diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index c5be1ef..6f0aedc 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -690,6 +690,26 @@ ] }, { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_f_fit.normalization(obs_toy)" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ @@ -698,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -744,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -759,7 +779,7 @@ }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -788,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -804,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -820,12 +840,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (x_min, x_max), norm_range=False)\n", + "total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# inte = total_f.integrate(limits = (950., 1050.), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl/4500)\n", "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" @@ -833,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -849,32 +869,32 @@ "\n", "# print(name+\":\", inte_fl)\n", "# print(\"Increase am by factor:\", np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", - "# print(\"New amp:\", pdg[name][3]*np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# print(\"New amp:\", pdg[name][0]*np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", "\n", - "# # print(x_min)\n", - "# # print(x_max)\n", - "# # # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", - "# # total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", - "# # dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", - "# # draws_per_dim=1000000)\n", - "# # # _ = []\n", + "# print(x_min)\n", + "# print(x_max)\n", + "# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", + "# dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", + "# draws_per_dim=1000000)\n", + "# # _ = []\n", "\n", - "# # # for i in range(10):\n", + "# # for i in range(10):\n", "\n", - "# # # inte = total_f.integrate(limits = (x_min, x_max))\n", - "# # # inte_fl = zfit.run(inte)\n", - "# # # print(inte_fl)\n", - "# # # _.append(inte_fl)\n", + "# # inte = total_f.integrate(limits = (x_min, x_max))\n", + "# # inte_fl = zfit.run(inte)\n", + "# # print(inte_fl)\n", + "# # _.append(inte_fl)\n", "\n", - "# # # print(\"mean:\", np.mean(_))\n", + "# # print(\"mean:\", np.mean(_))\n", "\n", - "# # _ = time.time()\n", + "# _ = time.time()\n", "\n", - "# # inte = total_f.integrate(limits = (x_min, x_max))\n", - "# # inte_fl = zfit.run(inte)\n", - "# # print(inte_fl)\n", - "# # print(\"Time taken: {}\".format(display_time(int(time.time() - _))))\n", + "# inte = total_f.integrate(limits = (x_min, x_max))\n", + "# inte_fl = zfit.run(inte)\n", + "# print(inte_fl)\n", + "# print(\"Time taken: {}\".format(display_time(int(time.time() - _))))\n", "\n", "# print(pdg['NR_BR']/pdg['NR_auc']*inte_fl)\n", "# print(0.25**2*4.2/1000)" @@ -890,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -974,7 +994,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -983,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -992,7 +1012,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1001,7 +1021,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "scrolled": false }, @@ -1049,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1066,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1090,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1117,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1140,7 +1160,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1149,7 +1169,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1165,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1195,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1209,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1227,7 +1247,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1241,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1262,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1272,7 +1292,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1303,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1320,7 +1340,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1361,48 +1381,48 @@ "\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\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(0.014/3.)\n", - "sigma2 = ztf.constant(0.128/3.)\n", - "sigma3 = ztf.constant(0.548/3.)\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", + "# 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", @@ -1418,7 +1438,7 @@ "for part in sum_list_5:\n", " sum_ru_5 += part\n", "\n", - "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 100000., lambda: 0.)\n", + "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), ztf.constant(0.02)), lambda: 100000., lambda: 0.)\n", "\n", "# 6. Constraint on phases of Jpsi and Psi2s for cut out fit\n", "\n", @@ -1431,13 +1451,19 @@ "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", + "# 7. Constraint on Ctt with higher limits\n", + "\n", + "# Ctt_abs = tf.pow(tf.pow(Ctt, 2.), 0.5)\n", + "\n", + "# constraint7 = tf.cond(tf.greater_equal(Ctt_abs, 0.5), lambda: 100000., lambda: 0.)\n", + "\n", "# zfit.run(constraint6_0)\n", "\n", "# ztf.convert_to_tensor(constraint6_0)\n", "\n", "#List of all constraints\n", "\n", - "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4_0, constraint4_1, constraint4_2,\n", + "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4, #constraint4_0, constraint4_1, constraint4_2,\n", " constraint6_0, constraint6_1]" ] }, @@ -1450,7 +1476,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1520,7 +1546,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": { "scrolled": false }, @@ -1541,8 +1567,87 @@ "Toy 0: Loading data finished\n", "Toy 0: Fitting pdf...\n", "------------------------------------------------------------------\n", - "| FCN = 291.3 | Ncalls=753 (753 total) |\n", - "| EDM = 4.03E-05 (Goal: 5E-06) | up = 0.5 |\n", + "| FCN = 3.516E+05 | Ncalls=867 (867 total) |\n", + "| EDM = 6.79E-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 | False | False | True |\n", + "------------------------------------------------------------------\n", + "Function minimum: 351616.34467140574\n", + "----------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "----------------------------------------------------------------------------------------------\n", + "| 0 | DDstar_p | 1.95 | 0.29 | | |-6.28319 | 6.28319 | |\n", + "| 1 | p3770_s | 3.27 | 0.21 | | |0.918861 | 4.08114 | |\n", + "| 2 | bplus_0 | 0.479 | 0.018 | | | -2 | 2 | |\n", + "| 3 | Ctt | -0.44 | 0.19 | | | -1 | 1 | |\n", + "| 4 | bplus_2 | -0.23 | 0.08 | | | -2 | 2 | |\n", + "| 5 | Dbar_p | 5.30 | 0.26 | | |-6.28319 | 6.28319 | |\n", + "| 6 | p4040_p | 3.79 | 0.17 | | |-6.28319 | 6.28319 | |\n", + "| 7 | psi2s_p | 1.903 | 0.028 | | |-6.28319 | 6.28319 | |\n", + "| 8 | bplus_1 | -0.89 | 0.04 | | | -2 | 2 | |\n", + "| 9 | p4415_s | 1.09 | 0.18 | | |0.126447 | 2.35355 | |\n", + "| 10| p3770_p | -2.60 | 0.09 | | |-6.28319 | 6.28319 | |\n", + "| 11| DDstar_s | -0.300 | 0.016 | | | -0.3 | 0.3 | |\n", + "| 12| p4040_s | 1.02 | 0.16 | | |0.00501244| 2.01499 | |\n", + "| 13| p4160_p | -2.08 | 0.10 | | |-6.28319 | 6.28319 | |\n", + "| 14| p4415_p | 4.22 | 0.18 | | |-6.28319 | 6.28319 | |\n", + "| 15| Dbar_s | 0.300 | 0.013 | | | -0.3 | 0.3 | |\n", + "| 16| jpsi_p | 4.640 | 0.023 | | |-6.28319 | 6.28319 | |\n", + "| 17| p4160_s | 2.15 | 0.16 | | | 0.71676 | 3.68324 | |\n", + "----------------------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | DDstar_p p3770_s bplus_0 Ctt bplus_2 Dbar_p p4040_p psi2s_p bplus_1 p4415_s p3770_p DDstar_s p4040_s p4160_p p4415_p Dbar_s jpsi_p p4160_s |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| DDstar_p | 1.000 0.173 0.000 -0.171 -0.324 -0.080 0.106 0.003 0.389 -0.061 0.262 0.029 -0.140 0.217 -0.024 0.003 0.173 -0.100 |\n", + "| p3770_s | 0.173 1.000 0.045 -0.234 -0.148 0.058 -0.027 -0.423 0.095 0.000 -0.171 0.024 0.080 0.020 -0.021 0.025 -0.006 -0.011 |\n", + "| bplus_0 | 0.000 0.045 1.000 -0.008 -0.011 0.019 0.022 -0.007 -0.832 0.017 0.025 0.000 0.018 0.014 0.018 0.001 -0.064 0.035 |\n", + "| Ctt | -0.171 -0.234 -0.008 1.000 0.689 -0.326 -0.291 0.166 -0.184 0.221 -0.263 -0.004 0.368 -0.425 -0.073 0.009 0.130 0.258 |\n", + "| bplus_2 | -0.324 -0.148 -0.011 0.689 1.000 -0.134 -0.069 -0.013 -0.337 -0.054 -0.134 0.005 0.099 -0.085 0.177 0.004 0.052 0.123 |\n", + "| Dbar_p | -0.080 0.058 0.019 -0.326 -0.134 1.000 0.011 0.052 0.180 -0.008 0.366 0.002 -0.089 0.105 -0.044 0.015 0.302 -0.091 |\n", + "| p4040_p | 0.106 -0.027 0.022 -0.291 -0.069 0.011 1.000 -0.228 0.020 0.031 0.180 0.029 -0.241 0.163 0.099 0.022 -0.071 0.295 |\n", + "| psi2s_p | 0.003 -0.423 -0.007 0.166 -0.013 0.052 -0.228 1.000 0.051 0.010 0.058 0.024 0.009 -0.131 -0.105 0.024 0.004 -0.083 |\n", + "| bplus_1 | 0.389 0.095 -0.832 -0.184 -0.337 0.180 0.020 0.051 1.000 0.100 0.128 -0.005 0.010 0.019 -0.100 -0.005 0.105 0.001 |\n", + "| p4415_s | -0.061 0.000 0.017 0.221 -0.054 -0.008 0.031 0.010 0.100 1.000 -0.081 -0.000 0.154 -0.055 -0.131 -0.001 -0.039 0.309 |\n", + "| p3770_p | 0.262 -0.171 0.025 -0.263 -0.134 0.366 0.180 0.058 0.128 -0.081 1.000 0.019 -0.177 0.252 0.072 0.022 0.115 -0.082 |\n", + "| DDstar_s | 0.029 0.024 0.000 -0.004 0.005 0.002 0.029 0.024 -0.005 -0.000 0.019 1.000 0.003 0.038 0.026 -0.001 0.054 0.007 |\n", + "| p4040_s | -0.140 0.080 0.018 0.368 0.099 -0.089 -0.241 0.009 0.010 0.154 -0.177 0.003 1.000 -0.562 -0.246 -0.001 -0.036 0.024 |\n", + "| p4160_p | 0.217 0.020 0.014 -0.425 -0.085 0.105 0.163 -0.131 0.019 -0.055 0.252 0.038 -0.562 1.000 0.282 0.024 0.016 -0.187 |\n", + "| p4415_p | -0.024 -0.021 0.018 -0.073 0.177 -0.044 0.099 -0.105 -0.100 -0.131 0.072 0.026 -0.246 0.282 1.000 0.014 -0.019 -0.216 |\n", + "| Dbar_s | 0.003 0.025 0.001 0.009 0.004 0.015 0.022 0.024 -0.005 -0.001 0.022 -0.001 -0.001 0.024 0.014 1.000 0.040 0.004 |\n", + "| jpsi_p | 0.173 -0.006 -0.064 0.130 0.052 0.302 -0.071 0.004 0.105 -0.039 0.115 0.054 -0.036 0.016 -0.019 0.040 1.000 -0.068 |\n", + "| p4160_s | -0.100 -0.011 0.035 0.258 0.123 -0.091 0.295 -0.083 0.001 0.309 -0.082 0.007 0.024 -0.187 -0.216 0.004 -0.068 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.2850165410517804}), (, {'error': 0.2124961626399453}), (, {'error': 0.01829244045185119}), (, {'error': 0.19189240704670774}), (, {'error': 0.07723151076900314}), (, {'error': 0.26113738425875166}), (, {'error': 0.16593345938513693}), (, {'error': 0.02822480404964267}), (, {'error': 0.037964767888795214}), (, {'error': 0.17890980886132019}), (, {'error': 0.09336821683842733}), (, {'error': 0.016393727908621925}), (, {'error': 0.1615214697208751}), (, {'error': 0.0976483880480612}), (, {'error': 0.17860121118891303}), (, {'error': 0.01277429819793291}), (, {'error': 0.022652863795177502}), (, {'error': 0.15625965574324152})])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:166: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Toy 1/2\n", + "Time taken: 4 min, 18 s\n", + "Projected time left: 4 min, 18 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 = 7.032E+05 | Ncalls=914 (914 total) |\n", + "| EDM = 0.000618 (Goal: 5E-06) | up = 0.5 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", @@ -1552,92 +1657,66 @@ "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", - "Function minimum: 291.34660987562074\n", + "Function minimum: 703225.2607413743\n", "----------------------------------------------------------------------------------------------\n", "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", "----------------------------------------------------------------------------------------------\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", + "| 0 | DDstar_p | 1.96 | 0.22 | | |-6.28319 | 6.28319 | |\n", + "| 1 | p3770_s | 3.08 | 0.16 | | |0.918861 | 4.08114 | |\n", + "| 2 | bplus_0 | 0.475 | 0.013 | | | -2 | 2 | |\n", + "| 3 | Ctt | -0.39 | 0.14 | | | -1 | 1 | |\n", + "| 4 | bplus_2 | -0.24 | 0.05 | | | -2 | 2 | |\n", + "| 5 | Dbar_p | -4.08 | 0.22 | | |-6.28319 | 6.28319 | |\n", + "| 6 | p4040_p | 3.71 | 0.12 | | |-6.28319 | 6.28319 | |\n", + "| 7 | psi2s_p | 1.961 | 0.025 | | |-6.28319 | 6.28319 | |\n", + "| 8 | bplus_1 | -0.875 | 0.027 | | | -2 | 2 | |\n", + "| 9 | p4415_s | 1.09 | 0.13 | | |0.126447 | 2.35355 | |\n", + "| 10| p3770_p | 3.69 | 0.07 | | |-6.28319 | 6.28319 | |\n", + "| 11| DDstar_s | -0.300 | 0.011 | | | -0.3 | 0.3 | |\n", + "| 12| p4040_s | 1.02 | 0.12 | | |0.00501244| 2.01499 | |\n", + "| 13| p4160_p | -2.10 | 0.07 | | |-6.28319 | 6.28319 | |\n", + "| 14| p4415_p | 4.18 | 0.13 | | |-6.28319 | 6.28319 | |\n", + "| 15| Dbar_s | -0.300 | 0.008 | | | -0.3 | 0.3 | |\n", + "| 16| jpsi_p | -1.642 | 0.017 | | |-6.28319 | 6.28319 | |\n", + "| 17| p4160_s | 2.12 | 0.11 | | | 0.71676 | 3.68324 | |\n", "----------------------------------------------------------------------------------------------\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", + "| | DDstar_p p3770_s bplus_0 Ctt bplus_2 Dbar_p p4040_p psi2s_p bplus_1 p4415_s p3770_p DDstar_s p4040_s p4160_p p4415_p Dbar_s jpsi_p p4160_s |\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", + "| DDstar_p | 1.000 0.165 -0.005 -0.159 -0.333 -0.114 0.101 -0.005 0.405 -0.058 0.248 0.040 -0.141 0.222 -0.026 0.004 0.172 -0.100 |\n", + "| p3770_s | 0.165 1.000 0.043 -0.254 -0.143 0.044 0.025 -0.489 0.087 0.000 -0.162 0.023 0.074 0.047 -0.001 0.024 0.005 0.006 |\n", + "| bplus_0 | -0.005 0.043 1.000 -0.011 -0.013 0.020 0.025 -0.011 -0.821 0.016 0.023 0.000 0.015 0.017 0.020 0.001 -0.062 0.035 |\n", + "| Ctt | -0.159 -0.254 -0.011 1.000 0.685 -0.354 -0.282 0.181 -0.196 0.215 -0.292 -0.004 0.377 -0.430 -0.067 0.012 0.077 0.257 |\n", + "| bplus_2 | -0.333 -0.143 -0.013 0.685 1.000 -0.142 -0.063 -0.025 -0.347 -0.058 -0.155 0.004 0.105 -0.093 0.179 0.004 0.025 0.130 |\n", + "| Dbar_p | -0.114 0.044 0.020 -0.354 -0.142 1.000 0.005 0.093 0.195 -0.001 0.407 0.002 -0.085 0.119 -0.048 0.022 0.369 -0.099 |\n", + "| p4040_p | 0.101 0.025 0.025 -0.282 -0.063 0.005 1.000 -0.275 0.020 0.039 0.168 0.034 -0.245 0.148 0.095 0.026 -0.063 0.304 |\n", + "| psi2s_p | -0.005 -0.489 -0.011 0.181 -0.025 0.093 -0.275 1.000 0.070 0.015 0.075 0.038 0.031 -0.155 -0.129 0.038 0.015 -0.104 |\n", + "| bplus_1 | 0.405 0.087 -0.821 -0.196 -0.347 0.195 0.020 0.070 1.000 0.100 0.156 -0.004 0.001 0.034 -0.101 -0.005 0.138 -0.010 |\n", + "| p4415_s | -0.058 0.000 0.016 0.215 -0.058 -0.001 0.039 0.015 0.100 1.000 -0.078 -0.001 0.151 -0.055 -0.135 -0.001 -0.037 0.312 |\n", + "| p3770_p | 0.248 -0.162 0.023 -0.292 -0.155 0.407 0.168 0.075 0.156 -0.078 1.000 0.025 -0.185 0.260 0.061 0.029 0.164 -0.093 |\n", + "| DDstar_s | 0.040 0.023 0.000 -0.004 0.004 0.002 0.034 0.038 -0.004 -0.001 0.025 1.000 0.002 0.049 0.032 -0.002 0.069 0.008 |\n", + "| p4040_s | -0.141 0.074 0.015 0.377 0.105 -0.085 -0.245 0.031 0.001 0.151 -0.185 0.002 1.000 -0.559 -0.243 -0.003 -0.039 0.007 |\n", + "| p4160_p | 0.222 0.047 0.017 -0.430 -0.093 0.119 0.148 -0.155 0.034 -0.055 0.260 0.049 -0.559 1.000 0.277 0.030 0.040 -0.183 |\n", + "| p4415_p | -0.026 -0.001 0.020 -0.067 0.179 -0.048 0.095 -0.129 -0.101 -0.135 0.061 0.032 -0.243 0.277 1.000 0.017 -0.023 -0.205 |\n", + "| Dbar_s | 0.004 0.024 0.001 0.012 0.004 0.022 0.026 0.038 -0.005 -0.001 0.029 -0.002 -0.003 0.030 0.017 1.000 0.051 0.003 |\n", + "| jpsi_p | 0.172 0.005 -0.062 0.077 0.025 0.369 -0.063 0.015 0.138 -0.037 0.164 0.069 -0.039 0.040 -0.023 0.051 1.000 -0.078 |\n", + "| p4160_s | -0.100 0.006 0.035 0.257 0.130 -0.099 0.304 -0.104 -0.010 0.312 -0.093 0.008 0.007 -0.183 -0.205 0.003 -0.078 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" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:160: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + "Hesse errors: OrderedDict([(, {'error': 0.21976002920084792}), (, {'error': 0.15829775347665453}), (, {'error': 0.013027835592936077}), (, {'error': 0.14012275276008618}), (, {'error': 0.0548143444334922}), (, {'error': 0.21679058977592525}), (, {'error': 0.11774283300825683}), (, {'error': 0.02483027542093197}), (, {'error': 0.027345870305282904}), (, {'error': 0.12735002863519618}), (, {'error': 0.07047893274838923}), (, {'error': 0.010590941888212607}), (, {'error': 0.1157783313417895}), (, {'error': 0.07019659457672}), (, {'error': 0.12646052173735356}), (, {'error': 0.008140502524761783}), (, {'error': 0.016511357460833764}), (, {'error': 0.11145758454315047})])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "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 \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1336\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\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_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 1317\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1318\u001b[0m return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m 1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1321\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1405\u001b[0m return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1409\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "Toy 2/2\n", + "Time taken: 9 min, 6 s\n", + "Projected time left: \n" ] }, { "data": { - "image/png": "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\n", + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAD4CAYAAABMtfkzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO29eXzc1XX3/z4zo9XWbsmrbAks7xgbjIGYBAcINiFgkkLjJBCehD60CWmztE3glzbpQ0Of0KaQtAkQ/wItJYuhhBSHGBwCGEICxgZveJd3WV60WdauWe7zx/fOaDye0czIGs1IOu/Xyy/N3Lnfc+98Lc1nzrnnnivGGBRFURQlE3ClewKKoiiKEkRFSVEURckYVJQURVGUjEFFSVEURckYVJQURVGUjMGT7glkGuPGjTNVVVXpnoaiZDzvH2tl3NgcJhTlntXe7Q2w71QbU0vzKcrLStPsHMLn6PUH2H2ijcnFeZSOyU7rvEYi7777bqMxpvx87agoRVBVVcWmTZvSPQ1FyXhmfPNFPn9VNffeMOus9tpT7Vz30Ov866cWcvPFk9I0O4fwOZ48083l//QK//jxeXzm8mlpnddIREQOD4YdDd8pijIg/Mbgcck57W7b5g8EhnpK52AwBKfoEudBIKB7MzMZFSVFUZLGGIM/YHBFESVPSJSGelbnEjBgtShMLFWUMhkVJUVRkib4ue6Wc0XJlUmekjEIznyCc/WrJmU0uqakKErSBL0Nd5SvtZnkKRkIhe/c7rPDdy+9f5z//OMhfv5nV0T1+AYLr9dLXV0d3d3dKRtjKMnNzWXKlClkZaUmiUVFSVGUpAnYmpnRPsyDazeZ4SkRit8FPSWfFaUvr95Cjy/A6S5vSrPx6urqKCgooKqqConiWQ4njDE0NTVRV1dHdXV1SsbQ8J2iKEkT8pSifMh6MmTtJlhsOjhDl/20Cwpqtsdp6PL6UzqP7u5uysrKhr0gAYgIZWVlKfX6VJQURUkavwmG72KvKfnSLkrOT1eEpxQUy2wbe+zq9aV8LiNBkIKk+r2oKCmKkjTBdRlXP55SIM3H4gTHj5V9F3ze7U1/mFHpQ0VJUZSkCXpBHnfsfUpp95Tsz6AzJyKInCuW6Z5nprB+/Xo+9rGPAdDT08N1113HggULePrpp4d0HprooChK0vTnKYU8kjTnXvd5Sn1z9Lgk5CkF5+7LhDTBDGPz5s14vV62bNky5GMn5CmJyHIR2SMitSJyb5TXc0Tkafv6BhGpCnvtPtu+R0SWxbMpItXWxj5rM7u/MUSkTEReE5F2EflhjPmvEZH3E7sliqLEo781pb79QJmxphSOS/pEKahV3lGwcenQoUPMmjWLO++8k/nz53PrrbfS2dnJSy+9xKxZs7jqqqt47rnnADh16hS33347W7ZsYcGCBezfv39I5xrXUxIRN/Aj4CNAHbBRRNYYY3aGdbsLaDHGTBeRlcCDwCdFZA6wEpgLTAJ+JyIz7DWxbD4IPGyMWS0ij1nbj8YaA+gG/h6YZ/9Fzv8TQHtSd0VRlH7pL/vO5XLCZOnOvgsS7s25wzylIL4hTF3/P7/ewc76M4Nqc86kQr5909y4/fbs2cPjjz/OkiVL+PznP89DDz3Ej3/8Y1599VWmT5/OJz/5SQAqKir4yU9+wve+9z1eeOGFQZ1rIiTiKS0Gao0xB4wxvcBqYEVEnxXAk/bxs8C14vjMK4DVxpgeY8xBoNbai2rTXnONtYG1eUt/YxhjOowxb+KI01mIyFjga8B3EnifiqIkSPBzPNamU0+UD/+hJjLRARwR9UekivtGgacEUFlZyZIlSwC4/fbb2bRpE9XV1dTU1CAi3H777WmeoUMia0qTgaNhz+uAy2P1Mcb4RKQVKLPtb0dcO9k+jmazDDhtjPFF6R9rjMZ+5v6PwL8Cnf29QRG5G7gbYOrUqf11VRSF8PBd9NfDw2TpIhi+C5dNl0tC62HBtSbvEK4pJeLRpIrIVO7W1taMTFVPxFOKNuvI37ZYfQarPdF59E1IZAEw3Rjzq1h9QkaMWWWMWWSMWVReft7HgSjKiCcyWSCSTPCU+rLvIsJ3EYtNo2FNCeDIkSO89dZbAPziF7/guuuu4+DBg6E1o1/84hfpnF6IRESpDqgMez4FqI/VR0Q8QBHQ3M+1sdobgWJrI3KsWGPE4krgUhE5BLwJzBCR9f2+U0VREiIYGvO4on+EuFyS9lTrqOE7l5xTk28o15TSyezZs3nyySeZP38+zc3NfPWrX2XVqlXceOONXHXVVUyblhlnTCUSvtsI1IhINXAMJ3Hh0xF91gB3Am8BtwKvGmOMiKwBfi4iD+EkOtQA7+B4PefYtNe8Zm2stjaf72+MWJM2xjyKkyCBzdR7wRizNIH3qyhKHILrMLHCdx6XpH3zbCh8F+4piYRq8o2m7DsAl8vFY489dlbb8uXL2b179zl9ly5dytKlS4doZmcTV5Ts+s2XgHWAG3jCGLNDRO4HNhlj1gCPA0+JSC2O97LSXrtDRJ4BdgI+4B5jjB8gmk075DeA1SLyHWCztU2sMaytQ0AhkC0itwDXR2QHKooyiIQKssYI37kzwFOKrH0HZ3tKwanrPqXMIqHNs8aYtcDaiLZvhT3uBm6Lce0DwAOJ2LTtB3Cy8yLb+xujKs78DxElXVxRlIERWaonEndYQkG66POU+tpcrnMrOngzJHU9lVRVVfH++8Njq6aWGVIUJWn8/RxdAU6YLO2ekv3pOid8F/Sghq6iQz8rDcOOVL8XFSVFUZIm0M/mWXAO1Et39l20RAdXWPZdX/gutfPMzc2lqalpRAhT8Dyl3NzclI2hte8URUmaeOE7j8uVdlGKtk/JEyWs6E1x9t2UKVOoq6ujoaEhpeMMFcGTZ1OFipKiKEnTX+07cCpzp12UOLcgqyssrDhUFR2ysrJSdkrrSETDd4qiJM2w8pQi9ikFPaXg7DT7LrNQUVIUJWniVXTIhM2zkSfPwtkVHYIe0mjIvhtOqCgpipI0gTjhu0zYPBuIsk8pvCZf8Kd6SpmFipKiKEkT/ByPlX2XEZ6S/XlO+C7oKdn5jZaKDsMFFSVFUZImFL7rr8xQukUpysmz4ecpBWvejZbad8MFFSVFUZImXvjO2Tyb3g/7aCnh4Ztng8e1pzshQzkbFSVFUZKmv5NnIZjlNpQzOpdoBVmzPK5QuE7Dd5mJipKiKEmTSO27tHtKBDME+9qyXBI61C8UvtNEh4xCRUlRlKRJRJTSHRYLhDylvrYstyuUCh7ylDR8l1GoKCmKkjT+OEdXZLkl7WExE2WOHrfjKQUCJhTeU08ps1BRUhQlaQJxPKUstyvt4btoDlCW24U3EDgrXT3VZYaU5FBRUhQlaeLVvvO4XWn3lIhS+y7LLfj85izB1PBdZqGipChK0gTilBnKcvclFKSLvjJDfW2OWEZ6Shq+yyRUlBRFSZp4iQ5ZLlfaRSmU6BC2UynbenD+MC8u3ZUnlLNRUVIUJWmCn+kxRckjaV+r6Tu6oq/NY1PCw89QUk8ps1BRUhQlafz2Q72/oyt6MzR85/Obs9LV1VPKLBISJRFZLiJ7RKRWRO6N8nqOiDxtX98gIlVhr91n2/eIyLJ4NkWk2trYZ21m9zeGiJSJyGsi0i4iPwyzky8ivxGR3SKyQ0S+m/ztURQlGsEkBk/M7Lv0e0p9VcrDw3dCrz9w1tzSn5ChhBNXlETEDfwIuAGYA3xKROZEdLsLaDHGTAceBh60184BVgJzgeXAIyLijmPzQeBhY0wN0GJtxxwD6Ab+HvibKNP/njFmFrAQWCIiN8R7v4qixCfoacQWpfSvKcXylAB6fBq+y1QS8ZQWA7XGmAPGmF5gNbAios8K4En7+FngWnHyMFcAq40xPcaYg0CttRfVpr3mGmsDa/OW/sYwxnQYY97EEacQxphOY8xr9nEv8B6QuoPlFWUUEfwg7y8l3BcwoQ2s6SBq7TsrSt1ef6hNw3eZRSKiNBk4Gva8zrZF7WOM8QGtQFk/18ZqLwNOWxuRY8UaIy4iUgzcBLwS4/W7RWSTiGxqaGhIxKSijGq8AUOWW876wA8n2+20pzM0Fkp0CGvLsvPqsqKUl+VOu0ennE0iohTtty7yNy1Wn8FqT3Qe5yAiHuAXwL8ZYw5E62OMWWWMWWSMWVReXh7PpKKMevwBgyfWYUr0hcnSWdUhFL4Lm2bQU+rqtaKU7U772pdyNomIUh1QGfZ8ClAfq48VgSKguZ9rY7U3AsXWRuRYscaIxypgnzHm+wn0VRQlAbz+QMz1JOj78E+np9R3HPrZte+gz1PK9aS/HJJyNomI0kagxmbFZeMkLqyJ6LMGuNM+vhV41TjB5DXASps5Vw3UAO/Esmmvec3awNp8Ps4YMRGR7+CI11cSeJ+KoiSIz29CH/DRyAqF79L3gR+1Srjr7DWl3Cy3rillGJ54HYwxPhH5ErAOcANPGGN2iMj9wCZjzBrgceApEanF8V5W2mt3iMgzwE7AB9xjjPEDRLNph/wGsNoKymZrm1hjWFuHgEIgW0RuAa4HzgDfBHYD79nY9w+NMT9J/jYpihKOL2BCIbpoBD2ldIbGTJT6fFke5/FZoqThu4wirigBGGPWAmsj2r4V9rgbuC3GtQ8ADyRi07YfwMnOi2zvb4yqGFOP/VVOUZQB44sTvgu+lk5PyR+lPl9wHSy4ppSblf7UdeVstKKDoihJ43hKsUUp2xNcU0p/+M4VLSXc7lPS8F3moaKkxGTX8TN881fbQxWhFSWI1x8Irc9EI+iRZEKiw1nHoQcTHXr7wnf+NO+nUs5GRUmJyef/cyM/23CEE2e643dWRhX+gIm5cRb6stzS6ymdu6bkcUcmOqRfPJWzUVFSFCVpvP7+Ex2y3ZkTvos85A/CU8LdQHr3Uylno6KkKErS+AKB0Ad8NIKeUjrXa/oOIuxri9w8m5vtiJJ6SpmDipKiKEkTL3wX2jzry6zwXdCD64z0lDQDL2NQUVLiot8hlUjiJTqENs+m0VOKlhKeY9eQOnqc8prBNSW/JvNkDCpKiqIkTfyKDpngKTk/w0Up6Bm1dzuilJdlw3cqShmDipISF92BrEQSr6JDMCU8nQkEoZTwsGnmWhFqD3lKGr7LNFSUlLjod0glEl+g/4oO2Z70H10RWlMK95SC4bteK0qa6JBxqCgpipI0Pr+JU2YoM1PCg55RR09flXDQlPBMQkVJUZSk8QVMaN0oGlmZUGYoSkp4jp3XueE79ZQyBRUlRVGSxucP9JsSHky97s2wlHARIcfjCs0rKEpalDVzUFFSFCVpvHGy74Kp1z1pFKVoKeHQJ0QifWtMQ7HJ94+1jdzzs/fSKtTDARUlJSaadafEwhfof59SMEyWTlHqOw797N/k4NxyPK4hXft6YO0ufrP9OJuPtKR8rOGMipISE42yK7HwBwzu/o6ucLsQgR5bOSEd+KNUCYc+TynH4w5t8h2KzbP7TrUDsL+hI+VjDWdUlBRFSRqv35DVz5pScO0mnZ5StJRw6AvZ5XhcfZt8h8BTyrfp5ydau1I+1nBGRUmJiYbvlFj4/IF+N8+C44mkV5ScnxJjTSkny9W39uVN7TyNMaHSRg3tPSkda7ijoqQoStJ445w8C1hPKX3hu2gp4dBXaijH4w497k7xPLu9gdAG3Ya23pSONdxRUVIUJSmMMfT6AuTE85SyXCn3QPojWko4wNhcD+CIZtBr6k7xPM90e0OP1VPqn4RESUSWi8geEakVkXujvJ4jIk/b1zeISFXYa/fZ9j0isiyeTRGptjb2WZvZ/Y0hImUi8pqItIvIDyPmdamIbLfX/JtE+vGKoiRN8Bt/tiezw3fB5IXIP/uivCzAEaVgJl53ihMyznQ5ouR2Cac71VPqj7iiJCJu4EfADcAc4FMiMiei211AizFmOvAw8KC9dg6wEpgLLAceERF3HJsPAg8bY2qAFms75hhAN/D3wN9Emf6jwN1Ajf23PN77VRSlf3ptUkB8UUpv+C6YEh7pKQVFKS/bPeSeUmVJHqc7vXF6j24S8ZQWA7XGmAPGmF5gNbAios8K4En7+FngWuuVrABWG2N6jDEHgVprL6pNe8011gbW5i39jWGM6TDGvIkjTiFEZCJQaIx5yxhjgP8Ks6UoygAJbv7sr8wQOAkFafWUYqSEB0VpTLZnCD0lJ8mhsjSfM93e0HqXci6JiNJk4GjY8zrbFrWPMcYHtAJl/Vwbq70MOG1tRI4Va4z+5l0XZ94AiMjdIrJJRDY1NDT0Y1JRFG8SnlKqP+z7I3R0RUT4rjg/K/TY5RKyPa6UJzqEPKXSfIyBtm5fnCtGL4mIUrR1mEiZj9VnsNoTnUciczq30ZhVxphFxphF5eXl/ZgcnRij3+qUPoKeUnbclPD07lMKVXSIEKXSMdlAX7WJXE/qEzKCa0pTS/MBaO3SEF4sEhGlOqAy7PkUoD5WHxHxAEVAcz/XxmpvBIqtjcixYo3R37ynxJm3oihJEvwwTyjRIY3Zd/4YKeGLq0vJzXKx8jLnIygny53yta8z1jOqLHFE6XSXJjvEIhFR2gjU2Ky4bJzEhTURfdYAd9rHtwKv2nWcNcBKmzlXjZNs8E4sm/aa16wNrM3n44wRFWPMcaBNRK6wa1WfDbOlKMoACXpKOfFEKSvN+5RipIRPLMpj27eXccNFEwGnwkPKEx26vOR4XFQU5gDqKfWHJ14HY4xPRL4ErAPcwBPGmB0icj+wyRizBngceEpEanG8l5X22h0i8gywE/AB9xhj/ADRbNohvwGsFpHvAJutbWKNYW0dAgqBbBG5BbjeGLMT+ALwn0Ae8KL9pyjKeZBc9l36z1OKthMkfO65HnfqEx26vRTmZYWSLDQDLzZxRQnAGLMWWBvR9q2wx93AbTGufQB4IBGbtv0ATnZeZHt/Y1TFaN8EzIv2mqIoA6NvTcndb79071MKmHO9pGjkZg2BKHX5KMz1UGxFST2l2GhFByUumueghNOXEp5AmaE0Z98loEk2SzD1+5QK87IoVFGKi4qSoihJkWhKeCbsU0qkiEvuUCQ6dHkpzM0iN8tNbpZLRakfVJQURUmKxLPvXPgCBl+ajho35txjK6KRm+WiK+Weki/kJRXlZdGqa0oxUVFS4qLhOyWcYKJDvOy7PHt+UFeaQnj+QGLhu/xsD129qd3M2trlpdAWgi3Oy6ZF69/FREVJUZSkSDTRIT/b+RDu7E2fKMU78wmgINeT0goLxhgnfGc9peL8LM2+6wcVJSUuRg9GV8LoTTB8FzxpNV2i5PUH8CTgKo3N9dDWkzpR6vL68QUMhbmOKJXkq6fUHypKiqIkRa9NCkhUlDpS+IHfH/4EDiIEKMjx0OsLpCzZIViMtTDP8RxLxqgo9YeKkhIXXVNSwgmuKcVLCR+Tk97wnS9g8Ljif8SNtfPs6EmRKNlirH2ekhO+05qS0VFRUhQlKRI95C/kKaU4iSAW/oBJaPPsWCsW7SlaVwoWYy2wiQ4l+dn4AialIcPhjIqSEhf9PqeE05NglfBQokOKPJB4OJ5SAqJkPaW2ntQkHwT3JAVLDJXYKuUtHRrCi4aKkhIXDTMo4fR4/eRmueJuTE2/pxRIyFMKejAp85S6I0TJnufUohl4UVFRUhQlKbq8fvKy+k8Hh741pa50rSn5Ewzf2Xm2pyicFtwoWxRKCVdPqT9UlJS4qJ+khNPV6yc3AVFKv6dk4h7ZDmGeUqpEKZR954hS8JBBzcCLjoqSoihJkainlONx4ZL0rSl5E0x0KEzxcRKtXV7ys90hgdTwXf+oKClx0SUlJZxubyAhT0lEGJPtSeuaUiKJDiX52YhAU4rCaWe6vaHQHTip4S7R8F0sVJSUBFBVUvro9vpDde3ikZ/jTl/2XYJrSm6XUJKfTVN7T0rm0dp1tii5XEKxVnWIiYqSoihJ0WWz7xIhvZ5SYhUdAMrGZNOcIs+lNazuXZCS/KyUjTfcUVFS4qLhOyWcrt7E1pTA8ZTSVWbIFzC4E6joAE7yQVN7isJ3EZ4SwPjCXE6e6U7JeMMdFSVFUZKi25dY9h1AQU5WSitw94c/YMhK5OwKYNzYHJo6UhO+O93pDR2DHsQRpdSMN9xRUVLioo6SEk53Ep5SUV5W2k5Z9SWYfQfWU0pBOC0QMDS29zCuIOes9vGFuZxq69aN6VFISJREZLmI7BGRWhG5N8rrOSLytH19g4hUhb12n23fIyLL4tkUkWprY5+1mX0eY3xVRHaIyPsi8gsRyU3u9iiKEklXEokO6RQlfyCQ8JrSuLE5nO70Dnql8NYuL76AoXxspCjl4PUbXVeKQlxREhE38CPgBmAO8CkRmRPR7S6gxRgzHXgYeNBeOwdYCcwFlgOPiIg7js0HgYeNMTVAi7U9kDEmA38FLDLGzAPctp+SJPplTgkn0ZRwcI5rCJbZGWqc7LvEgkGTip3vq8dPD2ydx+sPRPV6Gm1GX3kUTwnQEF4UEvkfWwzUGmMOGGN6gdXAiog+K4An7eNngWvFKYy1AlhtjOkxxhwEaq29qDbtNddYG1ibtwxwDAAPkCciHiAfqE/g/SoR6CF/ShBjjM2+S9xT6vam7qyi/ki0ICvAlJJ8AI6d7kp6nEONHVz6jy9z33Pbz3mtoc0RnXHneEpWlNo02SGSRERpMnA07HmdbYvaxxjjA1qBsn6ujdVeBpy2NiLHSmoMY8wx4HvAEeA40GqM+W20Nygid4vIJhHZ1NDQEPNGKMpoJ1ghPJk1JSAtIbxEj64AmFKSB8CxluRF6bnNxzjT7WP1xqMcbe4867WGmJ6S8/yUZuCdQyKiFO1/NfKrc6w+g9We9BgiUoLjRVUDk4AxInJ7lL4YY1YZYxYZYxaVl5dH6zKq0fCdEiRYXDXRfUrB/Tln0iBKvkAg7kGEQSYU5eISqGvpjN85gg0Hmiiz9exe3nnyrNeOtzqiExShIBUFuYhA/QDDhSOZRH6z6oDKsOdTODcMFupjQ2VFQHM/18ZqbwSKrY3IsZId4zrgoDGmwRjjBZ4DPpDA+1UUJQbBjbDBCuDx6POUhj4tPNGKDgBZbhcTCnOpG4CndKipgw/PquCC8jGs33t2pOVIcyelY7IpyD07JTzb42JSUR5HmpMXwZFOIqK0EaixWXHZOMkCayL6rAHutI9vBV41zqrfGmClzZyrBmqAd2LZtNe8Zm1gbT4/wDGOAFeISL5de7oW2JXYbVHCUU9JCRKspF2QoCil01Pq9QXIdicWZgSoLh/D/ob2pMbo6vVz8kwPVWX5LJ1RwdsHms46quNocyeVpflRr51ams/hpo6kxhsNxBUlu37zJWAdzof6M8aYHSJyv4jcbLs9DpSJSC3wNeBee+0O4BlgJ/AScI8xxh/LprX1DeBr1laZtT2QMTbgJES8B2y373XVAO6RoiiW4EF4yXtKQy9KPf4AWZ7EPCWAGeML2HuynUAg8W9hQU9nWtkYPjyrnF5fgDdrG0OvH27qpNKuV0UyrSyfw03qKUWS0G+WMWYtsDai7Vthj7uB22Jc+wDwQCI2bfsB+rLnwtsHMsa3gW9Hu0ZJHM2+U4IEPaWxuYmJUnHoWIih3Y9jjKHXFyAngfOUgswcX0CX109dSxdTy6J7N5Ecsp7OtLJ8Zk0opCDHw8s7T/CROeM50+3lSHMnt106Jeq108rG0NTRS1u395zw3mhGKzoocdHwnRIk2fBdcX42rhQeCxELr9/5pc32JCFKEwoA2H3iTMLXBMNv00rHkO1xsXRWBa/sOoU/YNhxzLEzb0pR1GunWeFTb+lsVJQURUmYZMN3bpdQOiYntIl0qOj1O6nryYjSjPEFuF3C9mOtCV9zuKmTkvwsiuzBfR+dN4Gmjl5e3X2KP+5vxCWwsLI46rVVZWMAONCo60rhqCgpipIwyYbvAMaNzQ5tIh0qvHY/VXYS4bsxOR7mTCxk46HmhK853NTJVCsuAB+ZM57JxXn880u7eWbTURZXl1Kcnx312gsrxuBxCbuPJ+6ZjQZUlBRFSZigKI3JTlyUygtyaEjRsRCx6POUEs++A7isqpQtR0/Ta0UtHoeaOqgKW3/yuF1866Y51Da009DWw19eUxPz2hyPmwvLx7JLReksVJSUuOiakhKkvdtHfrY74f0/AOVjc2gcYk8pKCrJhO8ALqsqodsbYGvd6YTGqD/dxbSIlO9lcyfw4pc/yG/+6oMsmT6uXxuzJxaw63hbUnMc6agoKTFRLVIiae/xMTbB9aQgjqfUM6THNATLISVa0SHIkppxZLnlnMoM0Tja0knAQNW4Mee8NmtCIbMnFsa1MXtiISfOdNOi1cJDqCgpcdGUcCVIe48vqfUkcIqR9voCtA3hCbRBTyknSU+pMDeLKy8cx7odJ+KKaCjzruxcUUqUeZOdzLxEPLPRgoqSEhcN3ylB2rp9CaeDBxlX4Cz0nxrCYxoGkn0XZNnc8Rxu6oybhXeo0UnlrkpwT1M0FlQW43YJmw61DNjGSENFSYmJipESyenOXopiZJPFYmKRU9HgROvQFR8NrSklUWYoyE0XTyIvy81P3z7cb7/DTR0U5HgoHZPc/QhnTI6HeZMKeSdOxt+pM93c/+udo+JQQBUlJS6qTUqQlk4vJfnJVR8IHgsxkArcA2WgiQ7ghPBuWTiJNVvraepnf9Xek+1cUDEWp7TmwFlkM/76O3PqkfX7eeIPB3n8zQPnNdZwQEVJiYmuJSmRtHT2UpKkpzShMBe3SwZUgXug9PqdD/iBiBLAXVddQK8vwCPr90d93RjD+/WtzJ0UP5khHpdXl9LrC/BuPyG84JrTaAjzqSgpcRnKrCklc/H5A7R1+yhO0lPyuF1MLModUk+pqze5wwgjmV4xlj+5ZApPvXWYQ1EqLhxt7qKt28e8SdFLCCXDkunjyPa4+N2uU1FfDwQMe044aeO1p5KrYj4cUVFSYiL2/ESVJAXgtK30naynBE4Ibyg9pU577lN+9sBECeCvr59JTpaLv/nvrfgjKoe/dcCpBH7JtOglhJJhTI6HD1xYxiu7T0b9AnikuZPOXj/TK8bS1NFLa+fQV1wfSlSUlJho+E4JJ1jpO1lPCWBKSStHYvkAACAASURBVP6QilKX1wnf5Z2HKE0oyuUfbprLpsMtfPfFs49ie2XXKSYU5jJzfMF5zTPItbOdjL99UTyhYNsN8yYAsL9xZHtLKkpKXDR6p4CT5AAD85SmleZz4kw3HUO0V6nTHrR3Pp4SwCcumcxnr5zG///7g3xv3R4CAcOhxg5e2X2KmxdMOu8khyDL5o7H7RJ+tfnYOa8FQ3bXzh4PEDWcOJJIbsOBMqpQMVLCCVYdGIgo1YwfCzgfsBfHqJo9mARFKTfJ2neRiAjfvmku3V4/P3ytlpd2nAiVWvr8kurBmCoAFQW5LJ1RznPv1fE31888q4zT/oZ2KgpymD2xAJfAoRF+1IV6SkoCqDophPbIlIxJPnw3w4a59p4cmjpvnT2OcLiSqNEXC7dLePBP5vODlQsozc+malw+//m5xUwoyh2EmfZx66VTOHmmhzf2NZzVvr+hnQvLx5LjcTOpOG/EH6GunpISE5UiJZxTtqhqeUFO0tdOK3MOwYu2ZpIKOr3+8w7dhSMirFgwmRULJg+azUiumV1BRUEOP/n9AT48swJwMu9qT7azYuEkwDmDST0lZdSjYTwF4OSZbkrys8gZQEjM7RIuLB8bSm1ONV29/vNKckgHOR43d11VzR9qm9h61NmXdKCxnbYeHxdPcUKe08ryR7ynpKKkxEU1SQHHUxpfOPCQ1eyJBeyobx2SfW+dvT7ys4ZfIOjTl0+lKC+L7764G2MMG+1m2YVTHVGqKhvD6U5vKBNyJJKQKInIchHZIyK1InJvlNdzRORp+/oGEakKe+0+275HRJbFsyki1dbGPmsz+zzGKBaRZ0Vkt4jsEpErk7s9iqIEOXWme0ChuyCXTC2hsb2Xo82pTw3v6PGTnzO8PCWAgtws/mbZTN460MTP3znCmi31TC3N58JyJ1Fkmi3+OpJDeHFFSUTcwI+AG4A5wKdEZE5Et7uAFmPMdOBh4EF77RxgJTAXWA48IiLuODYfBB42xtQALdZ20mPYa34AvGSMmQVcDJy92UBJCA3fKQAnz5yfp3TptBIA3j2S+HHjA6W1y0txXvIJGZnApxdP5YM14/jmr97nrQNN3HHFtFDqebU9u2kkh/AS8ZQWA7XGmAPGmF5gNbAios8K4En7+FngWnHu4gpgtTGmxxhzEKi19qLatNdcY21gbd4ykDFEpBD4EPA4gDGm1xijh5YkgYqREiQQMDS091BxHp7SjPEFjM3xDEn9ttNdvRQNU1Fyu4Qf33Epf7tsJn9342w+f1Vf6nllaT4ifcdmjEQSCbpOBo6GPa8DLo/VxxjjE5FWoMy2vx1xbTB9JZrNMuC0McYXpX+yY3QBDcB/iMjFwLvAl40x53zFEJG7gbsBpk6dGus+jFq09p3S0N6DP2DOKw3a7RIuqyrhzdpGjDGDtvE0Gq2dXooHsJ8qU8jP9nDPh6ef056b5WZiYe6o95Si/eZEfkrF6jNY7QMZwwNcAjxqjFkIdADnrIcBGGNWGWMWGWMWlZeXR+sySlExUhyONDvfzKeWDvxAO4BrbDmdAymsSuAPGM50+ygcpp5SPKaVjeHQKBelOqAy7PkUoD5WHxHxAEVAcz/XxmpvBIqtjcixBjJGnTFmg21/FkeklCRRaVKONA2SKM1y9t+8suvkec8pFmds4djhuqYUj6px+RwezYkOwEagxmbFZeMkFayJ6LMGuNM+vhV41TgxnzXASps5Vw3UAO/Esmmvec3awNp8fiBjGGNOAEdFZKa95lpgZwLvV4lAo3fKkeZORJzCqufD5OI85k0u5H82R36vHTyC1cyH65pSPKaVjaGpo5cz3SOzWnhcUbLrO18C1uFkrz1jjNkhIveLyM222+NAmYjUAl/DhsmMMTuAZ3DE4CXgHmOMP5ZNa+sbwNesrTJrO+kx7DV/CfxMRLYBC4B/SvYGKYoCR5s7mVSUN+BD88L500WV7Dx+hvePtQ7CzM7l1Bnn2PXzSV/PZKqCaeEjtDBrQrvLjDFrgbURbd8Ke9wN3Bbj2geABxKxadsP4GTnRbYPZIwtwKJo1yiJo0dYKIebO6kszRsUWysunswDv9nFU28d5sFb5w+KzXBOWFGaOMi16TKFYB3B3cfbmD8l9cVthxqt6KDERMN2CjjZl3tPtlFTMThnBxXlZ7Hyskp++V4dR5sHf23kpBWl8SNUlKrKxjA2x8P2FHma6UZFSYmPitOopr61m7ZuHzMnDI4oAXxh6XRcLuGf1+0ZNJtBTrT2kJ/tpiBn+JUZSgSXS5gzqVBFSRl9mIifyuhkz4kzAMwaRFGaUJTLF5deyK+31vO7nYObiXe0pZNJxXkp3QeVbi6aXMSu42fw+QPpnsqgo6KkKEq/7DruVPaeMYiiBPDFpdOZNaGAv/7vrRwcxEX7/afamW5rxY1ULppcRI8vQG3DyDsaXUVJiYuuLY1utte1MrU0n8LcwU2xzva4WHXHIlwCdz7xzqCsL/X6Ahxu7mR6xcgWpeDpve8dHnmV01SUFEWJiTGGTYdbQsVUB5upZfk88b8uo7XLy62P/ZF3Dp5fsda9J9vwB8yge3WZRlVZPuUFObxzsCndUxl0VJSUuGhK+OjlaHMXje09KRMlgIVTS3jmz68kN8vNylVv8Z0XdtLaNbCNoUFRu6wqdfPNBESExdWlbDjYPOJqU6ooKTEZab/sSvJsPOR8yKdSlABmTijgN3/1Qf50USWP/+EgS//lNf7tlX00tfckZefN2kYqS/OYWDQ4e6oymcurSzne2k1dS+rPpxpKVJSUuKg2jV7W721g3NgcZo5PfThsbI6H7/7JfF74y6tYUFnMQy/v5QPffZVvPLstoeoPzR29vLG3gY/Om5jyuWYCi6tLAc475JlpjMxEfmVQUU0anfj8AV7fc4plcyfgcg1devXcSUX8x+cWs+9kG0/84SC/2nyMpzcd5ZKpxXz2yipuuGgCOZ5zT5V94s2D+AKGP7l0ypDNNZ3MqCigJD+LP+5vGlHvWT0lJSYqRqObjYdaONPt49rZFWkZv2Z8Af/3E/PZcN91/N2Ns2nu6OUrT29hyXdf5V/W7ab+dF/YasOBJn78xn5uunhSqAzPSMflEpZMH8fv9zWMqFC7ekpKXEbSL7ySOM+9V8eYbDcfmpHeM8aK8rP4sw9ewOeXVPP72kaeeusQj6zfz6Pr93Pd7PF8+vKpfOXpLUwtzec7K+alda5DzYdmlPPCtuPsPtHG7ImF6Z7OoKCipMREtWj00tHj4zfbj3PT/EnkZ2fGx4TLJVw9o5yrZ5RztLmTn204wn/84SC/3XmSbI+L5++5jKL8kXlcRSw+VON8YXhjb8OIESUN3ylxUW0afTz3Xh2dvX5uW5SZaxWVpfnce8MsfnKncwjAv9w6n2llY9I8q6FnQlEuM8cX8Ma+hnRPZdDIjK9AiqJkDF5/gMdeP8AlU4tTngp+vnywppyt375+xB7olwgfmjGOJ/94mM5eX8Z4teeDekpKfNRVGlU8s+kox0538cWl04dFUdPRLEjgrCv1+gNsODAyUsNVlJS4aEWH0UNLRy//sm4Pl1eXpi3rTkmOy6pKyc1y8frekRHCU1FSYqJZd6MLYwzfXrODtm4f/3Dz3GHhJSmQm+XmygvKeENFSRktqDaNDp7eeJQ1W+v56nU1IyaTa7Rw9YxyDjR2cKRp8E/yHWpUlJSYqBaNHt7Y28DfP/8+V00fxxeWTk/3dJQkuXqmE2p9fQRk4SUkSiKyXET2iEitiNwb5fUcEXnavr5BRKrCXrvPtu8RkWXxbIpItbWxz9rMHugY9jW3iGwWkRcSvy0KEFIl9ZRGNm8faOIvfvou0ysKeOT2S3APYUkhZXCoKstnamk+r+85le6pnDdxRUlE3MCPgBuAOcCnRGRORLe7gBZjzHTgYeBBe+0cYCUwF1gOPGJFoj+bDwIPG2NqgBZrO+kxwub2ZWBXYrdDCSegajTieXH7cT77xDtMKs7jyc9dNugH+SlDg4izsfiP+5vo8fnTPZ3zIhFPaTFQa4w5YIzpBVYDKyL6rACetI+fBa4VZ5V0BbDaGNNjjDkI1Fp7UW3aa66xNrA2bxngGIjIFOBG4CeJ3Q4lnEDQU0rvNJQU4PUH+O6Lu/nCz95j3qRC/vvPr6SiMDfd01LOg6tnlNPZ6+fdQy3pnsp5kYgoTQaOhj2vs21R+xhjfEArUNbPtbHay4DT1kbkWMmOAfB94OtAoL83KCJ3i8gmEdnU0DD8Y7KDRTAVXLPwRhb7G9pZueptHnt9P59aPJWf/+8rKBmTne5pKefJlReWkeWWYZ8anogoRQswR35KxeozWO1JjyEiHwNOGWPejfL62Z2NWWWMWWSMWVRent7ik5lEQLVoRNHj8/P93+3lhu//nn0n2/jBygX8309cRG7WucdAKMOPMTkeLqsqHfailEhNijqgMuz5FKA+Rp86EfEARUBznGujtTcCxSLisd5QeP9kx7gZuFlEPgrkAoUi8lNjzO0JvGeFPg9JtWl4EwgYfr2tnode3svhpk5WLJjE3904h/KCnHRPTRlkls4s55/W7uZEazcTioZnODYRT2kjUGOz4rJxkgrWRPRZA9xpH98KvGqcT7Q1wEqbOVcN1ADvxLJpr3nN2sDafH4gYxhj7jPGTDHGVFn7r6ogJYdG7YY3xhjW7znFx/79Tb68egv52R6eumsxP1i5UAVphHL1DCc1fDhvpI3rKRljfCLyJWAd4AaeMMbsEJH7gU3GmDXA48BTIlKL472stNfuEJFngJ2AD7jHGOMHiGbTDvkNYLWIfAfYbG0zkDGU8yOYfafiNLzwBwwvvX+Cx17fz/ZjrVSW5vH9Ty7g5osnDekJssrQM2P8WCYU5rJ+7yn+9LLK+BdkIAmVlDXGrAXWRrR9K+xxN3BbjGsfAB5IxKZtP4DNnotoT3qMsNfXA+tjva5Ep29NSVVpONDt9fPce8dY9cZ+DjV1UlWWzz99/CJuvXQK2R7dJz8aCKaGr33/OD5/AI97+P2/D/8654oyyqk/3cXPNxxh9cYjNLb3Mn9KEY985hKWzZ2gG2FHIVfPLOfpTUfZcvQ0i6pK0z2dpFFRUuKi4bvMwxjDH2qb+K+3DvG7XScxwLWzKvjckmo+cGGZFlMdxSyZPg63y0kNV1FSFCWlnOn28st363jq7cMcaOigJD+Luz90IZ+5fCqVpfnpnp6SARTlZbGwspjX9zbw19fPTPd0kkZFSYmLOkrpJRAwbDjYzH9vOsra94/T7Q2woLKYh/70Yj560UTdZ6Scw9UzyvnXl/fS2N7DuLHDK9NSRUlRMpTjrV388t06ntlUx5HmTgpyPPzJJVNYedlULppSlO7pKRnM1TMdUfr9vgY+vnBKuqeTFCpKSlx0TWno6PH5eWXXKZ7eeJTf72sgYODKC8r46kdqWD53InnZ6hUp8Zk3qYiS/Cze3NekoqSMPPQ49NRijGHn8TM8+24d/7P5GC2dXiYW5XLPh6dz26WVTC3TtSIlOVwu4YoLynj7QBPGmGGV+KKipChpov50F89vqedXm+vYe7KdLLdw/ZwJ3LZoCh+sKdd0buW8uPLCMl58/wRHm7uG1RcbFSUlLhq+GzzOdHt5afsJfrX5GG8fbMIYuHRaCf94yzw+dtFErdatDBpXXlAGwFsHGplaNjXNs0kcFSVFSTFef4DX9zTwqy3H+N3Ok/T4AlSV5fOVa2dwy8JJTCsbk+4pKiOQ6RVjGTc2h7f2N/HJy1SUlBGEOkrJY4xhy9HT/GrzMV7Ydpzmjl5Kx2Sz8rJKblk4mQWVxcMqzq8MP0SEKy4o5a1htq6koqQog8jek238ems9v95az6GmTnI8Lq6bM55PLJzMh2aUkzUMa5Epw5crLyzjhW3HOdjYwQXlY9M9nYRQUVLioifP9s/Bxg5e2FrPr7fVs/dkOy6BD1w4ji8unc7yiyZQmJuV7ikqo5S+daUmFSVFGckcO93FC1vreWHbcbYfawXgsqoS7l8xlxvmTdTzipSMoHrcGMYXOutKn7l8WrqnkxAqSkpUAnoW+jmcautm7bbj/Hrbcd493ALAxVOK+LsbZ/PRiyYyqTgvzTNUlLMREa68oIw3a4fPupKKkhIVbyAQejyao3fNHb289P4Jfr21PpTCPWtCAX+7bCYfmz9RM+eUjOeKC8r4ny317G9oZ3pFQbqnExcVJSUqXv/oVaL60138dscJ1u04yTuHmvEHDBeMG8NfXlPDTfMnUjM+8/+wFSXIZdXO8RWbDrWoKCnDF68vzFMaBUnhtafaWbfjBL/dcYKtdc4a0fSKsfzF1Rdww7yJzJ1UOCxCH4oSyQXjxlA6JptNh1tYuTjz9yupKI1wHl2/n0unlbC4OrnDvsLDdyMRYwzb6lpZt+ME63acYH9DB+CsEX19+UyWzZ3AhcMkW0lR+kNEuHRaCZsONad7KgmhojTCefCl3QAc+u6NSV0XHr4bKWtKPn+Adw4189sdJ/ntjhPUt3bjdgmXV5fy2SuruH7ueCYWabKCMvK4rKqEl3eepKGtJ+MzQxMSJRFZDvwAcAM/McZ8N+L1HOC/gEuBJuCTxphD9rX7gLsAP/BXxph1/dkUkWpgNVAKvAfcYYzpTXYMEam0/ScAAWCVMeYHyd6g4YzXP3Bvp9vrDz0ezqLU7fXz+32NrNtxgld2naSl00uOx8WHZpTztetncu2sCq03p4x4gseiv3u4meXzJqZ5Nv0TV5RExA38CPgIUAdsFJE1xpidYd3uAlqMMdNFZCXwIPBJEZkDrATmApOA34nIDHtNLJsPAg8bY1aLyGPW9qMDGMMH/LUx5j0RKQDeFZGXI+Y9omnv9g342o6egV+bbs50e3lt9ynW7TjB+j0NdPb6Kcj1cO2sCpbNncDVM8vJz9YggTJ6mDepiByPi42HWoa/KAGLgVpjzAEAEVkNrADCP9xXAP9gHz8L/FCcVeEVwGpjTA9wUERqrT2i2RSRXcA1wKdtnyet3UeTHcMY8xZwHMAY02ZtT46Y94im/TyEpaMnzFMajMmkmFNt3by88yTrdpzkrf2NeP2G8oIcPr5wMsvmTuCKC8rI9miJH2V0ku1xcXFl8bBYV0pElCYDR8Oe1wGXx+pjjPGJSCtQZtvfjrh2sn0czWYZcNoY44vSfyBjACAiVcBCYEO0NygidwN3A0ydmvnZKYnSFRaCS5bO3sz3lI40dYYSFd490oIxMK0sn88tqWbZ3PEsrCzBpWcSKQrgrCv9+PUDdPb6MjpSkMjMov1VR355jtUnVnu0r6z99R/IGM5FImOBXwJfMcacidIXY8wqYBXAokWLhoNjkBDnE4Lr6A1fU8qMW2KMYdfxtpAQ7T7RBsCciYV85doZLJs3npnjCzR1W1GisKiqlB+9tp8tR0/zgQvHpXs6MUlElOqAyrDnU4D6GH3qRMQDFAHNca6N1t4IFIuIx3pL4f2THkNEsnAE6WfGmOcSeK8jik4rLAM5wfRMlzf0OJ2SFAgY3jvSYoXoJEeaOxGBRdNK+LsbZ7Ns7gQqS4fPqZqKki4umVqCiLOJdriL0kagxmbFHcNJKvh0RJ81wJ3AW8CtwKvGGCMia4Cfi8hDOEkINcA7ON7NOTbtNa9ZG6utzecHMoZdb3oc2GWMeSjZGzMSCHpK2QM4LqGxvWewp5Mwvb4Af9zfyLodJ3l550ka23vIcgtLpo/jC0sv5LrZ4zM+rVVRMo2ivCxmji9gY4avK8UVJbt+8yVgHU769hPGmB0icj+wyRizBufD/ymbZNCMIzLYfs/gJBf4gHuMMX6AaDbtkN8AVovId4DN1jbJjiEiVwF3ANtFZIu18f8ZY9YO7FYNP4Ke0kAW+BvawkRpCFyljh4fr+9t4KX3T/Da7lO09fjIz3bz4ZkVXD93PB+eVaFHQCjKeXLptBKe31KPP2AGFEEZChJa7bIf5Gsj2r4V9rgbuC3GtQ8ADyRi07YfoC9DL7w9qTGMMW8Sfb1p1NBqQ3B5We6krz3V1kNBjoe2FKaGN3f08rtdzkbWN/Y10usLUJKfxQ0XTWDZ3AksmT6O3AHMXVGU6CyqKuFnG46w50QbcyYVpns6UcncFAzlvAmG4Apyk/9v3t/QTnX5GLbVtQ5q7btgsdOXdpzgnYPNBAxMKsrl04unsmzuBC6rKsGjp7MqSkpYNK1vE62KkjLkNLb3AuBPMnuu2+vncFMntyyYzDZbnPR8ONzUwYvvn+DF90+w9ehpAGoqxvLFpdNZNncC8yZrsVNFGQqmlORRUZDDpsMt3HFlVbqnExUVpRHMsdNdAPiSPIZioz2uYeHUYn75Xt2AygzVnmrjxe0nWPv+CXYddzLxL5pcxN8um8nyeVrsVFHSgYiwqKqETYda0j2VmKgojWD2nXT28fiSrIG3dvtxcjwurrywDEg8z+Focyf/s/kYz2+tp/ZUO+AsrGrqtqJkDpdOK2Xt9hOcaO1mQlFuuqdzDipKI5QjTZ0cb+0GwJvE0eaHmzp47r1jfOKSyaG1KF8/17d1e1mztZ7/2XyMjfbb1+LqUu5fMZdlcycwvjDzfukVZTSzaFoJAJsON/Ox+ZPSPJtzUVEagRhjeGR9LS6Bq2eUs9mu48TjTLeXL/18M9luF391bQ05bifzLfzAvyAHGtr5zz8e4pfv1tHR66emYixfXz6TFQsmM7lYj39QlExlzqRC8rLcbDrUoqKkpBafP8Bvth/n0fX72X2ijf/9wWp8ARPyYPpj38k2vvTzzRxobOfHd1zKxKK8UP273rDw3/HWLv71t3t57r06PC4XH7t4IndeWcX8KUWarKAow4Ast4uLK4t493BmriupKI0AfP4Az2+p54ev1XKwsYOairH8620X8/GFk3nwpd39nqvU1u3lkfX7efzNgxTkeHjif13GB2vKgb5KEEFP6YVt9dz33HZ6fAHuuqqaP7/6QsaN1coKijLcWDStlEdf309Hj48xOZklA5k1GyUpfP4Av9p8jB++Vsvhpk7mTCzksdsv4fo5E0LVsT1uibom5PUHWP3OEb7/u300dfTy8YWTue+GWVSErQG5XYKI4yn94p0j3Pfcdi6ZWszDn1zAtLIxQ/Y+FUUZXC6tKsH/mmHr0dN8YHpm1cFTURqGeINi9GotR5o7mTupkFV3XMpH5ow/J4TmcbnwBwzGGEQEYwxrt5/ge7/dw8HGDhZXl/IfN85m/pTic8YREbLdLjYfOc2j6/ezdGY5P77jUnI8WmVBUYYzl1QGkx1aVJSUgdPU3sPqjUf5+YYjHDvdxUWTi/jJZxdx7eyKmOs5WW6n3es31J/u5OvPbuOdQ83MGD827rXg1M17s7aRcWOz+cEnF6ogKcoIoCg/ixnjx7IpA9eVVJQyHH/AsOFgE89uquOFbcfp9QdYMr2M+1fM5ZpZ/QsKECrZs+FgE1/86Xsg8N1PXMRtiyoTKsjYZo9U//xV1RTla0FURRkpXDqtlBe2Zl5xVhWlDMQYw9a6VtZsqeeFbfWcauthTLabTy2u5I4rpzG9oiBhWx77y/bFn71H2dhsfvpnlzOlJPFNrAsqi9ly9DSfWTwt6fehKErmsmhaCb945wh7T7Yxe2Lm1MFTUcog9p5sY82Wen69rZ7DTZ1ku11cPbOcmy+exLWzKwZ0hHGW9ZTaun388NOXJCVIAP+2ciGdXp96SYoywlhU1beupKKkhDja3MmarfX8ems9u0+04RL4wIXjuGfpdJbNm0BR3vmJQY49S2lKSR4fqkl+QXNqmZYGUpSRyNTSfMaNzeHdQ83ccUXmREJUlNJAQ1sPa7cf5/ktx3jviFNt4ZKpxfzDTXP46PyJVBQMXmmeJdPHUVMxln+57WLd3KooSggRYdG0koxLdlBRGiK6ev2OEG2t5w+1jfgDhlkTCvj68pncNH9SyoqVVpbm8/LXrk6JbUVRhjeLqkp4accJTp7pzpg6lSpKKWZ/Qzs/ffswz75bR1u3jyklefzF1Rdw88WTmTkh8YQFRVGUwebSYHHWQy3cOH9immfjoKKUImpPtfODV/bxwrZ6PC7hhnkT+czlU1lcXaphNEVRMoK5k4rI8bjYdLhZRWmk0uPz88NXa3l0/X6yPS6+cPWFfG5JNeUFWiNOUZTMItvjYkFlMRsONKd7KiFciXQSkeUiskdEakXk3iiv54jI0/b1DSJSFfbafbZ9j4gsi2dTRKqtjX3WZvZgj5EqGtp6WLnqbf791VpuXjCJN77+Yb6+fJYKkqIoGcs1syrYefwMR5s70z0VIAFREhE38CPgBmAO8CkRmRPR7S6gxRgzHXgYeNBeOwdYCcwFlgOPiIg7js0HgYeNMTVAi7U92GMMOk3tPXzyx2+x6/gZHv3MJTz0pwu0graiKBnPDfOcsN1vth9P80wcEvGUFgO1xpgDxpheYDWwIqLPCuBJ+/hZ4FpxFk5WAKuNMT3GmINArbUX1aa95hprA2vzlsEcI7Hbkhy9vgCff3ITx0538dRdl3PDRZkRm1UURYnH1LJ8FleX8u+v7OOEPa06nSSypjQZOBr2vA64PFYfY4xPRFqBMtv+dsS1k+3jaDbLgNPGGF+U/oM1xjmIyN3A3fZpu4g0AY3R+sZj8QMDuSpjGccA78MIRO9FH3ovHEbcfZj4jwO+dBwwKDtwExGlaKlikQf0xOoTqz2ah9Zf/8Ec49xGY1YBq4LPRWSTMWZRtL6jCb0Pfei96EPvhYPehz7svagaDFuJhO/qgMqw51OA+lh9RMQDFAHN/Vwbq70RKLY2IscarDEURVGUDCURUdoI1NisuGycpII1EX3WAHfax7cCrxpjjG1faTPnqoEa4J1YNu01r1kbWJvPD+YYid0WRVEUJR3EDd/Z9ZsvAesAN/CEMWaHiNwPbDLGrAEeB54SkVoc72WlvXaHiDwD7AR8wD3GGD9ANJt2yG8ATTpK0wAAA9BJREFUq0XkO8Bma5tBHiMeq+J3GRXofehD70Ufei8c9D70MWj3QhxnQ1EURVHST0KbZxVFURRlKFBRUhRFUTIGFaUwhrosUToQkSdE5JSIvB/WVioiL9vSTi+LSIltFxH5N3s/tonIJWHX3Gn77xORO6ONlcmISKWIvCYiu0Rkh4h82baPxnuRKyLviMhWey/+j20ftJJfwwlbEWaziLxgn4/W+3BIRLaLyBYR2WTbUv/3YYzRf866mhvYD1wAZANbgTnpnlcK3ueHgEuA98Pa/hm41z6+F3jQPv4o8CLOXrArgA22vRQ4YH+W2Mcl6X5vSd6HicAl9nEBsBenHNVovBcCjLWPs4AN9j0+A6y07Y8BX7CPvwg8Zh+vBJ62j+fYv5scoNr+PbnT/f4GcD++BvwceME+H6334RAwLqIt5X8f6in1MWRlidKJMeYNnOzFcMJLOEWWdvov4/A2zh6yicAy4GVjTLMxpgV4Gafu4LDBGHPcGPOefdwG7MKpBDIa74UxxrTbp1n2n2HwSn4NG0RkCnAj8BP7fDBLn40EUv73oaLUR7RySpNj9B1pjDfGHAfnwxqosO2x7smIulc27LIQx0MYlffChqy2AKdwPjj2k2DJLyC85NdwvxffB74OBOzzhEufMbLuAzhfTH4rIu+KU4oNhuDvQ89T6iORckqjjWRLOw07RGQs8EvgK8aYMxL7AMYRfS+Ms7dvgYgUA78CZkfrZn+OyHshIh8DThlj3hWRpcHmKF1H9H0IY4kxpl5EKoCXRWR3P30H7V6op9THaC5LdNK62tifp2z7iC7hJCJZOIL0M2PMc7Z5VN6LIMaY08B6nHWBwSr5NVxYAtwsIodwwvfX4HhOo+0+AGCMqbc/T+F8UVnMEPx9qCj1MZrLEoWXcIos7fRZm1lzBdBqXfZ1wPUiUmKzb663bcMGG/t/HNhljHko7KXReC/KrYeEiOQB1+GssQ1Wya9hgTHmPmPMFOMUFl2J874+wyi7DwAiMkZECoKPcX6v32co/j7SneGRSf9wMkj24sTTv5nu+aToPf4COA54cb7F3IUTB38F2Gd/ltq+gnNQ4n5gO7AozM7ncRZwa4HPpft9DeA+XIUTRtgGbLH/PjpK78V8nJJe2+wHz7ds+wU4H6a1wH8DObY91z6vta9fEGbrm/Ye7QFuSPd7O497spS+7LtRdx/se95q/+0Ifh4Oxd+HlhlSFEVRMgYN3ymKoigZg4qSoiiKkjGoKCmKoigZg4qSoiiKkjGoKCmKoigZg4qSoiiKkjGoKCmKoigZw/8DzKwcax3qENcAAAAASUVORK5CYII=\n", "text/plain": [ "
" ] @@ -1656,7 +1735,7 @@ "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", - "nr_of_toys = 10\n", + "nr_of_toys = 25\n", "nevents = int(pdg[\"number_of_decays\"])\n", "nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", @@ -1759,11 +1838,17 @@ " \n", " if fitting_range == 'cut':\n", " \n", - " tot_sam_1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " \n", + " tot_sam_1 = total_samp[_1]\n", " \n", - " tot_sam_2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + " _2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + " \n", + " tot_sam_2 = total_samp[_2]\n", "\n", - " tot_sam_3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\n", + " _3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\n", + " \n", + " tot_sam_3 = total_samp[_3]\n", "\n", " tot_sam = np.append(tot_sam_1, tot_sam_2)\n", " tot_sam = np.append(tot_sam, tot_sam_3)\n", @@ -1807,7 +1892,7 @@ " plt.clf()\n", " plt.plot(test_q, calcs_test, label = 'pdf')\n", " plt.legend()\n", - " plt.ylim(0.0, 6e-6)\n", + " plt.ylim(0.0, 1.5e-6)\n", " plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))\n", "\n", " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", @@ -1825,25 +1910,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2/2 fits converged\n", + "Mean Ctt value = -0.41593044149928\n", + "Mean Ctt error = 0.16600757990339696\n", + "Sensitivy = 0.00011574576965860746\n" + ] + } + ], "source": [ "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", - "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))" + "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))\n", + "print('Sensitivy = {}'.format(np.mean(Ctt_error_list)**2*4.2/1000))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(tot_sam, bins = int((x_max-x_min)/7.))\n", + "\n", + "plt.show()\n", + "# _ = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + "\n", + "# total_samp[_]" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index 8ca162d..ee40e58 100644 --- a/__pycache__/pdg_const.cpython-37.pyc +++ b/__pycache__/pdg_const.cpython-37.pyc Binary files differ diff --git a/data/plots/toy_fit_cut_region0.png b/data/plots/toy_fit_cut_region0.png index ca0c853..a0acb13 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 457fe08..875a5ae 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 1e95827..acc4826 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 a2e2259..7638f08 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 e47b455..092fe1f 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 82ed102..8f99389 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 17f09c8..bcc2ceb 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 3eef7ad..85e7a02 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 031fd23..1deaf6a 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 new file mode 100644 index 0000000..6c2cc6e --- /dev/null +++ b/data/plots/toy_fit_cut_region9.png Binary files differ diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index 10cb0d2..b13a0f3 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 e75f319..bd43d79 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 58e8121..2f1168c 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 edb43bc..18b185e 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 51ae529..273190b 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 0a5d7e2..b430037 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 9181b3a..6fa2347 100644 --- a/pdg_const.py +++ b/pdg_const.py @@ -95,11 +95,11 @@ # after scaling - "rho": (775.26, 149.0, -0.22, 1.05), + "rho": (743.2, 149.0, -0.22, 1.05), "omega": (782.7, 8.5, 0.38, 6.8), - "phi": (1019.46, 4.25, 0.62, 19.2), + "phi": (1013.5, 4.25, 0.62, 19.2), "jpsi": (3096.1, 0.09, 1.63, 9897.0), "jpsi_auc": 0.2126825758464027, diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index c5be1ef..6f0aedc 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -690,6 +690,26 @@ ] }, { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "total_f_fit.normalization(obs_toy)" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ @@ -698,7 +718,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 12, "metadata": {}, "outputs": [], "source": [ @@ -744,7 +764,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [ { @@ -759,7 +779,7 @@ }, { "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAD4CAYAAABMtfkzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO29eXzc1XX3/z4zo92WZG3eZCPZFjYyGBMcGzBhsQmYbOZpoTF5SElCS9qGX54kXYAnTfI0CW1o0tCmIQuFJCQlGELS4hIHQ7CBsNkYDBgvsuVdeNEuWfss9/fH9440Gs8qS5rR6LxfL780c+d+z73ztTSfOeeee64YY1AURVGUdMCV6gkoiqIoShAVJUVRFCVtUFFSFEVR0gYVJUVRFCVtUFFSFEVR0gZPqieQbpSVlZmqqqpUT0NR0p59p06Tm+Vmbkn+sPaOXi9HW3uoqZhCbpY7RbNz2N/YRbbbxTml+fgChj0nOplVnEdpQXZK55WJvPHGG83GmPKztaOiFEZVVRXbt29P9TQUJe25+jvPc8HsIr5380XD2p9+9yR/8Z9v8NjnL2fxrKIUzc5hzb++yJySfP7jT5fR1j3ARd94lq9+tJZPr6xO6bwyERE5Mhp2NHynKMqI8AcMLjmz3W0bA4FxnlAUglN02Xn5A7o3M51RUVIUZUQEjBn8oA/FE/zwT4ON+caA2CkOimUazEuJjoqSoigjIhAwuORMURrySFLvKhkMYn0ltwTnlcoZKfHQNSVFUUZEwAx90IeSTh/+oZ6Sy34FH29Pyev10tDQQF9f37iOO1bk5uZSWVlJVlbWmNhXUVIUZUT4jRn8oA/FnUZrN4aQ8J2kZl4NDQ1MnTqVqqoqJIKITySMMbS0tNDQ0EB19dgki2j4TlGUEWFM5PBdWomSMYNCkKp59fX1UVpaOuEFCUBEKC0tHVOvT0VJUZQRETBEESXnZ9okOtjHIoJIahIdMkGQgoz1e1FRUhRlRERPCXc+VgLp4Ckx/EPULZIWHpwSHRUlRVFGRLSU8ODajS8NPvyNMYTO0OWStPDg0pHnn3+ej3zkIwD09/dzzTXXsHTpUh577LFxnYcmOiiKMiKip4Q7P9PBIwlNdABHMNPBg0t3duzYgdfr5a233hr3sRPylERkjYjUiUi9iNwV4fUcEXnMvr5VRKpCXrvbtteJyHXxbIpItbWx39rMjjWGiJSKyBYR6RKR70eZ/wYReTexW6IoSiIEzFDyQCjptEk1dE0JnLmlQ6r6eHP48GEWLVrErbfeypIlS7jxxhvp6enh6aefZtGiRVx++eX85je/AaCxsZFbbrmFt956i6VLl3LgwIFxnWtcT0lE3MD9wAeBBuB1EdlgjNkd0u02oM0Ys0BE1gH3Ah8XkVpgHbAYmAX8XkTOtddEs3kvcJ8xZr2I/Mja/mG0MYA+4CvA+fZf+Pz/COhK6q4oihIXvzFEWvMOVnRIi/AdZtiakitFiQ5B/uF/drH7eOeo2qydVcjXPro4br+6ujoeeughVq5cyWc+8xm++93v8uMf/5jNmzezYMECPv7xjwNQUVHBgw8+yHe+8x2eeuqpUZ1rIiTiKS0H6o0xB40xA8B6YG1Yn7XAw/bxE8BqcX4T1gLrjTH9xphDQL21F9GmvWaVtYG1eUOsMYwx3caYl3DEaRgiMgX4EvDNBN6noihJYIyJuHk2GNJLhzBZZE8p9fNKBXPmzGHlypUA3HLLLWzfvp3q6mpqamoQEW655ZYUz9AhkTWl2cCxkOcNwIpofYwxPhHpAEpt+2th1862jyPZLAXajTG+CP2jjdEcY+7fAP4F6In1BkXkduB2gLlz58bqqiiKxR9lTSm99imFZd+lONEhEY9mrAhP5e7o6EjLVPVEPKVIsw7/X43WZ7TaE53H0IRElgILjDH/Fa3PoBFjHjDGLDPGLCsvP+vjQBQl4zHG2H1KZ77mTquCrMNDjG6X4Penfl6p4OjRo7z66qsAPProo1xzzTUcOnRocM3o0UcfTeX0BklElBqAOSHPK4Hj0fqIiAcoAlpjXButvRkotjbCx4o2RjQuBS4WkcPAS8C5IvJ8zHeqKEpCBJ0gd4Q6Q2nlKREWvpPJmxJ+3nnn8fDDD7NkyRJaW1v54he/yAMPPMCHP/xhLr/8cs4555xUTxFILHz3OlAjItXAeziJC58I67MBuBV4FbgR2GyMMSKyAfiliHwXJ9GhBtiG83tyhk17zRZrY721+WSsMaJN2hjzQ5wECWym3lPGmKsSeL+KosQhKDjuSLXvUlRjLhKhBVnB2aeUDmtdqcDlcvGjH/1oWNuaNWvYu3fvGX2vuuoqrrrqqnGa2XDiipJdv7kD2AS4gZ8YY3aJyNeB7caYDcBDwC9EpB7He1lnr90lIo8DuwEf8DljjB8gkk075J3AehH5JrDD2ibaGNbWYaAQyBaRG4Brw7IDFUUZRYIZbLE8pbRICQ85ugJSv6akxCehzbPGmI3AxrC2r4Y87gNuinLtPcA9idi07QdxsvPC22ONURVn/oeJkC6uKMrI8MXylIIp4WmwdhPuKU3WMkNVVVW8++7E2KqpZYYURUma4Ad7rEP+0sNTihC+S8G8Yqw0TDjG+r2oKCmKkjRBUfLEOg49DTySM1LCU+Ap5ebm0tLSkhHCFDxPKTc3d8zG0Np3iqIkzVCiQ/TNs2lR0SFSQdZxLjNUWVlJQ0MDTU1N4zvwGBE8eXasUFFSFCVpEkp0SAdRImxNyTX+YcWsrKwxO6U1E9HwnaIoSRMz0UHSbPMsqQ3fKcmhoqQoStIE4iQ6iKTJmhLpkeigJI6KkqIoSRP0lDzuyLXT0sUjOaMga5rMS4mOipKiKEkTKyUc0ueEV6f2XcjRFZO4SvhEQUVJUZSkCYbAPBESHZz29CjnEz4Dt2j4Lt1RUVIUJWmC1RoiJTqA8+GfDinhmOHe3GQ+T2mioKKkKErSxEoJh/QpfBoIO7rCCSumbj5KfFSUFEVJmlgp4eCE79JiTYnhiQ4el+APOLtnO/u8vLQ/1hmhSipQUVIUJWkSSnRIA0/pjIKsLhkMPf7dr97hloe20nS6P0WzUyKhoqQoStLES3RIl9Rrw/Dsuyz30FrXW8faAejo9aZkbkpkVJQURUmaoLcRRZMcjyQdRMmEh+9c+Gzxu9wsZ/Kn+1SU0gkVJUVRkiZuSrg7XTwlhqmSxy14BwXVeaHfN84VWpWYqCgpipI0iSQ6pMMhf+Ep4VkuFz6b6BBsHVBRSitUlBRFSZpYte8AstwuvON9RkQEAmFHV2R5hsQyuNakopReqCgpipI0Q4f8Rf4ISRdRCi/I6nENzSt4FNRAGsxTGUJFSVGUpAmG76IlOnjc6ZLoMPzoiqyQNaVgu3pK6UVCoiQia0SkTkTqReSuCK/niMhj9vWtIlIV8trdtr1ORK6LZ1NEqq2N/dZmdqwxRKRURLaISJeIfD/ETr6I/FZE9orILhH5VvK3R1GUSMRLdMhypamn5A5ZUwp6SipKaUVcURIRN3A/cD1QC9wsIrVh3W4D2owxC4D7gHvttbXAOmAxsAb4gYi449i8F7jPGFMDtFnbUccA+oCvAH8TYfrfMcYsAi4CVorI9fHer6Io8Ymb6OBOj0SH8JTwLJfjKYVWD+9PA/FUhkjEU1oO1BtjDhpjBoD1wNqwPmuBh+3jJ4DV4vyPrwXWG2P6jTGHgHprL6JNe80qawNr84ZYYxhjuo0xL+GI0yDGmB5jzBb7eAB4Exi7g+UVZRIRL9HB43bhTYPwHTDMVfJYFfUHjGbfpSmJiNJs4FjI8wbbFrGPMcYHdAClMa6N1l4KtFsb4WNFGyMuIlIMfBR4Lsrrt4vIdhHZ3tTUlIhJRZnUxE10cMngJtVUYUxw7WiILCtKvoAZXA/r9/nHeWZKLBIRpUhfhcK/AkXrM1rtic7jDETEAzwKfM8YczBSH2PMA8aYZcaYZeXl5fFMKsqkx59IokOKw3fBerCusDJDAF5/YDDRwetLE49OARITpQZgTsjzSuB4tD5WBIqA1hjXRmtvBoqtjfCxoo0RjweA/caYf02gr6IoCeCPW9HBhTeQWk8pmIwxPCXceeLzm0FhHfCrp5ROJCJKrwM1NisuGydxYUNYnw3ArfbxjcBm4/jOG4B1NnOuGqgBtkWzaa/ZYm1gbT4ZZ4yoiMg3ccTrCwm8T0VREiReSnhWGlR0iBRiCa4pef2BwexAXVNKLzzxOhhjfCJyB7AJcAM/McbsEpGvA9uNMRuAh4BfiEg9jveyzl67S0QeB3YDPuBzxhg/QCSbdsg7gfVWUHZY20Qbw9o6DBQC2SJyA3At0Al8GdgLvGkzbb5vjHkw+dukKEoowUQHd4xEh9SvKTk/Q6c4GL4LmEFh9aZBlqAyRFxRAjDGbAQ2hrV9NeRxH3BTlGvvAe5JxKZtP4iTnRfeHmuMqihTj/wXoyjKWRG/ooMwkHJPaXg5IRiar88fGPSQtCBreqEVHRRFSZp4iQ5ZIZtUU0Wk4H6WJxi+Mxq+S1NUlBRFSZq4iQ4uV8rXlIIMC98FEx0CgcHwnda+Sy9UlBRFSZr4npKkvMzQ4JoSZ26e9fkNXushedVTSitUlBRFSZqg4GTFOOQv1QVZg2tKrtCU8JB9SkEPST2l9EJFSVGUpPH5DS4ZOr01HI/LhT9giLNrY0wJRMq+cw2tKQ2G79RTSitUlBRFSRpvIDAYCovEUOWE1IlSUBAjVXQY8AVCNs+qKKUTKkqKoiSNz28GkwYiMbh2k8IMvCFP6cw1pV7vUBUH9ZTSCxUlRVGSxueP7SkFy/mkh6c01Bb0lFSU0hcVJUVRksYbMIMf8JEYrMadwtBY0FNyRdg82zvgG2zT8F16oaKkKErS+PyBqHuUIDTLLZWJDmd6SjlZzpy7+4c8pVSnrivDUVFSYtLR4x1cEFaUID6/GRSeSGSFFD5NFUNVwofmmWMrOnT1h3hKGr5LK1SUlKj0DPi48OvP8I2ndqd6Kkqa4YTv4mffpXKvUqTzlHKz3MCQKOVmuVSU0gwVJSUqwRDHU++EH5+lTHac8F2M7DtXOqwpRQjfWU/pdJ8jSgXZHhWlNENFSVGUpPH6TdrvUxoshRTBU+q2nlJ+jlsTHdIMFSVFUZLGFwjEzL4b9JRSuE8p0nlKHpfgkqHwXUG2hwF/IKWVJ5ThqCgpipI0Pr+JGb4LHhGRytBYMHznDpmniJCb5R4UpbxsN8akdu1LGY6KkqIoSeONs3k2Jy1EyfnpCjsdN8fjoitkTQk0LTydUFFSFCVpvP7Y4bugKPWnQaJD+IntOZ4hTyk/21lj0mSH9EFFSVGUpPEFTMzNs9lBUfKmck3pzEQHcNLAg6I0JcfxlFSU0gcVJUVRksbrj71PKcfjeCD9Pn/UPmNN9PDd8DUlgH4VpbQhIVESkTUiUici9SJyV4TXc0TkMfv6VhGpCnntbtteJyLXxbMpItXWxn5rMzvWGCJSKiJbRKRLRL4fNq+LRWSnveZ7IuGOvKIoI8GXYPguHRIdwvMxQjfMFuTomlK6EVeURMQN3A9cD9QCN4tIbVi324A2Y8wC4D7gXnttLbAOWAysAX4gIu44Nu8F7jPG1ABt1nbUMYA+4CvA30SY/g+B24Ea+29NvPerKEp8fIHY+5SCNeZS6YEEs9HDv4sGvTgIWVNSUUobEvGUlgP1xpiDxpgBYD2wNqzPWuBh+/gJYLX1StYC640x/caYQ0C9tRfRpr1mlbWBtXlDrDGMMd3GmJdwxGkQEZkJFBpjXjVOcPnnIbYURTkLvP5AzPOUctypD4tF85SCggma6JCOJCJKs4FjIc8bbFvEPsYYH9ABlMa4Nlp7KdBubYSPFW2MWPNuiDNvAETkdhHZLiLbm5qaYphUFAXiF2Qd8pRSt6YUqfYdDPeUpuZmARq+SycSEaVIv3nhO82i9Rmt9kTnkciczmw05gFjzDJjzLLy8vIYJhVFAadSQ6zwXbY7jdaUwqYZTG4AKLSiNB4e3Yv7mvj0T7fRO5A6oZ4IJCJKDcCckOeVQHiFzsE+IuIBioDWGNdGa28Giq2N8LGijRFr3pVx5q0oygjwxjkO3eUSstySFuG78DWlqbmewceFeeOXEv7tTXVsqWti2+FYH1tKIqL0OlBjs+KycRIXNoT12QDcah/fCGy26zgbgHU2c64aJ9lgWzSb9pot1gbW5pNxxoiIMeYEcFpELrFrVX8aYktRlLMg3nHo4ITJUrlPKVpKeNA7gqHw3XiI0omOXgAONnWN+VgTGU+8DsYYn4jcAWwC3MBPjDG7ROTrwHZjzAbgIeAXIlKP472ss9fuEpHHgd2AD/icMcYPEMmmHfJOYL2IfBPYYW0TbQxr6zBQCGSLyA3AtcaY3cBfAj8D8oDf2X+Kopwl3kDsNSVw0sIH/KlcU4qc6BDqKeXZquFjXc3cGDN4XMbx9t4xHWuiE1eUAIwxG4GNYW1fDXncB9wU5dp7gHsSsWnbD+Jk54W3xxqjKkr7duD8SK8p8TExl+yUyYzPHyArRkUHcEQpLT2lvCFPKVh5YqzFs9frHwxlHm/vi9N7cqMVHZTo2D9qreqvhOLzBwiYoQ/0aGR7XGmypjS8vTDEUxqvckgdvd7Bxy3d/WM61kRHRUmJi9bBUEIJCk1OHFHK8bhTW2YowiF/MDx8l2/Dd73esZ1nZ69v8HFbtzdGT0VFSYmKOkhKJIJJAfE8pZyQcj6pIBi+c4ctKpUU5Aw+zs9xRKlnjNO0g55S5bQ8WnsGxnSsiY6KkhIVDdspkQiW5AndhBqJbHd6hO/CEx3mlxcAcE5pPtluFx6XDB6PPlYERam6rIC27gE96TYGCSU6KJMTTXRQIhFcf0nEU+pLaaJDtH1KWXxj7WKWzpmGiJCf7R43T6mqtIA/7G/mdL9vWGq6MoSKkqIoSRHMVEtkTSl0gX+8iVZmCOCTl1YNPs7P9tAzMLaeUmdQlMocL621a0BFKQoavlOiohEGJRL9ia4ppTwlPHL4Lpz8HDfd4+Yp5QPoulIMVJSUqAxpkqbfKUMkKkq5WW760vCQv3AKsj1jXo+uo9fL1BwPZVOcJIt2FaWoqCgpURlajFWXSRliIMGU8Lxsd0qLj0bbpxROfrZ7zBMdOvu8FOZlUVKQDUCrpoVHRUVJUZSkSHSfUn6Wm+7+dCgzFMdTyvGMeaJDZ6+XorwspllRautWTykaKkpKVIbWlDR8pwwx5CnFTgnPz/HQ6/UPbmIdbxIN3+Vlu+ke40SHDitKBdlustyia0oxUFFSFCUpEt08GzzVNVXrSokmOhSMQ5ixs9dHYZ4HEWFafrZ6SjFQUVIUJSmCpYOy4xxdERSlVIXwgp5S+D6lcPKzPXSNx5qSTQEvKcimVUUpKipKSlSGwnea6KAMMRi+y4onSs42yFQlO0Q7uiKcorwsuvp9+McwzHi6zzd4dtO0/GzaNHwXFRUlJSom5kn0ymQlWGYoUU+pxzu2Xkg0/FEKsoZTnJ+FMUMbXMdiHl39vsFTbksKsmnr0ey7aKgoKYqSFMENsTlZsRMd8lIcvguKUnhB1nCK8x0Ppn2MRKnLHu436CkVZOmaUgxUlJSoaEUHJRIJe0rBYyFSFL5LWJTybJr2GIXUOvscsQue4xQM36UqKzHdUVFSoqJ/Mkok+u3ZQ1lxjkMvyHE+hMe6rlw0/PZblSdBT6ljjEJqQVEKXVMKmKF2ZTgqSoqiJEW/P0COxxU3qy0YvhvrA/SikXj4zvGU2nvHyFOyB/yFrikBmoEXBRUlJSp65osSib4B/6DgxCLVKeE+f6LhO8eDGasTYYfCd8E1pbENF050EhIlEVkjInUiUi8id0V4PUdEHrOvbxWRqpDX7rbtdSJyXTybIlJtbey3NrPPYowvisguEXlXRB4Vkdzkbs/kZjD3TpPvlBB6BvyD60WxyM9KbfguuHk2nigV5mUhMnaJDqdtosPgPqX8YKkhDd9FIq4oiYgbuB+4HqgFbhaR2rButwFtxpgFwH3AvfbaWmAdsBhYA/xARNxxbN4L3GeMqQHarO2RjDEb+DywzBhzPuC2/ZQkUYdJCaXH6yc3AU9pMHyXokQHXyC4phT7Y87tEorzsmju6h+TeQRTzYPhu+AalpYaikwintJyoN4Yc9AYMwCsB9aG9VkLPGwfPwGsFifgvBZYb4zpN8YcAuqtvYg27TWrrA2szRtGOAY4hxjmiYgHyAeOJ/B+FYuKkRKJvgH/YGguFtkeF9ke15hXS4jG4D6lBD7lphfm0tjZNybzCHpKU3KGrylpWnhkEhGl2cCxkOcNti1iH2OMD+gASmNcG629FGi3NsLHSmoMY8x7wHeAo8AJoMMY80ykNygit4vIdhHZ3tTUFPVGTD4SK/2vTC56BvzkJRC+AydklaosM3+CnhLAjKJcTo6RKHX0einIduOxKfT52W6yPS71lKKQiChF+kgK/w4drc9otSc9hohMw/GiqoFZQIGI3BKhL8aYB4wxy4wxy8rLyyN1mdSox6SE0uv1k2dLCMWjMM8zmH023vgCiZUZAphRmMvJjrEJ37X1DAwmN4BTi69Ei7JGJRFRagDmhDyv5Mww2GAfGyorAlpjXButvRkotjbCx0p2jGuAQ8aYJmOMF/gNcFkC71exqBgpkegd8JMXp+5dkNR6SgHcLombug5O+K6lux+vf/SPb2/pHqA0RJTACeE1d6koRSKR36zXgRqbFZeNkyywIazPBuBW+/hGYLNx8ok3AOts5lw1UANsi2bTXrPF2sDafHKEYxwFLhGRfLv2tBrYk9htUUCz75TI9Hh9g8VW41GUlzVmNeXi4Q/Ez7wLMqMoF2Og8fToe0ut3f3DPCWAmUW5nOwYm3DhRCeuKNn1mzuATTgf6o8bY3aJyNdF5GO220NAqYjUA18C7rLX7gIeB3YDTwOfM8b4o9m0tu4EvmRtlVrbIxljK05CxJvATvteHxjBPVIUJYTegQC5ia4p5WXR2ZeqRIcA7gS/Uc0scnaLHG/vHfV5tHYNDCY3BBnLNayJTkJfd4wxG4GNYW1fDXncB9wU5dp7gHsSsWnbDzKUPRfaPpIxvgZ8LdI1Snw0fKdEonfAl1D2HTj13lLlKfkCJm6JoSDVZQUAHGru5v1VJaM2B2NMxPDdzKJcWrsH6PP6Exb4yYJWdFCiYrT6nRKGMcZJdEjKU/KmpDpIIGBwx6nPF2R2cR5ZbuFgU/eIx4pEz4Cffl+AkoKcYe0zivIAOKXe0hmoKClRUU9JCaffFyBgSKjMEDiJDl6/oc87+gkE8fAFTMLhO4/bxdySfA41dyU9zv5Tp1n69Wf4p41nLlm32GSG0ilnekoAJ3Rd6QxUlBRFSZg+W1w1cU/JWSFIRQaeP2ASTnQAmFc+hUPNyXtKv3jtCJ19Pn784kFawqpCHO9w1qhmWc8oyAwrSprscCYqSkpU1FNSwumxJYOS8ZRg7E51jYU/iTUlgAUVjij1+5Iri7TreCdT7VlJT+86Oey1YOLErOLhZTdnFNrEio7RT6yY6KgoKVEJrilpRrgSJFgyKFgyJx7BOm+pOP7bHzC4khClC2YX4fUb6k6eTvgaYwz1jV189MJZVJXm8/S70URpuKdUkOOhpCCbY609CY81WVBRUuKiDpMS5PTggXWJiVKpXeAPD2uNB8lk34EjSgBvN3QkfE1z1wAdvV4WlE/husUzePVACx0hXuF77b2UFGRHzLCrLisYUbgw01FRUqKi4TslnOCeo+ApqvEoswv8zSkoqeM3ya0pVU7Lo6Qgm50N7QlfU9/oJEYsqJjCtYtn4AsYnq9rHPb6/PKCiNdWlaooRUJFSYmLhu+UIF2DZwMl5ikFN402j0GlhHj4/cmJkoiwdE4x2w61JnxNfdOQKF00p5iKqTlssutKxjihwHOnT4147bzyAk519qfsvKl0RUVJiYs6TEqQwWMYEhQlj9vFtPwsWrpTE75zJ3JuRQiXLyjjcEtPwms9Bxq7KMh2M7MoF5dL+GDtdJ6va6LP66ehrZfOPh+LZkQWpapSx4M63KzrSqGoKClR0fCdEs7QmlJi4TuAsik5NJ9OQfguEEhqTQnginPLAPjD/uaE+tc3djG/Yspg0ddrF8+gZ8DPH/Y388oBx8aKeaURrw1WkTjQlPzeqExGRUmJimbfKeF09ftwCRQkmBIOzsbRsTrVNRZevyHbk9xH3PzyKcwqyuW5PacS6l/f2MWC8imDzy+dV8r0why+99x+Htl6lNnFedRUTIl47fyKAjwuYc+JzqTmmOmoKCmKkjCn+3xMyfEkdBxEkLIpObSkINFhwBcgK8EyQ0FEhA8vmckL+5rinnd0us/Lyc4+5oeITrbHxZc/XMvO9zp4p6GDv7hqftR7leNxUzN9KruOqyiFklhgWJmUaPhOCaezz5tU6A6C4bvx95QG/AEKs5ObK8ANF83mP/5wiN/uPMEtl5wTtV9o5l0oH7twFmVTsjnd5+Pa2ukxxzp/ViGb9zZijElK6DMZ9ZSUqKgmKeF09fkS3qMUZGZRLqf7fYPrUeOF1x8gO0lPCaB2ZiGLZkzlP187ErOQ7P5TjihFyq67bH4Z1y2eEVdoFs8qpKV7gFOdsUW7u9/Hd5+pmxRliVSUFEVJGMdTSk6UZk9zqhm8NwZnFcXCCd8l/xEnIvzZB+ax9+TpmAkP+06dJsfjFHIdKRdUFgPw1rG2mP1+/WYD39tczzd/u3vEY00UVJSUqKTiuAElvWnr9jItPzt+xxBm2xI7Da3jK0pe/8hECZwQ3PTCHP7tuf1R/w72NXYxv3xKUnuhwrlgdhF5WW5eOxh7b9SOo86G3nffS7zaxERFRUmJikqSEk5rz8AZxzDEI1We0kiy74Jke1x88ZpzeeNIG0+9c+KM1wMBw86GdhbPKjyrOWZ7XCyrmsZrB1ti9jtoKz8cbe0ZrNSeqagoKYqSEMYY2roHkvaUygpyyPa4xj98dxaeEsBNy+ZQO7OQbzy1m9awTLwDTV209Xh5f/XZn1J7ybxS9p48fcYYQYwxHGrqoigvi4CBwy2ZXZpIRUmJikbvlFA6+3z4AmawdFCiuFzC7OI83msb/zWlkcwu7xIAACAASURBVCQ6BHG7hG/ftIT2Hi9/+6u38YecLvt8XRMAl1RH3hibDJfNd2y8uK8p4uut3QN09vn4oM3kO9CooqRMWlSVlCGC+3aSFSWAOSX54/4N/2zWlIIsnlXElz98Hs/tbeTv//td/AGDP2D41RvHuGB2EXNLR57kEOTCyuE188IJFm29emEFoJ4SACKyRkTqRKReRO6K8HqOiDxmX98qIlUhr91t2+tE5Lp4NkWk2trYb21mn8UYxSLyhIjsFZE9InJpcrdHUZQgrT2OKE0bgSjVVEzhQFMXgcD4fdHx+gMjXlMK5dbLqvirq+bz6LajfPzHr/L59TvYd6qLP79i3ijMksGaeS/sa4q4XnS4xamNVzurkOmFORxsmuSiJCJu4H7geqAWuFlEasO63Qa0GWMWAPcB99pra4F1wGJgDfADEXHHsXkvcJ8xpgZos7aTHsNe82/A08aYRcCFwJ5Eb4yi4TtlOK1d1lNKck0JHFHq8wbGbV3JGIPXb87aUwryt9ct5Ns3LqGhrZdN757ks1fM46NLZo6KbYDrbM28YFgwlCMt3bhtCLSqtEA9JWA5UG+MOWiMGQDWA2vD+qwFHraPnwBWi7NrbC2w3hjTb4w5BNRbexFt2mtWWRtYmzeMZAwRKQSuAB4CMMYMGGMSPyhF0eCdMozWswjf1Ux3qh7sb0z8VNezYcAfABgVTwmcvUs3LZvDq3evYu831nD3h84b1QoMl80vpWJqDr/afuyM14609DCrOJdsj4vqsgIOZ/gZTIn8j80GQu9Ug22L2McY4wM6gNIY10ZrLwXarY3wsZIdYx7QBPxURHaIyIMiEvG0LRG5XUS2i8j2pqbIi42KMtlpPO1UEyifmpP0tQvKnaoHwSoIY82AzxGlZGvfxUNE8IyS9xWKx+3ixosr2VLXeEbVhiMt3ZxT4nx0VZcV0NI9MOx020wjkbsb6X81/Et0tD6j1T6SMTzA+4AfGmMuArqBM9bDAIwxDxhjlhljlpWXl0fqMinR8J0SyomOPqblZ0U82jseRflZzCzKHbfio31eR5TyRjDXVPHx988hYODRbUcH24wxHGzu5hybUFFVFjyDKXO9pUREqQGYE/K8EjgerY+IeIAioDXGtdHam4FiayN8rJGM0WCM2Wrbn8ARKSVBgjvZtU6kAnCqs48ZRXkjvn7pnGJ2xCmnM1r0DjgJA3nZE6fm9DmlBVxz3nQefvUwXf1OsKihrZfTfT4WzyoChs5gyuR1pURE6XWgxmbFZeMkFWwI67MBuNU+vhHYbJxPtA3AOps5Vw3UANui2bTXbLE2sDafHMkYxpiTwDERWWivWQ1kfuGoMUA9JgUcT2lGYfKhuyAXzS3mWGsvTeNQMbzXZrFNJE8J4I5VC2jv8fLzVw8D8E6DU1YoWDlibkk+IkNp4plIXFGy6zd3AJtwstceN8bsEpGvi8jHbLeHgFIRqQe+hA2TGWN2AY/jiMHTwOeMMf5oNq2tO4EvWVul1nbSY9hr/j/gERF5B1gK/GOyN2gyo1qkhHK2ntL75k4DYMfRsfeWegYcTyM/icMI04Glc4pZvaiC72+u51hrDy/sa2RqrodaK0q5WW5mFeVltCgl5NsaYzYCG8PavhryuA+4Kcq19wD3JGLTth/Eyc4Lbx/JGG8ByyJdo8Qn6CFp+E7p9/lp7hpgRmHuiG2cP7uIHI+LVw+2cO3iGaM4uzMZ9JQmmCgB/MPaxVx334t88qGtnOjo46MXzhqW2p7pGXha0UGJi4bvlBPtTkbYzOKRi1JulptL55eyZW/jaE0rKoNrShMsfAdQOS2fH39yGZ19PqYX5vKFa2qGvV5dVsCh5u6MreI/cVYBlXHHaABPsRyyC+vBhfaRcvXCCr5Wt4tDzd1nbSsWQU9pooXvglxeU8Ybf38NwBn7oarKCujs89HaPUDplJGv8aUr6ikp0dHwnWI5YsNFVaVnJySrFjn12zbuPPM4iNGkx3pKI0lfTxdEJOIG3eoyJz08UzPwVJQURYnL4ZYeCrLdlCV5llI4c0ryWVFdwq+2HxvT8FMwfDdRPaVYVJc51TEONfekeCZjg4qSEhUN3ilBDjV3U1VWMCqldW5aNofDLT28Gudgu7Oh01Y8mJqbNWZjpIrKaXlkuWXcSjaNNypKSlQydB1VGQEHmrpGbQ3oI0tmUjYlh/u31I+KvUh09HrJy3KPWu27dCLL7eK8mYW8cywzj0bPvP8xRVFGlY5eLw1tvZw38+yO/g6Sm+Xms1fM4+X6Fl49MDbeUkevl+L8zPOSgiypLOLd9zrG9SiQ8UJFSYmKZt8pAHtOOPXqglUFRoNbLjmHyml5fPm/d0Y8Q+hsae/1UpSXyaJUzOl+HwczcL+SipISFQ3fKQC7bRHV2lEUpbxsN//4vy7gYFM333hq9Kt/dfR6KcxgUbqwshiAt49l3mk8KkqKosTk3fc6KJ+aQ8XUkW+cjcQV55bz2Svn8cjWozzw4oFRtd3eM0BxBovSgoopFGS7x63A7Xiim2eVqKijpABsPdTKsnOmjYntv7tuEcdae/jHjXs53efjC9eci9t19hl+pzr7uWRe6SjMMD1xu4RlVSW8drA11VMZddRTUqKSqWVMlMQ51trDe+29rKguGRP7bpfwvXUXcdPFlfz75nr+9CdbOdZ6dvtvegZ8dPR6mVE0up5dunHJvFLqG7sGD1/MFFSUFEWJytZDzjfxFWPodXjcLv75xiV8648u4M0j7az+7gt8e9PewePXkyV4cuvMDBelS+c7/ydbM8xbUlFSoqJ+kvLcnlOUT81h4fSpYzqOiLBu+Vye++srWbN4BvdvOcBl33qOrz75LgeakjtC/UiL42nNLs4fi6mmDefPKmRKjmdMNyGnAhUlJTqqSpOaPq+fF/Y1cW3tdFyjsM6TCLOK8/jezRfx7Bev4KNLZvHotqOs/pcX+MR/vMbGnSfw+gNxbdSdciodjLWQphqP28Xy6hJeqW9O9VRGFRUlJSrBfUqCVmSdjLywr4meAT/XjfHZR5GomT6Vb990IS/ftYq/ufZcjrT08FePvMnKb23mu8/Ucby9N+q1777XwcyiXIoyePNskCtqyjjc0pNR5yupKClRCdgvpeP0JVlJMx57/RgVU3MG1y5SQcXUXO5YVcOLf3c1D926jMWzCvn3LfVcfu9m/vzn23lhX9OwqgY+f4CX65u5bH5ZyuY8nly10Km6/nzd2J9RNV5oSrgSlYDNvhuNIpzKxOK99l6er2vkc1cvGHbqaapwu4TV501n9XnTOdbawy+3HeXx14/x7O5TzC3J5xMr5vIny+awaddJ2nq8XH/++Ht3qaCqrIB5ZQVsqWviUyurUz2dUUFFSYlKBpbVUhLkxy8cwGWTD9KNOSX53LlmEV+4poZNu07xn68d4Vu/28s/P72XgIHlVSWD5zZNBq5eVMEvXjtC74B/Qh7/Ho6KkhKV4D4lV+q/KCvjyHvtvazfdoybls1hdnFeqqcTlRyPm49dOIuPXTiL/adO85sd7zElx8Otl1WNW2JGOnD1wgoeeukQrx5sZtWi6amezlmT0MeNiKwRkToRqReRuyK8niMij9nXt4pIVchrd9v2OhG5Lp5NEam2NvZbm9kjHcO+5haRHSLyVOK3RYEhT0kTHSYX3/if3YjAHasWpHoqCVMzfSp3rlnE565ewJScyfVd+/3V08jPdrN5b2asK8UVJRFxA/cD1wO1wM0iUhvW7TagzRizALgPuNdeWwusAxYDa4AfWJGIZfNe4D5jTA3QZm0nPUbI3P4PsCex26GEMrSmlOKJKOPGxp0neHrXSb5wzblp7SUpQ+R43KxcUMaWvU0ZUYUlEU9pOVBvjDlojBkA1gNrw/qsBR62j58AVouzOr4WWG+M6TfGHALqrb2INu01q6wNrM0bRjgGIlIJfBh4MLHboYQSFCWXqtKk4EBTF3c+8Q5LKov4sw9kxqL5ZGHVogrea+9l36nkNhqnI4mI0mzgWMjzBtsWsY8xxgd0AKUxro3WXgq0WxvhYyU7BsC/An8HxNxxJyK3i8h2Edne1NQUq+ukwgyG75RMp+l0P3/+8HayPC5+eMvFaZFxpyTO1TY1PBNCeIn85kX6TAr3EaP1Ga32pMcQkY8AjcaYNyK8PryzMQ8YY5YZY5aVl5fH6z5pGDzkT1Upo2nu6ud/P/gaJzr6+PEnL9aw3QRkRlEui2cVsnnvqVRP5axJRJQagDkhzyuB49H6iIgHKAJaY1wbrb0ZKLY2wsdKdoyVwMdE5DBOeHCViPxnAu9XsQQ3z6omZS57T3ay9vsvc7S1h4c+tYz3V41NNXBl7Fm9qII3jrTR3jOyQrbpQiKi9DpQY7PisnGSCjaE9dkA3Gof3whsNs6K2wZgnc2cqwZqgG3RbNprtlgbWJtPjmQMY8zdxphKY0yVtb/ZGHNLgvdFQTfPZjLGGJ54o4E//sEr+AIBHv/spZOmCkKmcvWiCgLGKQ81kYmbO2mM8YnIHcAmwA38xBizS0S+Dmw3xmwAHgJ+ISL1ON7LOnvtLhF5HNgN+IDPGWP8AJFs2iHvBNaLyDeBHdY2IxlDOTuCa0qTaMvHpKCxs4+//+93eWb3KZZXl/C9dRdl/NlDk4ELK4spLchm895G1i4NX/afOCSU0G+M2QhsDGv7asjjPuCmKNfeA9yTiE3bfhCbPRfWnvQYIa8/Dzwf7XUlMoOekgbwMoI+r5+HXjrED7bU4/Ubvvyh87jt8upJtdE0k3G5hKsWVvD7Pafw+QN4JmiyyuTaZaYkxeDmWf3MmtB4/QH+a8d7fO+5/TS09XJt7XT+74fOo6qsINVTU0aZ1edV8Os3G9hxrH3Crg+qKClR0TWliU2/z88TbzTww+cP0NDWy/mzC/nnP17CZQt07ShTubymDI9LeG5Po4qSknmYwfCdMpFoOt3Po9uO8sjWI5zq7GfpnGK+sfZ8rlpYrl8wMpzC3CyWV5ewZW8jd12/KNXTGREqSkpUNHw3sXj7WDsPv3KYp945wYA/wAdqyvj2jRfygZoyFaNJxKpFFXzzt3toaOuhctrEOxJeRUmJitHad2nP6T4vv33nBOtfP8Zbx9opyHZz8/I5/OllVcwvn5Lq6Skp4GorSlv2NvLJS6tSPZ2kUVFSoqJVwtMTYwzbDrXy+PYGNu48Qa/Xz4KKKfy/j9byxxdXMjU3848BV6Izr6yAqtJ8nlNRUjKNoYKsKZ6IAsDJjj5+/WYDv9p+jMMtPUzJ8XDDRbP5k2WVLJ1TrCE6BXASk65eVMEjW4/SM+AjP3tifcxPrNkq48pgFXz9sEsZp/u8PLPrFE++fZyX9jcRMLCiuoTPr65hzfkzJtwHjjI+rF40nZ++fJhX6lu4pnZiHfynv9FKVAKafZcS+n1+Xqhr4sm3j/P73afo9wWonJbHX121gBsvrtT9RUpclleXUJDtZnNdo4qSkjn47KKSR+N3Y04gYNh6qJUNb7/Hxp0n6ej1UlKQzcffP4e1S2fzvrkanlMSJ9vj4gM15WzZ24gxZkL97qgoKVHx+R1RcqsojQnGGHYd72TD28fZ8NZxTnb2kZ/t5rrFM/jY0llcvqBMzzVSRsyqRRU8veske06cpnZWYaqnkzAqSkpUfPbsCj15dvQICtHGnSfYuPMEh1t68LiEK88t5+4PLeKDtdN1nUgZFa5c6JwN91J9k4qSkhkEw3fmjDMdlWQICtFvd57gd1aI3C7h0nml/PkV87j+/JmUFGSneppKhjG9MJcFFVN4qb6F26+Yn+rpJIyKkhIVn9/xlIxqUtKECtHGnSc4YoXosvmlfPbK+VxbO53SKTmpnqaS4Vy+oIz1rx+l3+cnx+NO9XQSQkVJiYrXr2qUDMYY3n1vSIiOtg4J0V9eOZ9rF89Qj0gZV1YuKONnrxxmx9F2LplXmurpJISKkhIV/2D4TomGP2DYcbSNTbtOsmnXKY62OmtEly0o43NXz+fa2hlMUyFSUsSKeSW4XcLL9c0qSsrEJ5jooAyn3+fnlfoWntl9kmd3n6K5a4Bst4tL55dyx9UL+GDtdBUiJS0ozM3iwsoiXqpv5q+vXZjq6SSEipISlcHwnbpKnO7zsqWuiWd2neT5uia6+n1MyfFw1cJyrls8g6sWlmvNOSUtWbmgjPu31NPZ56VwAvyOqihlODuOtlE5LZ/yqckvqvsnefZd4+k+fr+7kU27TvLKgWa8fkPZlGw+euFMrl08g8vml06YxWNl8rJyQRn/vrme1w60cO3iGameTlxUlDKc//WDV6iYmsO2L1+T9LVe/+QL3x1q7ubZ3c760JtH2zAG5pbk86nLqrhu8QwumjtNNxMrE4qL5haTl+Xm5frmzBElEVkD/BvgBh40xnwr7PUc4OfAxUAL8HFjzGH72t3AbYAf+LwxZlMsmyJSDawHSoA3gU8aYwaSHUNE5tj+M4AA8IAx5t+SvUETmYD1dBpP94/o+n5f5qeE9/v8bDvUypa9TWypa+RQczcAi2cV8sVrzuXaxdNZOH3qhCrToiih5HjcLK8u4eUDLameSkLEFSURcQP3Ax8EGoDXRWSDMWZ3SLfbgDZjzAIRWQfcC3xcRGqBdcBiYBbwexE5114Tzea9wH3GmPUi8iNr+4cjGMMH/LUx5k0RmQq8ISLPhs07o+nx+s/q+t6Bs7s+XTnV2ceWvY1s3tvIy/XNdA/4yfa4uHReKZ+6rIpViyqYUzLxTuxUlGhcOr+Ub/1uL81d/ZSl+f64RDyl5UC9MeYggIisB9YCoR/ua4H/Zx8/AXxfnK+Wa4H1xph+4JCI1Ft7RLIpInuAVcAnbJ+Hrd0fJjuGMeZV4ASAMea0tT07bN4ZTXe/76yu77WiNtEdJX/A8HZD+6AQ7TreCcCsolxuuGg2qxZVcNn8MvKydX1IyUyWV5cAsO1QKx+6YGaKZxObRERpNnAs5HkDsCJaH2OMT0Q6gFLb/lrYtbPt40g2S4F2Y4wvQv+RjAGAiFQBFwFbI71BEbkduB1g7ty5kbpMSLrOUpR6rKdkJmD8rqPXy4v7mtiyt5Hn9zXR2j2AS+Dic6bxd2sWsmpRhYbllEnDBbOLyMtys/VgS0aIUqS/2vBPqWh9orVHKn0cq/9IxnAuEpkC/Br4gjGmM0JfjDEPAA8ALFu2bOJ9AkfhbD2lvrMM/40nxhj2nepi895GttQ18saRNvwBw7T8LK5aWMFVC8u58txyivN1/5Ay+chyu7j4nGlsPdSa6qnEJRFRagDmhDyvBI5H6dMgIh6gCGiNc22k9magWEQ81lsK7Z/0GCKShSNIjxhjfpPAe80oTvc5opTtGdnxBz0DzvXpqtK9A35ePdjsCNHeJt5r7wWgdmYhf3nlfK5eVMHSOcWaLacoOCcWf/f3+2jvGUjrL2eJiNLrQI3NinsPJ6ngE2F9NgC3Aq8CNwKbjTFGRDYAvxSR7+IkIdQA23C8mzNs2mu2WBvrrc0nRzKGXW96CNhjjPlusjcmE+js9QKQP8K1kg57fTpF7xraegbXhl450EK/L0B+tpuVC8q4Y9UCrl5YwYyi3FRPU1HSjuXVJRgDrx9u44NpfBptXFGy6zd3AJtw0rd/YozZJSJfB7YbYzbgfPj/wiYZtOKIDLbf4zjJBT7gc8YYP0Akm3bIO4H1IvJNYIe1TbJjiMjlwCeBnSLylrXxf40xG0d2qyYeQU8pPyt5UeoZ8NHnTf0+JZ8/wBtH2thc18iWvY3sO9UFwDml+dy8fC6rFlWwYl6JbmJVlDhcOKeYbI+LrQdbJrYoAdgP8o1hbV8NedwH3BTl2nuAexKxadsPMpShF9qe1BjGmJeIvN40aejsczyd3BF4Si1dA4OPx9tRaunq5/m6JjbXNfLiviZO9/nIcgvLq0v4k2VzuHpRBfPKCjRJQVGSIDfLzdI5xWw7nN7rSlrRIYNp7XaEZSRexEg33I6E4NlDm21Y7u2GdoyB8qk5XH/+DFYtqmDlgjKtLacoZ8kl1SV8f0s9p/u8afv3pKKUwRy3C//Byg7JcNhWNphbkj8mi0pd/T5e2t/MFpst13i6HxFYUlnMF1afy6pFFSyeVYhLkxQUZdRYXl1KYHM9bxxp46qFFameTkRUlDKY4+19AHhHcARFfVMXbpcwtyR/MAx4thxr7eH3e06xeW8jrx1swes3TM3xcMW55Vy9yEnbTvfd5ooykXnfOcV4XMLWQ60qSsr4EggY9pxwtmX5R+ApbT/cyvmzi8hyj9xT8fkD7DjWznN7Gnluzyn2NzpJCvPLC/j0ymquXljBsqppZLlHlrKuKEpy5Gd7WFJZxNaD6VsHT0UpQ9l1vJPT/T5cAr4kjzVv7OzjzaPt/MWV89h9vDOp6F2f18/zdY1s2nWKLXWNtPd48biEFfNKWLd8LqsXVVBVVpDku1EUZbRYXl3Kg384SO+APy1La6koZSg/feUQ2W4X19RWsP1wW1LXPvDiQfwBw40Xz+EbJ3bHPU+pd8ARot/uPMHmvY30DPiZlp/FqkUVrF40nQ+cWzYhDhdTlMnAinkl/OiFA7x5tI2VC8pSPZ0zUFHKQB7ddpTfvPkef3HlfLr7ffiSCN89u/sUP3n5EDcvn0N1WQFul0T1tHY2dLD+9aNseOs4p/t9lBZkc8NFs/nwBTNZUV2CR8NyipJ2LDtnGi6BrQdbVJSUsaXf5+efNu7lZ68c5spzy/niB2v41u/2JnRYnz9gePAPB/n2pjouqCzmyx+uBSDH42LAN/z6l+ub+bfn9rPtUCs5HhcfXjKTG99XyYp5pVrSR1HSnKm5WSyeVcRraVoHT0UpQ3i+rpF/+J/dHGru5jMrq7nr+kVke1x4Yng64OwRemFfE/+0cS91p05z/fkzuPfGJUzJcX41cjzuwcP+WrsH+Mp/v8tvd55gemEOX/lILTdeXElRnobmFGUisaK6hJ+/doQ+r5/cEVR8GUtUlCY42w+38u+b63lhXxPzygr4+WeWc8W55YOve9wufBFSwvu8fv7n7eP89OXD7D7RydySfO7/xPv40AUzhlVKyPa46PcFONnRxyf+4zUa2nr5m2vP5c8+MC/tfpkVRUmMFfNKefClQ7x9rJ0V80pTPZ1hqChNQLz+AM/taeSnLx9i66FWSgqyufv6RXx6ZfUZFcGzXILXbzDGICI0nu7jkdeO8sjWIzR3DXDu9Cl8648u4I/eVxmxmniOx0XvgI+/fOQNTnX28cs/X8GyqpLxequKoowBy6tKEIGth1pVlJSRU9/Yxa/eOMav32iguWuAmUW5fOUjtdy8fA752ZH/K4PJBq3dA/zLs/v41fZjeP2GVYsq+MzKalYuKI1ZQy4ny0X3gJ8dR9v5148vVUFSlAygKD+LhdOnsvVQC87BCumDilIaY4zhnYYONu06yTO7T1Hf6FRZWLWogpuXz+GKmvK4GW4eu/n1j3/4Csfaerl5+Rw+s7KaeeVTEppDsG5eTcUU1i6ddXZvSFGUtOGSeaWsf/0oA77AiM9cGwtUlNIMrz/A1oOtPLP7JM/sOsXJzj7cLmFFdQmfvOQcrj9/BhWFiZ8XlOVyftkOt/Scsd6UCO+bWwzA51fXaFVuRckgVlSX8LNXDrPzvQ4uPmdaqqcziIpSGtAz4OPFfU1s2nWK5/acorPPR26WiyvPLedvaxey+ryKEZ8UGdyxfWFlUdKCBHDVwgq2fXk1FVP14DxFySSWVzuh+K2HWlSUFGjrHuD3e06xadcp/rC/iX5fgOL8LD5YO4PrFk/nAzXlo1ICZOWCMiqn5fG1jy0esQ0VJEXJPEqn5LCgYgpbD7byV1elejZDqCiNI42dfWzadZKnd53ktYOt+AOGWUW53Lx8Ltcuns7yqtGvglBdVsBLd64aVZuKomQGK6pLePKt4/j8gbSpwKKiNMb0ef1s2nWSX21v4OUDzRgD88oL+Isr53Hd4hlcMLtI12oURUkJK+aV8sjWo+w+0cmSyuJUTwdQURoz2nsG+Nkrh/nZK4dp7/FSOS2Pz6+q4SNLZlIzfWqqp6coisIldl3p1QMtKkqZij9g+OXWI/zzpjpO9/m45rzpfHplFZfOK9VTVBVFSSsqCnOpnVnIs7tP8dkr56d6OgAkFEQUkTUiUici9SJyV4TXc0TkMfv6VhGpCnntbtteJyLXxbMpItXWxn5rM3u0xxgr2nsG+NRPt/GVJ3dxYWUxT3/hAzx46zJWLihTQVIUJS1Zc/4M3jjaRuPpvlRPBUhAlETEDdwPXA/UAjeLSG1Yt9uANmPMAuA+4F57bS2wDlgMrAF+ICLuODbvBe4zxtQAbdb2aI8x6rR1D3DTj15l68FW/umPLuAXty1n0YzCsRpOURRlVLhu8QyMgafePpHqqQCJeUrLgXpjzEFjzACwHlgb1mct8LB9/ASwWpzV+7XAemNMvzHmEFBv7UW0aa9ZZW1gbd4wmmMkdluSY8AX4FM/e50jrT387NPv5+blczV5QVGUCcG506ewvKqE723ez/H23lRPJ6E1pdnAsZDnDcCKaH2MMT4R6QBKbftrYdfOto8j2SwF2o0xvgj9R2uMMxCR24Hb7dMuEWkBmiP1jcfKe0ZyVdpSxgjvQwai92IIvRcOGXcfZn9txJeWAeeMxhwSEaVIX/nDD+iJ1idaeyQPLVb/0RzjzEZjHgAeCD4Xke3GmGWR+k4m9D4MofdiCL0XDnofhrD3omo0bCUSvmsA5oQ8rwSOR+sjIh6gCGiNcW209mag2NoIH2u0xlAURVHSlERE6XWgxmbFZeMkFWwI67MBuNU+vhHYbIwxtn2dzZyrxqmRvi2aTXvNFmsDa/PJ0RwjsduiKIqipIK44Tu7fnMHsAlwAz8xxuwSka8D240xG4CHgF+ISD2O97LOXrtLRB4HdgM+4HPGGD9AJJt2yDuB9SLyTWCHtc0ojxGPTCcqJAAAA8RJREFUB+J3mRTofRhC78UQei8c9D4MMWr3QhxnQ1EURVFST3pU4FMURVEUVJQURVGUNEJFKYTxLkuUCkTkJyLSKCLvhrSViMiztrTTsyIyzbaLiHzP3o93ROR9IdfcavvvF5FbI42VzojIHBHZIiJ7RGSXiPwf2z4Z70WuiGwTkbftvfgH2z5qJb8mErYizA4Reco+n6z34bCI7BSRt0Rku20b+78PY4z+c9bV3MABYB6QDbwN1KZ6XmPwPq8A3ge8G9L2z8Bd9vFdwL328YeA3+HsBbsE2GrbS4CD9uc0+3haqt9bkvdhJvA++3gqsA+nHNVkvBcCTLGPs4Ct9j0+Dqyz7T8C/tI+/ivgR/bxOuAx+7jW/t3kANX278md6vc3gvvxJeCXwFP2+WS9D4eBsrC2Mf/7UE9piHErS5RKjDEv4mQvhhJawim8tNPPjcNrOHvIZgLXAc8aY1qNMW3Aszh1BycMxpgTxpg37ePTwB6cSiCT8V4YY0yXfZpl/xlGr+TXhEFEKoEPAw/a56NZ+iwTGPO/DxWlISKVU5odpW+mMd0YcwKcD2ugwrZHuycZda9s2OUiHA9hUt4LG7J6C2jE+eA4QIIlv4DQkl8T/V78K/B3QMA+T7j0GZl1H8D5YvKMiLwhTik2GIe/Dz1PaYhEyilNNpIt7TThEJEpwK+BLxhjOiV6Id2MvhfG2du3VESKgf8CzovUzf7MyHshIh8BGo0xb4jIVcHmCF0z+j6EsNIYc1xEKoBnRWRvjL6jdi/UUxpiMpclOmVdbezPRtue0SWcRCQLR5AeMcb8xjZPynsRxBjTDjyPsy4wWiW/JgorgY+JyGGc8P0qHM9pst0HAIwxx+3PRpwvKssZh78PFaUhJnNZotASTuGlnf7UZtZcAnRYl30TcK2ITLPZN9fatgmDjf0/BOwxxnw35KXJeC/KrYeEiOQB1+CssY1Wya8JgTHmbmNMpXEKi67DeV//m0l2HwBEpEBEpgYf4/xev8t4/H2kOsMjnf7hZJDsw4mnfznV8xmj9/gocALw4nyLuQ0nDv4csN/+LLF9BeegxAPATmBZiJ3P4Czg1gOfTvX7GsF9uBwnjPAO8Jb996FJei+W4JT0esd+8HzVts/D+TCtB34F5Nj2XPu83r4+L8TWl+09qgOuT/V7O4t7chVD2XeT7j7Y9/y2/bcr+Hk4Hn8fWmZIURRFSRs0fKcoiqKkDSpKiqIoStqgoqQoiqKkDSpKiqIoStqgoqQoiqKkDSpKiqIoStqgoqQoiqKkDf8/Z+T2JUHicG0AAAAASUVORK5CYII=\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -788,7 +808,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -804,7 +824,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -820,12 +840,12 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ - "total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (x_min, x_max), norm_range=False)\n", + "total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# inte = total_f.integrate(limits = (950., 1050.), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl/4500)\n", "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" @@ -833,7 +853,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -849,32 +869,32 @@ "\n", "# print(name+\":\", inte_fl)\n", "# print(\"Increase am by factor:\", np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", - "# print(\"New amp:\", pdg[name][3]*np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", + "# print(\"New amp:\", pdg[name][0]*np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", "\n", - "# # print(x_min)\n", - "# # print(x_max)\n", - "# # # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", - "# # total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", - "# # dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", - "# # draws_per_dim=1000000)\n", - "# # # _ = []\n", + "# print(x_min)\n", + "# print(x_max)\n", + "# # total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "# total_f.update_integration_options(mc_sampler=lambda dim, num_results,\n", + "# dtype: tf.random_uniform(maxval=1., shape=(num_results, dim), dtype=dtype),\n", + "# draws_per_dim=1000000)\n", + "# # _ = []\n", "\n", - "# # # for i in range(10):\n", + "# # for i in range(10):\n", "\n", - "# # # inte = total_f.integrate(limits = (x_min, x_max))\n", - "# # # inte_fl = zfit.run(inte)\n", - "# # # print(inte_fl)\n", - "# # # _.append(inte_fl)\n", + "# # inte = total_f.integrate(limits = (x_min, x_max))\n", + "# # inte_fl = zfit.run(inte)\n", + "# # print(inte_fl)\n", + "# # _.append(inte_fl)\n", "\n", - "# # # print(\"mean:\", np.mean(_))\n", + "# # print(\"mean:\", np.mean(_))\n", "\n", - "# # _ = time.time()\n", + "# _ = time.time()\n", "\n", - "# # inte = total_f.integrate(limits = (x_min, x_max))\n", - "# # inte_fl = zfit.run(inte)\n", - "# # print(inte_fl)\n", - "# # print(\"Time taken: {}\".format(display_time(int(time.time() - _))))\n", + "# inte = total_f.integrate(limits = (x_min, x_max))\n", + "# inte_fl = zfit.run(inte)\n", + "# print(inte_fl)\n", + "# print(\"Time taken: {}\".format(display_time(int(time.time() - _))))\n", "\n", "# print(pdg['NR_BR']/pdg['NR_auc']*inte_fl)\n", "# print(0.25**2*4.2/1000)" @@ -890,7 +910,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -974,7 +994,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -983,7 +1003,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -992,7 +1012,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -1001,7 +1021,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": { "scrolled": false }, @@ -1049,7 +1069,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -1066,7 +1086,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -1090,7 +1110,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, "outputs": [], "source": [ @@ -1117,7 +1137,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -1140,7 +1160,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": {}, "outputs": [], "source": [ @@ -1149,7 +1169,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1165,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [], "source": [ @@ -1195,7 +1215,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [], "source": [ @@ -1209,7 +1229,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": 31, "metadata": {}, "outputs": [], "source": [ @@ -1227,7 +1247,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": 32, "metadata": {}, "outputs": [], "source": [ @@ -1241,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": 33, "metadata": {}, "outputs": [], "source": [ @@ -1262,7 +1282,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": 34, "metadata": {}, "outputs": [], "source": [ @@ -1272,7 +1292,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1303,7 +1323,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [], "source": [ @@ -1320,7 +1340,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1361,48 +1381,48 @@ "\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\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(0.014/3.)\n", - "sigma2 = ztf.constant(0.128/3.)\n", - "sigma3 = ztf.constant(0.548/3.)\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", + "# 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", @@ -1418,7 +1438,7 @@ "for part in sum_list_5:\n", " sum_ru_5 += part\n", "\n", - "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), 0.02), lambda: 100000., lambda: 0.)\n", + "constraint5 = tf.cond(tf.greater_equal(tf.abs(sum_ru_5), ztf.constant(0.02)), lambda: 100000., lambda: 0.)\n", "\n", "# 6. Constraint on phases of Jpsi and Psi2s for cut out fit\n", "\n", @@ -1431,13 +1451,19 @@ "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", + "# 7. Constraint on Ctt with higher limits\n", + "\n", + "# Ctt_abs = tf.pow(tf.pow(Ctt, 2.), 0.5)\n", + "\n", + "# constraint7 = tf.cond(tf.greater_equal(Ctt_abs, 0.5), lambda: 100000., lambda: 0.)\n", + "\n", "# zfit.run(constraint6_0)\n", "\n", "# ztf.convert_to_tensor(constraint6_0)\n", "\n", "#List of all constraints\n", "\n", - "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4_0, constraint4_1, constraint4_2,\n", + "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4, #constraint4_0, constraint4_1, constraint4_2,\n", " constraint6_0, constraint6_1]" ] }, @@ -1450,7 +1476,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1520,7 +1546,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": 39, "metadata": { "scrolled": false }, @@ -1541,8 +1567,87 @@ "Toy 0: Loading data finished\n", "Toy 0: Fitting pdf...\n", "------------------------------------------------------------------\n", - "| FCN = 291.3 | Ncalls=753 (753 total) |\n", - "| EDM = 4.03E-05 (Goal: 5E-06) | up = 0.5 |\n", + "| FCN = 3.516E+05 | Ncalls=867 (867 total) |\n", + "| EDM = 6.79E-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 | False | False | True |\n", + "------------------------------------------------------------------\n", + "Function minimum: 351616.34467140574\n", + "----------------------------------------------------------------------------------------------\n", + "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", + "----------------------------------------------------------------------------------------------\n", + "| 0 | DDstar_p | 1.95 | 0.29 | | |-6.28319 | 6.28319 | |\n", + "| 1 | p3770_s | 3.27 | 0.21 | | |0.918861 | 4.08114 | |\n", + "| 2 | bplus_0 | 0.479 | 0.018 | | | -2 | 2 | |\n", + "| 3 | Ctt | -0.44 | 0.19 | | | -1 | 1 | |\n", + "| 4 | bplus_2 | -0.23 | 0.08 | | | -2 | 2 | |\n", + "| 5 | Dbar_p | 5.30 | 0.26 | | |-6.28319 | 6.28319 | |\n", + "| 6 | p4040_p | 3.79 | 0.17 | | |-6.28319 | 6.28319 | |\n", + "| 7 | psi2s_p | 1.903 | 0.028 | | |-6.28319 | 6.28319 | |\n", + "| 8 | bplus_1 | -0.89 | 0.04 | | | -2 | 2 | |\n", + "| 9 | p4415_s | 1.09 | 0.18 | | |0.126447 | 2.35355 | |\n", + "| 10| p3770_p | -2.60 | 0.09 | | |-6.28319 | 6.28319 | |\n", + "| 11| DDstar_s | -0.300 | 0.016 | | | -0.3 | 0.3 | |\n", + "| 12| p4040_s | 1.02 | 0.16 | | |0.00501244| 2.01499 | |\n", + "| 13| p4160_p | -2.08 | 0.10 | | |-6.28319 | 6.28319 | |\n", + "| 14| p4415_p | 4.22 | 0.18 | | |-6.28319 | 6.28319 | |\n", + "| 15| Dbar_s | 0.300 | 0.013 | | | -0.3 | 0.3 | |\n", + "| 16| jpsi_p | 4.640 | 0.023 | | |-6.28319 | 6.28319 | |\n", + "| 17| p4160_s | 2.15 | 0.16 | | | 0.71676 | 3.68324 | |\n", + "----------------------------------------------------------------------------------------------\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| | DDstar_p p3770_s bplus_0 Ctt bplus_2 Dbar_p p4040_p psi2s_p bplus_1 p4415_s p3770_p DDstar_s p4040_s p4160_p p4415_p Dbar_s jpsi_p p4160_s |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "| DDstar_p | 1.000 0.173 0.000 -0.171 -0.324 -0.080 0.106 0.003 0.389 -0.061 0.262 0.029 -0.140 0.217 -0.024 0.003 0.173 -0.100 |\n", + "| p3770_s | 0.173 1.000 0.045 -0.234 -0.148 0.058 -0.027 -0.423 0.095 0.000 -0.171 0.024 0.080 0.020 -0.021 0.025 -0.006 -0.011 |\n", + "| bplus_0 | 0.000 0.045 1.000 -0.008 -0.011 0.019 0.022 -0.007 -0.832 0.017 0.025 0.000 0.018 0.014 0.018 0.001 -0.064 0.035 |\n", + "| Ctt | -0.171 -0.234 -0.008 1.000 0.689 -0.326 -0.291 0.166 -0.184 0.221 -0.263 -0.004 0.368 -0.425 -0.073 0.009 0.130 0.258 |\n", + "| bplus_2 | -0.324 -0.148 -0.011 0.689 1.000 -0.134 -0.069 -0.013 -0.337 -0.054 -0.134 0.005 0.099 -0.085 0.177 0.004 0.052 0.123 |\n", + "| Dbar_p | -0.080 0.058 0.019 -0.326 -0.134 1.000 0.011 0.052 0.180 -0.008 0.366 0.002 -0.089 0.105 -0.044 0.015 0.302 -0.091 |\n", + "| p4040_p | 0.106 -0.027 0.022 -0.291 -0.069 0.011 1.000 -0.228 0.020 0.031 0.180 0.029 -0.241 0.163 0.099 0.022 -0.071 0.295 |\n", + "| psi2s_p | 0.003 -0.423 -0.007 0.166 -0.013 0.052 -0.228 1.000 0.051 0.010 0.058 0.024 0.009 -0.131 -0.105 0.024 0.004 -0.083 |\n", + "| bplus_1 | 0.389 0.095 -0.832 -0.184 -0.337 0.180 0.020 0.051 1.000 0.100 0.128 -0.005 0.010 0.019 -0.100 -0.005 0.105 0.001 |\n", + "| p4415_s | -0.061 0.000 0.017 0.221 -0.054 -0.008 0.031 0.010 0.100 1.000 -0.081 -0.000 0.154 -0.055 -0.131 -0.001 -0.039 0.309 |\n", + "| p3770_p | 0.262 -0.171 0.025 -0.263 -0.134 0.366 0.180 0.058 0.128 -0.081 1.000 0.019 -0.177 0.252 0.072 0.022 0.115 -0.082 |\n", + "| DDstar_s | 0.029 0.024 0.000 -0.004 0.005 0.002 0.029 0.024 -0.005 -0.000 0.019 1.000 0.003 0.038 0.026 -0.001 0.054 0.007 |\n", + "| p4040_s | -0.140 0.080 0.018 0.368 0.099 -0.089 -0.241 0.009 0.010 0.154 -0.177 0.003 1.000 -0.562 -0.246 -0.001 -0.036 0.024 |\n", + "| p4160_p | 0.217 0.020 0.014 -0.425 -0.085 0.105 0.163 -0.131 0.019 -0.055 0.252 0.038 -0.562 1.000 0.282 0.024 0.016 -0.187 |\n", + "| p4415_p | -0.024 -0.021 0.018 -0.073 0.177 -0.044 0.099 -0.105 -0.100 -0.131 0.072 0.026 -0.246 0.282 1.000 0.014 -0.019 -0.216 |\n", + "| Dbar_s | 0.003 0.025 0.001 0.009 0.004 0.015 0.022 0.024 -0.005 -0.001 0.022 -0.001 -0.001 0.024 0.014 1.000 0.040 0.004 |\n", + "| jpsi_p | 0.173 -0.006 -0.064 0.130 0.052 0.302 -0.071 0.004 0.105 -0.039 0.115 0.054 -0.036 0.016 -0.019 0.040 1.000 -0.068 |\n", + "| p4160_s | -0.100 -0.011 0.035 0.258 0.123 -0.091 0.295 -0.083 0.001 0.309 -0.082 0.007 0.024 -0.187 -0.216 0.004 -0.068 1.000 |\n", + "--------------------------------------------------------------------------------------------------------------------------------------------------------------------------------\n", + "Hesse errors: OrderedDict([(, {'error': 0.2850165410517804}), (, {'error': 0.2124961626399453}), (, {'error': 0.01829244045185119}), (, {'error': 0.19189240704670774}), (, {'error': 0.07723151076900314}), (, {'error': 0.26113738425875166}), (, {'error': 0.16593345938513693}), (, {'error': 0.02822480404964267}), (, {'error': 0.037964767888795214}), (, {'error': 0.17890980886132019}), (, {'error': 0.09336821683842733}), (, {'error': 0.016393727908621925}), (, {'error': 0.1615214697208751}), (, {'error': 0.0976483880480612}), (, {'error': 0.17860121118891303}), (, {'error': 0.01277429819793291}), (, {'error': 0.022652863795177502}), (, {'error': 0.15625965574324152})])\n" + ] + }, + { + "name": "stderr", + "output_type": "stream", + "text": [ + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:166: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + ] + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Toy 1/2\n", + "Time taken: 4 min, 18 s\n", + "Projected time left: 4 min, 18 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 = 7.032E+05 | Ncalls=914 (914 total) |\n", + "| EDM = 0.000618 (Goal: 5E-06) | up = 0.5 |\n", "------------------------------------------------------------------\n", "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", "------------------------------------------------------------------\n", @@ -1552,92 +1657,66 @@ "------------------------------------------------------------------\n", "| False | True | True | True | False |\n", "------------------------------------------------------------------\n", - "Function minimum: 291.34660987562074\n", + "Function minimum: 703225.2607413743\n", "----------------------------------------------------------------------------------------------\n", "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", "----------------------------------------------------------------------------------------------\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", + "| 0 | DDstar_p | 1.96 | 0.22 | | |-6.28319 | 6.28319 | |\n", + "| 1 | p3770_s | 3.08 | 0.16 | | |0.918861 | 4.08114 | |\n", + "| 2 | bplus_0 | 0.475 | 0.013 | | | -2 | 2 | |\n", + "| 3 | Ctt | -0.39 | 0.14 | | | -1 | 1 | |\n", + "| 4 | bplus_2 | -0.24 | 0.05 | | | -2 | 2 | |\n", + "| 5 | Dbar_p | -4.08 | 0.22 | | |-6.28319 | 6.28319 | |\n", + "| 6 | p4040_p | 3.71 | 0.12 | | |-6.28319 | 6.28319 | |\n", + "| 7 | psi2s_p | 1.961 | 0.025 | | |-6.28319 | 6.28319 | |\n", + "| 8 | bplus_1 | -0.875 | 0.027 | | | -2 | 2 | |\n", + "| 9 | p4415_s | 1.09 | 0.13 | | |0.126447 | 2.35355 | |\n", + "| 10| p3770_p | 3.69 | 0.07 | | |-6.28319 | 6.28319 | |\n", + "| 11| DDstar_s | -0.300 | 0.011 | | | -0.3 | 0.3 | |\n", + "| 12| p4040_s | 1.02 | 0.12 | | |0.00501244| 2.01499 | |\n", + "| 13| p4160_p | -2.10 | 0.07 | | |-6.28319 | 6.28319 | |\n", + "| 14| p4415_p | 4.18 | 0.13 | | |-6.28319 | 6.28319 | |\n", + "| 15| Dbar_s | -0.300 | 0.008 | | | -0.3 | 0.3 | |\n", + "| 16| jpsi_p | -1.642 | 0.017 | | |-6.28319 | 6.28319 | |\n", + "| 17| p4160_s | 2.12 | 0.11 | | | 0.71676 | 3.68324 | |\n", "----------------------------------------------------------------------------------------------\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", + "| | DDstar_p p3770_s bplus_0 Ctt bplus_2 Dbar_p p4040_p psi2s_p bplus_1 p4415_s p3770_p DDstar_s p4040_s p4160_p p4415_p Dbar_s jpsi_p p4160_s |\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", + "| DDstar_p | 1.000 0.165 -0.005 -0.159 -0.333 -0.114 0.101 -0.005 0.405 -0.058 0.248 0.040 -0.141 0.222 -0.026 0.004 0.172 -0.100 |\n", + "| p3770_s | 0.165 1.000 0.043 -0.254 -0.143 0.044 0.025 -0.489 0.087 0.000 -0.162 0.023 0.074 0.047 -0.001 0.024 0.005 0.006 |\n", + "| bplus_0 | -0.005 0.043 1.000 -0.011 -0.013 0.020 0.025 -0.011 -0.821 0.016 0.023 0.000 0.015 0.017 0.020 0.001 -0.062 0.035 |\n", + "| Ctt | -0.159 -0.254 -0.011 1.000 0.685 -0.354 -0.282 0.181 -0.196 0.215 -0.292 -0.004 0.377 -0.430 -0.067 0.012 0.077 0.257 |\n", + "| bplus_2 | -0.333 -0.143 -0.013 0.685 1.000 -0.142 -0.063 -0.025 -0.347 -0.058 -0.155 0.004 0.105 -0.093 0.179 0.004 0.025 0.130 |\n", + "| Dbar_p | -0.114 0.044 0.020 -0.354 -0.142 1.000 0.005 0.093 0.195 -0.001 0.407 0.002 -0.085 0.119 -0.048 0.022 0.369 -0.099 |\n", + "| p4040_p | 0.101 0.025 0.025 -0.282 -0.063 0.005 1.000 -0.275 0.020 0.039 0.168 0.034 -0.245 0.148 0.095 0.026 -0.063 0.304 |\n", + "| psi2s_p | -0.005 -0.489 -0.011 0.181 -0.025 0.093 -0.275 1.000 0.070 0.015 0.075 0.038 0.031 -0.155 -0.129 0.038 0.015 -0.104 |\n", + "| bplus_1 | 0.405 0.087 -0.821 -0.196 -0.347 0.195 0.020 0.070 1.000 0.100 0.156 -0.004 0.001 0.034 -0.101 -0.005 0.138 -0.010 |\n", + "| p4415_s | -0.058 0.000 0.016 0.215 -0.058 -0.001 0.039 0.015 0.100 1.000 -0.078 -0.001 0.151 -0.055 -0.135 -0.001 -0.037 0.312 |\n", + "| p3770_p | 0.248 -0.162 0.023 -0.292 -0.155 0.407 0.168 0.075 0.156 -0.078 1.000 0.025 -0.185 0.260 0.061 0.029 0.164 -0.093 |\n", + "| DDstar_s | 0.040 0.023 0.000 -0.004 0.004 0.002 0.034 0.038 -0.004 -0.001 0.025 1.000 0.002 0.049 0.032 -0.002 0.069 0.008 |\n", + "| p4040_s | -0.141 0.074 0.015 0.377 0.105 -0.085 -0.245 0.031 0.001 0.151 -0.185 0.002 1.000 -0.559 -0.243 -0.003 -0.039 0.007 |\n", + "| p4160_p | 0.222 0.047 0.017 -0.430 -0.093 0.119 0.148 -0.155 0.034 -0.055 0.260 0.049 -0.559 1.000 0.277 0.030 0.040 -0.183 |\n", + "| p4415_p | -0.026 -0.001 0.020 -0.067 0.179 -0.048 0.095 -0.129 -0.101 -0.135 0.061 0.032 -0.243 0.277 1.000 0.017 -0.023 -0.205 |\n", + "| Dbar_s | 0.004 0.024 0.001 0.012 0.004 0.022 0.026 0.038 -0.005 -0.001 0.029 -0.002 -0.003 0.030 0.017 1.000 0.051 0.003 |\n", + "| jpsi_p | 0.172 0.005 -0.062 0.077 0.025 0.369 -0.063 0.015 0.138 -0.037 0.164 0.069 -0.039 0.040 -0.023 0.051 1.000 -0.078 |\n", + "| p4160_s | -0.100 0.006 0.035 0.257 0.130 -0.099 0.304 -0.104 -0.010 0.312 -0.093 0.008 0.007 -0.183 -0.205 0.003 -0.078 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" - ] - }, - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:160: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n" + "Hesse errors: OrderedDict([(, {'error': 0.21976002920084792}), (, {'error': 0.15829775347665453}), (, {'error': 0.013027835592936077}), (, {'error': 0.14012275276008618}), (, {'error': 0.0548143444334922}), (, {'error': 0.21679058977592525}), (, {'error': 0.11774283300825683}), (, {'error': 0.02483027542093197}), (, {'error': 0.027345870305282904}), (, {'error': 0.12735002863519618}), (, {'error': 0.07047893274838923}), (, {'error': 0.010590941888212607}), (, {'error': 0.1157783313417895}), (, {'error': 0.07019659457672}), (, {'error': 0.12646052173735356}), (, {'error': 0.008140502524761783}), (, {'error': 0.016511357460833764}), (, {'error': 0.11145758454315047})])\n" ] }, { "name": "stdout", "output_type": "stream", "text": [ - "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 \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1336\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mcompat\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mas_text\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mmessage\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_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 1317\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1318\u001b[0m return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m 1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1321\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_prun_fn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1405\u001b[0m return tf_session.TF_SessionRun_wrapper(\n\u001b[0;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1409\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_tf_sessionprun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mKeyboardInterrupt\u001b[0m: " + "Toy 2/2\n", + "Time taken: 9 min, 6 s\n", + "Projected time left: \n" ] }, { "data": { - "image/png": "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\n", + "image/png": "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\n", "text/plain": [ "
" ] @@ -1656,7 +1735,7 @@ "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", - "nr_of_toys = 10\n", + "nr_of_toys = 25\n", "nevents = int(pdg[\"number_of_decays\"])\n", "nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", @@ -1759,11 +1838,17 @@ " \n", " if fitting_range == 'cut':\n", " \n", - " tot_sam_1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " \n", + " tot_sam_1 = total_samp[_1]\n", " \n", - " tot_sam_2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + " _2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + " \n", + " tot_sam_2 = total_samp[_2]\n", "\n", - " tot_sam_3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\n", + " _3 = np.where((total_samp >= (psi2s_mass + 50.)) & (total_samp <= x_max))\n", + " \n", + " tot_sam_3 = total_samp[_3]\n", "\n", " tot_sam = np.append(tot_sam_1, tot_sam_2)\n", " tot_sam = np.append(tot_sam, tot_sam_3)\n", @@ -1807,7 +1892,7 @@ " plt.clf()\n", " plt.plot(test_q, calcs_test, label = 'pdf')\n", " plt.legend()\n", - " plt.ylim(0.0, 6e-6)\n", + " plt.ylim(0.0, 1.5e-6)\n", " plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))\n", "\n", " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", @@ -1825,25 +1910,57 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "2/2 fits converged\n", + "Mean Ctt value = -0.41593044149928\n", + "Mean Ctt error = 0.16600757990339696\n", + "Sensitivy = 0.00011574576965860746\n" + ] + } + ], "source": [ "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", - "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))" + "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))\n", + "print('Sensitivy = {}'.format(np.mean(Ctt_error_list)**2*4.2/1000))" ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 41, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "data": { + "image/png": "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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], + "source": [ + "plt.hist(tot_sam, bins = int((x_max-x_min)/7.))\n", + "\n", + "plt.show()\n", + "# _ = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + "\n", + "# total_samp[_]" + ] }, { "cell_type": "code", - "execution_count": null, + "execution_count": 42, "metadata": {}, "outputs": [], "source": [ diff --git a/test.png b/test.png index 697a2e2..30d59d7 100644 --- a/test.png +++ b/test.png Binary files differ