diff --git a/.gitignore b/.gitignore index fd98963..fb9b9bb 100644 --- a/.gitignore +++ b/.gitignore @@ -8,3 +8,4 @@ data/zfit_toys/toy_7/ data/zfit_toys/toy_8/ data/zfit_toys/toy_9/ +2180573/ diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index 2bb8c92..4f10b65 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 49b4cfa..c2768ce 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 7761382..87d8510 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 6cac02f..89c4967 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 aecfe9d..9d68762 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 45b15d3..f79f627 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 1dbc810..8e018b7 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 7793fa8..92a9684 100644 --- a/pdg_const.py +++ b/pdg_const.py @@ -66,16 +66,16 @@ "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": (3096.0, 0.09, -1.66, 2e-2), #-------> pre scaling +"jpsi": (3096.0, 0.09, -1.66, 9897.0), #---> after scaling +# "jpsi": (3096.0, 0.09, -1.66, 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": (3686.0, 0.3, -1.93, 3.14e-3), #-------> pre scaling +"psi2s": (3686.0, 0.3, -1.93, 1396.0), #--------> after scaling +# "psi2s": (3686.0, 0.3, -1.93, 0.0), "psi2s_BR": 4.97e-6, "psi2s_auc": 2.802257483178487e-10, #------------> pre scaling "psi2s_auc": 0.0151332263 #--------------------> after scaling diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 55870af..902dc0c 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -16,7 +16,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd_test\\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", + "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" ] }, @@ -418,7 +418,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd_test\\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", + "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" ] @@ -432,7 +432,7 @@ "\n", "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n", "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n", - "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), floating = False)\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase))\n", "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale))\n", "\n", "#psi2s\n", @@ -441,7 +441,7 @@ "\n", "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n", "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n", - "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False)\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase))\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale))\n", "\n", "#cusp\n", @@ -531,7 +531,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -551,8 +551,8 @@ "# plt.plot(test_q, fplus_y, label = '+')\n", "# plt.plot(test_q, res_y, label = 'res')\n", "plt.legend()\n", - "# plt.ylim(0.0, 6e-6)\n", - "plt.yscale('log')\n", + "plt.ylim(0.0, 6e-6)\n", + "# plt.yscale('log')\n", "# plt.xlim(3080, 3110)\n", "plt.savefig('test.png')\n", "# print(jpsi_width)" @@ -954,8 +954,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "6/6\n", - "Time taken: 6 min, 55 s\n", + "6/6 of Toy 2/2\n", + "Time taken: 1 min, 27 s\n", "Projected time left: \n" ] } @@ -963,7 +963,7 @@ "source": [ "# zfit.run.numeric_checks = False \n", "\n", - "nr_of_toys = 10\n", + "nr_of_toys = 2\n", "nevents = int(pdg[\"number_of_decays\"])\n", "nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", @@ -991,10 +991,11 @@ " sam = zfit.run(s)\n", " clear_output(wait=True)\n", "\n", - " c = call + 1 \n", - " print(\"{0}/{1}\".format(c, calls))\n", + " c = call + 1\n", + " \n", + " print(\"{0}/{1} of Toy {2}/{3}\".format(c, calls, toy+1, nr_of_toys))\n", " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", - " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/c*(calls-c)))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", "\n", " with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n", " pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)" @@ -1026,7 +1027,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time to generate full toy: 415 s\n", + "Time to generate full toy: 87 s\n", "(5404696,)\n" ] } @@ -1064,7 +1065,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1089,7 +1090,7 @@ "# plt.plot(sam, calcs, '.')\n", "# plt.plot(test_q, calcs_test)\n", "# plt.yscale('log')\n", - "plt.ylim(0, 1000)\n", + "plt.ylim(0, 200)\n", "# plt.xlim(3080, 3110)\n", "\n", "plt.legend()\n", @@ -1155,8 +1156,8 @@ "output_type": "stream", "text": [ "------------------------------------------------------------------\n", - "| FCN = -8.534E+05 | Ncalls=31 (31 total) |\n", - "| EDM = 1.83E-05 (Goal: 5E-06) | up = 0.5 |\n", + "| FCN = -8.724E+05 | Ncalls=153 (153 total) |\n", + "| EDM = 8.38E-06 (Goal: 5E-06) | up = 0.5 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", @@ -1166,16 +1167,18 @@ "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", - "psi2s_s: ^{+3.6164112195259897}_{-3.6412375056684962}\n", - "jpsi_s: ^{+28.565826484711366}_{-28.76638576587415}\n", - "Function minimum: -853446.6101229072\n" + "jpsi_p: ^{+0.016484670160195978}_{-0.016715939969484755}\n", + "psi2s_s: ^{+3.5429799872792804}_{-3.5286564682010337}\n", + "psi2s_p: ^{+0.01844073930972661}_{-0.018643934945039613}\n", + "jpsi_s: ^{+28.041395536069153}_{-27.882719703773358}\n", + "Function minimum: -872362.5577928417\n" ] } ], "source": [ "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n", "\n", - "minimizer = zfit.minimize.MinuitMinimizer()\n", + "minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", "# minimizer._use_tfgrad = False\n", "result = minimizer.minimize(nll)\n", "\n", @@ -1206,7 +1209,7 @@ "outputs": [ { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1267,21 +1270,21 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 42, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "10173.339691942534\n", - "1237.0113581511123\n" + "1.2311725935705207\n", + "1.4004132078629088\n" ] } ], "source": [ - "print(zfit.run(jpsi_s))\n", - "print(zfit.run(psi2s_s))" + "print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n", + "print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)" ] }, { diff --git a/raremodel-nb.py b/raremodel-nb.py index 9700fb7..b378112 100644 --- a/raremodel-nb.py +++ b/raremodel-nb.py @@ -3,9 +3,13 @@ # # Import -# In[1]: +# In[ ]: +import os + +# os.environ["CUDA_VISIBLE_DEVICES"] = "-1" + import numpy as np from pdg_const import pdg import matplotlib @@ -23,11 +27,13 @@ import tensorflow as tf import zfit from zfit import ztf -# from IPython.display import clear_output +from IPython.display import clear_output import os +import tensorflow_probability as tfp +tfd = tfp.distributions -# In[2]: +# In[ ]: # chunksize = 1000000 @@ -38,7 +44,7 @@ # # Build model and graphs # ## Create graphs -# In[3]: +# In[ ]: def formfactor( q2, subscript): #returns real value @@ -111,7 +117,7 @@ p0 = 0.5 * ztf.sqrt(_mass**2 - 4*mmu**2) - gamma_j = tf.divide(p, q2) * _mass * width / p0 + gamma_j = tf.divide(p, q) * _mass * width / p0 #Calculate the resonance @@ -236,7 +242,7 @@ return c9 -# In[4]: +# In[ ]: def G(y): @@ -283,7 +289,7 @@ # ## Build pdf -# In[5]: +# In[ ]: class total_pdf(zfit.pdf.ZPDF): @@ -319,7 +325,7 @@ # ## Load data -# In[6]: +# In[ ]: x_min = 2*pdg['muon_M'] @@ -339,7 +345,7 @@ # ## Setup parameters -# In[7]: +# In[ ]: #jpsi @@ -349,7 +355,7 @@ jpsi_m = zfit.Parameter("jpsi_m", ztf.constant(jpsi_mass), floating = False) jpsi_w = zfit.Parameter("jpsi_w", ztf.constant(jpsi_width), floating = False) -jpsi_p = zfit.Parameter("jpsi_p", ztf.constant(jpsi_phase)) +jpsi_p = zfit.Parameter("jpsi_p", ztf.constant(jpsi_phase), floating = False) jpsi_s = zfit.Parameter("jpsi_s", ztf.constant(jpsi_scale)) #psi2s @@ -358,7 +364,7 @@ psi2s_m = zfit.Parameter("psi2s_m", ztf.constant(psi2s_mass), floating = False) psi2s_w = zfit.Parameter("psi2s_w", ztf.constant(psi2s_width), floating = False) -psi2s_p = zfit.Parameter("psi2s_p", ztf.constant(psi2s_phase)) +psi2s_p = zfit.Parameter("psi2s_p", ztf.constant(psi2s_phase), floating = False) psi2s_s = zfit.Parameter("psi2s_s", ztf.constant(psi2s_scale)) #cusp @@ -373,19 +379,19 @@ # ## Setup pdf -# In[8]: +# In[ ]: total_f = total_pdf(obs=obs, jpsi_mass = jpsi_m, jpsi_scale = jpsi_s, jpsi_phase = jpsi_p, jpsi_width = jpsi_w, psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w)#, - #cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s) - + #cusp_mass = cusp_m, sigma_L = sig_L, sigma_R = sig_R, cusp_scale = cusp_s) + # print(total_pdf.obs) # ## Test if graphs actually work and compute values -# In[9]: +# In[ ]: def total_test_tf(xq): @@ -414,42 +420,86 @@ # calcs = zfit.run(total_test_tf(x_part)) -test_q = np.linspace(x_min, x_max, 2000000) +test_q = np.linspace(x_min, x_max, 200000) probs = total_f.pdf(test_q) calcs_test = zfit.run(probs) res_y = zfit.run(jpsi_res(test_q)) +f0_y = zfit.run(formfactor(test_q,"0")) +fplus_y = zfit.run(formfactor(test_q,"+")) +fT_y = zfit.run(formfactor(test_q,"T")) -# In[10]: +# In[ ]: plt.clf() # plt.plot(x_part, calcs, '.') plt.plot(test_q, calcs_test, label = 'pdf') +# plt.plot(test_q, f0_y, label = '0') +# plt.plot(test_q, fT_y, label = 'T') +# plt.plot(test_q, fplus_y, label = '+') # plt.plot(test_q, res_y, label = 'res') plt.legend() -plt.ylim(0.0, 4e-4) -# plt.yscale('log') +# plt.ylim(0.0, 6e-6) +plt.yscale('log') # plt.xlim(3080, 3110) plt.savefig('test.png') # print(jpsi_width) +# In[ ]: + + +dtype = tf.float64 +mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.007, dtype=dtype), + tf.constant(0.965, dtype=dtype), + tf.constant(0.04, dtype=dtype), + tf.constant(0.03, dtype=dtype), + tf.constant(0.006, dtype=dtype)]), + components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), + tf.constant(3092, dtype=dtype), + tf.constant(3682, dtype=dtype), + tf.constant(3070, dtype=dtype), + tf.constant(3660, dtype=dtype)], + high=[tf.constant(x_max, dtype=dtype), + tf.constant(3100, dtype=dtype), + tf.constant(3690, dtype=dtype), + tf.constant(3110, dtype=dtype), + tf.constant(3710, dtype=dtype)])) + +# probs = mixture.prob(test_q) +# probs_np = zfit.run(probs) +# probs_np *= np.max(calcs_test) / np.max(probs_np) +# plt.figure() +# plt.semilogy(test_q, probs_np,label="importance sampling") +# plt.semilogy(test_q, calcs_test, label = 'pdf') + + # ## Adjust scaling of different parts -# In[11]: +# In[ ]: -# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None) -# inte = total_f.integrate(limits = (3090, 3102), norm_range=False) +# total_f.update_integration_options(draws_per_dim=20000000, mc_sampler=None) +# inte = total_f.integrate(limits = (3080, 3112), norm_range=False) # inte_fl = zfit.run(inte) # print(inte_fl) -# print(pdg["jpsi_BR"]/pdg["NR_BR"], inte_fl/pdg["NR_auc"]) +# print(pdg["jpsi_BR"]/pdg["NR_BR"], inte_fl*pdg["psi2s_auc"]/pdg["NR_auc"]) -# In[12]: +# In[ ]: + + +# print("jpsi:", inte_fl) +# print("Increase am by factor:", np.sqrt(pdg["jpsi_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl)) +# print("New amp:", pdg["jpsi"][3]*np.sqrt(pdg["jpsi_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl)) + +# print("psi2s:", inte_fl) +# print("Increase am by factor:", np.sqrt(pdg["psi2s_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl)) +# print("New amp:", pdg["psi2s"][3]*np.sqrt(pdg["psi2s_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl)) + # print(x_min) @@ -479,7 +529,7 @@ # ## Tensorflow scaling -# In[13]: +# In[ ]: # def scaling_func(x): @@ -505,7 +555,7 @@ # print(integrate.quad(s_func, x_min, x_max, limit = 50)) -# In[14]: +# In[ ]: # factor_jpsi = pdg["NR_auc"]*pdg["jpsi_BR"]/(pdg["NR_BR"]*pdg["jpsi_auc"]) @@ -519,7 +569,7 @@ # print(np.sqrt(factor_psi2s)) -# In[33]: +# In[ ]: # def _t_f(xq): @@ -546,13 +596,13 @@ # return probs -# In[34]: +# In[ ]: # print(36000*(1+ pdg["jpsi_BR"]/pdg["NR_BR"] + pdg["psi2s_BR"]/pdg["NR_BR"])) -# In[35]: +# In[ ]: # start = time.time() @@ -566,7 +616,7 @@ # ## One sample # ! total_f.sample() always returns the same set ! -# In[18]: +# In[ ]: # nevents = int(pdg["number_of_decays"]) @@ -600,7 +650,7 @@ # pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL) -# In[19]: +# In[ ]: # print("Time to generate full toy: {} s".format(int(time.time()-start))) @@ -619,7 +669,7 @@ # print(total_samp[:nevents].shape) -# In[20]: +# In[ ]: # bins = int((x_max-x_min)/7) @@ -634,17 +684,101 @@ # # plt.xlim(3000, 3750) # plt.savefig('test2.png') +# 1-(0.21+0.62) # ## Toys -# In[21]: +# In[ ]: -nr_of_toys = 1 + + +# print(list_of_borders[:9]) +# print(list_of_borders[-9:]) + + +class UniformSampleAndWeights(zfit.util.execution.SessionHolderMixin): + def __call__(self, limits, dtype, n_to_produce): + # n_to_produce = tf.cast(n_to_produce, dtype=tf.int32) + low, high = limits.limit1d + low = tf.cast(low, dtype=dtype) + high = tf.cast(high, dtype=dtype) +# uniform = tfd.Uniform(low=low, high=high) +# uniformjpsi = tfd.Uniform(low=tf.constant(3080, dtype=dtype), high=tf.constant(3112, dtype=dtype)) +# uniformpsi2s = tfd.Uniform(low=tf.constant(3670, dtype=dtype), high=tf.constant(3702, dtype=dtype)) + + list_of_borders = [] + _p = [] + splits = 10 + + _ = np.linspace(x_min, x_max, splits) + + for i in range(splits): + list_of_borders.append(tf.constant(_[i], dtype=dtype)) + _p.append(tf.constant(1/splits, dtype=dtype)) + +# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]), +# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], +# high=list_of_borders[-(splits-1):])) + mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.007, dtype=dtype), + tf.constant(0.95, dtype=dtype), + tf.constant(0.07, dtype=dtype), + tf.constant(0.025, dtype=dtype), + tf.constant(0.006, dtype=dtype)]), + components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), + tf.constant(3093, dtype=dtype), + tf.constant(3683, dtype=dtype), + tf.constant(3070, dtype=dtype), + tf.constant(3660, dtype=dtype)], + high=[tf.constant(x_max, dtype=dtype), + tf.constant(3099, dtype=dtype), + tf.constant(3690, dtype=dtype), + tf.constant(3110, dtype=dtype), + tf.constant(3710, dtype=dtype)])) +# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype)) +# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype) + sample = mixture.sample((n_to_produce, 1)) +# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype) + weights = mixture.prob(sample)[:,0] +# weights = tf.broadcast_to(tf.constant(1., dtype=dtype), shape=(n_to_produce,)) + # sample = tf.expand_dims(sample, axis=-1) +# print(sample, weights) + +# weights = tf.ones(shape=(n_to_produce,), dtype=dtype) + weights_max = None + thresholds = tf.random_uniform(shape=(n_to_produce,), dtype=dtype) + return sample, thresholds, weights, weights_max, n_to_produce + + +# In[ ]: + + +total_f._sample_and_weights = UniformSampleAndWeights + + +# In[ ]: + + +# psi2s_mass + + +# In[ ]: + + +# zfit.settings.set_verbosity(10) + + +# In[ ]: + + +# zfit.run.numeric_checks = False + +nr_of_toys = 2 nevents = int(pdg["number_of_decays"]) -event_stack = 100000 - +nevents = pdg["number_of_decays"] +event_stack = 1000000 +# zfit.settings.set_verbosity(10) calls = int(nevents/event_stack + 1) total_samp = [] @@ -666,18 +800,20 @@ sampler.resample(n=event_stack) s = sampler.unstack_x() sam = zfit.run(s) -# clear_output(wait=True) + clear_output(wait=True) - c = call + 1 + c = call + 1 + print("{0}/{1}".format(c, calls)) + print("Toy {}/{}".format(toy+1, nr_of_toys)) print("Time taken: {}".format(display_time(int(time.time() - start)))) - print("Projected time left: {}".format(display_time(int((time.time() - start)/c*(calls-c))))) + print("Projected time left: {}".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c))))) with open("data/zfit_toys/toy_{0}/{1}.pkl".format(toy, call), "wb") as f: pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL) -# In[22]: +# In[ ]: # with open(r"data/zfit_toys/toy_0/0.pkl", "rb") as input_file: @@ -691,7 +827,7 @@ # print(np.sum(sam-sam2)) -# In[23]: +# In[ ]: print("Time to generate full toy: {} s".format(int(time.time()-start))) @@ -712,20 +848,23 @@ print(total_samp[:nevents].shape) -# In[24]: +# In[ ]: +plt.clf() + bins = int((x_max-x_min)/7) # calcs = zfit.run(total_test_tf(samp)) +print(total_samp[:nevents].shape) plt.hist(total_samp[:nevents], bins = bins, range = (x_min,x_max), label = 'data') -plt.plot(test_q, calcs_test*nevents*4.5 , label = 'pdf') +# plt.plot(test_q, calcs_test*nevents , label = 'pdf') # plt.plot(sam, calcs, '.') # plt.plot(test_q, calcs_test) # plt.yscale('log') -plt.ylim(0, 12000) +plt.ylim(0, 200) # plt.xlim(3080, 3110) plt.legend() @@ -733,7 +872,7 @@ plt.savefig('test2.png') -# In[25]: +# In[ ]: # sampler = total_f.create_sampler(n=nevents) @@ -753,15 +892,27 @@ # minimum = minimizer.minimize(nll) +# In[ ]: + + +# jpsi_width + + +# In[ ]: + + +# plt.hist(sample, weights=1 / prob(sample)) + + # # Fitting -# In[26]: +# In[ ]: -nll = zfit.loss.UnbinnedNLL(model=total_f, data=data3, fit_range = (x_min, x_max)) +nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max)) minimizer = zfit.minimize.MinuitMinimizer() -minimizer._use_tfgrad = False +# minimizer._use_tfgrad = False result = minimizer.minimize(nll) param_errors = result.error() @@ -775,30 +926,6 @@ # In[ ]: -(-7.95933+2*np.pi)/np.pi+np.pi - - -# In[ ]: - - - - - -# In[ ]: - - -display_time(int(395*pdg["number_of_decays"]/100000)) - - -# In[ ]: - - -print(display_time(22376)) - - -# In[ ]: - - # probs = total_f.pdf(test_q) calcs_test = zfit.run(probs) @@ -813,11 +940,47 @@ plt.plot(test_q, calcs_test, label = 'pdf') # plt.plot(test_q, res_y, label = 'res') plt.legend() -plt.ylim(0.0, 5e-4) +plt.ylim(0.0, 5e-6) # plt.yscale('log') # plt.xlim(3080, 3110) plt.savefig('test3.png') -print(jpsi_width) +# print(jpsi_width) + + +# In[ ]: + + +# _tot = 4.37e-7+6.02e-5+4.97e-6 +# _probs = [] +# _probs.append(6.02e-5/_tot) +# _probs.append(4.97e-6/_tot) +# _probs.append(4.37e-7/_tot) +# print(_probs) + + +# In[ ]: + + +# dtype = 'float64' +# # mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype)) +# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.007, dtype=dtype), +# tf.constant(0.917, dtype=dtype), +# tf.constant(0.076, dtype=dtype)]), +# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), +# tf.constant(3080, dtype=dtype), +# tf.constant(3670, dtype=dtype)], +# high=[tf.constant(x_max, dtype=dtype), +# tf.constant(3112, dtype=dtype), +# tf.constant(3702, dtype=dtype)])) +# # for i in range(10): +# # print(zfit.run(mixture.prob(mixture.sample((10, 1))))) + + +# In[ ]: + + +print(zfit.run(jpsi_s)) +print(zfit.run(psi2s_s)) # In[ ]: diff --git a/test.png b/test.png index fbd79cc..3e3aa87 100644 --- a/test.png +++ b/test.png Binary files differ diff --git a/test2.png b/test2.png index f8bab4f..9e39f61 100644 --- a/test2.png +++ b/test2.png Binary files differ diff --git a/test3.png b/test3.png index 4e0eafe..25d099d 100644 --- a/test3.png +++ b/test3.png Binary files differ