diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 73fc233..1767cf4 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -21,16 +21,35 @@ ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", - "For more information, please see:\n", - " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", - " * https://github.com/tensorflow/addons\n", - "If you depend on functionality not listed there, please file an issue.\n", - "\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;31m# Copyright (c) 2019 zfit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mztf\u001b[0m \u001b[1;31m# legacy\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mztf\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mz\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 26\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0msettings\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mztypes\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\ztf\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 42\u001b[0m from .wrapping_tf import (log, exp, random_normal, random_uniform, convert_to_tensor, reduce_sum, reduce_prod, square,\n\u001b[0;32m 43\u001b[0m sqrt, complex, check_numerics, pow)\n\u001b[1;32m---> 44\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mrandom\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\ztf\\random.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSized\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\bijectors\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 44\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbijectors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmasked_autoregressive\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMaskedAutoregressiveFlow\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 45\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbijectors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmatrix_inverse_tril\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMatrixInverseTriL\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 46\u001b[1;33m \u001b[1;32mfrom\u001b[0m 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\bijectors\\matveclu.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 22\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mbijectors\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbijector\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 24\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmath\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlinalg\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mlu_reconstruct\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 25\u001b[0m \u001b[1;32mfrom\u001b[0m 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item)\u001b[0m\n\u001b[0;32m 59\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 60\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__getattr__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 61\u001b[1;33m \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_load\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 62\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mgetattr\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmodule\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mitem\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\lazy_loader.py\u001b[0m in \u001b[0;36m_load\u001b[1;34m(self)\u001b[0m\n\u001b[0;32m 42\u001b[0m \u001b[1;34m\"\"\"Load the module and insert it into the parent's globals.\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 43\u001b[0m \u001b[1;31m# Import the target module and insert it into the parent's namespace\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\contrib\\distributions\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 62\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistributions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpoisson_lognormal\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 63\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcontrib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdistributions\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mquantized_distribution\u001b[0m \u001b[1;32mimport\u001b[0m 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mget_code\u001b[1;34m(self, fullname)\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mpath_stats\u001b[1;34m(self, path)\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36m_path_stat\u001b[1;34m(path)\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -56,7 +75,7 @@ "import tensorflow as tf\n", "import zfit\n", "from zfit import ztf\n", - "# from IPython.display import clear_output\n", + "from IPython.display import clear_output\n", "import os\n", "import tensorflow_probability as tfp\n", "tfd = tfp.distributions" @@ -64,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -86,11 +105,15 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# t = np.array([1,2,3,6,8,4,-2,4])\n", + "\n", + "# np.where((t >= 6) & (t <=10))" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -439,19 +462,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "# formfactors\n", "\n", @@ -502,7 +515,7 @@ "\n", "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n", "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n", - "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale), floating = False) #, lower_limit=jpsi_scale-np.sqrt(jpsi_scale), upper_limit=jpsi_scale+np.sqrt(jpsi_scale))\n", "\n", "#psi2s\n", @@ -511,7 +524,7 @@ "\n", "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n", "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n", - "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale), floating = False) #, lower_limit=psi2s_scale-np.sqrt(psi2s_scale), upper_limit=psi2s_scale+np.sqrt(psi2s_scale))\n", "\n", "#psi(3770)\n", @@ -560,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -590,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -607,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -618,7 +631,7 @@ "\n", "# # Full spectrum\n", "\n", - "# obs = zfit.Space('q', limits = (x_min, x_max))\n", + "obs_toy = zfit.Space('q', limits = (x_min, x_max))\n", "\n", "# Jpsi and Psi2s cut out\n", "\n", @@ -626,7 +639,7 @@ "obs2 = zfit.Space('q', limits = (jpsi_mass + 50. + epsilon, psi2s_mass - 50. - epsilon))\n", "obs3 = zfit.Space('q', limits = (psi2s_mass + 50. + epsilon, x_max - epsilon))\n", "\n", - "obs = obs1 + obs2 + obs3\n", + "obs_fit = obs1 + obs2 + obs3\n", "\n", "# with open(r\"./data/slim_points/slim_points_toy_0_range({0}-{1}).pkl\".format(int(x_min), int(x_max)), \"rb\") as input_file:\n", "# part_set = pkl.load(input_file)\n", @@ -647,11 +660,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n", + "total_f = total_pdf(obs=obs_toy, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n", " psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n", " p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n", " p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n", @@ -665,7 +678,21 @@ " tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,\n", " bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,\n", " bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)\n", - " \n", + "\n", + "total_f_fit = total_pdf(obs=obs_fit, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n", + " psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n", + " p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n", + " p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n", + " p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,\n", + " p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n", + " rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n", + " omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n", + " phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,\n", + " Dstar_mass = Dstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p, D_mass = D_m,\n", + " Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p,\n", + " tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,\n", + " bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,\n", + " bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)\n", " \n", "# print(total_pdf.obs)\n", "\n", @@ -684,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -730,32 +757,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " if sys.path[0] == '':\n", - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -774,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -790,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -806,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -819,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -876,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -960,16 +964,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# total_f._sample_and_weights = UniformSampleAndWeights" + "total_f._sample_and_weights = UniformSampleAndWeights" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -978,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -987,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1035,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1052,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1076,7 +1080,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1103,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1126,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1135,7 +1139,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1151,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1181,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1195,7 +1199,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1213,7 +1217,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1227,7 +1231,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1248,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1258,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1289,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1306,7 +1310,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1396,15 +1400,19 @@ "\n", "# 6. Constraint on phases of Jpsi and Psi2s for cut out fit\n", "\n", + "\n", "constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg[\"jpsi_phase_unc\"]),\n", " sigma = ztf.constant(jpsi_phase))\n", + "constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]),\n", + " sigma = ztf.constant(psi2s_phase))\n", "\n", - "constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]), \n", - " sigma = ztf.constant(psi2s_phase))\n", + "# zfit.run(constraint6_0)\n", + "\n", + "# ztf.convert_to_tensor(constraint6_0)\n", "\n", "#List of all constraints\n", "\n", - "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4, constraint6_0, constraint6_1]" + "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4]#, ztf.convert_to_tensor(constraint6_0)]#, ztf.convert_to_tensor(constraint6_1)]" ] }, { @@ -1416,47 +1424,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n", - "Toy 0: Generating data...\n" - ] - }, - { - "ename": "InvalidArgumentError", - "evalue": "Incompatible shapes: [799998] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n\nCaused by op 'create_sampler/while/truediv_1', defined at:\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in \n lambda f: self._run_callback(functools.partial(callback, future))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 378, in dispatch_queue\n yield self.process_one()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 225, in wrapper\n runner = Runner(result, future, yielded)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 708, in __init__\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 365, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 272, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 542, in execute_request\n user_expressions, allow_stdin,\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 17, in \n sampler = total_f.create_sampler(n=event_stack)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 821, in create_sampler\n limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 841, in _create_sampler_tensor\n sample = self._single_hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 884, in _single_hook_sample\n return self._hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 491, in _hook_sample\n samples = super()._hook_sample(limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 887, in _hook_sample\n return self._norm_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 891, in _norm_sample\n return self._limits_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 895, in _limits_sample\n return self._call_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 905, in _call_sample\n return self._fallback_sample(n=n, limits=limits)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 938, in _fallback_sample\n sample_and_weights_factory=self._sample_and_weights)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 355, in accept_reject_sample\n back_prop=False)[1] # backprop not needed here\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3556, in while_loop\n return_same_structure)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3087, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3022, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 270, in sample_body\n prob_weights_ratio = probabilities / weights_clipped\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 812, in binary_op_wrapper\n return func(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 920, in _truediv_python3\n return gen_math_ops.real_div(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 6897, in real_div\n \"RealDiv\", x=x, y=y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): Incompatible shapes: [799998] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1333\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1335\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 1318\u001b[0m return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m 1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mInvalidArgumentError\u001b[0m: Incompatible shapes: [799998] vs. [800000]\n\t [[{{node create_sampler/while/truediv_1}}]]\n\t [[{{node create_sampler/while/LoopCond}}]]", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcall\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 34\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0msam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\data.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(self, param_values, n)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot set a new `n` if not a Tensor-like object was given\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_holder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initial_resampled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 927\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 928\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1150\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1151\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m 1153\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1154\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1329\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1346\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1347\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1350\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mInvalidArgumentError\u001b[0m: Incompatible shapes: [799998] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n\nCaused by op 'create_sampler/while/truediv_1', defined at:\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in \n lambda f: self._run_callback(functools.partial(callback, future))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 378, in dispatch_queue\n yield self.process_one()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 225, in wrapper\n runner = Runner(result, future, yielded)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 708, in __init__\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 365, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 272, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 542, in execute_request\n user_expressions, allow_stdin,\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 17, in \n sampler = total_f.create_sampler(n=event_stack)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 821, in create_sampler\n limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 841, in _create_sampler_tensor\n sample = self._single_hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 884, in _single_hook_sample\n return self._hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 491, in _hook_sample\n samples = super()._hook_sample(limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 887, in _hook_sample\n return self._norm_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 891, in _norm_sample\n return self._limits_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 895, in _limits_sample\n return self._call_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 905, in _call_sample\n return self._fallback_sample(n=n, limits=limits)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 938, in _fallback_sample\n sample_and_weights_factory=self._sample_and_weights)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 355, in accept_reject_sample\n back_prop=False)[1] # backprop not needed here\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3556, in while_loop\n return_same_structure)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3087, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3022, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 270, in sample_body\n prob_weights_ratio = probabilities / weights_clipped\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 812, in binary_op_wrapper\n return func(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 920, in _truediv_python3\n return gen_math_ops.real_div(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 6897, in real_div\n \"RealDiv\", x=x, y=y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): Incompatible shapes: [799998] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n" - ] - } - ], + "outputs": [], "source": [ "# zfit.run.numeric_checks = False \n", "\n", + "fitting_range = 'cut'\n", + "\n", "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", @@ -1477,6 +1454,8 @@ " \n", " ### Generate data\n", " \n", + "# clear_output(wait=True)\n", + " \n", " print(\"Toy {}: Generating data...\".format(toy))\n", " \n", " dirName = 'data/zfit_toys/toy_{0}'.format(toy)\n", @@ -1490,7 +1469,6 @@ " sampler.resample(n=event_stack)\n", " s = sampler.unstack_x()\n", " sam = zfit.run(s)\n", - "# clear_output(wait=True)\n", "\n", " c = call + 1\n", " \n", @@ -1509,51 +1487,112 @@ " total_samp = np.append(total_samp, sam)\n", "\n", " total_samp = total_samp.astype('float64')\n", - "\n", - " data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)\n", " \n", - " print(\"Toy {}: Loading data finished\".format(toy))\n", + " if fitting_range == 'full':\n", "\n", - " ### Fit data\n", + " data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)\n", " \n", - " print(\"Toy {}: Fitting pdf...\".format(toy))\n", + " print(\"Toy {}: Loading data finished\".format(toy))\n", "\n", - " for param in total_f.get_dependents():\n", - " param.randomize()\n", + " ### Fit data\n", "\n", - " nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints)\n", + " print(\"Toy {}: Fitting pdf...\".format(toy))\n", "\n", - " minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", - " # minimizer._use_tfgrad = False\n", - " result = minimizer.minimize(nll)\n", - " \n", - " print(\"Toy {}: Fitting finished\".format(toy))\n", + " for param in total_f.get_dependents():\n", + " param.randomize()\n", "\n", - " print(\"Function minimum:\", result.fmin)\n", - " print(\"Hesse errors:\", result.hesse())\n", - " \n", - " params = result.params\n", - " Ctt_list.append(params[Ctt]['value'])\n", - " Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n", - " \n", - " #plotting the result\n", - " \n", - " plotdirName = 'data/plots'.format(toy)\n", - " \n", - " if not os.path.exists(plotdirName):\n", - " os.mkdir(plotdirName)\n", - "# print(\"Directory \" , dirName , \" Created \")\n", - " calcs_test = zfit.run(probs)\n", - " res_y = zfit.run(jpsi_res(test_q))\n", - " plt.clf()\n", - " plt.plot(test_q, calcs_test, label = 'pdf')\n", - " plt.legend()\n", - " plt.ylim(0.0, 6e-6)\n", - " plt.savefig(plotdirName + '/toy_fit{}.png'.format(toy))\n", + " nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints)\n", "\n", - " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", - " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", - " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n" + " minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", + " # minimizer._use_tfgrad = False\n", + " result = minimizer.minimize(nll)\n", + "\n", + " print(\"Toy {}: Fitting finished\".format(toy))\n", + "\n", + " print(\"Function minimum:\", result.fmin)\n", + " print(\"Hesse errors:\", result.hesse())\n", + "\n", + " params = result.params\n", + " Ctt_list.append(params[Ctt]['value'])\n", + " Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n", + "\n", + " #plotting the result\n", + "\n", + " plotdirName = 'data/plots'.format(toy)\n", + "\n", + " if not os.path.exists(plotdirName):\n", + " os.mkdir(plotdirName)\n", + "# print(\"Directory \" , dirName , \" Created \")\n", + " \n", + " probs = total_f.pdf(test_q, norm_range=False)\n", + " calcs_test = zfit.run(probs)\n", + " plt.clf()\n", + " plt.plot(test_q, calcs_test, label = 'pdf')\n", + " plt.legend()\n", + " plt.ylim(0.0, 6e-6)\n", + " plt.savefig(plotdirName + '/toy_fit_full_range{}.png'.format(toy))\n", + "\n", + " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", + " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", + " \n", + " if fitting_range == 'cut':\n", + " \n", + " tot_sam_1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " \n", + " tot_sam_2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + "\n", + " tot_sam_3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\n", + "\n", + " tot_sam = np.append(tot_sam_1, tot_sam_2)\n", + " tot_sam = np.append(tot_sam, tot_sam_3)\n", + " \n", + " data = zfit.data.Data.from_numpy(array=tot_sam[:int(nevents)], obs=obs_fit)\n", + " \n", + " print(\"Toy {}: Loading data finished\".format(toy))\n", + " \n", + " ### Fit data\n", + "\n", + " print(\"Toy {}: Fitting pdf...\".format(toy))\n", + "\n", + " for param in total_f_fit.get_dependents():\n", + " param.randomize()\n", + "\n", + " nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints)\n", + "\n", + " minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", + " # minimizer._use_tfgrad = False\n", + " result = minimizer.minimize(nll)\n", + "\n", + " print(\"Function minimum:\", result.fmin)\n", + " print(\"Hesse errors:\", result.hesse())\n", + "\n", + " params = result.params\n", + " \n", + " if result.converged:\n", + " Ctt_list.append(params[Ctt]['value'])\n", + " Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n", + "\n", + " #plotting the result\n", + "\n", + " plotdirName = 'data/plots'.format(toy)\n", + "\n", + " if not os.path.exists(plotdirName):\n", + " os.mkdir(plotdirName)\n", + " # print(\"Directory \" , dirName , \" Created \")\n", + " \n", + " probs = total_f_fit.pdf(test_q, norm_range=False)\n", + " calcs_test = zfit.run(probs)\n", + " plt.clf()\n", + " plt.plot(test_q, calcs_test, label = 'pdf')\n", + " plt.legend()\n", + " plt.ylim(0.0, 6e-6)\n", + " plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))\n", + "\n", + " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", + " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", + " " ] }, { @@ -1569,6 +1608,7 @@ "metadata": {}, "outputs": [], "source": [ + "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))" ] @@ -1585,7 +1625,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# sample from original values" + ] } ], "metadata": { diff --git a/data/plots/toy_fit_cut_region0.png b/data/plots/toy_fit_cut_region0.png new file mode 100644 index 0000000..5106864 --- /dev/null +++ b/data/plots/toy_fit_cut_region0.png Binary files differ diff --git a/data/plots/toy_fit_cut_region1.png b/data/plots/toy_fit_cut_region1.png new file mode 100644 index 0000000..30b838d --- /dev/null +++ b/data/plots/toy_fit_cut_region1.png Binary files differ diff --git a/data/plots/toy_fit_cut_region2.png b/data/plots/toy_fit_cut_region2.png new file mode 100644 index 0000000..57c4ae8 --- /dev/null +++ b/data/plots/toy_fit_cut_region2.png Binary files differ diff --git a/data/plots/toy_fit_cut_region3.png 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b/data/zfit_toys/toy_0/3.pkl index aecc450..240e3db 100644 --- a/data/zfit_toys/toy_0/3.pkl +++ b/data/zfit_toys/toy_0/3.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/4.pkl b/data/zfit_toys/toy_0/4.pkl index a8441ac..cd61d6b 100644 --- a/data/zfit_toys/toy_0/4.pkl +++ b/data/zfit_toys/toy_0/4.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/5.pkl b/data/zfit_toys/toy_0/5.pkl index 3ed21b5..fe64c9f 100644 --- a/data/zfit_toys/toy_0/5.pkl +++ b/data/zfit_toys/toy_0/5.pkl Binary files differ diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 45cd74e..1767cf4 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -21,16 +21,35 @@ ] }, { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", - "For more information, please see:\n", - " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", - " * https://github.com/tensorflow/addons\n", - "If you depend on functionality not listed there, please file an issue.\n", - "\n" + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mitertools\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcompress\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 21\u001b[0m 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\u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 3\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mUnion\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mIterable\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mSized\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 4\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 5\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow_probability\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtfp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 6\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 7\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 76\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 77\u001b[0m \u001b[1;31m# from tensorflow_probability.google import staging # DisableOnExport\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 78\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[1;33m*\u001b[0m \u001b[1;31m# pylint: disable=wildcard-import\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 79\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mversion\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0m__version__\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[1;31m# pylint: enable=g-import-not-at-top\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\__init__.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0m__future__\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mprint_function\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 20\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 21\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mbijectors\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 22\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtensorflow_probability\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mpython\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mdistributions\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 23\u001b[0m \u001b[1;32mfrom\u001b[0m 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namespace\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 44\u001b[1;33m \u001b[0mmodule\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mimportlib\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mimport_module\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m__name__\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 45\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_parent_module_globals\u001b[0m\u001b[1;33m[\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_local_name\u001b[0m\u001b[1;33m]\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mmodule\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 46\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\__init__.py\u001b[0m in \u001b[0;36mimport_module\u001b[1;34m(name, package)\u001b[0m\n\u001b[0;32m 125\u001b[0m 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"\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_find_and_load_unlocked\u001b[1;34m(name, import_)\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap.py\u001b[0m in \u001b[0;36m_load_unlocked\u001b[1;34m(spec)\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mexec_module\u001b[1;34m(self, module)\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mget_code\u001b[1;34m(self, fullname)\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36mpath_stats\u001b[1;34m(self, path)\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\importlib\\_bootstrap_external.py\u001b[0m in \u001b[0;36m_path_stat\u001b[1;34m(path)\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] } ], @@ -56,7 +75,7 @@ "import tensorflow as tf\n", "import zfit\n", "from zfit import ztf\n", - "# from IPython.display import clear_output\n", + "from IPython.display import clear_output\n", "import os\n", "import tensorflow_probability as tfp\n", "tfd = tfp.distributions" @@ -64,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -86,11 +105,15 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# t = np.array([1,2,3,6,8,4,-2,4])\n", + "\n", + "# np.where((t >= 6) & (t <=10))" + ] }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +301,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +356,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -439,19 +462,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "# formfactors\n", "\n", @@ -502,7 +515,7 @@ "\n", "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n", "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n", - "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale), floating = False) #, lower_limit=jpsi_scale-np.sqrt(jpsi_scale), upper_limit=jpsi_scale+np.sqrt(jpsi_scale))\n", "\n", "#psi2s\n", @@ -511,7 +524,7 @@ "\n", "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n", "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n", - "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale), floating = False) #, lower_limit=psi2s_scale-np.sqrt(psi2s_scale), upper_limit=psi2s_scale+np.sqrt(psi2s_scale))\n", "\n", "#psi(3770)\n", @@ -560,7 +573,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -590,7 +603,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -607,7 +620,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -618,7 +631,7 @@ "\n", "# # Full spectrum\n", "\n", - "# obs = zfit.Space('q', limits = (x_min, x_max))\n", + "obs_toy = zfit.Space('q', limits = (x_min, x_max))\n", "\n", "# Jpsi and Psi2s cut out\n", "\n", @@ -626,7 +639,7 @@ "obs2 = zfit.Space('q', limits = (jpsi_mass + 50. + epsilon, psi2s_mass - 50. - epsilon))\n", "obs3 = zfit.Space('q', limits = (psi2s_mass + 50. + epsilon, x_max - epsilon))\n", "\n", - "obs = obs1 + obs2 + obs3\n", + "obs_fit = obs1 + obs2 + obs3\n", "\n", "# with open(r\"./data/slim_points/slim_points_toy_0_range({0}-{1}).pkl\".format(int(x_min), int(x_max)), \"rb\") as input_file:\n", "# part_set = pkl.load(input_file)\n", @@ -647,11 +660,11 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n", + "total_f = total_pdf(obs=obs_toy, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n", " psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n", " p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n", " p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n", @@ -665,7 +678,21 @@ " tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,\n", " bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,\n", " bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)\n", - " \n", + "\n", + "total_f_fit = total_pdf(obs=obs_fit, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w,\n", + " psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w,\n", + " p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n", + " p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n", + " p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,\n", + " p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n", + " rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n", + " omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n", + " phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w,\n", + " Dstar_mass = Dstar_m, DDstar_scale = DDstar_s, DDstar_phase = DDstar_p, D_mass = D_m,\n", + " Dbar_mass = Dbar_m, Dbar_scale = Dbar_s, Dbar_phase = Dbar_p,\n", + " tau_mass = tau_m, C_tt = Ctt, b0_0 = b0_0, b0_1 = b0_1, b0_2 = b0_2,\n", + " bplus_0 = bplus_0, bplus_1 = bplus_1, bplus_2 = bplus_2,\n", + " bT_0 = bT_0, bT_1 = bT_1, bT_2 = bT_2)\n", " \n", "# print(total_pdf.obs)\n", "\n", @@ -684,7 +711,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -730,32 +757,9 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " if sys.path[0] == '':\n", - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -774,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -790,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -806,7 +810,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -819,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -876,7 +880,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -960,16 +964,16 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# total_f._sample_and_weights = UniformSampleAndWeights" + "total_f._sample_and_weights = UniformSampleAndWeights" ] }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -978,7 +982,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -987,7 +991,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1035,7 +1039,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1052,7 +1056,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1076,7 +1080,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1103,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1126,7 +1130,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1135,7 +1139,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1151,7 +1155,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1181,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1195,7 +1199,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1213,7 +1217,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1227,7 +1231,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1248,7 +1252,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1258,7 +1262,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1289,7 +1293,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1306,7 +1310,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1396,15 +1400,19 @@ "\n", "# 6. Constraint on phases of Jpsi and Psi2s for cut out fit\n", "\n", + "\n", "constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg[\"jpsi_phase_unc\"]),\n", " sigma = ztf.constant(jpsi_phase))\n", + "constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]),\n", + " sigma = ztf.constant(psi2s_phase))\n", "\n", - "constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]), \n", - " sigma = ztf.constant(psi2s_phase))\n", + "# zfit.run(constraint6_0)\n", + "\n", + "# ztf.convert_to_tensor(constraint6_0)\n", "\n", "#List of all constraints\n", "\n", - "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4, constraint6_0, constraint6_1]" + "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4]#, ztf.convert_to_tensor(constraint6_0)]#, ztf.convert_to_tensor(constraint6_1)]" ] }, { @@ -1416,47 +1424,16 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n", - "Toy 0: Generating data...\n" - ] - }, - { - "ename": "InvalidArgumentError", - "evalue": "Incompatible shapes: [799999] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n\nCaused by op 'create_sampler/while/truediv_1', defined at:\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in \n lambda f: self._run_callback(functools.partial(callback, future))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 378, in dispatch_queue\n yield self.process_one()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 225, in wrapper\n runner = Runner(result, future, yielded)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 708, in __init__\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 365, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 272, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 542, in execute_request\n user_expressions, allow_stdin,\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 17, in \n sampler = total_f.create_sampler(n=event_stack)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 821, in create_sampler\n limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 841, in _create_sampler_tensor\n sample = self._single_hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 884, in _single_hook_sample\n return self._hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 491, in _hook_sample\n samples = super()._hook_sample(limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 887, in _hook_sample\n return self._norm_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 891, in _norm_sample\n return self._limits_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 895, in _limits_sample\n return self._call_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 905, in _call_sample\n return self._fallback_sample(n=n, limits=limits)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 938, in _fallback_sample\n sample_and_weights_factory=self._sample_and_weights)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 355, in accept_reject_sample\n back_prop=False)[1] # backprop not needed here\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3556, in while_loop\n return_same_structure)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3087, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3022, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 270, in sample_body\n prob_weights_ratio = probabilities / weights_clipped\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 812, in binary_op_wrapper\n return func(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 920, in _truediv_python3\n return gen_math_ops.real_div(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 6897, in real_div\n \"RealDiv\", x=x, y=y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): Incompatible shapes: [799999] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1333\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1335\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 1318\u001b[0m return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m 1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mInvalidArgumentError\u001b[0m: Incompatible shapes: [799999] vs. [800000]\n\t [[{{node create_sampler/while/truediv_1}}]]\n\t [[{{node create_sampler/while/LoopCond}}]]", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 31\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcall\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 32\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 33\u001b[1;33m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 34\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 35\u001b[0m \u001b[0msam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\data.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(self, param_values, n)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot set a new `n` if not a Tensor-like object was given\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_holder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initial_resampled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 927\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 928\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1150\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1151\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m 1153\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1154\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1329\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1346\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1347\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1350\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mInvalidArgumentError\u001b[0m: Incompatible shapes: [799999] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n\nCaused by op 'create_sampler/while/truediv_1', defined at:\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in \n lambda f: self._run_callback(functools.partial(callback, future))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 378, in dispatch_queue\n yield self.process_one()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 225, in wrapper\n runner = Runner(result, future, yielded)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 708, in __init__\n self.run()\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 365, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 272, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 542, in execute_request\n user_expressions, allow_stdin,\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 17, in \n sampler = total_f.create_sampler(n=event_stack)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 821, in create_sampler\n limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 841, in _create_sampler_tensor\n sample = self._single_hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 884, in _single_hook_sample\n return self._hook_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 491, in _hook_sample\n samples = super()._hook_sample(limits=limits, n=n, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 887, in _hook_sample\n return self._norm_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 891, in _norm_sample\n return self._limits_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 895, in _limits_sample\n return self._call_sample(n=n, limits=limits, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 905, in _call_sample\n return self._fallback_sample(n=n, limits=limits)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 938, in _fallback_sample\n sample_and_weights_factory=self._sample_and_weights)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 355, in accept_reject_sample\n back_prop=False)[1] # backprop not needed here\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3556, in while_loop\n return_same_structure)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3087, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3022, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 270, in sample_body\n prob_weights_ratio = probabilities / weights_clipped\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 812, in binary_op_wrapper\n return func(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\math_ops.py\", line 920, in _truediv_python3\n return gen_math_ops.real_div(x, y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_math_ops.py\", line 6897, in real_div\n \"RealDiv\", x=x, y=y, name=name)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): Incompatible shapes: [799999] vs. [800000]\n\t [[node create_sampler/while/truediv_1 (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:270) ]]\n\t [[node create_sampler/while/LoopCond (defined at C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:355) ]]\n" - ] - } - ], + "outputs": [], "source": [ "# zfit.run.numeric_checks = False \n", "\n", + "fitting_range = 'cut'\n", + "\n", "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", @@ -1477,6 +1454,8 @@ " \n", " ### Generate data\n", " \n", + "# clear_output(wait=True)\n", + " \n", " print(\"Toy {}: Generating data...\".format(toy))\n", " \n", " dirName = 'data/zfit_toys/toy_{0}'.format(toy)\n", @@ -1490,7 +1469,6 @@ " sampler.resample(n=event_stack)\n", " s = sampler.unstack_x()\n", " sam = zfit.run(s)\n", - "# clear_output(wait=True)\n", "\n", " c = call + 1\n", " \n", @@ -1509,51 +1487,112 @@ " total_samp = np.append(total_samp, sam)\n", "\n", " total_samp = total_samp.astype('float64')\n", - "\n", - " data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)\n", " \n", - " print(\"Toy {}: Loading data finished\".format(toy))\n", + " if fitting_range == 'full':\n", "\n", - " ### Fit data\n", + " data = zfit.data.Data.from_numpy(array=total_samp[:int(nevents)], obs=obs)\n", " \n", - " print(\"Toy {}: Fitting pdf...\".format(toy))\n", + " print(\"Toy {}: Loading data finished\".format(toy))\n", "\n", - " for param in total_f.get_dependents():\n", - " param.randomize()\n", + " ### Fit data\n", "\n", - " nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints)\n", + " print(\"Toy {}: Fitting pdf...\".format(toy))\n", "\n", - " minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", - " # minimizer._use_tfgrad = False\n", - " result = minimizer.minimize(nll)\n", - " \n", - " print(\"Toy {}: Fitting finished\".format(toy))\n", + " for param in total_f.get_dependents():\n", + " param.randomize()\n", "\n", - " print(\"Function minimum:\", result.fmin)\n", - " print(\"Hesse errors:\", result.hesse())\n", - " \n", - " params = result.params\n", - " Ctt_list.append(params[Ctt]['value'])\n", - " Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n", - " \n", - " #plotting the result\n", - " \n", - " plotdirName = 'data/plots'.format(toy)\n", - " \n", - " if not os.path.exists(plotdirName):\n", - " os.mkdir(plotdirName)\n", - "# print(\"Directory \" , dirName , \" Created \")\n", - " calcs_test = zfit.run(probs)\n", - " res_y = zfit.run(jpsi_res(test_q))\n", - " plt.clf()\n", - " plt.plot(test_q, calcs_test, label = 'pdf')\n", - " plt.legend()\n", - " plt.ylim(0.0, 6e-6)\n", - " plt.savefig(plotdirName + '/toy_fit{}.png'.format(toy))\n", + " nll = zfit.loss.UnbinnedNLL(model=total_f, data=data, fit_range = (x_min, x_max), constraints = constraints)\n", "\n", - " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", - " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", - " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n" + " minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", + " # minimizer._use_tfgrad = False\n", + " result = minimizer.minimize(nll)\n", + "\n", + " print(\"Toy {}: Fitting finished\".format(toy))\n", + "\n", + " print(\"Function minimum:\", result.fmin)\n", + " print(\"Hesse errors:\", result.hesse())\n", + "\n", + " params = result.params\n", + " Ctt_list.append(params[Ctt]['value'])\n", + " Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n", + "\n", + " #plotting the result\n", + "\n", + " plotdirName = 'data/plots'.format(toy)\n", + "\n", + " if not os.path.exists(plotdirName):\n", + " os.mkdir(plotdirName)\n", + "# print(\"Directory \" , dirName , \" Created \")\n", + " \n", + " probs = total_f.pdf(test_q, norm_range=False)\n", + " calcs_test = zfit.run(probs)\n", + " plt.clf()\n", + " plt.plot(test_q, calcs_test, label = 'pdf')\n", + " plt.legend()\n", + " plt.ylim(0.0, 6e-6)\n", + " plt.savefig(plotdirName + '/toy_fit_full_range{}.png'.format(toy))\n", + "\n", + " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", + " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", + " \n", + " if fitting_range == 'cut':\n", + " \n", + " tot_sam_1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " \n", + " tot_sam_2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + "\n", + " tot_sam_3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\n", + "\n", + " tot_sam = np.append(tot_sam_1, tot_sam_2)\n", + " tot_sam = np.append(tot_sam, tot_sam_3)\n", + " \n", + " data = zfit.data.Data.from_numpy(array=tot_sam[:int(nevents)], obs=obs_fit)\n", + " \n", + " print(\"Toy {}: Loading data finished\".format(toy))\n", + " \n", + " ### Fit data\n", + "\n", + " print(\"Toy {}: Fitting pdf...\".format(toy))\n", + "\n", + " for param in total_f_fit.get_dependents():\n", + " param.randomize()\n", + "\n", + " nll = zfit.loss.UnbinnedNLL(model=total_f_fit, data=data, constraints = constraints)\n", + "\n", + " minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", + " # minimizer._use_tfgrad = False\n", + " result = minimizer.minimize(nll)\n", + "\n", + " print(\"Function minimum:\", result.fmin)\n", + " print(\"Hesse errors:\", result.hesse())\n", + "\n", + " params = result.params\n", + " \n", + " if result.converged:\n", + " Ctt_list.append(params[Ctt]['value'])\n", + " Ctt_error_list.append(params[Ctt]['minuit_hesse']['error'])\n", + "\n", + " #plotting the result\n", + "\n", + " plotdirName = 'data/plots'.format(toy)\n", + "\n", + " if not os.path.exists(plotdirName):\n", + " os.mkdir(plotdirName)\n", + " # print(\"Directory \" , dirName , \" Created \")\n", + " \n", + " probs = total_f_fit.pdf(test_q, norm_range=False)\n", + " calcs_test = zfit.run(probs)\n", + " plt.clf()\n", + " plt.plot(test_q, calcs_test, label = 'pdf')\n", + " plt.legend()\n", + " plt.ylim(0.0, 6e-6)\n", + " plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))\n", + "\n", + " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", + " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", + " " ] }, { @@ -1569,6 +1608,7 @@ "metadata": {}, "outputs": [], "source": [ + "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))" ] @@ -1585,7 +1625,9 @@ "execution_count": null, "metadata": {}, "outputs": [], - "source": [] + "source": [ + "# sample from original values" + ] } ], "metadata": {