVLlqhTp06124EL1bz//vuutm3bur788kuXJNfu3butHqnOmjt3rqtVq1ZWj2GcHj16uFJSUty3KyoqXM2bN3dlZGRYOFXddOLECZck15YtW6wepc4oLS113Xzzza7s7GxX3759XVOmTPF6Hxx5/cL333+vSZMm6e9//7vCw8OtHqfOKy4uVqNGjawewyjnz5/Xzp07lZiY6F4XFBSkxMREffrppxZOVjcVFxdLEn9PfSglJUWDBg2q8nfYW9fUG/P+GpfLpeTkZE2ePFndu3fX0aNHrR6pTjt8+LAWLlyoefPmWT2KUU6dOqWKigpFRUVVWR8VFaW8vDyLpqqbKisrNXXqVN1xxx3q0KGD1ePUCatXr9auXbuUk5Pzm/ZzTRx5paWlyWaz1bjk5eVp4cKFKi0tVXp6utUjG8XTx/fnCgsLlZSUpBEjRmjSpEkWTQ7ULCUlRfv27dPq1autHqVOKCgo0JQpU7Ry5UqFhob+pn1dE28PdfLkSf3www81bhMbG6sHH3xQ7777bpXP+qqoqFBwcLBGjRqlFStW+HtUI3n6+NavX1+SVFRUpPj4eP3+979XVlZWjZ/Zg+rOnz+v8PBwvfHGGxo6dKh7/bhx43T27FmtW7fOuuHqkNTUVK1bt05bt25Vq1atrB6nTli7dq2GDRum4OBg97qKigrZbDYFBQXJ6XRWua8m10S8PJWfn+/+CGrp0i/ZAQMG6I033lDPnj1r/CwweKawsFAJCQnq1q2b/vGPf3j8FxVV9ezZUz169NDChQslXTq9FRMTo9TUVKWlpVk8ndlcLpcee+wxvf3229q8ebNuvvlmq0eqM0pLS/Xtt99WWTd+/Hi1bdtWM2fO9OrULM95/UxMTEyV2w0bNpQktW7dmnD5QGFhoeLj43XTTTdp3rx5OnnypPu+Zs2aWTiZeaZNm6Zx48ape/fu6tGjhxYsWKDy8nKNHz/e6tGMl5KSolWrVmndunWKiIjQ8ePHJV36gMSwsDCLpzNbREREtUA1aNBAN9xwg9fPKRIvBEx2drYOHz6sw4cPV/vHACcAvPPQQw/p5MmTmj17to4fP64uXbpow4YN1S7igPcyMzMlSfHx8VXWL1++XMnJyYEfCJfFaUMAgHF4phwAYBziBQAwDvECABiHeAEAjEO8AADGIV4AAOMQLwCAcYgXAMA4xAswRHx8vPtd+nNzc6+43ebNm2Wz2XT27Fmffv+WLVu6v7+v9w14i3gBPpScnOz+BV+vXj21atVKM2bM0Llz56pt+91336l+/fpevafbpEmTdOzYMUs+WyonJ0dvvvlmwL8vcDnEC/CxpKQkHTt2TF9//bXmz5+vJUuWaM6cOdW2y8rK0oMPPqiSkhJ99tlnHu07PDxczZo1U0hI4N+WtEmTJnyaMK4axAvwMbvdrmbNmik6OlpDhw5VYmKisrOzq2zjcrm0fPlyjRkzRiNHjtTSpUtr/f3ef/993XLLLQoLC1NCQsJlPwF827ZtuvPOOxUWFqbo6Gg9/vjjKi8vd99/7NgxDRo0SGFhYWrVqpVWrVqlli1basGCBbWeC/An4gX40b59+/TJJ5+4P4jzJx9//LH++9//KjExUaNHj9bq1aurxMRTBQUFuv/++zV48GDl5uZq4sSJ1T7P68iRI0pKStLw4cO1d+9erVmzRtu2bVNqaqp7m7Fjx6qoqEibN2/Wm2++qZdfflknTpyo3R8aCAA+EgXwsffee08NGzbUxYsX5XQ6FRQUpJdeeqnKNkuXLtUf/vAHBQcHq0OHDoqNjdXrr7/u9UduZGZmqnXr1nrhhRckSW3atNEXX3yh559/3r1NRkaGRo0apalTp0qSbr75Zr344ovq27evMjMzdfToUX300UfKyclR9+7dJUmvvPIKH8KIqxrxAnwsISFBmZmZKi8v1/z58xUSEqLhw4e77z979qzeeustbdu2zb1u9OjRWrp0qdfx2r9/v3r27FllXa9evarc3rNnj/bu3auVK1e617lcLlVWVuqbb77RwYMHFRISoq5du7rvj4uL0/XXX+/VLEAgES/Axxo0aKC4uDhJ0rJly9S5c2ctXbpUEyZMkCStWrVK586dqxKdn2Jy8OBB3XLLLT6dp6ysTI888ogef/zxavfFxMTo4MGDPv1+QCDwnBfgR0FBQZo1a5b+9Kc/6ccff5R06ZTh9OnTlZub61727NmjO++8U8uWLfNq/+3atdOOHTuqrNu+fXuV2127dtVXX32luLi4akv9+vXVpk0bXbx4Ubt373Z/zeHDh3XmzJla/qkB/yNegJ+NGDFCwcHBWrRokXJzc7Vr1y5NnDhRHTp0qLL88Y9/1IoVK3Tx4kWP9z158mQdOnRITz75pA4cOKBVq1YpKyuryjYzZ87UJ598otTUVOXm5urQoUNat26d+4KNtm3bKjExUQ8//LB27Nih3bt36+GHH1ZYWJhsNpsvHwrAZ4gX4GchISFKTU3V3LlztWjRIrVv315t27attt2wYcN04sQJvf/++x7vOyYmRm+++abWrl2rzp07a/HixXr22WerbNOpUydt2bJFBw8e1J133qnbbrtNs2fPVvPmzd3bvPrqq4qKitJdd92lYcOGadKkSYqIiFBoaGjt/+CAH9lcLpfL6iEA/Lr4+Hh16dIlIK+9+u677xQdHa2PPvpI/fr1c6/fvHmzEhISdObMGV133XV+nwO4EuIFGCI+Pt79mrFPP/1UHTt29Nm+N23apLKyMnXs2FHHjh3TjBkzVFhYqIMHD6pevXqSpFtvvVVff/21zp07R7xgOa42BAyxcuVK90UfMTExPt33hQsXNGvWLH399deKiIhQ7969tXLlSne4pEvv5HHhwgVJUmRkpE+/P+AtjrwAAMbhgg0AgHGIFwDAOMQLAGAc4gUAMA7xAgAYh3gBAIxDvAAAxiFeAADj/C+PkS5nPfo7mgAAAABJRU5ErkJggg==", "text/plain": [ "
" ] @@ -105,7 +98,14 @@ "cell_type": "markdown", "metadata": {}, "source": [ - "#### Set the cosmology" + "## SNIa simulation" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Set the cosmology" ] }, { @@ -120,13 +120,6 @@ "cosmo = snsim.utils.set_cosmo(cosmology)" ] }, - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "## SNIa simulation" - ] - }, { "cell_type": "markdown", "metadata": {}, @@ -136,7 +129,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 5, "metadata": {}, "outputs": [], "source": [ @@ -146,37 +139,29 @@ "\n", "#parameters of SNIa object\n", "sn_par = {'zcos': zcos,\n", - " 'z2cmb': 0.0,\n", + " 'zpcmb': 0.0,\n", " 'como_dist': cosmo.comoving_distance(zcos).value,\n", " 'vpec': 300,\n", - " 'sim_t0': 58030,#simulated peak time of the event\n", + " 't0': 58057,\n", " 'ra': coords[0],\n", " 'dec': coords[1],\n", - " 'mag_sct': 0.0,\n", - " 'sct_model': 'G10',\n", - " 'sncosmo':{'x1':1 , 'c':0.1} #parameter of of Salt model for SNIa\n", - " }\n", - "\n", - "\n", - "# Set the sncosmo model for SNIa\n", - "sn_model = snsim.utils.init_sn_model('salt2')\n", - "\n", - "\n", - "#parameters of SNIa model\n", - "model_par = {'M0': -19.3,\n", - " 'alpha': 0.14,\n", - " 'beta': 3.1,\n", - " 'mod_fcov': False}\n", - "\n", + " 'coh_sct': 0.0,\n", + " 'x1':1, \n", + " 'c':0.1,\n", + " 'M0': -19.3,\n", + " 'alpha': 0.14,\n", + " 'beta': 3.1,\n", + " 'model_name': 'salt2',\n", + " 'model_version': '2.0'\n", + " }\n", "\n", "#Init SNIa object\n", - "SNIa = snsim.astrobj.SNIa(sn_par, sn_model, model_par=model_par)\n", - "\n" + "SNIa = snsim.astrobj.SNIa(sn_par, relation='SALTTripp')" ] }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 6, "metadata": {}, "outputs": [ { @@ -203,7 +188,7 @@ " min_t\n", " max_t\n", " 1_zobs\n", - " sim_t0\n", + " t0\n", " ra\n", " dec\n", " \n", @@ -211,10 +196,10 @@ " \n", " \n", " 0\n", - " 58008.978985\n", - " 58082.552536\n", + " 58035.978985\n", + " 58109.552536\n", " 1.051051\n", - " 58030\n", + " 58057\n", " 0.733038\n", " 0.733038\n", " \n", @@ -223,11 +208,11 @@ "" ], "text/plain": [ - " min_t max_t 1_zobs sim_t0 ra dec\n", - "0 58008.978985 58082.552536 1.051051 58030 0.733038 0.733038" + " min_t max_t 1_zobs t0 ra dec\n", + "0 58035.978985 58109.552536 1.051051 58057 0.733038 0.733038" ] }, - "execution_count": 17, + "execution_count": 6, "metadata": {}, "output_type": "execute_result" } @@ -237,11 +222,11 @@ "#evaluate Z-obs and time range where we can observed the event in the rest frame\n", "#( [-20,50] phase respect to t_peak where Salt model is defined)\n", "dict_obs_par={}\n", - "_1_zobs_ = (1 +sn_par['zcos']) * (1+sn_par['z2cmb'])*(1 + sn_par['vpec'] / C_LIGHT_KMS) \n", - "dict_obs_par['min_t'] = sn_par['sim_t0'] -20 * _1_zobs_\n", - "dict_obs_par['max_t'] = sn_par['sim_t0'] + 50 * _1_zobs_\n", + "_1_zobs_ = (1 +sn_par['zcos']) * (1+sn_par['zpcmb'])*(1 + sn_par['vpec'] / C_LIGHT_KMS) \n", + "dict_obs_par['min_t'] = sn_par['t0'] - 20 * _1_zobs_\n", + "dict_obs_par['max_t'] = sn_par['t0'] + 50 * _1_zobs_\n", "dict_obs_par['1_zobs'] = _1_zobs_\n", - "dict_obs_par['sim_t0']=sn_par['sim_t0']\n", + "dict_obs_par['t0']=sn_par['t0']\n", "dict_obs_par['ra']=sn_par['ra']\n", "dict_obs_par['dec']=sn_par['dec']\n", "\n", @@ -259,7 +244,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 7, "metadata": {}, "outputs": [ { @@ -309,162 +294,6 @@ " \n", " \n", " 0\n", - " 58000.000000\n", - " ztfg\n", - " 1\n", - " 629.554693\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58002.040816\n", - " ztfg\n", - " 1\n", - " 794.918147\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58004.081633\n", - " ztfg\n", - " 1\n", - " 857.808033\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58006.122449\n", - " ztfg\n", - " 1\n", - " 736.170271\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58008.163265\n", - " ztfg\n", - " 1\n", - " 716.202148\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58010.204082\n", - " ztfg\n", - " 1\n", - " 558.747930\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58012.244898\n", - " ztfr\n", - " 1\n", - " 242.029050\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58014.285714\n", - " ztfr\n", - " 1\n", - " 107.626067\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58016.326531\n", - " ztfg\n", - " 1\n", - " 18.283325\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58018.367347\n", - " ztfr\n", - " 1\n", - " 571.811236\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58020.408163\n", - " ztfg\n", - " 1\n", - " 213.885387\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58022.448980\n", - " ztfg\n", - " 1\n", - " 134.582496\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", - " 58024.489796\n", - " ztfg\n", - " 1\n", - " 743.396108\n", - " 25.0\n", - " 0.0\n", - " 0.0\n", - " 1.0\n", - " ab\n", - " \n", - " \n", - " 0\n", " 58026.530612\n", " ztfg\n", " 1\n", @@ -914,19 +743,6 @@ "text/plain": [ " time band fieldID skynoise zp sig_zp sig_psf gain zpsys\n", "ID \n", - "0 58000.000000 ztfg 1 629.554693 25.0 0.0 0.0 1.0 ab\n", - "0 58002.040816 ztfg 1 794.918147 25.0 0.0 0.0 1.0 ab\n", - "0 58004.081633 ztfg 1 857.808033 25.0 0.0 0.0 1.0 ab\n", - "0 58006.122449 ztfg 1 736.170271 25.0 0.0 0.0 1.0 ab\n", - "0 58008.163265 ztfg 1 716.202148 25.0 0.0 0.0 1.0 ab\n", - "0 58010.204082 ztfg 1 558.747930 25.0 0.0 0.0 1.0 ab\n", - "0 58012.244898 ztfr 1 242.029050 25.0 0.0 0.0 1.0 ab\n", - "0 58014.285714 ztfr 1 107.626067 25.0 0.0 0.0 1.0 ab\n", - "0 58016.326531 ztfg 1 18.283325 25.0 0.0 0.0 1.0 ab\n", - "0 58018.367347 ztfr 1 571.811236 25.0 0.0 0.0 1.0 ab\n", - "0 58020.408163 ztfg 1 213.885387 25.0 0.0 0.0 1.0 ab\n", - "0 58022.448980 ztfg 1 134.582496 25.0 0.0 0.0 1.0 ab\n", - "0 58024.489796 ztfg 1 743.396108 25.0 0.0 0.0 1.0 ab\n", "0 58026.530612 ztfg 1 405.460001 25.0 0.0 0.0 1.0 ab\n", "0 58028.571429 ztfg 1 582.286411 25.0 0.0 0.0 1.0 ab\n", "0 58030.612245 ztfg 1 862.602785 25.0 0.0 0.0 1.0 ab\n", @@ -966,20 +782,20 @@ "0 58100.000000 ztfg 1 535.020328 25.0 0.0 0.0 1.0 ab" ] }, - "execution_count": 18, + "execution_count": 7, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#define the observation of the object\n", - "epochs,param= survey.get_observations(obs_par,[-50,70])\n", + "epochs,param= survey.get_observations(obs_par,[-30,60])\n", "epochs" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 8, "metadata": {}, "outputs": [ { @@ -1003,9 +819,9 @@ " \n", " \n", " \n", - " ID\n", " time\n", " fluxtrue\n", + " fluxerrtrue\n", " flux\n", " fluxerr\n", " mag\n", @@ -1041,17 +857,17 @@ " \n", " \n", " 0\n", - " 0\n", - " 58010.204082\n", - " 3.818568\n", - " -618.869138\n", - " 558.751347\n", - " NaN\n", - " NaN\n", + " 58036.734694\n", + " 0.362527\n", + " 347.366583\n", + " 152.165823\n", + " 347.585020\n", + " 19.544207\n", + " 2.480095\n", " 25.0\n", " ab\n", " 1.0\n", - " 558.747930\n", + " 347.366061\n", " ztfg\n", " 1\n", " 0.0\n", @@ -1059,17 +875,17 @@ " \n", " \n", " 1\n", - " 0\n", - " 58012.244898\n", - " 33.355528\n", - " -136.571458\n", - " 242.097948\n", + " 58038.775510\n", + " -30.070167\n", + " 476.544091\n", + " -1836.897998\n", + " 478.436097\n", " NaN\n", " NaN\n", " 25.0\n", " ab\n", " 1.0\n", - " 242.029050\n", + " 476.512540\n", " ztfr\n", " 1\n", " 0.0\n", @@ -1077,17 +893,17 @@ " \n", " \n", " 2\n", - " 0\n", - " 58014.285714\n", - " 104.412586\n", - " 55.621494\n", - " 108.110050\n", - " 20.636893\n", - " 2.110317\n", + " 58040.816327\n", + " -29.465715\n", + " 822.313522\n", + " 1050.885756\n", + " 822.934353\n", + " 17.446111\n", + " 0.850225\n", " 25.0\n", " ab\n", " 1.0\n", - " 107.626067\n", + " 822.295606\n", " ztfr\n", " 1\n", " 0.0\n", @@ -1095,35 +911,35 @@ " \n", " \n", " 3\n", - " 0\n", - " 58016.326531\n", - " 225.985564\n", - " 226.423139\n", - " 23.669929\n", - " 19.112698\n", - " 0.113501\n", + " 58042.857143\n", + " 229.389300\n", + " 746.711113\n", + " 472.125062\n", + " 746.873632\n", + " 18.314857\n", + " 1.717570\n", " 25.0\n", " ab\n", " 1.0\n", - " 18.283325\n", - " ztfg\n", + " 746.557497\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 4\n", - " 0\n", - " 58018.367347\n", - " 355.661833\n", - " 716.265013\n", - " 572.122147\n", - " 17.862316\n", - " 0.867240\n", + " 58044.897959\n", + " 446.377632\n", + " 415.482014\n", + " 978.318237\n", + " 416.121670\n", + " 17.523800\n", + " 0.461811\n", " 25.0\n", " ab\n", " 1.0\n", - " 571.811236\n", + " 414.944486\n", " ztfr\n", " 1\n", " 0.0\n", @@ -1131,89 +947,89 @@ " \n", " \n", " 5\n", - " 0\n", - " 58020.408163\n", - " 517.812637\n", - " 503.518334\n", - " 215.092472\n", - " 18.244962\n", - " 0.463804\n", + " 58046.938776\n", + " 646.022936\n", + " 290.846376\n", + " 1058.431344\n", + " 291.554494\n", + " 17.438343\n", + " 0.299076\n", " 25.0\n", " ab\n", " 1.0\n", - " 213.885387\n", - " ztfg\n", + " 289.733657\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 6\n", - " 0\n", - " 58022.448980\n", - " 667.885154\n", - " 744.306630\n", - " 137.041357\n", - " 17.820620\n", - " 0.199905\n", + " 58048.979592\n", + " 854.227878\n", + " 597.868949\n", + " 779.471228\n", + " 597.806427\n", + " 17.770500\n", + " 0.832693\n", " 25.0\n", " ab\n", " 1.0\n", - " 134.582496\n", - " ztfg\n", + " 597.154128\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 7\n", - " 0\n", - " 58024.489796\n", - " 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58055.102041\n", - " 178.841935\n", - " 364.438817\n", - " 285.913293\n", - " 18.595938\n", - " 0.851793\n", + " 58081.632653\n", + " 644.178985\n", + " 211.858934\n", + " 395.310331\n", + " 211.270773\n", + " 18.507655\n", + " 0.580264\n", " 25.0\n", " ab\n", " 1.0\n", - " 285.600366\n", - " ztfg\n", + " 210.333138\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 23\n", - " 0\n", - " 58057.142857\n", - " 157.240872\n", - " 103.254701\n", - " 892.129345\n", - " 19.965225\n", - " 9.380853\n", + " 58083.673469\n", + " 619.778601\n", + " 28.516658\n", + " 633.864500\n", + " 28.762575\n", + " 17.995009\n", + " 0.049267\n", " 25.0\n", " ab\n", " 1.0\n", - " 892.041214\n", - " ztfg\n", + " 13.907595\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 24\n", - " 0\n", - " 58059.183673\n", - " 139.254791\n", - " 65.077242\n", - " 184.775461\n", - " 20.466427\n", - " 3.082758\n", + " 58085.714286\n", + " 198.810860\n", + " 318.020828\n", + " 269.923765\n", + " 318.132614\n", + " 18.921897\n", + " 1.279651\n", " 25.0\n", " ab\n", " 1.0\n", - " 184.398254\n", + " 317.708099\n", " ztfg\n", " 1\n", " 0.0\n", @@ -1491,71 +1307,71 @@ " \n", " \n", " 25\n", - " 0\n", - " 58061.224490\n", - " 122.906545\n", - " 602.402107\n", - " 229.071534\n", - " 18.050284\n", - " 0.412866\n", + " 58087.755102\n", + " 529.634700\n", + " 718.371202\n", + " -223.445306\n", + " 718.158056\n", + " NaN\n", + " NaN\n", " 25.0\n", " ab\n", " 1.0\n", - " 228.803106\n", - " ztfg\n", + " 718.002471\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 26\n", - " 0\n", - " 58063.265306\n", - " 338.510891\n", - " 282.278625\n", - " 126.919757\n", - " 18.873305\n", - " 0.488175\n", + " 58089.795918\n", + " 154.134043\n", + " 838.460653\n", + " 476.716807\n", + " 838.652997\n", + " 18.304349\n", + " 1.910056\n", " 25.0\n", " ab\n", " 1.0\n", - " 125.579114\n", - " ztfr\n", + " 838.368733\n", + " ztfg\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 27\n", - " 0\n", - " 58065.306122\n", - " 96.010751\n", - " 191.610671\n", - " 336.723012\n", - " 19.293951\n", - " 1.907996\n", + " 58091.836735\n", + " 432.569689\n", + " 882.294185\n", + " 410.488220\n", + " 882.281672\n", + " 18.466748\n", + " 2.333624\n", " 25.0\n", " ab\n", " 1.0\n", - " 336.580416\n", - " ztfg\n", + " 882.049012\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 28\n", - " 0\n", - " 58067.346939\n", - " 86.693541\n", - " 95.073249\n", - " 21.737995\n", - " 20.054854\n", - " 0.248248\n", + " 58093.877551\n", + " 123.283963\n", + " 999.046770\n", + " -363.133113\n", + " 999.166802\n", + " NaN\n", + " NaN\n", " 25.0\n", " ab\n", " 1.0\n", - " 19.642986\n", + " 998.985068\n", " ztfg\n", " 1\n", " 0.0\n", @@ -1563,17 +1379,17 @@ " \n", " \n", " 29\n", - " 0\n", - " 58069.387755\n", - " 79.630727\n", - " -57.033372\n", - " 95.080236\n", - " NaN\n", - " NaN\n", + " 58095.918367\n", + " 111.941063\n", + " 210.567328\n", + " 223.418892\n", + " 210.831870\n", + " 19.127200\n", + " 1.024568\n", " 25.0\n", " ab\n", " 1.0\n", - " 94.660555\n", + " 210.301351\n", " ztfg\n", " 1\n", " 0.0\n", @@ -1581,17 +1397,17 @@ " \n", " \n", " 30\n", - " 0\n", - " 58071.428571\n", - " 74.394583\n", - " 64.380350\n", - " 185.698569\n", - " 20.478117\n", - " 3.131696\n", + " 58097.959184\n", + " 104.052198\n", + " 903.854354\n", + " 314.693983\n", + " 903.970871\n", + " 18.755279\n", + " 3.118820\n", " 25.0\n", " ab\n", " 1.0\n", - " 185.498151\n", + " 903.796792\n", " ztfg\n", " 1\n", " 0.0\n", @@ -1599,179 +1415,134 @@ " \n", " \n", " 31\n", - " 0\n", - " 58073.469388\n", - " 70.263098\n", - " -701.281958\n", - " 636.814576\n", - " NaN\n", - " NaN\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 636.759406\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 32\n", - " 0\n", - " 58075.510204\n", - " 66.986893\n", - " -175.612266\n", - " 363.730137\n", - " NaN\n", - " NaN\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 363.638042\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 33\n", - " 0\n", - " 58077.551020\n", - " 64.441472\n", - " -612.780622\n", - " 725.530589\n", + " 58100.000000\n", + " 95.254080\n", + " 535.109340\n", + " -123.184288\n", + " 535.135437\n", " NaN\n", " NaN\n", " 25.0\n", " ab\n", " 1.0\n", - " 725.486177\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 34\n", - " 0\n", - " 58079.591837\n", - " 62.509673\n", - " 600.342725\n", - " 737.942173\n", - " 18.054002\n", - " 1.334589\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 737.899817\n", + " 535.020328\n", " ztfg\n", " 1\n", " 0.0\n", " 0.0\n", " \n", - " \n", - " 35\n", - " 0\n", - " 58081.632653\n", - " 151.909028\n", - " 596.723672\n", - " 210.693944\n", - " 18.060567\n", - " 0.383357\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 210.333138\n", - " ztfr\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", " \n", "\n", "" ], "text/plain": [ - " ID time fluxtrue flux fluxerr mag \\\n", + " time fluxtrue fluxerrtrue flux fluxerr \\\n", + "epochs \n", + "0 58036.734694 0.362527 347.366583 152.165823 347.585020 \n", + "1 58038.775510 -30.070167 476.544091 -1836.897998 478.436097 \n", + "2 58040.816327 -29.465715 822.313522 1050.885756 822.934353 \n", + "3 58042.857143 229.389300 746.711113 472.125062 746.873632 \n", + "4 58044.897959 446.377632 415.482014 978.318237 416.121670 \n", + "5 58046.938776 646.022936 290.846376 1058.431344 291.554494 \n", + "6 58048.979592 854.227878 597.868949 779.471228 597.806427 \n", + "7 58051.020408 1050.570836 848.913265 1741.903288 849.320354 \n", + "8 58053.061224 1054.372615 995.033692 1474.884326 995.244974 \n", + "9 58055.102041 1241.990289 287.766502 958.703818 287.273864 \n", + "10 58057.142857 1207.907331 892.718004 1338.601880 892.791202 \n", + "11 58059.183673 1201.956430 187.629082 1197.869626 187.618191 \n", + "12 58061.224490 1096.008457 231.185791 891.679491 230.743452 \n", + "13 58063.265306 1079.349027 129.805481 943.462373 129.280997 \n", + "14 58065.306122 938.532586 337.971757 680.285679 337.589487 \n", + "15 58067.346939 787.643243 34.256242 794.168160 34.351347 \n", + "16 58069.387755 733.751459 98.460003 687.951808 98.227147 \n", + "17 58071.428571 554.930918 186.987954 815.581074 187.683631 \n", + "18 58073.469388 482.904910 637.138483 371.888832 637.051356 \n", + "19 58075.510204 422.137092 364.218016 655.379442 364.538071 \n", + "20 58077.551020 347.772452 725.725820 -603.036254 725.901667 \n", + "21 58079.591837 294.797096 738.099545 793.777303 738.437484 \n", + "22 58081.632653 644.178985 211.858934 395.310331 211.270773 \n", + "23 58083.673469 619.778601 28.516658 633.864500 28.762575 \n", + "24 58085.714286 198.810860 318.020828 269.923765 318.132614 \n", + "25 58087.755102 529.634700 718.371202 -223.445306 718.158056 \n", + "26 58089.795918 154.134043 838.460653 476.716807 838.652997 \n", + "27 58091.836735 432.569689 882.294185 410.488220 882.281672 \n", + "28 58093.877551 123.283963 999.046770 -363.133113 999.166802 \n", + "29 58095.918367 111.941063 210.567328 223.418892 210.831870 \n", + "30 58097.959184 104.052198 903.854354 314.693983 903.970871 \n", + "31 58100.000000 95.254080 535.109340 -123.184288 535.135437 \n", + "\n", + " mag magerr zp zpsys gain skynoise band fieldID \\\n", "epochs \n", - "0 0 58010.204082 3.818568 -618.869138 558.751347 NaN \n", - "1 0 58012.244898 33.355528 -136.571458 242.097948 NaN \n", - "2 0 58014.285714 104.412586 55.621494 108.110050 20.636893 \n", - "3 0 58016.326531 225.985564 226.423139 23.669929 19.112698 \n", - "4 0 58018.367347 355.661833 716.265013 572.122147 17.862316 \n", - "5 0 58020.408163 517.812637 503.518334 215.092472 18.244962 \n", - "6 0 58022.448980 667.885154 744.306630 137.041357 17.820620 \n", - "7 0 58024.489796 784.210411 1766.003588 743.923372 16.882521 \n", - "8 0 58026.530612 854.137002 90.370213 406.511930 20.109937 \n", - "9 0 58028.571429 887.070230 1336.464649 583.047626 17.185106 \n", - "10 0 58030.612245 884.473478 1856.416036 863.115310 16.828312 \n", - "11 0 58032.653061 867.991069 1059.404915 169.635128 17.437345 \n", - "12 0 58034.693878 793.308775 623.545366 638.450115 18.012830 \n", - "13 0 58036.734694 726.002772 451.977593 348.409505 18.362208 \n", - "14 0 58038.775510 709.960100 1118.786336 477.256913 17.378132 \n", - "15 0 58040.816327 631.747671 1288.707976 822.679653 17.224614 \n", - "16 0 58042.857143 561.454234 266.817005 746.933432 18.934466 \n", - "17 0 58044.897959 503.386881 997.814913 415.550614 17.502375 \n", - "18 0 58046.938776 470.062384 54.543372 290.543722 20.658145 \n", - "19 0 58048.979592 459.744781 139.527623 597.538951 19.638350 \n", - "20 0 58051.020408 450.472445 511.764758 848.559740 18.227324 \n", - "21 0 58053.061224 440.442834 1771.482475 994.725147 16.879158 \n", - "22 0 58055.102041 178.841935 364.438817 285.913293 18.595938 \n", - "23 0 58057.142857 157.240872 103.254701 892.129345 19.965225 \n", - "24 0 58059.183673 139.254791 65.077242 184.775461 20.466427 \n", - "25 0 58061.224490 122.906545 602.402107 229.071534 18.050284 \n", - "26 0 58063.265306 338.510891 282.278625 126.919757 18.873305 \n", - "27 0 58065.306122 96.010751 191.610671 336.723012 19.293951 \n", - "28 0 58067.346939 86.693541 95.073249 21.737995 20.054854 \n", - "29 0 58069.387755 79.630727 -57.033372 95.080236 NaN \n", - "30 0 58071.428571 74.394583 64.380350 185.698569 20.478117 \n", - "31 0 58073.469388 70.263098 -701.281958 636.814576 NaN \n", - "32 0 58075.510204 66.986893 -175.612266 363.730137 NaN \n", - "33 0 58077.551020 64.441472 -612.780622 725.530589 NaN \n", - "34 0 58079.591837 62.509673 600.342725 737.942173 18.054002 \n", - "35 0 58081.632653 151.909028 596.723672 210.693944 18.060567 \n", + "0 19.544207 2.480095 25.0 ab 1.0 347.366061 ztfg 1 \n", + "1 NaN NaN 25.0 ab 1.0 476.512540 ztfr 1 \n", + "2 17.446111 0.850225 25.0 ab 1.0 822.295606 ztfr 1 \n", + "3 18.314857 1.717570 25.0 ab 1.0 746.557497 ztfr 1 \n", + "4 17.523800 0.461811 25.0 ab 1.0 414.944486 ztfr 1 \n", + "5 17.438343 0.299076 25.0 ab 1.0 289.733657 ztfr 1 \n", + "6 17.770500 0.832693 25.0 ab 1.0 597.154128 ztfr 1 \n", + "7 16.897440 0.529385 25.0 ab 1.0 848.294265 ztfr 1 \n", + "8 17.078105 0.732650 25.0 ab 1.0 994.503733 ztfr 1 \n", + "9 17.545789 0.325339 25.0 ab 1.0 285.600366 ztfg 1 \n", + "10 17.183371 0.724140 25.0 ab 1.0 892.041214 ztfg 1 \n", + "11 17.303976 0.170055 25.0 ab 1.0 184.398254 ztfg 1 \n", + "12 17.624478 0.280960 25.0 ab 1.0 228.803106 ztfg 1 \n", + "13 17.563189 0.148777 25.0 ab 1.0 125.579114 ztfr 1 \n", + "14 17.918272 0.538793 25.0 ab 1.0 336.580416 ztfg 1 \n", + "15 17.750219 0.046963 25.0 ab 1.0 19.642986 ztfg 1 \n", + "16 17.906105 0.155024 25.0 ab 1.0 94.660555 ztfg 1 \n", + "17 17.721332 0.249852 25.0 ab 1.0 185.498151 ztfg 1 \n", + "18 18.573967 1.859883 25.0 ab 1.0 636.759406 ztfg 1 \n", + "19 17.958768 0.603913 25.0 ab 1.0 363.638042 ztfg 1 \n", + "20 NaN NaN 25.0 ab 1.0 725.486177 ztfg 1 \n", + "21 17.750753 1.010042 25.0 ab 1.0 737.899817 ztfg 1 \n", + "22 18.507655 0.580264 25.0 ab 1.0 210.333138 ztfr 1 \n", + "23 17.995009 0.049267 25.0 ab 1.0 13.907595 ztfr 1 \n", + "24 18.921897 1.279651 25.0 ab 1.0 317.708099 ztfg 1 \n", + "25 NaN NaN 25.0 ab 1.0 718.002471 ztfr 1 \n", + "26 18.304349 1.910056 25.0 ab 1.0 838.368733 ztfg 1 \n", + "27 18.466748 2.333624 25.0 ab 1.0 882.049012 ztfr 1 \n", + "28 NaN NaN 25.0 ab 1.0 998.985068 ztfg 1 \n", + "29 19.127200 1.024568 25.0 ab 1.0 210.301351 ztfg 1 \n", + "30 18.755279 3.118820 25.0 ab 1.0 903.796792 ztfg 1 \n", + "31 NaN NaN 25.0 ab 1.0 535.020328 ztfg 1 \n", "\n", - " magerr zp zpsys gain skynoise band fieldID sig_zp sig_psf \n", - "epochs \n", - "0 NaN 25.0 ab 1.0 558.747930 ztfg 1 0.0 0.0 \n", - "1 NaN 25.0 ab 1.0 242.029050 ztfr 1 0.0 0.0 \n", - "2 2.110317 25.0 ab 1.0 107.626067 ztfr 1 0.0 0.0 \n", - "3 0.113501 25.0 ab 1.0 18.283325 ztfg 1 0.0 0.0 \n", - "4 0.867240 25.0 ab 1.0 571.811236 ztfr 1 0.0 0.0 \n", - "5 0.463804 25.0 ab 1.0 213.885387 ztfg 1 0.0 0.0 \n", - "6 0.199905 25.0 ab 1.0 134.582496 ztfg 1 0.0 0.0 \n", - "7 0.457363 25.0 ab 1.0 743.396108 ztfg 1 0.0 0.0 \n", - "8 4.883962 25.0 ab 1.0 405.460001 ztfg 1 0.0 0.0 \n", - "9 0.473665 25.0 ab 1.0 582.286411 ztfg 1 0.0 0.0 \n", - "10 0.504798 25.0 ab 1.0 862.602785 ztfg 1 0.0 0.0 \n", - "11 0.173851 25.0 ab 1.0 167.057133 ztfr 1 0.0 0.0 \n", - "12 1.111689 25.0 ab 1.0 637.828536 ztfg 1 0.0 0.0 \n", - "13 0.836946 25.0 ab 1.0 347.366061 ztfg 1 0.0 0.0 \n", - "14 0.463158 25.0 ab 1.0 476.512540 ztfr 1 0.0 0.0 \n", - "15 0.693107 25.0 ab 1.0 822.295606 ztfr 1 0.0 0.0 \n", - "16 3.039434 25.0 ab 1.0 746.557497 ztfr 1 0.0 0.0 \n", - "17 0.452166 25.0 ab 1.0 414.944486 ztfr 1 0.0 0.0 \n", - "18 5.783541 25.0 ab 1.0 289.733657 ztfr 1 0.0 0.0 \n", - "19 4.649758 25.0 ab 1.0 597.154128 ztfr 1 0.0 0.0 \n", - "20 1.800265 25.0 ab 1.0 848.294265 ztfr 1 0.0 0.0 \n", - "21 0.609664 25.0 ab 1.0 994.503733 ztfr 1 0.0 0.0 \n", - "22 0.851793 25.0 ab 1.0 285.600366 ztfg 1 0.0 0.0 \n", - "23 9.380853 25.0 ab 1.0 892.041214 ztfg 1 0.0 0.0 \n", - "24 3.082758 25.0 ab 1.0 184.398254 ztfg 1 0.0 0.0 \n", - "25 0.412866 25.0 ab 1.0 228.803106 ztfg 1 0.0 0.0 \n", - "26 0.488175 25.0 ab 1.0 125.579114 ztfr 1 0.0 0.0 \n", - "27 1.907996 25.0 ab 1.0 336.580416 ztfg 1 0.0 0.0 \n", - "28 0.248248 25.0 ab 1.0 19.642986 ztfg 1 0.0 0.0 \n", - "29 NaN 25.0 ab 1.0 94.660555 ztfg 1 0.0 0.0 \n", - "30 3.131696 25.0 ab 1.0 185.498151 ztfg 1 0.0 0.0 \n", - "31 NaN 25.0 ab 1.0 636.759406 ztfg 1 0.0 0.0 \n", - "32 NaN 25.0 ab 1.0 363.638042 ztfg 1 0.0 0.0 \n", - "33 NaN 25.0 ab 1.0 725.486177 ztfg 1 0.0 0.0 \n", - "34 1.334589 25.0 ab 1.0 737.899817 ztfg 1 0.0 0.0 \n", - "35 0.383357 25.0 ab 1.0 210.333138 ztfr 1 0.0 0.0 " + " sig_zp sig_psf \n", + "epochs \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "17 0.0 0.0 \n", + "18 0.0 0.0 \n", + "19 0.0 0.0 \n", + "20 0.0 0.0 \n", + "21 0.0 0.0 \n", + "22 0.0 0.0 \n", + "23 0.0 0.0 \n", + "24 0.0 0.0 \n", + "25 0.0 0.0 \n", + "26 0.0 0.0 \n", + "27 0.0 0.0 \n", + "28 0.0 0.0 \n", + "29 0.0 0.0 \n", + "30 0.0 0.0 \n", + "31 0.0 0.0 " ] }, - "execution_count": 19, + "execution_count": 8, "metadata": {}, "output_type": "execute_result" } @@ -1784,33 +1555,36 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 9, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'zobs': 0.05105072689987433,\n", - " 'sim_t0': 58030,\n", - " 'sim_x1': 1,\n", - " 'sim_c': 0.1,\n", - " 'sim_x0': 0.0013413322249302211,\n", - " 'type': 'snIa',\n", - " 'ra': 0.7330382858376184,\n", - " 'dec': 0.7330382858376184,\n", - " 'zcos': 0.05,\n", + "{'mu': 36.81185565719685,\n", + " 'zobs': 0.05105072689987433,\n", " 'zCMB': 0.05105072689987433,\n", - " 'zpec': 0.0010006922855944561,\n", - " 'vpec': 300,\n", - " 'z2cmb': 0.0,\n", - " 'sim_mu': 36.81185565719685,\n", + " 'zcos': 0.05,\n", + " 'zpcmb': 0.0,\n", " 'como_dist': 218.93393187129323,\n", - " 'sim_mb': 17.68185565719685,\n", - " 'mag_sct': 0.0,\n", - " 'template': 'salt2'}" + " 'vpec': 300,\n", + " 't0': 58057,\n", + " 'ra': 0.7330382858376184,\n", + " 'dec': 0.7330382858376184,\n", + " 'coh_sct': 0.0,\n", + " 'x1': 1,\n", + " 'c': 0.1,\n", + " 'M0': -19.3,\n", + " 'alpha': 0.14,\n", + " 'beta': 3.1,\n", + " 'model_name': 'salt2',\n", + " 'model_version': '2.0',\n", + " 'ID': 0,\n", + " 'mb': 17.341855657196852,\n", + " 'x0': 0.00183328102063278}" ] }, - "execution_count": 20, + "execution_count": 9, "metadata": {}, "output_type": "execute_result" } @@ -1829,12 +1603,12 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 10, "metadata": {}, "outputs": [ { "data": { - "image/png": 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"text/plain": [ "
" ] @@ -1844,9 +1618,74 @@ } ], "source": [ - "bandcol = {'ztfg': 'g', 'ztfr': 'r', 'ztfi': 'gold'}\n", + "bandcol = {'ztfg': 'C2', 'ztfr': 'C3', 'ztfi': 'C5'}\n", "snsim.plot_utils.plot_lc(lc,lc.attrs, snc_sim_model=SNIa.sim_model,\n", - " bandcol=bandcol,phase_limit=[-70,70])" + " bandcol=bandcol,phase_limit=[-30,60])" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Add intrinsic scattering" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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+ "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "# Set the SNIa parameters\n", + "zcos = 0.05\n", + "coords = np.radians([42, 42])\n", + "\n", + "# Set the sncosmo source for SNIa\n", + "sn_source = sncosmo.get_source(name='salt2', version='2.0')\n", + "\n", + "effects = [snsim.scatter.init_sn_sct_model('G10', sn_source)]\n", + "\n", + "#parameters of SNIa object\n", + "sn_par = {\n", + " 'zcos': zcos,\n", + " 'zpcmb': 0.0,\n", + " 'como_dist': cosmo.comoving_distance(zcos).value,\n", + " 'vpec': 300,\n", + " 't0': 58057,\n", + " 'ra': coords[0],\n", + " 'dec': coords[1],\n", + " 'coh_sct': 0.0,\n", + " 'x1':1, \n", + " 'c':0.1,\n", + " 'M0': -19.3,\n", + " 'alpha': 0.14,\n", + " 'beta': 3.1,\n", + " 'model_name': 'salt2',\n", + " 'model_version': '2.0'\n", + " }\n", + "\n", + "\n", + "\n", + "\n", + "#Init SNIa object\n", + "SNIa = snsim.astrobj.SNIa(sn_par, effects=effects, relation='SALTTripp')\n", + "\n", + "lc=SNIa.gen_flux(epochs,np.random.default_rng(1200))\n", + "\n", + "bandcol = {'ztfg': 'C2', 'ztfr': 'C3', 'ztfi': 'C5'}\n", + "snsim.plot_utils.plot_lc(lc, lc.attrs, snc_sim_model=SNIa.sim_model,\n", + " bandcol=bandcol,phase_limit=[-30,60])" ] }, { @@ -1872,7 +1711,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -1882,33 +1721,27 @@ "\n", "#parameters of SNIc object\n", "sn_par = {'zcos': zcos,\n", - " 'z2cmb': 0.0,\n", + " 't0': 0.,\n", + " 'zpcmb': 0.0,\n", " 'como_dist': cosmo.comoving_distance(zcos).value,\n", " 'vpec': 500,\n", - " 'sim_t0': 58030,#simulated peak time of the event\n", + " 't0': 58057, #simulated peak time of the event\n", " 'ra': coords[0],\n", " 'dec': coords[1],\n", - " 'mag_sct': 0.3,\n", - " 'sncosmo': {} #no sncosmo model parameter needed in this case\n", + " 'coh_sct': 0.3,\n", + " 'M0': -18,\n", + " 'model_name': 'v19-2007gr' # Name of sncosmo built-in source\n", " }\n", "\n", "\n", - "# Set the sncosmo model for SNIc\n", - "sn_model = snsim.utils.init_sn_model('v19-2007gr') #name of sncosmo built-in source\n", - "\n", - "\n", - "#parameters of SNIc model\n", - "model_par = {'M0': -18,\n", - " 'mod_fcov': False} #absolute magnitude in r-band\n", - "\n", "\n", "#Init SNIc object\n", - "SNIc = snsim.astrobj.SNIc(sn_par, sn_model, model_par=model_par)" + "SNIc = snsim.astrobj.SNIc(sn_par)" ] }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -1935,7 +1768,7 @@ " min_t\n", " max_t\n", " 1_zobs\n", - " sim_t0\n", + " t0\n", " ra\n", " dec\n", " \n", @@ -1943,10 +1776,10 @@ " \n", " \n", " 0\n", - " 58012.958024\n", - " 58216.000699\n", + " 58039.958024\n", + " 58243.000699\n", " 1.021701\n", - " 58030\n", + " 58057\n", " 0.733038\n", " 0.733038\n", " \n", @@ -1955,11 +1788,11 @@ "" ], "text/plain": [ - " min_t max_t 1_zobs sim_t0 ra dec\n", - "0 58012.958024 58216.000699 1.021701 58030 0.733038 0.733038" + " min_t max_t 1_zobs t0 ra dec\n", + "0 58039.958024 58243.000699 1.021701 58057 0.733038 0.733038" ] }, - "execution_count": 12, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -1969,11 +1802,11 @@ "#evaluate Z-obs and time range where we can observed the event in the rest frame\n", "#( [-20,50] phase respect to t_peak where Salt model is defined)\n", "dict_obs_par={}\n", - "_1_zobs_ = (1 +sn_par['zcos']) * (1+sn_par['z2cmb'])*(1 + sn_par['vpec'] / C_LIGHT_KMS) \n", + "_1_zobs_ = 1 + SNIc.zobs \n", "dict_obs_par['min_t'] = SNIc.sim_model.mintime()\n", "dict_obs_par['max_t'] = SNIc.sim_model.maxtime()\n", "dict_obs_par['1_zobs'] = _1_zobs_\n", - "dict_obs_par['sim_t0']=sn_par['sim_t0']\n", + "dict_obs_par['t0']=sn_par['t0']\n", "dict_obs_par['ra']=sn_par['ra']\n", "dict_obs_par['dec']=sn_par['dec']\n", "\n", @@ -1984,7 +1817,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [ { @@ -2691,7 +2524,7 @@ "0 58100.000000 ztfg 1 535.020328 25.0 0.0 0.0 1.0 ab" ] }, - "execution_count": 13, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -2704,7 +2537,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [ { @@ -2728,9 +2561,9 @@ " \n", " \n", " \n", - " ID\n", " time\n", " fluxtrue\n", + " fluxerrtrue\n", " flux\n", " fluxerr\n", " mag\n", @@ -2766,391 +2599,31 @@ " \n", " \n", " 0\n", - " 0\n", - " 58000.000000\n", - " 0.000000\n", - " -701.592882\n", - " 629.554693\n", - " NaN\n", - " NaN\n", + " 58040.816327\n", + " 54.736590\n", + " 822.328888\n", + " 1329.823197\n", + " 823.103813\n", + " 17.190515\n", + " 0.672024\n", " 25.0\n", " ab\n", " 1.0\n", - " 629.554693\n", - " ztfg\n", + " 822.295606\n", + " ztfr\n", " 1\n", " 0.0\n", " 0.0\n", " \n", " \n", " 1\n", - " 0\n", - " 58002.040816\n", - " 0.000000\n", - " -557.947914\n", - " 794.918147\n", - " NaN\n", - " NaN\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 794.918147\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 2\n", - " 0\n", - " 58004.081633\n", - " 0.000000\n", - " -387.136914\n", - " 857.808033\n", - " NaN\n", - " NaN\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 857.808033\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 3\n", - " 0\n", - " 58006.122449\n", - " 0.000000\n", - " 13.609242\n", - " 736.170271\n", - " 22.165415\n", - " 58.731170\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 736.170271\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 4\n", - " 0\n", - " 58008.163265\n", - " 0.000000\n", - " 451.415442\n", - " 716.202148\n", - " 18.363559\n", - " 1.722596\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 716.202148\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 5\n", - " 0\n", - " 58010.204082\n", - " 0.000000\n", - " -37.132459\n", - " 558.747930\n", - " NaN\n", - " NaN\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 558.747930\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 6\n", - " 0\n", - " 58012.244898\n", - " 0.000000\n", - " 134.968140\n", - " 242.029050\n", - " 19.674422\n", - " 1.946976\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 242.029050\n", - " ztfr\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 7\n", - " 0\n", - " 58014.285714\n", - " 81.750504\n", - " 224.290397\n", - " 108.005189\n", - " 19.122973\n", - " 0.522827\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 107.626067\n", - " ztfr\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 8\n", - " 0\n", - " 58016.326531\n", - " 214.888424\n", - " 170.859280\n", - " 23.434342\n", - " 19.418404\n", - " 0.148915\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 18.283325\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 9\n", - " 0\n", - " 58018.367347\n", - " 404.143158\n", - " 845.149224\n", - " 572.164516\n", - " 17.682667\n", - " 0.735041\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 571.811236\n", - " ztfr\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 10\n", - " 0\n", - " 58020.408163\n", - " 519.573567\n", - " 761.790921\n", - " 215.096566\n", - " 17.795411\n", - " 0.306565\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 213.885387\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 11\n", - " 0\n", - " 58022.448980\n", - " 844.795981\n", - " 1000.158109\n", - " 137.685309\n", - " 17.499828\n", - " 0.149466\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 134.582496\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 12\n", - " 0\n", - " 58024.489796\n", - " 941.127335\n", - " 743.290626\n", - " 744.028830\n", - " 17.822103\n", - " 1.086815\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 743.396108\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 13\n", - " 0\n", - " 58026.530612\n", - " 1144.891636\n", - " 824.887569\n", - " 406.869394\n", - " 17.709013\n", - " 0.535531\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 405.460001\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 14\n", - " 0\n", - " 58028.571429\n", - " 1231.915149\n", - " 1731.616736\n", - " 583.343278\n", - " 16.903871\n", - " 0.365760\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 582.286411\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 15\n", - " 0\n", - " 58030.612245\n", - " 1169.527926\n", - " 1858.910445\n", - " 863.280426\n", - " 16.826854\n", - " 0.504217\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 862.602785\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 16\n", - " 0\n", - " 58032.653061\n", - " 1534.222443\n", - " 1466.537566\n", - " 171.587610\n", - " 17.084267\n", - " 0.127033\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 167.057133\n", - " ztfr\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 17\n", - " 0\n", - " 58034.693878\n", - " 1011.558474\n", - " 1771.398841\n", - " 638.621014\n", - " 16.879209\n", - " 0.391427\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 637.828536\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 18\n", - " 0\n", - " 58036.734694\n", - " 884.971927\n", - " 386.370420\n", - " 348.637566\n", - " 18.532490\n", - " 0.979703\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 347.366061\n", - " ztfg\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 19\n", - " 0\n", - " 58038.775510\n", - " 1354.723065\n", - " 1098.602519\n", - " 477.931924\n", - " 17.397899\n", - " 0.472335\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 476.512540\n", - " ztfr\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 20\n", - " 0\n", - " 58040.816327\n", - " 1606.677319\n", - " 1666.143071\n", - " 823.271972\n", - " 16.945719\n", - " 0.536482\n", - " 25.0\n", - " ab\n", - " 1.0\n", - " 822.295606\n", - " ztfr\n", - " 1\n", - " 0.0\n", - " 0.0\n", - " \n", - " \n", - " 21\n", - " 0\n", - " 58042.857143\n", - " 1029.667383\n", - " 2029.556752\n", - " 747.246789\n", - " 16.731497\n", - " 0.399749\n", + " 58042.857143\n", + " 193.216446\n", + " 746.686891\n", + " 624.717967\n", + " 746.975779\n", + " 18.010790\n", + " 1.298216\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3161,14 +2634,14 @@ " 0.0\n", " \n", " \n", - " 22\n", - " 0\n", + " 2\n", " 58044.897959\n", - " 942.902481\n", - " 1212.994805\n", - " 416.079114\n", - " 17.290353\n", - " 0.372427\n", + " 376.092745\n", + " 415.397423\n", + " 158.626840\n", + " 415.135584\n", + " 19.499058\n", + " 2.841434\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3179,14 +2652,14 @@ " 0.0\n", " \n", " \n", - " 23\n", - " 0\n", + " 3\n", " 58046.938776\n", - " 822.749047\n", - " 805.130441\n", - " 291.150031\n", - " 17.735334\n", - " 0.392622\n", + " 640.871788\n", + " 290.837521\n", + " 483.896011\n", + " 290.567527\n", + " 18.288120\n", + " 0.651958\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3197,14 +2670,14 @@ " 0.0\n", " \n", " \n", - " 24\n", - " 0\n", + " 4\n", " 58048.979592\n", - " 719.412118\n", - " 479.444705\n", - " 597.756192\n", - " 18.298154\n", - " 1.353661\n", + " 875.793269\n", + " 597.886984\n", + " 1375.511686\n", + " 598.304742\n", + " 17.153839\n", + " 0.472261\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3215,14 +2688,14 @@ " 0.0\n", " \n", " \n", - " 25\n", - " 0\n", + " 5\n", " 58051.020408\n", - " 619.054488\n", - " 2395.478859\n", - " 848.659068\n", - " 16.551519\n", - " 0.384650\n", + " 1108.468092\n", + " 848.947365\n", + " 1506.724123\n", + " 849.181891\n", + " 17.054916\n", + " 0.611915\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3233,14 +2706,14 @@ " 0.0\n", " \n", " \n", - " 26\n", - " 0\n", + " 6\n", " 58053.061224\n", - " 550.856940\n", - " 110.115710\n", - " 994.780645\n", - " 19.895377\n", - " 9.808495\n", + " 1289.690522\n", + " 995.151931\n", + " 2580.209644\n", + " 995.800123\n", + " 16.470863\n", + " 0.419027\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3251,14 +2724,14 @@ " 0.0\n", " \n", " \n", - " 27\n", - " 0\n", + " 7\n", " 58055.102041\n", - " 196.377813\n", - " 277.560917\n", - " 285.943958\n", - " 18.891604\n", - " 1.118528\n", + " 1236.065648\n", + " 287.756207\n", + " 1255.317742\n", + " 287.789657\n", + " 17.253116\n", + " 0.248912\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3269,14 +2742,14 @@ " 0.0\n", " \n", " \n", - " 28\n", - " 0\n", + " 8\n", " 58057.142857\n", - " 185.240437\n", - " 529.150462\n", - " 892.145038\n", - " 18.191052\n", - " 1.830546\n", + " 1181.456270\n", + " 892.703189\n", + " 567.285010\n", + " 892.359128\n", + " 18.115497\n", + " 1.707901\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3287,14 +2760,14 @@ " 0.0\n", " \n", " \n", - " 29\n", - " 0\n", + " 9\n", " 58059.183673\n", - " 177.856155\n", - " -87.881962\n", - " 184.879886\n", - " NaN\n", - " NaN\n", + " 1134.930120\n", + " 187.450383\n", + " 802.140236\n", + " 186.560597\n", + " 17.739374\n", + " 0.252519\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3305,14 +2778,14 @@ " 0.0\n", " \n", " \n", - " 30\n", - " 0\n", + " 10\n", " 58061.224490\n", - " 166.738534\n", - " 154.380153\n", - " 229.167188\n", - " 19.528521\n", - " 1.611704\n", + " 1039.167061\n", + " 231.062823\n", + " 1210.892014\n", + " 231.434123\n", + " 17.292236\n", + " 0.207513\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3323,14 +2796,14 @@ " 0.0\n", " \n", " \n", - " 31\n", - " 0\n", + " 11\n", " 58063.265306\n", - " 365.102888\n", - " 211.203923\n", - " 127.024473\n", - " 19.188245\n", - " 0.652995\n", + " 1436.929859\n", + " 131.175622\n", + " 1483.669236\n", + " 131.353657\n", + " 17.071657\n", + " 0.096123\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3341,14 +2814,14 @@ " 0.0\n", " \n", " \n", - " 32\n", - " 0\n", + " 12\n", " 58065.306122\n", - " 153.521794\n", - " -71.121220\n", - " 336.808399\n", - " NaN\n", - " NaN\n", + " 784.588989\n", + " 337.743934\n", + " 1137.046055\n", + " 338.265313\n", + " 17.360555\n", + " 0.323001\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3359,14 +2832,14 @@ " 0.0\n", " \n", " \n", - " 33\n", - " 0\n", + " 13\n", " 58067.346939\n", - " 147.556264\n", - " 125.998526\n", - " 23.095522\n", - " 19.749086\n", - " 0.199015\n", + " 775.297373\n", + " 34.075567\n", + " 756.246803\n", + " 33.794877\n", + " 17.803341\n", + " 0.048519\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3377,14 +2850,14 @@ " 0.0\n", " \n", " \n", - " 34\n", - " 0\n", + " 14\n", " 58069.387755\n", - " 146.976973\n", - " 216.531768\n", - " 95.433734\n", - " 19.161196\n", - " 0.478525\n", + " 475.728220\n", + " 97.140871\n", + " 471.446216\n", + " 97.118829\n", + " 18.316420\n", + " 0.223664\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3395,14 +2868,14 @@ " 0.0\n", " \n", " \n", - " 35\n", - " 0\n", + " 15\n", " 58071.428571\n", - " 142.702240\n", - " 535.135035\n", - " 185.882399\n", - " 18.178842\n", - " 0.377137\n", + " 428.092077\n", + " 186.648483\n", + " 448.356923\n", + " 186.702761\n", + " 18.370940\n", + " 0.452117\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3413,14 +2886,14 @@ " 0.0\n", " \n", " \n", - " 36\n", - " 0\n", + " 16\n", " 58073.469388\n", - " 141.541962\n", - " -635.475733\n", - " 636.870539\n", - " NaN\n", - " NaN\n", + " 363.767630\n", + " 637.044982\n", + " 804.946074\n", + " 637.391157\n", + " 17.735583\n", + " 0.859733\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3431,12 +2904,12 @@ " 0.0\n", " \n", " \n", - " 37\n", - " 0\n", + " 17\n", " 58075.510204\n", - " 139.092612\n", - " -257.018910\n", - " 363.829243\n", + " 304.597195\n", + " 364.056621\n", + " -22.874211\n", + " 363.669493\n", " NaN\n", " NaN\n", " 25.0\n", @@ -3449,14 +2922,14 @@ " 0.0\n", " \n", " \n", - " 38\n", - " 0\n", + " 18\n", " 58077.551020\n", - " 132.943521\n", - " 132.385978\n", - " 725.577795\n", - " 19.695395\n", - " 5.950676\n", + " 250.270548\n", + " 725.658642\n", + " 93.037394\n", + " 725.550295\n", + " 20.078356\n", + " 8.467093\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3467,14 +2940,14 @@ " 0.0\n", " \n", " \n", - " 39\n", - " 0\n", + " 19\n", " 58079.591837\n", - " 133.884731\n", - " 646.696993\n", - " 737.990532\n", - " 17.973248\n", - " 1.239008\n", + " 223.575010\n", + " 738.051296\n", + " 158.626559\n", + " 738.007295\n", + " 19.499060\n", + " 5.051369\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3485,14 +2958,14 @@ " 0.0\n", " \n", " \n", - " 40\n", - " 0\n", + " 20\n", " 58081.632653\n", - " 244.596173\n", - " 52.086315\n", - " 210.913786\n", - " 20.708191\n", - " 4.396486\n", + " 504.677971\n", + " 211.529447\n", + " 485.177680\n", + " 211.483349\n", + " 18.285248\n", + " 0.473260\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3503,14 +2976,14 @@ " 0.0\n", " \n", " \n", - " 41\n", - " 0\n", + " 21\n", " 58083.673469\n", - " 232.639086\n", - " 256.165834\n", - " 20.641228\n", - " 18.978697\n", - " 0.087486\n", + " 455.807594\n", + " 25.479969\n", + " 426.407460\n", + " 24.896358\n", + " 18.425438\n", + " 0.063392\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3521,14 +2994,14 @@ " 0.0\n", " \n", " \n", - " 42\n", - " 0\n", + " 22\n", " 58085.714286\n", - " 133.874534\n", - " -150.961514\n", - " 317.918717\n", - " NaN\n", - " NaN\n", + " 180.157801\n", + " 317.991500\n", + " 413.760051\n", + " 318.358597\n", + " 18.458129\n", + " 0.835396\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3539,14 +3012,14 @@ " 0.0\n", " \n", " \n", - " 43\n", - " 0\n", + " 23\n", " 58087.755102\n", - " 216.988260\n", - " 893.282611\n", - " 718.153561\n", - " 17.622528\n", - " 0.872876\n", + " 390.173966\n", + " 718.274128\n", + " 551.495002\n", + " 718.386417\n", + " 18.146146\n", + " 1.414298\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3557,14 +3030,14 @@ " 0.0\n", " \n", " \n", - " 44\n", - " 0\n", + " 24\n", " 58089.795918\n", - " 128.470640\n", - " -786.020627\n", - " 838.445349\n", - " NaN\n", - " NaN\n", + " 166.054749\n", + " 838.467762\n", + " 1085.213249\n", + " 839.015701\n", + " 17.411212\n", + " 0.839420\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3575,14 +3048,14 @@ " 0.0\n", " \n", " \n", - " 45\n", - " 0\n", + " 25\n", " 58091.836735\n", - " 202.038380\n", - " 1032.343748\n", - " 882.163533\n", - " 17.465439\n", - " 0.927789\n", + " 351.094402\n", + " 882.248012\n", + " 83.024537\n", + " 882.096074\n", + " 20.201984\n", + " 11.535429\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3593,14 +3066,14 @@ " 0.0\n", " \n", " \n", - " 46\n", - " 0\n", + " 26\n", " 58093.877551\n", - " 126.370681\n", - " 1055.125340\n", - " 999.048315\n", - " 17.441740\n", - " 1.028032\n", + " 147.667461\n", + " 999.058974\n", + " -84.305483\n", + " 999.027262\n", + " NaN\n", + " NaN\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3611,14 +3084,14 @@ " 0.0\n", " \n", " \n", - " 47\n", - " 0\n", + " 27\n", " 58095.918367\n", - " 116.619084\n", - " 28.862508\n", - " 210.578435\n", - " 21.349165\n", - " 7.921440\n", + " 147.451037\n", + " 210.651630\n", + " -51.612772\n", + " 210.424027\n", + " NaN\n", + " NaN\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3629,14 +3102,14 @@ " 0.0\n", " \n", " \n", - " 48\n", - " 0\n", + " 28\n", " 58097.959184\n", - " 110.484981\n", - " 28.147742\n", - " 903.857913\n", - " 21.376391\n", - " 34.864298\n", + " 143.866124\n", + " 903.876379\n", + " -1314.471338\n", + " 904.523694\n", + " NaN\n", + " NaN\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3647,14 +3120,14 @@ " 0.0\n", " \n", " \n", - " 49\n", - " 0\n", + " 29\n", " 58100.000000\n", - " 105.057776\n", - " -191.044331\n", - " 535.118500\n", - " NaN\n", - " NaN\n", + " 140.989219\n", + " 535.152073\n", + " 733.622685\n", + " 535.705492\n", + " 17.836318\n", + " 0.792826\n", " 25.0\n", " ab\n", " 1.0\n", @@ -3669,204 +3142,145 @@ "" ], "text/plain": [ - " ID time fluxtrue flux fluxerr mag \\\n", - "epochs \n", - "0 0 58000.000000 0.000000 -701.592882 629.554693 NaN \n", - "1 0 58002.040816 0.000000 -557.947914 794.918147 NaN \n", - "2 0 58004.081633 0.000000 -387.136914 857.808033 NaN \n", - "3 0 58006.122449 0.000000 13.609242 736.170271 22.165415 \n", - "4 0 58008.163265 0.000000 451.415442 716.202148 18.363559 \n", - "5 0 58010.204082 0.000000 -37.132459 558.747930 NaN \n", - "6 0 58012.244898 0.000000 134.968140 242.029050 19.674422 \n", - "7 0 58014.285714 81.750504 224.290397 108.005189 19.122973 \n", - "8 0 58016.326531 214.888424 170.859280 23.434342 19.418404 \n", - "9 0 58018.367347 404.143158 845.149224 572.164516 17.682667 \n", - "10 0 58020.408163 519.573567 761.790921 215.096566 17.795411 \n", - "11 0 58022.448980 844.795981 1000.158109 137.685309 17.499828 \n", - "12 0 58024.489796 941.127335 743.290626 744.028830 17.822103 \n", - "13 0 58026.530612 1144.891636 824.887569 406.869394 17.709013 \n", - "14 0 58028.571429 1231.915149 1731.616736 583.343278 16.903871 \n", - "15 0 58030.612245 1169.527926 1858.910445 863.280426 16.826854 \n", - "16 0 58032.653061 1534.222443 1466.537566 171.587610 17.084267 \n", - "17 0 58034.693878 1011.558474 1771.398841 638.621014 16.879209 \n", - "18 0 58036.734694 884.971927 386.370420 348.637566 18.532490 \n", - "19 0 58038.775510 1354.723065 1098.602519 477.931924 17.397899 \n", - "20 0 58040.816327 1606.677319 1666.143071 823.271972 16.945719 \n", - "21 0 58042.857143 1029.667383 2029.556752 747.246789 16.731497 \n", - "22 0 58044.897959 942.902481 1212.994805 416.079114 17.290353 \n", - "23 0 58046.938776 822.749047 805.130441 291.150031 17.735334 \n", - "24 0 58048.979592 719.412118 479.444705 597.756192 18.298154 \n", - "25 0 58051.020408 619.054488 2395.478859 848.659068 16.551519 \n", - "26 0 58053.061224 550.856940 110.115710 994.780645 19.895377 \n", - "27 0 58055.102041 196.377813 277.560917 285.943958 18.891604 \n", - "28 0 58057.142857 185.240437 529.150462 892.145038 18.191052 \n", - "29 0 58059.183673 177.856155 -87.881962 184.879886 NaN \n", - "30 0 58061.224490 166.738534 154.380153 229.167188 19.528521 \n", - "31 0 58063.265306 365.102888 211.203923 127.024473 19.188245 \n", - "32 0 58065.306122 153.521794 -71.121220 336.808399 NaN \n", - "33 0 58067.346939 147.556264 125.998526 23.095522 19.749086 \n", - "34 0 58069.387755 146.976973 216.531768 95.433734 19.161196 \n", - "35 0 58071.428571 142.702240 535.135035 185.882399 18.178842 \n", - "36 0 58073.469388 141.541962 -635.475733 636.870539 NaN \n", - "37 0 58075.510204 139.092612 -257.018910 363.829243 NaN \n", - "38 0 58077.551020 132.943521 132.385978 725.577795 19.695395 \n", - "39 0 58079.591837 133.884731 646.696993 737.990532 17.973248 \n", - "40 0 58081.632653 244.596173 52.086315 210.913786 20.708191 \n", - "41 0 58083.673469 232.639086 256.165834 20.641228 18.978697 \n", - "42 0 58085.714286 133.874534 -150.961514 317.918717 NaN \n", - "43 0 58087.755102 216.988260 893.282611 718.153561 17.622528 \n", - "44 0 58089.795918 128.470640 -786.020627 838.445349 NaN \n", - "45 0 58091.836735 202.038380 1032.343748 882.163533 17.465439 \n", - "46 0 58093.877551 126.370681 1055.125340 999.048315 17.441740 \n", - "47 0 58095.918367 116.619084 28.862508 210.578435 21.349165 \n", - "48 0 58097.959184 110.484981 28.147742 903.857913 21.376391 \n", - "49 0 58100.000000 105.057776 -191.044331 535.118500 NaN \n", + " time fluxtrue fluxerrtrue flux fluxerr \\\n", + "epochs \n", + "0 58040.816327 54.736590 822.328888 1329.823197 823.103813 \n", + "1 58042.857143 193.216446 746.686891 624.717967 746.975779 \n", + "2 58044.897959 376.092745 415.397423 158.626840 415.135584 \n", + "3 58046.938776 640.871788 290.837521 483.896011 290.567527 \n", + "4 58048.979592 875.793269 597.886984 1375.511686 598.304742 \n", + "5 58051.020408 1108.468092 848.947365 1506.724123 849.181891 \n", + "6 58053.061224 1289.690522 995.151931 2580.209644 995.800123 \n", + "7 58055.102041 1236.065648 287.756207 1255.317742 287.789657 \n", + "8 58057.142857 1181.456270 892.703189 567.285010 892.359128 \n", + "9 58059.183673 1134.930120 187.450383 802.140236 186.560597 \n", + "10 58061.224490 1039.167061 231.062823 1210.892014 231.434123 \n", + "11 58063.265306 1436.929859 131.175622 1483.669236 131.353657 \n", + "12 58065.306122 784.588989 337.743934 1137.046055 338.265313 \n", + "13 58067.346939 775.297373 34.075567 756.246803 33.794877 \n", + "14 58069.387755 475.728220 97.140871 471.446216 97.118829 \n", + "15 58071.428571 428.092077 186.648483 448.356923 186.702761 \n", + "16 58073.469388 363.767630 637.044982 804.946074 637.391157 \n", + "17 58075.510204 304.597195 364.056621 -22.874211 363.669493 \n", + "18 58077.551020 250.270548 725.658642 93.037394 725.550295 \n", + "19 58079.591837 223.575010 738.051296 158.626559 738.007295 \n", + "20 58081.632653 504.677971 211.529447 485.177680 211.483349 \n", + "21 58083.673469 455.807594 25.479969 426.407460 24.896358 \n", + "22 58085.714286 180.157801 317.991500 413.760051 318.358597 \n", + "23 58087.755102 390.173966 718.274128 551.495002 718.386417 \n", + "24 58089.795918 166.054749 838.467762 1085.213249 839.015701 \n", + "25 58091.836735 351.094402 882.248012 83.024537 882.096074 \n", + "26 58093.877551 147.667461 999.058974 -84.305483 999.027262 \n", + "27 58095.918367 147.451037 210.651630 -51.612772 210.424027 \n", + "28 58097.959184 143.866124 903.876379 -1314.471338 904.523694 \n", + "29 58100.000000 140.989219 535.152073 733.622685 535.705492 \n", "\n", - " magerr zp zpsys gain skynoise band fieldID sig_zp \\\n", - "epochs \n", - "0 NaN 25.0 ab 1.0 629.554693 ztfg 1 0.0 \n", - "1 NaN 25.0 ab 1.0 794.918147 ztfg 1 0.0 \n", - "2 NaN 25.0 ab 1.0 857.808033 ztfg 1 0.0 \n", - "3 58.731170 25.0 ab 1.0 736.170271 ztfg 1 0.0 \n", - "4 1.722596 25.0 ab 1.0 716.202148 ztfg 1 0.0 \n", - "5 NaN 25.0 ab 1.0 558.747930 ztfg 1 0.0 \n", - "6 1.946976 25.0 ab 1.0 242.029050 ztfr 1 0.0 \n", - "7 0.522827 25.0 ab 1.0 107.626067 ztfr 1 0.0 \n", - "8 0.148915 25.0 ab 1.0 18.283325 ztfg 1 0.0 \n", - "9 0.735041 25.0 ab 1.0 571.811236 ztfr 1 0.0 \n", - "10 0.306565 25.0 ab 1.0 213.885387 ztfg 1 0.0 \n", - "11 0.149466 25.0 ab 1.0 134.582496 ztfg 1 0.0 \n", - "12 1.086815 25.0 ab 1.0 743.396108 ztfg 1 0.0 \n", - "13 0.535531 25.0 ab 1.0 405.460001 ztfg 1 0.0 \n", - "14 0.365760 25.0 ab 1.0 582.286411 ztfg 1 0.0 \n", - "15 0.504217 25.0 ab 1.0 862.602785 ztfg 1 0.0 \n", - "16 0.127033 25.0 ab 1.0 167.057133 ztfr 1 0.0 \n", - "17 0.391427 25.0 ab 1.0 637.828536 ztfg 1 0.0 \n", - "18 0.979703 25.0 ab 1.0 347.366061 ztfg 1 0.0 \n", - "19 0.472335 25.0 ab 1.0 476.512540 ztfr 1 0.0 \n", - "20 0.536482 25.0 ab 1.0 822.295606 ztfr 1 0.0 \n", - "21 0.399749 25.0 ab 1.0 746.557497 ztfr 1 0.0 \n", - "22 0.372427 25.0 ab 1.0 414.944486 ztfr 1 0.0 \n", - "23 0.392622 25.0 ab 1.0 289.733657 ztfr 1 0.0 \n", - "24 1.353661 25.0 ab 1.0 597.154128 ztfr 1 0.0 \n", - "25 0.384650 25.0 ab 1.0 848.294265 ztfr 1 0.0 \n", - "26 9.808495 25.0 ab 1.0 994.503733 ztfr 1 0.0 \n", - "27 1.118528 25.0 ab 1.0 285.600366 ztfg 1 0.0 \n", - "28 1.830546 25.0 ab 1.0 892.041214 ztfg 1 0.0 \n", - "29 NaN 25.0 ab 1.0 184.398254 ztfg 1 0.0 \n", - "30 1.611704 25.0 ab 1.0 228.803106 ztfg 1 0.0 \n", - "31 0.652995 25.0 ab 1.0 125.579114 ztfr 1 0.0 \n", - "32 NaN 25.0 ab 1.0 336.580416 ztfg 1 0.0 \n", - "33 0.199015 25.0 ab 1.0 19.642986 ztfg 1 0.0 \n", - "34 0.478525 25.0 ab 1.0 94.660555 ztfg 1 0.0 \n", - "35 0.377137 25.0 ab 1.0 185.498151 ztfg 1 0.0 \n", - "36 NaN 25.0 ab 1.0 636.759406 ztfg 1 0.0 \n", - "37 NaN 25.0 ab 1.0 363.638042 ztfg 1 0.0 \n", - "38 5.950676 25.0 ab 1.0 725.486177 ztfg 1 0.0 \n", - "39 1.239008 25.0 ab 1.0 737.899817 ztfg 1 0.0 \n", - "40 4.396486 25.0 ab 1.0 210.333138 ztfr 1 0.0 \n", - "41 0.087486 25.0 ab 1.0 13.907595 ztfr 1 0.0 \n", - "42 NaN 25.0 ab 1.0 317.708099 ztfg 1 0.0 \n", - "43 0.872876 25.0 ab 1.0 718.002471 ztfr 1 0.0 \n", - "44 NaN 25.0 ab 1.0 838.368733 ztfg 1 0.0 \n", - "45 0.927789 25.0 ab 1.0 882.049012 ztfr 1 0.0 \n", - "46 1.028032 25.0 ab 1.0 998.985068 ztfg 1 0.0 \n", - "47 7.921440 25.0 ab 1.0 210.301351 ztfg 1 0.0 \n", - "48 34.864298 25.0 ab 1.0 903.796792 ztfg 1 0.0 \n", - "49 NaN 25.0 ab 1.0 535.020328 ztfg 1 0.0 \n", + " mag magerr zp zpsys gain skynoise band fieldID \\\n", + "epochs \n", + "0 17.190515 0.672024 25.0 ab 1.0 822.295606 ztfr 1 \n", + "1 18.010790 1.298216 25.0 ab 1.0 746.557497 ztfr 1 \n", + "2 19.499058 2.841434 25.0 ab 1.0 414.944486 ztfr 1 \n", + "3 18.288120 0.651958 25.0 ab 1.0 289.733657 ztfr 1 \n", + "4 17.153839 0.472261 25.0 ab 1.0 597.154128 ztfr 1 \n", + "5 17.054916 0.611915 25.0 ab 1.0 848.294265 ztfr 1 \n", + "6 16.470863 0.419027 25.0 ab 1.0 994.503733 ztfr 1 \n", + "7 17.253116 0.248912 25.0 ab 1.0 285.600366 ztfg 1 \n", + "8 18.115497 1.707901 25.0 ab 1.0 892.041214 ztfg 1 \n", + "9 17.739374 0.252519 25.0 ab 1.0 184.398254 ztfg 1 \n", + "10 17.292236 0.207513 25.0 ab 1.0 228.803106 ztfg 1 \n", + "11 17.071657 0.096123 25.0 ab 1.0 125.579114 ztfr 1 \n", + "12 17.360555 0.323001 25.0 ab 1.0 336.580416 ztfg 1 \n", + "13 17.803341 0.048519 25.0 ab 1.0 19.642986 ztfg 1 \n", + "14 18.316420 0.223664 25.0 ab 1.0 94.660555 ztfg 1 \n", + "15 18.370940 0.452117 25.0 ab 1.0 185.498151 ztfg 1 \n", + "16 17.735583 0.859733 25.0 ab 1.0 636.759406 ztfg 1 \n", + "17 NaN NaN 25.0 ab 1.0 363.638042 ztfg 1 \n", + "18 20.078356 8.467093 25.0 ab 1.0 725.486177 ztfg 1 \n", + "19 19.499060 5.051369 25.0 ab 1.0 737.899817 ztfg 1 \n", + "20 18.285248 0.473260 25.0 ab 1.0 210.333138 ztfr 1 \n", + "21 18.425438 0.063392 25.0 ab 1.0 13.907595 ztfr 1 \n", + "22 18.458129 0.835396 25.0 ab 1.0 317.708099 ztfg 1 \n", + "23 18.146146 1.414298 25.0 ab 1.0 718.002471 ztfr 1 \n", + "24 17.411212 0.839420 25.0 ab 1.0 838.368733 ztfg 1 \n", + "25 20.201984 11.535429 25.0 ab 1.0 882.049012 ztfr 1 \n", + "26 NaN NaN 25.0 ab 1.0 998.985068 ztfg 1 \n", + "27 NaN NaN 25.0 ab 1.0 210.301351 ztfg 1 \n", + "28 NaN NaN 25.0 ab 1.0 903.796792 ztfg 1 \n", + "29 17.836318 0.792826 25.0 ab 1.0 535.020328 ztfg 1 \n", "\n", - " sig_psf \n", - "epochs \n", - "0 0.0 \n", - "1 0.0 \n", - "2 0.0 \n", - "3 0.0 \n", - "4 0.0 \n", - "5 0.0 \n", - "6 0.0 \n", - "7 0.0 \n", - "8 0.0 \n", - "9 0.0 \n", - "10 0.0 \n", - "11 0.0 \n", - "12 0.0 \n", - "13 0.0 \n", - "14 0.0 \n", - "15 0.0 \n", - "16 0.0 \n", - "17 0.0 \n", - "18 0.0 \n", - "19 0.0 \n", - "20 0.0 \n", - "21 0.0 \n", - "22 0.0 \n", - "23 0.0 \n", - "24 0.0 \n", - "25 0.0 \n", - "26 0.0 \n", - "27 0.0 \n", - "28 0.0 \n", - "29 0.0 \n", - "30 0.0 \n", - "31 0.0 \n", - "32 0.0 \n", - "33 0.0 \n", - "34 0.0 \n", - "35 0.0 \n", - "36 0.0 \n", - "37 0.0 \n", - "38 0.0 \n", - "39 0.0 \n", - "40 0.0 \n", - "41 0.0 \n", - "42 0.0 \n", - "43 0.0 \n", - "44 0.0 \n", - "45 0.0 \n", - "46 0.0 \n", - "47 0.0 \n", - "48 0.0 \n", - "49 0.0 " + " sig_zp sig_psf \n", + "epochs \n", + "0 0.0 0.0 \n", + "1 0.0 0.0 \n", + "2 0.0 0.0 \n", + "3 0.0 0.0 \n", + "4 0.0 0.0 \n", + "5 0.0 0.0 \n", + "6 0.0 0.0 \n", + "7 0.0 0.0 \n", + "8 0.0 0.0 \n", + "9 0.0 0.0 \n", + "10 0.0 0.0 \n", + "11 0.0 0.0 \n", + "12 0.0 0.0 \n", + "13 0.0 0.0 \n", + "14 0.0 0.0 \n", + "15 0.0 0.0 \n", + "16 0.0 0.0 \n", + "17 0.0 0.0 \n", + "18 0.0 0.0 \n", + "19 0.0 0.0 \n", + "20 0.0 0.0 \n", + "21 0.0 0.0 \n", + "22 0.0 0.0 \n", + "23 0.0 0.0 \n", + "24 0.0 0.0 \n", + "25 0.0 0.0 \n", + "26 0.0 0.0 \n", + "27 0.0 0.0 \n", + "28 0.0 0.0 \n", + "29 0.0 0.0 " ] }, - "execution_count": 14, + "execution_count": 15, "metadata": {}, "output_type": "execute_result" } ], "source": [ "#simulate the LC\n", - "lc=SNIc.gen_flux(epochs,np.random.default_rng(1200))\n", + "lc=SNIc.gen_flux(epochs)\n", "lc" ] }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'zobs': 0.021701176885510653,\n", - " 'sim_t0': 58030,\n", - " 'sim_amplitude': 3.537229263043246e-14,\n", - " 'type': 'snIc',\n", - " 'ra': 0.7330382858376184,\n", - " 'dec': 0.7330382858376184,\n", - " 'zcos': 0.02,\n", + "{'mu': 34.77763241667154,\n", + " 'zobs': 0.021701176885510653,\n", " 'zCMB': 0.021701176885510653,\n", - " 'zpec': 0.0016678204759907602,\n", - " 'vpec': 500,\n", - " 'z2cmb': 0.0,\n", - " 'sim_mu': 34.77763241667154,\n", + " 'zcos': 0.02,\n", + " 't0': 58057,\n", + " 'zpcmb': 0.0,\n", " 'como_dist': 88.20208831459321,\n", - " 'sim_mb': 17.258117429121988,\n", - " 'mag_sct': 0.3,\n", - " 'template': 'v19-2007gr'}" + " 'vpec': 500,\n", + " 'ra': 0.7330382858376184,\n", + " 'dec': 0.7330382858376184,\n", + " 'coh_sct': 0.3,\n", + " 'M0': -18,\n", + " 'model_name': 'v19-2007gr',\n", + " 'ID': 0,\n", + " 'model_version': '1.0',\n", + " 'mb': 17.258117429121988,\n", + " 'amplitude': 3.537229263043246e-14}" ] }, - "execution_count": 15, + "execution_count": 16, "metadata": {}, "output_type": "execute_result" } @@ -3877,12 +3291,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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"text/plain": [ "
" ] @@ -3896,27 +3310,13 @@ "snsim.plot_utils.plot_lc(lc,lc.attrs, snc_sim_model=SNIc.sim_model,\n", " bandcol=bandcol,phase_limit=[-30,70])" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "snsim_dev", "language": "python", - "name": "python3" + "name": "snsim_dev" }, "language_info": { "codemirror_mode": { @@ -3928,7 +3328,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.6" + "version": "3.10.13" } }, "nbformat": 4, diff --git a/Examples/SN_simulation.ipynb b/Examples/SN_simulation.ipynb index 46b6c92..a7d3873 100644 --- a/Examples/SN_simulation.ipynb +++ b/Examples/SN_simulation.ipynb @@ -10,7 +10,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": 1, "id": "3f7c6f0f", "metadata": {}, "outputs": [ @@ -45,84 +45,6 @@ "import pandas as pd" ] }, - { - "cell_type": "code", - "execution_count": 4, - "id": "ed7692e4", - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2000.0 9200.0\n" - ] - }, - { - "data": { - "text/plain": [ - "array([2000., 2800., 3600., 4400., 5200., 6000., 6800., 7600., 8400.,\n", - " 9200.])" - ] - }, - "execution_count": 4, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "import sncosmo\n", - "model=sncosmo.Model('salt2')\n", - "_minwave = model.source.minwave()\n", - "_maxwave = model.source.maxwave()\n", - "print(_minwave,_maxwave)\n", - "lam_nodes = np.arange(_minwave, _maxwave + 800, 800)\n", - "lam_nodes" - ] - }, - { - "cell_type": "code", - "execution_count": 5, - "id": "9372f58f", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "array([6870., 6872., 6874., ..., 8956., 8958., 8960.])" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "band = sncosmo.get_bandpass('ztfi')\n", - "band.wave" - ] - }, - { - "cell_type": "code", - "execution_count": 8, - "id": "ec26deaf", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "'salt2'" - ] - }, - "execution_count": 8, - "metadata": {}, - "output_type": "execute_result" - } - ], - "source": [ - "model.source.name\n" - ] - }, { "cell_type": "markdown", "id": "947fd04c", @@ -137,16 +59,6 @@ "id": "23b7681f", "metadata": {}, "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "/home/rosselli/.local/lib/python3.10/site-packages/distributed/node.py:182: UserWarning: Port 8787 is already in use.\n", - "Perhaps you already have a cluster running?\n", - "Hosting the HTTP server on port 40831 instead\n", - " warnings.warn(\n" - ] - }, { "data": { "text/html": [ @@ -154,7 +66,7 @@ "
\n", "
\n", "

Client

\n", - "

Client-fdf72f86-1a78-11ee-a51d-d362f53ed17f

\n", + "

Client-39c1b53c-cd01-11ee-924e-58ef68e79040

\n", " \n", "\n", " \n", @@ -167,7 +79,7 @@ " \n", " \n", " \n", " \n", " \n", @@ -185,11 +97,11 @@ " \n", "
\n", "

LocalCluster

\n", - "

c033093f

\n", + "

a4102d60

\n", "
\n", - " Dashboard: http://127.0.0.1:40831/status\n", + " Dashboard: http://127.0.0.1:8787/status\n", "
\n", " \n", " \n", " \n", " \n", " \n", "
\n", - " Dashboard: http://127.0.0.1:40831/status\n", + " Dashboard: http://127.0.0.1:8787/status\n", " \n", " Workers: 4\n", @@ -197,7 +109,7 @@ "
\n", - " Total threads: 8\n", + " Total threads: 16\n", " \n", " Total memory: 15.32 GiB\n", @@ -222,11 +134,11 @@ "
\n", "
\n", "

Scheduler

\n", - "

Scheduler-42ee27af-c134-4e92-aa91-ce8c20fae1e2

\n", + "

Scheduler-0d5a866c-e5f0-47b5-a907-2d8aac3fded7

\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", @@ -268,15 +180,15 @@ "
\n", - " Comm: tcp://127.0.0.1:41931\n", + " Comm: tcp://127.0.0.1:39751\n", " \n", " Workers: 4\n", @@ -234,10 +146,10 @@ "
\n", - " Dashboard: http://127.0.0.1:40831/status\n", + " Dashboard: http://127.0.0.1:8787/status\n", " \n", - " Total threads: 8\n", + " Total threads: 16\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -313,15 +225,15 @@ "
\n", - " Comm: tcp://127.0.0.1:41589\n", + " Comm: tcp://127.0.0.1:36639\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: http://127.0.0.1:39395/status\n", + " Dashboard: http://127.0.0.1:40519/status\n", " \n", " Memory: 3.83 GiB\n", @@ -284,13 +196,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:37907\n", + " Nanny: tcp://127.0.0.1:38913\n", "
\n", - " Local directory: /tmp/dask-worker-space/worker-gedykt21\n", + " Local directory: /tmp/dask-scratch-space/worker-rz93ba86\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -358,15 +270,15 @@ "
\n", - " Comm: tcp://127.0.0.1:42519\n", + " Comm: tcp://127.0.0.1:37295\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: http://127.0.0.1:43887/status\n", + " Dashboard: http://127.0.0.1:40065/status\n", " \n", " Memory: 3.83 GiB\n", @@ -329,13 +241,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:38755\n", + " Nanny: tcp://127.0.0.1:39909\n", "
\n", - " Local directory: /tmp/dask-worker-space/worker-s3wryhcv\n", + " Local directory: /tmp/dask-scratch-space/worker-c6comlfx\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -403,15 +315,15 @@ "
\n", - " Comm: tcp://127.0.0.1:41273\n", + " Comm: tcp://127.0.0.1:45415\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: http://127.0.0.1:44479/status\n", + " Dashboard: http://127.0.0.1:36697/status\n", " \n", " Memory: 3.83 GiB\n", @@ -374,13 +286,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:36159\n", + " Nanny: tcp://127.0.0.1:36369\n", "
\n", - " Local directory: /tmp/dask-worker-space/worker-8p4_jud5\n", + " Local directory: /tmp/dask-scratch-space/worker-oesm2lst\n", "
\n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", " \n", "\n", @@ -452,7 +364,7 @@ "" ], "text/plain": [ - "" + "" ] }, "execution_count": 2, @@ -509,22 +421,23 @@ "metadata": {}, "outputs": [], "source": [ - "snia_gen = {'M0': 'jla',\n", - " 'sigM': 0.1,\n", - " 'sct_model': 'G10',\n", - " 'force_n': 1000 ,\n", - " 'model_config': {'model_name': 'salt2',\n", - " 'alpha': 0.14,\n", - " 'beta': 2.9,\n", - " 'dist_x1': 'N21',\n", - " 'dist_c': [-0.055, 0.023, 0.150]}}\n", + "snia_gen = {\n", + " 'M0': 'jla',\n", + " 'sigM': 0.1,\n", + " 'sct_model': 'G10',\n", + " 'force_n': 1000 ,\n", + " 'model_name': 'salt2',\n", + " 'alpha': 0.14,\n", + " 'beta': 2.9,\n", + " 'dist_x1': 'N21',\n", + " 'dist_c': [-0.055, 0.023, 0.150]}\n", "\n", "# for simplicity we use same configuration for SN core collapse\n", "sncc_gen = {'M0': -19,\n", " 'sigM': [1.5,1.5],\n", " 'rate': 'ztf20',\n", - " 'model_config': {'model_name': 'vinc_nocorr',\n", - " }}\n", + " 'model_name': 'vin19_nocorr'\n", + " }\n", "\n", "cosmology = {'name':'planck18'}\n", "\n", @@ -557,18 +470,18 @@ "outputs": [], "source": [ "config_dic = {'data': {'write_path': './',\n", - " 'sim_name': 'Test.simulation.allSN',\n", + " 'sim_name': 'Test_simulation_allSN',\n", " 'write_format': 'parquet'},\n", " 'survey_config': survey_conf,\n", " 'sim_par': {'randseed': 1234, # Optional\n", " 'z_range': [0.01, 0.3]},\n", " 'snia_gen': snia_gen,\n", - " #'sniipl_gen': sncc_gen,\n", - " #'sniin_gen': sncc_gen,\n", - " # 'sniib_gen': sncc_gen,\n", - " # 'snib_gen': sncc_gen,\n", - " #'snic_gen': sncc_gen,\n", - " #'snic-bl_gen': sncc_gen,\n", + " 'sniipl_gen': sncc_gen,\n", + " 'sniin_gen': sncc_gen,\n", + " 'sniib_gen': sncc_gen,\n", + " 'snib_gen': sncc_gen,\n", + " 'snic_gen': sncc_gen,\n", + " 'snic-bl_gen': sncc_gen,\n", " 'cosmology': cosmology,\n", " 'mw_dust': mw_dust,\n", " 'vpec_dist': vpec_dist,\n", @@ -607,8 +520,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "CPU times: user 474 ms, sys: 52.1 ms, total: 526 ms\n", - "Wall time: 491 ms\n" + "CPU times: user 9.56 s, sys: 346 ms, total: 9.9 s\n", + "Wall time: 9.43 s\n" ] } ], @@ -622,6 +535,34 @@ { "cell_type": "code", "execution_count": 8, + "id": "d132c43f-e62d-4fe2-b627-25ea38ec775a", + "metadata": {}, + "outputs": [], + "source": [ + "# -- Generate n base param\n", + "param_tmp = sim.generators[0].gen_basic_par(1000, 1234, \n", + " min_max_t=True)" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "id": "c79f795f-0b83-406c-9051-0f3ee1e02b9d", + "metadata": {}, + "outputs": [], + "source": [ + "epochs, params = sim.survey.get_observations(\n", + " param_tmp,\n", + " phase_cut=None,\n", + " nep_cut=sim.nep_cut,\n", + " IDmin=0,\n", + " use_dask=sim.config['dask']['use'],\n", + " npartitions=sim.config['dask']['nworkers'])" + ] + }, + { + "cell_type": "code", + "execution_count": 10, "id": "d9bc4cac", "metadata": {}, "outputs": [ @@ -638,7 +579,7 @@ "================================= Version : 0.4.5+dev ====== \n", "-----------------------------------------------------------\n", "\n", - "SIM NAME : Test.simulation.allSN\n", + "SIM NAME : Test_simulation_allSN\n", "CONFIG FILE : No config file\n", "SIM WRITE DIRECTORY : ./\n", "SIMULATION RANDSEED : 1234\n", @@ -659,19 +600,165 @@ "-----------------------------------------------------------\n", "\n", "OBJECT TYPE : SNIa\n", - "SIM MODEL : salt2 from sncosmo\n", + "SIM MODEL(S) :\n", + "- salt2 vT23 from sncosmo\n", "\n", - "Peak mintime : 57935.00 MJD\n", + "Peak mintime : 58000.00 MJD\n", "\n", - "Peak maxtime : 58126.00 MJD\n", + "Peak maxtime : 58100.00 MJD\n", "\n", "Redshift distribution computed using rate\n", "\n", "Use intrinsic scattering model : G10\n", - "Model COV OFF\n", + "\n", + "\n", "\n", "Rate lambda z: 3e-5 /Mpc^3/year (only for redshifts simulation)\n", "\n", + "OBJECT TYPE : SNIIpl\n", + "SIM MODEL(S) :\n", + "- v19-asassn14jb v1.0 from sncosmo\n", + "- v19-asassn15oz v1.0 from sncosmo\n", + "- v19-1987a v1.0 from sncosmo\n", + "- v19-1999em v1.0 from sncosmo\n", + "- v19-2004et v1.0 from sncosmo\n", + "- v19-2007od v1.0 from sncosmo\n", + "- v19-2008bj v1.0 from sncosmo\n", + "- v19-2008in v1.0 from sncosmo\n", + "- v19-2009n v1.0 from sncosmo\n", + "- v19-2009bw v1.0 from sncosmo\n", + "- v19-2009dd v1.0 from sncosmo\n", + "- v19-2009ib v1.0 from sncosmo\n", + "- v19-2009kr v1.0 from sncosmo\n", + "- v19-2012a v1.0 from sncosmo\n", + "- v19-2012aw v1.0 from sncosmo\n", + "- v19-2013ab v1.0 from sncosmo\n", + "- v19-2013am v1.0 from sncosmo\n", + "- v19-2013by v1.0 from sncosmo\n", + "- v19-2013ej v1.0 from sncosmo\n", + "- v19-2013fs v1.0 from sncosmo\n", + "- v19-2014g v1.0 from sncosmo\n", + "- v19-2016x v1.0 from sncosmo\n", + "- v19-2016bkv v1.0 from sncosmo\n", + "\n", + "Peak mintime : 58000.00 MJD\n", + "\n", + "Peak maxtime : 58100.00 MJD\n", + "\n", + "Redshift distribution computed using rate\n", + "\n", + "\n", + "\n", + "\n", + "Rate lambda z: 9.10e-5 * 0.620136 * (0.6766/0.70)**3 /Mpc^3/year \n", + "OBJECT TYPE : SNIIb\n", + "SIM MODEL(S) :\n", + "- v19-1993j v1.0 from sncosmo\n", + "- v19-1999dn v1.0 from sncosmo\n", + "- v19-2006t v1.0 from sncosmo\n", + "- v19-2008aq v1.0 from sncosmo\n", + "- v19-2008ax v1.0 from sncosmo\n", + "- v19-2008bo v1.0 from sncosmo\n", + "- v19-2011dh v1.0 from sncosmo\n", + "- v19-2011ei v1.0 from sncosmo\n", + "- v19-2011fu v1.0 from sncosmo\n", + "- v19-2011hs v1.0 from sncosmo\n", + "- v19-2013df v1.0 from sncosmo\n", + "- v19-2016gkg v1.0 from sncosmo\n", + "\n", + "Peak mintime : 58000.00 MJD\n", + "\n", + "Peak maxtime : 58100.00 MJD\n", + "\n", + "Redshift distribution computed using rate\n", + "\n", + "\n", + "\n", + "\n", + "Rate lambda z: 9.10e-5 * 0.10944 * (0.6766/0.70)**3 /Mpc^3/year \n", + "OBJECT TYPE : SNIIn\n", + "SIM MODEL(S) :\n", + "- v19-2006aa v1.0 from sncosmo\n", + "- v19-2007pk v1.0 from sncosmo\n", + "- v19-2008fq v1.0 from sncosmo\n", + "- v19-2009ip v1.0 from sncosmo\n", + "- v19-2010al v1.0 from sncosmo\n", + "- v19-2011ht v1.0 from sncosmo\n", + "\n", + "Peak mintime : 58000.00 MJD\n", + "\n", + "Peak maxtime : 58100.00 MJD\n", + "\n", + "Redshift distribution computed using rate\n", + "\n", + "\n", + "\n", + "\n", + "Rate lambda z: 9.10e-5 * 0.046632 * (0.6766/0.70)**3 /Mpc^3/year \n", + "OBJECT TYPE : SNIc\n", + "SIM MODEL(S) :\n", + "- v19-1994i v1.0 from sncosmo\n", + "- v19-2004aw v1.0 from sncosmo\n", + "- v19-2004fe v1.0 from sncosmo\n", + "- v19-2004gt v1.0 from sncosmo\n", + "- v19-2007gr v1.0 from sncosmo\n", + "- v19-2011bm v1.0 from sncosmo\n", + "- v19-2013ge v1.0 from sncosmo\n", + "\n", + "Peak mintime : 58000.00 MJD\n", + "\n", + "Peak maxtime : 58100.00 MJD\n", + "\n", + "Redshift distribution computed using rate\n", + "\n", + "\n", + "\n", + "\n", + "Rate lambda z: 9.10e-5 * 0.075088 * (0.6766/0.70)**3 /Mpc^3/year \n", + "OBJECT TYPE : SNIb\n", + "SIM MODEL(S) :\n", + "- v19-2004gq v1.0 from sncosmo\n", + "- v19-2004gv v1.0 from sncosmo\n", + "- v19-2005bf v1.0 from sncosmo\n", + "- v19-2005hg v1.0 from sncosmo\n", + "- v19-2006ep v1.0 from sncosmo\n", + "- v19-2007y v1.0 from sncosmo\n", + "- v19-2007uy v1.0 from sncosmo\n", + "- v19-2008d v1.0 from sncosmo\n", + "- v19-2009iz v1.0 from sncosmo\n", + "- v19-2009jf v1.0 from sncosmo\n", + "- v19-2012au v1.0 from sncosmo\n", + "- v19-iptf13bvn v1.0 from sncosmo\n", + "\n", + "Peak mintime : 58000.00 MJD\n", + "\n", + "Peak maxtime : 58100.00 MJD\n", + "\n", + "Redshift distribution computed using rate\n", + "\n", + "\n", + "\n", + "\n", + "Rate lambda z: 9.10e-5 * 0.108224 * (0.6766/0.70)**3 /Mpc^3/year \n", + "OBJECT TYPE : SNIc_BL\n", + "SIM MODEL(S) :\n", + "- v19-1998bw v1.0 from sncosmo\n", + "- v19-2002ap v1.0 from sncosmo\n", + "- v19-2006aj v1.0 from sncosmo\n", + "- v19-2007ru v1.0 from sncosmo\n", + "- v19-2009bb v1.0 from sncosmo\n", + "- v19-2012ap v1.0 from sncosmo\n", + "\n", + "Peak mintime : 58000.00 MJD\n", + "\n", + "Peak maxtime : 58100.00 MJD\n", + "\n", + "Redshift distribution computed using rate\n", + "\n", + "\n", + "\n", + "\n", + "Rate lambda z: 9.10e-5 * 0.011248 * (0.6766/0.70)**3 /Mpc^3/year \n", "\n", "-----------------------------------------------------------\n", "\n", @@ -683,38 +770,37 @@ "- At least 1 epochs between -20 and 50 rest-frame phase in any band\n", "\n", "-----------------------------------------------------------\n", - "\n" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "WARNING: AstropyDeprecationWarning: The update_default_config function is deprecated and may be removed in a future version. [sncosmo]\n", - "WARNING: AstropyDeprecationWarning: The update_default_config function is deprecated and may be removed in a future version. [sncosmo]\n", - "WARNING: AstropyDeprecationWarning: The update_default_config function is deprecated and may be removed in a future version. [sncosmo]\n", - "WARNING: AstropyDeprecationWarning: The update_default_config function is deprecated and may be removed in a future version. [sncosmo]\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1000 SNIa lcs generated in 8.3 seconds\n", + "\n", + "1000 SNIa lcs generated in 5.3 seconds\n", + "Sim file write in 0.3 seconds\n", + "49 SNIIpl lcs generated in 0.5 seconds\n", + "Sim file write in 0.0 seconds\n", + "5 SNIIb lcs generated in 0.2 seconds\n", + "Sim file write in 0.0 seconds\n", + "2 SNIIn lcs generated in 0.1 seconds\n", + "Sim file write in 0.0 seconds\n", + "3 SNIc lcs generated in 0.2 seconds\n", + "Sim file write in 0.0 seconds\n", + "4 SNIb lcs generated in 0.2 seconds\n", + "Sim file write in 0.0 seconds\n", + "1 SNIc_BL lcs generated in 0.1 seconds\n", "Sim file write in 0.0 seconds\n", "\n", "-----------------------------------------------------------\n", "\n", "OUTPUT FILE(S) : \n", - "- ./Test.simulation.allSN_SNIa.parquet\n", - "\n", - "CPU times: user 6.25 s, sys: 373 ms, total: 6.62 s\n", - "Wall time: 8.33 s\n" + "- ./Test_simulation_allSN_SNIa.parquet\n", + "- ./Test_simulation_allSN_SNIIpl.parquet\n", + "- ./Test_simulation_allSN_SNIIb.parquet\n", + "- ./Test_simulation_allSN_SNIIn.parquet\n", + "- ./Test_simulation_allSN_SNIc.parquet\n", + "- ./Test_simulation_allSN_SNIb.parquet\n", + "- ./Test_simulation_allSN_SNIc_BL.parquet\n", + "\n" ] } ], "source": [ - "%%time\n", "#run the simulation\n", "sim.simulate()" ] @@ -729,17 +815,23 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 11, "id": "e6f6a2ac", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "[]" + "[,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ,\n", + " ]" ] }, - "execution_count": 9, + "execution_count": 11, "metadata": {}, "output_type": "execute_result" } @@ -752,40 +844,31 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "id": "2906b2aa", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'seed': 979371,\n", + "{'seed': 1234,\n", + " 'seed_key': (0,),\n", " 'obj_type': 'SNIa',\n", " 'rate': 'lambda z: 3e-5',\n", " 'model_name': ['salt2'],\n", - " 'mw_dust': {'model': 'CCM89', 'rv': 3.1},\n", - " 'mod_fcov': False,\n", - " 'M0': -19.123830232811475,\n", - " 'sigM': 0.1,\n", - " 'alpha': 0.14,\n", - " 'beta': 2.9,\n", + " 'model_version': ['T23'],\n", " 'm_vp': 0,\n", " 's_vp': 300,\n", " 'M0_band': 'bessell_b',\n", " 'dist_x1': 'N21',\n", - " 'mean_c': -0.055,\n", + " 'peak_c': -0.055,\n", " 'dist_c': 'asym_gauss',\n", " 'sig_c_low': 0.023,\n", " 'sig_c_hi': 0.15,\n", - " 'sct_mod': 'G10',\n", - " 'G10_L0': 2157.3,\n", - " 'G10_F0': 0.0,\n", - " 'G10_F1': 0.000108,\n", - " 'G10_dL': 800.0,\n", " 'cosmo': {'cosmod_name': 'planck18'}}" ] }, - "execution_count": 11, + "execution_count": 12, "metadata": {}, "output_type": "execute_result" } @@ -797,38 +880,43 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "id": "da4b99e8", "metadata": {}, "outputs": [ { "data": { "text/plain": [ - "{'zobs': 0.08514039365428472,\n", - " 'G10_RndS': 353574,\n", - " 'mw_r_v': 3.1,\n", - " 'mw_ebv': 0.0894629005790134,\n", - " 'sim_t0': 58008.88437023253,\n", - " 'sim_x1': 0.39903049707041854,\n", - " 'sim_c': 0.08494218973928579,\n", - " 'sim_x0': 0.0004463834760406595,\n", - " 'type': 'snIa',\n", + "{'mu': 40.28117331936717,\n", + " 'zobs': 0.22321532306922487,\n", + " 'zCMB': 0.22321532306922487,\n", " 'ID': 0,\n", - " 'ra': 0.7828707370610833,\n", - " 'dec': 0.6664040790647171,\n", - " 'zcos': 0.08439615731630058,\n", - " 'zCMB': 0.08514039365428472,\n", - " 'zpec': 0.0006863140679381759,\n", - " 'vpec': 205.75178138716473,\n", - " 'z2cmb': 0.0,\n", - " 'sim_mu': 37.99912258538066,\n", - " 'como_dist': 366.4718552501182,\n", - " 'sim_mb': 18.8770954270438,\n", - " 'mag_sct': -0.188665006179458,\n", - " 'template': 'salt2'}" + " 'zcos': 0.2215437856652841,\n", + " 'como_dist': 929.2613557467457,\n", + " 'zpcmb': 0.0,\n", + " 't0': 58034.90950342247,\n", + " 'ra': 0.6614847077168625,\n", + " 'dec': 0.7580354265768443,\n", + " 'vpec': 410.23032726860123,\n", + " 'min_t': 58010.44519696108,\n", + " 'max_t': 58096.070269575925,\n", + " '1_zobs': 1.2232153230692249,\n", + " 'model_name': 'salt2',\n", + " 'model_version': 'T23',\n", + " 'M0': -19.123830232811475,\n", + " 'coh_sct': -0.06105215635050651,\n", + " 'x1': 1.1158189250371295,\n", + " 'c': -0.01899610101045291,\n", + " 'alpha': 0.14,\n", + " 'beta': 2.9,\n", + " 'G10_RndS': 998124575180,\n", + " 'mw_ebv': 0.06874377744226459,\n", + " 'mw_r_v': 3.1,\n", + " 'mb': 21.3075942726407,\n", + " 'x0': 4.750351490477434e-05}" ] }, - "execution_count": 11, + "execution_count": 13, "metadata": {}, "output_type": "execute_result" } @@ -840,7 +928,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 14, "id": "2d78191a", "metadata": {}, "outputs": [ @@ -868,6 +956,7 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", @@ -898,56 +987,60 @@ " \n", " \n", " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -955,33 +1048,35 @@ " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", " \n", " \n", " \n", " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", - " \n", + " \n", " \n", " \n", " \n", @@ -1004,16 +1099,18 @@ " \n", " \n", " \n", + " \n", " \n", " \n", - " \n", - " \n", + " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1024,13 +1121,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1041,13 +1139,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1058,13 +1157,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1075,13 +1175,14 @@ " \n", " \n", " \n", - " \n", + " \n", " \n", - " \n", - " \n", - " \n", - " \n", - " \n", + " \n", + " \n", + " \n", + " \n", + " \n", + " \n", " \n", " \n", " \n", @@ -1093,56 +1194,56 @@ " \n", " \n", "
\n", - " Comm: tcp://127.0.0.1:33747\n", + " Comm: tcp://127.0.0.1:44685\n", " \n", - " Total threads: 2\n", + " Total threads: 4\n", "
\n", - " Dashboard: http://127.0.0.1:40557/status\n", + " Dashboard: http://127.0.0.1:40165/status\n", " \n", " Memory: 3.83 GiB\n", @@ -419,13 +331,13 @@ "
\n", - " Nanny: tcp://127.0.0.1:37939\n", + " Nanny: tcp://127.0.0.1:34329\n", "
\n", - " Local directory: /tmp/dask-worker-space/worker-bn2jh9mg\n", + " Local directory: /tmp/dask-scratch-space/worker-fqqs0n20\n", "
timefluxtruefluxerrtruefluxfluxerrmag
0058000.0000005.544801-495.709935629.55909958012.2448980.252037242.029571-112.653090242.263886NaNNaN25ab1629.554693ztfg242.029050ztfr10.010.0
158002.0408165.061043329.785896794.92133218.7044202.61707658014.2857141.213546107.63170594.121886108.06592320.0657731.24658725ab1794.918147ztfg107.626067ztfr10.010.0
258004.0816334.669393-274.616237857.81075658016.3265312.55731218.353142-24.04688518.930819NaNNaN25ab1857.80803318.283325ztfg10.01
358006.1224494.359750158.501341736.17323319.4999185.04279658018.3673477.162953571.81750326.584485571.83453421.43842923.35427825ab1736.170271ztfg571.811236ztfr10.010.0
458008.1632654.1116411760.295120716.20501916.8860360.44175058020.40816311.737806213.912852162.710405214.27065919.4714621.42978825ab1716.202148213.885387ztfg10.01............
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10996 rows × 14 columns

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31597 rows × 15 columns

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" ], "text/plain": [ - " time fluxtrue flux fluxerr mag \\\n", - "ID epochs \n", - "0 0 58000.000000 5.544801 -495.709935 629.559099 NaN \n", - " 1 58002.040816 5.061043 329.785896 794.921332 18.704420 \n", - " 2 58004.081633 4.669393 -274.616237 857.810756 NaN \n", - " 3 58006.122449 4.359750 158.501341 736.173233 19.499918 \n", - " 4 58008.163265 4.111641 1760.295120 716.205019 16.886036 \n", - "... ... ... ... ... ... \n", - "224 29 58091.836735 0.000000 124.919797 882.049012 19.758422 \n", - " 30 58093.877551 0.000000 1232.953559 998.985068 17.272633 \n", - " 31 58095.918367 0.000000 372.227737 210.301351 18.572978 \n", - " 32 58097.959184 0.000000 71.697165 903.796792 20.361245 \n", - " 33 58100.000000 0.000000 -122.251439 535.020328 NaN \n", + " time fluxtrue fluxerrtrue flux fluxerr \\\n", + "ID epochs \n", + "0 0 58012.244898 0.252037 242.029571 -112.653090 242.263886 \n", + " 1 58014.285714 1.213546 107.631705 94.121886 108.065923 \n", + " 2 58016.326531 2.557312 18.353142 -24.046885 18.930819 \n", + " 3 58018.367347 7.162953 571.817503 26.584485 571.834534 \n", + " 4 58020.408163 11.737806 213.912852 162.710405 214.270659 \n", + "... ... ... ... ... ... \n", + "999 33 58091.836735 7.924679 882.053507 -1267.227588 882.844218 \n", + " 34 58093.877551 2.099412 998.986119 -493.277426 999.242255 \n", + " 35 58095.918367 2.022352 210.306160 323.369010 211.089786 \n", + " 36 58097.959184 1.949532 903.797871 -584.853531 904.136335 \n", + " 37 58100.000000 1.871824 535.022078 753.098987 535.768572 \n", "\n", - " magerr zp zpsys gain skynoise band fieldID sig_zp \\\n", - "ID epochs \n", - "0 0 NaN 25 ab 1 629.554693 ztfg 1 0.01 \n", - " 1 2.617076 25 ab 1 794.918147 ztfg 1 0.01 \n", - " 2 NaN 25 ab 1 857.808033 ztfg 1 0.01 \n", - " 3 5.042796 25 ab 1 736.170271 ztfg 1 0.01 \n", - " 4 0.441750 25 ab 1 716.202148 ztfg 1 0.01 \n", - "... ... .. ... ... ... ... ... ... \n", - "224 29 7.666299 25 ab 1 882.049012 ztfr 1 0.01 \n", - " 30 0.879704 25 ab 1 998.985068 ztfg 1 0.01 \n", - " 31 0.613420 25 ab 1 210.301351 ztfg 1 0.01 \n", - " 32 13.686523 25 ab 1 903.796792 ztfg 1 0.01 \n", - " 33 NaN 25 ab 1 535.020328 ztfg 1 0.01 \n", + " mag magerr zp zpsys gain skynoise band fieldID \\\n", + "ID epochs \n", + "0 0 NaN NaN 25 ab 1 242.029050 ztfr 1 \n", + " 1 20.065773 1.246587 25 ab 1 107.626067 ztfr 1 \n", + " 2 NaN NaN 25 ab 1 18.283325 ztfg 1 \n", + " 3 21.438429 23.354278 25 ab 1 571.811236 ztfr 1 \n", + " 4 19.471462 1.429788 25 ab 1 213.885387 ztfg 1 \n", + "... ... ... .. ... ... ... ... ... \n", + "999 33 NaN NaN 25 ab 1 882.049012 ztfr 1 \n", + " 34 NaN NaN 25 ab 1 998.985068 ztfg 1 \n", + " 35 18.725754 0.708750 25 ab 1 210.301351 ztfg 1 \n", + " 36 NaN NaN 25 ab 1 903.796792 ztfg 1 \n", + " 37 17.807870 0.772413 25 ab 1 535.020328 ztfg 1 \n", "\n", - " sig_psf \n", - "ID epochs \n", - "0 0 0.0 \n", - " 1 0.0 \n", - " 2 0.0 \n", - " 3 0.0 \n", - " 4 0.0 \n", - "... ... \n", - "224 29 0.0 \n", - " 30 0.0 \n", - " 31 0.0 \n", - " 32 0.0 \n", - " 33 0.0 \n", + " sig_zp sig_psf \n", + "ID epochs \n", + "0 0 0.01 0.0 \n", + " 1 0.01 0.0 \n", + " 2 0.01 0.0 \n", + " 3 0.01 0.0 \n", + " 4 0.01 0.0 \n", + "... ... ... \n", + "999 33 0.01 0.0 \n", + " 34 0.01 0.0 \n", + " 35 0.01 0.0 \n", + " 36 0.01 0.0 \n", + " 37 0.01 0.0 \n", "\n", - "[10996 rows x 14 columns]" + "[31597 rows x 15 columns]" ] }, - "execution_count": 23, + "execution_count": 14, "metadata": {}, "output_type": "execute_result" } @@ -1152,197 +1253,10 @@ "sim.samples[1].sim_lcs" ] }, - { - "cell_type": "code", - "execution_count": 9, - "id": "9c200ee8", - "metadata": {}, - "outputs": [], - "source": [ - "def sine_interp(x_new, fun_x, fun_y):\n", - " \"\"\"Return the sinus interpolation of a function at x.\n", - "\n", - " Parameters\n", - " ----------\n", - " x_new : float\n", - " New x where evaluate the function.\n", - " fun_x : list(float)\n", - " Existing function x.\n", - " fun_y : list(float)\n", - " Existing function y.\n", - "\n", - " Returns\n", - " -------\n", - " float\n", - " The sinus interpolation value of f at x_new.\n", - "\n", - " \"\"\"\n", - " if len(fun_x) != len(fun_y):\n", - " raise ValueError('x and y must have the same len')\n", - "\n", - " if (x_new > fun_x[-1]) or (x_new < fun_x[0]):\n", - " raise ValueError('x_new is out of range of fun_x')\n", - "\n", - " inf_sel = x_new >= fun_x[:-1]\n", - " sup_sel = x_new < fun_x[1:]\n", - " if inf_sel.all():\n", - " idx_inf = -2\n", - " elif sup_sel.all():\n", - " idx_inf = 0\n", - " else:\n", - " idx_inf = np.where(inf_sel * sup_sel)[0][0]\n", - "\n", - " x_inf = fun_x[idx_inf]\n", - " x_sup = fun_x[idx_inf + 1]\n", - " Value_inf = fun_y[idx_inf]\n", - " Value_sup = fun_y[idx_inf + 1]\n", - " sin_interp = np.sin(np.pi * (x_new - 0.5 * (x_inf + x_sup)) / (x_sup - x_inf))\n", - "\n", - " return 0.5 * (Value_sup + Value_inf) + 0.5 * (Value_sup - Value_inf) * sin_interp\n", - "\n", - "\n", - "def scattering_law(_parameters,_minwave,_colordisp,_maxwave):\n", - " L0, F0, F1, dL = _parameters[:-1]\n", - " lam = _minwave\n", - " sigma_lam = []\n", - " sigma_val = []\n", - "\n", - " while lam < _maxwave:\n", - " sigma_lam.append(lam)\n", - " val = _colordisp(lam)\n", - " if lam > L0:\n", - " val *= 1 + (lam - L0) * F1\n", - " elif lam < L0:\n", - " val *= 1 + (lam - L0) * F0\n", - "\n", - " sigma_val.append(val)\n", - " lam += dL\n", - " return np.asarray(sigma_lam), np.asarray(sigma_val)\n", - "\n", - " \n", - " \n", - "\n", - " \n", - "def lam_scatter(_parameters,_minwave,_colordisp,_maxwave):\n", - " sigma_lam, sigma_val = scattering_law(_parameters,_minwave,_colordisp,_maxwave)\n", - " RS = _parameters[-1]\n", - " return sigma_lam, sigma_val# * np.random.default_rng(int(RS)).normal(0, 1, size=len(sigma_val))\n", - "\n", - "\n", - "\n", - "def propagate(_parameters,_minwave,_colordisp,_maxwave, wave):\n", - " \n", - " lam, scatter = lam_scatter(_parameters,_minwave,_colordisp,_maxwave)\n", - " print(lam)\n", - " scattering = np.asarray([sine_interp(w, lam, scatter) for w in wave])\n", - " return scattering,10**(-0.4 * scattering)\n" - ] - }, - { - "cell_type": "code", - "execution_count": 10, - "id": "81813550", - "metadata": {}, - "outputs": [ - { - "ename": "NameError", - "evalue": "name 'sim' is not defined", - "output_type": "error", - "traceback": [ - "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[0;31mNameError\u001b[0m Traceback (most recent call last)", - "Cell \u001b[0;32mIn[10], line 8\u001b[0m\n\u001b[1;32m 2\u001b[0m dust \u001b[38;5;241m=\u001b[39m sncosmo\u001b[38;5;241m.\u001b[39mCCM89Dust()\n\u001b[1;32m 3\u001b[0m model \u001b[38;5;241m=\u001b[39m sncosmo\u001b[38;5;241m.\u001b[39mModel(source\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msalt2\u001b[39m\u001b[38;5;124m'\u001b[39m,\n\u001b[1;32m 4\u001b[0m effects\u001b[38;5;241m=\u001b[39m[dust],\n\u001b[1;32m 5\u001b[0m effect_names\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmw_\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 6\u001b[0m effect_frames\u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mobs\u001b[39m\u001b[38;5;124m'\u001b[39m])\n\u001b[0;32m----> 8\u001b[0m fixpar \u001b[38;5;241m=\u001b[39m {\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mz\u001b[39m\u001b[38;5;124m'\u001b[39m: \u001b[43msim\u001b[49m\u001b[38;5;241m.\u001b[39msamples[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmeta[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzobs\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 9\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmw_ebv\u001b[39m\u001b[38;5;124m'\u001b[39m: sim\u001b[38;5;241m.\u001b[39msamples[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmeta[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmw_ebv\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 10\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mt0\u001b[39m\u001b[38;5;124m'\u001b[39m: sim\u001b[38;5;241m.\u001b[39msamples[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmeta[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msim_t0\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 11\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mc\u001b[39m\u001b[38;5;124m'\u001b[39m: sim\u001b[38;5;241m.\u001b[39msamples[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmeta[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msim_c\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 12\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx1\u001b[39m\u001b[38;5;124m'\u001b[39m: sim\u001b[38;5;241m.\u001b[39msamples[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmeta[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msim_x1\u001b[39m\u001b[38;5;124m'\u001b[39m],\n\u001b[1;32m 13\u001b[0m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mx0\u001b[39m\u001b[38;5;124m'\u001b[39m: sim\u001b[38;5;241m.\u001b[39msamples[\u001b[38;5;241m0\u001b[39m]\u001b[38;5;241m.\u001b[39mmeta[\u001b[38;5;241m0\u001b[39m][\u001b[38;5;124m'\u001b[39m\u001b[38;5;124msim_x0\u001b[39m\u001b[38;5;124m'\u001b[39m]}\n\u001b[1;32m 15\u001b[0m model\u001b[38;5;241m.\u001b[39mset(\u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mfixpar)\n\u001b[1;32m 18\u001b[0m _param_names \u001b[38;5;241m=\u001b[39m [\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mL0\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mF0\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mF1\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdL\u001b[39m\u001b[38;5;124m'\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mRndS\u001b[39m\u001b[38;5;124m'\u001b[39m]\n", - "\u001b[0;31mNameError\u001b[0m: name 'sim' is not defined" - ] - } - ], - "source": [ - "import sncosmo\n", - "dust = sncosmo.CCM89Dust()\n", - "model = sncosmo.Model(source='salt2',\n", - " effects=[dust],\n", - " effect_names= ['mw_'],\n", - " effect_frames= ['obs'])\n", - "\n", - "fixpar = {'z': sim.samples[0].meta[0]['zobs'],\n", - " 'mw_ebv': sim.samples[0].meta[0]['mw_ebv'],\n", - " 't0': sim.samples[0].meta[0]['sim_t0'],\n", - " 'c': sim.samples[0].meta[0]['sim_c'],\n", - " 'x1': sim.samples[0].meta[0]['sim_x1'],\n", - " 'x0': sim.samples[0].meta[0]['sim_x0']}\n", - " \n", - "model.set(**fixpar)\n", - "\n", - "\n", - "_param_names = ['L0', 'F0', 'F1', 'dL', 'RndS']\n", - "param_names_latex = [r'\\lambda_0', 'F_0', 'F_1', 'd_L', 'RS']\n", - "\n", - " \n", - " \n", - "_parameters = np.array([2157.3, 0.0, 1.08e-4, 800,\n", - " sim.samples[0].meta[4]['G10_RndS']])\n", - "\n", - "_minwave = model.source.minwave()\n", - "\n", - "_maxwave = model.source.maxwave()\n", - "print(_minwave,_maxwave)\n", - "_colordisp = model.source._colordisp\n", - "wave=np.linspace(_minwave,_maxwave,800)\n", - "#wave=np.linspace(2800,8000,1000) \n", - "sct,st=propagate(_parameters,_minwave,_colordisp,_maxwave, wave)\n", - "len(sct)" - ] - }, - { - "cell_type": "code", - "execution_count": 39, - "id": "e700e21d", - "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 39, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", 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" - ] - }, - "metadata": {}, - "output_type": "display_data" - } - ], - "source": [ - "plt.figure(dpi=150)\n", - "plt.plot(wave,sct)\n", - "plt.yscale('log')\n", - "\n", - "plt.figure(dpi=150)\n", - "plt.plot(wave,st)\n" - ] - }, { "cell_type": "code", "execution_count": null, - "id": "5094b5ae", + "id": "32b1b844-d4d8-4c2b-8637-f9827aa30d7a", "metadata": {}, "outputs": [], "source": [] @@ -1350,9 +1264,9 @@ ], "metadata": { "kernelspec": { - "display_name": "Python 3 (ipykernel)", + "display_name": "snsim_dev", "language": "python", - "name": "python3" + "name": "snsim_dev" }, "language_info": { "codemirror_mode": { @@ -1364,8 +1278,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.12" - } + "version": "3.10.13" }, "nbformat": 4, "nbformat_minor": 5 diff --git a/Examples/Test_simulation_allSN_SNIIb.parquet b/Examples/Test_simulation_allSN_SNIIb.parquet new file mode 100644 index 0000000..eb47bd0 Binary files /dev/null and b/Examples/Test_simulation_allSN_SNIIb.parquet differ diff --git a/Examples/Test_simulation_allSN_SNIIn.parquet b/Examples/Test_simulation_allSN_SNIIn.parquet new file mode 100644 index 0000000..a42cf3b Binary files /dev/null and b/Examples/Test_simulation_allSN_SNIIn.parquet differ diff --git a/Examples/Test_simulation_allSN_SNIIpl.parquet b/Examples/Test_simulation_allSN_SNIIpl.parquet new file mode 100644 index 0000000..9155010 Binary files /dev/null and b/Examples/Test_simulation_allSN_SNIIpl.parquet differ diff --git a/Examples/Test_simulation_allSN_SNIa.parquet b/Examples/Test_simulation_allSN_SNIa.parquet new file mode 100644 index 0000000..7b9f350 Binary files /dev/null and b/Examples/Test_simulation_allSN_SNIa.parquet differ diff --git a/Examples/Test_simulation_allSN_SNIb.parquet b/Examples/Test_simulation_allSN_SNIb.parquet new file mode 100644 index 0000000..817ec86 Binary files /dev/null and b/Examples/Test_simulation_allSN_SNIb.parquet differ diff --git a/Examples/Test_simulation_allSN_SNIc.parquet b/Examples/Test_simulation_allSN_SNIc.parquet new file mode 100644 index 0000000..c53e7a7 Binary files /dev/null and b/Examples/Test_simulation_allSN_SNIc.parquet differ diff --git a/Examples/Test_simulation_allSN_SNIc_BL.parquet b/Examples/Test_simulation_allSN_SNIc_BL.parquet new file mode 100644 index 0000000..dbc2474 Binary files /dev/null and b/Examples/Test_simulation_allSN_SNIc_BL.parquet differ diff --git a/README.md b/README.md index ca1a4f2..f33ecc8 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ # Code for simulation of transient surveys -[![Documentation Status](https://readthedocs.org/projects/snsim/badge/?version=dev)](https://snsim.readthedocs.io/en/main/?badge=dev) +[![Documentation Status](https://readthedocs.org/projects/snsim/badge/?version=dev)](https://snsim.readthedocs.io/en/main/?badge=dev) ![Tests](https://github.com/bastiencarreres/snsim/actions/workflows/python-package.yml/badge.svg?branch=dev_2) ## Installation In the setup.py directory use: ``` @@ -18,3 +18,5 @@ The documentation is [here](https://snsim.readthedocs.io/en/main/). ## Acknowledgements: The code use [sncosmo](https://sncosmo.readthedocs.io/en/stable/) SED templates and for fluxes generation. [Numba](https://numba.pydata.org/), [Dask](https://www.dask.org/) and [GeoPandas](https://geopandas.org/en/stable/) are used for optimisation. + +Logo: Background image from NASA HST. Observatory svg originally created by Game Icons.net in CC Attribution License via SVG Repo. diff --git a/Untitled.ipynb b/Untitled.ipynb new file mode 100644 index 0000000..bc2b12e --- /dev/null +++ b/Untitled.ipynb @@ -0,0 +1,278 @@ +{ + "cells": [ + { + "cell_type": "code", + "execution_count": 13, + "id": "199ed8ce-1f78-40af-8ac9-19b783c51484", + "metadata": {}, + "outputs": [], + "source": [ + "import snsim\n", + "import numpy as np\n", + "import astropy.cosmology as acosmo\n", + "cosmo = acosmo.Planck18\n", + "import matplotlib.pyplot as plt" + ] + }, + { + "cell_type": "code", + "execution_count": 27, + "id": "908e0af7-894b-48be-a9c5-a6dfb6ed6d33", + "metadata": {}, + "outputs": [], + "source": [ + "def rate(z):\n", + " return 1e-5\n", + "\n", + "def _compute_zcdf(z_range):\n", + " \"\"\"Give the time rate SN/years in redshift shell.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : numpy.ndarray\n", + " The redshift bins.\n", + "\n", + " Returns\n", + " -------\n", + " numpy.ndarray(float)\n", + " Numpy array containing the time rate in each redshift bin.\n", + "\n", + " \"\"\"\n", + " z_min, z_max = z_range\n", + "\n", + " # -- Set the precision to dz = 1e-5\n", + " dz = 1e-5\n", + "\n", + " z_shell = np.linspace(z_min, z_max, int((z_max - z_min) / dz))\n", + " z_shell_center = 0.5 * (z_shell[1:] + z_shell[:-1])\n", + " co_dist = cosmo.comoving_distance(z_shell).value\n", + " shell_vol = 4 * np.pi / 3 * (co_dist[1:]**3 - co_dist[:-1]**3)\n", + "\n", + " # -- Compute the sn time rate in each volume shell [( SN / year )(z)]\n", + " shell_time_rate = rate(z_shell_center) * shell_vol / (1 + z_shell_center)\n", + "\n", + " z_pdf = lambda x : np.interp(x, z_shell, np.append(0, shell_time_rate))\n", + "\n", + " return snsim.utils.CustomRandom(z_pdf, z_min, z_max, dx=1e-5), (z_shell, shell_time_rate)\n", + "\n", + "\n", + "def _compute_zcdf2(z_range):\n", + " \"\"\"Give the time rate SN/years in redshift shell.\n", + "\n", + " Parameters\n", + " ----------\n", + " z : numpy.ndarray\n", + " The redshift bins.\n", + "\n", + " Returns\n", + " -------\n", + " numpy.ndarray(float)\n", + " Numpy array containing the time rate in each redshift bin.\n", + "\n", + " \"\"\"\n", + " z_min, z_max = z_range\n", + "\n", + " # -- Set the precision to dz = 1e-5\n", + " dz = 1e-5\n", + "\n", + " z_shell = np.linspace(z_min, z_max, int((z_max - z_min) / dz))\n", + " co_dist = cosmo.comoving_distance(z_shell).value\n", + " shell_vol = 4 * np.pi / 3 * co_dist**2 * snsim.constants.C_LIGHT_KMS / cosmo.H(z_shell).value * dz\n", + "\n", + " # -- Compute the sn time rate in each volume shell [( SN / year )(z)]\n", + " shell_time_rate = rate(z_shell) * shell_vol / (1 + z_shell)\n", + "\n", + " z_pdf = lambda x : np.interp(x, z_shell, shell_time_rate)\n", + "\n", + " return snsim.utils.CustomRandom(z_pdf, z_min, z_max, dx=1e-5), (z_shell, shell_time_rate)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "id": "93587277-6102-4c19-a320-bc9050e8fe98", + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": 56, + "id": "66480fc9-88e1-4c53-bbd3-783b87bcd74f", + "metadata": {}, + "outputs": [], + "source": [ + "pdf, _ = _compute_zcdf((0.01, 0.1))\n", + "pdf2, _ = _compute_zcdf2((0.001, 0.1))\n", + "\n" + ] + }, + { + "cell_type": "code", + "execution_count": 57, + "id": "13019400-b1ac-411e-bc2f-33fa40fce1ef", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 57, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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3sdXSmS3epvIhIiKVxvx3XmKg/V0AMro+RY1m3SxOVDmpfIiISKXw2cJP+FP66TNb0hvfRnyP2y1OVHmpfIiISIW3cv1G2q8af/rMlppXUU9ntlhK5UNERCq0XQczCZt3yy9ntozRmS1WU/kQEZEK61huAQffGklzIx27Xxg1x8zHCA61Olalp/IhIiIVUmGxk9Qpf+Vq10qKCcAY+i5BkXWtjiWofIiISAVkmiZzpz7F4PxZAJzo8SyhTbpanEp+pvIhIiIVzry5H3DDkdMHlR5oOY7orrdanEh+TeVDREQqlGXffEuPjfcTaDjZV7sv8YP/Y3Uk+Q2VDxERqTA27txNg8W3Em7kcyCkJYm3TQfDsDqW/IbKh4iIVAgHs07imnkziUYmWQExxN41HwKDrY4lZ6HyISIiPi+3oIidb4ykLdvJM0IIuXUu/tVrWR1LzkHlQ0REfJrTZbJ0Sgo9ilMpwZ/CQdOpWqeF1bHkPFQ+RETEpy2Y8TwD7e8AcOTK/xDZupfFieSPqHyIiIjP+vzTj+iXfvpslt2Nbyc+eazFieRCqHyIiIhP+nr1ajr/cA82o4Q9kT1oMOwZqyPJBSpT+Zg0aRIdOnSgevXqREVFMXDgQHbs2HHGMoWFhYwbN46aNWtSrVo1Bg8eTGZmpltDi4hI5bYlbS9xn48kwsjjQJVm1LvjXfDT/6d9RZn+plJTUxk3bhyrVq1i8eLFFBcX06tXL/Lz80uXue+++/jkk0+YPXs2qampHD58mOuvv97twUVEpHI6eCybgvdupp5xhGP+UdS+ax5GUIjVsaQMDNM0zYt9cVZWFlFRUaSmptK1a1fsdju1atVi5syZDBkyBIDt27fTrFkzVq5cSefOnf9wnTk5OYSFhWG32wkN1Z0HRUTkF/ZTRax6fhi9i5eSTxW4bREhCW2sjiWU7fP7kvZR2e12ACIiIgBYs2YNxcXFJCcnly7TtGlTEhISWLly5aW8lYiIVHJFJS6+fO1+ehcvpQQ/HIOmqnj4qICLfaHL5eLee++lS5cutGzZEoCMjAyCgoIIDw8/Y9no6GgyMjLOuh6Hw4HD4Sh9nJOTc7GRRESkgjJNk4/eepqbcmcAcPTKJ4ht08/iVHKxLnrPx7hx49i8eTMffPDBJQWYNGkSYWFhpVN8fPwlrU9ERCqeeR/NZMihpwHY1+wOYpPHW5xILsVFlY/x48fz6aefsnz5cuLi4krnx8TEUFRURHZ29hnLZ2ZmEhMTc9Z1TZw4EbvdXjodOHDgYiKJiEgFtTR1GcmbTt+ldm/tPiTe8JTVkeQSlal8mKbJ+PHjmTdvHsuWLaNevXpnPN+uXTsCAwNZunRp6bwdO3awf/9+kpKSzrpOm81GaGjoGZOIiAjAmk1baL5sNKFGAfurX0bd0TN0Sm0FUKZjPsaNG8fMmTNZsGAB1atXLz2OIywsjCpVqhAWFsbo0aNJSUkhIiKC0NBQ7r77bpKSki7oTBcREZGf7T54mJCPbqK2cYKMwATi7poLATarY4kblKl8vPbaawB07979jPnTpk1j1KhRADz//PP4+fkxePBgHA4HvXv35tVXX3VLWBERqRyysvM49tZNdGIfJ/1qUOOOBfiFRFgdS9zkkq7z4Qm6zoeISOVW4Cjhm+dv4prCLynAhmP4J4Q37GR1LPkDXrvOh4iIiDs5XSZfTrmfawq/xImBvd/rKh4VkMqHiIiUC6ZpsmD6M1x3cjoA+zs/QUyHQdaGEo9Q+RARkXLhk/nv03/fkwCkNb6den3utjiReIrKh4iIWG5Z6nK6r08h0HCyO7oPDYc9Y3Uk8SCVDxERsdT3GzbTbNlthBoF7KvWlgZjdC2Pik5/uyIiYpltew8SOvfm09fyCEogfuw8XcujElD5EBERSxzMOknu28Noauwj268GNcboWh6VhcqHiIh4XXZ+ITtfH0FHcxOnqIL/iNnYatW3OpZ4icqHiIh4VWGxk68nj6VHydeU4E/BoOlUr9fB6ljiRSofIiLiNU6XyadT/k7/U3MBONrjOWq26WNxKvE2lQ8REfEK0zSZP+MFhhyfAsDeyx8ktusoa0OJJVQ+RETEKz5fMJP+6U8AsLvBLdTtP9HiRGIVlQ8REfG4r75aTLd1KQQZTnZH9aLBn18Ew7A6llhE5UNERDxqzfq1tFg+mmpGIenVL6f+mHd0EbFKTn/7IiLiMdvSdhM57yZqGXYOBDUgcew8jMBgq2OJxVQ+RETEI/YdPorr3RtJNDI46h9Frbs+wa9quNWxpBxQ+RAREbc7mp3LkTeH0oI07EYoVW9bQHBEHatjSTmh8iEiIm6VU1DEhldH0tm1lkKCcA57n2p1mlsdS8oRlQ8REXGbwmInS18ZzzVFSynBj5w//Y+IJldaHUvKGZUPERFxC6fL5JMp/2BQ/iwAMq96kqj2A60NJeWSyoeIiFwy0zT5aNoz3HD8NQD2tU2hTs87LU4l5ZXKh4iIXLIFs6Zy/f5JAKQ3HEnidY9anEjKM5UPERG5JAs/m0ufbQ8RYLjYE/sn6t38gq5eKuel8iEiIhctdcUyunw/jmCjmD01rqT+6Om6eqn8IX2HiIjIRflx7RqaLx1FqHGKvSGtqTf2Q/APtDqW+ACVDxERKbOtO3cQteD0ZdMPBtUnftzHGEEhVscSH6HyISIiZbL3wEECZw4hwcgk0782tcZ+in/VGlbHEh+i8iEiIhfscNZx7G8NphH7OWmEE3L7x9hq6LLpUjYqHyIickGO2fPYN+VG2pjbySMEY8RcqtVubHUs8UEqHyIi8odyChxsnDycJOePOAii8Ib3CK/fzupY4qNUPkRE5LwKHCV8/fIYehQtpwQ/TvZ7g8gWV1sdS3yYyoeIiJxTUYmLha+m0O/UAgAyuv+XmA6DLE4lvk7lQ0REzsrpMvn49Ye53v42APs7PEJc99ssTiUVgcqHiIj8jmmazHvrSYZkTQYgveU9JPSbYHEqqShUPkRE5Hfmv/cKgw48BcCehqOoN/hfFieSikTlQ0REzvDpR9P5067H8DdMdscPpv6fX9CN4sStVD5ERKTUl59/RPLGCQQaTtKietPg1v+peIjbqXyIiAgAqcsXcsXqvxBsFJNW4yoa3vke+PlbHUsqIJUPERFh9aoVtPlqNNWMQvZUu5wGf5mtO9SKx6h8iIhUcmvW/Uj9hcMJN/LZG9yMuuMWYARWsTqWVGAqHyIildiGLZuJmT+MWoadA0H1qTP+M/yqhFodSyo4lQ8RkUpqy840Qj8cQh0ji4yAOkT95XMCq9W0OpZUAiofIiKV0I69+/GfeT31jCNk+UcRfudCbOG1rY4llYTKh4hIJbP7YAaO6YNpyj5O+tWg6u2fElwr0epYUomofIiIVCIHMo6RM3UgrdlJrlGNgFELCKndxOpYUsmofIiIVBKHj50g842BXGZuI5+quIbPp3pCG6tjSSWk8iEiUgkcPWnnwGuDae/axCmCcQybTViDDlbHkkpK5UNEpII7kZPP7sk30Mm5lgJsnLrhAyKaXml1LKnEylw+VqxYQf/+/YmNjcUwDObPn3/G86NGjcIwjDOmPn36uCuviIiUgT2/gK2v3EBSyWoKCSJn4DtEtrja6lhSyZW5fOTn59OmTRsmT558zmX69OnDkSNHSqf333//kkKKiEjZ5Rc42PDSMK4s+pYiAjjxp7eIbtvb6lgiBJT1BX379qVv377nXcZmsxETE3PRoURE5NIUOIr5/qXhXO34imL8yez9OvHt+1sdSwTw0DEfX331FVFRUTRp0oSxY8dy/Pjxcy7rcDjIyck5YxIRkYtXWFTCty+N5OqCL3GaBod6vEx80hCrY4mUcnv56NOnDzNmzGDp0qU89dRTpKam0rdvX5xO51mXnzRpEmFhYaVTfHy8uyOJiFQahUUlfPXS7STnf4bLNNjX7QXqdv2z1bFEzmCYpmle9IsNg3nz5jFw4MBzLrNnzx4aNGjAkiVL6Nmz5++edzgcOByO0sc5OTnEx8djt9sJDdXNjURELlRhUQlLXx5Lv9wPAUjv8jT1rrnT4lRSWeTk5BAWFnZBn98eP9W2fv36REZGkpaWdtbnbTYboaGhZ0wiIlI2jhIniybf+0vx6PSEioeUWx4vHwcPHuT48ePUrq0bFomIeEJRiYsvXrmX6+zvAJDe/hHq9b3H4lQi51bms13y8vLO2IuRnp7O+vXriYiIICIigscff5zBgwcTExPD7t27+dvf/kbDhg3p3Vund4mIuFux08XCyfdyXfYMAPZe9iD1/jTB4lQi51fm8vHjjz9y9dW/XKAmJSUFgJEjR/Laa6+xceNG3n77bbKzs4mNjaVXr1488cQT2Gw296UWERGKnS6+mHwf1518G4A9bR+k/nV/tziVyB+7pANOPaEsB6yIiFRWJU4XC19Nof/xaQDsbvs3Ggz8h8WppDIrVwecioiIe5U4XSx69f7S4pHW5gEVD/EpKh8iIj7E6TL5csoE+h1/C4BdrSbQcNDDFqcSKRuVDxERH+F0mXz52v1cmzUVgB0t76fR4EcsTiVSdiofIiI+wOky+eK1CfQtLR4pNBnyqMWpRC6OyoeISDlX4nTx+asT6Jf1JgDbW6bQZMhjFqcSuXhlPtVWRES8p9jpYuGrExhw/PQej50tU2iq4iE+Tns+RETKqWKni88mP1BaPHa1SqGxiodUANrzISJSDhUVO1n46n0M/OkCYrtb3UejwSoeUjGofIiIlDOO4hKWvHI319lnArCnzQQaDNJZLVJxqHyIiJQjhUUlLH/lLvrlzAZg92UTaXDdQxanEnEvlQ8RkXKisKiE1JdG0zdvPgC7OzxGg34p1oYS8QCVDxGRcqDAUcx3L42kd/5nuEyD9M7/pkHf8VbHEvEIlQ8REYudKnTw/UvD6XnqS1ymwd4rn6bBNXdYHUvEY1Q+REQslFdQyLoXh9G9cDklph/7uz1H/R63Wh1LxKNUPkRELGLPPcXmV27gKsc3FJv+7L/6JRp0H251LBGPU/kQEbHAcXsuO14ZQpfiVRTjz6Fer9Ogyw1WxxLxCpUPEREvyzyRTfqrg7mi5EccBHK075vU7TTQ6lgiXqPyISLiRYeOHuPQ64Pp7FxPIUGcHPA28Zdfa3UsEa9S+RAR8ZK9h45gf3MgHc3tnCKYvOvfo3brZKtjiXidyoeIiBfs3ruXoumDaMMecgmhaOgsoppdZXUsEUuofIiIeNj2nTsImjmIZhwi2wjDHDGXmvXbWx1LxDIqHyIiHrRp80ZqzBlMHEc55hdJ0K0fExrfwupYIpZS+RAR8ZB1a1cRu2AY0cZJMvxrU23MZ1SLaWB1LBHLqXyIiHjAD98tp8GiEUQYuRwMSCTiL59TNSLO6lgi5YKf1QFERCqar5d+QtNFNxFh5JIe1JjIu5eqeIj8ivZ8iIi40ZJPP+CKH+6hquEgrUprEu/+hMCq4VbHEilXVD5ERNzANE0WznmTnpsfwmaUsLN6ZxqOn4ufLcTqaCLljsqHiMglMk2Tj2c8R789/ybAcLEzogeNxr6PERhsdTSRcknlQ0TkEpQ4XXz+xiNcl/kKGLCzdn8a3z4d/PXjVeRc9K9DROQiFRaVsPzV8QzIfh+AnfVG0HjES+CnY/lFzkflQ0TkIuQVOPj+lZH0zV8IwM6WKTQe/CgYhsXJRMo/lQ8RkTI6ac9h2+Sh9Cj6DqdpkN753zTuO97qWCI+Q+VDRKQMMo9mcfj1QVzh3EQRARxKfoWGV91kdSwRn6LyISJygfbu24tj+iAuM/eQTzAn+79NvXZ9rI4l4nNUPkRELsDWrZsI+fAGmnCEk4ThGDaLuKZJVscS8UkqHyIif+CH1d+Q8Plwoo2TZPpFEThqATEJza2OJeKzVD5ERM7jqyWfcNnXdxJm5HMwIJEad31KSGSC1bFEfJrKh4jIWZimyRdzp9F944NUMYpIr9KCOn/5hKDqNa2OJuLzVD5ERH7D5TL5fPr/0XffM/gbJmlhSdQfOwe/4GpWRxOpEFQ+RER+xVFcwrIp9/Gn4zPAgB21r6PJ7VPBP9DqaCIVhsqHiMhPcvNPsWbySPqe+hKA7Y3H0vSmSbpqqYibqXyIiABZx4+zb8oNdC9eg9M02N3pCZpee7fVsUQqJJUPEan09u/fy6np19PetZtCgsjo9RqNuwyxOpZIhaXyISKV2uaNawmbO4ymZJJNKKeGzKRuy6usjiVSoal8iEil9V3qFzRddjsRRi5H/GKwjZpPbEIzq2OJVHgqHyJS6ZimyZfz36br+geoYhSxN6gxUXctoGpErNXRRCoFlQ8RqVScLpPPp/0f1+4/fQ2PndU702DcHPyDq1sdTaTSUPkQkUqjwFHMilf/Qn/7hz9dw2MAjUdPxQgIsjqaSKXiV9YXrFixgv79+xMbG4thGMyfP/+M503T5NFHH6V27dpUqVKF5ORkdu3a5a68IiIX5djJk6z77wB62z8EYEeze2hyxwwVDxELlLl85Ofn06ZNGyZPnnzW559++mleeuklpkyZwurVqwkJCaF3794UFhZeclgRkYuxd186R1++hiuKvqOIAPZc9TxNhj6hi4eJWKTMv3bp27cvffv2Petzpmnywgsv8PDDD3PdddcBMGPGDKKjo5k/fz7Dhg27tLQiImW0cd1qai74M83Jwk518q+fQf3WPayOJVKplXnPx/mkp6eTkZFBcnJy6bywsDA6derEypUrz/oah8NBTk7OGZOIiDusXPwRdecPpA5ZHPaPxXXbl8SqeIhYzq3lIyMjA4Do6Ogz5kdHR5c+91uTJk0iLCysdIqPj3dnJBGphEzTZMl7z9L+mzGEGqdIC25JxD2p1EhobnU0EcHN5eNiTJw4EbvdXjodOHDA6kgi4sMKi4pZ/Mp4knc9QaDhZHPENdRLWUJwWJTV0UTkJ2491TYmJgaAzMxMateuXTo/MzOTtm3bnvU1NpsNm83mzhgiUkkdy7az/bUR9HKkArC54R20vPkp8LP8/1ki8itu/RdZr149YmJiWLp0aem8nJwcVq9eTVJSkjvfSkTkDGl79nDkpWu40pFKMf6kJT1Fy+HPqHiIlENl3vORl5dHWlpa6eP09HTWr19PREQECQkJ3Hvvvfz73/+mUaNG1KtXj0ceeYTY2FgGDhzoztwiIqV+WJVKnYW30dA4Ri4h5F73Fg0v62N1LBE5hzKXjx9//JGrr7669HFKSgoAI0eOZPr06fztb38jPz+fO+64g+zsbK688kq++OILgoOD3ZdaROQny+ZNpdP6iYQYDg771yFk1EfExuvmcCLlmWGapml1iF/LyckhLCwMu91OaGio1XFEpJwqKXGyfOpEeh5+Az/DZGdIe+reNZug6hFWRxOplMry+a17u4iIz7Hn5rLptZFcc2opGLClzo00v3WyLpUu4iNUPkTEpxzYl07ujKFc6dxBienHznaP0GJAitWxRKQMVD5ExGesXZ1K7YW30pzj2KnGiX5v0LxjP6tjiUgZqXyISLlnmiZL5k6ly8a/U9VwcNA/jiq3zKZeoq5YKuKLVD5EpFwrLCph2f8e4tqs/4EBO0Pakzh2NrZqOrBUxFepfIhIuZV17Dg737iFa4u+AWBL/E00H/Uyhn+gxclE5FKofIhIubR1ywaC5gyni7mfYvzZ2+lftOg73upYIuIGKh8iUu5888UsWq68j3Ajn+NGDYoHT6dRy+5WxxIRN1H5EJFyo6TEyVfTH+XqA5PxN0z22JoRPWY2NSPjrY4mIm6k8iEi5UK2PZttr48i+dRyMGBzVH+a3/4mfkG6NYNIRaPyISKW27ljM3wwnCQznWLTnx1t/0HLgSlgGFZHExEPUPkQEUt9vWgOLb+7lxpGLicII2fAm7Rs18vqWCLiQSofImKJomInS6c/xjUHJxNguEgPakTN2+ZQN6au1dFExMNUPkTE644eO8au/91KX8cKMGBrrX40vf1N/GxVrY4mIl6g8iEiXrVx3fdUXzCKLhyiGH/2XDaR5gMm6PgOkUpE5UNEvMI0Tb6a+zodNz5KiOEgy6hJyfXTaNKqm9XRRMTLVD5ExOMKCgpZ/cZfuPrkR6fvz1L1MuLGvE/VGrWtjiYiFlD5EBGP2rc3jbx3htPduQ2ADXVvo/WIp3V/FpFKTOVDRDxm5dL5NF5xD4mGnTyqcqj787TpPszqWCJiMZUPEXE7R3EJqdMeoeeh1/A3TPYG1KPaiPdpktjM6mgiUg6ofIiIWx06fIgD02+jV9Gq05dJj+xL09vfJCC4mtXRRKScUPkQEbf5/utFxC39C505RhEBpLd/hJb9/qrTaEXkDCofInLJikucrHj7Mbruf5VAw8kRv9r4D51OkyadrY4mIuWQyoeIXJKMjEMcfGsUPYu+BwO21OhJo9FTCapWw+poIlJOqXyIyEVb9+0iYhb/hfYcw2EGsuvyf9BywL36NYuInJfKh4iUWVFxCd/MeJSu+18jwHBxyC8W48bptGzayepoIuIDVD5EpEwOHNxP5tu30qP4RzBgQ3gyTW6fSnC1cKujiYiPUPkQkQv2zZIFNPz6PtobxykkkLTLH6FN/3v0axYRKROVDxH5Q/mnClj51gP0yHoXP8PkkH8cAcNm0LJRO6ujiYgPUvkQkfPauX0jJR+OJtm18/RFw6IG0PTWyQRUCbU6moj4KJUPETkr0zRZ8dFk2m36N9WMAnKpypGuT9Gyxy1WRxMRH6fyISK/c+LEcXZOHUO3/KVgwE5bK6JGzaBx7fpWRxORCkDlQ0TOsObbRcQsHk9njlJi+rGx4V1cdvMTGP76cSEi7qGfJiICQEFhEd+9/Xe6HZ5KgOHiiBFFwYApXH55T6ujiUgFo/IhIuzYtgnHnDvo6dwKBmyqcQ2NbvsftavrEuki4n4qHyKVmNPpInXWc3Tc8QzVjELyCeZA0hO06jVG1+4QEY9R+RCppA4d3MeRd8bQw7EaDEgLbkWtEW/RtE5jq6OJSAWn8iFSyZimyarP3qbJDw/T3silyAxge/O/0mrI33VQqYh4hX7SiFQiJ45nsWP6OJJyF4EBewPqEXzjm7Ru3N7qaCJSiah8iFQS3y+bT/yK+0niGE7TYH3CSNqOeAr/oGCro4lIJaPyIVLBncy2s/7t+7n65GwADvvFUNhvMu3aJVucTEQqK5UPkQrs+xULiV6WwtUcBmBD1CCajnoRW9Uwi5OJSGWm8iFSAdntdta9fT9dj8/BzzA5ZkSQk/wsbboMtjqaiIjKh0hFs/brT6m19H66kwEGbIzsR+NbXiYytKbV0UREAJUPkQrDbs9m89spdDnxEQBZRk2yk5+ldZfrLU4mInImlQ+RCuD75QuITX2ALmQCsDZyAM1ueYlaobo8uoiUPyofIj4s6/gxts5IoZt9AQCZRiQne/6Xy68caG0wEZHzUPkQ8UGmafLN5+/S8IfH6MZxANZHDaLpLc8TXU17O0SkfPNz9wr/+c9/YhjGGVPTpk3d/TYildbB/el8//QArvphPLU5ToZfNHv7zaTtX6YTrOIhIj7AI3s+WrRowZIlS355kwDtYBG5VE6nk+9mP0+bbf8lzjhFienH5oThtLz5/wioUt3qeCIiF8wjrSAgIICYmBhPrFqkUtq9dS2F88ZzVfEWMGB3YCOqXP8KbZt1tjqaiEiZeaR87Nq1i9jYWIKDg0lKSmLSpEkkJCScdVmHw4HD4Sh9nJOT44lIIj4pPz+fNe89SqdD07EZJZzCxvZmf+WyIX/D8A+0Op6IyEUxTNM03bnChQsXkpeXR5MmTThy5AiPP/44hw4dYvPmzVSv/vtdw//85z95/PHHfzffbrcTGhrqzmgiPsM0TVanfkZ06oPUMw8CsLlqR6JvfpVacY0sTici8ns5OTmEhYVd0Oe328vHb2VnZ5OYmMhzzz3H6NGjf/f82fZ8xMfHq3xIpXXo4H72vn8/XfK/BOAEYRy54nFaXDMKDMPacCIi51CW8uHxI0HDw8Np3LgxaWlpZ33eZrNhs9k8HUOk3CsqKmbl7Gdou/MVuhj5AGyIGkiT4f+lRWikxelERNzH4+UjLy+P3bt3M2LECE+/lYjP2rx6KUGLHqCbazcYkB7YgIABz9OmVTero4mIuJ3by8eECRPo378/iYmJHD58mMceewx/f39uuukmd7+ViM/LyjxM2vsP0OnkZ/gZJrlUZXer+2gzMAXDX6eoi0jF5PafbgcPHuSmm27i+PHj1KpViyuvvJJVq1ZRq1Ytd7+ViM8qKi5h5ZwXaL39BZKMXDBgbY0+NLj5v7StFWd1PBERj3J7+fjggw/cvUqRCmXtd4sJXvJ3url2ggF7/RMp6fMsl3foZXU0ERGv0H5dES85tG83+2Y/yBV5iwHIJ5hdze+m9aC/4RcYZHE6ERHvUfkQ8bBTp/JY8/6/uXz/W9QxTp9WvqHmtdQf9hRta5394nsiIhWZyoeIh7icLn784m3ifvg/ruIoGLArqBnB/Z+mTauuVscTEbGMyoeIB2xZ+w2uhRPpWLwRgKPU5EjHh2jd53YMP7ffTFpExKeofIi40YH96eyd8zBX2D/D3zApNAPZmDCS1sMeIypEV+wVEQGVDxG3sGefZMOsJ2h3+F3iDQcYsDGsB7E3Pk3HOroXi4jIr6l8iFyCoqIifpj7Ik22v0xX7GBAWlBTgvr+h9aXJVsdT0SkXFL5ELkIpsvF2sUziVw1iS4/3XX2kF8M2Vf8nRY9b9EN4EREzkPlQ6SMNqxaQsCSR2lXsgWAbKqT1mwcbQfdR52gYIvTiYiUfyofIhdo+6YfsH/+OJ0KvgY4fTBp3M00v/GftA+LsDidiIjvUPkQ+QN707aSseAxOuQsxt8wcZkGGyP7kjD4P3SMrW91PBERn6PyIXIOmYfS2fPRP2l3/BPqGk4wYFP1q6jV/3HaNm5ndTwREZ+l8iHyG1mZh9j10RNcnjmHJKMYDNhSpR3V+vyTVm10ZVIRkUul8iHyk6yjmWyd9yTtD8/kCqMQDNge2Bwj+VFadOprdTwRkQpD5UMqvayjR9g27ykuO/wB3YwCMGB3QEMKr5pI86uu1+XQRUTcTOVDKq1jmYfZMW8SbY98SNef9nTs908k74oHaHb1n1U6REQ8ROVDKp2sI/vZNf9J2mTMoctPl0JPD6jPqaT7aX71TRh+/lZHFBGp0FQ+pNI4uH8Pez9+knZZ87jCKDp9KfSAhhQkTaDl1UO1p0NExEtUPqTC271tPRlfPEP77C+IM0rAgJ2BTSi4YgKtuw1R6RAR8TKVD6mwtv24jPxlz3F5/jc0MMzTpcPWArPbgzRJGqD7r4iIWETlQyoUl9PFptSPCFj1Ei2KNp6eacCmkCsI6Xk/jS/XnWZFRKym8iEVQmFhIWs/f4voza/TxrUXgGLTnw0RvYjp8zdaNbnc2oAiIlJK5UN8WtbRw2z/9BUa7f+AKzgOQL4ZzKaYQdTr/wDt4xpYnFBERH5L5UN80p6tP3B08Yu0ObGIq4wiAI4TTnqD4TTtfx+dwyMtTigiIuei8iE+w+l0smH5bAJ/mEIrxzrqAxiwJ6ABuW3H0OKaUbS3VbE6poiI/AGVDyn3jh/PYsvC16m7+10uN48A4DQNNla/iqpXjadJx146c0VExIeofEi5ZJom29euIHvFFNpkL6Wr4QAgl6psqT2IhN5/5bK6TSxOKSIiF0PlQ8qVU3l2tix6i/Ct79LMmXZ6pgH7/BM53nwEzfvcQeeQMGtDiojIJVH5EMuZpsnOjas5seJ1Wh5bSAejAIAiM4BN4VcTduWdNGyfTKJ+tSIiUiGofIhl7Cey2LJ4OjV2zqaZc8fpmQYcNGI4UH8YzfrcRbtata0NKSIibqfyIV7lKilh68pPKfx+Bi1zVnCFUQxAienHlupXEtD5dpol/Yk4f91ZVkSkolL5EK/Yt2sjh756i/qHPqElx07PNGCvXwLHGg2hcfLttKlVx9qQIiLiFSof4jFZmQfZuexdwncvoEXJVhJ/mp9DCNtq9iLyqtto0OYq6upYDhGRSkXlQ9wqP+cE25a/T+C2ebQoWEMXwwWAyzTYWrU9Ra1uovnVw+hUJcTipCIiYhWVD7lk+fl5bF3xEX6b59AibyXtfzqOAwPSAhqS3WAADa4eScuYupbmFBGR8kHlQy5Kfm42W7+eh7n1Y5rnriw9PRYD9huxHIr/EwldR9CwYWtrg4qISLmj8iEXzH4yi7Sv5+C34xOa5X1Ph1/t4Thq1GRvTG8iOv+ZBq2uIMHPz9qwIiJSbql8yHkd2pdG+sp5hKYvpFnhetoZztNPGHDIiOZQTDI1OwyhfttuRPnp9FgREfljKh9yBmdJCbvWLufk+k+IykilgWsvpSfAGpDul8iR2GSiOt1AgxadqKM9HCIiUkYqH0LW0cOkr/wYv92LaZiziqbklT7nMg3SgppiT+hJ7c5DqdeoNfUszCoiIr5P5aMSKjyVx64fl5C7bQmRR1fSsGQ3tQyz9PkcM4SdoZ0wGveiUdIgGkfGWJhWREQqGpWPSqC4uJi0Dd9g37KY6oe/pWHhFlr9fLAonL7SqH8iGdHdqNbyWhq370n7oCDrAouISIWm8lEBOQrz2b3+a7K3r6Bqxg/UL9hMM+PULwsYcJQI9oV3xK9+d+p1vJa6MYnUtSyxiIhUJiofFUDW0SMc2PAVRenfEZ71I/WLdtLcKPllAQNyqUpayGUUxl1FzGV9qNu4DVE6WFRERCyg8uFjCvNz2Ld5JSfTVhGQsZ6YvK3EmRnU+vVCBhwjnP3V2lAS14moFleT0KwDlwUEWhVbRESklMpHOWY/cZQD237Evm8DgZnrqZW7jQTnfpr86uDQnx30q8ORsLa44jsT3aoHiQ2aE6k9GyIiUg6pfJQHJQ44ngaZW9i1aTWFBzcSVbCbaI4T9ttlDcgkgkNVm1EU1Ybq9TuS0KoLcTWiiLMiu4iISBl5rHxMnjyZZ555hoyMDNq0acPLL79Mx44dPfV25Z/LBTmHTpeM42lwfDcc33X6z9n7wTx999dGv3nZEaMWWVUaUFizBUEJ7YlpfgXRsYlE6zb0IiLiozxSPmbNmkVKSgpTpkyhU6dOvPDCC/Tu3ZsdO3YQFRXlibe0nrMEcg9D9gGw/zRlHwD7wV/+XFJw7tfbQimp1YwP9lZju5nAjf36kNisPbVrRFLbe1shIiLicYZpmr8/gOASderUiQ4dOvDKK68A4HK5iI+P5+677+ahhx4672tzcnIICwvDbrcTGhrq7mgXxjSh+BQU5kCh/Zfp1HHIPwp5P02//vOp48AfDKVfIETUg5oNoWaDn77+NFWL5mieg47/WYphwJ7/uxZDezdERMRHlOXz2+17PoqKilizZg0TJ04snefn50dycjIrV6783fIOhwOHw1H6OCcnx92RflrxEUh9CkoKf5ocp78W/+px8Slw/FQ4XCV/vM7f8guEsDgIj4ewn6bwX39NAP9zD7mj+PSvXmwBfioeIiJSYbm9fBw7dgyn00l0dPQZ86Ojo9m+ffvvlp80aRKPP/64u2P8XlEerJlWttcY/hAc9tMUClVqQLVoCKkF1aLO/HNI1Ok/X8IZJo6S03eMDQ7U3WFFRKTisvxsl4kTJ5KSklL6OCcnh/j4ePe/UUgkdP87BAZDwK8nGwRWOf01IPiXsmELhaAQ8OIeiLAqQdzToyEB/jpFVkREKi63l4/IyEj8/f3JzMw8Y35mZiYxMb+/QZnNZsNms7k7xu9VqQHdH/T8+1yCWtVtpPRqYnUMERERj3L7f7GDgoJo164dS5cuLZ3ncrlYunQpSUlJ7n47ERER8TEe+bVLSkoKI0eOpH379nTs2JEXXniB/Px8br31Vk+8nYiIiPgQj5SPoUOHkpWVxaOPPkpGRgZt27bliy+++N1BqCIiIlL5eOQ6H5eiXFznQ0RERMqkLJ/fOq1CREREvErlQ0RERLxK5UNERES8SuVDREREvErlQ0RERLxK5UNERES8SuVDREREvErlQ0RERLxK5UNERES8yiOXV78UP19wNScnx+IkIiIicqF+/ty+kAunl7vykZubC0B8fLzFSURERKSscnNzCQsLO+8y5e7eLi6Xi8OHD1O9enUMw7jo9eTk5BAfH8+BAwd0jxgP01h7l8bbezTW3qOx9h5PjbVpmuTm5hIbG4uf3/mP6ih3ez78/PyIi4tz2/pCQ0P1jewlGmvv0nh7j8baezTW3uOJsf6jPR4/0wGnIiIi4lUqHyIiIuJVFbZ82Gw2HnvsMWw2m9VRKjyNtXdpvL1HY+09GmvvKQ9jXe4OOBUREZGKrcLu+RAREZHySeVDREREvErlQ0RERLxK5UNERES8yqfKx+TJk6lbty7BwcF06tSJ77///rzLz549m6ZNmxIcHEyrVq34/PPPz3jeNE0effRRateuTZUqVUhOTmbXrl2e3ASf4c6xLi4u5sEHH6RVq1aEhIQQGxvLLbfcwuHDhz29GT7B3d/Xv3bXXXdhGAYvvPCCm1P7Jk+M9bZt2xgwYABhYWGEhITQoUMH9u/f76lN8BnuHuu8vDzGjx9PXFwcVapUoXnz5kyZMsWTm+AzyjLWW7ZsYfDgwdStW/e8PxvK+vdXZqaP+OCDD8ygoCDzrbfeMrds2WKOGTPGDA8PNzMzM8+6/Lfffmv6+/ubTz/9tLl161bz4YcfNgMDA81NmzaVLvPkk0+aYWFh5vz5880NGzaYAwYMMOvVq2cWFBR4a7PKJXePdXZ2tpmcnGzOmjXL3L59u7ly5UqzY8eOZrt27by5WeWSJ76vfzZ37lyzTZs2ZmxsrPn88897eEvKP0+MdVpamhkREWE+8MAD5tq1a820tDRzwYIF51xnZeGJsR4zZozZoEEDc/ny5WZ6err5+uuvm/7+/uaCBQu8tVnlUlnH+vvvvzcnTJhgvv/++2ZMTMxZfzaUdZ0Xw2fKR8eOHc1x48aVPnY6nWZsbKw5adKksy5/4403mv369TtjXqdOncw777zTNE3TdLlcZkxMjPnMM8+UPp+dnW3abDbz/fff98AW+A53j/XZfP/99yZg7tu3zz2hfZSnxvrgwYNmnTp1zM2bN5uJiYkqH6Znxnro0KHm8OHDPRPYh3lirFu0aGH+61//OmOZyy+/3PzHP/7hxuS+p6xj/Wvn+tlwKeu8UD7xa5eioiLWrFlDcnJy6Tw/Pz+Sk5NZuXLlWV+zcuXKM5YH6N27d+ny6enpZGRknLFMWFgYnTp1Ouc6KwNPjPXZ2O12DMMgPDzcLbl9kafG2uVyMWLECB544AFatGjhmfA+xhNj7XK5+Oyzz2jcuDG9e/cmKiqKTp06MX/+fI9thy/w1Pf1FVdcwccff8yhQ4cwTZPly5ezc+dOevXq5ZkN8QEXM9ZWrPNsfKJ8HDt2DKfTSXR09Bnzo6OjycjIOOtrMjIyzrv8z1/Lss7KwBNj/VuFhYU8+OCD3HTTTZX6BlKeGuunnnqKgIAA7rnnHveH9lGeGOujR4+Sl5fHk08+SZ8+ffjyyy8ZNGgQ119/PampqZ7ZEB/gqe/rl19+mebNmxMXF0dQUBB9+vRh8uTJdO3a1f0b4SMuZqytWOfZlLu72krFVlxczI033ohpmrz22mtWx6lw1qxZw4svvsjatWsxDMPqOBWay+UC4LrrruO+++4DoG3btnz33XdMmTKFbt26WRmvwnn55ZdZtWoVH3/8MYmJiaxYsYJx48YRGxv7u70mUv75xJ6PyMhI/P39yczMPGN+ZmYmMTExZ31NTEzMeZf/+WtZ1lkZeGKsf/Zz8di3bx+LFy+u1Hs9wDNj/fXXX3P06FESEhIICAggICCAffv2cf/991O3bl2PbIcv8MRYR0ZGEhAQQPPmzc9YplmzZpX6bBdPjHVBQQF///vfee655+jfvz+tW7dm/PjxDB06lGeffdYzG+IDLmasrVjn2fhE+QgKCqJdu3YsXbq0dJ7L5WLp0qUkJSWd9TVJSUlnLA+wePHi0uXr1atHTEzMGcvk5OSwevXqc66zMvDEWMMvxWPXrl0sWbKEmjVremYDfIgnxnrEiBFs3LiR9evXl06xsbE88MADLFq0yHMbU855YqyDgoLo0KEDO3bsOGOZnTt3kpiY6OYt8B2eGOvi4mKKi4vx8zvzI8vf3790D1RldDFjbcU6z8pth6562AcffGDabDZz+vTp5tatW8077rjDDA8PNzMyMkzTNM0RI0aYDz30UOny3377rRkQEGA+++yz5rZt28zHHnvsrKfahoeHmwsWLDA3btxoXnfddTrV1nT/WBcVFZkDBgww4+LizPXr15tHjhwpnRwOhyXbWF544vv6t3S2y2meGOu5c+eagYGB5htvvGHu2rXLfPnll01/f3/z66+/9vr2lSeeGOtu3bqZLVq0MJcvX27u2bPHnDZtmhkcHGy++uqrXt++8qSsY+1wOMx169aZ69atM2vXrm1OmDDBXLdunblr164LXqc7+Ez5ME3TfPnll82EhAQzKCjI7Nixo7lq1arS57p162aOHDnyjOU//PBDs3HjxmZQUJDZokUL87PPPjvjeZfLZT7yyCNmdHS0abPZzJ49e5o7duzwxqaUe+4c6/T0dBM467R8+XIvbVH55e7v699S+fiFJ8Z66tSpZsOGDc3g4GCzTZs25vz58z29GT7B3WN95MgRc9SoUWZsbKwZHBxsNmnSxPzvf/9rulwub2xOuVaWsT7Xz+Nu3bpd8DrdwTBN03TffhQRERGR8/OJYz5ERESk4lD5EBEREa9S+RARERGvUvkQERERr1L5EBEREa9S+RARERGvUvkQERERr1L5EBEREa9S+RARERGvUvkQERERr1L5EBEREa9S+RARERGv+n/z57usewiMoQAAAABJRU5ErkJggg==", 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pdf.x, pdf.pdfx)\n", + "plt.plot(pdf2.x, pdf2.pdfx)" + ] + }, + { + "cell_type": "code", + "execution_count": 58, + "id": "38087a45-7d78-4d83-96b1-d4cb6fc2e79b", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 58, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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", + "text/plain": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.plot(pdf.x, pdf.cdf)\n", + "plt.plot(pdf2.x, pdf2.cdf)" + ] + }, + { + "cell_type": "code", + "execution_count": 55, + "id": "27362eb8-5ec6-4514-9aca-42bce7b61ded", + "metadata": {}, + "outputs": [ + { + "data": { + "image/png": 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" + ] + }, + "metadata": {}, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(pdf.draw(10000000), bins=500)\n", + "plt.hist(pdf2.draw(10000000), bins=500, histtype='step');" + ] + }, + { + "cell_type": "code", + "execution_count": 62, + "id": "0c150651-97e0-4b6b-bf6c-34729fe362f7", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3.45704207e-03, 4.10371336e-01, 1.46706705e+00, 3.13588799e+00,\n", + " 5.38069438e+00, 8.16680786e+00, 1.14609591e+01, 1.52312376e+01,\n", + " 1.94470438e+01, 2.40790431e+01])" + ] + }, + "execution_count": 62, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "pdf2.pdfx[::1000]" + ] + }, + { + "cell_type": "code", + "execution_count": 63, + "id": "d905b559-d875-49ad-89ba-854a7a903af4", + "metadata": {}, + "outputs": [], + "source": [ + " test_array = np.array([\n", + " 3.45704207e-03, 4.10371336e-01, 1.46706705e+00, 3.13588799e+00,\n", + " 5.38069438e+00, 8.16680786e+00, 1.14609591e+01, 1.52312376e+01,\n", + " 1.94470438e+01, 2.40790431e+01\n", + " ])" + ] + }, + { + "cell_type": "code", + "execution_count": 64, + "id": "44dd46b1-06dd-4921-b3e3-515dd133b560", + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "array([3.45704207e-03, 4.10371336e-01, 1.46706705e+00, 3.13588799e+00,\n", + " 5.38069438e+00, 8.16680786e+00, 1.14609591e+01, 1.52312376e+01,\n", + " 1.94470438e+01, 2.40790431e+01])" + ] + }, + "execution_count": 64, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "test_array" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "snsim_dev", + "language": "python", + "name": "snsim_dev" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.13" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/docs/astrobj.rst b/docs/astrobj.rst new file mode 100644 index 0000000..d8a3a5e --- /dev/null +++ b/docs/astrobj.rst @@ -0,0 +1,39 @@ +AstrObj +========= + +The AstrObj abstract class +--------------------------- + +`AstrObj` is an abstract class of objects that snsim used to model transients. + +The basics attributes of an AstrObj are: + ++--------------------+--------------------------------------------------+ +| :code:`ID` | Identification number, set to 0 by default | ++--------------------+--------------------------------------------------+ +| :code:`ra` | Right ascension [rad] | ++--------------------+--------------------------------------------------+ +| :code:`dec` | Declination [rad] | ++--------------------+--------------------------------------------------+ +| :code:`zcos` | Cosmological redshift | ++--------------------+--------------------------------------------------+ +| :code:`vpec` | Peculiar velocities [km/s] | ++--------------------+--------------------------------------------------+ +| :code:`zpcmb` | Redshift contribution from the CMB dipole motion | ++--------------------+--------------------------------------------------+ +| :code:`como_dist` | Comoving distance [Mpc] | ++--------------------+--------------------------------------------------+ + +Derived properties can be called: + + +* The peculiar redshift :code:`zpec`: :math:`z_p = v_p / c` +* The redshift corrected from CMB dipole :code:`zCMB`: :math:`z_\mathrm{CMB} = (1 + z_\mathrm{cos}) (1 + z_p) - 1` +* The observed redshift :code:`zobs`: :math:`z_\mathrm{obs} = (1 + z_\mathrm{cos}) (1 + z_{p,\mathrm{CMB}}) (1 + z_p) - 1` +* The distance modulus :code:`mu`: :math:`\mu = 5 \log_{10}((1 + z_\mathrm{cos}) (1 + z_{p,\mathrm{CMB}}) (1 + z_p)^2 r(z_\mathrm{cos}))` + + +Any model will add attributes depanding on what is needed to models the transient. +These new attributes are added through the definition of :code:`_obj_attrs`. + +During the :code:`__init__` call, \ No newline at end of file diff --git a/docs/index.rst b/docs/index.rst index e75c590..052fdc2 100644 --- a/docs/index.rst +++ b/docs/index.rst @@ -17,6 +17,7 @@ Welcome to snsim's documentation! installation.rst basicusage.rst configfile.rst + astrobj.rst obsfile.rst hostfile.rst snsample.rst diff --git a/setup.cfg b/setup.cfg index 3f72d4f..97e28b6 100644 --- a/setup.cfg +++ b/setup.cfg @@ -19,11 +19,13 @@ install_requires = shapely >= 1.8.0 numba >= 0.56 dask >= 2023 - sfdmap == 0.1.1 + sfdmap2 pyarrow matplotlib + scipy pyyaml requests + rtree [options.extras_require] docs = diff --git a/snsim/__init__.py b/snsim/__init__.py index 6051f5d..8f2f76a 100644 --- a/snsim/__init__.py +++ b/snsim/__init__.py @@ -22,3 +22,4 @@ from . import survey_host from . import utils from . import io_utils +from . import scatter diff --git a/snsim/astrobj.py b/snsim/astrobj.py index 8d6d4aa..f27a142 100644 --- a/snsim/astrobj.py +++ b/snsim/astrobj.py @@ -4,72 +4,114 @@ import abc import numpy as np import pandas as pd +import sncosmo as snc from .constants import C_LIGHT_KMS from . import utils as ut -class BasicAstrObj(abc.ABC): +class AstrObj(abc.ABC): """Basic class for transients. Parameters ---------- - parameters : dict - Parameters of the obj. - - | parameters - | ├── zcos, cosmological redshift - | ├── z2cmb, CMB dipole redshift contribution - | ├── como_dist, comoving distance of the obj - | ├── vpec, obj peculiar velocity - | ├── ra, obj Right Ascension - | ├── dec, obj Declinaison - | ├── sim_t0, obj peak time - | ├── dip_dM, magnitude modification due to a dipole - | └── sncosmo - | └── sncosmo parameters - sim_model : sncosmo.Model - sncosmo Model to use. - model_par : dict - General model parameters. - model_par - └── mod_fcov, boolean to use or not model flux covariance + sim_par: dict + Simulation parameters. + effect: list(snc.PropagationEffect) + Effects to apply to the model. + + + | sim_par + | ├── zcos, cosmological redshift + | ├── zpcmb, CMB dipole redshift contribution + | ├── como_dist, comoving distance of the obj + | ├── vpec, obj peculiar velocity + | ├── ra, obj Right Ascension + | ├── dec, obj Declinaison + | └── t0, obj peak time + | sncosmo """ _type = '' - _base_attrs = ['ID', 'coord', 'zcos', 'zCMB', 'zpec', - 'vpec', 'z2cmb', 'sim_mu', 'como_dist'] - - def __init__(self, parameters, sim_model, model_par): + _base_attrs = [ + 'ID', 'ra', 'dec', 'zcos', + 'vpec', 'zpcmb', 'como_dist', + 'model_name'] + + _obj_attrs = [''] + _available_models = [''] + + def __init__(self, sim_par, relation=None, effects=[]): + + # -- Copy input parameters dic + self._sim_par = copy.copy(sim_par) + + self._relation = relation + if 'ID' not in self._sim_par: + self._sim_par['ID'] = 0 + + # -- Check model name + if self.sim_par['model_name'] not in self._available_models: + raise ValueError(f"{self.sim_par['model_name']} not available.") + # -- Update attrs - self._base_attrs = self._base_attrs + self._attrs + for k in self._base_attrs: + setattr(self, k, self._sim_par[k]) # -- sncosmo model - self.sim_model = copy.deepcopy(sim_model) - - # -- Intrinsic parameters of the astrobj - self._params = parameters - self._model_par = model_par - self._update_model_par() + self._sim_model = self._init_model(effects) + + # -- Update attr of astrobj class + for k in [*self._obj_attrs, 'model_version']: + setattr(self, k, self._sim_par[k]) + + @abc.abstractmethod + def _set_model_par(self, model): + """This method set model parameters that are not t0 or z.""" + pass + + def _init_model(self, effects): + """Initialise sncosmo model using the good source. - if 'dip_dM' in self._params: - self.dip_dM = self._params['dip_dM'] + Returns + ------- + sncosmo.Model + sncosmo.Model(source) object where source depends on the + SN simulation model. - # -- set parameters of the sncosmo model - self._set_model() + """ + if 'model_version' not in self._sim_par: + version = None + else: + version = self._sim_par['model_version'] + + snc_source = snc.get_source( + name=self._sim_par['model_name'], + version=version) + + if 'model_version' not in self._sim_par: + self._sim_par['model_version'] = snc_source.version + + model = snc.Model( + source=snc_source, + effects=[eff['source'] for eff in effects], + effect_names=[eff['name'] for eff in effects], + effect_frames=[eff['frame'] for eff in effects]) + + for eff in effects: + for k in eff['source'].param_names: + if k in self._sim_par: + model.set(self._sim_par[eff['name'] + k]) - def _set_model(self): - # Set sncosmo model parameters - params = {**{'z': self.zobs, 't0': self.sim_t0}, - **self._params['sncosmo']} - self.sim_model.set(**params) + model.set( + t0=self._sim_par['t0'], + z=self.zobs) + + model = self._set_model_par(model) - @abc.abstractmethod - def _update_model_par(self): - """Abstract method to add general model parameters call during __init__.""" - pass + return model - def gen_flux(self, obs, seed=None): + def gen_flux(self, obs, mod_fcov=False, seed=None): """Generate the obj lightcurve. Parameters @@ -90,19 +132,22 @@ def gen_flux(self, obs, seed=None): else: random_seeds = np.random.default_rng(seed).integers(1e3, 1e6, size=2) - # Re - set the parameters - self._set_model() - + # -- Check for fcov + if mod_fcov: + if not hasattr(self.sim_model, 'bandfluxcov'): + raise ValueError('This sncosmo model has no flux covariance available') + # mask to delete observed points outside time range of the model obs = obs[(obs['time'] > self.sim_model.mintime()) & (obs['time'] < self.sim_model.maxtime())] - if self._model_par['mod_fcov']: + if mod_fcov: # -- Implement the flux variation due to simulation model covariance gen = np.random.default_rng(random_seeds[0]) - fluxtrue, fluxcov = self.sim_model.bandfluxcov(obs['band'], - obs['time'], - zp=obs['zp'], - zpsys=obs['zpsys']) + fluxtrue, fluxcov = self.sim_model.bandfluxcov( + obs['band'], + obs['time'], + zp=obs['zp'], + zpsys=obs['zpsys']) fluxtrue += gen.multivariate_normal(np.zeros(len(fluxcov)), fluxcov, @@ -110,23 +155,26 @@ def gen_flux(self, obs, seed=None): method='eigh') else: - fluxtrue = self.sim_model.bandflux(obs['band'], - obs['time'], - zp=obs['zp'], - zpsys=obs['zpsys']) + fluxtrue = self.sim_model.bandflux( + obs['band'], + obs['time'], + zp=obs['zp'], + zpsys=obs['zpsys']) # -- Noise computation : Poisson Noise + Skynoise + ZP noise - fluxerrtrue = np.sqrt(np.abs(fluxtrue) / obs.gain - + obs.skynoise**2 - + (np.log(10) / 2.5 * fluxtrue * obs.sig_zp)**2) + fluxerrtrue = np.sqrt( + np.abs(fluxtrue) / obs.gain + + obs.skynoise**2 + + (np.log(10) / 2.5 * fluxtrue * obs.sig_zp)**2) gen = np.random.default_rng(random_seeds[1]) flux = fluxtrue + gen.normal(loc=0., scale=fluxerrtrue) - fluxerr = np.sqrt(np.abs(flux) / obs.gain - + obs.skynoise**2 - + (np.log(10) / 2.5 * flux * obs.sig_zp)**2) + fluxerr = np.sqrt( + np.abs(flux) / obs.gain + + obs.skynoise**2 + + (np.log(10) / 2.5 * flux * obs.sig_zp)**2) - # Set magnitude + # -- Set magnitude mag = np.zeros_like(flux) magerr = np.zeros_like(flux) @@ -140,105 +188,35 @@ def gen_flux(self, obs, seed=None): mag[~positive_fmask] = np.nan magerr[~positive_fmask] = np.nan - # Create astropy Table lightcurve - sim_lc = pd.DataFrame({'time': obs['time'], - 'fluxtrue': fluxtrue, - 'fluxerrtrue': fluxerrtrue, - 'flux': flux, - 'fluxerr': fluxerr, - 'mag': mag, - 'magerr': magerr, - 'zp': obs['zp'], - 'zpsys': obs['zpsys'], - 'gain': obs['gain'], - 'skynoise': obs['skynoise']}) - + # -- Create DataFrame of the lightcurve + sim_lc = pd.DataFrame({ + 'time': obs['time'], + 'fluxtrue': fluxtrue, + 'fluxerrtrue': fluxerrtrue, + 'flux': flux, + 'fluxerr': fluxerr, + 'mag': mag, + 'magerr': magerr, + 'zp': obs['zp'], + 'zpsys': obs['zpsys'], + 'gain': obs['gain'], + 'skynoise': obs['skynoise'] + }) + + # TODO - BC: Maybe remove this "for loop" for k in obs.columns: if k not in sim_lc.columns: sim_lc[k] = obs[k].values - sim_lc.attrs = {**sim_lc.attrs, - **{'zobs': self.zobs, 't0': self.sim_t0}, - **self._params['sncosmo']} + sim_lc.attrs = {'mu': self.mu, + 'zobs': self.zobs, + 'zCMB': self.zCMB, + **self._sim_par} - sim_lc.reset_index(inplace=True) + sim_lc.reset_index(inplace=True, drop=True) sim_lc.index.set_names('epochs', inplace=True) - return self._reformat_sim_table(sim_lc) - - def _reformat_sim_table(self, sim_lc): - """Give the good format to the sncosmo output Table. - - Returns - ------- - None - - Notes - ----- - Directly change the sim_lc attribute - - """ - # Keys that don't need renaming - not_to_change = ['G10', 'C11', 'mw_'] - dont_touch = ['zobs', 'mw_r_v', 'fcov_seed'] - - for k in sim_lc.attrs.copy(): - if k not in dont_touch and k[:3] not in not_to_change: - sim_lc.attrs['sim_' + k] = sim_lc.attrs.pop(k) - - sim_lc.attrs['type'] = self._type - - for meta in self._base_attrs: - if meta == 'coord': - sim_lc.attrs['ra'] = self.coord[0] - sim_lc.attrs['dec'] = self.coord[1] - else: - attrs = getattr(self, meta) - if attrs is not None: - sim_lc.attrs[meta] = getattr(self, meta) - - if 'dip_dM' in self._params: - sim_lc.attrs['dip_dM'] = self.dip_dM - if 'template' in self._params: - sim_lc.attrs['template']= self._params['template'] - return sim_lc - @property - def ID(self): - """Get ID.""" - if 'ID' in self._params: - return self._params['ID'] - - @property - def sim_t0(self): - """Get peakmag time.""" - return self._params['sim_t0'] - - @property - def vpec(self): - """Get peculiar velocity.""" - return self._params['vpec'] - - @property - def zcos(self): - """Get cosmological redshift.""" - return self._params['zcos'] - - @property - def como_dist(self): - """Get comoving distance.""" - return self._params['como_dist'] - - @property - def coord(self): - """Get coordinates (ra,dec).""" - return self._params['ra'], self._params['dec'] - - @property - def mag_sct(self): - """Get coherent scattering term.""" - return self._params['mag_sct'] - @property def zpec(self): """Get peculiar velocity redshift.""" @@ -249,88 +227,103 @@ def zCMB(self): """Get CMB frame redshift.""" return (1 + self.zcos) * (1 + self.zpec) - 1. - @property - def z2cmb(self): - """Get redshift due to our motion relative to CMB.""" - return self._params['z2cmb'] - @property def zobs(self): """Get observed redshift.""" - return (1 + self.zcos) * (1 + self.zpec) * (1 + self.z2cmb) - 1. + return (1 + self.zcos) * (1 + self.zpec) * (1 + self.zpcmb) - 1. @property - def sim_mu(self): + def mu(self): """Get distance moduli.""" - return 5 * np.log10((1 + self.zcos) * (1 + self.z2cmb) * + return 5 * np.log10((1 + self.zcos) * (1 + self.zpcmb) * (1 + self.zpec)**2 * self.como_dist) + 25 + @property + def sim_model(self): + return self._sim_model + + @property + def sim_par(self): + return self._sim_par + -class SNIa(BasicAstrObj): +class SNIa(AstrObj): """SNIa class. Parameters ---------- - sn_par : dict + sim_par : dict Parameters of the SN. - - | same as BasicAstrObj parameters - | └── mag_sct, coherent mag scattering. + | same as AstrObj parameters + | └── coh_sct, coherent mag scattering. sim_model : sncosmo.Model sncosmo Model to use. - model_par : dict - General model parameters. - | same as BasicAstrObj model_par - | ├── M0, SNIa absolute magnitude - | ├── sigM, sigma of coherent scattering - | └── used model parameters + | same as AstrObj model_par + | ├── M0, SNIa absolute magnitude + | ├── sigM, sigma of coherent scattering + | └── used model parameters """ _type = 'snIa' _available_models = ['salt2', 'salt3'] - _attrs = ['sim_mb', 'mag_sct'] + _obj_attrs = ['M0', 'mb', 'coh_sct'] - def _update_model_par(self): - """Extract and compute SN parameters that depends on used model. + def _set_model_par(self, model): + """Set SN Ia parameters to sncosmo model. Notes ----- Set attributes dependant on SN model - SALT: - - alpha -> _model_par['alpha'] - - beta -> _model_par['beta'] - - mb -> self.sim_mb - - x0 -> self.sim_x0 - - x1 -> self.sim_x1 - - c -> self.sim_c """ - M0 = self._model_par['M0'] + self.mag_sct - if self.sim_model.source.name in ['salt2', 'salt3']: - self._params['template']=self.sim_model.source.name + M0 = self._sim_par['M0'] + self._sim_par['coh_sct'] + + if self._relation is None: + self._relation = 'salttripp' + + if self._relation.lower() == 'salttripp': + if model.source.name not in ['salt2', 'salt3']: + raise ValueError('SALTTripp only available for salt2 & salt3 models') + + self._obj_attrs.extend(['alpha', 'beta', 'x0', 'x1', 'c']) + # Compute mB : { mu + M0 : the standard magnitude} + {-alpha*x1 + # beta*c : scattering due to color and stretch} + {coherent intrinsic scattering} - alpha = self._model_par['alpha'] - beta = self._model_par['beta'] - x1 = self._params['sncosmo']['x1'] - c = self._params['sncosmo']['c'] - mb = self.sim_mu + M0 - alpha * x1 + beta * c #add mass step if you have host - - self.sim_x1 = x1 - self.sim_c = c - - if 'dip_dM' in self._params: - mb += self._params['dip_dM'] + self._sim_par['mb'] = self.SALTTripp( + M0, + self._sim_par['alpha'], + self._sim_par['beta'], + self._sim_par['x1'], + self._sim_par['c']) + self.mu + + else: + # TODO - BC : Find a way to use lambda function for relation + raise ValueError('Relation not available') + + if 'mass_step' in self._sim_par: + if 'host_mass' in self._sim_par: + if self._sim_par['host_mass'] > 10. : + mb += self._sim_par['mass_step'] + else: + raise ValueError('Provide SN host mass to account for the magnitude mass step') - self.sim_mb = mb + # Compute the x0 parameter + model.set_source_peakmag(self._sim_par['mb'], 'bessellb', 'ab') + self._sim_par['x0'] = model.get('x0') - # Compute the x0 parameter - self.sim_model.set(x1=self.sim_x1, c=self.sim_c) - self.sim_model.set_source_peakmag(self.sim_mb, 'bessellb', 'ab') - self.sim_x0 = self.sim_model.get('x0') - self._params['sncosmo']['x0'] = self.sim_x0 + model.set( + x1=self._sim_par['x1'], + c=self._sim_par['c']) + return model + + @staticmethod + def SALTTripp(M0, alpha, beta, x1, c): + return M0 - alpha * x1 + beta * c + @staticmethod + def SALTTrippBS20(M0, alpha, beta, RV, Edust, x1, c): + return M0 - alpha * x1 + beta * c + (RV + 1) * Edust -class TimeSeries(BasicAstrObj): +class TimeSeries(AstrObj): """TimeSeries class. Parameters @@ -338,64 +331,41 @@ class TimeSeries(BasicAstrObj): sn_par : dict Parameters of the object. - | same as BasicAstrObj parameters - | └── mag_sct, coherent mag scattering. + | same as AstrObj parameters + | └── coh_sct, coherent mag scattering. sim_model : sncosmo.Model sncosmo Model to use. model_par : dict General model parameters. - | same as BasicAstrObj model_par - | ├── M0, absolute magnitude - | ├── sigM, sigma of coherent scattering - | └── used model parameters + | same as AstrObj model_par + | ├── M0, absolute magnitude + | ├── sigM, sigma of coherent scattering + | └── used model parameters """ - _attrs = ['sim_mb', 'mag_sct'] + _obj_attrs = ['M0','amplitude', 'mb', 'coh_sct'] - def _update_model_par(self): + def _set_model_par(self, model): """Extract and compute SN parameters that depends on used model. Notes ----- Set attributes dependant on SN model - - - mb -> self.sim_mb + - mb -> self.mb - amplitude -> self.sim_amplitude - - Template -> self._params['template'] Sed template used - + - Template -> self._params['template'] SED template used """ - M0 = self._model_par['M0'] + self.mag_sct - self._params['M0'] = M0 - if self.sim_model.source.name in self._available_models: - self._params['template']=self.sim_model.source.name - m_r= self.sim_mu + M0 - - if 'dip_dM' in self._params: - m_r += self._params['dip_dM'] + M0 = self._sim_par['M0'] + self._sim_par['coh_sct'] - # Compute the amplitude parameter - self.sim_model.set_source_peakmag(m_r, 'bessellr', 'ab') - self.sim_mb = self.sim_model.source_peakmag( 'bessellb', 'ab') - self.sim_amplitude = self.sim_model.get('amplitude') - self._params['sncosmo']['amplitude'] = self.sim_amplitude + m_r = self.mu + M0 + + # Compute the amplitude parameter + model.set_source_peakmag(m_r, 'bessellr', 'ab') + self._sim_par['mb'] = model.source_peakmag('bessellb', 'ab') + self._sim_par['amplitude'] = model.get('amplitude') + return model - @property - def mag_sct(self): - """SN coherent scattering term.""" - return self._params['mag_sct'] - - @property - def M0(self): - """SN absolute magnitude in B-band""" - return self._params['M0'] - - @property - def template(self): - """sncosmo.Model source name """ - return self._params['template'] - - class SNII(TimeSeries): """SNII class. @@ -420,7 +390,7 @@ class SNIIb(TimeSeries): Parameters ---------- - same as TimeSeries class """ + same as TimeSeries class.""" _type = 'snIIb' _available_models = ut.Templatelist_fromsncosmo('sniib') @@ -429,7 +399,7 @@ class SNIIn(TimeSeries): Parameters ---------- - same as TimeSeries class """ + same as TimeSeries class """ _type = 'snIIn' _available_models = ut.Templatelist_fromsncosmo('sniin') @@ -443,12 +413,13 @@ class SNIbc(TimeSeries): _type = 'snIb/c' _available_models = ut.Templatelist_fromsncosmo('snib/c') + class SNIc(TimeSeries): """SNIIn class. Parameters ---------- - same as TimeSeries class """ + same as TimeSeries class """ _type = 'snIc' _available_models = ut.Templatelist_fromsncosmo('snic') @@ -458,7 +429,7 @@ class SNIb(TimeSeries): Parameters ---------- - same as TimeSeries class """ + same as TimeSeries class """ _type = 'snIb' _available_models = ut.Templatelist_fromsncosmo('snib') @@ -468,6 +439,6 @@ class SNIc_BL(TimeSeries): Parameters ---------- - same as TimeSeries class """ + same as TimeSeries class """ _type = 'snIc-BL' _available_models = ut.Templatelist_fromsncosmo('snic-bl') diff --git a/snsim/constants.py b/snsim/constants.py index 67c2768..5094cb1 100644 --- a/snsim/constants.py +++ b/snsim/constants.py @@ -34,6 +34,13 @@ #Value of h used in the various articles h_article = { 'jla' : 0.70, - 'li11': 0.73 + 'li11': 0.73, + 'sullivan06': 0.70 } +#value of fitted parameter of SNIa-Host_galaxy from Sullivan et al 2006 https://iopscience.iop.org/article/10.1086/506137/pdf +sullivan_para = { + 'mass': 5.3 * 1.e-14, + 'SFR': 3.9 * 1.e-4 + } + diff --git a/snsim/dust_utils.py b/snsim/dust_utils.py index ebc54b6..19ad227 100644 --- a/snsim/dust_utils.py +++ b/snsim/dust_utils.py @@ -2,7 +2,7 @@ import os import sncosmo as snc -import sfdmap +from sfdmap2 import sfdmap from snsim import __snsim_dir_path__ import glob import requests @@ -45,7 +45,7 @@ def check_files_and_download(): break -def init_mw_dust(model, mw_dust): +def init_mw_dust(mw_dust): """Set MW dut effect on sncosmo model. Parameters @@ -57,8 +57,8 @@ def init_mw_dust(model, mw_dust): Returns ------- - None - Directly modify the sncosmo model. + dict + Dust effects. """ f99_r_v = 3.1 @@ -73,7 +73,7 @@ def init_mw_dust(model, mw_dust): else: raise ValueError(f"{mw_dust['model']} model does not exist in sncosmo") - model.add_effect(dust, frame='obs', name='mw_') + return {'source': dust, 'frame': 'obs', 'name': 'mw_'} def add_mw_to_fit(fit_model, mwebv, mod_name, rv=3.1): diff --git a/snsim/generators.py b/snsim/generators.py index 0fc4440..c93e486 100644 --- a/snsim/generators.py +++ b/snsim/generators.py @@ -2,9 +2,12 @@ import abc import numpy as np import pandas as pd +import copy +import time import geopandas as gpd +import sncosmo as snc from inspect import getsource -from .constants import C_LIGHT_KMS +from .constants import C_LIGHT_KMS, VCMB, L_CMB, B_CMB from . import utils as ut from . import nb_fun as nbf from . import dust_utils as dst_ut @@ -14,17 +17,19 @@ from . import constants as cst -__GEN_DIC__ = {'snia_gen': 'SNIaGen', - 'timeseries_gen':'TimeSeriesGen', - 'snii_gen': 'SNIIGen', - 'sniipl_gen': 'SNIIplGen', - 'sniib_gen': 'SNIIbGen', - 'sniin_gen': 'SNIInGen', - 'snib/c_gen':'SNIbcGen', - 'snic_gen': 'SNIcGen', - 'snib_gen': 'SNIbGen', - 'snic-bl_gen': 'SNIc_BLGen'} +__GEN_DIC__ = { + 'snia_gen': 'SNIaGen', + 'timeseries_gen':'TimeSeriesGen', + 'snii_gen': 'SNIIGen', + 'sniipl_gen': 'SNIIplGen', + 'sniib_gen': 'SNIIbGen', + 'sniin_gen': 'SNIInGen', + 'snib/c_gen':'SNIbcGen', + 'snic_gen': 'SNIcGen', + 'snib_gen': 'SNIbGen', + 'snic-bl_gen': 'SNIc_BLGen' + } class BaseGen(abc.ABC): """Abstract class for basic astrobj generator. @@ -33,52 +38,42 @@ class BaseGen(abc.ABC): ---------- params : dict Basic generator configuration. - - | params - | ├── General obj parameters - | └── model_config - | └── General sncosmo model parameters + | params + | ├── General obj parameters + | └── model_config cmb : dict The CMB dipole configuration. - - | cmb - | ├── vcmb - | ├── l_cmb - | └── b_cmb + | cmb + | ├── v_cmb + | ├── l_cmb + | └── b_cmb cosmology : astropy.cosmology The astropy cosmological model to use. vpec_dist : dict The parameters of the peculiar velocity distribution. - - | vpec_dist - | ├── mean_vpec - | └── sig_vpec + | vpec_dist + | ├── mean_vpec + | └── sig_vpec host : class SnHost, opt The host class to introduce sn host. - dipole : dict, opt - The alpha dipole parameters. - - | dipole - | ├── coord list(ra, dec) dipole vector coordinates in ra, dec - | ├── A parameter of the A + B * cos(theta) dipole - | └── B B parameter of the A + B * cos(theta) dipole """ # General attributes + _object_type = '' _available_models = [] # Flux models _available_rates = {} # Rate models - - def __init__(self, params, cmb, cosmology, time_range, z_range=None, peak_out_trange=False, - vpec_dist=None, host=None, mw_dust=None, dipole=None, geometry=None): + + def __init__(self, params, cosmology, time_range, z_range=None, + vpec_dist=None, host=None, mw_dust=None, cmb=None, geometry=None): if vpec_dist is not None and host is not None: raise ValueError("You can't set vpec_dist and host at the same time") # -- Mandatory parameters - self._params = params - self._cmb = cmb + self._params = copy.copy(params) self._cosmology = cosmology self._time_range = time_range + # -- At least one mandatory if vpec_dist is not None and host is None: @@ -93,42 +88,47 @@ def __init__(self, params, cmb, cosmology, time_range, z_range=None, peak_out_tr # -- If no host need to define a z_range if host is None: self._z_range = z_range - else: + elif host is not None: self._z_range = self.host._z_range + else: + raise ValueError('Set zrange xor host') + + if cmb is None: + self._cmb = { + 'v_cmb': VCMB, + 'l_cmb': L_CMB, + 'b_cmb': B_CMB + } + else: + self._cmb = cmb self._mw_dust = mw_dust - self._dipole = dipole self._geometry = geometry - self.rate, self._rate_expr = self._init_rate() - # -- Init sncosmo model - self.sim_model = self._init_sim_model() - self._init_dust() - - # -- Init general_par - self._general_par = {} - self._init_general_par() - + # -- Init sncosmo model & effects + self.sim_sources, self._sources_prange = self._init_snc_sources() + self.sim_effects = self._init_snc_effects() + # -- Init redshift distribution self._z_dist, self._z_time_rate = self._compute_zcdf() # -- Get the astrobj class self._astrobj_class = getattr(astr, self._object_type) - if peak_out_trange: - t0 = self.time_range[0] - self.snc_model_time[1] * (1 + self.z_range[1]) - t1 = self.time_range[1] - self.snc_model_time[0] * (1 + self.z_range[1]) - self._time_range = (t0, t1) + # if peak_out_trange: + # t0 = self.time_range[0] - self.snc_model_time[1] * (1 + self.z_range[1]) + # t1 = self.time_range[1] - self.snc_model_time[0] * (1 + self.z_range[1]) + # self._time_range = (t0, t1) - def __call__(self, n_obj=None, rand_seed=None, astrobj_par=None): + def __call__(self, n_obj=None, seed=None, basic_par=None, use_dask=False): """Launch the simulation of obj. Parameters ---------- n_obj : int Number of obj to simulate. - rand_seed : int + seed : int or np.random.SeedSequence The random seed of the simulation. astrobj_par : np.records An array that contains pre-generated parameters @@ -138,72 +138,87 @@ def __call__(self, n_obj=None, rand_seed=None, astrobj_par=None): list(AstrObj) A list containing Astro Object. """ - if rand_seed is None: - rand_seed = np.random.integer(1e3, 1e6) - # -- Initialise 4 seeds for differents generation calls - seeds = np.random.default_rng(rand_seed).integers(1e3, 1e6, size=4) + # -- Initialise 3 seeds for differents generation calls + seeds = ut.gen_rndchilds(seed, 3) - if astrobj_par is not None: - n_obj = len(astrobj_par) + if basic_par is not None: + n_obj = len(basic_par['zcos']) elif n_obj is not None: - astrobj_par = self.gen_astrobj_par(n_obj, seed=seeds[0]) + basic_par = self.gen_basic_par(n_obj, seed=seeds[0]) else: raise ValueError('n_obj and astrobj_par cannot be None at the same time') - # -- Add astrobj par sepecific to the obj generated - self._update_astrobj_par(n_obj, astrobj_par, seed=seeds[1]) - - # -- Add sncosmo par specific to the generated obj - snc_par = self.gen_snc_par(n_obj, astrobj_par, seed=seeds[2]) - + # -- Add parameters specific to the generated obj + obj_par = self.gen_par(n_obj, basic_par, seed=seeds[1]) + # -- randomly chose the number of object for each model - rand_gen = np.random.default_rng(seeds[3]) - random_models = rand_gen.choice(list(self.sim_model.keys()), n_obj) - + random_models = self.random_models(n_obj, seed=seeds[2]) + # -- Check if there is dust - if 'mw_' in self.sim_model[0].effect_names: - dust_par = self._compute_dust_par(astrobj_par['ra'], astrobj_par['dec']) + if self.mw_dust is not None: + dust_par = self._compute_dust_par(basic_par['ra'], basic_par['dec']) else: - dust_par = [{}] * len(astrobj_par['ra']) - - if snc_par is not None: - par_list = ({**{'ID': astrobj_par.index.values[i]}, - **{k: astrobj_par[k][i+astrobj_par.index.values[0]] for k in astrobj_par}, - **{'sncosmo': {**sncp, **dstp}}} - for i, (sncp, dstp) in enumerate(zip(snc_par, dust_par))) + dust_par = {} + + par = pd.DataFrame({**random_models, **obj_par, **dust_par}, index=basic_par.index) + + par = pd.concat([basic_par, par], axis=1) + + if 'relation' not in self._params: + relation = None else: - par_list = ({**{'ID': astrobj_par.index.values[i]}, - **{k: astrobj_par[k][i+astrobj_par.index.values[0]] for k in astrobj_par}, - **{'sncosmo': { **dstp}}} - for i, dstp in enumerate(dust_par)) + relation = self._params['relation'] - return [self._astrobj_class(snp, - self.sim_model[k], - self._general_par) - for k, snp in zip(random_models, par_list)] + # TODO - BC: Dask that part or vectorize it for more efficiency + return [self._astrobj_class(par_dic, + effects=self.sim_effects, + relation=relation) + for par_dic in par.reset_index().to_dict(orient='records')] - def _init_registered_rate(self): - """SNII rates registry.""" - if self._params['rate'].lower() in self._available_rates(): - return self._available_rates[self._params['rate'].lower()].format(h=self.cosmology.h) + def __str__(self): + """Print config.""" + str = '' + + if 'model_dir' in self._params: + model_dir = self._params['model_dir'] + model_dir_str = f" from {model_dir}" else: - raise ValueError(f"{self._params['rate']} is not available! Available rate are {self._available_rates}") + model_dir = None + model_dir_str = " from sncosmo" - @abc.abstractmethod - def _init_sim_model(self): - """Abstract method that return sncosmo sim model, - called in __init__""" - pass + str += 'OBJECT TYPE : ' + self._object_type + '\n' + str += "SIM MODEL(S) :\n" + for sn, snv in zip(self.sim_sources['model_name'], self.sim_sources['model_version']): + str += f"- {sn}" + str += f" v{snv}" + str += model_dir_str + '\n' + str += '\n' - @abc.abstractmethod - def _update_general_par(self): - """Abstract method to add parameters to _general_par, - called in _init_general_par""" - pass + str += ("Peak mintime : " + f"{self.time_range[0]:.2f} MJD\n\n" + "Peak maxtime : " + f"{self.time_range[1]:.2f} MJD\n\n") + + str += 'Redshift distribution computed' + if self.host is not None: + if self.host.config['distrib'] == 'random': + str += ' using host redshift distribution\n' + elif self.host.config['distrib'] == 'survey_rate': + str += ' using rate\n\n' + else: + str += ' using rate\n' + + str += self._add_print() + '\n\n' + return str + + ################################################## + # FUNCTIONS TO ADAPT FOR EACH GENERATOR SUBCLASS # + ################################################## + @abc.abstractmethod - def _update_astrobj_par(self, n_obj, astrobj_par, seed=None): + def gen_par(self, n_obj, basic_par, seed=None): """Abstract method to add random generated parameters specific to the astro object used, called in __call__ @@ -214,49 +229,91 @@ def _update_astrobj_par(self, n_obj, astrobj_par, seed=None): seed : int, optional Random seed. """ - rand_gen = np.random.default_rng(seed) pass - - @abc.abstractmethod - def gen_snc_par(self, n_obj, astrobj_par, seed=None): - """Abstract method to add random generated parameters - specific to the sncosmo model used, called in __call__ - - Parameters - ---------- - n_obj: int - Number of parameters to generate. - astrobj_par: dict(key: np.ndarray()) - Contains basic random generated properties. - seed : int, optional - Random seed. - - Return - ------ - dict - A dictionnary of the sncosmo parameters (not t0 or z). - """ - rand_gen = np.random.default_rng(seed) - pass - - def _update_header(self, header): + + def _update_header(self): """Method to add information in header, called in _get_header - Parameters + Returns ---------- - header: dict - dict to directly modify in the function. + dict + dict is added to header dict in _get_header(). """ pass - + + def _add_effects(self): + """Method that return a list of effect dict. + + Notes + ----- + Effect dict are like + { + 'name': name of the effect, + 'source': snc.PropagationEffect subclass + 'frame': 'obs' or 'rest' + } + """ + return [] + def _add_print(self): - """Method to print something in print_config.""" + """Method to print something in __str__.""" pass + + def _init_sources_list(self): + return [self._params['model_name']] + + #################### + # COMMON FUNCTIONS # + #################### + + # -- INIT FUNCTIONS -- # + + def _init_registered_rate(self): + """Rates registry.""" + if self._params['rate'].lower() in self._available_rates: + return self._available_rates[self._params['rate'].lower()].format(h=self.cosmology.h) + else: + raise ValueError(f"{self._params['rate']} is not available! Available rate are {self._available_rates}") + + def _init_snc_effects(self): + effects = [] + # -- MW dust + if self.mw_dust is not None: + effects.append(dst_ut.init_mw_dust(self.mw_dust)) + effects += self._add_effects() + return effects + + def _init_snc_sources(self): + + # -- Check existence of the model + if (isinstance(self._params['model_name'], str) & + (self._params['model_name'] not in self._available_models)): + raise ValueError(f"{self._params['model_name']} is not available") + elif isinstance(self._params['model_name'], list): + for s in self._params['model_name']: + if s not in self._available_models: + raise ValueError(f"{s} is not available") + + sources = {'model_name': self._init_sources_list()} + + if 'model_version' in self._params: + if not isinstance(self._params['model_version'], list): + sources['model_version'] = [self._params['model_version']] + else: + sources['model_version'] = [None] * len(sources['model_name']) + + # -- Compute max, min phase + snc_sources = [snc.get_source(name=n, version=v) for n, v in zip(sources['model_name'], + sources['model_version'])] + + sources['model_version'] = [s.version for s in snc_sources] + maxphase = np.max([s.maxphase() for s in snc_sources]) + minphase = np.min([s.minphase() for s in snc_sources]) + return sources, (minphase, maxphase) def _init_rate(self): """Initialise rate in obj/Mpc^-3/year - Returns ------- lambda funtion, str @@ -284,29 +341,83 @@ def _init_rate(self): expr = 'lambda z: 3e-5' return eval(expr), expr.strip() - def _init_general_par(self): - """Init general parameters.""" - if self.mw_dust is not None: - self._general_par['mw_dust'] = {'model' : self.mw_dust['model'], - 'rv': self.mw_dust['rv']} - for model in self.sim_model.values(): - if not hasattr(model, 'bandfluxcov'): - raise ValueError('This sncosmo model has no flux covariance available') - - if 'mod_fcov' in self._params: - self._general_par['mod_fcov'] = self._params['mod_fcov'] - else: - self._general_par['mod_fcov'] = False - self._update_general_par() + def _compute_zcdf(self): + """Give the time rate SN/years in redshift shell. - def _init_dust(self): - """Initialise dust.""" - if self.mw_dust is not None: - if 'rv' not in self.mw_dust: - self.mw_dust['rv'] = 3.1 - for model in self.sim_model.values(): - dst_ut.init_mw_dust(model, self.mw_dust) + Parameters + ---------- + z : numpy.ndarray + The redshift bins. + + Returns + ------- + numpy.ndarray(float) + Numpy array containing the time rate in each redshift bin. + + """ + z_min, z_max = self.z_range + + # -- Set the precision to dz = 1e-5 + dz = 1e-5 + + z_shell = np.linspace(z_min, z_max, int((z_max - z_min) / dz)) + co_dist = self.cosmology.comoving_distance(z_shell).value + shell_vol = 4 * np.pi / 3 * co_dist**2 * C_LIGHT_KMS / self.cosmology.H(z_shell).value * dz + + # -- Compute the sn time rate in each volume shell [( SN / year )(z)] + shell_time_rate = self.rate(z_shell) * shell_vol / (1 + z_shell) + + z_pdf = lambda x : np.interp(x, z_shell, shell_time_rate) + + return ut.CustomRandom(z_pdf, z_min, z_max, dx=1e-5), (z_shell, shell_time_rate) + + def _compute_dust_par(self, ra, dec): + """Compute dust parameters. + Parameters + ---------- + ra : numpy.ndaray(float) + SN Right Ascension. + dec : numpy.ndarray(float) + SN Declinaison. + Returns + ------- + list(dict) + List of Dictionnaries that contains Rv and E(B-V) for each SN. + """ + mod_name = self.mw_dust['model'] + dust_par = {'mw_ebv': dst_ut.compute_ebv(ra, dec)} + + if mod_name.lower() in ['ccm89', 'od94']: + if 'r_v' in self.mw_dust: + rv_val = self.mw_dust['r_v'] + else: + rv_val = 3.1 + dust_par['mw_r_v'] = np.ones(len(ra)) * rv_val + return dust_par + + def _get_header(self): + """Generate header of sim file.. + + Returns + ------- + None + """ + header = { + 'obj_type': self._object_type, + 'rate': self._rate_expr, + **self.sim_sources + } + + if self.vpec_dist is not None: + header['m_vp'] = self.vpec_dist['mean_vpec'] + header['s_vp'] = self.vpec_dist['sig_vpec'] + + header = {**header, **self._update_header()} + return header + + # -- RANDOM FUNCTIONS -- # + def gen_peak_time(self, n, seed=None): """Generate uniformly n peak time in the survey time range. @@ -370,7 +481,6 @@ def gen_coord(self, n, seed=None): ra.extend(ra_tmp[intersects]) dec.extend(dec_tmp[intersects]) n_to_sim = n - len(ra) - return ra, dec def gen_zcos(self, n, seed=None): @@ -414,7 +524,7 @@ def gen_vpec(self, n, seed=None): size=n) return vpec - def gen_astrobj_par(self, n_obj, seed=None, min_max_t=False): + def gen_basic_par(self, n_obj, seed=None, min_max_t=False): """Generate basic obj properties. Parameters @@ -427,18 +537,17 @@ def gen_astrobj_par(self, n_obj, seed=None, min_max_t=False): Notes ----- List of parameters: - * sim_t0 : obj peak + * t0 : obj peak * zcos : cosmological redshift * ra : Right Ascension * dec : Declinaison * vpec : peculiar velocity * como_dist : comoving distance - * z2cmb : CMB dipole redshift contribution + * zpcmb : CMB dipole redshift contribution * mw_ebv, opt : Milky way dust extinction - * dip_dM, opt : Dipole magnitude modification """ # -- Generate seeds for random calls - seeds = np.random.default_rng(seed).integers(1e3, 1e6, size=4) + seeds = ut.gen_rndchilds(seed, 4) # -- Generate peak time t0 = self.gen_peak_time(n_obj, seed=seeds[0]) @@ -447,9 +556,10 @@ def gen_astrobj_par(self, n_obj, seed=None, min_max_t=False): if self.host is None: zcos = self.gen_zcos(n_obj, seed=seeds[1]) else: - host = self.host.random_choice(n_obj, seed=seeds[1], rate=self.rate) # change self random choiche depend on type + host = self.host.random_choice(n_obj, seed=seeds[1], rate=self.rate, sn_type=self._object_type, cosmology=self.cosmology) zcos = host['zcos'].values + # -- Generate ra, dec if self.host is not None: ra = host['ra'].values @@ -465,161 +575,40 @@ def gen_astrobj_par(self, n_obj, seed=None, min_max_t=False): else: vpec = np.zeros(len(ra)) - astrobj_par = {'zcos': zcos, - 'como_dist': self.cosmology.comoving_distance(zcos).value, - 'z2cmb': ut.compute_z2cmb(ra, dec, self.cmb), - 'sim_t0': t0, - 'ra': ra, - 'dec': dec, - 'vpec': vpec} + basic_par = { + 'zcos': zcos, + 'como_dist': self.cosmology.comoving_distance(zcos).value, + 'zpcmb': ut.compute_zpcmb(ra, dec, self.cmb), + 't0': t0, + 'ra': ra, + 'dec': dec, + 'vpec': vpec} if min_max_t: - _1_zobs_ = (1 + astrobj_par['zcos']) - _1_zobs_ *= (1 + astrobj_par['z2cmb']) - _1_zobs_ *= (1 + astrobj_par['vpec'] / C_LIGHT_KMS) - astrobj_par['min_t'] = astrobj_par['sim_t0'] + self.snc_model_time[0] * _1_zobs_ - astrobj_par['max_t'] = astrobj_par['sim_t0'] + self.snc_model_time[1] * _1_zobs_ - astrobj_par['1_zobs'] = _1_zobs_ - - if self.dipole is not None: - astrobj_par['dip_dM'] = self._compute_dipole(ra, dec) - - return pd.DataFrame(astrobj_par) - - def _compute_zcdf(self): - """Give the time rate SN/years in redshift shell. - - Parameters - ---------- - z : numpy.ndarray - The redshift bins. - - Returns - ------- - numpy.ndarray(float) - Numpy array containing the time rate in each redshift bin. - - """ - z_min, z_max = self.z_range - - # -- Set the precision to dz = 1e-5 - dz = 1e-5 - - z_shell = np.linspace(z_min, z_max, int((z_max - z_min) / dz)) - z_shell_center = 0.5 * (z_shell[1:] + z_shell[:-1]) - co_dist = self.cosmology.comoving_distance(z_shell).value - shell_vol = 4 * np.pi / 3 * (co_dist[1:]**3 - co_dist[:-1]**3) - - # -- Compute the sn time rate in each volume shell [( SN / year )(z)] - shell_time_rate = self.rate(z_shell_center) * shell_vol / (1 + z_shell_center) - - z_pdf = lambda x : np.interp(x, z_shell, np.append(0, shell_time_rate)) - - return ut.CustomRandom(z_pdf, z_min, z_max, dx=1e-5), (z_shell, shell_time_rate) - - def _compute_dust_par(self, ra, dec): - """Compute dust parameters. - Parameters - ---------- - ra : numpy.ndaray(float) - SN Right Ascension. - dec : numpy.ndarray(float) - SN Declinaison. - Returns - ------- - list(dict) - List of Dictionnaries that contains Rv and E(B-V) for each SN. - """ - ebv = dst_ut.compute_ebv(ra, dec) - mod_name = self.mw_dust['model'] - - if mod_name.lower() in ['ccm89', 'od94']: - r_v = np.ones(len(ra)) * self.mw_dust['rv'] - dust_par = [{'mw_r_v': r, 'mw_ebv': e} for r, e in zip(r_v, ebv)] - elif mod_name.lower() in ['f99']: - dust_par = [{'mw_ebv': e} for e in ebv] - else: - raise ValueError(f'{mod_name} is not implemented') - return dust_par - - def _compute_dipole(self, ra, dec): - """Compute dipole.""" - cart_vec = nbf.radec_to_cart(self.dipole['coord'][0], - self.dipole['coord'][1]) - - sn_vec = nbf.radec_to_cart_2d(ra, dec) - delta_M = self.dipole['A'] + self.dipole['B'] * sn_vec @ cart_vec - return delta_M - - def __str__(self): - """Print config.""" - str = '' - - if 'model_dir' in self._params['model_config']: - model_dir = self._params['model_config']['model_dir'] - model_dir_str = f" from {model_dir}" - else: - model_dir = None - model_dir_str = " from sncosmo" - - str += 'OBJECT TYPE : ' + self._object_type + '\n' - str += f"SIM MODEL : {self._params['model_config']['model_name']}" + model_dir_str + '\n\n' - - str += ("Peak mintime : " - f"{self.time_range[0]:.2f} MJD\n\n" - "Peak maxtime : " - f"{self.time_range[1]:.2f} MJD\n\n") - - str += 'Redshift distribution computed' - - if self.host is not None: - if self.host.config['distrib'] == 'random': - str += ' using host redshift distribution\n' - elif self.host.config['distrib'] == 'survey_rate': - str += ' using rate\n\n' - else: - str += ' using rate\n' - - - str += self._add_print() + '\n' - - if self._general_par['mod_fcov']: - str += "Model COV ON" - else: - str += "Model COV OFF" - return str - - def _get_header(self): - """Generate header of sim file.. - - Returns - ------- - None - - """ - header = { - 'obj_type': self._object_type, - 'rate': self._rate_expr, - 'model_name': [model.source.name - for model in self.sim_model.values()] - } - - header = {**header, **self._general_par} + _1_zobs_ = (1 + basic_par['zcos']) + _1_zobs_ *= (1 + basic_par['zpcmb']) + _1_zobs_ *= (1 + basic_par['vpec'] / C_LIGHT_KMS) + basic_par['min_t'] = basic_par['t0'] + self._sources_prange[0] * _1_zobs_ + basic_par['max_t'] = basic_par['t0'] + self._sources_prange[1] * _1_zobs_ + basic_par['1_zobs'] = _1_zobs_ + + # save in astrobj_par all the column of the host_table that start with host, to save the data in final sim + # col_name = [column for column in host if column.startswith('host_')] + # if len(col_name) > 0 : + # for c in col_name: + # astrobj_par[c] = host[c].values + + return pd.DataFrame(basic_par) - if self.vpec_dist is not None: - header['m_vp'] = self.vpec_dist['mean_vpec'] - header['s_vp'] = self.vpec_dist['sig_vpec'] - - self._update_header(header) - return header - + def random_models(self, n_obj, seed=None): + rand_gen = np.random.default_rng(seed) - @property - def snc_model_time(self): - """Get the sncosmo model mintime and maxtime.""" - maxtime = np.max([model.maxtime() for model in self.sim_model.values()]) - mintime = np.min([model.mintime() for model in self.sim_model.values()]) - return mintime, maxtime + idx = rand_gen.integers(low=0, high=len(self.sim_sources['model_name']), size=n_obj) + random_models = { + 'model_name': np.array(self.sim_sources['model_name'])[idx], + 'model_version': np.array(self.sim_sources['model_version'])[idx]} + return random_models + @property def host(self): @@ -636,11 +625,6 @@ def mw_dust(self): """Get the mw_dust parameters.""" return self._mw_dust - @property - def dipole(self): - """Get alpha dipole parameters.""" - return self._dipole - @property def cosmology(self): """Get astropy cosmological model.""" @@ -698,132 +682,97 @@ class SNIaGen(BaseGen): | └── sig_vpec host : class SnHost, opt The host class to introduce sn host. - dipole : dict, opt - The alpha dipole parameters. - - | dipole - | ├── coord list(ra, dec) dipole vector coordinates in ra, dec - | ├── A parameter of the A + B * cos(theta) dipole - | └── B B parameter of the A + B * cos(theta) dipole """ _object_type = 'SNIa' _available_models = ['salt2', 'salt3'] _available_rates = { 'ptf19': 'lambda z: 2.43e-5 * ({h}/0.70)**3', # Rate from https://arxiv.org/abs/1903.08580 'ztf20': 'lambda z: 2.35e-5 * ({h}/0.70)**3', # Rate from https://arxiv.org/abs/2009.01242 - 'ptf19_pw': 'lambda z: 2.35e-5 * ({h}/0.70)**3 * (1 + z)**1.7' # Rate from https://arxiv.org/abs/1903.08580 - + 'ptf19_pw': 'lambda z: 2.35e-5 * ({h}/0.70)**3 * (1 + z)**1.7' # Rate from https://arxiv.org/abs/1903.08580 } - # M0 SNIA from JLA paper (https://arxiv.org/abs/1401.4064) + SNIA_M0 = { - 'jla': -19.05 - } + 'jla': -19.05 # M0 SNIA from JLA paper (https://arxiv.org/abs/1401.4064) + } def _init_M0(self): """Initialise absolute magnitude.""" if isinstance(self._params['M0'], (float, np.floating, int, np.integer)): return self._params['M0'] - elif self._params['M0'].lower() in self.SNIA_M0: - return ut.scale_M0_cosmology(self.cosmology.h, - self.SNIA_M0[self._params['M0'].lower()], - cst.h_article[self._params['M0'].lower()]) + return ut.scale_M0_cosmology( + self.cosmology.h, + self.SNIA_M0[self._params['M0'].lower()], + cst.h_article[self._params['M0'].lower()]) else: raise ValueError(f"{self._params['M0']} is not available! Available M0 are {self.SNIA_M0.keys()}") - def _init_sim_model(self): - """Initialise sncosmo model using the good source. - - Returns - ------- - sncosmo.Model - sncosmo.Model(source) object where source depends on the - SN simulation model. - - """ - if self._params['model_config']['model_name'].lower() not in self._available_models: - raise ValueError(f"Model {self._params['model_config']['model_name']} not available! Avaliable Models are {self._available_models}") - - model_dir = None - if 'model_dir' in self._params['model_config']: - model_dir = self._params['model_config']['model_dir'] - - model = ut.init_sn_model(self._params['model_config']['model_name'], - model_dir) - - if 'sct_model' in self._params: - sct.init_sn_sct_model(model, self._params['sct_model']) - return {0: model} - - def _update_general_par(self): - """Initialise the general parameters, depends on the SN simulation model. - - Returns - ------- - list - A dict containing all the usefull keys of the SN model. - """ - model_name = self._params['model_config']['model_name'] - if model_name[:5] in ('salt2', 'salt3'): - model_keys = ['alpha', 'beta'] - - self._general_par['M0'] = self._init_M0() - self._general_par['sigM'] = self._params['sigM'] - - for k in model_keys: - self._general_par[k] = self._params['model_config'][k] - - return - - def _update_astrobj_par(self, n_obj, astrobj_par, seed=None): - # -- Generate coherent mag scattering - astrobj_par['mag_sct'] = self.gen_coh_scatter(n_obj, seed=seed) - def _add_print(self): str = '' if 'sct_model' in self._params: str += ("\nUse intrinsic scattering model : " f"{self._params['sct_model']}") return str - - def _update_header(self, header): - model_name = self._params['model_config']['model_name'] + + def _add_effects(self): + effects = [] + # Add scattering model if needed + if 'sct_model' in self._params: + if self._params['sct_model'] == 'G10': + if (len(self.sim_sources['model_name']) > 1 + or self.sim_sources['model_name'][0] not in ['salt2', 'salt3']): + raise ValueError('G10 cannot be used') + effects.append({ + 'source': sct.G10(snc.get_source( + name=self.sim_sources['model_name'][0], + version=self.sim_sources['model_version'][0])), + 'frame': 'rest', + 'name': 'G10_' + }) + elif self._params['sct_model'] in ['C11_0', 'C11_1', 'C11_2']: + effects.append({'source': sct.C11(), + 'frame': 'rest', + 'name': 'C11_' + }) + return effects + + def _update_header(self): + model_name = self._params['model_name'] + + header = {} header['M0_band'] = 'bessell_b' if model_name.lower()[:4] == 'salt': - if isinstance(self._params['model_config']['dist_x1'], str): - header['dist_x1'] = self._params['model_config']['dist_x1'] + if isinstance(self._params['dist_x1'], str): + header['dist_x1'] = self._params['dist_x1'] else: - header['mean_x1'] = self._params['model_config']['dist_x1'][0] - if len(self._params['model_config']['dist_x1']) == 3: + header['peak_x1'] = self._params['dist_x1'][0] + if len(self._params['dist_x1']) == 3: header['dist_x1'] = 'asym_gauss' - header['sig_x1_low'] = self._params['model_config']['dist_x1'][1] - header['sig_x1_hi'] = self._params['model_config']['dist_x1'][2] - elif len(self._params['model_config']['dist_x1']) == 2: + header['sig_x1_low'] = self._params['dist_x1'][1] + header['sig_x1_hi'] = self._params['dist_x1'][2] + elif len(self._params['dist_x1']) == 2: header['dist_x1'] = 'gauss' - header['sig_x1'] = self._params['model_config']['dist_x1'][1] - - header['mean_c'] = self._params['model_config']['dist_c'][0] - - if len(self._params['model_config']['dist_c']) == 3: - header['dist_c'] = 'asym_gauss' - header['sig_c_low'] = self._params['model_config']['dist_c'][1] - header['sig_c_hi'] = self._params['model_config']['dist_c'][2] - else: - header['dist_c'] = 'gauss' - header['sig_c'] = self._params['model_config']['dist_c'][1] + header['sig_x1'] = self._params['dist_x1'][1] - if 'sct_model' in self._params: - header['sct_mod'] = self._params['sct_model'] - if self._params['sct_model'].lower() == 'g10': - params = ['G10_L0', 'G10_F0', 'G10_F1', 'G10_dL'] - for par in params: - pos = np.where(np.array(self.sim_model[0].param_names) == par)[0] - header[par] = self.sim_model[0].parameters[pos][0] - elif self._params['sct_model'].lower() == 'c11': - params = ['C11_Cuu', 'C11_Sc'] - for par in params: - pos = np.where(np.array(self.sim_model[0].param_names) == par)[0] - header[par] = self.sim_model[0].parameters[pos][0] + + if isinstance(self._params['dist_c'], str): + if self._params['dist_c'].lower() == 'bs20': + header['mean_c'] = 'BS20' + header['dist_c'] = 'c_int BS20' + header['sig_c'] = 'c_int BS20' + + + elif isinstance(self._params['dist_c'], list): + if len(self._params['dist_c']) == 3: + header['mean_c'] = self._params['dist_c'][0] + header['dist_c'] = 'asym_gauss' + header['sig_c_low'] = self._params['dist_c'][1] + header['sig_c_hi'] = self._params['dist_c'][2] + else: + header['mean_c'] = self._params['dist_c'][0] + header['dist_c'] = 'gauss' + header['sig_c'] = self._params['dist_c'][1] + return header def gen_coh_scatter(self, n_sn, seed=None): """Generate n coherent mag scattering term. @@ -854,8 +803,7 @@ def gen_snc_par(self, n_obj, astrobj_par, seed=None): n_obj : int Number of parameters to generate. seed : int, optional - Random seed - . + Random seed. Returns ------- @@ -863,39 +811,38 @@ def gen_snc_par(self, n_obj, astrobj_par, seed=None): One dictionnary containing 'parameters names': numpy.ndarray(float). """ - rand_gen = np.random.default_rng(seed) - + seeds = ut.gen_rndchilds(seed=seed, size=3) + + params = { + 'M0': np.ones(n_obj) * self._init_M0(), + 'coh_sct': self.gen_coh_scatter(n_obj, seed=seeds[0])} + # -- Spectra model parameters - model_name = self._params['model_config']['model_name'] + model_name = self._params['model_name'] if model_name in ('salt2', 'salt3'): - if self._params['model_config']['dist_x1'] in ['N21']: - z_for_dist = astrobj_par['zcos'] - else: - z_for_dist = None - sim_x1, sim_c = self.gen_salt_par(n_obj, rand_gen.integers(1000, 1e6), - z=z_for_dist) - snc_par = [{'x1': x1, 'c': c} for x1, c in zip(sim_x1, sim_c)] + sim_x1, sim_c, alpha, beta = self.gen_salt_par( + n_obj, + seeds[1], + z=basic_par['zcos'], + astrobj_par=astrobj_par) + params = {**params, 'x1': sim_x1, 'c': sim_c, 'alpha': alpha, 'beta': beta} # -- Non-coherent scattering effects - if 'G10_' in self.sim_model[0].effect_names: - seeds = rand_gen.integers(low=1e3, high=1e6, size=n_obj) - for par, s in zip(snc_par, seeds): - par['G10_RndS'] = s - - elif 'C11_' in self.sim_model[0].effect_names: - seeds = rand_gen.integers(low=1e3, high=1e6, size=n_obj) - for par, s in zip(snc_par, seeds): - par['C11_RndS'] = s - - return snc_par + if 'sct_model' in self._params: + randgen = np.random.default_rng(seeds[2]) + if self._params['sct_model'] == 'G10': + params['G10_RndS'] = randgen.integers(1e12, size=n_obj) + elif self._params['sct_model'] == 'C11': + params['C11_RndS'] = randgen.integers(1e12, size=n_obj) + return params - def gen_salt_par(self, n_sn, seed=None, z=None): - """Generate n SALT parameters. + def gen_salt_par(self, n_sn, seed=None, z=None, astrobj_par=None): + """Generate SALT parameters. Parameters ---------- - n : int + n_sn : int Number of parameters to generate. seed : int Random seed. @@ -906,21 +853,64 @@ def gen_salt_par(self, n_sn, seed=None, z=None): 2 numpy arrays containing SALT2 x1 and c generated parameters. """ - seeds = np.random.default_rng(seed).integers(1e3, 1e6, size=2) + seeds = ut.gen_rndchilds(seed=seed, size=4) - if isinstance(self._params['model_config']['dist_x1'], str): - if self._params['model_config']['dist_x1'].lower() == 'n21': + if isinstance(self._params['dist_x1'], str): + if self._params['dist_x1'].lower() == 'n21': sim_x1 = salt_ut.n21_x1_model(z, seed=seeds[0]) + elif self._params['dist_x1'].lower() == 'n21+mass': + sim_x1 = salt_ut.n21_x1_mass_model(z, astrobj_par['host_mass'], seed=seeds[0]) + elif self._params['dist_x1'].lower() == 'mass': + sim_x1 = salt_ut.x1_mass_model(astrobj_par['host_mass'], seed=seeds[0]) + elif isinstance(self._params['dist_x1'], list): + sim_x1 = ut.asym_gauss(*self._params['dist_x1'], + seed=seeds[0], + size=n_sn) + + if isinstance(self._params['model_config']['dist_c'], str): + if self._params['model_config']['dist_c'].lower() == 'bs20': + sim_c = astrobj_par['c_int'] else: - sim_x1 = ut.asym_gauss(*self._params['model_config']['dist_x1'], - seed=seeds[0], - size=n_sn) + sim_c = ut.asym_gauss(*self._params['dist_c'], + seed=seeds[1], + size=n_sn) + + # -- Alpha dist + if isinstance(self._params['alpha'], float): + alpha = np.ones(n_sn) * self._params['alpha'] + elif isinstance(self._params['alpha'], list): + alpha = ut.asym_gauss(*self._params['alpha'], + seed=seeds[2], + size=n_sn) + # -- Beta dist + if isinstance(self._params['beta'], float): + beta = np.ones(n_sn) * self._params['beta'] + elif isinstance(self._params['alpha'], list): + beta = ut.asym_gauss(*self._params['beta'], + seed=seeds[2], + size=n_sn) + return sim_x1, sim_c, alpha, beta + + def gen_coh_scatter(self, n_sn, seed=None): + """Generate n coherent mag scattering term. + + Parameters + ---------- + n : int + Number of mag scattering terms to generate. + seed : int, optional + Random seed. - sim_c = ut.asym_gauss(*self._params['model_config']['dist_c'], - seed=seeds[1], - size=n_sn) - return sim_x1, sim_c + Returns + ------- + numpy.ndarray(float) + numpy array containing scattering terms generated. + """ + rand_gen = np.random.default_rng(seed) + + coh_sct = rand_gen.normal(loc=0, scale=self._params['sigM'], size=n_sn) + return coh_sct class TimeSeriesGen(BaseGen): @@ -952,13 +942,6 @@ class TimeSeriesGen(BaseGen): | └── sig_vpec host : class SnHost, opt The host class to introduce sn host. - dipole : dict, opt - The alpha dipole parameters. - - | dipole - | ├── coord list(ra, dec) dipole vector coordinates in ra, dec - | ├── A parameter of the A + B * cos(theta) dipole - | └── B B parameter of the A + B * cos(theta) dipole General Info about parameters: @@ -978,57 +961,7 @@ def _init_M0(self): else: return self.init_M0_for_type() - - def _init_sim_model(self): - """Initialise sncosmo model using the good source. - - Returns - ------- - sncosmo.Model - sncosmo.Model(source) object where source depends on the - SN simulation model. - """ - - if isinstance(self._params['model_config']['model_name'], str): - if self._params['model_config']['model_name'].lower() == 'all': - selected_models = self._available_models - elif self._params['model_config']['model_name'].lower() == 'vinc_nocorr': - selected_models = ut.select_Vincenzi_template(self._available_models,corr=False) - elif self._params['model_config']['model_name'].lower() == 'vinc_corr': - selected_models = ut.select_Vincenzi_template(self._available_models,corr=True) - else: - selected_models = [self._params['model_config']['model_name']] - - model= [ut.init_sn_model(m) - for m in selected_models] - else: - model = [ut.init_sn_model(m) - for m in self._params['model_config']['model_name']] - - model = {i :m for i, m in enumerate(model)} - - return model - - - def _update_general_par(self): - """Initialise the general parameters, depends on the SN simulation model. - - Returns - ------- - list - A dict containing all the usefull keys of the SN model. - """ - self._general_par['M0'] = self._init_M0() - self._general_par['sigM'] = self._params['sigM'] - - return - - def _update_astrobj_par(self, n_obj, astrobj_par, seed=None): - # -- Generate coherent mag scattering - astrobj_par['mag_sct'] = self.gen_coh_scatter(n_obj, seed=seed) - - def gen_coh_scatter(self, n_sn, seed=None): """Generate n coherent mag scattering term. @@ -1045,8 +978,6 @@ def gen_coh_scatter(self, n_sn, seed=None): numpy array containing scattering terms generated. """ - if seed is None: - seed = np.random.random_integers(1e3, 1e6) rand_gen = np.random.default_rng(seed) if isinstance(self._params['sigM'], (float, np.floating, int, np.integer)): @@ -1059,10 +990,8 @@ def gen_coh_scatter(self, n_sn, seed=None): else: return self.gen_coh_scatter_for_type(n_sn, seed) - - - def gen_snc_par(self, n_obj, astrobj_par, seed=None): + def gen_par(self, n_obj, astrobj_par, seed=None): """Generate sncosmo model dependant parameters (others than redshift and t0). Parameters ---------- @@ -1076,20 +1005,26 @@ def gen_snc_par(self, n_obj, astrobj_par, seed=None): dict One dictionnary containing 'parameters names': numpy.ndarray(float). """ - - return None + params = { + 'M0': np.ones(n_obj) * self._init_M0(), + 'coh_sct': self.gen_coh_scatter(n_obj, seed=seed)} + return params def _add_print(self): str = '' return str - def _update_header(self, header): + def _update_header(self): + header={} header['M0_band']='bessell_r' + return header class CCGen(TimeSeriesGen): """Template for CoreColapse.""" - def init_M0_for_type(): + _available_models = ["vin19_corr", "vin19_nocorr"] + + def init_M0_for_type(self): """Initialise absolute magnitude using default values from past literature works based on the type.""" if self._params['M0'].lower() == 'li11_gaussian': return ut.scale_M0_cosmology( @@ -1104,22 +1039,45 @@ def init_M0_for_type(): cst.h_article['li11']) else: raise ValueError(f"{self._params['M0']} is not available! Available M0 are {self._sn_lumfunc['M0'].keys()} ") + + def _init_sources_list(self): + """Initialise sncosmo model using the good source. - def gen_coh_scatter_for_type(n_sn, seed): + Returns + ------- + sncosmo.Model + sncosmo.Model(source) object where source depends on the + SN simulation model. + """ + if isinstance(self._params['model_name'], str): + if self._params['model_name'].lower() == 'all': + sources = self._available_models + elif self._params['model_name'].lower() == 'vin19_nocorr': + sources = ut.select_Vincenzi_template(self._available_models,corr=False) + elif self._params['model_name'].lower() == 'vin19_corr': + sources = ut.select_Vincenzi_template(self._available_models,corr=True) + else: + sources = [self._params['model_name']] + else: + sources = self._params['model_name'] + + return sources + + def gen_coh_scatter_for_type(self, n_sn, seed): """Generate n coherent mag scattering term using default values from past literature works based on the type.""" if self._params['sigM'].lower() == 'li11_gaussian': return ut.asym_gauss(mu=0, - sig_low=self._sn_lumfunc['mag_sct']['li11_gaussian'][0], - sig_high=self._sn_lumfunc['mag_sct']['li11_gaussian'][1], + sig_low=self._sn_lumfunc['coh_sct']['li11_gaussian'][0], + sig_high=self._sn_lumfunc['coh_sct']['li11_gaussian'][1], seed=seed, size=n_sn) elif self._params['sigM'].lower() == 'li11_skewed': return ut.asym_gauss(mu=0, - sig_low=self._sn_lumfunc['mag_sct']['li11_skewed'][0], - sig_high=self._sn_lumfunc['mag_sct']['li11_skewed'][1], + sig_low=self._sn_lumfunc['coh_sct']['li11_skewed'][0], + sig_high=self._sn_lumfunc['coh_sct']['li11_skewed'][1], seed=seed, size=n_sn) else: - raise ValueError(f"{self._params['sigM']} is not available! Available sigM are {self._sn_lumfunc['mag_scatter'].keys()} ") + raise ValueError(f"{self._params['sigM']} is not available! Available sigM are {self._sn_lumfunc['coh_scatter'].keys()} ") class SNIIGen(CCGen): @@ -1130,7 +1088,7 @@ class SNIIGen(CCGen): same as TimeSeriesGen """ _object_type = 'SNII' - _available_models = ut.Templatelist_fromsncosmo('snii') + _available_models = ut.Templatelist_fromsncosmo('snii') + CCGen._available_models _sn_fraction = { 'ztf20': 0.776208, @@ -1139,9 +1097,9 @@ class SNIIGen(CCGen): _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19' : f"lambda z: 1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19' : f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6" } @@ -1161,25 +1119,25 @@ class SNIIplGen(CCGen): same as TimeSeriesGen """ _object_type = 'SNIIpl' - _available_models = ut.Templatelist_fromsncosmo('sniipl') + _available_models = ut.Templatelist_fromsncosmo('sniipl') + CCGen._available_models _sn_lumfunc= { 'M0': {'li11_gaussian': -15.97, 'li11_skewed': -17.51}, - 'mag_sct': {'li11_gaussian': [1.31, 1.31], 'li11_skewed': [2.01, 3.18]} - } + 'coh_sct': {'li11_gaussian': [1.31, 1.31], 'li11_skewed': [2.01, 3.18]} + } _sn_fraction={ 'shivers17': 0.620136, 'ztf20': 0.546554, - } + } _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19': f"1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19': f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf - 'ptf19_pw': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" + 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" } @@ -1191,11 +1149,11 @@ class SNIIbGen(CCGen): same as TimeSeriesGen """ _object_type = 'SNIIb' - _available_models = ut.Templatelist_fromsncosmo('sniib') + _available_models = ut.Templatelist_fromsncosmo('sniib') + CCGen._available_models _available_rates = ['ptf19', 'ztf20', 'ptf19_pw'] _sn_lumfunc= { 'M0': {'li11_gaussian': -16.69, 'li11_skewed': -18.30}, - 'mag_sct': {'li11_gaussian': [1.38, 1.38], 'li11_skewed': [2.03, 7.40]} + 'coh_sct': {'li11_gaussian': [1.38, 1.38], 'li11_skewed': [2.03, 7.40]} } _sn_fraction={ @@ -1205,20 +1163,13 @@ class SNIIbGen(CCGen): _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19': f"1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19': f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf - 'ptf19_pw': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" + 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" } - - def _init_registered_rate(self): - """SNIIPL rates registry.""" - if self._params['rate'].lower() in self._available_rates(): - return self._available_rates[self._params['rate'].lower()].format(h=self.cosmology.h) - else: - raise ValueError(f"{self._params['rate']} is not available! Available rate are {self._available_rates}") - + class SNIInGen(CCGen): """SNIIn parameters generator. @@ -1228,11 +1179,11 @@ class SNIInGen(CCGen): same as TimeSeriesGen """ _object_type = 'SNIIn' - _available_models = ut.Templatelist_fromsncosmo('sniin') + _available_models = ut.Templatelist_fromsncosmo('sniin') + CCGen._available_models _sn_lumfunc= { 'M0': {'li11_gaussian': -17.90, 'li11_skewed': -19.13}, - 'mag_sct': {'li11_gaussian': [0.95, 0.95], 'li11_skewed' :[1.53, 6.83]} + 'coh_sct': {'li11_gaussian': [0.95, 0.95], 'li11_skewed' :[1.53, 6.83]} } _sn_fraction={ @@ -1242,11 +1193,11 @@ class SNIInGen(CCGen): _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19': f"1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19': f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf - 'ptf19_pw': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" + 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" } @@ -1259,7 +1210,7 @@ class SNIbcGen(CCGen): same as TimeSeriesGen """ _object_type = 'SNIb/c' - _available_models = ut.Templatelist_fromsncosmo('snib/c') + _available_models = ut.Templatelist_fromsncosmo('snib/c') + CCGen._available_models _sn_fraction= { 'ztf20': 0.217118, 'shivers17': 0.19456 @@ -1267,11 +1218,11 @@ class SNIbcGen(CCGen): _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19': f"1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19': f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf - 'ptf19_pw': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" + 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" } def init_M0_for_type(self): @@ -1288,10 +1239,10 @@ class SNIcGen(CCGen): ---------- same as TimeSeriesGen class """ _object_type = 'SNIc' - _available_models =ut.Templatelist_fromsncosmo('snic') + _available_models = ut.Templatelist_fromsncosmo('snic') + CCGen._available_models _sn_lumfunc= { 'M0': {'li11_gaussian': -16.75, 'li11_skewed': -17.51}, - 'mag_sct': {'li11_gaussian': [0.97, 0.97], 'li11_skewed': [1.24, 1.22]} + 'coh_sct': {'li11_gaussian': [0.97, 0.97], 'li11_skewed': [1.24, 1.22]} } _sn_fraction={ @@ -1300,11 +1251,11 @@ class SNIcGen(CCGen): } _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19': f"1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19': f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf - 'ptf19_pw': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" + 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" } @@ -1315,10 +1266,10 @@ class SNIbGen(CCGen): ---------- same as TimeSeriesGen class.""" _object_type = 'SNIb' - _available_models =ut.Templatelist_fromsncosmo('snib') + _available_models =ut.Templatelist_fromsncosmo('snib') + CCGen._available_models _sn_lumfunc= { 'M0': {'li11_gaussian': -16.07, 'li11_skewed': -17.71}, - 'mag_sct': {'li11_gaussian': [1.34, 1.34], 'li11_skewed': [2.11, 7.15]} + 'coh_sct': {'li11_gaussian': [1.34, 1.34], 'li11_skewed': [2.11, 7.15]} } _sn_fraction={ @@ -1328,11 +1279,11 @@ class SNIbGen(CCGen): _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19': f"1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19': f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf - 'ptf19_pw': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" + 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" } @@ -1343,10 +1294,10 @@ class SNIc_BLGen(CCGen): ---------- Same as TimeSeriesGen class.""" _object_type = 'SNIc_BL' - _available_models =ut.Templatelist_fromsncosmo('snic-bl') + _available_models = ut.Templatelist_fromsncosmo('snic-bl') + CCGen._available_models _sn_lumfunc= { 'M0': {'li11_gaussian': -16.79, 'li11_skewed': -17.74}, - 'mag_sct': {'li11_gaussian': [0.95, 0.95], 'li11_skewed': [1.35, 2.06]} + 'coh_sct': {'li11_gaussian': [0.95, 0.95], 'li11_skewed': [1.35, 2.06]} } _sn_fraction={ @@ -1356,11 +1307,11 @@ class SNIc_BLGen(CCGen): _available_rates = { # Rate from https://arxiv.org/abs/2009.01242, rates of subtype from figure 6 - 'ptf19': f"1.01e-4 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", + 'ptf19': f"lambda z: 1.01e-4 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270 - 'ztf20': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3", + 'ztf20': f"lambda z: 9.10e-5 * {_sn_fraction['ztf20']} * ({{h}}/0.70)**3", # Rate from https://arxiv.org/abs/2010.15270, pw from https://arxiv.org/pdf/1403.0007.pdf - 'ptf19_pw': f"9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" + 'ptf19_pw': f"lambda z: 9.10e-5 * {_sn_fraction['shivers17']} * ({{h}}/0.70)**3 * ((1 + z)**2.7/(1 + ((1 + z) / 2.9))**5.6)" } @@ -1404,27 +1355,26 @@ def _init_sim_model(self): SN simulation model. """ - if isinstance(self._params['model_config']['model_name'], str): - if self._params['model_config']['model_name'].lower() == 'all': + if isinstance(self._params['model_name'], str): + if self._params['model_name'].lower() == 'all': selected_models = self._available_models - elif self._params['model_config']['model_name'].lower() == 'vinc_nocorr': + elif self._params['model_name'].lower() == 'vin19_nocorr': selected_models = ut.select_Vincenzi_template(self._available_models,corr=False) - elif self._params['model_config']['model_name'].lower() == 'vinc_corr': + elif self._params['model_name'].lower() == 'vin19_corr': selected_models = ut.select_Vincenzi_template(self._available_models,corr=True) else: - selected_models = [self._params['model_config']['model_name']] + selected_models = [self._params['model_name']] model= [ut.init_sn_model(m) for m in selected_models] else: model = [ut.init_sn_model(m) - for m in self._params['model_config']['model_name']] + for m in self._params['model_name']] model = {i :m for i, m in enumerate(model)} return model - def _update_general_par(self): """Initialise the general parameters, depends on the SN simulation model. @@ -1441,7 +1391,7 @@ def _update_general_par(self): def _update_astrobj_par(self, n_obj, astrobj_par, seed=None): # -- Generate coherent mag scattering - astrobj_par['mag_sct'] = self.gen_coh_scatter(n_obj, seed=seed) + astrobj_par['coh_sct'] = self.gen_coh_scatter(n_obj, seed=seed) def gen_coh_scatter(self, n_sn, seed=None): @@ -1498,22 +1448,24 @@ def _add_print(self): str = '' return str - def _update_header(self, header): + def _update_header(self): + header = {} header['M0_band']='bessell_r' + + return header def _init_registered_rate(self): """SNIa_peculiar rates registry.""" - def init_M0_for_type(): + def init_M0_for_type(self): """Initialise absolute magnitude using default values from past literature works based on the type.""" - def gen_coh_scatter_for_type(n_sn, seed): + def gen_coh_scatter_for_type(self,n_sn, seed): """Generate n coherent mag scattering term using default values from past literature works based on the type.""" - class SNIax(SNIa_peculiar): """SNIaxclass. @@ -1534,4 +1486,5 @@ class SNIa_91bg(SNIa_peculiar): Parameters ---------- - same as TimeSeriesGen class """ \ No newline at end of file + same as TimeSeriesGen class """ + \ No newline at end of file diff --git a/snsim/geo_utils.py b/snsim/geo_utils.py new file mode 100644 index 0000000..1249695 --- /dev/null +++ b/snsim/geo_utils.py @@ -0,0 +1,68 @@ +"""This module contains usefull function for the survey and field geometry.""" +import numpy as np +import geopandas as gpd +from shapely import geometry as shp_geo +from shapely import ops as shp_ops +from .constants import _SPHERE_LIMIT_ + + +def _format_corner(corner, RA): + # -- Replace corners that cross sphere edges + # + # 0 ---- 1 + # | | + # 3 ---- 2 + # + # conditions : + # - RA_0 < RA_1 + # - RA_3 < RA_2 + # - RA_0 and RA_3 on the same side of the field center + # corner[fields, corner, subfields, ra/dec] + + sign = (corner[:, 3, :, 0] - RA[:, None]) * (corner[:, 0, :, 0] - RA[:, None]) < 0 + comp = corner[:, 0, :, 0] < corner[:, 3, :, 0] + + corner[:, 1, :, 0][corner[:, 1, :, 0] < corner[:, 0, :, 0]] += 2 * np.pi + corner[:, 2, :, 0][corner[:, 2, :, 0] < corner[:, 3, :, 0]] += 2 * np.pi + + corner[:, 0, :, 0][sign & comp] += 2 * np.pi + corner[:, 1, :, 0][sign & comp] += 2 * np.pi + + corner[:, 2, :, 0][sign & ~comp] += 2 * np.pi + corner[:, 3, :, 0][sign & ~comp] += 2 * np.pi + return corner + + +def _compute_area(polygon): + """Compute survey total area.""" + # It's an integration by dec strip + area = 0 + strip_dec = np.linspace(-np.pi/2, np.pi/2, 10_000) + for da, db in zip(strip_dec[:-1], strip_dec[1:]): + line = shp_geo.LineString([[0, (da + db) * 0.5], [2 * np.pi, (da + db) * 0.5]]) + if line.intersects(polygon): + dRA = line.intersection(polygon).length + area += dRA * (np.sin(db) - np.sin(da)) + return area + + +def _compute_polygon(corners): + """Create polygon on a sphere, check for edges conditions. + + Notes + ----- + corners[corner, subfields, ra/dec] + """ + + # Create polygons + polygons = gpd.GeoSeries([shp_geo.Polygon(corners[:, j, :]) for j in range(corners.shape[1])]) + + # Check if they intersect the 2pi edge line + int_mask = polygons.intersects(_SPHERE_LIMIT_) + + # If they do cut divide them in 2 and translate the one that is beyond the edge at -2pi + polydiv = gpd.GeoSeries(shp_ops.polygonize(polygons[int_mask].boundary.union(_SPHERE_LIMIT_))) + transl_mask = polydiv.boundary.bounds['maxx'] > 2 * np.pi + polydiv[transl_mask] = polydiv[transl_mask].translate(-2*np.pi) + + return shp_geo.MultiPolygon([*polygons[~int_mask].values, *polydiv.values]) diff --git a/snsim/io_utils.py b/snsim/io_utils.py index 9ee20df..c60ed70 100644 --- a/snsim/io_utils.py +++ b/snsim/io_utils.py @@ -224,7 +224,7 @@ def open_fit(file): return fit -def _read_sub_field_map(size, field_config): +def _read_sub_field_map(field_size_rad, field_config): """Read the sub-field map file. Parameters @@ -271,28 +271,33 @@ def _read_sub_field_map(size, field_config): dec_space += dic_symbol[lines[0]]['size'] used_dec -= 1 - subfield_ra_size = (size[0] - ra_space) / used_ra - subfield_dec_size = (size[1] - dec_space) / used_dec + subfield_ra_size = (field_size_rad[0] - ra_space) / used_ra + subfield_dec_size = (field_size_rad[1] - dec_space) / used_dec # Compute all ccd corner corner_dic = {} - dec_metric = size[1] / 2 + dec_metric = field_size_rad[1] / 2 for i, l in enumerate(subfield_map): if l[0] in dic_symbol and dic_symbol[l[0]]['type'] == 'dec': dec_metric -= dic_symbol[l[0]]['size'] else: - ra_metric = - size[0] / 2 + ra_metric = - field_size_rad[0] / 2 for j, elmt in enumerate(l): if elmt in dic_symbol.keys() and dic_symbol[elmt]['type'] == 'ra': ra_metric += dic_symbol[elmt]['size'] elif int(elmt) == -1: ra_metric += subfield_ra_size else: - corner_dic[int(elmt)] = np.array([ + field = np.array([ [ra_metric, dec_metric], [ra_metric + subfield_ra_size, dec_metric], [ra_metric + subfield_ra_size, dec_metric - subfield_dec_size], [ra_metric, dec_metric - subfield_dec_size]]) + if int(elmt) not in corner_dic: + corner_dic[int(elmt)] = np.array([field]) + else: + corner_dic[int(elmt)] = np.vstack([corner_dic[int(elmt)], [field]]) + ra_metric += subfield_ra_size dec_metric -= subfield_dec_size return corner_dic \ No newline at end of file diff --git a/snsim/nb_fun.py b/snsim/nb_fun.py index 2b948b1..daec8b1 100644 --- a/snsim/nb_fun.py +++ b/snsim/nb_fun.py @@ -77,8 +77,8 @@ def R_base(theta, phi, vec): return R @ vec -@guvectorize(["void(float64[:, :], float64[:, :], float64[:,:])"], - "(m, n),(m, n)->(m, n)", nopython=True) +@guvectorize(["(float64[:, :, :], float64[:, :], float64[:, :, :])"], + "(m, n, k),(k, m)->(m, n, k)", nopython=True) def new_coord_on_fields(ra_dec, ra_dec_frame, new_radec): """Compute new coordinates of an object in a list of fields frames. Parameters @@ -94,15 +94,15 @@ def new_coord_on_fields(ra_dec, ra_dec_frame, new_radec): numpy.ndarray(float, size = (2, ?)) The new coordinates of the obect in each field frame. """ - - for i in range(len(ra_dec_frame[0])): - vec = np.array([np.cos(ra_dec[0][i]) * np.cos(ra_dec[1][i]), - np.sin(ra_dec[0][i]) * np.cos(ra_dec[1][i]), - np.sin(ra_dec[1][i])]) - x, y, z = R_base(ra_dec_frame[0][i], ra_dec_frame[1][i], vec) - new_radec[0][i] = np.arctan2(y, x) - if new_radec[0][i] < 0: new_radec[0][i] += 2 * np.pi - new_radec[1][i] = np.arcsin(z) + for i in range(ra_dec_frame.shape[1]): + ra = ra_dec[i] + vec = np.vstack((np.cos(ra_dec[i, :, 0]) * np.cos(ra_dec[i, :, 1]), + np.sin(ra_dec[i, :, 0]) * np.cos(ra_dec[i, :, 1]), + np.sin(ra_dec[i, :, 1]))) + x, y, z = R_base(ra_dec_frame[0, i], ra_dec_frame[1, i], vec) + new_radec[i, :, 0] = np.arctan2(y, x) + new_radec[i, :, 0][new_radec[i, :, 0] < 0] += 2 * np.pi + new_radec[i, :, 1] = np.arcsin(z) @njit(cache=True) diff --git a/snsim/plasticc_model.py b/snsim/plasticc_model.py new file mode 100644 index 0000000..f743265 --- /dev/null +++ b/snsim/plasticc_model.py @@ -0,0 +1,83 @@ +import os +import sncosmo as snc +from snsim import __snsim_dir_path__ +import glob +import requests +import tarfile +import shutil + + +plasticc_repo = 'https://zenodo.org/records/6672739/' + +model_repo = { + 'slsn' : plasticc_repo + 'SIMSED.SLSN-I-MOSFIT.tar.gz', + 'sniax' : plasticc_repo + 'SIMSED.SNIax.tar.gz', + 'snia91bg' : plasticc_repo + 'SISIMSED.SNIa-91bg.tar.gz' + } + +def check_files_and_download(model_name): + """Check if model files are here and download from Plasticc repository if not. + availabele model are SLSN, SNIax, SNIa91bg + + Returns + ------- + None + No return, just download files. + + Notes + ----- + TODO : Change that for environement variable or cleaner solution + + """ + + data_dir_name = model_name.lower() + '_data' + + if not os.path.isdir(_snsim_dir_path__ + data_dir_name + '/'): + print("Dowloading model template files files from ", model_repo[model_name.lower()]) + os.mkdir(snsim_dir_path__ + data_dir_name) + url = model_repo[model_name.lower()] + response = requests.get(url, stream=True) + dir_tar = tarfile.open(fileobj=response.raw, mode="r|gz") + for file in dir_tar.getmembers(): + file.extractall( path=_snsim_dir_path__ + data_dir_name ) + shutil.rmtree(snsim_dir_path_ + model_repo[model_name.lower()] ) + + +def get_sed_listname(model_name): + + check_files_and_download(model_name) + data_dir_name = model_name.lower() + '_data' + + file_list=[] + for file in os.listdir(snsim_dir_path__ + data_dir_name): + name = filename.replace('.SED','') + file_list.append(name) + + return file_list + + + +def sncosmo_model_from_SED(model): + + import numpy as np + +phase = np.linspace(-50., 50., 11) + +disp = np.linspace(3000., 8000., 6) + +flux = np.repeat(np.array([[0.], [1.], [2.], [3.], [4.], [5.], + + [4.], [3.], [2.], [1.], [0.]]), + + 6, axis=1) + +source = sncosmo.TimeSeriesSource(phase, disp, flux) + +#the file are already well organized, just ask rick the unit of the flux + #create sncosmo model from it + + model = sncosmo.Model(source) + # init the model + + + diff --git a/snsim/plot_utils.py b/snsim/plot_utils.py index d8a07eb..3490bfb 100644 --- a/snsim/plot_utils.py +++ b/snsim/plot_utils.py @@ -75,7 +75,7 @@ def plot_lc( set_res=None, flux_limit=None, phase_limit=[-21,51], - mtpstyle='seaborn-deep', + mtpstyle='seaborn-v0_8-deep', dpi=100, savefig=False, savepath='LC', saveformat='png'): """Ploting a lightcurve flux table. @@ -127,7 +127,7 @@ def plot_lc( time = flux_table['time'] - t0 = meta['sim_t0'] + t0 = meta['t0'] z = meta['zobs'] time_th = np.linspace(t0 + ((phase_limit[0]+1.2) * (1 + z)), t0 + ((phase_limit[1]-1.2) * (1 + z)), 200) diff --git a/snsim/salt_utils.py b/snsim/salt_utils.py index 7ebba32..b2f35bf 100644 --- a/snsim/salt_utils.py +++ b/snsim/salt_utils.py @@ -3,6 +3,7 @@ import sncosmo as snc import numpy as np from . import utils as ut +from scipy.interpolate import RectBivariateSpline as spline2d def n21_x1_model(z, seed=None): """X1 distribution redshift dependant model from Nicolas et al. 2021. @@ -46,6 +47,84 @@ def n21_x1_model(z, seed=None): X1[~is_young] = dist_old.draw(np.sum(~is_young), seed=rand_gen.integers(low=1e3, high=1e6)) return X1 +def x1_mass_model(host_mass, seed=None): + + if host_mass is None: + raise ValueError('provide host_mass') + + rand_gen = np.random.default_rng(seed) + + #probability x1-mass from Popovic et al. 2021b + x1_bin,mass_bin,prob = ut.reshape_prob_data() + prob_x1_mass = spline2d(mass_bin,x1_bin,prob.T) + + #to avoid errors + host_mass = np.atleast_1d(host_mass) + + + dist_mass = (CustomRandom(lambda x: prob_x1_mass(m, x), + x1_bin.min(), x1_bin.max(),ndiv=10000) for m in host_mass) + + + return np.asarray([dist.draw(1, seed=rand_gen.integers(low=1e3, high=1e6))[0] for dist in dist_mass]) + + + +def n21_x1_mass_model(z, host_mass=None, seed=None): + + rand_gen = np.random.default_rng(seed) + + # Constants defines in the paper Nicolas et al 2021 + a = 0.51 + K = 0.87 + mu1 = 0.37 + mu2 = -1.22 + sig1 = 0.61 + sig2 = 0.56 + + if host_mass is None: + raise ValueError('provide host_mass') + + #probability x1-mass from Popovic et al. 2021b + x1_bin,mass_bin,prob = ut.reshape_prob_data() + prob_x1_mass = spline2d(mass_bin,x1_bin,prob.T) + + + # Just to avoid errors + z = np.atleast_1d(z) + host_mass = np.atleast_1d(host_mass) + + # Constants defines in the paper + a = 0.51 + K = 0.87 + mu1 = 0.37 + mu2 = -1.22 + sig1 = 0.61 + sig2 = 0.56 + + young_or_old = rand_gen.random(size=len(z)) + + # Apply the pdf eq 2 from Nicolas et al. 2021 + delta_z = 1 / (1 / (K * (1 + z)**2.8) + 1) # Probability to be young + is_young = young_or_old < delta_z + X1 = np.zeros(len(z)) + + + # Compute the distribution for old galaxies + pdf_old = lambda x: a * ut.gauss(mu1, sig1, x) + (1 - a) * ut.gauss(mu2, sig2, x) + dist_old = (CustomRandom(lambda x: pdf_old(x) * prob_x1_mass(m, x), + mu2 - 10 * sig2, mu1 + 10 * sig1,ndiv=10000) for m in host_mass[~is_young]) + + + #compute distribution for young galaxies + pdf_young = lambda x: ut.gauss(mu1,sig1,x) + dist_young = (CustomRandom(lambda x: pdf_old(x) * prob_x1_mass(m, x), + mu1 - 10 * sig1, mu1 + 10 * sig1,ndiv=10000) for m in host_mass[is_young]) + + X1[is_young] = np.asarray([dist.draw(1, seed=rand_gen.integers(low=1e3, high=1e6))[0] for dist in dist_young]) + X1[~is_young] = np.asarray([dist.draw(1, seed=rand_gen.integers(low=1e3, high=1e6))[0] for dist in dist_old]) + return X1 + def cov_x0_to_mb(x0, cov): """Convert x0,x1,c covariance into mB,x1,c covariance. diff --git a/snsim/sample.py b/snsim/sample.py index cc2cbc8..36116f8 100644 --- a/snsim/sample.py +++ b/snsim/sample.py @@ -37,7 +37,7 @@ def __init__(self, sample_name, sim_lcs, header, model_dir=None, dir_path=None): self._model_dir = model_dir self._dir_path = dir_path - self._sim_model = self._init_sim_model() + # self._sim_model = self._init_sim_model() self._fit_model = None self._fit_res = None @@ -75,9 +75,8 @@ def fromDFlist(cls, sample_name, sim_lcs, header, model_dir=None, dir_path=None) A SimSample class with the simulated lcs. """ - lcs = pd.concat(sim_lcs) - lcs.set_index(['ID'], append=True, inplace=True) - lcs = lcs.swaplevel() + IDs = (sim_lcs[i].attrs['ID'] for i in range(len(sim_lcs))) + lcs = pd.concat(sim_lcs, keys=IDs, names=['ID']) lcs.attrs = {lc.attrs['ID']: lc.attrs for lc in sim_lcs} return cls(sample_name, lcs, header, model_dir=model_dir, dir_path=dir_path) diff --git a/snsim/scatter.py b/snsim/scatter.py index e609b1d..0fc32b8 100644 --- a/snsim/scatter.py +++ b/snsim/scatter.py @@ -6,241 +6,166 @@ from . import nb_fun as nbf -def init_sn_sct_model(model, sct_mod): +def init_sn_sct_model(sct_mod, *args): """Add scattering effect on sncosmo model. Parameters ---------- - model : sncosmo.Model - The model on which add effects. sct_mod : str Name of the model to use. - Returns ------- None """ if sct_mod == 'G10': - model.add_effect(G10(model), 'G10_', 'rest') - + eff_dic = {'source': G10(*args), 'name': 'G10_', 'frame': 'rest'} elif sct_mod[:3] == 'C11': - model.add_effect(C11(model), 'C11_', 'rest') - if sct_mod == 'C11_1': - model.set(C11_Cuu=1.) - elif sct_mod == 'C11_2': - model.set(C11_Cuu=-1.) + eff_dic = {'source': C11, 'name': 'C11_', 'frame': 'rest'} + return eff_dic class G10(snc.PropagationEffect): - """G10 scattering effect for sncosmo. - - Parameters - ---------- - model : sncosmo.Model - The sncosmo Model of the SN. - - Attributes - ---------- - _parameters : list - List containing all the model parameters. - _minwave : float - The minimal wavelength of the effect. - _maxwave : float - The maximal wavelength of the effect. - _colordisp : function - The color dispersion of SALT model. - _param_names : list(str) - Names of the parameters. - param_names_latex : list(str) - Latex version of parameters names. - - Notes - ----- - Use colordisp file of salt and follow SNANA formalism, see arXiv:1209.2482 - - """ + """Guy (2010) SNe Ia non-coherent scattering. + + Implementation is done following arxiv:1209.2482.""" _param_names = ['L0', 'F0', 'F1', 'dL', 'RndS'] - param_names_latex = [r'\lambda_0', 'F_0', 'F_1', 'd_L', 'RS'] + param_names_latex = [r'\lambda_0', 'F_0', 'F_1', 'd_L', 'RndS'] - def __init__(self, model): + def __init__(self, SALTsource): """Initialize G10 class.""" - self._parameters = np.array([2157.3, 0.0, 1.08e-4, 800, - np.random.randint(low=1000, high=100000)]) - self._minwave = model.source.minwave() - self._maxwave = model.source.maxwave() - self._colordisp = model.source._colordisp + self._parameters = np.array([2157.3, 0.0, 1.08e-4, 800, np.random.randint(1e11)]) + self._colordisp = SALTsource._colordisp + self._minwave = SALTsource.minwave() + self._maxwave = SALTsource.maxwave() + + + #self._seed = np.random.SeedSequence() - def compute_sigma_nodes(self): """Computes the sigma nodes.""" - L0, F0, F1, dL, RS = self._parameters - - # Computes the sigma values + L0, F0, F1, dL = self._parameters[:-1] lam_nodes = np.arange(self._minwave, self._maxwave, dL) - - # Cover the whole wavelength range if lam_nodes.max() < self._maxwave: lam_nodes = np.append(lam_nodes, self._maxwave) - siglam_values = self._colordisp(lam_nodes) + siglam_values[lam_nodes < L0] *= 1 + (lam_nodes[lam_nodes < L0] - L0) * F0 siglam_values[lam_nodes > L0] *= 1 + (lam_nodes[lam_nodes > L0] - L0) * F1 - # Random drawing - siglam_values *= np.random.default_rng(int(RS)).normal(size=len(sigma_val)) - return lam_nodes, siglam_values def propagate(self, wave, flux): - """Propagate the effect to the flux. - - Parameters - ---------- - wave : float - wavelength. - flux : float - flux density at wavelength. - - Returns - ------- - numpy.ndarray(float) - Flux density with effect applied. - """ + """Propagate the effect to the flux."""# Draw the scattering lam_nodes, siglam_values = self.compute_sigma_nodes() + siglam_values *= np.random.default_rng(int(self._parameters[-1])).normal(size=len(lam_nodes)) magscat = ut.sine_interp(wave, lam_nodes, siglam_values) return flux * 10**(-0.4 * magscat) class C11(snc.PropagationEffect): """C11 scattering effect for sncosmo. + Use COV matrix between the vUBVRI bands from N. Chottard thesis. + Implementation is done following arxiv:1209.2482.""" - Parameters - ---------- - model : sncosmo.Model - The sncosmo Model of the SN. - - Attributes - ---------- - _parameters : list - List containing all the model parameters. - _minwave : float - The minimal wavelength of the effect. - _maxwave : float - The maximal wavelength of the effect. - _sigma_lam : numpy.ndarray(float, size = 6) - Value of the effective wavelengths of U'UBVRI bands. - _CORR_matrix : numpy.ndarray(float, sizee = (6,6)) - Correlation matrix of U'UBVRI bands scattering. - _sigma : numpy.ndarray(float, size = 6) - Mean scattering in U'UBVRI bands. - _param_names : list(str) - Names of the parameters. - param_names_latex : list(str) - Latex version of parameters names. - - Notes - ----- - Use COV matrix from N. Chottard thesis and follow SNANA formalism, see arXiv:1209.2482 + _param_names = ["CvU", 'Sf', 'RndS'] + param_names_latex = ["\rho_\mathrm{vU}", 'S_f', 'RndS'] + _minwave = 2000 + _maxwave = 11000 - """ - - _param_names = ['Cuu', 'Sc', 'RndS'] - param_names_latex = ["\rho_{u'u}", 'Sc', 'RS'] - - def __init__(self, model): + def __init__(self): """Initialise C11 class.""" - self._parameters = np.array([0., 1.3, np.random.randint(low=1000, high=100000)]) - self._minwave = model.source.minwave() - self._maxwave = model.source.maxwave() + self._parameters = np.array([0., 1.3, np.random.randint(1e11)]) - # U'UBVRI lambda eff - self._sigma_lam = np.array([2500.0, 3560.0, 4390.0, 5490.0, 6545.0, 8045.0]) - # U'UBVRI correlation matrix extract from SNANA, came from N.Chotard thesis + # vUBVRI lambda eff + self._lam_nodes = np.array([2500.0, 3560.0, 4390.0, 5490.0, 6545.0, 8045.0]) + + # vUBVRI correlation matrix extract from SNANA, came from N.Chotard thesis self._corr_matrix = np.array( - [[+1.000, 0.000, 0.000, 0.000, 0.000, 0.000], - [0.000, +1.000000, -0.118516, -0.768635, -0.908202, -0.219447], - [0.000, -0.118516, +1.000000, +0.570333, -0.238470, -0.888611], - [0.000, -0.768635, +0.570333, +1.000000, +0.530320, -0.399538], - [0.000, -0.908202, -0.238470, +0.530320, +1.000000, +0.490134], - [0.000, -0.219447, -0.888611, -0.399538, +0.490134, +1.000000]]) - # U'UBVRI sigma - self._sigma = np.array([0.5900, 0.06001, 0.040034, 0.050014, 0.040017, 0.080007]) - - @property - def covmat(self): - """Define the covariance matrix according to the choice made for COV_U'U. + [ + [+1.000000, 0.000000, 0.000000, 0.000000, 0.000000, 0.000000], + [ 0.000000, +1.000000, -0.118516, -0.768635, -0.908202, -0.219447], + [ 0.000000, -0.118516, +1.000000, +0.570333, -0.238470, -0.888611], + [ 0.000000, -0.768635, +0.570333, +1.000000, +0.530320, -0.399538], + [ 0.000000, -0.908202, -0.238470, +0.530320, +1.000000, +0.490134], + [ 0.000000, -0.219447, -0.888611, -0.399538, +0.490134, +1.000000] + ] + ) + + # vUBVRI sigma + self._variance = np.array([0.5900, 0.06001, 0.040034, 0.050014, 0.040017, 0.080007]) + + #self._seed = np.random.SeedSequence() - Returns - ------- - numpy.ndarray(float, size = (6,6)) - Matrice de covariance U'UBVRI. + def build_cov(self): + CvU, Sf = self._parameters[:-1] + + cov_matrix = self._corr_matrix.copy() + + # Set up the vU correlation + cov_matrix[0, 1:] = CvU * self._corr_matrix[1, 1:] + cov_matrix[1:, 0] = CvU * self._corr_matrix[1:, 1] - Notes - ----- - cor2cov is multiply by _parameters[1] to rescale the error due to the - fact taht we pass from broadband to continuous wavelengths. - """ - covmat = np.zeros((6, 6)) - for i in range(6): - for j in range(i + 1): - cor2cov = self._corr_matrix[i, j] - if i != 0 and j == 0: - cor2cov = self._parameters[0] * self._corr_matrix[i, 1] - sigi_sigj = self._sigma[i] * self._sigma[j] - cor2cov *= sigi_sigj - covmat[i, j] = covmat[j, i] = cor2cov * self._parameters[1] - return covmat - - @property - def scatter(self): - """Generate random scatter. + # Convert corr to cov + cov_matrix *= np.outer(self._variance, + self._variance) + + # Rescale covariance as in arXiv:1209.2482 + cov_matrix *= Sf + return cov_matrix + + def propagate(self, wave, flux): + """Propagate the effect to the flux.""" + + cov_matrix = self.build_cov() + + # Draw the scattering + siglam_values = np.random.default_rng(int(self._parameters[-1])).multivariate_normal(np.zeros(len(self._lam_nodes)), + cov_matrix) - Returns - ------- - numpy.ndarray - The 6 values of random scatter of the SN. - """ - RS = self._parameters[-1] - mu = np.zeros(6) - scat = np.random.default_rng(int(RS)).multivariate_normal(mu, - self.covmat, - check_valid='raise') - return scat + inf_mask = wave <= self._lam_nodes[0] + sup_mask = wave >= self._lam_nodes[-1] + + magscat = np.zeros(len(wave)) + magscat[inf_mask] = siglam_values[0] + magscat[sup_mask] = siglam_values[-1] + magscat[~inf_mask & ~sup_mask] = ut.sine_interp(wave[~inf_mask & ~sup_mask], + self._lam_nodes, siglam_values) + + return flux * 10**(-0.4 * magscat) - def propagate(self, wave, flux): - """Propagate the effect to the flux. +########################################## +#GENERATE terms for BS20 scattering model# +########################################## +def gen_BS20_scatter(n_sn, seed=None): + """Generate n coherent mag scattering term. Parameters ---------- - wave : float - wavelength. - flux : float - flux density at wavelength. + n : int + Number of mag scattering terms to generate. + seed : int, optional + Random seed. Returns ------- numpy.ndarray(float) - Flux density with effect applied. + numpy array containing scattering terms generated. + """ - if self._parameters[0] not in [0., 1., -1.]: - raise ValueError('Cov_uu must be 1,-1 or 0') - scatter = self.scatter - scattering = np.zeros(len(wave)) - for i, w in enumerate(wave): - if w >= self._sigma_lam[-1]: - scattering[i] = scatter[-1] - elif w <= self._sigma_lam[0]: - scattering[i] = scatter[0] - else: - scattering[i] = ut.sine_interp(w, self._sigma_lam, scatter) - return flux * 10**(-0.4 * scattering) + rand_gen = np.random.default_rng(seed) + + lower,upper = 0.5, 1000 + mu, sigma = 2,1.4 + X = stats.truncnorm((lower - mu) / sigma, (upper - mu) / sigma, loc=mu, scale=sigma) + Rv= X.rvs(n_sn, random_state=seed) + E_dust = rand_gen.exponential(scale=0.1, size=n_sn) #value fitted in Brout and Scolnic 2020 shown in table 1 + beta_sn = rand_gen.normal(loc=1.98, scale=0.35 , size=n_sn) #value of mean and sigma are fitted in Brout and Scolnic 2020 + c_int = rand_gen.normal(loc= -0.084 , scale=0.042 , size=n_sn) #value of mean and sigma are fitted in Brout and Scolnic 2020 -class BS21(snc.PropagationEffect): - """G10 scattering effect for sncosmo. - ask Rick """ \ No newline at end of file + return beta_sn, Rv, E_dust, c_int \ No newline at end of file diff --git a/snsim/simu.py b/snsim/simu.py index d4062a6..0e676bb 100644 --- a/snsim/simu.py +++ b/snsim/simu.py @@ -10,7 +10,6 @@ from . import survey_host as sh from .constants import SN_SIM_PRINT, VCMB, L_CMB, B_CMB from . import dust_utils as dst_ut - from .generators import __GEN_DIC__ from .sample import SimSample @@ -56,7 +55,7 @@ class Simulator: | duration_for_rate: FAKE DURATION ONLY USE TO GENERATE N OBJ # Optional | mw_dust: | model: MOD_NAME - | rv: Rv # Optional, default Rv = 3.1 + | r_v: Rv # Optional, default Rv = 3.1 | snia_gen: | n_sn: NUMBER OF SN TO GENERATE # Optional | rate: rate of SN/Mpc^3/year # Optional, default=3e-5 @@ -84,10 +83,6 @@ class Simulator: | host_file: 'PATH/TO/HOSTFILE' | distrib: 'rate' or 'random' # Optional, default = 'rate' | key_dic: {'column_name': 'new_column_name', etc} # Optional, to change columns names - | dipole: # Optional, add a dipole as dM = A + B * cos(theta) - | coord: [RA, Dec] # Direction of the dipole - | A: A_parameter - | B: B_parameter | dask: # Optional for using dask parallelization | use: True or False | nworkers: NUMBER OF WORKERS # used to adjust work distribution @@ -139,23 +134,21 @@ def __init__(self, param_dic, print_config=False): # -- Init host object if 'host' in self.config: - self._host = sh.SnHost(self.config['host'], z_range=self.z_range, - geometry=self.survey._envelope) + self._host = sh.SnHost( + self.config['host'], + z_range=self.z_range, + geometry=self.survey._envelope) else: self._host = None # -- Init mw dust if 'mw_dust' in self.config: mw_dust = self.config['mw_dust'] + if 'r_v' not in self.config['mw_dust']: + self.config['mw_dust']['r_v'] = 3.1 else: mw_dust = None - # -- Init dipole - if 'dipole' in self.config: - dipole = self.config['dipole'] - else: - dipole = None - # Init the cuts on lightcurves self._nep_cut = self._init_nep_cuts() @@ -169,17 +162,16 @@ def __init__(self, param_dic, print_config=False): if object_name in self.config: # -- Get which generator correspond to which transient in snsim.generators gen_class = getattr(generators, object_genclass) - self._generators.append(gen_class(self.config[object_name], - self.cmb, - self.cosmology, - time_range, - z_range=self.z_range, - peak_out_trange=True, - vpec_dist=self.vpec_dist, - host=self.host, - mw_dust=mw_dust, - dipole=dipole, - geometry=self.survey._envelope)) + self._generators.append(gen_class( + self.config[object_name], + self.cosmology, + time_range, + cmb=self.cmb, + z_range=self.z_range, + vpec_dist=self.vpec_dist, + host=self.host, + mw_dust=mw_dust, + geometry=self.survey._envelope)) # -- Cadence sim or n fixed if 'force_n' in self.config[object_name]: self._use_rate.append(False) @@ -209,7 +201,7 @@ def _init_nep_cuts(self): # -- Set default mintime, maxtime (restframe) snc_mintime = -20 snc_maxtime = 50 - #maybe default timerange to change to be more flexible with sncc + # TODO : maybe default timerange to change to be more flexible with sncc cut_list = [] if 'nep_cut' in self.config['sim_par']: @@ -229,7 +221,8 @@ def _init_nep_cuts(self): dt = [('nep', np.int8), ('mintime', np.int16), ('maxtime', np.int16), ('band', np.str_, 8)] return np.asarray(cut_list, dtype=dt) - def _gen_n_sn(self, rand_gen, z_shell_time_rate, duration_in_days, area=4 * np.pi): + @staticmethod + def _gen_n_sn(z_shell_time_rate, duration_in_days, seed=None, area=4 * np.pi): """Generate the number of obj with Poisson law. Parameters @@ -243,9 +236,10 @@ def _gen_n_sn(self, rand_gen, z_shell_time_rate, duration_in_days, area=4 * np.p Number of obj to simulate. """ + rng = np.random.default_rng(seed) nsn = duration_in_days / 365.25 * area / (4 * np.pi) * np.sum(z_shell_time_rate) nsn = int(np.round(nsn)) - return rand_gen.poisson(nsn) + return np.max([rng.poisson(nsn), 1]) def _get_cosmo_header(self): """Return the header for cosmology model used.""" @@ -275,10 +269,11 @@ def simulate(self): print('-----------------------------------------------------------\n') - print(f"SIM NAME : {self.sim_name}\n" - f"CONFIG FILE : {self._yml_path}\n" - f"SIM WRITE DIRECTORY : {self.config['data']['write_path']}\n" - f"SIMULATION RANDSEED : {self.randseed}") + print( + f"SIM NAME : {self.sim_name}\n" + f"CONFIG FILE : {self._yml_path}\n" + f"SIM WRITE DIRECTORY : {self.config['data']['write_path']}\n" + f"SIMULATION RANDSEED : {self.randseed}") if 'host_file' in self.config: print(f"HOST FILE : {self.config['host_file']}") @@ -303,8 +298,9 @@ def simulate(self): print('\n-----------------------------------------------------------\n') if 'mw_dust' in self.config: - print("Use mw dust model : " - f"{self.config['mw_dust']['model']} with RV = {self.config['mw_dust']['rv']}") + print( + "Use mw dust model : " + f"{self.config['mw_dust']['model']} with RV = {self.config['mw_dust']['r_v']}") print('\n-----------------------------------------------------------\n') @@ -318,36 +314,41 @@ def simulate(self): print('\n-----------------------------------------------------------\n') - # -- Create the random generator object with the rand seed - rand_gen = np.random.default_rng(self.randseed) - seed_list = rand_gen.integers(1000, 1e6, size=len(self.generators)) - + # -- Create the SeedSequence object with the root rand seed + SeedSeq = np.random.SeedSequence(self.randseed) + # -- Change the samples attribute to store obj, init ID self._samples = [] Obj_ID = 0 file_str='' # -- Simulation for each of the selected obj. - for use_rate, seed, gen in zip(self._use_rate, seed_list, self.generators): + for use_rate, gen in zip(self._use_rate, self.generators): sim_time = time.time() + seed = SeedSeq.spawn(1)[0] if use_rate: - lcs_list = self._cadence_sim(np.random.default_rng(seed), gen, Obj_ID) + lcs_list = self._cadence_sim(seed, gen, Obj_ID) else: - lcs_list = self._fix_nsn_sim(np.random.default_rng(seed), gen, Obj_ID) - - self._samples.append(SimSample.fromDFlist(self.sim_name + '_' + gen._object_type, - lcs_list, - {'seed': seed, - **gen._get_header(), - 'cosmo': self._get_cosmo_header()}, - model_dir=None, - dir_path=self.config['data']['write_path'])) - - print(f'{len(lcs_list)} {gen._object_type} lcs generated' - f' in {time.time() - sim_time:.1f} seconds') + lcs_list = self._fix_nsn_sim(seed, gen, Obj_ID) + + self._samples.append(SimSample.fromDFlist( + self.sim_name + '_' + gen._object_type, + lcs_list, + {'seed': seed.entropy, + 'seed_key': seed.spawn_key, + **gen._get_header(), + 'cosmo': self._get_cosmo_header()}, + model_dir=None, + dir_path=self.config['data']['write_path'])) + + print( + f'{len(lcs_list)} {gen._object_type} lcs generated' + f' in {time.time() - sim_time:.1f} seconds') write_time = time.time() - self._samples[-1]._write_sim(self.config['data']['write_path'], - self.config['data']['write_format']) + + self._samples[-1]._write_sim( + self.config['data']['write_path'], + self.config['data']['write_format']) print(f'Sim file write in {time.time() - write_time:.1f} seconds') @@ -360,17 +361,17 @@ def simulate(self): print('OUTPUT FILE(S) : ') print(file_str) - def _sim_lcs(self, generator, n_obj, Obj_ID=0, seed=None): + def _sim_lcs(self, seed, generator, n_obj, Obj_ID=0): """Simulate AstrObj lcs. Parameters ---------- - generator : snsim.generator - The parameter generator class + seed : np.random.SeedSequence + Random Seed. n_obj : int The nummber of object to generate Obj_ID : int, optional - The first ID of AstrObj, by default 0 + The first ID of AstrObj, by default 0 seed : int, optional The random seed to generate parameters, by default None @@ -380,35 +381,31 @@ def _sim_lcs(self, generator, n_obj, Obj_ID=0, seed=None): List of the AstrObj LCs """ - if seed is None: - seed = np.random.randint(1e3, 1e6) - - rand_gen = np.random.default_rng(seed) - + + seeds = seed.spawn(2) # -- Init lcs list lcs = [] # -- Generate n base param - param_tmp = generator.gen_astrobj_par(n_obj, rand_gen.integers(1000, 1e6), - min_max_t=True) - - # -- Set up obj parameters - model_t_range = (generator.snc_model_time[0], generator.snc_model_time[1]) + param_tmp = generator.gen_basic_par(n_obj, seeds[0], + min_max_t=True) # -- Select observations that pass all the cuts - epochs, params = self.survey.get_observations(param_tmp, - phase_cut=model_t_range, - nep_cut=self.nep_cut, - IDmin=Obj_ID, - use_dask=self.config['dask']['use'], - npartitions=self.config['dask']['nworkers']) + epochs, params = self.survey.get_observations( + param_tmp, + phase_cut=None, + nep_cut=self.nep_cut, + IDmin=Obj_ID, + use_dask=self.config['dask']['use'], + npartitions=self.config['dask']['nworkers']) if params is None: raise RuntimeError('None of the object pass the cuts...') - - # -- Generate the object - obj_list = generator(rand_seed=rand_gen.integers(1e3, 1e6), - astrobj_par=params) + # -- Generate the object + obj_list = generator( + seed=seeds[1], + basic_par=params) + # -- TO DO: dask it when understanding the random pickel-sncosmo error # if self.config['dask']['use']: @@ -420,19 +417,18 @@ def _sim_lcs(self, generator, n_obj, Obj_ID=0, seed=None): # lcs += dask.compute([dask.delayed(obj).gen_flux(epochs.loc[[obj.ID]]) # for obj in obj_list[imin:imax]])[0] # else: - lcs = [obj.gen_flux(epochs.loc[[obj.ID]]) for obj in obj_list] + lcs = [obj.gen_flux(epochs.loc[obj.ID]) for obj in obj_list] + return lcs - def _cadence_sim(self, rand_gen, generator, Obj_ID=0): + def _cadence_sim(self, seed, generator, Obj_ID=0): """Simulate a number of AstrObj according to poisson law. Parameters ---------- - rand_gen : numpy.random.default_rng - Numpy random generator. - shell_time_rate : numpy.ndarray - An array that contains sn time rate in each shell. - + seed : np.random.SeedSequence + Random Seed. + Returns ------- list(pandas.Dataframe) @@ -450,27 +446,31 @@ def _cadence_sim(self, rand_gen, generator, Obj_ID=0): 7- Apply observation and selection cuts to SN 8- Genertate fluxes """ + seeds = seed.spawn(2) # -- Generate the number of SN if 'duration_for_rate' in self.config['sim_par']: duration = self.config['sim_par']['duration_for_rate'] else: duration = generator.time_range[1] - generator.time_range[0] - n_obj = self._gen_n_sn(rand_gen, generator._z_time_rate[1], - duration, area=self.survey._envelope_area) + n_obj = self._gen_n_sn( + generator._z_time_rate[1], + duration, + seed=seeds[0], + area=self.survey._envelope_area) - lcs = self._sim_lcs(generator, n_obj, - Obj_ID=Obj_ID, seed=rand_gen.integers(1e3, 1e6)) + lcs = self._sim_lcs(seeds[1], generator, n_obj, + Obj_ID=Obj_ID) return lcs - def _fix_nsn_sim(self, rand_gen, generator, Obj_ID=0): + def _fix_nsn_sim(self, seed, generator, Obj_ID=0): """Simulate a fixed number of AstrObj. Parameters ---------- - rand_gen : numpy.random.default_rng - Numpy random generator. + seed : np.random.SeedSequence + Random Seed. Returns ------- @@ -485,8 +485,11 @@ def _fix_nsn_sim(self, rand_gen, generator, Obj_ID=0): raise_trigger = 0 n_to_sim = generator._params['force_n'] while len(lcs) < generator._params['force_n']: - lcs += self._sim_lcs(generator, n_to_sim, - Obj_ID=len(lcs), seed=rand_gen.integers(1e3, 1e6)) + lcs += self._sim_lcs( + seed, + generator, + n_to_sim, + Obj_ID=len(lcs)) # -- Arbitrary cut to stop the simulation if no SN are geenrated if n_to_sim == generator._params['force_n'] - len(lcs): @@ -494,7 +497,7 @@ def _fix_nsn_sim(self, rand_gen, generator, Obj_ID=0): if raise_trigger > 2 * len(self.survey.obs_table['expMJD']): raise RuntimeError('Cuts are too stricts') continue - + n_to_sim = generator._params['force_n'] - len(lcs) return lcs diff --git a/snsim/survey_host.py b/snsim/survey_host.py index 86513b1..bc1cc47 100644 --- a/snsim/survey_host.py +++ b/snsim/survey_host.py @@ -13,9 +13,9 @@ from shapely import ops as shp_ops import dask.dataframe as daskdf from . import utils as ut +from . import geo_utils as geo_ut from . import io_utils as io_ut from . import nb_fun as nbf -from .constants import C_LIGHT_KMS class SurveyObs: @@ -26,27 +26,28 @@ class SurveyObs: survey_config : dic It contains all the survey configuration. - | survey_config - | ├── survey_file PATH TO SURVEY FILE - | ├── ra_size RA FIELD SIZE IN DEG -> float - | ├── dec_size DEC FIELD SIZE IN DEG -> float - | ├── gain CCD GAIN e-/ADU -> float - | ├── start_day STARTING DAY -> float or str, opt - | ├── end_day ENDING DAY -> float or str, opt - | ├── duration SURVEY DURATION -> float, opt - | ├── zp FIXED ZEROPOINT -> float, opt - | ├── survey_cut, CUT ON DB FILE -> dict, opt - | ├── add_data, LIST OF KEY TO ADD METADATA -> list(str), opt - | ├── field_map, PATH TO SUBFIELD MAP FILE -> str, opt - | └── sub_field, SUBFIELD KEY -> str, opt + | survey_config + | ├── survey_file PATH TO SURVEY FILE + | ├── ra_size RA FIELD SIZE IN DEG -> float + | ├── dec_size DEC FIELD SIZE IN DEG -> float + | ├── gain CCD GAIN e-/ADU -> float + | ├── start_day STARTING DAY -> float or str, opt + | ├── end_day ENDING DAY -> float or str, opt + | ├── duration SURVEY DURATION -> float, opt + | ├── zp FIXED ZEROPOINT -> float, opt + | ├── survey_cut, CUT ON DB FILE -> dict, opt + | ├── add_data, LIST OF KEY TO ADD METADATA -> list(str), opt + | ├── field_map, PATH TO SUBFIELD MAP FILE -> str, opt + | └── sub_field, SUBFIELD KEY -> str, opt """ # -- Basic keys needed in survey file (+ noise) - _base_keys = ['expMJD', - 'filter', - 'fieldID', - 'fieldRA', - 'fieldDec'] + _base_keys = [ + 'expMJD', + 'filter', + 'fieldID', + 'fieldRA', + 'fieldDec'] def __init__(self, survey_config): """Initialize SurveyObs class.""" @@ -64,7 +65,7 @@ def __init__(self, survey_config): self._sub_field_corners = self._init_fields_map(field_map) self._envelope, self._envelope_area = self._compute_envelope() - + def _compute_envelope(self): """Compute envelope of survey geometry and it's area. @@ -82,21 +83,18 @@ def _compute_envelope(self): # Represent them as rectangle restfield_corners = self._init_fields_map('rectangle') - f_RA = [minRA, maxRA, maxRA, minRA] - f_Dec = [maxDec, maxDec, minDec, minDec] - - sub_fields_corners = np.broadcast_to(restfield_corners[0], (4, 4, 2)) - - corners = {} - for i in range(4): - corners[i] = nbf.new_coord_on_fields(sub_fields_corners[:, i].T, - [f_RA, f_Dec]) - corners = ut._format_corner(corners, f_RA) - envelope = shp_ops.unary_union([ut._compute_polygon([[corners[i][0][j], - corners[i][1][j]] - for i in range(4)]) - for j in range(4)]).envelope - envelope_area = ut._compute_area(envelope) + f_RA = np.array([minRA, maxRA, maxRA, minRA]) + f_Dec = np.array([maxDec, maxDec, minDec, minDec]) + + sub_fields_corners = np.broadcast_to(restfield_corners[0], (4, *restfield_corners[0].shape)) + + corners = np.stack([nbf.new_coord_on_fields(sub_fields_corners[:, :, i, :], + np.stack([f_RA, f_Dec])) for i in range(4)], axis=1) + + corners = geo_ut._format_corner(corners, f_RA) + + envelope = shp_ops.unary_union([geo_ut._compute_polygon(corners[i]) for i in range(4)]).envelope + envelope_area = geo_ut._compute_area(envelope) return envelope, envelope_area def __str__(self): @@ -162,10 +160,11 @@ def _read_start_end_days(self, obs_dic): end_day = ut.init_astropy_time(end_day) if end_day.mjd > max_mjd or start_day.mjd < min_mjd: - warnings.warn(f'Starting day {start_day.mjd:.3f} MJD or' - f'Ending day {end_day.mjd:.3f} MJD is outer of' - f'the survey range : {min_mjd:.3f} - {max_mjd:.3f}', - UserWarning) + warnings.warn( + f'Starting day {start_day.mjd:.3f} MJD or' + f'Ending day {end_day.mjd:.3f} MJD is outer of' + f'the survey range : {min_mjd:.3f} - {max_mjd:.3f}', + UserWarning) if end_day.mjd < start_day.mjd: raise ValueError("The ending day is before the starting day !") @@ -343,7 +342,6 @@ def _init_data(self): end_day = ut.init_astropy_time(maxMJDinObs) return obs_dic, (start_day, end_day) - def _init_fields_map(self, field_config): """Init the subfield map parameters. @@ -359,10 +357,11 @@ def _init_fields_map(self, field_config): """ if field_config == 'rectangle': - sub_fields_corners = {0: np.array([[-self.field_size_rad[0] / 2, self.field_size_rad[1] / 2], - [ self.field_size_rad[0] / 2, self.field_size_rad[1] / 2], - [ self.field_size_rad[0] / 2, -self.field_size_rad[1] / 2], - [-self.field_size_rad[0] / 2, -self.field_size_rad[1] / 2]])} + sub_fields_corners = {0: np.array( + [[[-self.field_size_rad[0] / 2, self.field_size_rad[1] / 2], + [ self.field_size_rad[0] / 2, self.field_size_rad[1] / 2], + [ self.field_size_rad[0] / 2, -self.field_size_rad[1] / 2], + [-self.field_size_rad[0] / 2, -self.field_size_rad[1] / 2]]])} else: sub_fields_corners = io_ut._read_sub_field_map(self.field_size_rad, field_config) @@ -392,34 +391,33 @@ def _match_radec_to_obs(df, ObjPoints, config, sub_fields_corners): # -- Compute max and min of table section minMJD = df.expMJD.min() maxMJD = df.expMJD.max() - + ObjPoints = ObjPoints[(maxMJD >= ObjPoints.min_t) & (ObjPoints.max_t >= minMJD)] # -- Map field and rcid corners to their coordinates if 'sub_field' in config: field_corners = np.stack(df[config['sub_field']].map(sub_fields_corners).values) else: - field_corners = np.broadcast_to(sub_fields_corners[0], (len(df), 4, 2)) - - corner = {} - for i in range(4): - corner[i] = nbf.new_coord_on_fields(field_corners[:, i].T, - [df.fieldRA.values, df.fieldDec.values]) + field_corners = np.broadcast_to(sub_fields_corners[0], (len(df), *sub_fields_corners[0].shape)) - corner = ut._format_corner(corner, df.fieldRA.values) + corners = np.stack([nbf.new_coord_on_fields( + field_corners[:, :, i, :], + np.array([df.fieldRA.values, df.fieldDec.values])) + for i in range(4)], axis=1) + corners = geo_ut._format_corner(corners, df.fieldRA.values) + # -- Create shapely polygon - geometry = [ut._compute_polygon([[corner[i][0][j], corner[i][1][j]] for i in range(4)]) - for j in range(len(df))] + fgeo = np.vectorize(lambda i: geo_ut._compute_polygon(corners[i])) GeoS = gpd.GeoDataFrame(data=df, - geometry=geometry) + geometry=fgeo(np.arange(df.shape[0]))) join = ObjPoints.sjoin(GeoS, how="inner", predicate="intersects") - join['phase'] = (join['expMJD'] - join['sim_t0']) / join['1_zobs'] + join['phase'] = (join['expMJD'] - join['t0']) / join['1_zobs'] - return join.drop(columns=['geometry', 'index_right', 'min_t', 'max_t', '1_zobs', 'sim_t0']) + return join.drop(columns=['geometry', 'index_right', 'min_t', 'max_t', '1_zobs', 't0']) def get_observations(self, params, phase_cut=None, nep_cut=None, IDmin=0, use_dask=False, npartitions=None): @@ -431,7 +429,7 @@ def get_observations(self, params, phase_cut=None, nep_cut=None, IDmin=0, Obj ra coord [rad]. dec : numpy.ndarray(float) or float Obj dec coord [rad]. - sim_t0 : numpy.ndarray(float) or float + t0 : numpy.ndarray(float) or float Obj sncosmo model peak time. MinT : numpy.ndarray(float) or float Obj sncosmo model mintime. @@ -449,7 +447,7 @@ def get_observations(self, params, phase_cut=None, nep_cut=None, IDmin=0, """ params = params.copy() - ObjPoints = gpd.GeoDataFrame(data=params[['sim_t0', 'min_t', 'max_t', '1_zobs']], + ObjPoints = gpd.GeoDataFrame(data=params[['t0', 'min_t', 'max_t', '1_zobs']], geometry=gpd.points_from_xy(params['ra'], params['dec']), index=params.index) @@ -466,8 +464,9 @@ def get_observations(self, params, phase_cut=None, nep_cut=None, IDmin=0, align_dataframes=False, meta=meta).compute() else: - ObsObj = self._match_radec_to_obs(self.obs_table, ObjPoints, - self.config, self._sub_field_corners) + ObsObj = self._match_radec_to_obs( + self.obs_table, ObjPoints, + self.config, self._sub_field_corners) # -- Phase cut if phase_cut is not None: ObsObj = ObsObj[(ObsObj.phase >= phase_cut[0]) & (ObsObj.phase <= phase_cut[1])] @@ -484,7 +483,7 @@ def get_observations(self, params, phase_cut=None, nep_cut=None, IDmin=0, params = params.loc[ObsObj.index.unique()] # -- Reset index - new_idx = {k:IDmin + i for i, k in enumerate(ObsObj.index.unique())} + new_idx = {k: IDmin + i for i, k in enumerate(ObsObj.index.unique())} ObsObj['ID'] = ObsObj.index.map(new_idx) params['ID'] = params.index.map(new_idx) @@ -550,16 +549,17 @@ def show_map(self, ax=None): fig, ax = plt.subplots() for k, corners in self._sub_field_corners.items(): corners_deg = np.degrees(corners) - p = Polygon(corners_deg, color='r', fill=False) - ax.add_patch(p) - x_text = 0.5 * (corners_deg[0][0] + corners_deg[1][0]) - y_text = 0.5 * (corners_deg[0][1] + corners_deg[3][1]) - ax.text(x_text, y_text, k, ha='center', va='center') + polist = [Polygon(cd, color='r', fill=False) for cd in corners_deg] + for p in polist: + ax.add_patch(p) + x_text = 0.5 * (p.xy[0][0] + p.xy[1][0]) + y_text = 0.5 * (p.xy[0][1] + p.xy[3][1]) + ax.text(x_text, y_text, k, ha='center', va='center') ax.set_xlabel('RA [deg]') ax.set_ylabel('Dec [deg]') ax.set_xlim(-self.config['ra_size'] / 2 - 0.5, - self.config['ra_size'] / 2 + 0.5) - ax.set_ylim(-self.config['dec_size'] / 2 - 0.5, + self.config['ra_size'] / 2 + 0.5) + ax.set_ylim(-self.config['dec_size'] / 2 - 0.5, self.config['dec_size'] / 2 + 0.5) ax.set_aspect('equal') if ax is None: @@ -640,7 +640,7 @@ class SnHost: z_range : list(float), opt The redshift range. """ - _dist_options = ['rate', 'random'] + _dist_options = ['rate', 'random', 'mass', 'mass_sfr', 'sfr'] def __init__(self, config, z_range=None, geometry=None): """Initialize SnHost class.""" @@ -655,6 +655,7 @@ def __init__(self, config, z_range=None, geometry=None): elif self.config['distrib'].lower() not in self._dist_options: raise ValueError(f"{self.config['distrib']} is not an available option," f"distributions are {self._dist_options}") + @property def config(self): """Get the configuration dic of host.""" @@ -722,7 +723,7 @@ def _read_host_file(self, z_range): host_list.reset_index(drop=True, inplace=True) return z_range, host_list - def compute_weights(self, rate=None): + def compute_weights(self, rate=None, sn_type=None, cosmology = None): """Compute the weights for random choice. Parameters @@ -744,11 +745,42 @@ def compute_weights(self, rate=None): weights = rate(self.table['zcos']) / (1 + self.table['zcos']) # X mass X # Normalize the weights weights /= weights.sum() + elif self.config['distrib'].lower() == 'mass': + if rate is None: + raise ValueError("rate should be set to use 'rate' distribution") + # Take into account rate is divide by (1 + z) + weights_rate = rate(self.table['zcos']) / (1 + self.table['zcos']) + #compute mass weight + weights_mass = ut.compute_weight_mass_for_type(mass=self.table['host_mass'], sn_type=sn_type, cosmology=cosmology) + weights = weights_rate * weights_mass + #normalize + weights /= weights.sum() + elif self.config['distrib'].lower() == 'sfr': + if rate is None: + raise ValueError("rate should be set to use 'rate' distribution") + # Take into account rate is divide by (1 + z) + weights_rate = rate(self.table['zcos']) / (1 + self.table['zcos']) + #compute SFR weight + weights_SFR = ut.compute_weight_SFR_for_type(SFR=self.table['host_SFR'], sn_type=sn_type, cosmology=cosmology) + weights = weights_rate * weights_SFR + #normalize + weights /= weights.sum() + elif self.config['distrib'].lower() == 'mass_sfr': + if rate is None: + raise ValueError("rate should be set to use 'rate' distribution") + # Take into account rate is divide by (1 + z) + weights_rate = rate(self.table['zcos']) / (1 + self.table['zcos']) + #compute SFR and mass weight + weights_mass = ut.compute_weight_mass_for_type(mass=self.table['host_mass'], sn_type=sn_type, cosmology=cosmology) + weights_SFR = ut.compute_weight_SFR_for_type(SFR=self.table['host_SFR'], sn_type=sn_type, cosmology=cosmology) + weights = weights_rate * (weights_mass + weights_SFR) + #normalize + weights /= weights.sum() return weights - #elif self.config['distrib'].lower() == 'gal_prop': - #weights that depends on galaxy properties, it will depend on the SN type, to figure out implementation - #see vincenzi et al, and ask alex kim fo his model + # elif self.config['distrib'].lower() == 'gal_prop': + # weights that depends on galaxy properties, it will depend on the SN type, to figure out implementation + # see vincenzi et al, and ask alex kim fo his model def random_choice(self, n, seed=None, rate=None): """Randomly select hosts. @@ -768,7 +800,7 @@ def random_choice(self, n, seed=None, rate=None): """ rand_gen = np.random.default_rng(seed) - weights = self.compute_weights(rate=rate) + weights = self.compute_weights(rate=rate, sn_type=sn_type, cosmology=cosmology) if self._geometry is None: idx = rand_gen.choice(self.table.index, p=weights, size=n) diff --git a/snsim/tests/__init__.py b/snsim/tests/__init__.py new file mode 100644 index 0000000..8a18ce8 --- /dev/null +++ b/snsim/tests/__init__.py @@ -0,0 +1,3 @@ +""" +Test package. +""" \ No newline at end of file diff --git a/snsim/tests/test_astrobj.py b/snsim/tests/test_astrobj.py new file mode 100644 index 0000000..c21e39c --- /dev/null +++ b/snsim/tests/test_astrobj.py @@ -0,0 +1,74 @@ +import snsim +import sncosmo as snc +import numpy as np +import pandas as pd +from numpy.testing import assert_allclose, assert_approx_equal +from snsim import astrobj as sn_astrobj + + +class TestSNIa: + def setup_class(self): + """Create a SNIa.""" + + # Set the cosmology (astropy.cosmology object) + cosmology = {'name': 'planck18'} + cosmo = snsim.utils.set_cosmo(cosmology) + + # Fake position + zcos = 0.1 + coords = [0., 0.] + + # Params dic + sim_par = { + 'zcos': zcos, + 'zpcmb': 0.0, + 'como_dist': cosmo.comoving_distance(zcos).value, + 'vpec': 300, + 't0': 0,#simulated peak time of the event + 'ra': coords[0], + 'dec': coords[1], + 'coh_sct': 0.0, + 'x1':1, + 'c':0.1, + 'M0': -19.3, + 'alpha': 0.14, + 'beta': 3.1, + 'model_name': 'salt2', + 'model_version': '2.4' + } + + self.SNIa_Tripp = sn_astrobj.SNIa(sim_par, relation='SALTTripp') + + self.obs = pd.DataFrame({ + 'time': [-10, 0, 20, 50], + 'band': ['bessellb', 'bessellv', 'bessellr', 'besselli'], + 'zp': np.ones(4) * 30, + 'zpsys': ['ab'] * 4, + 'gain': np.ones(4), + 'skynoise': np.zeros(4), + 'sig_zp': np.zeros(4) + }) + + def test_tripp(self): + mb = self.SNIa_Tripp.sim_par['M0'] + self.SNIa_Tripp.mu + mb -= self.SNIa_Tripp.sim_par['alpha'] * self.SNIa_Tripp.sim_par['x1'] + mb += self.SNIa_Tripp.sim_par['beta'] * self.SNIa_Tripp.sim_par['c'] + assert(self.SNIa_Tripp.mb == mb) + + def test_genflux(self): + lcs = self.SNIa_Tripp.gen_flux(self.obs, seed=1234, mod_fcov=False) + print(lcs) + test = { + 'fluxtrue': np.array([15425.39490416, 28576.9759029 , 15492.50307286, 4990.21114817]), + 'fluxerrtrue': np.array([124.1990133 , 169.04725938, 124.46888395, 70.64142657]), + 'flux': np.array([15559.40785314, 28473.85136389, 15251.03732738, 4972.76245161]), + 'fluxerr': np.array([124.73735548, 168.74196681, 123.4950903 , 70.51781655]) + } + + for k in ['flux', 'fluxerr', 'fluxtrue', 'fluxerrtrue']: + assert_allclose(lcs[k].values, test[k]) + + + + + \ No newline at end of file diff --git a/snsim/tests/test_generators.py b/snsim/tests/test_generators.py new file mode 100644 index 0000000..c86b6e3 --- /dev/null +++ b/snsim/tests/test_generators.py @@ -0,0 +1,89 @@ +import snsim +import sncosmo as snc +import numpy as np +import pandas as pd +from numpy.testing import assert_allclose, assert_approx_equal, assert_array_almost_equal +from snsim import generators as gen + + +FlatSource = snc.TimeSeriesSource( + np.linspace(0., 100., 10), + np.linspace(800., 20000., 100), + np.ones((10, 100), dtype=float)) + +snc.register(FlatSource, name='flatsource') + +class FakeGen(gen.BaseGen): + _object_type = 'TimeSeries' + _available_models = ['flatsource'] + _available_rates = {'testrate': 'lambda z: 1e-5 * z * ({h}/0.70)**3'} + + def gen_par(self, n_obj, basic_par, seed=None): + return {} + +class TestGenerators: + def setup_class(self): + self.config = { + 'M0': -19., + 'model_name': 'flatsource'} + + # Set the cosmology (astropy.cosmology object) + cosmology = {'name': 'planck18'} + self.cosmo = snsim.utils.set_cosmo(cosmology) + + #distribution of peculiar velocities of SNe + self.vpec_dist = { + 'mean_vpec':0, + 'sig_vpec': 0} + + self.time_range = [-1000, 1000] + self.z_range = [0.001, 0.1] + + + def test_rate(self): + Gen_str_rate = FakeGen( + {**self.config, 'rate': 'lambda z: 1e-5 * z'}, + self.cosmo, + self.time_range, + z_range=self.z_range, + vpec_dist=self.vpec_dist) + + rate = lambda z: 1e-5 * z + Gen_lambda_rate = FakeGen( + {**self.config, 'rate': rate}, + self.cosmo, + self.time_range, + z_range=self.z_range, + vpec_dist=self.vpec_dist) + + Gen_register_rate = FakeGen( + {**self.config, 'rate': 'testrate'}, + self.cosmo, + self.time_range, + z_range=self.z_range, + vpec_dist=self.vpec_dist) + + assert_approx_equal(Gen_str_rate.rate(2), 1e-5 * 2) + (Gen_lambda_rate.rate(2), 1e-5 * 2) + assert_approx_equal(Gen_register_rate.rate(2), 1e-5 * 2 * (self.cosmo.h / 0.70)**3) + + def test_zcdf(self): + Gen_str_rate = FakeGen( + {**self.config, 'rate': 'lambda z: 1e-5'}, + self.cosmo, + self.time_range, + z_range=self.z_range, + vpec_dist=self.vpec_dist) + + test_array = np.array([ + 3.45704207e-03, 4.10371336e-01, 1.46706705e+00, 3.13588799e+00, + 5.38069438e+00, 8.16680786e+00, 1.14609591e+01, 1.52312376e+01, + 1.94470438e+01, 2.40790431e+01 + ]) + + assert_array_almost_equal(Gen_str_rate._z_dist.pdfx[::1000], test_array) + + + + + diff --git a/snsim/tests/test_geout.py b/snsim/tests/test_geout.py new file mode 100644 index 0000000..d60330b --- /dev/null +++ b/snsim/tests/test_geout.py @@ -0,0 +1,16 @@ +import numpy as np +import shapely.geometry as shp_geo +from numpy.testing import assert_almost_equal +from snsim import geo_utils as geo_ut + +def test_compute_area(): + corners = np.array([[[0, np.pi / 2]], [[2 * np.pi, np.pi / 2]] , + [[2 * np.pi, -np.pi / 2]], [[0, -np.pi / 2]]]) + + polygon = geo_ut._compute_polygon(corners) + + area = geo_ut._compute_area(polygon) + assert_almost_equal(area, 4 * np.pi) + + + \ No newline at end of file diff --git a/snsim/tests/test_survey.py b/snsim/tests/test_survey.py new file mode 100644 index 0000000..9220507 --- /dev/null +++ b/snsim/tests/test_survey.py @@ -0,0 +1,2 @@ +import snsim +from snsim.survey_host import SurveyObs diff --git a/snsim/utils.py b/snsim/utils.py index 1d2b7b1..294f390 100644 --- a/snsim/utils.py +++ b/snsim/utils.py @@ -6,10 +6,13 @@ from astropy.coordinates import SkyCoord from astropy import cosmology as acosmo import astropy.units as astu +import geopandas as gpd from shapely import geometry as shp_geo from shapely import ops as shp_ops from .constants import C_LIGHT_KMS, _SPHERE_LIMIT_ - +from . import constants as cst +from snsim import __snsim_dir_path__ +import pandas as pdimport matplotlib.pyplot as plt def gauss(mu, sig, x): """Gaussian function. @@ -119,6 +122,18 @@ def draw(self, n, seed=None): return np.interp(rand_gen.random(n), self.cdf, self.x) +def reshape_prob_data(): + """ function that read DES X1-mass probability file and return + grid values fro interpolation """ + + prob_data=pd.read_csv(__snsim_dir_path__+'data_probability/DES-SN5YR_DES_S3_x1.DAT', sep=',') + prob = np.zeros((len(np.unique(prob_data.x1.values)),len(np.unique(prob_data.logmass.values)))) + for i, (name, group) in enumerate(prob_data.groupby('x1')): + prob[i]=group.prob.values + + return np.unique(prob_data.x1.values), np.unique(prob_data.logmass.values), prob + + def set_cosmo(cosmo_dic): """Load an astropy cosmological model. @@ -163,6 +178,10 @@ def set_cosmo(cosmo_dic): cosmo_dic['Ode0'] = 1 - cosmo_dic['Om0'] - Ok0 return acosmo.w0waCDM(**cosmo_dic) +def init_snc_source(name, version=None): + # TODO - BC: Not very usefull, maybe has to be reimplemented later + return snc.get_source(name=name, version=version) + def scale_M0_cosmology(h, M0_art, h_art): """Compute a value of M0 corresponding the cosmology used in the simulation. @@ -174,7 +193,7 @@ def scale_M0_cosmology(h, M0_art, h_art): M0_art: float M0 value to be scaled h_art: float - the H0/100 constant used in the article to retrive M0_art + the H0/100 constant used in the article to retrive M0_art Returns ------- @@ -182,9 +201,7 @@ def scale_M0_cosmology(h, M0_art, h_art): Scaled SN Absolute Magnitude. """ - - dh = (h_art - h) / h - return M0_art - 5 * np.log10(1 + dh) + return M0_art + 5 * np.log10(h / h_art) def init_astropy_time(date): @@ -266,7 +283,7 @@ def asym_pdf(x): return asym_dist.draw(size, seed=seed) -def compute_z2cmb(ra, dec, cmb): +def compute_zpcmb(ra, dec, cmb): """Compute the redshifts of a list of objects relative to the CMB. Parameters @@ -303,8 +320,8 @@ def compute_z2cmb(ra, dec, cmb): return (1 - v_cmb * (ss + ccc) / C_LIGHT_KMS) - 1. -def init_sn_model(name, model_dir=None): - """Initialise a sncosmo model. +def init_snia_source(name, model_dir=None, version=None): + """Initialise a sncosmo source. Parameters ---------- @@ -319,12 +336,12 @@ def init_sn_model(name, model_dir=None): sncosmo Model corresponding to input configuration. """ if model_dir is None: - return snc.Model(source=name) + return snc.get_source(name=name, version=version) else: if name == 'salt2': - return snc.Model(source=snc.SALT2Source(model_dir, name='salt2')) + return snc.SALT2Source(model_dir, name='salt2') elif name == 'salt3': - return snc.Model(source=snc.SALT3Source(model_dir, name='salt3')) + return snc.SALT3Source(model_dir, name='salt3') return None @@ -416,7 +433,7 @@ def flux_to_Jansky(zp, band): return norm def zobs_MinT_MaxT(par, model_t_range): - zobs = (1. + par['zcos']) * (1. + par['z2cmb']) * (1. + par['vpec'] / C_LIGHT_KMS) - 1. + zobs = (1. + par['zcos']) * (1. + par['zpcmb']) * (1. + par['vpec'] / C_LIGHT_KMS) - 1. MinT = par['sim_t0'] + model_t_range[0] * (1. + zobs) MaxT = par['sim_t0'] + model_t_range[1] * (1. + zobs) return zobs, MinT, MaxT @@ -430,69 +447,6 @@ def print_dic(dic, prefix=''): else: print(prefix + f'{K}: {dic[K]}') - -def _format_corner(corner, RA): - # -- Replace corners that cross sphere edges - # - # 0 ---- 1 - # | | - # 3 ---- 2 - # - # conditions : - # - RA_0 < RA_1 - # - RA_3 < RA_2 - # - RA_0 and RA_3 on the same side of the field center - - sign = (corner[3][0] - RA) * (corner[0][0] - RA) < 0 - comp = corner[0][0] < corner[3][0] - - corner[1][0][corner[1][0] < corner[0][0]] += 2 * np.pi - corner[2][0][corner[2][0] < corner[3][0]] += 2 * np.pi - - - corner[0][0][sign & comp] += 2 * np.pi - corner[1][0][sign & comp] += 2 * np.pi - - corner[2][0][sign & ~comp] += 2 * np.pi - corner[3][0][sign & ~comp] += 2 * np.pi - return corner - - -def _compute_area(polygon): - """Compute survey total area.""" - # It's an integration by dec strip - area = 0 - strip_dec = np.linspace(-np.pi/2, np.pi/2, 10_000) - for da, db in zip(strip_dec[:-1], strip_dec[1:]): - line = shp_geo.LineString([[0, (da + db) * 0.5], [2 * np.pi, (da + db) * 0.5]]) - if line.intersects(polygon): - dRA = line.intersection(polygon).length - area += dRA * (np.sin(db) - np.sin(da)) - return area - - -def _compute_polygon(corners): - """Create polygon on a sphere, check for edges conditions.""" - polygon = shp_geo.Polygon(corners) - # -- Cut into 2 polygon if cross the edges - if polygon.intersects(_SPHERE_LIMIT_): - unioned = polygon.boundary.union(_SPHERE_LIMIT_) - polygon = [p for p in shp_ops.polygonize(unioned) - if p.representative_point().within(polygon)] - - x0, y0 = polygon[0].boundary.xy - x1, y1 = polygon[1].boundary.xy - - if x1 > x0: - x1 = np.array(x1) - 2 * np.pi - polygon[1] = shp_geo.Polygon(np.array([x1, y1]).T) - else: - x0 = np.array(x0) - 2 * np.pi - polygon[0] = shp_geo.Polygon(np.array([x0, y0]).T) - polygon = shp_geo.MultiPolygon(polygon) - return polygon - - def Templatelist_fromsncosmo(source_type=None): """ list names of templates in sncosmo built-in sources catalogue Parameters @@ -604,3 +558,20 @@ def sine_interp(x_new, fun_x, fun_y): values = 0.5 * (fun_y_sup + fun_y_inf) + 0.5 * (fun_y_sup - fun_y_inf) * sin_interp return values +def gen_rndchilds(seed, size=1): + if isinstance(seed, np.random.SeedSequence): + return seed.spawn(size) + else: + return np.random.SeedSequence(seed).spawn(size)def compute_weight_mass_for_type(mass, sn_type, cosmology): + """ compute the mass dependent weights for HOST - SN matching """ + if sn_type.lower() == 'snia': + weights_mass = cst.sullivan_para['mass'] * (cosmology.h/cst.h_article['sullivan06']) * mass + + return weights_mass + +def compute_weight_SFR_for_type(SFR, sn_type, cosmology): + """ compute the SFR dependent weights for HOST - SN matching """ + if sn_type.lower() == 'snia': + weights_SFR = cst.sullivan_para['SFR'] * (cosmology.h/cst.h_article['sullivan06']) * SFR + + return weights_SFR \ No newline at end of file