diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index ef8d1be..2599e28 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -1448,9 +1448,9 @@ " mean1 = ztf.constant(0.466)\n", " mean2 = ztf.constant(-0.885)\n", " mean3 = ztf.constant(-0.213)\n", - " sigma1 = ztf.constant(0.014)\n", - " sigma2 = ztf.constant(0.128)\n", - " sigma3 = ztf.constant(0.548)\n", + " sigma1 = ztf.constant(0.014/3.)\n", + " sigma2 = ztf.constant(0.128/3.)\n", + " sigma3 = ztf.constant(0.548/3.)\n", " x1 = (val1-mean1)/sigma1\n", " x2 = (val2-mean2)/sigma2\n", " x3 = (val3-mean3)/sigma3\n", @@ -1892,8 +1892,8 @@ "pull_dic = {}\n", "\n", "mi = 1e-4\n", - "ma = 3e-3\n", - "ste = 20\n", + "ma = 6e-4\n", + "ste = 6\n", "\n", "for param in total_f_fit.get_dependents():\n", " if param.floating:\n", @@ -1971,7 +1971,7 @@ "nevents = int(pdg[\"number_of_decays\"]*cut_BR)\n", "# nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", - "# nevents *= 41\n", + "nevents *= 41\n", "# zfit.settings.set_verbosity(10)\n", "\n", "# mi = 1e-4\n", @@ -2110,7 +2110,7 @@ "source": [ "if load:\n", " \n", - " phase_combi = '--'\n", + " phase_combi = '-+'\n", " \n", " if D_contribs:\n", " D_dir = 'D-True'\n", @@ -2123,7 +2123,7 @@ " \n", " First = True\n", " \n", - "# print(jobs)\n", + " print('Number of jobs: {}'.format(len(jobs)))\n", " \n", " for job in jobs:\n", " \n", @@ -2263,42 +2263,42 @@ "metadata": {}, "outputs": [], "source": [ - "l = []\n", + "# l = []\n", "\n", "\n", - "if load:\n", - " CLs_values = -1*np.array(CLs_list)\n", + "# if load:\n", + "# CLs_values = -1*np.array(CLs_list)\n", "\n", - "if not os.path.exists('data/CLs/plots'):\n", - " os.mkdir('data/CLs/plots')\n", - " print(\"Directory \" , 'data/CLs/plots' , \" Created \")\n", + "# if not os.path.exists('data/CLs/plots'):\n", + "# os.mkdir('data/CLs/plots')\n", + "# print(\"Directory \" , 'data/CLs/plots' , \" Created \")\n", "\n", - "print(np.shape(CLs_values))\n", + "# print(np.shape(CLs_values))\n", " \n", - "for step in range(1,ste):\n", - " plt.clf()\n", - " plt.title('Ctt value: {:.2f}'.format(Ctt_steps[step]))\n", - " plt.hist(CLs_values[0], bins = 150, range = (-25, 50), label = 'Ctt fixed to 0')\n", - " plt.hist(CLs_values[step], bins = 150, range = (-25, 50), label = 'Ctt floating')\n", - " plt.axvline(x=np.mean(CLs_values[0]),color='red', linewidth=1.0, linestyle = 'dotted')\n", - " plt.legend()\n", - " plt.savefig('data/CLs/plots/CLs-BR({:.1E}).png'.format(BR_steps[step]))\n", + "# for step in range(1,ste):\n", + "# plt.clf()\n", + "# plt.title('Ctt value: {:.2f}'.format(Ctt_steps[step]))\n", + "# plt.hist(CLs_values[0], bins = 150, range = (-25, 50), label = 'Ctt fixed to 0')\n", + "# plt.hist(CLs_values[step], bins = 150, range = (-25, 50), label = 'Ctt floating')\n", + "# plt.axvline(x=np.mean(CLs_values[0]),color='red', linewidth=1.0, linestyle = 'dotted')\n", + "# plt.legend()\n", + "# plt.savefig('data/CLs/plots/CLs-BR({:.1E}).png'.format(BR_steps[step]))\n", " \n", - " l.append(len(np.where(np.array(CLs_values[step]) < np.mean(CLs_values[0]))[0]))\n", + "# l.append(len(np.where(np.array(CLs_values[step]) < np.mean(CLs_values[0]))[0]))\n", " \n", - "for step in range(2*ste):\n", - " if step%2 == 0:\n", - " floaty = True\n", - " else:\n", - " floaty = False\n", - " for key in pull_dic.keys():\n", - " if not os.path.exists('data/CLs/plots/{}'.format(key)):\n", - " os.mkdir('data/CLs/plots/{}'.format(key))\n", - " plt.clf()\n", - " plt.title('Pull {} - Ctt value {:.2f} - floating {}'.format(key, Ctt_steps[int(step/2)], floaty))\n", - " plt.hist(pull_dic[key][step], bins = 50, range = (-5,5))\n", - " plt.xlabel('Pull')\n", - " plt.savefig('data/CLs/plots/{}/{:.2f}Ctt{}s{}f.png'.format(key, Ctt_steps[int(step/2)], step, floaty))\n" + "# for step in range(2*ste):\n", + "# if step%2 == 0:\n", + "# floaty = True\n", + "# else:\n", + "# floaty = False\n", + "# for key in pull_dic.keys():\n", + "# if not os.path.exists('data/CLs/plots/{}'.format(key)):\n", + "# os.mkdir('data/CLs/plots/{}'.format(key))\n", + "# plt.clf()\n", + "# plt.title('Pull {} - Ctt value {:.2f} - floating {}'.format(key, Ctt_steps[int(step/2)], floaty))\n", + "# plt.hist(pull_dic[key][step], bins = 50, range = (-5,5))\n", + "# plt.xlabel('Pull')\n", + "# plt.savefig('data/CLs/plots/{}/{:.2f}Ctt{}s{}f.png'.format(key, Ctt_steps[int(step/2)], step, floaty))\n" ] }, { @@ -2309,10 +2309,10 @@ }, "outputs": [], "source": [ - "for s in range(len(l)):\n", - " print('BR: {:.4f}'.format(BR_steps[s+1]))\n", - " print(2*l[s]/len(CLs_values[s]))\n", - " print()" + "# for s in range(len(l)):\n", + "# print('BR: {:.4f}'.format(BR_steps[s+1]))\n", + "# print(2*l[s]/len(CLs_values[s]))\n", + "# print()" ] }, { @@ -2354,76 +2354,6 @@ "source": [ "# variab['mi'] =! mi" ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] } ], "metadata": { diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index f701626..2599e28 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": [ @@ -90,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -543,19 +521,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "# formfactors\n", "\n", @@ -664,7 +632,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +662,7 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -711,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -749,7 +717,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -793,7 +761,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -809,7 +777,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -869,32 +837,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:14: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " \n", - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -915,7 +860,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -931,7 +876,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -947,7 +892,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -960,7 +905,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1017,7 +962,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1101,7 +1046,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1110,7 +1055,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1119,7 +1064,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1128,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1176,7 +1121,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1193,7 +1138,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1217,7 +1162,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1244,7 +1189,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1267,7 +1212,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1276,7 +1221,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1292,7 +1237,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1322,7 +1267,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1336,7 +1281,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1354,7 +1299,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1368,7 +1313,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1389,7 +1334,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1399,7 +1344,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1430,7 +1375,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1447,7 +1392,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1503,9 +1448,9 @@ " mean1 = ztf.constant(0.466)\n", " mean2 = ztf.constant(-0.885)\n", " mean3 = ztf.constant(-0.213)\n", - " sigma1 = ztf.constant(0.014)\n", - " sigma2 = ztf.constant(0.128)\n", - " sigma3 = ztf.constant(0.548)\n", + " sigma1 = ztf.constant(0.014/3.)\n", + " sigma2 = ztf.constant(0.128/3.)\n", + " sigma3 = ztf.constant(0.548/3.)\n", " x1 = (val1-mean1)/sigma1\n", " x2 = (val2-mean2)/sigma2\n", " x3 = (val3-mean3)/sigma3\n", @@ -1583,7 +1528,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1662,7 +1607,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1873,7 +1818,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1885,7 +1830,7 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1897,7 +1842,7 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1912,7 +1857,7 @@ }, { "cell_type": "code", - "execution_count": 43, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1921,7 +1866,7 @@ }, { "cell_type": "code", - "execution_count": 44, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1930,7 +1875,7 @@ }, { "cell_type": "code", - "execution_count": 45, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1939,7 +1884,7 @@ }, { "cell_type": "code", - "execution_count": 46, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1947,8 +1892,8 @@ "pull_dic = {}\n", "\n", "mi = 1e-4\n", - "ma = 3e-3\n", - "ste = 20\n", + "ma = 6e-4\n", + "ste = 6\n", "\n", "for param in total_f_fit.get_dependents():\n", " if param.floating:\n", @@ -1973,7 +1918,7 @@ }, { "cell_type": "code", - "execution_count": 47, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1993,33 +1938,15 @@ }, { "cell_type": "code", - "execution_count": 48, + "execution_count": null, "metadata": { "scrolled": true }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "[0. 0.00025263 0.00040526 0.00055789 0.00071053 0.00086316\n", - " 0.00101579 0.00116842 0.00132105 0.00147368 0.00162632 0.00177895\n", - " 0.00193158 0.00208421 0.00223684 0.00238947 0.00254211 0.00269474\n", - " 0.00284737 0.003 ]\n", - "[0. 0.24525574 0.31063037 0.36446136 0.41130637 0.45333628\n", - " 0.49178719 0.5274424 0.5608354 0.59234888 0.62226847 0.65081403\n", - " 0.67815909 0.70444347 0.72978178 0.75426939 0.77798661 0.80100188\n", - " 0.82337407 0.84515425]\n", - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n" - ] - } - ], + "outputs": [], "source": [ "# zfit.run.numeric_checks = False \n", "\n", - "load = True\n", + "load = False\n", "\n", "bo = True\n", "\n", @@ -2044,7 +1971,7 @@ "nevents = int(pdg[\"number_of_decays\"]*cut_BR)\n", "# nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", - "# nevents *= 41\n", + "nevents *= 41\n", "# zfit.settings.set_verbosity(10)\n", "\n", "# mi = 1e-4\n", @@ -2177,17 +2104,9 @@ }, { "cell_type": "code", - "execution_count": 49, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Number of jobs: 331\n" - ] - } - ], + "outputs": [], "source": [ "if load:\n", " \n", @@ -2263,7 +2182,7 @@ }, { "cell_type": "code", - "execution_count": 50, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2324,7 +2243,7 @@ }, { "cell_type": "code", - "execution_count": 51, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2340,149 +2259,65 @@ }, { "cell_type": "code", - "execution_count": 52, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "(20, 231)\n" - ] - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "l = []\n", + "# l = []\n", "\n", "\n", - "if load:\n", - " CLs_values = -1*np.array(CLs_list)\n", + "# if load:\n", + "# CLs_values = -1*np.array(CLs_list)\n", "\n", - "if not os.path.exists('data/CLs/plots'):\n", - " os.mkdir('data/CLs/plots')\n", - " print(\"Directory \" , 'data/CLs/plots' , \" Created \")\n", + "# if not os.path.exists('data/CLs/plots'):\n", + "# os.mkdir('data/CLs/plots')\n", + "# print(\"Directory \" , 'data/CLs/plots' , \" Created \")\n", "\n", - "print(np.shape(CLs_values))\n", + "# print(np.shape(CLs_values))\n", " \n", - "for step in range(1,ste):\n", - " plt.clf()\n", - " plt.title('Ctt value: {:.2f}'.format(Ctt_steps[step]))\n", - " plt.hist(CLs_values[0], bins = 150, range = (-25, 50), label = 'Ctt fixed to 0')\n", - " plt.hist(CLs_values[step], bins = 150, range = (-25, 50), label = 'Ctt floating')\n", - " plt.axvline(x=np.mean(CLs_values[0]),color='red', linewidth=1.0, linestyle = 'dotted')\n", - " plt.legend()\n", - " plt.savefig('data/CLs/plots/CLs-BR({:.1E}).png'.format(BR_steps[step]))\n", + "# for step in range(1,ste):\n", + "# plt.clf()\n", + "# plt.title('Ctt value: {:.2f}'.format(Ctt_steps[step]))\n", + "# plt.hist(CLs_values[0], bins = 150, range = (-25, 50), label = 'Ctt fixed to 0')\n", + "# plt.hist(CLs_values[step], bins = 150, range = (-25, 50), label = 'Ctt floating')\n", + "# plt.axvline(x=np.mean(CLs_values[0]),color='red', linewidth=1.0, linestyle = 'dotted')\n", + "# plt.legend()\n", + "# plt.savefig('data/CLs/plots/CLs-BR({:.1E}).png'.format(BR_steps[step]))\n", " \n", - " l.append(len(np.where(np.array(CLs_values[step]) < np.mean(CLs_values[0]))[0]))\n", + "# l.append(len(np.where(np.array(CLs_values[step]) < np.mean(CLs_values[0]))[0]))\n", " \n", - "for step in range(2*ste):\n", - " if step%2 == 0:\n", - " floaty = True\n", - " else:\n", - " floaty = False\n", - " for key in pull_dic.keys():\n", - " if not os.path.exists('data/CLs/plots/{}'.format(key)):\n", - " os.mkdir('data/CLs/plots/{}'.format(key))\n", - " plt.clf()\n", - " plt.title('Pull {} - Ctt value {:.2f} - floating {}'.format(key, Ctt_steps[int(step/2)], floaty))\n", - " plt.hist(pull_dic[key][step], bins = 50, range = (-5,5))\n", - " plt.xlabel('Pull')\n", - " plt.savefig('data/CLs/plots/{}/{:.2f}Ctt{}s{}f.png'.format(key, Ctt_steps[int(step/2)], step, floaty))\n" + "# for step in range(2*ste):\n", + "# if step%2 == 0:\n", + "# floaty = True\n", + "# else:\n", + "# floaty = False\n", + "# for key in pull_dic.keys():\n", + "# if not os.path.exists('data/CLs/plots/{}'.format(key)):\n", + "# os.mkdir('data/CLs/plots/{}'.format(key))\n", + "# plt.clf()\n", + "# plt.title('Pull {} - Ctt value {:.2f} - floating {}'.format(key, Ctt_steps[int(step/2)], floaty))\n", + "# plt.hist(pull_dic[key][step], bins = 50, range = (-5,5))\n", + "# plt.xlabel('Pull')\n", + "# plt.savefig('data/CLs/plots/{}/{:.2f}Ctt{}s{}f.png'.format(key, Ctt_steps[int(step/2)], step, floaty))\n" ] }, { "cell_type": "code", - "execution_count": 53, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "BR: 0.0003\n", - "0.8744588744588745\n", - "\n", - "BR: 0.0004\n", - "0.8398268398268398\n", - "\n", - "BR: 0.0006\n", - "0.7272727272727273\n", - "\n", - "BR: 0.0007\n", - "0.6320346320346321\n", - "\n", - "BR: 0.0009\n", - "0.4935064935064935\n", - "\n", - "BR: 0.0010\n", - "0.45021645021645024\n", - "\n", - "BR: 0.0012\n", - "0.3722943722943723\n", - "\n", - "BR: 0.0013\n", - "0.2597402597402597\n", - "\n", - "BR: 0.0015\n", - "0.19047619047619047\n", - "\n", - "BR: 0.0016\n", - "0.17316017316017315\n", - "\n", - "BR: 0.0018\n", - "0.15584415584415584\n", - "\n", - "BR: 0.0019\n", - "0.1645021645021645\n", - "\n", - "BR: 0.0021\n", - "0.1038961038961039\n", - "\n", - "BR: 0.0022\n", - "0.06926406926406926\n", - "\n", - "BR: 0.0024\n", - "0.08658008658008658\n", - "\n", - "BR: 0.0025\n", - "0.07792207792207792\n", - "\n", - "BR: 0.0027\n", - "0.04329004329004329\n", - "\n", - "BR: 0.0028\n", - "0.05194805194805195\n", - "\n", - "BR: 0.0030\n", - "0.05194805194805195\n", - "\n" - ] - } - ], + "outputs": [], "source": [ - "for s in range(len(l)):\n", - " print('BR: {:.4f}'.format(BR_steps[s+1]))\n", - " print(2*l[s]/len(CLs_values[s]))\n", - " print()" + "# for s in range(len(l)):\n", + "# print('BR: {:.4f}'.format(BR_steps[s+1]))\n", + "# print(2*l[s]/len(CLs_values[s]))\n", + "# print()" ] }, { "cell_type": "code", - "execution_count": 54, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2493,7 +2328,7 @@ }, { "cell_type": "code", - "execution_count": 55, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -2504,24 +2339,16 @@ }, { "cell_type": "code", - "execution_count": 56, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2 min, 20 s\n" - ] - } - ], + "outputs": [], "source": [ "print(display_time(int(time.time()-start)))" ] }, { "cell_type": "code", - "execution_count": 57, + "execution_count": null, "metadata": {}, "outputs": [], "source": [