diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 679a6ff..5080c0b 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -9,31 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n", - " warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n" - ] - }, - { - "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" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -64,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -339,14 +317,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class total_pdf(zfit.pdf.ZPDF):\n", " _N_OBS = 1 # dimension, can be omitted\n", " _PARAMS = ['jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n", - " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width'\n", + " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',\n", + " 'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',\n", + " 'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',\n", + " 'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n", + " 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width'\n", " ] # the name of the parameters\n", "\n", " def _unnormalized_pdf(self, x):\n", @@ -354,15 +336,31 @@ " x = x.unstack_x()\n", "\n", " def jpsi_res(q):\n", - " return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'], phase = self.params['jpsi_phase'], width = self.params['jpsi_width'])\n", + " return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'],\n", + " phase = self.params['jpsi_phase'], width = self.params['jpsi_width'])\n", "\n", " def psi2s_res(q):\n", - " return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'], phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n", + " return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'],\n", + " phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n", + " \n", + " def p3770_res(q):\n", + " return resonance(q, _mass = self.params['p3770_mass'], scale = self.params['p3770_scale'],\n", + " phase = self.params['p3770_phase'], width = self.params['p3770_width'])\n", + " \n", + " def p4040_res(q):\n", + " return resonance(q, _mass = self.params['p4040_mass'], scale = self.params['p4040_scale'],\n", + " phase = self.params['p4040_phase'], width = self.params['p4040_width'])\n", + " \n", + " def p4160_res(q):\n", + " return resonance(q, _mass = self.params['p4160_mass'], scale = self.params['p4160_scale'],\n", + " phase = self.params['p4160_phase'], width = self.params['p4160_width'])\n", + " \n", + " def p4415_res(q):\n", + " return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'],\n", + " phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n", + " \n", "\n", - " def cusp(q):\n", - " return bifur_gauss(q, mean = self.params['cusp_mass'], sigma_L = self.params['sigma_L'], sigma_R = self.params['sigma_R'], scale = self.params['cusp_scale'])\n", - "\n", - " funcs = jpsi_res(x) + psi2s_res(x) #+ cusp(x)\n", + " funcs = jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x) + p4160_res(x) + p4415_res(x)\n", "\n", " vec_f = vec(x, funcs)\n", "\n", @@ -382,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -412,17 +410,7 @@ "cell_type": "code", "execution_count": 7, "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": [ "#jpsi\n", "\n", @@ -443,14 +431,41 @@ "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False)\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale))\n", "\n", - "#cusp\n", + "#psi(3770)\n", "\n", - "# cusp_mass, sigma_R, sigma_L, cusp_scale = 3550, 3e-7, 200, 0\n", + "p3770_mass, p3770_width, p3770_phase, p3770_scale = pdg[\"p3770\"]\n", "\n", - "# cusp_m = zfit.Parameter(\"cusp_m\", ztf.constant(cusp_mass), floating = False)\n", - "# sig_L = zfit.Parameter(\"sig_L\", ztf.constant(sigma_L), floating = False)\n", - "# sig_R = zfit.Parameter(\"sig_R\", ztf.constant(sigma_R), floating = False)\n", - "# cusp_s = zfit.Parameter(\"cusp_s\", ztf.constant(cusp_scale), floating = False)" + "p3770_m = zfit.Parameter(\"p3770_m\", ztf.constant(p3770_mass), floating = False)\n", + "p3770_w = zfit.Parameter(\"p3770_w\", ztf.constant(p3770_width), floating = False)\n", + "p3770_p = zfit.Parameter(\"p3770_p\", ztf.constant(p3770_phase), floating = False)\n", + "p3770_s = zfit.Parameter(\"p3770_s\", ztf.constant(p3770_scale))\n", + "\n", + "#psi(4040)\n", + "\n", + "p4040_mass, p4040_width, p4040_phase, p4040_scale = pdg[\"p4040\"]\n", + "\n", + "p4040_m = zfit.Parameter(\"p4040_m\", ztf.constant(p4040_mass), floating = False)\n", + "p4040_w = zfit.Parameter(\"p4040_w\", ztf.constant(p4040_width), floating = False)\n", + "p4040_p = zfit.Parameter(\"p4040_p\", ztf.constant(p4040_phase), floating = False)\n", + "p4040_s = zfit.Parameter(\"p4040_s\", ztf.constant(p4040_scale))\n", + "\n", + "#psi(4160)\n", + "\n", + "p4160_mass, p4160_width, p4160_phase, p4160_scale = pdg[\"p4160\"]\n", + "\n", + "p4160_m = zfit.Parameter(\"p4160_m\", ztf.constant(p4160_mass), floating = False)\n", + "p4160_w = zfit.Parameter(\"p4160_w\", ztf.constant(p4160_width), floating = False)\n", + "p4160_p = zfit.Parameter(\"p4160_p\", ztf.constant(p4160_phase), floating = False)\n", + "p4160_s = zfit.Parameter(\"p4160_s\", ztf.constant(p4160_scale))\n", + "\n", + "#psi(4415)\n", + "\n", + "p4415_mass, p4415_width, p4415_phase, p4415_scale = pdg[\"p4415\"]\n", + "\n", + "p4415_m = zfit.Parameter(\"p4415_m\", ztf.constant(p4415_mass), floating = False)\n", + "p4415_w = zfit.Parameter(\"p4415_w\", ztf.constant(p4415_width), floating = False)\n", + "p4415_p = zfit.Parameter(\"p4415_p\", ztf.constant(p4415_phase), floating = False)\n", + "p4415_s = zfit.Parameter(\"p4415_s\", ztf.constant(p4415_scale))" ] }, { @@ -467,10 +482,15 @@ "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", - " psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w)#,\n", - " #cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s) \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", " \n", - "# print(total_pdf.obs)" + "# print(total_pdf.obs)\n", + "\n", + "# print(calcs_test)" ] }, { @@ -482,30 +502,30 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "def total_test_tf(xq):\n", + "# def total_test_tf(xq):\n", "\n", - " def jpsi_res(q):\n", - " return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n", + "# def jpsi_res(q):\n", + "# return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n", "\n", - " def psi2s_res(q):\n", - " return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n", + "# def psi2s_res(q):\n", + "# return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n", "\n", - " def cusp(q):\n", - " return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)\n", + "# def cusp(q):\n", + "# return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)\n", "\n", - " funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq)\n", + "# funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq)\n", "\n", - " vec_f = vec(xq, funcs)\n", + "# vec_f = vec(xq, funcs)\n", "\n", - " axiv_nr = axiv_nonres(xq)\n", + "# axiv_nr = axiv_nonres(xq)\n", "\n", - " tot = vec_f + axiv_nr\n", + "# tot = vec_f + axiv_nr\n", " \n", - " return tot\n", + "# return tot\n", "\n", "def jpsi_res(q):\n", " return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n", @@ -525,12 +545,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -559,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -593,10 +613,29 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "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 1\u001b[0m \u001b[0mtotal_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_integration_options\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdraws_per_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20000000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmc_sampler\u001b[0m\u001b[1;33m=\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 2\u001b[0m \u001b[0minte\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtotal_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintegrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4250\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4600\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm_range\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0minte_fl\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[0minte\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 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minte_fl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"jpsi_BR\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"NR_BR\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minte_fl\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"psi2s_auc\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"NR_auc\"\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\\zfit\\util\\execution.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[0mkwargs\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---> 81\u001b[1;33m \u001b[1;32mreturn\u001b[0m \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[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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 82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[1;31m# def close(self):\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;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 1332\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\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[1;33m\u001b[0m\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[0;32m 1336\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\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[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 1317\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\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 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[0;32m 1321\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\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[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_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1405\u001b[0m return tf_session.TF_SessionRun_wrapper(\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[0;32m 1409\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\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[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "# total_f.update_integration_options(draws_per_dim=20000000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (3080, 3112), norm_range=False)\n", + "# inte = total_f.integrate(limits = (4250, 4600), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl)\n", "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" @@ -604,43 +643,48 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# print(\"jpsi:\", inte_fl)\n", - "# print(\"Increase am by factor:\", np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", - "# print(\"New amp:\", pdg[\"jpsi\"][3]*np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"jpsi:\", inte_fl)\n", + "# # print(\"Increase am by factor:\", np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"New amp:\", pdg[\"jpsi\"][3]*np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", - "# print(\"psi2s:\", inte_fl)\n", - "# print(\"Increase am by factor:\", np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", - "# print(\"New amp:\", pdg[\"psi2s\"][3]*np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"psi2s:\", inte_fl)\n", + "# # print(\"Increase am by factor:\", np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"New amp:\", pdg[\"psi2s\"][3]*np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "\n", + "# name = \"p4415\"\n", + "\n", + "# print(name+\":\", inte_fl)\n", + "# print(\"Increase am by factor:\", np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# print(\"New amp:\", pdg[name][3]*np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", "\n", + "# # print(x_min)\n", + "# # print(x_max)\n", + "# # # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# # total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", + "# # dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", + "# # draws_per_dim=1000000)\n", + "# # # _ = []\n", "\n", - "# print(x_min)\n", - "# print(x_max)\n", - "# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", - "# total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", - "# dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", - "# draws_per_dim=1000000)\n", - "# # _ = []\n", + "# # # for i in range(10):\n", "\n", - "# # for i in range(10):\n", + "# # # inte = total_f.integrate(limits = (x_min, x_max))\n", + "# # # inte_fl = zfit.run(inte)\n", + "# # # print(inte_fl)\n", + "# # # _.append(inte_fl)\n", "\n", - "# # inte = total_f.integrate(limits = (x_min, x_max))\n", - "# # inte_fl = zfit.run(inte)\n", - "# # print(inte_fl)\n", - "# # _.append(inte_fl)\n", + "# # # print(\"mean:\", np.mean(_))\n", "\n", - "# # print(\"mean:\", np.mean(_))\n", + "# # _ = time.time()\n", "\n", - "# _ = time.time()\n", - "\n", - "# inte = total_f.integrate(limits = (x_min, x_max))\n", - "# inte_fl = zfit.run(inte)\n", - "# print(inte_fl)\n", - "# print(\"Time taken: {}\".format(display_time(int(time.time() - _))))" + "# # inte = total_f.integrate(limits = (x_min, x_max))\n", + "# # inte_fl = zfit.run(inte)\n", + "# # print(inte_fl)\n", + "# # print(\"Time taken: {}\".format(display_time(int(time.time() - _))))" ] }, { @@ -652,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -681,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -698,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -728,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -737,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -759,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -818,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -846,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -927,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -936,27 +980,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001309082138940001" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -965,21 +998,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6/6 of Toy 1/1\n", - "Time taken: 1 min, 6 s\n", - "Projected time left: \n" - ] - } - ], + "outputs": [], "source": [ "# zfit.run.numeric_checks = False \n", "\n", @@ -1023,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1040,18 +1063,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time to generate full toy: 66 s\n", - "(5404696,)\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n", "\n", @@ -1073,29 +1087,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5404696,)\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "\n", @@ -1120,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1143,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1152,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1168,216 +1162,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
FCN = -861523.9193443996TOTAL NCALL = 31NCALLS = 31
EDM = 1.78687712418138e-05GOAL EDM = 5e-06\n", - " UP = 0.5
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ValidValid ParamAccurate CovarPosDefMade PosDef
TrueTrueTrueTrueFalse
Hesse FailHasCovAbove EDMReach calllim
FalseTrueFalseFalse
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
+NameValueHesse ErrorMinos Error-Minos Error+Limit-Limit+Fixed?
0jpsi_s10204.328.6702No
1psi2s_s1239.483.62611No
\n", - "
\n",
-       "\n",
-       "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Minos status for jpsi_s: VALID\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Error-28.78472940434378228.58795673661853
ValidTrueTrue
At LimitFalseFalse
Max FCNFalseFalse
New MinFalseFalse
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Minos status for psi2s_s: VALID\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Error-3.63829584578296843.6140145472909015
ValidTrueTrue
At LimitFalseFalse
Max FCNFalseFalse
New MinFalseFalse
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "jpsi_s: ^{+28.58795673661853}_{-28.784729404343782}\n", - "psi2s_s: ^{+3.6140145472909015}_{-3.6382958457829684}\n", - "Function minimum: -861523.9193443996\n" - ] - } - ], + "outputs": [], "source": [ "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n", "\n", @@ -1395,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1407,22 +1194,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "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", @@ -1438,7 +1212,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1452,7 +1226,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1473,18 +1247,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.522535170724314\n", - "1.522535170724314\n" - ] - } - ], + "outputs": [], "source": [ "print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n", "print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)" diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index 82e8b3a..5ef7254 100644 --- a/__pycache__/pdg_const.cpython-37.pyc +++ b/__pycache__/pdg_const.cpython-37.pyc Binary files differ diff --git a/pdg_const.py b/pdg_const.py index 62af75a..b20431a 100644 --- a/pdg_const.py +++ b/pdg_const.py @@ -1,113 +1,122 @@ pdg = { -###Particle masses### -"mbstar" : 5415.4, -"mbstar0" : 5711.0, -"B0_M" : 5279.5, -"Bs_M" : 5366.7, -"Bplus_M" : 5279.3, -"Lb_M" : 5619.4, -"D0_M" : 1864.8, -"Dst_M" : 2010, -"pi_M" : 139.6, -"Jpsi_M" : 3096.9, -"Psi2s_M" : 3685.6, -"kaon_M" : 493.7, -"Ks_M" : 497.6, -"phi_M" : 1019.5, -"rho_M" : 775.26, -"rho_width" : 149.1, -"omega_M" : 782.65, -"omega_width" : 8.49, + ###Particle masses### -"muon_M" : 105.7, + "mbstar" : 5415.4, + "mbstar0" : 5711.0, + "B0_M" : 5279.5, + "Bs_M" : 5366.7, + "Bplus_M" : 5279.3, + "Lb_M" : 5619.4, + "D0_M" : 1864.8, + "Dst_M" : 2010, + "pi_M" : 139.6, + "Jpsi_M" : 3096.9, + "Psi2s_M" : 3685.6, + "kaon_M" : 493.7, + "Ks_M" : 497.6, + "phi_M" : 1019.5, + "rho_M" : 775.26, + "rho_width" : 149.1, + "omega_M" : 782.65, + "omega_width" : 8.49, -"squark_M" : 95.0, -"bquark_M" : 4180.0, -"cquark_M" : 1275.0, + "muon_M" : 105.7, -"Bplus_tau" : 1.638e-12, + "squark_M" : 95.0, + "bquark_M" : 4180.0, + "cquark_M" : 1275.0, -###Wilson coefficients### + "Bplus_tau" : 1.638e-12, -"C1" : -0.257, -"C2" : 1.009, -"C3" : -0.005, -"C4" : -0.078, + ###Wilson coefficients### -# "C7eff" : -0.306, -"C7eff": 0.0, + "C1" : -0.257, + "C2" : 1.009, + "C3" : -0.005, + "C4" : -0.078, -# "C9eff" : 4.211, -# "C10eff" : -4.103, -"C9eff": 0.0, -"C10eff": 0.0, - -###Other constants - -"GF" : 1.1663787e-5, -"alpha_ew" : 1.0/137.0, -"Vts" : 0.0394, -"Vtb" : 1.019, -"number_of_decays": 5404696, - -#Formfactor z coefficients - -#"b0" : [0.285, 0.19, -0.17], -#"bplus" : [0.437, -1.41, -2.5], -#"bT" : [0.440, -1.47, -2.7] - -"b0" : [0.292, 0.281, 0.150], -"bplus" : [0.466, -0.885, -0.213], -"bT" : [0.460, -1.089, -1.114], - -"NR_BR": 4.37e-7, -"NR_auc": 0.00133, - -#Resonances format(mass, width, phase, scale) -# "jpsi": (3096.0, 0.09, -1.5, 2e-2), #-------> pre scaling -# "jpsi": (3096.0, 0.09, -1.5, 9897.0), #---> after scaling -"jpsi": (3096.0, 0.09, -1.5, 0.0), -"jpsi_BR": 6.02e-5, -# "jpsi_auc": 0.2126825758464027, #----------------> pre scaling -"jpsi_auc": 0.2126825758464027, #--------------> after scaling - -# "psi2s": (3686.0, 0.3, -1.5, 3.14e-3), #-------> pre scaling -# "psi2s": (3686.0, 0.3, -1.5, 1396.0), #--------> after scaling -"psi2s": (3686.0, 0.3, -1.5, 0.0), -"psi2s_BR": 4.97e-6, -# "psi2s_auc": 2.802257483178487e-10, #------------> pre scaling -"psi2s_auc": 0.0151332263, #--------------------> after scaling + "C7eff" : -0.306, + "C9eff" : 4.211, + "C10eff" : -4.103, -#------------------------------------------------------------------------------------- +# "C7eff": 0.0, +# "C9eff": 0.0, +# "C10eff": 0.0, + + ###Other constants + + "GF" : 1.1663787e-5, + "alpha_ew" : 1.0/137.0, + "Vts" : 0.0394, + "Vtb" : 1.019, + "number_of_decays": 5404696, + + #Formfactor z coefficients + + #"b0" : [0.285, 0.19, -0.17], + #"bplus" : [0.437, -1.41, -2.5], + #"bT" : [0.440, -1.47, -2.7] + + "b0" : [0.292, 0.281, 0.150], + "bplus" : [0.466, -0.885, -0.213], + "bT" : [0.460, -1.089, -1.114], + + "NR_BR": 4.37e-7, + "NR_auc": 0.00133, + + #Resonances format(mass, width, phase, scale) + + # pre scaling -# "p3770": (3773.0, 27.2, -2.13, 3.14e-3), #-------> pre scaling -# "p3770": (3773.0, 27.2, -2.13, 1396.0), #--------> after scaling -"p3770": (3773.0, 27.2, -2.13, 0.0), -"p3770_BR": 1.38e-9, -# "p3770_auc": 2.802257483178487e-10, #------------> pre scaling -"p3770_auc": 0.0151332263, #--------------------> after scaling +# "jpsi": (3096.0, 0.09, -1.5, 2e-2), +# "jpsi_auc": 0.2126825758464027, -# "p4040": (4039.0, 80.0, -2.52, 3.14e-3), #-------> pre scaling -# "p4040": (4039.0, 80.0,, -2.52, 1396.0), #--------> after scaling -"p4040": (4039.0, 80.0,, -2.52, 0.0), -"p4040_BR": 4.2e-10, -# "p4040_auc": 2.802257483178487e-10, #------------> pre scaling -"p4040_auc": 0.0151332263, #--------------------> after scaling +# "psi2s": (3686.0, 0.3, -1.5, 3.14e-3), +# "psi2s_auc": 2.802257483178487e-10, + +# "p3770": (3773.0, 27.2, -2.13, 1.0e-3), + +# "p4040": (4039.0, 80.0, -2.52, 2.0), + +# "p4160": (4147.0, 22.0, -1.9, 1.0), + +# "p4415": (4421.0, 62.0, -2.52, 1.0), + -# "p4160": (4147.0, 22.0, -1.9, 3.14e-3), #-------> pre scaling -# "p4160": (4147.0, 22.0, -1.9, 1396.0), #--------> after scaling -"p4160": (4147.0, 22.0, -1.9, 0.0), -"p4160_BR": 2.6e-9, -# "p4160_auc": 2.802257483178487e-10, #------------> pre scaling -"p4160_auc": 0.0151332263, #--------------------> after scaling + # after scaling + + "jpsi": (3096.0, 0.09, -1.5, 9897.0), + "jpsi_auc": 0.2126825758464027, + + "psi2s": (3686.0, 0.3, -1.5, 1396.0), + "psi2s_auc": 0.0151332263, + + "p3770": (3773.0, 27.2, -2.13, 2.5), + + "p4040": (4039.0, 80.0, -2.52, 1.01), + + "p4160": (4147.0, 22.0, -1.9, 3.94), + + "p4415": (4421.0, 62.0, -2.52, 1.24), -# "p4415": (4421.0, 62.0, -2.52, 3.14e-3), #-------> pre scaling -# "p4415": (4421.0, 62.0, -2.52, 1396.0), #--------> after scaling -"p4415": (4421.0, 62.0, -2.52, 0.0), -"p4415_BR": 6.1e-10, -# "p4415_auc": 2.802257483178487e-10, #------------> pre scaling -"p4415_auc": 0.0151332263, #--------------------> after scaling -} + # zeroing resonances + +# "jpsi": (3096.0, 0.09, -1.5, 0.0), +# "psi2s": (3686.0, 0.3, -1.5, 0.0), +# "p3770": (3773.0, 27.2, -2.13, 0.0), +# "p4040": (4039.0, 80.0, -2.52, 0.0), +# "p4160": (4147.0, 22.0, -1.9, 0.0), +# "p4415": (4421.0, 62.0, -2.52, 0.0), + + #general + "jpsi_BR": 6.02e-5, + "psi2s_BR": 4.97e-6, + "p3770_BR": 1.38e-9, + "p4040_BR": 4.2e-10, + "p4160_BR": 2.6e-9, + "p4415_BR": 6.1e-10, + + } diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 679a6ff..5080c0b 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -9,31 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n", - " warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n" - ] - }, - { - "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" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -64,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -83,7 +61,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -284,7 +262,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -339,14 +317,18 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "class total_pdf(zfit.pdf.ZPDF):\n", " _N_OBS = 1 # dimension, can be omitted\n", " _PARAMS = ['jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n", - " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width'\n", + " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',\n", + " 'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',\n", + " 'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',\n", + " 'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n", + " 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width'\n", " ] # the name of the parameters\n", "\n", " def _unnormalized_pdf(self, x):\n", @@ -354,15 +336,31 @@ " x = x.unstack_x()\n", "\n", " def jpsi_res(q):\n", - " return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'], phase = self.params['jpsi_phase'], width = self.params['jpsi_width'])\n", + " return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'],\n", + " phase = self.params['jpsi_phase'], width = self.params['jpsi_width'])\n", "\n", " def psi2s_res(q):\n", - " return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'], phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n", + " return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'],\n", + " phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n", + " \n", + " def p3770_res(q):\n", + " return resonance(q, _mass = self.params['p3770_mass'], scale = self.params['p3770_scale'],\n", + " phase = self.params['p3770_phase'], width = self.params['p3770_width'])\n", + " \n", + " def p4040_res(q):\n", + " return resonance(q, _mass = self.params['p4040_mass'], scale = self.params['p4040_scale'],\n", + " phase = self.params['p4040_phase'], width = self.params['p4040_width'])\n", + " \n", + " def p4160_res(q):\n", + " return resonance(q, _mass = self.params['p4160_mass'], scale = self.params['p4160_scale'],\n", + " phase = self.params['p4160_phase'], width = self.params['p4160_width'])\n", + " \n", + " def p4415_res(q):\n", + " return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'],\n", + " phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n", + " \n", "\n", - " def cusp(q):\n", - " return bifur_gauss(q, mean = self.params['cusp_mass'], sigma_L = self.params['sigma_L'], sigma_R = self.params['sigma_R'], scale = self.params['cusp_scale'])\n", - "\n", - " funcs = jpsi_res(x) + psi2s_res(x) #+ cusp(x)\n", + " funcs = jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x) + p4160_res(x) + p4415_res(x)\n", "\n", " vec_f = vec(x, funcs)\n", "\n", @@ -382,7 +380,7 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -412,17 +410,7 @@ "cell_type": "code", "execution_count": 7, "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": [ "#jpsi\n", "\n", @@ -443,14 +431,41 @@ "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False)\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale))\n", "\n", - "#cusp\n", + "#psi(3770)\n", "\n", - "# cusp_mass, sigma_R, sigma_L, cusp_scale = 3550, 3e-7, 200, 0\n", + "p3770_mass, p3770_width, p3770_phase, p3770_scale = pdg[\"p3770\"]\n", "\n", - "# cusp_m = zfit.Parameter(\"cusp_m\", ztf.constant(cusp_mass), floating = False)\n", - "# sig_L = zfit.Parameter(\"sig_L\", ztf.constant(sigma_L), floating = False)\n", - "# sig_R = zfit.Parameter(\"sig_R\", ztf.constant(sigma_R), floating = False)\n", - "# cusp_s = zfit.Parameter(\"cusp_s\", ztf.constant(cusp_scale), floating = False)" + "p3770_m = zfit.Parameter(\"p3770_m\", ztf.constant(p3770_mass), floating = False)\n", + "p3770_w = zfit.Parameter(\"p3770_w\", ztf.constant(p3770_width), floating = False)\n", + "p3770_p = zfit.Parameter(\"p3770_p\", ztf.constant(p3770_phase), floating = False)\n", + "p3770_s = zfit.Parameter(\"p3770_s\", ztf.constant(p3770_scale))\n", + "\n", + "#psi(4040)\n", + "\n", + "p4040_mass, p4040_width, p4040_phase, p4040_scale = pdg[\"p4040\"]\n", + "\n", + "p4040_m = zfit.Parameter(\"p4040_m\", ztf.constant(p4040_mass), floating = False)\n", + "p4040_w = zfit.Parameter(\"p4040_w\", ztf.constant(p4040_width), floating = False)\n", + "p4040_p = zfit.Parameter(\"p4040_p\", ztf.constant(p4040_phase), floating = False)\n", + "p4040_s = zfit.Parameter(\"p4040_s\", ztf.constant(p4040_scale))\n", + "\n", + "#psi(4160)\n", + "\n", + "p4160_mass, p4160_width, p4160_phase, p4160_scale = pdg[\"p4160\"]\n", + "\n", + "p4160_m = zfit.Parameter(\"p4160_m\", ztf.constant(p4160_mass), floating = False)\n", + "p4160_w = zfit.Parameter(\"p4160_w\", ztf.constant(p4160_width), floating = False)\n", + "p4160_p = zfit.Parameter(\"p4160_p\", ztf.constant(p4160_phase), floating = False)\n", + "p4160_s = zfit.Parameter(\"p4160_s\", ztf.constant(p4160_scale))\n", + "\n", + "#psi(4415)\n", + "\n", + "p4415_mass, p4415_width, p4415_phase, p4415_scale = pdg[\"p4415\"]\n", + "\n", + "p4415_m = zfit.Parameter(\"p4415_m\", ztf.constant(p4415_mass), floating = False)\n", + "p4415_w = zfit.Parameter(\"p4415_w\", ztf.constant(p4415_width), floating = False)\n", + "p4415_p = zfit.Parameter(\"p4415_p\", ztf.constant(p4415_phase), floating = False)\n", + "p4415_s = zfit.Parameter(\"p4415_s\", ztf.constant(p4415_scale))" ] }, { @@ -467,10 +482,15 @@ "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", - " psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w)#,\n", - " #cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s) \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", " \n", - "# print(total_pdf.obs)" + "# print(total_pdf.obs)\n", + "\n", + "# print(calcs_test)" ] }, { @@ -482,30 +502,30 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ - "def total_test_tf(xq):\n", + "# def total_test_tf(xq):\n", "\n", - " def jpsi_res(q):\n", - " return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n", + "# def jpsi_res(q):\n", + "# return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n", "\n", - " def psi2s_res(q):\n", - " return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n", + "# def psi2s_res(q):\n", + "# return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w)\n", "\n", - " def cusp(q):\n", - " return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)\n", + "# def cusp(q):\n", + "# return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s)\n", "\n", - " funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq)\n", + "# funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq)\n", "\n", - " vec_f = vec(xq, funcs)\n", + "# vec_f = vec(xq, funcs)\n", "\n", - " axiv_nr = axiv_nonres(xq)\n", + "# axiv_nr = axiv_nonres(xq)\n", "\n", - " tot = vec_f + axiv_nr\n", + "# tot = vec_f + axiv_nr\n", " \n", - " return tot\n", + "# return tot\n", "\n", "def jpsi_res(q):\n", " return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w)\n", @@ -525,12 +545,12 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": 18, "metadata": {}, "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -559,7 +579,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -593,10 +613,29 @@ "cell_type": "code", "execution_count": 13, "metadata": {}, - "outputs": [], + "outputs": [ + { + "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 1\u001b[0m \u001b[0mtotal_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mupdate_integration_options\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mdraws_per_dim\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m20000000\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmc_sampler\u001b[0m\u001b[1;33m=\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 2\u001b[0m \u001b[0minte\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtotal_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mintegrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m(\u001b[0m\u001b[1;36m4250\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;36m4600\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm_range\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[0minte_fl\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[0minte\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 4\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0minte_fl\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"jpsi_BR\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"NR_BR\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0minte_fl\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"psi2s_auc\"\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m/\u001b[0m\u001b[0mpdg\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;34m\"NR_auc\"\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\\zfit\\util\\execution.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 79\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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[0mkwargs\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---> 81\u001b[1;33m \u001b[1;32mreturn\u001b[0m \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[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\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 82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 83\u001b[0m \u001b[1;31m# def close(self):\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;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 1332\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\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[1;33m\u001b[0m\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[0;32m 1336\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\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[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 1317\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\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 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[0;32m 1321\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\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[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_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1405\u001b[0m return tf_session.TF_SessionRun_wrapper(\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[0;32m 1409\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\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[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + ] + } + ], "source": [ "# total_f.update_integration_options(draws_per_dim=20000000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (3080, 3112), norm_range=False)\n", + "# inte = total_f.integrate(limits = (4250, 4600), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl)\n", "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" @@ -604,43 +643,48 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "# print(\"jpsi:\", inte_fl)\n", - "# print(\"Increase am by factor:\", np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", - "# print(\"New amp:\", pdg[\"jpsi\"][3]*np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"jpsi:\", inte_fl)\n", + "# # print(\"Increase am by factor:\", np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"New amp:\", pdg[\"jpsi\"][3]*np.sqrt(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", - "# print(\"psi2s:\", inte_fl)\n", - "# print(\"Increase am by factor:\", np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", - "# print(\"New amp:\", pdg[\"psi2s\"][3]*np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"psi2s:\", inte_fl)\n", + "# # print(\"Increase am by factor:\", np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# # print(\"New amp:\", pdg[\"psi2s\"][3]*np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "\n", + "# name = \"p4415\"\n", + "\n", + "# print(name+\":\", inte_fl)\n", + "# print(\"Increase am by factor:\", np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# print(\"New amp:\", pdg[name][3]*np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", "\n", + "# # print(x_min)\n", + "# # print(x_max)\n", + "# # # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# # total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", + "# # dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", + "# # draws_per_dim=1000000)\n", + "# # # _ = []\n", "\n", - "# print(x_min)\n", - "# print(x_max)\n", - "# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", - "# total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", - "# dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", - "# draws_per_dim=1000000)\n", - "# # _ = []\n", + "# # # for i in range(10):\n", "\n", - "# # for i in range(10):\n", + "# # # inte = total_f.integrate(limits = (x_min, x_max))\n", + "# # # inte_fl = zfit.run(inte)\n", + "# # # print(inte_fl)\n", + "# # # _.append(inte_fl)\n", "\n", - "# # inte = total_f.integrate(limits = (x_min, x_max))\n", - "# # inte_fl = zfit.run(inte)\n", - "# # print(inte_fl)\n", - "# # _.append(inte_fl)\n", + "# # # print(\"mean:\", np.mean(_))\n", "\n", - "# # print(\"mean:\", np.mean(_))\n", + "# # _ = time.time()\n", "\n", - "# _ = time.time()\n", - "\n", - "# inte = total_f.integrate(limits = (x_min, x_max))\n", - "# inte_fl = zfit.run(inte)\n", - "# print(inte_fl)\n", - "# print(\"Time taken: {}\".format(display_time(int(time.time() - _))))" + "# # inte = total_f.integrate(limits = (x_min, x_max))\n", + "# # inte_fl = zfit.run(inte)\n", + "# # print(inte_fl)\n", + "# # print(\"Time taken: {}\".format(display_time(int(time.time() - _))))" ] }, { @@ -652,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -681,7 +725,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -698,7 +742,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -728,7 +772,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -737,7 +781,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -759,7 +803,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -796,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -818,7 +862,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -846,7 +890,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -927,7 +971,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -936,27 +980,16 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001309082138940001" - ] - }, - "execution_count": 25, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" ] }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -965,21 +998,11 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "6/6 of Toy 1/1\n", - "Time taken: 1 min, 6 s\n", - "Projected time left: \n" - ] - } - ], + "outputs": [], "source": [ "# zfit.run.numeric_checks = False \n", "\n", @@ -1023,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1040,18 +1063,9 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Time to generate full toy: 66 s\n", - "(5404696,)\n" - ] - } - ], + "outputs": [], "source": [ "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n", "\n", @@ -1073,29 +1087,9 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(5404696,)\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "\n", @@ -1120,7 +1114,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1143,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1152,7 +1146,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1168,216 +1162,9 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
FCN = -861523.9193443996TOTAL NCALL = 31NCALLS = 31
EDM = 1.78687712418138e-05GOAL EDM = 5e-06\n", - " UP = 0.5
\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
ValidValid ParamAccurate CovarPosDefMade PosDef
TrueTrueTrueTrueFalse
Hesse FailHasCovAbove EDMReach calllim
FalseTrueFalseFalse
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
+NameValueHesse ErrorMinos Error-Minos Error+Limit-Limit+Fixed?
0jpsi_s10204.328.6702No
1psi2s_s1239.483.62611No
\n", - "
\n",
-       "\n",
-       "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Minos status for jpsi_s: VALID\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Error-28.78472940434378228.58795673661853
ValidTrueTrue
At LimitFalseFalse
Max FCNFalseFalse
New MinFalseFalse
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/html": [ - "Minos status for psi2s_s: VALID\n", - "\n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - " \n", - "
Error-3.63829584578296843.6140145472909015
ValidTrueTrue
At LimitFalseFalse
Max FCNFalseFalse
New MinFalseFalse
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "jpsi_s: ^{+28.58795673661853}_{-28.784729404343782}\n", - "psi2s_s: ^{+3.6140145472909015}_{-3.6382958457829684}\n", - "Function minimum: -861523.9193443996\n" - ] - } - ], + "outputs": [], "source": [ "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n", "\n", @@ -1395,7 +1182,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1407,22 +1194,9 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "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", @@ -1438,7 +1212,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1452,7 +1226,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1473,18 +1247,9 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "1.522535170724314\n", - "1.522535170724314\n" - ] - } - ], + "outputs": [], "source": [ "print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n", "print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)" diff --git a/test.png b/test.png index b9496fe..3a43487 100644 --- a/test.png +++ b/test.png Binary files differ