diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 5080c0b..96b984a 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -9,9 +9,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "import os\n", "\n", @@ -42,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +304,7 @@ " \n", " return ztf.to_complex(2) - G(ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2)))\n", "\n", - "def h_P(m,q):\n", + "def h_P(m, q):\n", " \n", " return ztf.to_complex(2/3) + (ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2))) * h_S(m,q)\n", "\n", @@ -312,12 +334,68 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## C_q,qbar constraint" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", + "omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n", + "phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n", + "\n", + "\n", + "r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n", + "o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n", + "p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass\n", + "\n", + "phi_s = np.linspace(-500, 5000, 100000)\n", + "\n", + "p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", + "\n", + "p_y = r+o+p_\n", + "\n", + "plt.plot(phi_s, p_y)\n", + "\n", + "# print(r + o + p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "## Build pdf" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -380,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -408,9 +486,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "#jpsi\n", "\n", @@ -438,7 +526,7 @@ "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", + "p3770_s = zfit.Parameter(\"p3770_s\", ztf.constant(p3770_scale), floating = False)\n", "\n", "#psi(4040)\n", "\n", @@ -447,7 +535,7 @@ "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", + "p4040_s = zfit.Parameter(\"p4040_s\", ztf.constant(p4040_scale), floating = False)\n", "\n", "#psi(4160)\n", "\n", @@ -456,7 +544,7 @@ "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", + "p4160_s = zfit.Parameter(\"p4160_s\", ztf.constant(p4160_scale), floating = False)\n", "\n", "#psi(4415)\n", "\n", @@ -477,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -502,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -545,12 +633,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -579,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -595,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -611,39 +699,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, - "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", - 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"\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: " - ] - } - ], + "outputs": [], "source": [ - "# total_f.update_integration_options(draws_per_dim=20000000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (4250, 4600), norm_range=False)\n", + "# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# inte = total_f.integrate(limits = (4000, 4400), 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\"])" + "# # print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -655,7 +724,7 @@ "# # 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", + "# name = \"p4160\"\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", @@ -910,15 +979,15 @@ "# uniformjpsi = tfd.Uniform(low=tf.constant(3080, dtype=dtype), high=tf.constant(3112, dtype=dtype))\n", "# uniformpsi2s = tfd.Uniform(low=tf.constant(3670, dtype=dtype), high=tf.constant(3702, dtype=dtype))\n", "\n", - " list_of_borders = []\n", - " _p = []\n", - " splits = 10\n", + "# list_of_borders = []\n", + "# _p = []\n", + "# splits = 10\n", "\n", - " _ = np.linspace(x_min, x_max, splits)\n", + "# _ = np.linspace(x_min, x_max, splits)\n", "\n", - " for i in range(splits):\n", - " list_of_borders.append(tf.constant(_[i], dtype=dtype))\n", - " _p.append(tf.constant(1/splits, dtype=dtype))\n", + "# for i in range(splits):\n", + "# list_of_borders.append(tf.constant(_[i], dtype=dtype))\n", + "# _p.append(tf.constant(1/splits, dtype=dtype))\n", " \n", "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),\n", "# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], \n", @@ -938,22 +1007,22 @@ "# tf.constant(3691, dtype=dtype),\n", "# tf.constant(3110, dtype=dtype), \n", "# tf.constant(3710, dtype=dtype)]))\n", - " dtype = tf.float64\n", - " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", - " tf.constant(0.90, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype),\n", - " tf.constant(0.07, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype)]),\n", - " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - " tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype)], \n", - " high=[tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype), \n", - " tf.constant(x_max, dtype=dtype)]))\n", + " dtype = tf.float64\n", + " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", + " tf.constant(0.90, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype),\n", + " tf.constant(0.07, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype)]),\n", + " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + " tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype)], \n", + " high=[tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype), \n", + " tf.constant(x_max, dtype=dtype)]))\n", "# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n", "# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n", " sample = mixture.sample((n_to_produce, 1))\n", @@ -1166,6 +1235,8 @@ "metadata": {}, "outputs": [], "source": [ + "start = time.time()\n", + "\n", "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n", "\n", "minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", @@ -1186,6 +1257,8 @@ "metadata": {}, "outputs": [], "source": [ + "print(\"Time taken for fitting: {}\".format(display_time(int(time.time()-start))))\n", + "\n", "# probs = total_f.pdf(test_q)\n", "\n", "calcs_test = zfit.run(probs)\n", diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index 5ef7254..d8a0f6b 100644 --- a/__pycache__/pdg_const.cpython-37.pyc +++ b/__pycache__/pdg_const.cpython-37.pyc Binary files differ diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index aa45ff4..8da98c7 100644 --- a/data/zfit_toys/toy_0/0.pkl +++ b/data/zfit_toys/toy_0/0.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/1.pkl b/data/zfit_toys/toy_0/1.pkl index 344099a..b79899a 100644 --- a/data/zfit_toys/toy_0/1.pkl +++ b/data/zfit_toys/toy_0/1.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/2.pkl b/data/zfit_toys/toy_0/2.pkl index b67d41e..e58c433 100644 --- a/data/zfit_toys/toy_0/2.pkl +++ b/data/zfit_toys/toy_0/2.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/3.pkl b/data/zfit_toys/toy_0/3.pkl index f6fb010..b1faa49 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 b21b688..1beb224 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 d886d91..aa81eb6 100644 --- a/data/zfit_toys/toy_0/5.pkl +++ b/data/zfit_toys/toy_0/5.pkl Binary files differ diff --git a/pdg_const.py b/pdg_const.py index b20431a..a763428 100644 --- a/pdg_const.py +++ b/pdg_const.py @@ -69,6 +69,12 @@ #Resonances format(mass, width, phase, scale) # pre scaling + + "rho": (775.26, 149.0, -1.5, 1.0), + + "omega": (782.7, 8.5, -1.5, 50.0), + + "phi": (1019.46, 4.25, -1.6, 300.89), # "jpsi": (3096.0, 0.09, -1.5, 2e-2), # "jpsi_auc": 0.2126825758464027, @@ -80,7 +86,7 @@ # "p4040": (4039.0, 80.0, -2.52, 2.0), -# "p4160": (4147.0, 22.0, -1.9, 1.0), +# "p4160": (4191.0, 70.0, -1.9, 2.2), # "p4415": (4421.0, 62.0, -2.52, 1.0), @@ -97,7 +103,7 @@ "p4040": (4039.0, 80.0, -2.52, 1.01), - "p4160": (4147.0, 22.0, -1.9, 3.94), + "p4160": (4191.0, 70.0, -1.9, 2.2), "p4415": (4421.0, 62.0, -2.52, 1.24), diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 5080c0b..96b984a 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -9,9 +9,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 1, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "import os\n", "\n", @@ -42,7 +64,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ @@ -61,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -262,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -282,7 +304,7 @@ " \n", " return ztf.to_complex(2) - G(ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2)))\n", "\n", - "def h_P(m,q):\n", + "def h_P(m, q):\n", " \n", " return ztf.to_complex(2/3) + (ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2))) * h_S(m,q)\n", "\n", @@ -312,12 +334,68 @@ "cell_type": "markdown", "metadata": {}, "source": [ + "## C_q,qbar constraint" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "[]" + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + }, + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", + "omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n", + "phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n", + "\n", + "\n", + "r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n", + "o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n", + "p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass\n", + "\n", + "phi_s = np.linspace(-500, 5000, 100000)\n", + "\n", + "p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", + "\n", + "p_y = r+o+p_\n", + "\n", + "plt.plot(phi_s, p_y)\n", + "\n", + "# print(r + o + p)" + ] + }, + { + "cell_type": "markdown", + "metadata": {}, + "source": [ "## Build pdf" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -380,7 +458,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -408,9 +486,19 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": 8, "metadata": {}, - "outputs": [], + "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" + ] + } + ], "source": [ "#jpsi\n", "\n", @@ -438,7 +526,7 @@ "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", + "p3770_s = zfit.Parameter(\"p3770_s\", ztf.constant(p3770_scale), floating = False)\n", "\n", "#psi(4040)\n", "\n", @@ -447,7 +535,7 @@ "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", + "p4040_s = zfit.Parameter(\"p4040_s\", ztf.constant(p4040_scale), floating = False)\n", "\n", "#psi(4160)\n", "\n", @@ -456,7 +544,7 @@ "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", + "p4160_s = zfit.Parameter(\"p4160_s\", ztf.constant(p4160_scale), floating = False)\n", "\n", "#psi(4415)\n", "\n", @@ -477,7 +565,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -502,7 +590,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -545,12 +633,12 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 16, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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" ] @@ -579,7 +667,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -595,7 +683,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -611,39 +699,20 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, - "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", - 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"\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: " - ] - } - ], + "outputs": [], "source": [ - "# total_f.update_integration_options(draws_per_dim=20000000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (4250, 4600), norm_range=False)\n", + "# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# inte = total_f.integrate(limits = (4000, 4400), 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\"])" + "# # print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -655,7 +724,7 @@ "# # 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", + "# name = \"p4160\"\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", @@ -910,15 +979,15 @@ "# uniformjpsi = tfd.Uniform(low=tf.constant(3080, dtype=dtype), high=tf.constant(3112, dtype=dtype))\n", "# uniformpsi2s = tfd.Uniform(low=tf.constant(3670, dtype=dtype), high=tf.constant(3702, dtype=dtype))\n", "\n", - " list_of_borders = []\n", - " _p = []\n", - " splits = 10\n", + "# list_of_borders = []\n", + "# _p = []\n", + "# splits = 10\n", "\n", - " _ = np.linspace(x_min, x_max, splits)\n", + "# _ = np.linspace(x_min, x_max, splits)\n", "\n", - " for i in range(splits):\n", - " list_of_borders.append(tf.constant(_[i], dtype=dtype))\n", - " _p.append(tf.constant(1/splits, dtype=dtype))\n", + "# for i in range(splits):\n", + "# list_of_borders.append(tf.constant(_[i], dtype=dtype))\n", + "# _p.append(tf.constant(1/splits, dtype=dtype))\n", " \n", "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),\n", "# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], \n", @@ -938,22 +1007,22 @@ "# tf.constant(3691, dtype=dtype),\n", "# tf.constant(3110, dtype=dtype), \n", "# tf.constant(3710, dtype=dtype)]))\n", - " dtype = tf.float64\n", - " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", - " tf.constant(0.90, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype),\n", - " tf.constant(0.07, dtype=dtype),\n", - " tf.constant(0.02, dtype=dtype)]),\n", - " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - " tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype)], \n", - " high=[tf.constant(3089, dtype=dtype),\n", - " tf.constant(3103, dtype=dtype), \n", - " tf.constant(3681, dtype=dtype),\n", - " tf.constant(3691, dtype=dtype), \n", - " tf.constant(x_max, dtype=dtype)]))\n", + " dtype = tf.float64\n", + " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", + " tf.constant(0.90, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype),\n", + " tf.constant(0.07, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype)]),\n", + " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + " tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype)], \n", + " high=[tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype), \n", + " tf.constant(x_max, dtype=dtype)]))\n", "# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n", "# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n", " sample = mixture.sample((n_to_produce, 1))\n", @@ -1166,6 +1235,8 @@ "metadata": {}, "outputs": [], "source": [ + "start = time.time()\n", + "\n", "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n", "\n", "minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", @@ -1186,6 +1257,8 @@ "metadata": {}, "outputs": [], "source": [ + "print(\"Time taken for fitting: {}\".format(display_time(int(time.time()-start))))\n", + "\n", "# probs = total_f.pdf(test_q)\n", "\n", "calcs_test = zfit.run(probs)\n", diff --git a/test.png b/test.png index 3a43487..3b76a79 100644 --- a/test.png +++ b/test.png Binary files differ diff --git a/test2.png b/test2.png index f7eb98e..ab10d3b 100644 --- a/test2.png +++ b/test2.png Binary files differ diff --git a/test3.png b/test3.png index 0174991..5e94ef0 100644 --- a/test3.png +++ b/test3.png Binary files differ