diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 994958a..01987c5 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -502,7 +502,7 @@ "\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) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\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) #, lower_limit=jpsi_scale-np.sqrt(jpsi_scale), upper_limit=jpsi_scale+np.sqrt(jpsi_scale))\n", "\n", "#psi2s\n", @@ -511,7 +511,7 @@ "\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) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\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) #, lower_limit=psi2s_scale-np.sqrt(psi2s_scale), upper_limit=psi2s_scale+np.sqrt(psi2s_scale))\n", "\n", "#psi(3770)\n", @@ -1343,8 +1343,10 @@ " sum_ru_1 += part\n", "\n", "sum_1 = tf.math.real(sum_ru_1)\n", - "constraint1 = zfit.constraint.GaussianConstraint(params = sum_1, mu = ztf.constant(1.7*10**-8), \n", - " sigma = ztf.constant(2.2*10**-8))\n", + "# constraint1 = zfit.constraint.GaussianConstraint(params = sum_1, mu = ztf.constant(1.7*10**-8), \n", + "# sigma = ztf.constant(2.2*10**-8))\n", + "\n", + "constraint1 = tf.pow((sum_1-ztf.constant(1.7*10**-8))/ztf.constant(2.2*10**-8),2)/ztf.constant(2.)\n", "\n", "# 2. Constraint - Abs. of sum of Psi contribs and D contribs\n", "\n", @@ -1359,38 +1361,48 @@ "\n", "# 4. Constraint - Formfactor multivariant gaussian covariance fplus\n", "\n", - "Cov_matrix = [[ztf.constant( 1.), ztf.constant( 0.45), ztf.constant( 0.19), ztf.constant(0.857), ztf.constant(0.598), ztf.constant(0.531), ztf.constant(0.752), ztf.constant(0.229), ztf.constant(0,117)],\n", - " [ztf.constant( 0.45), ztf.constant( 1.), ztf.constant(0.677), ztf.constant(0.708), ztf.constant(0.958), ztf.constant(0.927), ztf.constant(0.227), ztf.constant(0.443), ztf.constant(0.287)],\n", - " [ztf.constant( 0.19), ztf.constant(0.677), ztf.constant( 1.), ztf.constant(0.595), ztf.constant(0.770), ztf.constant(0.819),ztf.constant(-0.023), ztf.constant( 0.07), ztf.constant(0.196)],\n", - " [ztf.constant(0.857), ztf.constant(0.708), ztf.constant(0.595), ztf.constant( 1.), ztf.constant( 0.83), ztf.constant(0.766), ztf.constant(0.582), ztf.constant(0.237), ztf.constant(0.192)],\n", - " [ztf.constant(0.598), ztf.constant(0.958), ztf.constant(0.770), ztf.constant( 0.83), ztf.constant( 1.), ztf.constant(0.973), ztf.constant(0.324), ztf.constant(0.372), ztf.constant(0.272)],\n", - " [ztf.constant(0.531), ztf.constant(0.927), ztf.constant(0.819), ztf.constant(0.766), ztf.constant(0.973), ztf.constant( 1.), ztf.constant(0.268), ztf.constant(0.332), ztf.constant(0.269)],\n", - " [ztf.constant(0.752), ztf.constant(0.227),ztf.constant(-0.023), ztf.constant(0.582), ztf.constant(0.324), ztf.constant(0.268), ztf.constant( 1.), ztf.constant( 0.59), ztf.constant(0.515)],\n", - " [ztf.constant(0.229), ztf.constant(0.443), ztf.constant( 0.07), ztf.constant(0.237), ztf.constant(0.372), ztf.constant(0.332), ztf.constant( 0.59), ztf.constant( 1.), ztf.constant(0.897)],\n", - " [ztf.constant(0.117), ztf.constant(0.287), ztf.constant(0.196), ztf.constant(0.192), ztf.constant(0.272), ztf.constant(0.269), ztf.constant(0.515), ztf.constant(0.897), ztf.constant( 1.)]]\n", + "# Cov_matrix = [[ztf.constant( 1.), ztf.constant( 0.45), ztf.constant( 0.19), ztf.constant(0.857), ztf.constant(0.598), ztf.constant(0.531), ztf.constant(0.752), ztf.constant(0.229), ztf.constant(0,117)],\n", + "# [ztf.constant( 0.45), ztf.constant( 1.), ztf.constant(0.677), ztf.constant(0.708), ztf.constant(0.958), ztf.constant(0.927), ztf.constant(0.227), ztf.constant(0.443), ztf.constant(0.287)],\n", + "# [ztf.constant( 0.19), ztf.constant(0.677), ztf.constant( 1.), ztf.constant(0.595), ztf.constant(0.770), ztf.constant(0.819),ztf.constant(-0.023), ztf.constant( 0.07), ztf.constant(0.196)],\n", + "# [ztf.constant(0.857), ztf.constant(0.708), ztf.constant(0.595), ztf.constant( 1.), ztf.constant( 0.83), ztf.constant(0.766), ztf.constant(0.582), ztf.constant(0.237), ztf.constant(0.192)],\n", + "# [ztf.constant(0.598), ztf.constant(0.958), ztf.constant(0.770), ztf.constant( 0.83), ztf.constant( 1.), ztf.constant(0.973), ztf.constant(0.324), ztf.constant(0.372), ztf.constant(0.272)],\n", + "# [ztf.constant(0.531), ztf.constant(0.927), ztf.constant(0.819), ztf.constant(0.766), ztf.constant(0.973), ztf.constant( 1.), ztf.constant(0.268), ztf.constant(0.332), ztf.constant(0.269)],\n", + "# [ztf.constant(0.752), ztf.constant(0.227),ztf.constant(-0.023), ztf.constant(0.582), ztf.constant(0.324), ztf.constant(0.268), ztf.constant( 1.), ztf.constant( 0.59), ztf.constant(0.515)],\n", + "# [ztf.constant(0.229), ztf.constant(0.443), ztf.constant( 0.07), ztf.constant(0.237), ztf.constant(0.372), ztf.constant(0.332), ztf.constant( 0.59), ztf.constant( 1.), ztf.constant(0.897)],\n", + "# [ztf.constant(0.117), ztf.constant(0.287), ztf.constant(0.196), ztf.constant(0.192), ztf.constant(0.272), ztf.constant(0.269), ztf.constant(0.515), ztf.constant(0.897), ztf.constant( 1.)]]\n", "\n", - "def triGauss(val1,val2,val3,m = Cov_matrix):\n", + "# def triGauss(val1,val2,val3,m = Cov_matrix):\n", "\n", - " 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", - " x1 = (val1-mean1)/sigma1\n", - " x2 = (val2-mean2)/sigma2\n", - " x3 = (val3-mean3)/sigma3\n", - " rho12 = m[0][1]\n", - " rho13 = m[0][2]\n", - " rho23 = m[1][2]\n", - " w = x1*x1*(rho23*rho23-1) + x2*x2*(rho13*rho13-1)+x3*x3*(rho12*rho12-1)+2*(x1*x2*(rho12-rho13*rho23)+x1*x3*(rho13-rho12*rho23)+x2*x3*(rho23-rho12*rho13))\n", - " d = 2*(rho12*rho12+rho13*rho13+rho23*rho23-2*rho12*rho13*rho23-1)\n", + "# 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", + "# x1 = (val1-mean1)/sigma1\n", + "# x2 = (val2-mean2)/sigma2\n", + "# x3 = (val3-mean3)/sigma3\n", + "# rho12 = m[0][1]\n", + "# rho13 = m[0][2]\n", + "# rho23 = m[1][2]\n", + "# w = x1*x1*(rho23*rho23-1) + x2*x2*(rho13*rho13-1)+x3*x3*(rho12*rho12-1)+2*(x1*x2*(rho12-rho13*rho23)+x1*x3*(rho13-rho12*rho23)+x2*x3*(rho23-rho12*rho13))\n", + "# d = 2*(rho12*rho12+rho13*rho13+rho23*rho23-2*rho12*rho13*rho23-1)\n", " \n", - " fcn = -w/d\n", - " chisq = -2*fcn\n", - " return chisq*10000.\n", + "# fcn = -w/d\n", + "# chisq = -2*fcn\n", + "# return chisq\n", "\n", - "constraint4 = triGauss(bplus_0, bplus_1, bplus_2)\n", + "# constraint4 = triGauss(bplus_0, bplus_1, bplus_2)\n", + "\n", + "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", + "constraint4_0 = tf.pow((bplus_0-mean1)/sigma1,2)/ztf.constant(2.)\n", + "constraint4_1 = tf.pow((bplus_1-mean2)/sigma2,2)/ztf.constant(2.)\n", + "constraint4_2 = tf.pow((bplus_2-mean3)/sigma3,2)/ztf.constant(2.)\n", "\n", "# 5. Constraint - Abs. of sum of light contribs\n", "\n", @@ -1411,10 +1423,13 @@ "# 6. Constraint on phases of Jpsi and Psi2s for cut out fit\n", "\n", "\n", - "constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg[\"jpsi_phase_unc\"]),\n", - " sigma = ztf.constant(jpsi_phase))\n", - "constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]),\n", - " sigma = ztf.constant(psi2s_phase))\n", + "# constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg[\"jpsi_phase_unc\"]),\n", + "# sigma = ztf.constant(jpsi_phase))\n", + "# constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]),\n", + "# sigma = ztf.constant(psi2s_phase))\n", + "\n", + "constraint6_0 = tf.pow((jpsi_p-ztf.constant(jpsi_phase))/ztf.constant(pdg[\"jpsi_phase_unc\"]),2)/ztf.constant(2.)\n", + "constraint6_1 = tf.pow((psi2s_p-ztf.constant(psi2s_phase))/ztf.constant(pdg[\"psi2s_phase_unc\"]),2)/ztf.constant(2.)\n", "\n", "# zfit.run(constraint6_0)\n", "\n", @@ -1422,7 +1437,8 @@ "\n", "#List of all constraints\n", "\n", - "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4]#, ztf.convert_to_tensor(constraint6_0)]#, ztf.convert_to_tensor(constraint6_1)]" + "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4_0, constraint4_1, constraint4_2,\n", + " constraint6_0, constraint6_1]" ] }, { @@ -1434,7 +1450,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1504,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": { "scrolled": false }, @@ -1513,14 +1529,20 @@ "name": "stdout", "output_type": "stream", "text": [ + "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", + "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\distributions\\categorical.py:263: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.random.categorical instead.\n", "Toy 0: Generating data...\n", "Toy 0: Data generation finished\n", "Toy 0: Loading data...\n", "Toy 0: Loading data finished\n", "Toy 0: Fitting pdf...\n", "------------------------------------------------------------------\n", - "| FCN = 241.5 | Ncalls=33 (33 total) |\n", - "| EDM = 2.47E-06 (Goal: 5E-06) | up = 0.5 |\n", + "| FCN = 291.3 | Ncalls=753 (753 total) |\n", + "| EDM = 4.03E-05 (Goal: 5E-06) | up = 0.5 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", @@ -1530,48 +1552,52 @@ "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", - "Function minimum: 241.45590864280427\n", + "Function minimum: 291.34660987562074\n", "----------------------------------------------------------------------------------------------\n", "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", "----------------------------------------------------------------------------------------------\n", - "| 0 | p4040_s | 1.1 | 0.5 | | |0.00501244| 2.01499 | |\n", - "| 1 | p4160_s | 2.7 | 0.6 | | | 0.71676 | 3.68324 | |\n", - "| 2 | p4415_p | 4.2 | 2.2 | | |-6.28319 | 6.28319 | |\n", - "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", - "| 4 | Ctt | 0.16 | 0.21 | | | -0.5 | 0.5 | |\n", - "| 5 | DDstar_p | -4.8 | 1.9 | | |-6.28319 | 6.28319 | |\n", - "| 6 | Dbar_p | 0.11 | 9.47 | | |-6.28319 | 6.28319 | |\n", - "| 7 | p4415_s | 0.18 | 0.16 | | |0.126447 | 2.35355 | |\n", - "| 8 | p3770_p | 2.7 | 2.7 | | |-6.28319 | 6.28319 | |\n", - "| 9 | DDstar_s | 0.020 | 0.138 | | | -0.3 | 0.3 | |\n", - "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", - "| 11| p3770_s | 1.6 | 0.6 | | |0.918861 | 4.08114 | |\n", - "| 12| Dbar_s | 0.07 | 0.13 | | | -0.3 | 0.3 | |\n", - "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", - "| 14| p4160_p | 6.0 | 0.6 | | |-6.28319 | 6.28319 | |\n", - "| 15| p4040_p | -0.11 | 3.88 | | |-6.28319 | 6.28319 | |\n", + "| 0 | bplus_2 | 2.0 | 3.3 | | | -2 | 2 | |\n", + "| 1 | p4415_p | -4.1 | 1.8 | | |-6.28319 | 6.28319 | |\n", + "| 2 | p4040_s | 2.0 | 2.0 | | |0.00501244| 2.01499 | |\n", + "| 3 | Ctt | -0.5 | 0.5 | | | -0.5 | 0.5 | |\n", + "| 4 | bplus_0 | 0.27 | 0.15 | | | -2 | 2 | |\n", + "| 5 | p4160_s | 3.3 | 2.5 | | | 0.71676 | 3.68324 | |\n", + "| 6 | DDstar_s | 0.30 | 0.55 | | | -0.3 | 0.3 | |\n", + "| 7 | p4415_s | 2.4 | 1.4 | | |0.126447 | 2.35355 | |\n", + "| 8 | Dbar_p | 6 | 10 | | |-6.28319 | 6.28319 | |\n", + "| 9 | p4160_p | 2.4 | 1.9 | | |-6.28319 | 6.28319 | |\n", + "| 10| p3770_s | 4.1 | 3.0 | | |0.918861 | 4.08114 | |\n", + "| 11| p3770_p | -3.8 | 1.7 | | |-6.28319 | 6.28319 | |\n", + "| 12| jpsi_p | 5.0 | 0.8 | | |-6.28319 | 6.28319 | |\n", + "| 13| psi2s_p | 1.80 | 0.03 | | |-6.28319 | 6.28319 | |\n", + "| 14| Dbar_s | 0.30 | 0.59 | | | -0.3 | 0.3 | |\n", + "| 15| bplus_1 | -0.48 | 0.28 | | | -2 | 2 | |\n", + "| 16| p4040_p | 5.4 | 1.9 | | |-6.28319 | 6.28319 | |\n", + "| 17| DDstar_p | -6 | 9 | | |-6.28319 | 6.28319 | |\n", "----------------------------------------------------------------------------------------------\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| p4040_s | 1.000 0.045 0.009 -0.001 -0.002 -0.003 -0.082 -0.005 -0.013 -0.009 -0.001 0.011 -0.032 -0.000 -0.065 0.621 |\n", - "| p4160_s | 0.045 1.000 0.003 -0.000 0.000 -0.001 -0.040 0.000 -0.009 -0.008 -0.000 0.002 -0.020 -0.000 0.093 0.075 |\n", - "| p4415_p | 0.009 0.003 1.000 0.000 -0.000 0.001 -0.008 -0.093 -0.002 -0.002 0.000 0.000 -0.008 0.000 0.008 0.013 |\n", - "| bplus_1 | -0.001 -0.000 0.000 1.000 0.000 -0.000 0.000 0.000 0.000 -0.000 0.677 -0.000 0.000 0.450 -0.000 -0.001 |\n", - "| Ctt | -0.002 0.000 -0.000 0.000 1.000 0.001 0.019 0.000 -0.001 0.002 0.000 -0.000 0.004 0.000 -0.002 -0.002 |\n", - "| DDstar_p | -0.003 -0.001 0.001 -0.000 0.001 1.000 -0.030 0.001 -0.003 0.298 -0.000 0.000 0.012 -0.000 -0.002 -0.004 |\n", - "| Dbar_p | -0.082 -0.040 -0.008 0.000 0.019 -0.030 1.000 0.011 0.059 -0.091 0.000 0.004 0.364 0.000 -0.008 -0.098 |\n", - "| p4415_s | -0.005 0.000 -0.093 0.000 0.000 0.001 0.011 1.000 0.002 0.002 0.000 -0.000 0.005 0.000 -0.003 -0.009 |\n", - "| p3770_p | -0.013 -0.009 -0.002 0.000 -0.001 -0.003 0.059 0.002 1.000 -0.000 0.000 -0.128 0.029 0.000 -0.003 -0.003 |\n", - "| DDstar_s | -0.009 -0.008 -0.002 -0.000 0.002 0.298 -0.091 0.002 -0.000 1.000 -0.000 0.001 -0.037 -0.000 -0.002 -0.008 |\n", - "| bplus_2 | -0.001 -0.000 0.000 0.677 0.000 -0.000 0.000 0.000 0.000 -0.000 1.000 -0.000 0.000 0.190 -0.000 -0.001 |\n", - "| p3770_s | 0.011 0.002 0.000 -0.000 -0.000 0.000 0.004 -0.000 -0.128 0.001 -0.000 1.000 0.002 -0.000 0.002 0.015 |\n", - "| Dbar_s | -0.032 -0.020 -0.008 0.000 0.004 0.012 0.364 0.005 0.029 -0.037 0.000 0.002 1.000 0.000 -0.001 -0.031 |\n", - "| bplus_0 | -0.000 -0.000 0.000 0.450 0.000 -0.000 0.000 0.000 0.000 -0.000 0.190 -0.000 0.000 1.000 -0.000 -0.001 |\n", - "| p4160_p | -0.065 0.093 0.008 -0.000 -0.002 -0.002 -0.008 -0.003 -0.003 -0.002 -0.000 0.002 -0.001 -0.000 1.000 -0.074 |\n", - "| p4040_p | 0.621 0.075 0.013 -0.001 -0.002 -0.004 -0.098 -0.009 -0.003 -0.008 -0.001 0.015 -0.031 -0.001 -0.074 1.000 |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "Hesse errors: OrderedDict([(, {'error': 0.5475039449324027}), (, {'error': 0.6200845337212617}), (, {'error': 2.1784786478303917}), (, {'error': 0.00045254373012648674}), (, {'error': 0.2132717560841134}), (, {'error': 1.8581258189915308}), (, {'error': 9.473191034944215}), (, {'error': 0.1559314560994186}), (, {'error': 2.6685592600641765}), (, {'error': 0.13789484579224884}), (, {'error': 0.0019374513205349109}), (, {'error': 0.5908195162116194}), (, {'error': 0.13394604673222416}), (, {'error': 4.94973454072678e-05}), (, {'error': 0.631080637656769}), (, {'error': 3.8768081157581706})])\n" + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | bplus_2 p4415_p p4040_s Ctt bplus_0 p4160_s DDstar_s p4415_s Dbar_p p4160_p p3770_s p3770_p jpsi_p psi2s_p Dbar_s bplus_1 p4040_p DDstar_p |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| bplus_2 | 1.000 0.002 0.000 -0.000 -0.024 -0.001 -0.000 -0.000 0.000 0.001 0.000 -0.001 0.002 0.000 -0.000 0.024 -0.001 -0.000 |\n", + "| p4415_p | 0.002 1.000 -0.002 -0.008 0.157 -0.212 0.001 0.005 0.092 0.237 -0.000 0.020 -0.063 0.001 0.003 0.164 -0.108 0.008 |\n", + "| p4040_s | 0.000 -0.002 1.000 0.000 0.002 -0.000 -0.000 0.000 0.001 -0.004 -0.000 0.001 0.001 0.000 -0.000 -0.003 0.002 -0.000 |\n", + "| Ctt | -0.000 -0.008 0.000 1.000 -0.060 0.010 0.000 -0.000 -0.010 -0.010 -0.000 -0.015 0.012 0.000 0.000 0.030 -0.021 0.000 |\n", + "| bplus_0 | -0.024 0.157 0.002 -0.060 1.000 0.134 0.001 -0.003 -0.129 0.005 0.001 -0.107 0.068 0.003 0.003 -0.536 -0.098 0.000 |\n", + "| p4160_s | -0.001 -0.212 -0.000 0.010 0.134 1.000 -0.005 -0.001 0.142 -0.561 -0.001 0.164 0.027 0.005 -0.002 -0.033 0.010 -0.001 |\n", + "| DDstar_s | -0.000 0.001 -0.000 0.000 0.001 -0.005 1.000 0.000 0.001 0.005 0.000 -0.002 0.001 0.000 -0.000 -0.015 -0.001 0.001 |\n", + "| p4415_s | -0.000 0.005 0.000 -0.000 -0.003 -0.001 0.000 1.000 -0.001 -0.005 -0.000 -0.001 -0.000 -0.000 0.000 0.003 0.001 0.000 |\n", + "| Dbar_p | 0.000 0.092 0.001 -0.010 -0.129 0.142 0.001 -0.001 1.000 0.016 -0.001 0.274 0.209 0.000 0.003 0.371 0.160 0.003 |\n", + "| p4160_p | 0.001 0.237 -0.004 -0.010 0.005 -0.561 0.005 -0.005 0.016 1.000 0.000 -0.059 -0.079 -0.002 0.004 0.216 -0.119 0.007 |\n", + "| p3770_s | 0.000 -0.000 -0.000 -0.000 0.001 -0.001 0.000 -0.000 -0.001 0.000 1.000 0.003 0.000 -0.000 0.000 -0.002 0.001 0.000 |\n", + "| p3770_p | -0.001 0.020 0.001 -0.015 -0.107 0.164 -0.002 -0.001 0.274 -0.059 0.003 1.000 -0.027 -0.003 0.005 0.243 0.114 0.007 |\n", + "| jpsi_p | 0.002 -0.063 0.001 0.012 0.068 0.027 0.001 -0.000 0.209 -0.079 0.000 -0.027 1.000 -0.003 0.003 -0.264 0.078 0.007 |\n", + "| psi2s_p | 0.000 0.001 0.000 0.000 0.003 0.005 0.000 -0.000 0.000 -0.002 -0.000 -0.003 -0.003 1.000 0.000 0.005 0.001 0.000 |\n", + "| Dbar_s | -0.000 0.003 -0.000 0.000 0.003 -0.002 -0.000 0.000 0.003 0.004 0.000 0.005 0.003 0.000 1.000 -0.006 0.003 0.000 |\n", + "| bplus_1 | 0.024 0.164 -0.003 0.030 -0.536 -0.033 -0.015 0.003 0.371 0.216 -0.002 0.243 -0.264 0.005 -0.006 1.000 -0.094 0.001 |\n", + "| p4040_p | -0.001 -0.108 0.002 -0.021 -0.098 0.010 -0.001 0.001 0.160 -0.119 0.001 0.114 0.078 0.001 0.003 -0.094 1.000 0.006 |\n", + "| DDstar_p | -0.000 0.008 -0.000 0.000 0.000 -0.001 0.001 0.000 0.003 0.007 0.000 0.007 0.007 0.000 0.000 0.001 0.006 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 3.325119189670308}), (, {'error': 1.7815174671180474}), (, {'error': 1.9926966547547778}), (, {'error': 0.5114351017394924}), (, {'error': 0.14819864700462704}), (, {'error': 2.4730090801780316}), (, {'error': 0.5456802813189129}), (, {'error': 1.4247502221049095}), (, {'error': 9.742999517381843}), (, {'error': 1.9168225450258651}), (, {'error': 2.9995956487093722}), (, {'error': 1.7105494289800758}), (, {'error': 0.7843308347897375}), (, {'error': 0.03332981923505862}), (, {'error': 0.5881037384446894}), (, {'error': 0.27508756809324075}), (, {'error': 1.8523591244884532}), (, {'error': 9.488064745244461})])\n" ] }, { @@ -1585,76 +1611,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Toy 1/2\n", - "Time taken: 2 min, 59 s\n", - "Projected time left: 2 min, 59 s\n", - "Toy 1: Generating data...\n", - "Toy 1: Data generation finished\n", - "Toy 1: Loading data...\n", - "Toy 1: Loading data finished\n", - "Toy 1: Fitting pdf...\n", - "------------------------------------------------------------------\n", - "| FCN = 241.4 | Ncalls=39 (39 total) |\n", - "| EDM = 1.08E-06 (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: 241.3777412895305\n", - "----------------------------------------------------------------------------------------------\n", - "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", - "----------------------------------------------------------------------------------------------\n", - "| 0 | p4040_s | 0.45 | 0.18 | | |0.00501244| 2.01499 | |\n", - "| 1 | p4160_s | 2.91 | 0.28 | | | 0.71676 | 3.68324 | |\n", - "| 2 | p4415_p | 3.1 | 1.4 | | |-6.28319 | 6.28319 | |\n", - "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", - "| 4 | Ctt | -0.022 | 0.107 | | | -0.5 | 0.5 | |\n", - "| 5 | DDstar_p | -1.8 | 1.3 | | |-6.28319 | 6.28319 | |\n", - "| 6 | Dbar_p | 4.3 | 1.0 | | |-6.28319 | 6.28319 | |\n", - "| 7 | p4415_s | 1.59 | 0.23 | | |0.126447 | 2.35355 | |\n", - "| 8 | p3770_p | -0.8 | 1.1 | | |-6.28319 | 6.28319 | |\n", - "| 9 | DDstar_s | -0.03 | 0.07 | | | -0.3 | 0.3 | |\n", - "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", - "| 11| p3770_s | 3.59 | 0.25 | | |0.918861 | 4.08114 | |\n", - "| 12| Dbar_s | -0.08 | 0.06 | | | -0.3 | 0.3 | |\n", - "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", - "| 14| p4160_p | -2.0 | 6.8 | | |-6.28319 | 6.28319 | |\n", - "| 15| p4040_p | -5.0 | 0.8 | | |-6.28319 | 6.28319 | |\n", - "----------------------------------------------------------------------------------------------\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| p4040_s | 1.000 -0.001 0.131 0.000 -0.006 0.021 0.032 0.001 0.075 0.045 0.000 -0.003 0.041 -0.000 0.178 0.017 |\n", - "| p4160_s | -0.001 1.000 -0.007 0.000 0.001 -0.001 -0.002 0.001 -0.006 -0.003 0.000 0.000 -0.003 0.000 -0.011 -0.004 |\n", - "| p4415_p | 0.131 -0.007 1.000 0.001 -0.023 0.089 0.137 -0.025 0.305 0.180 0.001 -0.010 0.165 -0.000 0.738 0.079 |\n", - "| bplus_1 | 0.000 0.000 0.001 1.000 0.000 -0.000 -0.000 -0.000 -0.000 0.000 0.677 -0.000 0.000 0.450 0.001 0.000 |\n", - "| Ctt | -0.006 0.001 -0.023 0.000 1.000 -0.003 -0.004 -0.000 -0.012 -0.009 0.000 0.000 -0.008 0.000 -0.032 -0.003 |\n", - "| DDstar_p | 0.021 -0.001 0.089 -0.000 -0.003 1.000 0.005 0.000 0.052 -0.034 0.000 -0.001 0.022 -0.000 0.116 0.012 |\n", - "| Dbar_p | 0.032 -0.002 0.137 -0.000 -0.004 0.005 1.000 0.001 0.078 0.043 0.000 -0.002 -0.030 -0.000 0.180 0.019 |\n", - "| p4415_s | 0.001 0.001 -0.025 -0.000 -0.000 0.000 0.001 1.000 0.003 0.001 -0.000 -0.000 0.001 -0.000 0.009 0.001 |\n", - "| p3770_p | 0.075 -0.006 0.305 -0.000 -0.012 0.052 0.078 0.003 1.000 0.103 0.000 0.002 0.092 -0.000 0.420 0.039 |\n", - "| DDstar_s | 0.045 -0.003 0.180 0.000 -0.009 -0.034 0.043 0.001 0.103 1.000 0.000 -0.003 0.052 -0.000 0.252 0.026 |\n", - "| bplus_2 | 0.000 0.000 0.001 0.677 0.000 0.000 0.000 -0.000 0.000 0.000 1.000 -0.000 0.000 0.190 0.002 0.000 |\n", - "| p3770_s | -0.003 0.000 -0.010 -0.000 0.000 -0.001 -0.002 -0.000 0.002 -0.003 -0.000 1.000 -0.003 -0.000 -0.014 -0.001 |\n", - "| Dbar_s | 0.041 -0.003 0.165 0.000 -0.008 0.022 -0.030 0.001 0.092 0.052 0.000 -0.003 1.000 -0.000 0.228 0.023 |\n", - "| bplus_0 | -0.000 0.000 -0.000 0.450 0.000 -0.000 -0.000 -0.000 -0.000 -0.000 0.190 -0.000 -0.000 1.000 -0.000 0.000 |\n", - "| p4160_p | 0.178 -0.011 0.738 0.001 -0.032 0.116 0.180 0.009 0.420 0.252 0.002 -0.014 0.228 -0.000 1.000 0.114 |\n", - "| p4040_p | 0.017 -0.004 0.079 0.000 -0.003 0.012 0.019 0.001 0.039 0.026 0.000 -0.001 0.023 0.000 0.114 1.000 |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "Hesse errors: OrderedDict([(, {'error': 0.1817980073330014}), (, {'error': 0.28105296332603724}), (, {'error': 1.4460585352975572}), (, {'error': 0.00045254152319595953}), (, {'error': 0.1070128591631859}), (, {'error': 1.274894582621453}), (, {'error': 0.9863367274899124}), (, {'error': 0.22635340269660909}), (, {'error': 1.0686131922004622}), (, {'error': 0.06595112952400507}), (, {'error': 0.0019374367701227024}), (, {'error': 0.24635197699053668}), (, {'error': 0.06369342157651345}), (, {'error': 4.949731518788525e-05}), (, {'error': 6.846065392563649}), (, {'error': 0.7735769115033309})])\n", - "Toy 2/2\n", - "Time taken: 5 min, 55 s\n", - "Projected time left: \n" + "Toy 1/10\n", + "Time taken: 4 min, 1 \n", + "Projected time left: 36 min, 10 s\n", + "Toy 1: Generating data...\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcall\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 40\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[0msam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\data.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(self, param_values, n)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot set a new `n` if not a Tensor-like object was given\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_holder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initial_resampled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 927\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 928\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1150\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1151\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m 1153\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1154\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1329\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in 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1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1321\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1405\u001b[0m return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1409\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -1673,7 +1656,7 @@ "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", - "nr_of_toys = 2\n", + "nr_of_toys = 10\n", "nevents = int(pdg[\"number_of_decays\"])\n", "nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", @@ -1842,19 +1825,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2/2 fits converged\n", - "Mean Ctt value = 0.07076623985541536\n", - "Mean Ctt error = 0.16014230762364964\n" - ] - } - ], + "outputs": [], "source": [ "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", diff --git a/data/plots/0..png b/data/plots/0..png new file mode 100644 index 0000000..9c37fe6 --- /dev/null +++ b/data/plots/0..png Binary files differ diff --git a/data/plots/toy_fit_cut_region0.png b/data/plots/toy_fit_cut_region0.png index 74f88d8..ca0c853 100644 --- a/data/plots/toy_fit_cut_region0.png +++ b/data/plots/toy_fit_cut_region0.png Binary files differ diff --git a/data/plots/toy_fit_cut_region1.png b/data/plots/toy_fit_cut_region1.png index 5d3b712..457fe08 100644 --- a/data/plots/toy_fit_cut_region1.png +++ b/data/plots/toy_fit_cut_region1.png Binary files differ diff --git a/data/plots/toy_fit_cut_region2.png b/data/plots/toy_fit_cut_region2.png index 57c4ae8..1e95827 100644 --- a/data/plots/toy_fit_cut_region2.png +++ b/data/plots/toy_fit_cut_region2.png Binary files differ diff --git a/data/plots/toy_fit_cut_region3.png b/data/plots/toy_fit_cut_region3.png index fef45d4..a2e2259 100644 --- a/data/plots/toy_fit_cut_region3.png +++ b/data/plots/toy_fit_cut_region3.png Binary files differ diff --git a/data/plots/toy_fit_cut_region4.png b/data/plots/toy_fit_cut_region4.png index 1816e8e..e47b455 100644 --- a/data/plots/toy_fit_cut_region4.png +++ b/data/plots/toy_fit_cut_region4.png Binary files differ diff --git a/data/plots/toy_fit_cut_region5.png b/data/plots/toy_fit_cut_region5.png index ed5de21..82ed102 100644 --- a/data/plots/toy_fit_cut_region5.png +++ b/data/plots/toy_fit_cut_region5.png Binary files differ diff --git a/data/plots/toy_fit_cut_region6.png b/data/plots/toy_fit_cut_region6.png index c508f13..17f09c8 100644 --- a/data/plots/toy_fit_cut_region6.png +++ b/data/plots/toy_fit_cut_region6.png Binary files differ diff --git a/data/plots/toy_fit_cut_region7.png b/data/plots/toy_fit_cut_region7.png index 23a63d6..3eef7ad 100644 --- a/data/plots/toy_fit_cut_region7.png +++ b/data/plots/toy_fit_cut_region7.png Binary files differ diff --git a/data/plots/toy_fit_cut_region8.png b/data/plots/toy_fit_cut_region8.png index 2f7ee7e..031fd23 100644 --- a/data/plots/toy_fit_cut_region8.png +++ b/data/plots/toy_fit_cut_region8.png Binary files differ diff --git a/data/plots/toy_fit_cut_region9.png b/data/plots/toy_fit_cut_region9.png deleted file mode 100644 index 73e9719..0000000 --- a/data/plots/toy_fit_cut_region9.png +++ /dev/null Binary files differ diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index 9b29e72..10cb0d2 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 3451e1e..e75f319 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 b659247..58e8121 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 236642e..edb43bc 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 2694e65..51ae529 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 bd59c4f..0a5d7e2 100644 --- a/data/zfit_toys/toy_0/5.pkl +++ b/data/zfit_toys/toy_0/5.pkl Binary files differ diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 994958a..01987c5 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -502,7 +502,7 @@ "\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) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\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) #, lower_limit=jpsi_scale-np.sqrt(jpsi_scale), upper_limit=jpsi_scale+np.sqrt(jpsi_scale))\n", "\n", "#psi2s\n", @@ -511,7 +511,7 @@ "\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) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\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) #, lower_limit=psi2s_scale-np.sqrt(psi2s_scale), upper_limit=psi2s_scale+np.sqrt(psi2s_scale))\n", "\n", "#psi(3770)\n", @@ -1343,8 +1343,10 @@ " sum_ru_1 += part\n", "\n", "sum_1 = tf.math.real(sum_ru_1)\n", - "constraint1 = zfit.constraint.GaussianConstraint(params = sum_1, mu = ztf.constant(1.7*10**-8), \n", - " sigma = ztf.constant(2.2*10**-8))\n", + "# constraint1 = zfit.constraint.GaussianConstraint(params = sum_1, mu = ztf.constant(1.7*10**-8), \n", + "# sigma = ztf.constant(2.2*10**-8))\n", + "\n", + "constraint1 = tf.pow((sum_1-ztf.constant(1.7*10**-8))/ztf.constant(2.2*10**-8),2)/ztf.constant(2.)\n", "\n", "# 2. Constraint - Abs. of sum of Psi contribs and D contribs\n", "\n", @@ -1359,38 +1361,48 @@ "\n", "# 4. Constraint - Formfactor multivariant gaussian covariance fplus\n", "\n", - "Cov_matrix = [[ztf.constant( 1.), ztf.constant( 0.45), ztf.constant( 0.19), ztf.constant(0.857), ztf.constant(0.598), ztf.constant(0.531), ztf.constant(0.752), ztf.constant(0.229), ztf.constant(0,117)],\n", - " [ztf.constant( 0.45), ztf.constant( 1.), ztf.constant(0.677), ztf.constant(0.708), ztf.constant(0.958), ztf.constant(0.927), ztf.constant(0.227), ztf.constant(0.443), ztf.constant(0.287)],\n", - " [ztf.constant( 0.19), ztf.constant(0.677), ztf.constant( 1.), ztf.constant(0.595), ztf.constant(0.770), ztf.constant(0.819),ztf.constant(-0.023), ztf.constant( 0.07), ztf.constant(0.196)],\n", - " [ztf.constant(0.857), ztf.constant(0.708), ztf.constant(0.595), ztf.constant( 1.), ztf.constant( 0.83), ztf.constant(0.766), ztf.constant(0.582), ztf.constant(0.237), ztf.constant(0.192)],\n", - " [ztf.constant(0.598), ztf.constant(0.958), ztf.constant(0.770), ztf.constant( 0.83), ztf.constant( 1.), ztf.constant(0.973), ztf.constant(0.324), ztf.constant(0.372), ztf.constant(0.272)],\n", - " [ztf.constant(0.531), ztf.constant(0.927), ztf.constant(0.819), ztf.constant(0.766), ztf.constant(0.973), ztf.constant( 1.), ztf.constant(0.268), ztf.constant(0.332), ztf.constant(0.269)],\n", - " [ztf.constant(0.752), ztf.constant(0.227),ztf.constant(-0.023), ztf.constant(0.582), ztf.constant(0.324), ztf.constant(0.268), ztf.constant( 1.), ztf.constant( 0.59), ztf.constant(0.515)],\n", - " [ztf.constant(0.229), ztf.constant(0.443), ztf.constant( 0.07), ztf.constant(0.237), ztf.constant(0.372), ztf.constant(0.332), ztf.constant( 0.59), ztf.constant( 1.), ztf.constant(0.897)],\n", - " [ztf.constant(0.117), ztf.constant(0.287), ztf.constant(0.196), ztf.constant(0.192), ztf.constant(0.272), ztf.constant(0.269), ztf.constant(0.515), ztf.constant(0.897), ztf.constant( 1.)]]\n", + "# Cov_matrix = [[ztf.constant( 1.), ztf.constant( 0.45), ztf.constant( 0.19), ztf.constant(0.857), ztf.constant(0.598), ztf.constant(0.531), ztf.constant(0.752), ztf.constant(0.229), ztf.constant(0,117)],\n", + "# [ztf.constant( 0.45), ztf.constant( 1.), ztf.constant(0.677), ztf.constant(0.708), ztf.constant(0.958), ztf.constant(0.927), ztf.constant(0.227), ztf.constant(0.443), ztf.constant(0.287)],\n", + "# [ztf.constant( 0.19), ztf.constant(0.677), ztf.constant( 1.), ztf.constant(0.595), ztf.constant(0.770), ztf.constant(0.819),ztf.constant(-0.023), ztf.constant( 0.07), ztf.constant(0.196)],\n", + "# [ztf.constant(0.857), ztf.constant(0.708), ztf.constant(0.595), ztf.constant( 1.), ztf.constant( 0.83), ztf.constant(0.766), ztf.constant(0.582), ztf.constant(0.237), ztf.constant(0.192)],\n", + "# [ztf.constant(0.598), ztf.constant(0.958), ztf.constant(0.770), ztf.constant( 0.83), ztf.constant( 1.), ztf.constant(0.973), ztf.constant(0.324), ztf.constant(0.372), ztf.constant(0.272)],\n", + "# [ztf.constant(0.531), ztf.constant(0.927), ztf.constant(0.819), ztf.constant(0.766), ztf.constant(0.973), ztf.constant( 1.), ztf.constant(0.268), ztf.constant(0.332), ztf.constant(0.269)],\n", + "# [ztf.constant(0.752), ztf.constant(0.227),ztf.constant(-0.023), ztf.constant(0.582), ztf.constant(0.324), ztf.constant(0.268), ztf.constant( 1.), ztf.constant( 0.59), ztf.constant(0.515)],\n", + "# [ztf.constant(0.229), ztf.constant(0.443), ztf.constant( 0.07), ztf.constant(0.237), ztf.constant(0.372), ztf.constant(0.332), ztf.constant( 0.59), ztf.constant( 1.), ztf.constant(0.897)],\n", + "# [ztf.constant(0.117), ztf.constant(0.287), ztf.constant(0.196), ztf.constant(0.192), ztf.constant(0.272), ztf.constant(0.269), ztf.constant(0.515), ztf.constant(0.897), ztf.constant( 1.)]]\n", "\n", - "def triGauss(val1,val2,val3,m = Cov_matrix):\n", + "# def triGauss(val1,val2,val3,m = Cov_matrix):\n", "\n", - " 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", - " x1 = (val1-mean1)/sigma1\n", - " x2 = (val2-mean2)/sigma2\n", - " x3 = (val3-mean3)/sigma3\n", - " rho12 = m[0][1]\n", - " rho13 = m[0][2]\n", - " rho23 = m[1][2]\n", - " w = x1*x1*(rho23*rho23-1) + x2*x2*(rho13*rho13-1)+x3*x3*(rho12*rho12-1)+2*(x1*x2*(rho12-rho13*rho23)+x1*x3*(rho13-rho12*rho23)+x2*x3*(rho23-rho12*rho13))\n", - " d = 2*(rho12*rho12+rho13*rho13+rho23*rho23-2*rho12*rho13*rho23-1)\n", + "# 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", + "# x1 = (val1-mean1)/sigma1\n", + "# x2 = (val2-mean2)/sigma2\n", + "# x3 = (val3-mean3)/sigma3\n", + "# rho12 = m[0][1]\n", + "# rho13 = m[0][2]\n", + "# rho23 = m[1][2]\n", + "# w = x1*x1*(rho23*rho23-1) + x2*x2*(rho13*rho13-1)+x3*x3*(rho12*rho12-1)+2*(x1*x2*(rho12-rho13*rho23)+x1*x3*(rho13-rho12*rho23)+x2*x3*(rho23-rho12*rho13))\n", + "# d = 2*(rho12*rho12+rho13*rho13+rho23*rho23-2*rho12*rho13*rho23-1)\n", " \n", - " fcn = -w/d\n", - " chisq = -2*fcn\n", - " return chisq*10000.\n", + "# fcn = -w/d\n", + "# chisq = -2*fcn\n", + "# return chisq\n", "\n", - "constraint4 = triGauss(bplus_0, bplus_1, bplus_2)\n", + "# constraint4 = triGauss(bplus_0, bplus_1, bplus_2)\n", + "\n", + "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", + "constraint4_0 = tf.pow((bplus_0-mean1)/sigma1,2)/ztf.constant(2.)\n", + "constraint4_1 = tf.pow((bplus_1-mean2)/sigma2,2)/ztf.constant(2.)\n", + "constraint4_2 = tf.pow((bplus_2-mean3)/sigma3,2)/ztf.constant(2.)\n", "\n", "# 5. Constraint - Abs. of sum of light contribs\n", "\n", @@ -1411,10 +1423,13 @@ "# 6. Constraint on phases of Jpsi and Psi2s for cut out fit\n", "\n", "\n", - "constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg[\"jpsi_phase_unc\"]),\n", - " sigma = ztf.constant(jpsi_phase))\n", - "constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]),\n", - " sigma = ztf.constant(psi2s_phase))\n", + "# constraint6_0 = zfit.constraint.GaussianConstraint(params = jpsi_p, mu = ztf.constant(pdg[\"jpsi_phase_unc\"]),\n", + "# sigma = ztf.constant(jpsi_phase))\n", + "# constraint6_1 = zfit.constraint.GaussianConstraint(params = psi2s_p, mu = ztf.constant(pdg[\"psi2s_phase_unc\"]),\n", + "# sigma = ztf.constant(psi2s_phase))\n", + "\n", + "constraint6_0 = tf.pow((jpsi_p-ztf.constant(jpsi_phase))/ztf.constant(pdg[\"jpsi_phase_unc\"]),2)/ztf.constant(2.)\n", + "constraint6_1 = tf.pow((psi2s_p-ztf.constant(psi2s_phase))/ztf.constant(pdg[\"psi2s_phase_unc\"]),2)/ztf.constant(2.)\n", "\n", "# zfit.run(constraint6_0)\n", "\n", @@ -1422,7 +1437,8 @@ "\n", "#List of all constraints\n", "\n", - "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4]#, ztf.convert_to_tensor(constraint6_0)]#, ztf.convert_to_tensor(constraint6_1)]" + "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4_0, constraint4_1, constraint4_2,\n", + " constraint6_0, constraint6_1]" ] }, { @@ -1434,7 +1450,7 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1504,7 +1520,7 @@ }, { "cell_type": "code", - "execution_count": 40, + "execution_count": 38, "metadata": { "scrolled": false }, @@ -1513,14 +1529,20 @@ "name": "stdout", "output_type": "stream", "text": [ + "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", + "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\distributions\\categorical.py:263: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n", + "Instructions for updating:\n", + "Use tf.random.categorical instead.\n", "Toy 0: Generating data...\n", "Toy 0: Data generation finished\n", "Toy 0: Loading data...\n", "Toy 0: Loading data finished\n", "Toy 0: Fitting pdf...\n", "------------------------------------------------------------------\n", - "| FCN = 241.5 | Ncalls=33 (33 total) |\n", - "| EDM = 2.47E-06 (Goal: 5E-06) | up = 0.5 |\n", + "| FCN = 291.3 | Ncalls=753 (753 total) |\n", + "| EDM = 4.03E-05 (Goal: 5E-06) | up = 0.5 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", @@ -1530,48 +1552,52 @@ "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", - "Function minimum: 241.45590864280427\n", + "Function minimum: 291.34660987562074\n", "----------------------------------------------------------------------------------------------\n", "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", "----------------------------------------------------------------------------------------------\n", - "| 0 | p4040_s | 1.1 | 0.5 | | |0.00501244| 2.01499 | |\n", - "| 1 | p4160_s | 2.7 | 0.6 | | | 0.71676 | 3.68324 | |\n", - "| 2 | p4415_p | 4.2 | 2.2 | | |-6.28319 | 6.28319 | |\n", - "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", - "| 4 | Ctt | 0.16 | 0.21 | | | -0.5 | 0.5 | |\n", - "| 5 | DDstar_p | -4.8 | 1.9 | | |-6.28319 | 6.28319 | |\n", - "| 6 | Dbar_p | 0.11 | 9.47 | | |-6.28319 | 6.28319 | |\n", - "| 7 | p4415_s | 0.18 | 0.16 | | |0.126447 | 2.35355 | |\n", - "| 8 | p3770_p | 2.7 | 2.7 | | |-6.28319 | 6.28319 | |\n", - "| 9 | DDstar_s | 0.020 | 0.138 | | | -0.3 | 0.3 | |\n", - "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", - "| 11| p3770_s | 1.6 | 0.6 | | |0.918861 | 4.08114 | |\n", - "| 12| Dbar_s | 0.07 | 0.13 | | | -0.3 | 0.3 | |\n", - "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", - "| 14| p4160_p | 6.0 | 0.6 | | |-6.28319 | 6.28319 | |\n", - "| 15| p4040_p | -0.11 | 3.88 | | |-6.28319 | 6.28319 | |\n", + "| 0 | bplus_2 | 2.0 | 3.3 | | | -2 | 2 | |\n", + "| 1 | p4415_p | -4.1 | 1.8 | | |-6.28319 | 6.28319 | |\n", + "| 2 | p4040_s | 2.0 | 2.0 | | |0.00501244| 2.01499 | |\n", + "| 3 | Ctt | -0.5 | 0.5 | | | -0.5 | 0.5 | |\n", + "| 4 | bplus_0 | 0.27 | 0.15 | | | -2 | 2 | |\n", + "| 5 | p4160_s | 3.3 | 2.5 | | | 0.71676 | 3.68324 | |\n", + "| 6 | DDstar_s | 0.30 | 0.55 | | | -0.3 | 0.3 | |\n", + "| 7 | p4415_s | 2.4 | 1.4 | | |0.126447 | 2.35355 | |\n", + "| 8 | Dbar_p | 6 | 10 | | |-6.28319 | 6.28319 | |\n", + "| 9 | p4160_p | 2.4 | 1.9 | | |-6.28319 | 6.28319 | |\n", + "| 10| p3770_s | 4.1 | 3.0 | | |0.918861 | 4.08114 | |\n", + "| 11| p3770_p | -3.8 | 1.7 | | |-6.28319 | 6.28319 | |\n", + "| 12| jpsi_p | 5.0 | 0.8 | | |-6.28319 | 6.28319 | |\n", + "| 13| psi2s_p | 1.80 | 0.03 | | |-6.28319 | 6.28319 | |\n", + "| 14| Dbar_s | 0.30 | 0.59 | | | -0.3 | 0.3 | |\n", + "| 15| bplus_1 | -0.48 | 0.28 | | | -2 | 2 | |\n", + "| 16| p4040_p | 5.4 | 1.9 | | |-6.28319 | 6.28319 | |\n", + "| 17| DDstar_p | -6 | 9 | | |-6.28319 | 6.28319 | |\n", "----------------------------------------------------------------------------------------------\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| p4040_s | 1.000 0.045 0.009 -0.001 -0.002 -0.003 -0.082 -0.005 -0.013 -0.009 -0.001 0.011 -0.032 -0.000 -0.065 0.621 |\n", - "| p4160_s | 0.045 1.000 0.003 -0.000 0.000 -0.001 -0.040 0.000 -0.009 -0.008 -0.000 0.002 -0.020 -0.000 0.093 0.075 |\n", - "| p4415_p | 0.009 0.003 1.000 0.000 -0.000 0.001 -0.008 -0.093 -0.002 -0.002 0.000 0.000 -0.008 0.000 0.008 0.013 |\n", - "| bplus_1 | -0.001 -0.000 0.000 1.000 0.000 -0.000 0.000 0.000 0.000 -0.000 0.677 -0.000 0.000 0.450 -0.000 -0.001 |\n", - "| Ctt | -0.002 0.000 -0.000 0.000 1.000 0.001 0.019 0.000 -0.001 0.002 0.000 -0.000 0.004 0.000 -0.002 -0.002 |\n", - "| DDstar_p | -0.003 -0.001 0.001 -0.000 0.001 1.000 -0.030 0.001 -0.003 0.298 -0.000 0.000 0.012 -0.000 -0.002 -0.004 |\n", - "| Dbar_p | -0.082 -0.040 -0.008 0.000 0.019 -0.030 1.000 0.011 0.059 -0.091 0.000 0.004 0.364 0.000 -0.008 -0.098 |\n", - "| p4415_s | -0.005 0.000 -0.093 0.000 0.000 0.001 0.011 1.000 0.002 0.002 0.000 -0.000 0.005 0.000 -0.003 -0.009 |\n", - "| p3770_p | -0.013 -0.009 -0.002 0.000 -0.001 -0.003 0.059 0.002 1.000 -0.000 0.000 -0.128 0.029 0.000 -0.003 -0.003 |\n", - "| DDstar_s | -0.009 -0.008 -0.002 -0.000 0.002 0.298 -0.091 0.002 -0.000 1.000 -0.000 0.001 -0.037 -0.000 -0.002 -0.008 |\n", - "| bplus_2 | -0.001 -0.000 0.000 0.677 0.000 -0.000 0.000 0.000 0.000 -0.000 1.000 -0.000 0.000 0.190 -0.000 -0.001 |\n", - "| p3770_s | 0.011 0.002 0.000 -0.000 -0.000 0.000 0.004 -0.000 -0.128 0.001 -0.000 1.000 0.002 -0.000 0.002 0.015 |\n", - "| Dbar_s | -0.032 -0.020 -0.008 0.000 0.004 0.012 0.364 0.005 0.029 -0.037 0.000 0.002 1.000 0.000 -0.001 -0.031 |\n", - "| bplus_0 | -0.000 -0.000 0.000 0.450 0.000 -0.000 0.000 0.000 0.000 -0.000 0.190 -0.000 0.000 1.000 -0.000 -0.001 |\n", - "| p4160_p | -0.065 0.093 0.008 -0.000 -0.002 -0.002 -0.008 -0.003 -0.003 -0.002 -0.000 0.002 -0.001 -0.000 1.000 -0.074 |\n", - "| p4040_p | 0.621 0.075 0.013 -0.001 -0.002 -0.004 -0.098 -0.009 -0.003 -0.008 -0.001 0.015 -0.031 -0.001 -0.074 1.000 |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "Hesse errors: OrderedDict([(, {'error': 0.5475039449324027}), (, {'error': 0.6200845337212617}), (, {'error': 2.1784786478303917}), (, {'error': 0.00045254373012648674}), (, {'error': 0.2132717560841134}), (, {'error': 1.8581258189915308}), (, {'error': 9.473191034944215}), (, {'error': 0.1559314560994186}), (, {'error': 2.6685592600641765}), (, {'error': 0.13789484579224884}), (, {'error': 0.0019374513205349109}), (, {'error': 0.5908195162116194}), (, {'error': 0.13394604673222416}), (, {'error': 4.94973454072678e-05}), (, {'error': 0.631080637656769}), (, {'error': 3.8768081157581706})])\n" + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | bplus_2 p4415_p p4040_s Ctt bplus_0 p4160_s DDstar_s p4415_s Dbar_p p4160_p p3770_s p3770_p jpsi_p psi2s_p Dbar_s bplus_1 p4040_p DDstar_p |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| bplus_2 | 1.000 0.002 0.000 -0.000 -0.024 -0.001 -0.000 -0.000 0.000 0.001 0.000 -0.001 0.002 0.000 -0.000 0.024 -0.001 -0.000 |\n", + "| p4415_p | 0.002 1.000 -0.002 -0.008 0.157 -0.212 0.001 0.005 0.092 0.237 -0.000 0.020 -0.063 0.001 0.003 0.164 -0.108 0.008 |\n", + "| p4040_s | 0.000 -0.002 1.000 0.000 0.002 -0.000 -0.000 0.000 0.001 -0.004 -0.000 0.001 0.001 0.000 -0.000 -0.003 0.002 -0.000 |\n", + "| Ctt | -0.000 -0.008 0.000 1.000 -0.060 0.010 0.000 -0.000 -0.010 -0.010 -0.000 -0.015 0.012 0.000 0.000 0.030 -0.021 0.000 |\n", + "| bplus_0 | -0.024 0.157 0.002 -0.060 1.000 0.134 0.001 -0.003 -0.129 0.005 0.001 -0.107 0.068 0.003 0.003 -0.536 -0.098 0.000 |\n", + "| p4160_s | -0.001 -0.212 -0.000 0.010 0.134 1.000 -0.005 -0.001 0.142 -0.561 -0.001 0.164 0.027 0.005 -0.002 -0.033 0.010 -0.001 |\n", + "| DDstar_s | -0.000 0.001 -0.000 0.000 0.001 -0.005 1.000 0.000 0.001 0.005 0.000 -0.002 0.001 0.000 -0.000 -0.015 -0.001 0.001 |\n", + "| p4415_s | -0.000 0.005 0.000 -0.000 -0.003 -0.001 0.000 1.000 -0.001 -0.005 -0.000 -0.001 -0.000 -0.000 0.000 0.003 0.001 0.000 |\n", + "| Dbar_p | 0.000 0.092 0.001 -0.010 -0.129 0.142 0.001 -0.001 1.000 0.016 -0.001 0.274 0.209 0.000 0.003 0.371 0.160 0.003 |\n", + "| p4160_p | 0.001 0.237 -0.004 -0.010 0.005 -0.561 0.005 -0.005 0.016 1.000 0.000 -0.059 -0.079 -0.002 0.004 0.216 -0.119 0.007 |\n", + "| p3770_s | 0.000 -0.000 -0.000 -0.000 0.001 -0.001 0.000 -0.000 -0.001 0.000 1.000 0.003 0.000 -0.000 0.000 -0.002 0.001 0.000 |\n", + "| p3770_p | -0.001 0.020 0.001 -0.015 -0.107 0.164 -0.002 -0.001 0.274 -0.059 0.003 1.000 -0.027 -0.003 0.005 0.243 0.114 0.007 |\n", + "| jpsi_p | 0.002 -0.063 0.001 0.012 0.068 0.027 0.001 -0.000 0.209 -0.079 0.000 -0.027 1.000 -0.003 0.003 -0.264 0.078 0.007 |\n", + "| psi2s_p | 0.000 0.001 0.000 0.000 0.003 0.005 0.000 -0.000 0.000 -0.002 -0.000 -0.003 -0.003 1.000 0.000 0.005 0.001 0.000 |\n", + "| Dbar_s | -0.000 0.003 -0.000 0.000 0.003 -0.002 -0.000 0.000 0.003 0.004 0.000 0.005 0.003 0.000 1.000 -0.006 0.003 0.000 |\n", + "| bplus_1 | 0.024 0.164 -0.003 0.030 -0.536 -0.033 -0.015 0.003 0.371 0.216 -0.002 0.243 -0.264 0.005 -0.006 1.000 -0.094 0.001 |\n", + "| p4040_p | -0.001 -0.108 0.002 -0.021 -0.098 0.010 -0.001 0.001 0.160 -0.119 0.001 0.114 0.078 0.001 0.003 -0.094 1.000 0.006 |\n", + "| DDstar_p | -0.000 0.008 -0.000 0.000 0.000 -0.001 0.001 0.000 0.003 0.007 0.000 0.007 0.007 0.000 0.000 0.001 0.006 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 3.325119189670308}), (, {'error': 1.7815174671180474}), (, {'error': 1.9926966547547778}), (, {'error': 0.5114351017394924}), (, {'error': 0.14819864700462704}), (, {'error': 2.4730090801780316}), (, {'error': 0.5456802813189129}), (, {'error': 1.4247502221049095}), (, {'error': 9.742999517381843}), (, {'error': 1.9168225450258651}), (, {'error': 2.9995956487093722}), (, {'error': 1.7105494289800758}), (, {'error': 0.7843308347897375}), (, {'error': 0.03332981923505862}), (, {'error': 0.5881037384446894}), (, {'error': 0.27508756809324075}), (, {'error': 1.8523591244884532}), (, {'error': 9.488064745244461})])\n" ] }, { @@ -1585,76 +1611,33 @@ "name": "stdout", "output_type": "stream", "text": [ - "Toy 1/2\n", - "Time taken: 2 min, 59 s\n", - "Projected time left: 2 min, 59 s\n", - "Toy 1: Generating data...\n", - "Toy 1: Data generation finished\n", - "Toy 1: Loading data...\n", - "Toy 1: Loading data finished\n", - "Toy 1: Fitting pdf...\n", - "------------------------------------------------------------------\n", - "| FCN = 241.4 | Ncalls=39 (39 total) |\n", - "| EDM = 1.08E-06 (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: 241.3777412895305\n", - "----------------------------------------------------------------------------------------------\n", - "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", - "----------------------------------------------------------------------------------------------\n", - "| 0 | p4040_s | 0.45 | 0.18 | | |0.00501244| 2.01499 | |\n", - "| 1 | p4160_s | 2.91 | 0.28 | | | 0.71676 | 3.68324 | |\n", - "| 2 | p4415_p | 3.1 | 1.4 | | |-6.28319 | 6.28319 | |\n", - "| 3 | bplus_1 | -0.885 | 0.000 | | | -2 | 2 | |\n", - "| 4 | Ctt | -0.022 | 0.107 | | | -0.5 | 0.5 | |\n", - "| 5 | DDstar_p | -1.8 | 1.3 | | |-6.28319 | 6.28319 | |\n", - "| 6 | Dbar_p | 4.3 | 1.0 | | |-6.28319 | 6.28319 | |\n", - "| 7 | p4415_s | 1.59 | 0.23 | | |0.126447 | 2.35355 | |\n", - "| 8 | p3770_p | -0.8 | 1.1 | | |-6.28319 | 6.28319 | |\n", - "| 9 | DDstar_s | -0.03 | 0.07 | | | -0.3 | 0.3 | |\n", - "| 10| bplus_2 | -2.130E-1 | 0.019E-1 | | | -2 | 2 | |\n", - "| 11| p3770_s | 3.59 | 0.25 | | |0.918861 | 4.08114 | |\n", - "| 12| Dbar_s | -0.08 | 0.06 | | | -0.3 | 0.3 | |\n", - "| 13| bplus_0 | 0.466 | 0.000 | | | -2 | 2 | |\n", - "| 14| p4160_p | -2.0 | 6.8 | | |-6.28319 | 6.28319 | |\n", - "| 15| p4040_p | -5.0 | 0.8 | | |-6.28319 | 6.28319 | |\n", - "----------------------------------------------------------------------------------------------\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| | p4040_s p4160_s p4415_p bplus_1 Ctt DDstar_p Dbar_p p4415_s p3770_p DDstar_s bplus_2 p3770_s Dbar_s bplus_0 p4160_p p4040_p |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "| p4040_s | 1.000 -0.001 0.131 0.000 -0.006 0.021 0.032 0.001 0.075 0.045 0.000 -0.003 0.041 -0.000 0.178 0.017 |\n", - "| p4160_s | -0.001 1.000 -0.007 0.000 0.001 -0.001 -0.002 0.001 -0.006 -0.003 0.000 0.000 -0.003 0.000 -0.011 -0.004 |\n", - "| p4415_p | 0.131 -0.007 1.000 0.001 -0.023 0.089 0.137 -0.025 0.305 0.180 0.001 -0.010 0.165 -0.000 0.738 0.079 |\n", - "| bplus_1 | 0.000 0.000 0.001 1.000 0.000 -0.000 -0.000 -0.000 -0.000 0.000 0.677 -0.000 0.000 0.450 0.001 0.000 |\n", - "| Ctt | -0.006 0.001 -0.023 0.000 1.000 -0.003 -0.004 -0.000 -0.012 -0.009 0.000 0.000 -0.008 0.000 -0.032 -0.003 |\n", - "| DDstar_p | 0.021 -0.001 0.089 -0.000 -0.003 1.000 0.005 0.000 0.052 -0.034 0.000 -0.001 0.022 -0.000 0.116 0.012 |\n", - "| Dbar_p | 0.032 -0.002 0.137 -0.000 -0.004 0.005 1.000 0.001 0.078 0.043 0.000 -0.002 -0.030 -0.000 0.180 0.019 |\n", - "| p4415_s | 0.001 0.001 -0.025 -0.000 -0.000 0.000 0.001 1.000 0.003 0.001 -0.000 -0.000 0.001 -0.000 0.009 0.001 |\n", - "| p3770_p | 0.075 -0.006 0.305 -0.000 -0.012 0.052 0.078 0.003 1.000 0.103 0.000 0.002 0.092 -0.000 0.420 0.039 |\n", - "| DDstar_s | 0.045 -0.003 0.180 0.000 -0.009 -0.034 0.043 0.001 0.103 1.000 0.000 -0.003 0.052 -0.000 0.252 0.026 |\n", - "| bplus_2 | 0.000 0.000 0.001 0.677 0.000 0.000 0.000 -0.000 0.000 0.000 1.000 -0.000 0.000 0.190 0.002 0.000 |\n", - "| p3770_s | -0.003 0.000 -0.010 -0.000 0.000 -0.001 -0.002 -0.000 0.002 -0.003 -0.000 1.000 -0.003 -0.000 -0.014 -0.001 |\n", - "| Dbar_s | 0.041 -0.003 0.165 0.000 -0.008 0.022 -0.030 0.001 0.092 0.052 0.000 -0.003 1.000 -0.000 0.228 0.023 |\n", - "| bplus_0 | -0.000 0.000 -0.000 0.450 0.000 -0.000 -0.000 -0.000 -0.000 -0.000 0.190 -0.000 -0.000 1.000 -0.000 0.000 |\n", - "| p4160_p | 0.178 -0.011 0.738 0.001 -0.032 0.116 0.180 0.009 0.420 0.252 0.002 -0.014 0.228 -0.000 1.000 0.114 |\n", - "| p4040_p | 0.017 -0.004 0.079 0.000 -0.003 0.012 0.019 0.001 0.039 0.026 0.000 -0.001 0.023 0.000 0.114 1.000 |\n", - "--------------------------------------------------------------------------------------------------------------------------------------------------------------\n", - "Hesse errors: OrderedDict([(, {'error': 0.1817980073330014}), (, {'error': 0.28105296332603724}), (, {'error': 1.4460585352975572}), (, {'error': 0.00045254152319595953}), (, {'error': 0.1070128591631859}), (, {'error': 1.274894582621453}), (, {'error': 0.9863367274899124}), (, {'error': 0.22635340269660909}), (, {'error': 1.0686131922004622}), (, {'error': 0.06595112952400507}), (, {'error': 0.0019374367701227024}), (, {'error': 0.24635197699053668}), (, {'error': 0.06369342157651345}), (, {'error': 4.949731518788525e-05}), (, {'error': 6.846065392563649}), (, {'error': 0.7735769115033309})])\n", - "Toy 2/2\n", - "Time taken: 5 min, 55 s\n", - "Projected time left: \n" + "Toy 1/10\n", + "Time taken: 4 min, 1 \n", + "Projected time left: 36 min, 10 s\n", + "Toy 1: Generating data...\n" + ] + }, + { + "ename": "KeyboardInterrupt", + "evalue": "", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mKeyboardInterrupt\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 37\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcall\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 38\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 39\u001b[1;33m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 40\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 41\u001b[0m \u001b[0msam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\data.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(self, param_values, n)\u001b[0m\n\u001b[0;32m 640\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot set a new `n` if not a Tensor-like object was given\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 642\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_holder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 643\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initial_resampled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 644\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 927\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 928\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1150\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1151\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m 1153\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1154\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1329\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1332\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1333\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1335\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m 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1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1321\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1405\u001b[0m return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1409\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mKeyboardInterrupt\u001b[0m: " ] }, { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -1673,7 +1656,7 @@ "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", - "nr_of_toys = 2\n", + "nr_of_toys = 10\n", "nevents = int(pdg[\"number_of_decays\"])\n", "nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", @@ -1842,19 +1825,9 @@ }, { "cell_type": "code", - "execution_count": 41, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "2/2 fits converged\n", - "Mean Ctt value = 0.07076623985541536\n", - "Mean Ctt error = 0.16014230762364964\n" - ] - } - ], + "outputs": [], "source": [ "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n",