diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 0d4efab..7de93aa 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -68,7 +68,7 @@ "metadata": {}, "outputs": [], "source": [ - "# chunksize = 1000000\n", + "# chunksize = 10000\n", "# zfit.run.chunking.active = True\n", "# zfit.run.chunking.max_n_points = chunksize" ] @@ -339,26 +339,31 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], "source": [ - "\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", + "# # phi_s = np.linspace(-500, 5000, 100000)\n", "\n", - "# p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", + "# # p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "# p_y = r+o+p_\n", + "# # p_y = r+o+p_\n", "\n", - "# plt.plot(phi_s, p_y)\n", + "# # plt.plot(phi_s, p_y)\n", "\n", - "# # print(r + o + p)" + "# print(r + o + p)" ] }, { @@ -493,10 +498,11 @@ "\n", "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", "\n", - "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False)\n", + "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False) #lower_limit = rho_mass - rho_width,\n", + "# upper_limit = rho_mass + rho_width)\n", "rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n", - "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), floating = False)\n", - "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale))\n", + "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), floating = False)\n", "\n", "#omega\n", "\n", @@ -504,8 +510,8 @@ "\n", "omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n", "omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n", - "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), floating = False)\n", - "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale))\n", + "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), floating = False)\n", "\n", "\n", "#phi\n", @@ -514,8 +520,8 @@ "\n", "phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n", "phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n", - "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), floating = False)\n", - "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale))\n", + "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), floating = False)\n", "\n", "#jpsi\n", "\n", @@ -524,8 +530,8 @@ "\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_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale))\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale), floating = False)\n", "\n", "#psi2s\n", "\n", @@ -533,8 +539,8 @@ "\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_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale))\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale), floating = False)\n", "\n", "#psi(3770)\n", "\n", @@ -542,7 +548,7 @@ "\n", "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_p = zfit.Parameter(\"p3770_p\", ztf.constant(p3770_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "p3770_s = zfit.Parameter(\"p3770_s\", ztf.constant(p3770_scale), floating = False)\n", "\n", "#psi(4040)\n", @@ -551,7 +557,7 @@ "\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_p = zfit.Parameter(\"p4040_p\", ztf.constant(p4040_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "p4040_s = zfit.Parameter(\"p4040_s\", ztf.constant(p4040_scale), floating = False)\n", "\n", "#psi(4160)\n", @@ -560,7 +566,7 @@ "\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_p = zfit.Parameter(\"p4160_p\", ztf.constant(p4160_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "p4160_s = zfit.Parameter(\"p4160_s\", ztf.constant(p4160_scale), floating = False)\n", "\n", "#psi(4415)\n", @@ -569,8 +575,8 @@ "\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))" + "p4415_p = zfit.Parameter(\"p4415_p\", ztf.constant(p4415_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "p4415_s = zfit.Parameter(\"p4415_s\", ztf.constant(p4415_scale), floating = False)" ] }, { @@ -598,7 +604,10 @@ " \n", "# print(total_pdf.obs)\n", "\n", - "# print(calcs_test)" + "# print(calcs_test)\n", + "\n", + "# for param in total_f.get_dependents():\n", + "# print(zfit.run(param))" ] }, { @@ -723,7 +732,7 @@ "metadata": {}, "outputs": [], "source": [ - "# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n", "# inte = total_f.integrate(limits = (1000, 1040), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl)\n", @@ -1071,20 +1080,9 @@ "cell_type": "code", "execution_count": 26, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "0.001309082138940001" - ] - }, - "execution_count": 26, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ - "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" + "# 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" ] }, { @@ -1108,7 +1106,7 @@ "output_type": "stream", "text": [ "6/6 of Toy 1/1\n", - "Time taken: 1 min, 33 s\n", + "Time taken: 1 min, 21 s\n", "Projected time left: \n" ] } @@ -1180,7 +1178,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time to generate full toy: 93 s\n", + "Time to generate full toy: 81 s\n", "(5404696,)\n" ] } @@ -1218,7 +1216,7 @@ }, { "data": { - 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\n", 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EDM = 1.41554916767651e-06GOAL EDM = 5e-06\n", - " UP = 0.5
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+NameValueHesse ErrorMinos Error-Minos Error+Limit-Limit+Fixed?
0omega_s5.202940.0355895No
1psi2s_s1256.640.734138No
2rho_s1.758890.235982No
3jpsi_s10341.62.34196No
4p4415_s1.049730.163853No
5phi_s17.08260.0113619No
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"------------------------------------------------------------------\n", + "| False | True | True | True | False |\n", + "------------------------------------------------------------------\n", + "Function minimum: -713057.7941560786\n", + "---------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "---------------------------------------------------------------------------------------------\n", + "| 0 | p4415_p | -2.93 | 0.12 | | |-6.28319 | 6.28319 | |\n", + "| 1 | p4160_p | 3.77 | 0.08 | | |-6.28319 | 6.28319 | |\n", + "| 2 | p3770_p | 2.45 | 0.09 | | |-6.28319 | 6.28319 | |\n", + "| 3 | phi_p | 6.28 | 0.04 | | |-6.28319 | 6.28319 | |\n", + "| 4 | omega_p | 6.283 | 0.027 | | |-6.28319 | 6.28319 | |\n", + "| 5 | p4040_p | -3.07 | 0.17 | | |-6.28319 | 6.28319 | |\n", + "| 6 | jpsi_p | -4.810 | 0.016 | | |-6.28319 | 6.28319 | |\n", + "| 7 | psi2s_p | -4.946 | 0.027 | | |-6.28319 | 6.28319 | |\n", + "| 8 | rho_p | 6.28 | 0.04 | | |-6.28319 | 6.28319 | |\n", + "---------------------------------------------------------------------------------------------\n", + "-------------------------------------------------------------------------------------\n", + "| | p4415_p p4160_p p3770_p phi_p omega_p p4040_p jpsi_p psi2s_p rho_p |\n", + "-------------------------------------------------------------------------------------\n", + "| p4415_p | 1.000 0.054 0.008 0.000 -0.000 0.001 -0.150 -0.158 -0.001 |\n", + "| p4160_p | 0.054 1.000 0.010 0.000 -0.000 -0.278 -0.111 -0.097 -0.001 |\n", + "| p3770_p | 0.008 0.010 1.000 0.000 0.000 -0.042 -0.116 -0.511 0.000 |\n", + "| phi_p | 0.000 0.000 0.000 1.000 0.000 0.000 0.004 0.002 -0.000 |\n", + "| omega_p | -0.000 -0.000 0.000 0.000 1.000 0.000 0.002 0.001 -0.003 |\n", + "| p4040_p | 0.001 -0.278 -0.042 0.000 0.000 1.000 -0.193 -0.278 -0.001 |\n", + "| jpsi_p | -0.150 -0.111 -0.116 0.004 0.002 -0.193 1.000 0.221 0.008 |\n", + "| psi2s_p | -0.158 -0.097 -0.511 0.002 0.001 -0.278 0.221 1.000 0.004 |\n", + "| rho_p | -0.001 -0.001 0.000 -0.000 -0.003 -0.001 0.008 0.004 1.000 |\n", + "-------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.12052654582200373}), (, {'error': 0.07990595220555008}), (, {'error': 0.09399014188036858}), (, {'error': 0.03841947108985533}), (, {'error': 0.027374388569219477}), (, {'error': 0.17137144953528938}), (, {'error': 0.015550554940964911}), (, {'error': 0.02746038930647643}), (, {'error': 0.03774225139990062})])\n" ] } ], "source": [ "start = time.time()\n", "\n", + "for param in total_f.get_dependents():\n", + " param.randomize()\n", + " \n", + "for param in total_f.get_dependents():\n", + " print(zfit.run(param))\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", @@ -1503,7 +1378,9 @@ "# for var, errors in param_errors.items():\n", "# print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))\n", "\n", - "print(\"Function minimum:\", result.fmin)" + "print(\"Function minimum:\", result.fmin)\n", + "# print(\"Results:\", result.params)\n", + "print(\"Hesse errors:\", result.hesse())" ] }, { @@ -1515,7 +1392,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "Time taken for fitting: 22 s\n" + "Time taken for fitting: 2 min, 33 s\n" ] } ], @@ -1535,7 +1412,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1598,19 +1475,10 @@ "cell_type": "code", "execution_count": 40, "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)" + "# print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n", + "# print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)" ] }, { diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index d46c993..d6b5aa5 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 ae410b7..e8234af 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 ad66105..45bb5d8 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 45b9f9d..b4586d0 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 5c804b0..9818c56 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 f00da81..363bf41 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 d01dd30..494c5b9 100644 --- a/pdg_const.py +++ b/pdg_const.py @@ -127,6 +127,10 @@ # "p4160": (4147.0, 22.0, -1.9, 0.0), # "p4415": (4421.0, 62.0, -2.52, 0.0), + # 2P contributions format(mass, amp, phase) + + "D_bar": ( + #general "rho_BR": 1.7e-10, diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index bd78d08..7de93aa 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -32,20 +32,6 @@ "If you depend on functionality not listed there, please file an issue.\n", "\n" ] - }, - { - "ename": "ModuleNotFoundError", - "evalue": "No module named 'iminuit'", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mModuleNotFoundError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 18\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mitertools\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mcompress\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtensorflow\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 20\u001b[1;33m \u001b[1;32mimport\u001b[0m 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31\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 32\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mconstraint\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mpdf\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mminimize\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloss\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcore\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdata\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfunc\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 33\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mparameter\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mParameter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComposedParameter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mComplexParameter\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_to_parameter\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 34\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mcore\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mlimits\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mSpace\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mconvert_to_space\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msupports\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\\minimize.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 1\u001b[0m \u001b[1;31m# from .minimizers.optimizers_tf import RMSPropMinimizer, GradientDescentMinimizer, AdagradMinimizer, AdadeltaMinimizer,\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[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mminimizers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0moptimizers_tf\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mAdamMinimizer\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mWrapOptimizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 3\u001b[1;33m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mminimizers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimizer_minuit\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mMinuitMinimizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 4\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[1;33m.\u001b[0m\u001b[0mminimizers\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mminimizers_scipy\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mScipyMinimizer\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\minimizers\\minimizer_minuit.py\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 5\u001b[0m \u001b[1;32mfrom\u001b[0m \u001b[0mtyping\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mList\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 6\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m----> 7\u001b[1;33m \u001b[1;32mimport\u001b[0m \u001b[0miminuit\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 8\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mnumpy\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mnp\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 9\u001b[0m \u001b[1;32mimport\u001b[0m \u001b[0mtexttable\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0mtt\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mModuleNotFoundError\u001b[0m: No module named 'iminuit'" - ] } ], "source": [ @@ -78,11 +64,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 2, "metadata": {}, "outputs": [], "source": [ - "# chunksize = 1000000\n", + "# chunksize = 10000\n", "# zfit.run.chunking.active = True\n", "# zfit.run.chunking.max_n_points = chunksize" ] @@ -97,7 +83,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 3, "metadata": {}, "outputs": [], "source": [ @@ -298,7 +284,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 4, "metadata": {}, "outputs": [], "source": [ @@ -353,26 +339,31 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "False\n" + ] + } + ], "source": [ - "\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", + "# # phi_s = np.linspace(-500, 5000, 100000)\n", "\n", - "# p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", + "# # p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "# p_y = r+o+p_\n", + "# # p_y = r+o+p_\n", "\n", - "# plt.plot(phi_s, p_y)\n", + "# # plt.plot(phi_s, p_y)\n", "\n", - "# # print(r + o + p)" + "# print(r + o + p)" ] }, { @@ -384,7 +375,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 6, "metadata": {}, "outputs": [], "source": [ @@ -461,7 +452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 7, "metadata": {}, "outputs": [], "source": [ @@ -489,17 +480,28 @@ }, { "cell_type": "code", - "execution_count": null, + "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": [ "#rho\n", "\n", "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", "\n", - "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False)\n", + "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False) #lower_limit = rho_mass - rho_width,\n", + "# upper_limit = rho_mass + rho_width)\n", "rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n", - "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase))\n", + "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), floating = False)\n", "\n", "#omega\n", @@ -508,7 +510,7 @@ "\n", "omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n", "omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n", - "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase))\n", + "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), floating = False)\n", "\n", "\n", @@ -518,7 +520,7 @@ "\n", "phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n", "phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n", - "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase))\n", + "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), floating = False)\n", "\n", "#jpsi\n", @@ -528,7 +530,7 @@ "\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))\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale), floating = False)\n", "\n", "#psi2s\n", @@ -537,7 +539,7 @@ "\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))\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale), floating = False)\n", "\n", "#psi(3770)\n", @@ -546,7 +548,7 @@ "\n", "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))\n", + "p3770_p = zfit.Parameter(\"p3770_p\", ztf.constant(p3770_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "p3770_s = zfit.Parameter(\"p3770_s\", ztf.constant(p3770_scale), floating = False)\n", "\n", "#psi(4040)\n", @@ -555,7 +557,7 @@ "\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))\n", + "p4040_p = zfit.Parameter(\"p4040_p\", ztf.constant(p4040_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "p4040_s = zfit.Parameter(\"p4040_s\", ztf.constant(p4040_scale), floating = False)\n", "\n", "#psi(4160)\n", @@ -564,7 +566,7 @@ "\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))\n", + "p4160_p = zfit.Parameter(\"p4160_p\", ztf.constant(p4160_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "p4160_s = zfit.Parameter(\"p4160_s\", ztf.constant(p4160_scale), floating = False)\n", "\n", "#psi(4415)\n", @@ -573,7 +575,7 @@ "\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))\n", + "p4415_p = zfit.Parameter(\"p4415_p\", ztf.constant(p4415_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", "p4415_s = zfit.Parameter(\"p4415_s\", ztf.constant(p4415_scale), floating = False)" ] }, @@ -586,7 +588,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 9, "metadata": {}, "outputs": [], "source": [ @@ -602,7 +604,10 @@ " \n", "# print(total_pdf.obs)\n", "\n", - "# print(calcs_test)" + "# print(calcs_test)\n", + "\n", + "# for param in total_f.get_dependents():\n", + "# print(zfit.run(param))" ] }, { @@ -614,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 10, "metadata": {}, "outputs": [], "source": [ @@ -657,9 +662,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 11, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -678,7 +696,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -694,7 +712,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -710,11 +728,11 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ - "# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n", "# inte = total_f.integrate(limits = (1000, 1040), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl)\n", @@ -723,7 +741,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -776,7 +794,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -805,7 +823,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -822,7 +840,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -852,7 +870,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -861,7 +879,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -883,7 +901,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -920,7 +938,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -942,7 +960,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -970,7 +988,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1051,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1060,16 +1078,16 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ - "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" + "# 0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1078,11 +1096,21 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": { "scrolled": false }, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "6/6 of Toy 1/1\n", + "Time taken: 1 min, 21 s\n", + "Projected time left: \n" + ] + } + ], "source": [ "# zfit.run.numeric_checks = False \n", "\n", @@ -1126,7 +1154,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1143,9 +1171,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time to generate full toy: 81 s\n", + "(5404696,)\n" + ] + } + ], "source": [ "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n", "\n", @@ -1167,9 +1204,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 31, "metadata": {}, - "outputs": [], + "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" + } + ], "source": [ "plt.clf()\n", "\n", @@ -1194,7 +1251,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1217,7 +1274,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1226,7 +1283,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1242,12 +1299,74 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "-0.6837632761492838\n", + "3.7285129659499887\n", + "4.5760200973853085\n", + "4.0873765340620665\n", + "5.696265762936989\n", + "-2.5717909121593525\n", + "-4.32139458348885\n", + "-4.6490244502769835\n", + "-2.4543520459301043\n", + "------------------------------------------------------------------\n", + "| FCN = -7.131E+05 | Ncalls=359 (359 total) |\n", + "| EDM = 6.99E-05 (Goal: 5E-06) | up = 0.5 |\n", + "------------------------------------------------------------------\n", + "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", + "------------------------------------------------------------------\n", + "| True | True | False | False |\n", + "------------------------------------------------------------------\n", + "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", + "------------------------------------------------------------------\n", + "| False | True | True | True | False |\n", + "------------------------------------------------------------------\n", + "Function minimum: -713057.7941560786\n", + "---------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "---------------------------------------------------------------------------------------------\n", + "| 0 | p4415_p | -2.93 | 0.12 | | |-6.28319 | 6.28319 | |\n", + "| 1 | p4160_p | 3.77 | 0.08 | | |-6.28319 | 6.28319 | |\n", + "| 2 | p3770_p | 2.45 | 0.09 | | |-6.28319 | 6.28319 | |\n", + "| 3 | phi_p | 6.28 | 0.04 | | |-6.28319 | 6.28319 | |\n", + "| 4 | omega_p | 6.283 | 0.027 | | |-6.28319 | 6.28319 | |\n", + "| 5 | p4040_p | -3.07 | 0.17 | | |-6.28319 | 6.28319 | |\n", + "| 6 | jpsi_p | -4.810 | 0.016 | | |-6.28319 | 6.28319 | |\n", + "| 7 | psi2s_p | -4.946 | 0.027 | | |-6.28319 | 6.28319 | |\n", + "| 8 | rho_p | 6.28 | 0.04 | | |-6.28319 | 6.28319 | |\n", + "---------------------------------------------------------------------------------------------\n", + "-------------------------------------------------------------------------------------\n", + "| | p4415_p p4160_p p3770_p phi_p omega_p p4040_p jpsi_p psi2s_p rho_p |\n", + "-------------------------------------------------------------------------------------\n", + "| p4415_p | 1.000 0.054 0.008 0.000 -0.000 0.001 -0.150 -0.158 -0.001 |\n", + "| p4160_p | 0.054 1.000 0.010 0.000 -0.000 -0.278 -0.111 -0.097 -0.001 |\n", + "| p3770_p | 0.008 0.010 1.000 0.000 0.000 -0.042 -0.116 -0.511 0.000 |\n", + "| phi_p | 0.000 0.000 0.000 1.000 0.000 0.000 0.004 0.002 -0.000 |\n", + "| omega_p | -0.000 -0.000 0.000 0.000 1.000 0.000 0.002 0.001 -0.003 |\n", + "| p4040_p | 0.001 -0.278 -0.042 0.000 0.000 1.000 -0.193 -0.278 -0.001 |\n", + "| jpsi_p | -0.150 -0.111 -0.116 0.004 0.002 -0.193 1.000 0.221 0.008 |\n", + "| psi2s_p | -0.158 -0.097 -0.511 0.002 0.001 -0.278 0.221 1.000 0.004 |\n", + "| rho_p | -0.001 -0.001 0.000 -0.000 -0.003 -0.001 0.008 0.004 1.000 |\n", + "-------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.12052654582200373}), (, {'error': 0.07990595220555008}), (, {'error': 0.09399014188036858}), (, {'error': 0.03841947108985533}), (, {'error': 0.027374388569219477}), (, {'error': 0.17137144953528938}), (, {'error': 0.015550554940964911}), (, {'error': 0.02746038930647643}), (, {'error': 0.03774225139990062})])\n" + ] + } + ], "source": [ "start = time.time()\n", "\n", + "for param in total_f.get_dependents():\n", + " param.randomize()\n", + " \n", + "for param in total_f.get_dependents():\n", + " print(zfit.run(param))\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", @@ -1259,14 +1378,24 @@ "# for var, errors in param_errors.items():\n", "# print('{}: ^{{+{}}}_{{{}}}'.format(var.name, errors['upper'], errors['lower']))\n", "\n", - "print(\"Function minimum:\", result.fmin)" + "print(\"Function minimum:\", result.fmin)\n", + "# print(\"Results:\", result.params)\n", + "print(\"Hesse errors:\", result.hesse())" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time taken for fitting: 2 min, 33 s\n" + ] + } + ], "source": [ "print(\"Time taken for fitting: {}\".format(display_time(int(time.time()-start))))\n", "\n", @@ -1278,9 +1407,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -1296,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1310,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, "outputs": [], "source": [ @@ -1331,12 +1473,12 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, "outputs": [], "source": [ - "print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n", - "print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)" + "# 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 b378112..1c50f0d 100644 --- a/raremodel-nb.py +++ b/raremodel-nb.py @@ -3,7 +3,7 @@ # # Import -# In[ ]: +# In[1]: import os @@ -27,13 +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[ ]: +# In[2]: # chunksize = 1000000 @@ -44,7 +44,7 @@ # # Build model and graphs # ## Create graphs -# In[ ]: +# In[3]: def formfactor( q2, subscript): #returns real value @@ -242,7 +242,7 @@ return c9 -# In[ ]: +# In[4]: def G(y): @@ -261,7 +261,7 @@ return ztf.to_complex(2) - G(ztf.to_complex(1) - 4*tf.pow(m, 2) / ztf.to_complex(tf.pow(q, 2))) -def h_P(m,q): +def h_P(m, q): 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) @@ -287,32 +287,88 @@ return left_part + right_part_D + right_part_D_star +# ## C_q,qbar constraint + +# In[5]: + + + + + +# r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass) +# o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass +# p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass + +# phi_s = np.linspace(-500, 5000, 100000) + +# p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass + +# p_y = r+o+p_ + +# plt.plot(phi_s, p_y) + +# # print(r + o + p) + + # ## Build pdf -# In[ ]: +# In[6]: class total_pdf(zfit.pdf.ZPDF): _N_OBS = 1 # dimension, can be omitted - _PARAMS = ['jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width', - 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width'#, - #'cusp_mass', 'sigma_L', 'sigma_R', 'cusp_scale' - ] # the name of the parameters + _PARAMS = ['rho_mass', 'rho_scale', 'rho_phase', 'rho_width', + 'jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width', + 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width', + 'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width', + 'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width', + 'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width', + 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width', + 'omega_mass', 'omega_scale', 'omega_phase', 'omega_width', + 'phi_mass', 'phi_scale', 'phi_phase', 'phi_width'] # the name of the parameters def _unnormalized_pdf(self, x): x = x.unstack_x() + + def rho_res(q): + return resonance(q, _mass = self.params['rho_mass'], scale = self.params['rho_scale'], + phase = self.params['rho_phase'], width = self.params['rho_width']) + + def omega_res(q): + return resonance(q, _mass = self.params['omega_mass'], scale = self.params['omega_scale'], + phase = self.params['omega_phase'], width = self.params['omega_width']) + + def phi_res(q): + return resonance(q, _mass = self.params['phi_mass'], scale = self.params['phi_scale'], + phase = self.params['phi_phase'], width = self.params['phi_width']) def jpsi_res(q): - return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'], phase = self.params['jpsi_phase'], width = self.params['jpsi_width']) + return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'], + phase = self.params['jpsi_phase'], width = self.params['jpsi_width']) def psi2s_res(q): - return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'], phase = self.params['psi2s_phase'], width = self.params['psi2s_width']) + return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'], + phase = self.params['psi2s_phase'], width = self.params['psi2s_width']) + + def p3770_res(q): + return resonance(q, _mass = self.params['p3770_mass'], scale = self.params['p3770_scale'], + phase = self.params['p3770_phase'], width = self.params['p3770_width']) + + def p4040_res(q): + return resonance(q, _mass = self.params['p4040_mass'], scale = self.params['p4040_scale'], + phase = self.params['p4040_phase'], width = self.params['p4040_width']) + + def p4160_res(q): + return resonance(q, _mass = self.params['p4160_mass'], scale = self.params['p4160_scale'], + phase = self.params['p4160_phase'], width = self.params['p4160_width']) + + def p4415_res(q): + return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'], + phase = self.params['p4415_phase'], width = self.params['p4415_width']) + - def cusp(q): - 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']) - - funcs = jpsi_res(x) + psi2s_res(x) #+ cusp(x) + funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x) vec_f = vec(x, funcs) @@ -325,7 +381,7 @@ # ## Load data -# In[ ]: +# In[7]: x_min = 2*pdg['muon_M'] @@ -345,9 +401,37 @@ # ## Setup parameters -# In[ ]: +# In[8]: +#rho + +rho_mass, rho_width, rho_phase, rho_scale = pdg["rho"] + +rho_m = zfit.Parameter("rho_m", ztf.constant(rho_mass), floating = False) +rho_w = zfit.Parameter("rho_w", ztf.constant(rho_width), floating = False) +rho_p = zfit.Parameter("rho_p", ztf.constant(rho_phase)) +rho_s = zfit.Parameter("rho_s", ztf.constant(rho_scale), floating = False) + +#omega + +omega_mass, omega_width, omega_phase, omega_scale = pdg["omega"] + +omega_m = zfit.Parameter("omega_m", ztf.constant(omega_mass), floating = False) +omega_w = zfit.Parameter("omega_w", ztf.constant(omega_width), floating = False) +omega_p = zfit.Parameter("omega_p", ztf.constant(omega_phase)) +omega_s = zfit.Parameter("omega_s", ztf.constant(omega_scale), floating = False) + + +#phi + +phi_mass, phi_width, phi_phase, phi_scale = pdg["phi"] + +phi_m = zfit.Parameter("phi_m", ztf.constant(phi_mass), floating = False) +phi_w = zfit.Parameter("phi_w", ztf.constant(phi_width), floating = False) +phi_p = zfit.Parameter("phi_p", ztf.constant(phi_phase)) +phi_s = zfit.Parameter("phi_s", ztf.constant(phi_scale), floating = False) + #jpsi jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] @@ -355,8 +439,8 @@ 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), floating = False) -jpsi_s = zfit.Parameter("jpsi_s", ztf.constant(jpsi_scale)) +jpsi_p = zfit.Parameter("jpsi_p", ztf.constant(jpsi_phase)) +jpsi_s = zfit.Parameter("jpsi_s", ztf.constant(jpsi_scale), floating = False) #psi2s @@ -364,56 +448,91 @@ 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), floating = False) -psi2s_s = zfit.Parameter("psi2s_s", ztf.constant(psi2s_scale)) +psi2s_p = zfit.Parameter("psi2s_p", ztf.constant(psi2s_phase)) +psi2s_s = zfit.Parameter("psi2s_s", ztf.constant(psi2s_scale), floating = False) -#cusp +#psi(3770) -# cusp_mass, sigma_R, sigma_L, cusp_scale = 3550, 3e-7, 200, 0 +p3770_mass, p3770_width, p3770_phase, p3770_scale = pdg["p3770"] -# cusp_m = zfit.Parameter("cusp_m", ztf.constant(cusp_mass), floating = False) -# sig_L = zfit.Parameter("sig_L", ztf.constant(sigma_L), floating = False) -# sig_R = zfit.Parameter("sig_R", ztf.constant(sigma_R), floating = False) -# cusp_s = zfit.Parameter("cusp_s", ztf.constant(cusp_scale), floating = False) +p3770_m = zfit.Parameter("p3770_m", ztf.constant(p3770_mass), floating = False) +p3770_w = zfit.Parameter("p3770_w", ztf.constant(p3770_width), floating = False) +p3770_p = zfit.Parameter("p3770_p", ztf.constant(p3770_phase)) +p3770_s = zfit.Parameter("p3770_s", ztf.constant(p3770_scale), floating = False) + +#psi(4040) + +p4040_mass, p4040_width, p4040_phase, p4040_scale = pdg["p4040"] + +p4040_m = zfit.Parameter("p4040_m", ztf.constant(p4040_mass), floating = False) +p4040_w = zfit.Parameter("p4040_w", ztf.constant(p4040_width), floating = False) +p4040_p = zfit.Parameter("p4040_p", ztf.constant(p4040_phase)) +p4040_s = zfit.Parameter("p4040_s", ztf.constant(p4040_scale), floating = False) + +#psi(4160) + +p4160_mass, p4160_width, p4160_phase, p4160_scale = pdg["p4160"] + +p4160_m = zfit.Parameter("p4160_m", ztf.constant(p4160_mass), floating = False) +p4160_w = zfit.Parameter("p4160_w", ztf.constant(p4160_width), floating = False) +p4160_p = zfit.Parameter("p4160_p", ztf.constant(p4160_phase)) +p4160_s = zfit.Parameter("p4160_s", ztf.constant(p4160_scale), floating = False) + +#psi(4415) + +p4415_mass, p4415_width, p4415_phase, p4415_scale = pdg["p4415"] + +p4415_m = zfit.Parameter("p4415_m", ztf.constant(p4415_mass), floating = False) +p4415_w = zfit.Parameter("p4415_w", ztf.constant(p4415_width), floating = False) +p4415_p = zfit.Parameter("p4415_p", ztf.constant(p4415_phase)) +p4415_s = zfit.Parameter("p4415_s", ztf.constant(p4415_scale), floating = False) # ## Setup pdf -# In[ ]: +# In[9]: 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) + psi2s_mass = psi2s_m, psi2s_scale = psi2s_s, psi2s_phase = psi2s_p, psi2s_width = psi2s_w, + p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w, + p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w, + p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w, + p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w, + rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w, + omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w, + phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w) # print(total_pdf.obs) +# print(calcs_test) + # ## Test if graphs actually work and compute values -# In[ ]: +# In[10]: -def total_test_tf(xq): +# def total_test_tf(xq): - def jpsi_res(q): - return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w) +# def jpsi_res(q): +# return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w) - def psi2s_res(q): - return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w) +# def psi2s_res(q): +# return resonance(q, psi2s_m, psi2s_s, psi2s_p, psi2s_w) - def cusp(q): - return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s) +# def cusp(q): +# return bifur_gauss(q, cusp_m, sig_L, sig_R, cusp_s) - funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq) +# funcs = jpsi_res(xq) + psi2s_res(xq) + cusp(xq) - vec_f = vec(xq, funcs) +# vec_f = vec(xq, funcs) - axiv_nr = axiv_nonres(xq) +# axiv_nr = axiv_nonres(xq) - tot = vec_f + axiv_nr +# tot = vec_f + axiv_nr - return tot +# return tot def jpsi_res(q): return resonance(q, jpsi_m, jpsi_s, jpsi_p, jpsi_w) @@ -431,7 +550,7 @@ fT_y = zfit.run(formfactor(test_q,"T")) -# In[ ]: +# In[11]: plt.clf() @@ -442,32 +561,17 @@ # plt.plot(test_q, fplus_y, label = '+') # plt.plot(test_q, res_y, label = 'res') plt.legend() -# plt.ylim(0.0, 6e-6) -plt.yscale('log') -# plt.xlim(3080, 3110) +plt.ylim(0.0, 6e-6) +# plt.yscale('log') +# plt.xlim(770, 785) plt.savefig('test.png') # print(jpsi_width) -# In[ ]: +# In[12]: -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) @@ -477,59 +581,70 @@ # plt.semilogy(test_q, calcs_test, label = 'pdf') +# In[13]: + + +# 0.213/(0.00133+0.213+0.015) + + # ## Adjust scaling of different parts -# In[ ]: +# In[14]: -# total_f.update_integration_options(draws_per_dim=20000000, mc_sampler=None) -# inte = total_f.integrate(limits = (3080, 3112), norm_range=False) +# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None) +# inte = total_f.integrate(limits = (1000, 1040), norm_range=False) # inte_fl = zfit.run(inte) # print(inte_fl) -# 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"]) -# In[ ]: +# In[15]: -# 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("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("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)) + +# name = "phi" + +# print(name+":", inte_fl) +# print("Increase am by factor:", np.sqrt(pdg[name+"_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl)) +# print("New amp:", pdg[name][3]*np.sqrt(pdg[name+"_BR"]/pdg["NR_BR"]*pdg["NR_auc"]/inte_fl)) +# # print(x_min) +# # print(x_max) +# # # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None) +# # total_f.update_integration_options(mc_sampler=lambda dim, num_results, +# # dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype), +# # draws_per_dim=1000000) +# # # _ = [] -# print(x_min) -# print(x_max) -# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None) -# total_f.update_integration_options(mc_sampler=lambda dim, num_results, -# dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype), -# draws_per_dim=1000000) -# # _ = [] +# # # for i in range(10): -# # for i in range(10): +# # # inte = total_f.integrate(limits = (x_min, x_max)) +# # # inte_fl = zfit.run(inte) +# # # print(inte_fl) +# # # _.append(inte_fl) -# # inte = total_f.integrate(limits = (x_min, x_max)) -# # inte_fl = zfit.run(inte) -# # print(inte_fl) -# # _.append(inte_fl) +# # # print("mean:", np.mean(_)) -# # print("mean:", np.mean(_)) +# # _ = time.time() -# _ = time.time() - -# inte = total_f.integrate(limits = (x_min, x_max)) -# inte_fl = zfit.run(inte) -# print(inte_fl) -# print("Time taken: {}".format(display_time(int(time.time() - _)))) +# # inte = total_f.integrate(limits = (x_min, x_max)) +# # inte_fl = zfit.run(inte) +# # print(inte_fl) +# # print("Time taken: {}".format(display_time(int(time.time() - _)))) # ## Tensorflow scaling -# In[ ]: +# In[16]: # def scaling_func(x): @@ -555,7 +670,7 @@ # print(integrate.quad(s_func, x_min, x_max, limit = 50)) -# In[ ]: +# In[17]: # factor_jpsi = pdg["NR_auc"]*pdg["jpsi_BR"]/(pdg["NR_BR"]*pdg["jpsi_auc"]) @@ -569,7 +684,7 @@ # print(np.sqrt(factor_psi2s)) -# In[ ]: +# In[18]: # def _t_f(xq): @@ -596,13 +711,13 @@ # return probs -# In[ ]: +# In[19]: # print(36000*(1+ pdg["jpsi_BR"]/pdg["NR_BR"] + pdg["psi2s_BR"]/pdg["NR_BR"])) -# In[ ]: +# In[20]: # start = time.time() @@ -616,7 +731,7 @@ # ## One sample # ! total_f.sample() always returns the same set ! -# In[ ]: +# In[21]: # nevents = int(pdg["number_of_decays"]) @@ -650,7 +765,7 @@ # pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL) -# In[ ]: +# In[22]: # print("Time to generate full toy: {} s".format(int(time.time()-start))) @@ -669,7 +784,7 @@ # print(total_samp[:nevents].shape) -# In[ ]: +# In[23]: # bins = int((x_max-x_min)/7) @@ -689,7 +804,7 @@ # ## Toys -# In[ ]: +# In[24]: @@ -708,34 +823,50 @@ # 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 +# list_of_borders = [] +# _p = [] +# splits = 10 - _ = np.linspace(x_min, x_max, splits) +# _ = 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)) +# 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)]), + mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype), + tf.constant(0.93, dtype=dtype), + tf.constant(0.05, dtype=dtype), + tf.constant(0.065, dtype=dtype), + tf.constant(0.05, 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(3090, dtype=dtype), + tf.constant(3681, 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(3102, dtype=dtype), + tf.constant(3691, dtype=dtype), tf.constant(3110, dtype=dtype), tf.constant(3710, dtype=dtype)])) +# dtype = tf.float64 +# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype), +# tf.constant(0.90, dtype=dtype), +# tf.constant(0.02, dtype=dtype), +# tf.constant(0.07, dtype=dtype), +# tf.constant(0.02, dtype=dtype)]), +# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), +# tf.constant(3089, dtype=dtype), +# tf.constant(3103, dtype=dtype), +# tf.constant(3681, dtype=dtype), +# tf.constant(3691, dtype=dtype)], +# high=[tf.constant(3089, dtype=dtype), +# tf.constant(3103, dtype=dtype), +# tf.constant(3681, dtype=dtype), +# tf.constant(3691, dtype=dtype), +# tf.constant(x_max, 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)) @@ -751,30 +882,30 @@ return sample, thresholds, weights, weights_max, n_to_produce -# In[ ]: +# In[25]: total_f._sample_and_weights = UniformSampleAndWeights -# In[ ]: +# In[26]: -# psi2s_mass +0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min) -# In[ ]: +# In[27]: # zfit.settings.set_verbosity(10) -# In[ ]: +# In[28]: # zfit.run.numeric_checks = False -nr_of_toys = 2 +nr_of_toys = 1 nevents = int(pdg["number_of_decays"]) nevents = pdg["number_of_decays"] event_stack = 1000000 @@ -800,12 +931,11 @@ sampler.resample(n=event_stack) s = sampler.unstack_x() sam = zfit.run(s) - clear_output(wait=True) +# clear_output(wait=True) c = call + 1 - print("{0}/{1}".format(c, calls)) - print("Toy {}/{}".format(toy+1, nr_of_toys)) + print("{0}/{1} of Toy {2}/{3}".format(c, calls, 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*(toy))*((nr_of_toys-toy)*calls-c))))) @@ -813,7 +943,7 @@ pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL) -# In[ ]: +# In[29]: # with open(r"data/zfit_toys/toy_0/0.pkl", "rb") as input_file: @@ -827,7 +957,7 @@ # print(np.sum(sam-sam2)) -# In[ ]: +# In[30]: print("Time to generate full toy: {} s".format(int(time.time()-start))) @@ -848,7 +978,7 @@ print(total_samp[:nevents].shape) -# In[ ]: +# In[31]: plt.clf() @@ -872,7 +1002,7 @@ plt.savefig('test2.png') -# In[ ]: +# In[32]: # sampler = total_f.create_sampler(n=nevents) @@ -892,13 +1022,13 @@ # minimum = minimizer.minimize(nll) -# In[ ]: +# In[33]: # jpsi_width -# In[ ]: +# In[34]: # plt.hist(sample, weights=1 / prob(sample)) @@ -909,9 +1039,11 @@ # In[ ]: +start = time.time() + nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max)) -minimizer = zfit.minimize.MinuitMinimizer() +minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5) # minimizer._use_tfgrad = False result = minimizer.minimize(nll) @@ -926,6 +1058,8 @@ # In[ ]: +print("Time taken for fitting: {}".format(display_time(int(time.time()-start)))) + # probs = total_f.pdf(test_q) calcs_test = zfit.run(probs) @@ -947,7 +1081,7 @@ # print(jpsi_width) -# In[ ]: +# In[38]: # _tot = 4.37e-7+6.02e-5+4.97e-6 @@ -958,7 +1092,7 @@ # print(_probs) -# In[ ]: +# In[39]: # dtype = 'float64' @@ -976,11 +1110,11 @@ # # print(zfit.run(mixture.prob(mixture.sample((10, 1))))) -# In[ ]: +# In[40]: -print(zfit.run(jpsi_s)) -print(zfit.run(psi2s_s)) +print((zfit.run(jpsi_p)%(2*np.pi))/np.pi) +print((zfit.run(psi2s_p)%(2*np.pi))/np.pi) # In[ ]: diff --git a/test2.png b/test2.png index 7418856..0bcad3d 100644 --- a/test2.png +++ b/test2.png Binary files differ diff --git a/test3.png b/test3.png index dcb51be..5731d21 100644 --- a/test3.png +++ b/test3.png Binary files differ diff --git a/zfit_git_update.txt b/zfit_git_update.txt new file mode 100644 index 0000000..1ed7373 --- /dev/null +++ b/zfit_git_update.txt @@ -0,0 +1 @@ +pip install git+https://github.com/zfit/zfit \ No newline at end of file