diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 6f0aedc..e3a1d69 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -9,31 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n", - " warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", - "For more information, please see:\n", - " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", - " * https://github.com/tensorflow/addons\n", - "If you depend on functionality not listed there, please file an issue.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -64,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -426,7 +404,13 @@ " axiv_nr = axiv_nonres(x, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\n", "\n", " tot = vec_f + axiv_nr\n", - "\n", + " \n", + " #Cut out jpsi and psi2s\n", + " \n", + " tot = tf.where(tf.math.logical_or(x < ztf.constant(jpsi_mass-60.), x > ztf.constant(jpsi_mass+70.)), tot, 0.0*tot)\n", + " \n", + " tot = tf.where(tf.math.logical_or(x < ztf.constant(psi2s_mass-50.), x > ztf.constant(psi2s_mass+50.)), tot, 0.0*tot)\n", + " \n", " return tot" ] }, @@ -439,19 +423,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "# formfactors\n", "\n", @@ -474,8 +448,8 @@ "\n", "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False) #lower_limit = rho_mass - rho_width, upper_limit = rho_mass + rho_width)\n", "rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n", - "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", - "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), floating = False) #, lower_limit=rho_scale-np.sqrt(rho_scale), upper_limit=rho_scale+np.sqrt(rho_scale))\n", + "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), lower_limit=rho_scale-np.sqrt(rho_scale), upper_limit=rho_scale+np.sqrt(rho_scale))\n", "\n", "#omega\n", "\n", @@ -483,8 +457,8 @@ "\n", "omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n", "omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n", - "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", - "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), floating = False) #, lower_limit=omega_scale-np.sqrt(omega_scale), upper_limit=omega_scale+np.sqrt(omega_scale))\n", + "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), lower_limit=omega_scale-np.sqrt(omega_scale), upper_limit=omega_scale+np.sqrt(omega_scale))\n", "\n", "\n", "#phi\n", @@ -493,8 +467,8 @@ "\n", "phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n", "phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n", - "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", - "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), floating = False) #, lower_limit=phi_scale-np.sqrt(phi_scale), upper_limit=phi_scale+np.sqrt(phi_scale))\n", + "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), lower_limit=phi_scale-np.sqrt(phi_scale), upper_limit=phi_scale+np.sqrt(phi_scale))\n", "\n", "#jpsi\n", "\n", @@ -560,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -590,12 +564,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tau_m = zfit.Parameter(\"tau_m\", ztf.constant(pdg['tau_M']), floating = False)\n", - "Ctt = zfit.Parameter(\"Ctt\", ztf.constant(0.0), lower_limit=-1.0, upper_limit=1.0)" + "Ctt = zfit.Parameter(\"Ctt\", ztf.constant(0.0), lower_limit=-1.5, upper_limit=1.5)" ] }, { @@ -607,24 +581,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_min = 2*pdg['muon_M']\n", "x_max = (pdg[\"Bplus_M\"]-pdg[\"Ks_M\"]-0.1)\n", "\n", - "epsilon = 0.3\n", - "\n", "# # Full spectrum\n", "\n", "obs_toy = zfit.Space('q', limits = (x_min, x_max))\n", "\n", "# Jpsi and Psi2s cut out\n", "\n", - "obs1 = zfit.Space('q', limits = (x_min + epsilon, jpsi_mass - 50. - epsilon))\n", - "obs2 = zfit.Space('q', limits = (jpsi_mass + 50. + epsilon, psi2s_mass - 50. - epsilon))\n", - "obs3 = zfit.Space('q', limits = (psi2s_mass + 50. + epsilon, x_max - epsilon))\n", + "obs1 = zfit.Space('q', limits = (x_min, jpsi_mass - 60.))\n", + "obs2 = zfit.Space('q', limits = (jpsi_mass + 70., psi2s_mass - 50.))\n", + "obs3 = zfit.Space('q', limits = (psi2s_mass + 50., x_max))\n", "\n", "obs_fit = obs1 + obs2 + obs3\n", "\n", @@ -647,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -691,20 +663,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "total_f_fit.normalization(obs_toy)" ] @@ -718,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -764,32 +725,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " if sys.path[0] == '':\n", - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -799,7 +737,7 @@ "# plt.plot(test_q, fplus_y, label = '+')\n", "# plt.plot(test_q, res_y, label = 'res')\n", "plt.legend()\n", - "plt.ylim(0.0, 1.5e-6)\n", + "# plt.ylim(0.0, 1.5e-6)\n", "# plt.yscale('log')\n", "# plt.xlim(770, 785)\n", "plt.savefig('test.png')\n", @@ -808,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -824,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -840,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -853,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -910,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -994,16 +932,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "total_f._sample_and_weights = UniformSampleAndWeights" + "# total_f._sample_and_weights = UniformSampleAndWeights" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1012,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1021,7 +959,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1069,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1086,7 +1024,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1110,7 +1048,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1137,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1160,7 +1098,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1169,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1185,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1215,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1229,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1247,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1261,7 +1199,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1220,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1292,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1323,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1340,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1396,9 +1334,9 @@ " mean1 = ztf.constant(0.466)\n", " mean2 = ztf.constant(-0.885)\n", " mean3 = ztf.constant(-0.213)\n", - " sigma1 = ztf.constant(0.014)\n", - " sigma2 = ztf.constant(0.128)\n", - " sigma3 = ztf.constant(0.548)\n", + " sigma1 = ztf.constant(0.014/3.)\n", + " sigma2 = ztf.constant(0.128/3.)\n", + " sigma3 = ztf.constant(0.548/3.)\n", " x1 = (val1-mean1)/sigma1\n", " x2 = (val2-mean2)/sigma2\n", " x3 = (val3-mean3)/sigma3\n", @@ -1453,9 +1391,9 @@ "\n", "# 7. Constraint on Ctt with higher limits\n", "\n", - "# Ctt_abs = tf.pow(tf.pow(Ctt, 2.), 0.5)\n", + "constraint7 = tf.cond(tf.greater_equal(Ctt*Ctt, 0.25), lambda: 100000., lambda: 0.)\n", "\n", - "# constraint7 = tf.cond(tf.greater_equal(Ctt_abs, 0.5), lambda: 100000., lambda: 0.)\n", + "constraint7dtype = tf.float64\n", "\n", "# zfit.run(constraint6_0)\n", "\n", @@ -1464,7 +1402,7 @@ "#List of all constraints\n", "\n", "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4, #constraint4_0, constraint4_1, constraint4_2,\n", - " constraint6_0, constraint6_1]" + " constraint6_0, constraint6_1]#, constraint7]" ] }, { @@ -1476,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1546,199 +1484,29 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n", - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\distributions\\categorical.py:263: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.random.categorical instead.\n", - "Toy 0: Generating data...\n", - "Toy 0: Data generation finished\n", - "Toy 0: Loading data...\n", - "Toy 0: Loading data finished\n", - "Toy 0: Fitting pdf...\n", - "------------------------------------------------------------------\n", - "| FCN = 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", - "| True | True | False | False |\n", - "------------------------------------------------------------------\n", - "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", - "------------------------------------------------------------------\n", - "| False | True | True | True | False |\n", - "------------------------------------------------------------------\n", - "Function minimum: 703225.2607413743\n", - "----------------------------------------------------------------------------------------------\n", - "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", - "----------------------------------------------------------------------------------------------\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", - "| | 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 0.165 -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': 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 2/2\n", - "Time taken: 9 min, 6 s\n", - "Projected time left: \n" - ] - }, - { - "data": { - "image/png": "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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# zfit.run.numeric_checks = False \n", "\n", "fitting_range = 'cut'\n", + "total_BR = 1.7e-10 + 4.9e-10 + 2.5e-9 + 6.02e-5 + 4.97e-6 + 1.38e-9 + 4.2e-10 + 2.6e-9 + 6.1e-10 + 4.37e-7\n", + "cut_BR = 1.0 - (6.02e-5 + 4.97e-6)/total_BR\n", "\n", "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", - "nr_of_toys = 25\n", - "nevents = int(pdg[\"number_of_decays\"])\n", - "nevents = pdg[\"number_of_decays\"]\n", + "nr_of_toys = 50\n", + "if fitting_range == 'cut':\n", + " nevents = int(pdg[\"number_of_decays\"]*cut_BR)\n", + "else:\n", + " nevents = int(pdg[\"number_of_decays\"])\n", + "# nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", + "nevents *= 41\n", "# zfit.settings.set_verbosity(10)\n", "calls = int(nevents/event_stack + 1)\n", "\n", @@ -1764,29 +1532,44 @@ " \n", " reset_param_values()\n", " \n", - " for call in range(calls):\n", - "\n", - " sampler.resample(n=event_stack)\n", + " if fitting_range == 'cut':\n", + " \n", + " sampler.resample(n=nevents)\n", " s = sampler.unstack_x()\n", " sam = zfit.run(s)\n", - "\n", - " c = call + 1\n", + " calls = 0\n", + " c = 1\n", " \n", - " with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n", - " pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)\n", + " else: \n", + " for call in range(calls):\n", + "\n", + " sampler.resample(n=event_stack)\n", + " s = sampler.unstack_x()\n", + " sam = zfit.run(s)\n", + "\n", + " c = call + 1\n", + "\n", + " with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n", + " pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)\n", " \n", " print(\"Toy {}: Data generation finished\".format(toy))\n", " \n", " ### Load data\n", " \n", " print(\"Toy {}: Loading data...\".format(toy))\n", + " \n", + " if fitting_range == 'cut':\n", + " \n", + " total_samp = sam\n", + " \n", + " else:\n", + " \n", + " for call in range(calls):\n", + " with open(r\"data/zfit_toys/toy_0/{}.pkl\".format(call), \"rb\") as input_file:\n", + " sam = pkl.load(input_file)\n", + " total_samp = np.append(total_samp, sam)\n", "\n", - " for call in range(calls):\n", - " with open(r\"data/zfit_toys/toy_0/{}.pkl\".format(call), \"rb\") as input_file:\n", - " sam = pkl.load(input_file)\n", - " total_samp = np.append(total_samp, sam)\n", - "\n", - " total_samp = total_samp.astype('float64')\n", + " total_samp = total_samp.astype('float64')\n", " \n", " if fitting_range == 'full':\n", "\n", @@ -1838,11 +1621,11 @@ " \n", " if fitting_range == 'cut':\n", " \n", - " _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 60.)))\n", " \n", " tot_sam_1 = total_samp[_1]\n", " \n", - " _2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + " _2 = np.where((total_samp >= (jpsi_mass + 70.)) & (total_samp <= (psi2s_mass - 50.)))\n", " \n", " tot_sam_2 = total_samp[_2]\n", "\n", @@ -1887,17 +1670,22 @@ " os.mkdir(plotdirName)\n", " # print(\"Directory \" , dirName , \" Created \")\n", " \n", + " plt.clf()\n", + " plt.hist(tot_sam, bins = int((x_max-x_min)/7.), label = 'toy data')\n", + " plt.savefig(plotdirName + '/toy_histo_cut_region{}.png'.format(toy))\n", + "\n", + " \n", " probs = total_f_fit.pdf(test_q, norm_range=False)\n", " calcs_test = zfit.run(probs)\n", " plt.clf()\n", " plt.plot(test_q, calcs_test, label = 'pdf')\n", " plt.legend()\n", - " plt.ylim(0.0, 1.5e-6)\n", + "# plt.ylim(0.0, 1.5e-6)\n", " plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))\n", - "\n", + " \n", " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", - " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(toy+1))*((nr_of_toys-toy-1)))))\n", " " ] }, @@ -1906,66 +1694,76 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "with open(\"data/results/Ctt_list.pkl\", \"wb\") as f:\n", + " pkl.dump(Ctt_list, f, pkl.HIGHEST_PROTOCOL)\n", + "with open(\"data/results/Ctt_error_list.pkl\", \"wb\") as f:\n", + " pkl.dump(Ctt_error_list, f, pkl.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", + "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", + "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))\n", + "print('95 Sensitivy = {}'.format((2*np.mean(Ctt_error_list)**2)*4.2/1000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(tot_sam, bins = int((x_max-x_min)/7.))\n", + "\n", + "plt.show()\n", + "\n", + "# _ = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + "\n", + "tot_sam.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# sample from original values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "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)))\n", - "print('Sensitivy = {}'.format(np.mean(Ctt_error_list)**2*4.2/1000))" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "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": 42, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# sample from original values" - ] + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/Untitled.ipynb b/Untitled.ipynb deleted file mode 100644 index 720b3d9..0000000 --- a/Untitled.ipynb +++ /dev/null @@ -1,282 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "metadata": {}, - "source": [ - "# Numpy backup for scaling in the beginning" - ] - }, - { - "cell_type": "code", - "execution_count": 1, - "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:53: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n", - " warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", - "For more information, please see:\n", - " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", - " * https://github.com/tensorflow/addons\n", - "If you depend on functionality not listed there, please file an issue.\n", - "\n" - ] - } - ], - "source": [ - "import numpy as np\n", - "from pdg_const import pdg\n", - "import matplotlib\n", - "import matplotlib.pyplot as plt\n", - "import pickle as pkl\n", - "import sys\n", - "import time\n", - "from helperfunctions import display_time, prepare_plot\n", - "import cmath as c\n", - "import scipy.integrate as integrate\n", - "from scipy.optimize import fminbound\n", - "from array import array as arr\n", - "import collections\n", - "from itertools import compress\n", - "import tensorflow as tf\n", - "import zfit\n", - "from zfit import ztf\n", - "from IPython.display import clear_output\n", - "import os" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [ - "def formfactor( q2, subscript): #returns real value\n", - " #check if subscript is viable\n", - "\n", - " if subscript != \"0\" and subscript != \"+\" and subscript != \"T\":\n", - " raise ValueError('Wrong subscript entered, choose either 0, + or T')\n", - "\n", - " #get constants\n", - "\n", - " mK = pdg['Ks_M']\n", - " mbstar0 = pdg[\"mbstar0\"]\n", - " mbstar = pdg[\"mbstar\"]\n", - " b0 = pdg[\"b0\"]\n", - " bplus = pdg[\"bplus\"]\n", - " bT = pdg[\"bT\"]\n", - "\n", - " mmu = pdg['muon_M']\n", - " mb = pdg['bquark_M']\n", - " ms = pdg['squark_M']\n", - " mB = pdg['Bplus_M']\n", - "\n", - " #N comes from derivation in paper\n", - "\n", - " N = 3\n", - "\n", - " #some helperfunctions\n", - "\n", - " tpos = (mB - mK)**2\n", - " tzero = (mB + mK)*(ztf.sqrt(mB)-ztf.sqrt(mK))**2\n", - "\n", - " z_oben = ztf.sqrt(tpos - q2) - ztf.sqrt(tpos - tzero)\n", - " z_unten = ztf.sqrt(tpos - q2) + ztf.sqrt(tpos - tzero)\n", - " z = tf.divide(z_oben, z_unten)\n", - "\n", - " #calculate f0\n", - "\n", - " if subscript == \"0\":\n", - " prefactor = 1/(1 - q2/(mbstar0**2))\n", - " _sum = 0\n", - "\n", - " for i in range(N):\n", - " _sum += b0[i]*(tf.pow(z,i))\n", - "\n", - " return tf.complex(prefactor * _sum, ztf.constant(0.0))\n", - "\n", - " #calculate f+ or fT\n", - "\n", - " else:\n", - " prefactor = 1/(1 - q2/(mbstar**2))\n", - " _sum = 0\n", - "\n", - " if subscript == \"T\":\n", - " b = bT\n", - " else:\n", - " b = bplus\n", - "\n", - " for i in range(N):\n", - " _sum += b[i] * (tf.pow(z, i) - ((-1)**(i-N)) * (i/N) * tf.pow(z, N))\n", - "\n", - " return tf.complex(prefactor * _sum, ztf.constant(0.0))\n", - "\n", - "def resonance(q, _mass, width, phase, scale):\n", - "\n", - " q2 = tf.pow(q, 2)\n", - "\n", - " mmu = ztf.constant(pdg['muon_M'])\n", - "\n", - " p = 0.5 * ztf.sqrt(q2 - 4*(mmu**2))\n", - "\n", - " p0 = 0.5 * ztf.sqrt(_mass**2 - 4*mmu**2)\n", - "\n", - " gamma_j = tf.divide(p, q2) * _mass * width / p0\n", - "\n", - " #Calculate the resonance\n", - "\n", - " _top = tf.complex(_mass * width, ztf.constant(0.0))\n", - "\n", - " _bottom = tf.complex(_mass**2 - q2, -_mass*gamma_j)\n", - "\n", - " com = _top/_bottom\n", - "\n", - " #Rotate by the phase\n", - "\n", - " r = ztf.to_complex(scale*tf.abs(com))\n", - "\n", - " _phase = tf.angle(com)\n", - "\n", - " _phase += phase\n", - "\n", - " com = r * tf.exp(tf.complex(ztf.constant(0.0), _phase))\n", - "\n", - " return com\n", - "\n", - "def bifur_gauss(q, mean, sigma_L, sigma_R, scale):\n", - "\n", - " _exp = tf.where(q < mean, ztf.exp(- tf.pow((q-mean),2) / (2 * sigma_L**2)), ztf.exp(- tf.pow((q-mean),2) / (2 * sigma_R**2)))\n", - "\n", - " #Scale so the total area under curve is 1 and the top of the cusp is continuous\n", - "\n", - " dgamma = scale*_exp/(ztf.sqrt(2*np.pi))*2*(sigma_L*sigma_R)/(sigma_L+sigma_R)\n", - "\n", - " com = ztf.complex(dgamma, ztf.constant(0.0))\n", - "\n", - " return com\n", - "\n", - "def axiv_nonres(q):\n", - "\n", - " GF = ztf.constant(pdg['GF'])\n", - " alpha_ew = ztf.constant(pdg['alpha_ew'])\n", - " Vtb = ztf.constant(pdg['Vtb'])\n", - " Vts = ztf.constant(pdg['Vts'])\n", - " C10eff = ztf.constant(pdg['C10eff'])\n", - "\n", - " mmu = ztf.constant(pdg['muon_M'])\n", - " mb = ztf.constant(pdg['bquark_M'])\n", - " ms = ztf.constant(pdg['squark_M'])\n", - " mK = ztf.constant(pdg['Ks_M'])\n", - " mB = ztf.constant(pdg['Bplus_M'])\n", - "\n", - " q2 = tf.pow(q, 2)\n", - "\n", - " #Some helperfunctions\n", - "\n", - " beta = ztf.sqrt(tf.abs(1. - 4. * mmu**2. / q2))\n", - "\n", - " kabs = ztf.sqrt(mB**2. +tf.pow(q2, 2)/mB**2. + mK**4./mB**2. - 2. * (mB**2. * mK**2. + mK**2. * q2 + mB**2. * q2) / mB**2.)\n", - "\n", - " #prefactor in front of whole bracket\n", - "\n", - " prefactor1 = GF**2. *alpha_ew**2. * (tf.abs(Vtb*Vts))**2. * kabs * beta / (128. * np.pi**5.)\n", - "\n", - " #left term in bracket\n", - "\n", - " bracket_left = 2./3. * kabs**2. * beta**2. *tf.abs(tf.complex(C10eff, ztf.constant(0.0))*formfactor(q2, \"+\"))**2.\n", - "\n", - " #middle term in bracket\n", - "\n", - " _top = 4. * mmu**2. * (mB**2. - mK**2.) * (mB**2. - mK**2.)\n", - "\n", - " _under = q2 * mB**2.\n", - "\n", - " bracket_middle = _top/_under *tf.pow(tf.abs(tf.complex(C10eff, ztf.constant(0.0)) * formfactor(q2, \"0\")), 2)\n", - "\n", - " #Note sqrt(q2) comes from derivation as we use q2 and plot q\n", - "\n", - " return prefactor1 * (bracket_left + bracket_middle) * 2 *ztf.sqrt(q2)\n", - "\n", - "def vec(q, funcs):\n", - " \n", - " q2 = tf.pow(q, 2)\n", - "\n", - " GF = ztf.constant(pdg['GF'])\n", - " alpha_ew = ztf.constant(pdg['alpha_ew'])\n", - " Vtb = ztf.constant(pdg['Vtb'])\n", - " Vts = ztf.constant(pdg['Vts'])\n", - " C7eff = ztf.constant(pdg['C7eff'])\n", - "\n", - " mmu = ztf.constant(pdg['muon_M'])\n", - " mb = ztf.constant(pdg['bquark_M'])\n", - " ms = ztf.constant(pdg['squark_M'])\n", - " mK = ztf.constant(pdg['Ks_M'])\n", - " mB = ztf.constant(pdg['Bplus_M'])\n", - "\n", - " #Some helperfunctions\n", - "\n", - " beta = ztf.sqrt(tf.abs(1. - 4. * mmu**2. / q2))\n", - "\n", - " kabs = ztf.sqrt(mB**2. + tf.pow(q2, 2)/mB**2. + mK**4./mB**2. - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2)\n", - "\n", - " #prefactor in front of whole bracket\n", - "\n", - " prefactor1 = GF**2. *alpha_ew**2. * (tf.abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.)\n", - "\n", - " #right term in bracket\n", - "\n", - " prefactor2 = kabs**2 * (1. - 1./3. * beta**2)\n", - "\n", - " abs_bracket = tf.abs(c9eff(q, funcs) * formfactor(q2, \"+\") + tf.complex(2.0 * C7eff * (mb + ms)/(mB + mK), ztf.constant(0.0)) * formfactor(q2, \"T\"))**2\n", - "\n", - " bracket_right = prefactor2 * abs_bracket\n", - "\n", - " #Note sqrt(q2) comes from derivation as we use q2 and plot q\n", - "\n", - " return prefactor1 * bracket_right * 2 * ztf.sqrt(q2)\n", - "\n", - "def c9eff(q, funcs):\n", - "\n", - " C9eff_nr = tf.complex(ztf.constant(pdg['C9eff']), ztf.constant(0.0))\n", - "\n", - " c9 = C9eff_nr\n", - "\n", - " c9 = c9 + funcs\n", - "\n", - " return c9" - ] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/Untitled1.ipynb b/Untitled1.ipynb deleted file mode 100644 index d038060..0000000 --- a/Untitled1.ipynb +++ /dev/null @@ -1,90 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "execution_count": 37, - "metadata": {}, - "outputs": [], - "source": [ - "import os\n", - "import re\n", - "import numpy as np" - ] - }, - { - "cell_type": "code", - "execution_count": 42, - "metadata": {}, - "outputs": [], - "source": [ - "Ctt = []\n", - "Ctt_err = []\n", - "\n", - "for filename in os.listdir('prelim_results'):\n", - " if filename.endswith(\".out\"):\n", - " with open('./prelim_results/' + filename) as file: \n", - " data = file.read() \n", - " _ = data.partition('value = ')[-1]\n", - "# print(_)\n", - " _ = _.partition('Mean Ctt error = ')\n", - "# print(_[-1])\n", - " err = _[0][:-2]\n", - " Ctt.append(float(err))\n", - "# print(err)\n", - " _ = _[-1].partition('\\nSimulation ended\\n')[0]\n", - "# print(_)\n", - " Ctt_err.append(float(_))\n", - " \n", - "Ctt_mean = np.mean(Ctt)\n", - "Ctt_err_mean = np.mean(Ctt_err)" - ] - }, - { - "cell_type": "code", - "execution_count": 43, - "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Ctt_mean = -0.09987017193178732\n", - "Ctt_err_mean = 0.14186649892786868\n" - ] - } - ], - "source": [ - "print('Ctt_mean = ', Ctt_mean)\n", - "print('Ctt_err_mean = ', Ctt_err_mean)" - ] - }, - { - "cell_type": "code", - "execution_count": null, - "metadata": {}, - "outputs": [], - "source": [] - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.7.3" - } - }, - "nbformat": 4, - "nbformat_minor": 2 -} diff --git a/data/plots/0..png b/data/plots/0..png deleted file mode 100644 index 9c37fe6..0000000 --- a/data/plots/0..png +++ /dev/null Binary files differ diff --git a/data/plots/0.png b/data/plots/0.png new file mode 100644 index 0000000..ed9c74b --- /dev/null +++ b/data/plots/0.png Binary files differ diff --git a/data/plots/toy_fit_cut_region0.png b/data/plots/toy_fit_cut_region0.png index a0acb13..c666f78 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 875a5ae..a9c963b 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_region10.png b/data/plots/toy_fit_cut_region10.png new file mode 100644 index 0000000..7b29561 --- /dev/null +++ b/data/plots/toy_fit_cut_region10.png Binary files differ diff --git a/data/plots/toy_fit_cut_region11.png b/data/plots/toy_fit_cut_region11.png new file mode 100644 index 0000000..77a48c0 --- /dev/null +++ b/data/plots/toy_fit_cut_region11.png Binary files differ diff --git a/data/plots/toy_fit_cut_region12.png b/data/plots/toy_fit_cut_region12.png new file mode 100644 index 0000000..95202c5 --- /dev/null +++ b/data/plots/toy_fit_cut_region12.png Binary files differ diff --git a/data/plots/toy_fit_cut_region13.png b/data/plots/toy_fit_cut_region13.png new file mode 100644 index 0000000..81d8225 --- /dev/null +++ b/data/plots/toy_fit_cut_region13.png Binary files differ diff --git a/data/plots/toy_fit_cut_region14.png b/data/plots/toy_fit_cut_region14.png new file mode 100644 index 0000000..11e140c --- /dev/null +++ b/data/plots/toy_fit_cut_region14.png Binary files differ diff --git a/data/plots/toy_fit_cut_region2.png b/data/plots/toy_fit_cut_region2.png index acc4826..edf8699 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 7638f08..7d18f5a 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 092fe1f..9fe4b41 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 8f99389..75de5b7 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 bcc2ceb..ec4c663 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 85e7a02..1e14b50 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 1deaf6a..1990821 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 index 6c2cc6e..b590aed 100644 --- a/data/plots/toy_fit_cut_region9.png +++ b/data/plots/toy_fit_cut_region9.png Binary files differ diff --git a/data/plots/toy_histo_cut_region0.png b/data/plots/toy_histo_cut_region0.png new file mode 100644 index 0000000..cf76664 --- /dev/null +++ b/data/plots/toy_histo_cut_region0.png Binary files differ diff --git a/data/plots/toy_histo_cut_region1.png b/data/plots/toy_histo_cut_region1.png new file mode 100644 index 0000000..03c311a --- /dev/null +++ b/data/plots/toy_histo_cut_region1.png Binary files differ diff --git a/data/plots/toy_histo_cut_region10.png b/data/plots/toy_histo_cut_region10.png new file mode 100644 index 0000000..9c2d65c --- /dev/null +++ b/data/plots/toy_histo_cut_region10.png Binary files differ diff --git a/data/plots/toy_histo_cut_region11.png b/data/plots/toy_histo_cut_region11.png new file mode 100644 index 0000000..6326ecc --- /dev/null +++ b/data/plots/toy_histo_cut_region11.png Binary files differ diff --git a/data/plots/toy_histo_cut_region12.png b/data/plots/toy_histo_cut_region12.png new file mode 100644 index 0000000..add1645 --- /dev/null +++ b/data/plots/toy_histo_cut_region12.png Binary files differ diff --git a/data/plots/toy_histo_cut_region13.png b/data/plots/toy_histo_cut_region13.png new file mode 100644 index 0000000..d78906d --- /dev/null +++ b/data/plots/toy_histo_cut_region13.png Binary files differ diff --git a/data/plots/toy_histo_cut_region14.png b/data/plots/toy_histo_cut_region14.png new file mode 100644 index 0000000..cc9b384 --- /dev/null +++ b/data/plots/toy_histo_cut_region14.png Binary files differ diff --git a/data/plots/toy_histo_cut_region2.png b/data/plots/toy_histo_cut_region2.png new file mode 100644 index 0000000..7299497 --- /dev/null +++ b/data/plots/toy_histo_cut_region2.png Binary files differ diff --git a/data/plots/toy_histo_cut_region3.png b/data/plots/toy_histo_cut_region3.png new file mode 100644 index 0000000..3fa2294 --- /dev/null +++ b/data/plots/toy_histo_cut_region3.png Binary files differ diff --git a/data/plots/toy_histo_cut_region4.png b/data/plots/toy_histo_cut_region4.png new file mode 100644 index 0000000..c926235 --- /dev/null +++ b/data/plots/toy_histo_cut_region4.png Binary files differ diff --git a/data/plots/toy_histo_cut_region5.png b/data/plots/toy_histo_cut_region5.png new file mode 100644 index 0000000..de35e2e --- /dev/null +++ b/data/plots/toy_histo_cut_region5.png Binary files differ diff --git a/data/plots/toy_histo_cut_region6.png b/data/plots/toy_histo_cut_region6.png new file mode 100644 index 0000000..cdb6067 --- /dev/null +++ b/data/plots/toy_histo_cut_region6.png Binary files differ diff --git a/data/plots/toy_histo_cut_region7.png b/data/plots/toy_histo_cut_region7.png new file mode 100644 index 0000000..6312870 --- /dev/null +++ b/data/plots/toy_histo_cut_region7.png Binary files differ diff --git a/data/plots/toy_histo_cut_region8.png b/data/plots/toy_histo_cut_region8.png new file mode 100644 index 0000000..0d88c96 --- /dev/null +++ b/data/plots/toy_histo_cut_region8.png Binary files differ diff --git a/data/plots/toy_histo_cut_region9.png b/data/plots/toy_histo_cut_region9.png new file mode 100644 index 0000000..6015538 --- /dev/null +++ b/data/plots/toy_histo_cut_region9.png Binary files differ diff --git a/data/results/Ctt_error_list.pkl b/data/results/Ctt_error_list.pkl new file mode 100644 index 0000000..66289db --- /dev/null +++ b/data/results/Ctt_error_list.pkl Binary files differ diff --git a/data/results/Ctt_list.pkl b/data/results/Ctt_list.pkl new file mode 100644 index 0000000..64c93e8 --- /dev/null +++ b/data/results/Ctt_list.pkl Binary files differ diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index b13a0f3..651b5ca 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 bd43d79..a36b0f7 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 2f1168c..1465d44 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 18b185e..8e961f6 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 273190b..7f5106b 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 b430037..4d1dcd0 100644 --- a/data/zfit_toys/toy_0/5.pkl +++ b/data/zfit_toys/toy_0/5.pkl Binary files differ diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 6f0aedc..e3a1d69 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -9,31 +9,9 @@ }, { "cell_type": "code", - "execution_count": 1, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n", - " warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n" - ] - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "\n", - "WARNING: The TensorFlow contrib module will not be included in TensorFlow 2.0.\n", - "For more information, please see:\n", - " * https://github.com/tensorflow/community/blob/master/rfcs/20180907-contrib-sunset.md\n", - " * https://github.com/tensorflow/addons\n", - "If you depend on functionality not listed there, please file an issue.\n", - "\n" - ] - } - ], + "outputs": [], "source": [ "import os\n", "\n", @@ -64,7 +42,7 @@ }, { "cell_type": "code", - "execution_count": 2, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -90,7 +68,7 @@ }, { "cell_type": "code", - "execution_count": 3, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -278,7 +256,7 @@ }, { "cell_type": "code", - "execution_count": 4, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -333,7 +311,7 @@ }, { "cell_type": "code", - "execution_count": 5, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -426,7 +404,13 @@ " axiv_nr = axiv_nonres(x, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\n", "\n", " tot = vec_f + axiv_nr\n", - "\n", + " \n", + " #Cut out jpsi and psi2s\n", + " \n", + " tot = tf.where(tf.math.logical_or(x < ztf.constant(jpsi_mass-60.), x > ztf.constant(jpsi_mass+70.)), tot, 0.0*tot)\n", + " \n", + " tot = tf.where(tf.math.logical_or(x < ztf.constant(psi2s_mass-50.), x > ztf.constant(psi2s_mass+50.)), tot, 0.0*tot)\n", + " \n", " return tot" ] }, @@ -439,19 +423,9 @@ }, { "cell_type": "code", - "execution_count": 6, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Colocations handled automatically by placer.\n" - ] - } - ], + "outputs": [], "source": [ "# formfactors\n", "\n", @@ -474,8 +448,8 @@ "\n", "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False) #lower_limit = rho_mass - rho_width, upper_limit = rho_mass + rho_width)\n", "rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n", - "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", - "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), floating = False) #, lower_limit=rho_scale-np.sqrt(rho_scale), upper_limit=rho_scale+np.sqrt(rho_scale))\n", + "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale), lower_limit=rho_scale-np.sqrt(rho_scale), upper_limit=rho_scale+np.sqrt(rho_scale))\n", "\n", "#omega\n", "\n", @@ -483,8 +457,8 @@ "\n", "omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n", "omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n", - "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", - "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), floating = False) #, lower_limit=omega_scale-np.sqrt(omega_scale), upper_limit=omega_scale+np.sqrt(omega_scale))\n", + "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale), lower_limit=omega_scale-np.sqrt(omega_scale), upper_limit=omega_scale+np.sqrt(omega_scale))\n", "\n", "\n", "#phi\n", @@ -493,8 +467,8 @@ "\n", "phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n", "phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n", - "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), floating = False) #, lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", - "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), floating = False) #, lower_limit=phi_scale-np.sqrt(phi_scale), upper_limit=phi_scale+np.sqrt(phi_scale))\n", + "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), lower_limit=-2*np.pi, upper_limit=2*np.pi)\n", + "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale), lower_limit=phi_scale-np.sqrt(phi_scale), upper_limit=phi_scale+np.sqrt(phi_scale))\n", "\n", "#jpsi\n", "\n", @@ -560,7 +534,7 @@ }, { "cell_type": "code", - "execution_count": 7, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -590,12 +564,12 @@ }, { "cell_type": "code", - "execution_count": 8, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "tau_m = zfit.Parameter(\"tau_m\", ztf.constant(pdg['tau_M']), floating = False)\n", - "Ctt = zfit.Parameter(\"Ctt\", ztf.constant(0.0), lower_limit=-1.0, upper_limit=1.0)" + "Ctt = zfit.Parameter(\"Ctt\", ztf.constant(0.0), lower_limit=-1.5, upper_limit=1.5)" ] }, { @@ -607,24 +581,22 @@ }, { "cell_type": "code", - "execution_count": 9, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ "x_min = 2*pdg['muon_M']\n", "x_max = (pdg[\"Bplus_M\"]-pdg[\"Ks_M\"]-0.1)\n", "\n", - "epsilon = 0.3\n", - "\n", "# # Full spectrum\n", "\n", "obs_toy = zfit.Space('q', limits = (x_min, x_max))\n", "\n", "# Jpsi and Psi2s cut out\n", "\n", - "obs1 = zfit.Space('q', limits = (x_min + epsilon, jpsi_mass - 50. - epsilon))\n", - "obs2 = zfit.Space('q', limits = (jpsi_mass + 50. + epsilon, psi2s_mass - 50. - epsilon))\n", - "obs3 = zfit.Space('q', limits = (psi2s_mass + 50. + epsilon, x_max - epsilon))\n", + "obs1 = zfit.Space('q', limits = (x_min, jpsi_mass - 60.))\n", + "obs2 = zfit.Space('q', limits = (jpsi_mass + 70., psi2s_mass - 50.))\n", + "obs3 = zfit.Space('q', limits = (psi2s_mass + 50., x_max))\n", "\n", "obs_fit = obs1 + obs2 + obs3\n", "\n", @@ -647,7 +619,7 @@ }, { "cell_type": "code", - "execution_count": 10, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -691,20 +663,9 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 11, - "metadata": {}, - "output_type": "execute_result" - } - ], + "outputs": [], "source": [ "total_f_fit.normalization(obs_toy)" ] @@ -718,7 +679,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -764,32 +725,9 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": null, "metadata": {}, - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py:12: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " if sys.path[0] == '':\n", - "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\pylabtools.py:128: UserWarning: Creating legend with loc=\"best\" can be slow with large amounts of data.\n", - " fig.canvas.print_figure(bytes_io, **kw)\n" - ] - }, - { - "data": { - "image/png": "iVBORw0KGgoAAAANSUhEUgAAAaUAAAD4CAYAAABMtfkzAAAABHNCSVQICAgIfAhkiAAAAAlwSFlzAAALEgAACxIB0t1+/AAAADh0RVh0U29mdHdhcmUAbWF0cGxvdGxpYiB2ZXJzaW9uMy4xLjAsIGh0dHA6Ly9tYXRwbG90bGliLm9yZy+17YcXAAAgAElEQVR4nO29eXzc1XX3/z4zo9FqSdbmTcaSbWFjgzHg2BBDsM1mEhqTBoLpA6EJfWjS0DTL04R0SZ+m0CekKaRtSIgLNGQphpLkh0PYYwMBjBdWr7JleZM3rZZkSSPNcn9/fL8zGo9nlSXNaHTer5de/s793nvuna+l+cy599xzxRiDoiiKomQCjnQPQFEURVGCqCgpiqIoGYOKkqIoipIxqCgpiqIoGYOKkqIoipIxuNI9gEyjoqLC1NTUpHsYipLx7DnRTV6Ok3PKCk4r7+zzcqi9l7qqIvJynGkancXe5lO4nQ5mlBfgCxh2Hetiamk+5YXutI4rG3nnnXdajTGVZ2tHRSmCmpoatm7dmu5hKErGs+xfNrCgupR/v/Wi08pf2H6cL/ziHZ788uXMn1qSptFZrPzB60wvK+A/P7uIjp4BLvqnl/n2H83jc0tr0zqubEREDg6HHZ2+UxRlSAQMOB1yRnmwLBAY7RFFJzhChz0uf0D3ZmYyKkqKogwJf8AgZ2oSruCHfwZszDeG0BhDYpkB41Jio6KkKMqQMMbgjKJKgx5J+l0lg0FsXyk4Vn/6h6XEQdeUFEUZEn5jcEQRpUz68A/3lBz2V/DR9pS8Xi9NTU14PJ5R7XekyMvLo7q6mpycnBGxr6KkKMqQCJhBrygcZwat3RjCpu8kPeNqampiwoQJ1NTUINHmO8cQxhja2tpoamqitnZkgkV0+k5RlCERCBiiaFJmiZIxISFI17g8Hg/l5eVjXpAARITy8vIR9fpUlBRFGRJ+Y2JE3w3eTzfGDEbfiQgi6Ql0yAZBCjLS70VFSVGUIWF5StFEyRG6n26s6bvBMTpFMsKDU2KjoqQoypAIGOIGOvgy4MPfGEP4CJ0OyQgPLhN59dVXueGGGwDo7+/n6quvZuHChTz55JOjOg4NdFAUZUgEjAlN1YUTjHLLBI8kPNABLFHKBA8u03nvvffwer28//77o953Up6SiKwUkXoRaRCRe6LczxWRJ+37m0SkJuzet+zyehG5LpFNEam1bey1bbrj9SEi5SKyQUROicgPY4x/nYhsT+6RKIqSDP6Y03eZs0k1fE0JgtN3aRtO2jhw4ABz587ljjvuYMGCBdx000309vbywgsvMHfuXC6//HJ+/etfA9Dc3Mxtt93G+++/z8KFC9m3b9+ojjWhpyQiTuAh4BqgCdgiIuuMMTvDqt0JdBhjZovIauB+4BYRmQesBuYDU4FXRORcu00sm/cDDxpj1orIw7btH8fqA/AAfw+cb/9Ejv+PgVMpPRVFURJiYoSEBzM6ZMT0Hea0NSWHQ9Iqlv/42x3sPNo1rDbnTS3mH/5ofsJ69fX1PProoyxdupTPf/7zPPDAA/zkJz9h/fr1zJ49m1tuuQWAqqoqHnnkEb7//e/z7LPPDutYkyEZT2kx0GCMaTTGDABrgVURdVYBj9vXTwNXifWbsApYa4zpN8bsBxpse1Ft2m1W2Dawbd4Yrw9jTI8x5g0scToNESkCvgbcm8T7VBQlBazNs2eWB72nTJgmC988C/aaUgaMKx1Mnz6dpUuXAnDbbbexdetWamtrqaurQ0S47bbb0jxCi2TWlKYBh8NeNwFLYtUxxvhEpBMot8vfjmg7zb6OZrMcOGmM8UWpH6uP1jhj/yfgX4HeeG9QRO4C7gI455xz4lVVFMXGH4ieZiiz9ikRSjMElmCmM9AhGY9mpIgM5e7s7MzIUPVkPKVoo478X41VZ7jKkx3H4IBEFgKzjTG/iVUnZMSYNcaYRcaYRZWVZ30ciKJkPcb+YI+b0SEj1pRMhKcEfn/6x5UODh06xMaNGwF44oknuPrqq9m/f39ozeiJJ55I5/BCJCNKTcD0sNfVwNFYdUTEBZQA7XHaxipvBUptG5F9xeojFpcBl4jIAeAN4FwReTXuO1UUJSmCXlC8QIeM8JSIEuiQAWKZDs477zwef/xxFixYQHt7O1/96ldZs2YNn/jEJ7j88suZMWNGuocIJDd9twWoE5Fa4AhW4MKfRNRZB9wBbARuAtYbY4yIrAP+W0QewAp0qAM2Y/2enGHTbrPBtrHWtvlMvD5iDdoY82OsAAnsSL1njTHLkni/iqIkIKg3UTM6pCnHXDQi15Qc4zgk3OFw8PDDD59WtnLlSnbv3n1G3WXLlrFs2bJRGtnpJBQle/3mbuBFwAk8ZozZISLfAbYaY9YBjwI/F5EGLO9ltd12h4g8BewEfMCXjDF+gGg27S6/CawVkXuB92zbxOrDtnUAKAbcInIjcG1EdKCiKMNIMIIt2pJERoWEhx1dAbp5diyQ1OZZY8xzwHMRZd8Ou/YAN8doex9wXzI27fJGrOi8yPJ4fdQkGP8BooSLK4oyNIKCEy/QwZcBazdnRN+N0zRDNTU1bN8+NrZqapohRVFSJt6akiOjPKUoGR3SMK44Kw1jjpF+LypKiqKkTPBQ2XibZzPBI7E8pYjpu1EeV15eHm1tbVkhTMHzlPLy8kasD819pyhKygTXZVxRRMmRwQlZHWlIM1RdXU1TUxMtLS2j2/EIETx5dqRQUVIUJWV8tqsU/TylDMroQPqn73JyckbslNZsRKfvFEVJmeAUWNyQ8AyYrrI8pdNz32XCtKISGxUlRVFSJp4oORzWCa+Z8OF/hqeUppNnleRRUVIUJWVCohQjd1qmhF6fcXSFekoZj4qSoigpE/xgdzmji5IjQzapWrnvIhKyqihlNCpKiqKkTLzpO7Ci8jTQQRkKKkqKoqSMP05Gh2B5ZoSEc2aaoQwYlxIbFSVFUVImmEIolqeUKYlPI4+usM5TSt94lMSoKCmKkjKh3Hdxpu8yYk2J0wMdXA7Bb++x6uzz8tqe7NjQmk2oKCmKkjK+BGtKmbIfKNpx6EEv76//5wPueGwzLd39aRqdEg0VJUVRUiYUfeeI/hGSMSHhnB59l+N0hAT1w6ZOwPKYlMxBRUlRlJQJZQmP8QnidGRSoMMgLqfgs5Pf5eZYg+/2qChlEipKiqKkTCJPyeXMFE+J01TJ5XDg9Z8eOdjvG+UMrUpcVJQURUmZwX1K0e+7wtZu0okx5rQzn3KcEkomGxSrARWljEJFSVGUlBkUpegfITlOB97RPiMiCtGn704/oFBFKbNQUVIUJWUS5b7LGFHi9Og7a/rOGleweCADxqkMoqKkKErKJAoJdzkzJdDh9KMr3K7BNSXR6buMJClREpGVIlIvIg0ick+U+7ki8qR9f5OI1ITd+5ZdXi8i1yWyKSK1to29tk13vD5EpFxENojIKRH5YZidAhH5nYjsFpEdIvLd1B+PoijRSJSQNceRqZ7S4JqSTt9lJglFSUScwEPA9cA84FYRmRdR7U6gwxgzG3gQuN9uOw9YDcwHVgI/EhFnApv3Aw8aY+qADtt2zD4AD/D3wP+JMvzvG2PmAhcBS0Xk+kTvV1GUxASzNThiTN+Fr92kkzPXlCxPyYRlm+jPAPFUBknGU1oMNBhjGo0xA8BaYFVEnVXA4/b108BVYu1YWwWsNcb0G2P2Aw22vag27TYrbBvYNm+M14cxpscY8waWOIUwxvQaYzbY1wPAu8DIHSyvKOOIYKoeV8zpOwfeDJi+A05zlXLs8foDg5tq+73+tAxLiU4yojQNOBz2uskui1rHGOMDOoHyOG1jlZcDJ20bkX3F6iMhIlIK/BHw+xj37xKRrSKytaVFc2EpSiKCzkWsNaUcx+Am1XRhQt7cYJnLjmH3BUyoXAMdMotkRCnab13kV6BYdYarPNlxnIGIuIAngH83xjRGq2OMWWOMWWSMWVRZWZnIpKKMe4KeUtxAhzRP3wUdtfBAhxx7DczrD4QcKK8vQzw6BUhOlJqA6WGvq4GjserYIlACtMdpG6u8FSi1bUT2FauPRKwB9hpjfpBEXUVRkiDoXMSfvssMTyky0AGsozeC72HAr9N3mUQyorQFqLOj4txYgQvrIuqsA+6wr28C1hvrN2IdsNqOnKsF6oDNsWzabTbYNrBtPpOgj5iIyL1Y4vWVJN6noihJEvSUHHGn79LrgUSbYslxWR95Xn8gNL2o0XeZhStRBWOMT0TuBl4EnMBjxpgdIvIdYKsxZh3wKPBzEWnA8l5W2213iMhTwE7AB3zJGOMHiGbT7vKbwFpbUN6zbROrD9vWAaAYcIvIjcC1QBfwt8Bu4F17UfOHxphHUn9MiqKE4wvlvovtKaV/Tcn6N9xTyrEzUHgDJhSyrqKUWSQUJQBjzHPAcxFl3w679gA3x2h7H3BfMjbt8kas6LzI8nh91MQYevS/GEVRzorBLOGxMjoIA2n3lILTd4NjDO6r8vkDoU206R6ncjqa0UFRlJTxJ/CUrHOLMsNTCicYfef1q6eUqagoKYqSMv6Ex6E70r6mFMQRZZ+SLxAYFCUNCc8oVJQURUkZf8SZRJHkOCXtaYYC0aLvgvuU/CYkml71lDIKFSVFUVImoaeUAQlZQ4EOYWWusH1KA+opZSQqSoqipIzfzoggsXLfORz4A6fnmBttQiHhYUN065pSxqOipChKyvgCJuZR6BCeOSGNohQlaWwwMGPAFwhlfFBRyixUlBRFSZlAwBBHk8JyzKXvAz+UZui0kHBrXH1hSVh1+i6zUFFSFCVlvH4T2ogajaBHkhme0mBZ0IM7TZTUU8ooVJQURUkZrz8QStkTjZxQlFv6PaXTp+9sT2nAFypTTymzUFFSFCVlfIFAzI2zEB7llj5PKRDFU8rNsT7yevoHPaV0h64rp6OipChKynj9JuQNRSPHOZj4NF0M7lMaVKVc27s71R/mKen0XUahoqQoSsp4/YHQ+kw0gvfSuVfJRJm+y8txAoOilJfjUFHKMFSUlJjsPdHNd367M617TZTMxOc3oUi2aATXbtK5phRKGhs+fWd7St0eS5QK3S4VpQxDRUmJyR2PbeaxN/dztNOT7qEoGYbXH39NKRP2KYXWlBxneko9tqdUkOvUQIcMQ0VJiUm8c+iV8Y3XH8AdJ/ou5CmlcZ9StOk7l0NwyOD0XaHbxYA/oLMBGYSKkpIQ/XNVIrEyOsTxlGzBSufUWLToOxEhL8cZEqV8txNj0rv2pZyOipKiKCkz4AvEXVPKzQhRsv51ROTny3U5OBW2pgQaFp5JqCgpCdHpOyUSX8CEkptGIyhK/RkREn56ea7LSY+9ebbAba0xabBD5qCipCREJzaUSHz+QGiDbDSC60393nSuKZ2ZkBWsMPCQp5RreUoqSpmDipKiKCkz4I+fJTzXZXkg/T5/zDojTezpOyfdYWtKAP0qShlDUqIkIitFpF5EGkTknij3c0XkSfv+JhGpCbv3Lbu8XkSuS2RTRGptG3ttm+54fYhIuYhsEJFTIvLDiHFdIiLb7Db/LrEOf1Hiog9NicTnD+B2xf7NCE3fZVigA5y+YbbQFiVdU8ocEoqSiDiBh4DrgXnArSIyL6LanUCHMWY28CBwv912HrAamA+sBH4kIs4ENu8HHjTG1AEdtu2YfQAe4O+B/xNl+D8G7gLq7J+Vid6vciY6fadEkug8pWCOubQGOthdR34XDXpxAAV2oIPuVcockvGUFgMNxphGY8wAsBZYFVFnFfC4ff00cJXtlawC1hpj+o0x+4EG215Um3abFbYNbJs3xuvDGNNjjHkDS5xCiMgUoNgYs9FYk8s/C7OlKMpZYEXfxfGUnOmfFovlKQUFE6AwVwMdMo1kRGkacDjsdZNdFrWOMcYHdALlcdrGKi8HTto2IvuK1Ue8cTclGDcAInKXiGwVka0tLS1xTCqKAtam2LjRdznB6bv0rSlF2zwLp3tKE/JyABWlTCIZUYr2dShyRidWneEqT3YcyYzpzEJj1hhjFhljFlVWVsYxOb7QtSQlFlbuuzjRd84MmL4LpRk6vTwY3ABQHBSlUZi+e7W+mc8+tpm+gfQJ9VggGVFqAqaHva4GjsaqIyIuoARoj9M2VnkrUGrbiOwrVh/xxl2dYNyKogyBAX8g7pqSwyHkOCUjpu8i15Qm5LlC18X5oxcS/v2X6nl9TwubD8T72FKSEaUtQJ0dFefGClxYF1FnHXCHfX0TsN5ex1kHrLYj52qxgg02x7Jpt9lg28C2+UyCPqJijDkGdIvIpfZa1WfDbCmKchb4/Cbu0RVgTZOlc59SrJDwoHcEozt9d9xObNzYcmrE+xrLuBJVMMb4RORu4EXACTxmjNkhIt8Bthpj1gGPAj8XkQYs72W13XaHiDwF7AR8wJeMMX6AaDbtLr8JrBWRe4H3bNvE6sO2dQAoBtwiciNwrTFmJ/BF4KdAPvC8/aMoylniCwTiHvIHVlh4eteUogc6hHtK+TnBkPCRjTE1xoSOyzjS0TeifY11EooSgDHmOeC5iLJvh117gJtjtL0PuC8Zm3Z5I1Z0XmR5vD5qYpRvBc6Pdk9RlKFhjMGb4DwlsEQpE3PfFecPekrBzBMD/pEVzz6vPzSVebRTRSkemtFBUZSUCGbUzomTJRysD/zMWFM6vbw4zFMarXRInX3e0HV7z8CI9jXWUVFSFCUlgtkPcuKcpwT2mlJa0wxFz30XvqZUYE/f9XlHdpxdfb7QdUePN05NRUVJSYgegKaEE1x/iXeeElh7ldI5fRdrn1JZoTt0XWBvnu0d4TDtoKdUPTGf9l71lOKhoqQoSkoEvZ/cHGfcem5nZkzfRWrnrKoiAGrKC3A7HbgcEjoefaQIilJtRSEdPQP6RS8OSQU6KOMbzWOrhBP0fnITTd/lOPBkQEh45O9vUa6Le288n4XTSxERCtzOUfOUaisK+cPeVro8PkrCAi6UQVSUlITotzolnP5kRcnlPG2Bf7SJ5SkB3HbpjNB1Ya6L3oGR9ZS67OdQU14IQEfPgIpSDHT6TlGUlAhGqiUWJUdGHvIXSb7bSc8oeUozygsAaNMIvJioKCmKkhKhNSVX/DWlvBwnnnRG39l6mEiUCt0uekdhTWlCrouKolwATmqwQ0xUlJSE6OydEk6y03f5bie9/ekPCU+0JDoaa0pdfV6K83NCkX+6Vyk2KkqKoqREKNAhJ/7HR0HOyH/YxyNWRodIrDWlERYlj5eS/Bwm2qLUoZ5STFSUFEVJiUFPKf70XUGuiz6vn0AgPa62iXF0RSTWmtLIT9+V5OdQ6Hbidjro6NUNtLFQUVIUJSWCa0ruBNN3Bfa5RelaV0raUxqFacauPh/F+S5EhNKCHDp0+i4mKkpKQnRNSQkn2ei7oCj1pGldKV5IeDgFbteIe0pdHm8ovVFZoVvXlOKgoqQkxMQ94FcZbwRPaU04fee2tkGm66TVWIf8RVKSn0O3x4d/BKcZuz2+0NlNEwvcuqYUBxUlRVFSot8bDAlPzlPq9Y6sFxKLWAlZIyktsMSia4Q2+vr8AU71+0Kn3KqnFB8VJSUhOn2nhBMMdEi0ppSf5uk726FLmDg2KEonR0iUTtl7oILTdxMLczTQIQ4qSoqipESy+5RCx0KkafrOb++edSYUpZEN0w6eOBs88baswM3J3oERnS4cy6goKQnRPx0lnH6fH6dDEp48W5hrfQiPdF65WAQPI0woSnYOus4R8l6CKYaCJ96WFrgJmJGbLhzrqCgpCdGErEo4A75AQi8JBqfv0rWBNpCsKNme0sm+UfKUglkdNNghKipKiqKkRH+SolSQZlEKekoJ15RsD2akToTt8tieUmhNyRZBFaWoJCVKIrJSROpFpEFE7olyP1dEnrTvbxKRmrB737LL60XkukQ2RaTWtrHXtuk+iz6+KiI7RGS7iDwhInmpPR4FdPpOOZ1+byBhkAMMhoSna/ouuGbjSCBKxfk5iIxcoENwmi60T6kgmP9Op++ikfA3S0ScwEPA9cA84FYRmRdR7U6gwxgzG3gQuN9uOw9YDcwHVgI/EhFnApv3Aw8aY+qADtv2UPqYBnwZWGSMOR9w2vWUFNHZOyWcXq+f/ASnzsKgp5S+QIfkPCWnQ5hY4Kb1VP+IjCM4fRcMCZ9YGPTM1FOKRjKe0mKgwRjTaIwZANYCqyLqrAIet6+fBq4Sa8faKmCtMabfGLMfaLDtRbVpt1lh28C2eeMQ+wDrEMN8EXEBBcDRJN6voihx6Bvwke9OfD5ojtOB2+UIhUSPNsHpu0T7lACqJuRyotMzIuMITt8V2YEfEwt0TSkeyYjSNOBw2OsmuyxqHWOMD+gEyuO0jVVeDpy0bUT2lVIfxpgjwPeBQ8AxoNMY81K0Nygid4nIVhHZ2tLSEvNBjF/UVVIG6R3wh7ygRBTn5YQ+lEebZD0lgMkleRzvGiFR6vNR6HaGohUL3E7cLod6SjFIRpSi/Y9GfkrFqjNc5Sn3ISITsbyoWmAqUCgit0WpizFmjTFmkTFmUWVlZbQqiqLYpCRK+S66+tK7ppQo+g5gcnEeJ0ZIlDp6B0LBDWClPSor0KwOsUhGlJqA6WGvqzlzGixUx54qKwHa47SNVd4KlNo2IvtKtY+rgf3GmBZjjBf4NfDRJN6vEoGuKSnh9A0kt6YEVl65dHpKDkmc+w5gUnEeracG8PqH//j2tp4BysNECayw8JFawxrrJCNKW4A6OyrOjRUssC6izjrgDvv6JmC9sTa3rANW25FztUAdsDmWTbvNBtsGts1nhtjHIeBSESmw156uAnYl91iUcFSTlHB6vb7QxthEFOflpG2TqC9gcCU6TMlmcokVmNvcPfxC0d7TH9qbFGRKSR7Hu1SUopHwf8xev7kbeBHrQ/0pY8wOEfmOiHzSrvYoUC4iDcDXgHvstjuAp4CdwAvAl4wx/lg2bVvfBL5m2yq3bQ+lj01YARHvAtvs97pmCM9IUZQw+gb8oY2xiSjOz6HLk76ErMlM3YElEgBHOvqGfRztpwYoK8w9rWxySR7HO4e/r2wgqa87xpjngOciyr4ddu0Bbo7R9j7gvmRs2uWNDEbPhZcPpY9/AP4hWhsleXT6Tgmnd8AfymuXiOI8V/o8JX/yolRbUQjAgdYeFteWDdsYjDHW9F3RmZ5SR68Xj9dPXpLPcrygGR0URUkaYwx93lQCHaw1pXSkqvIHAkmLUvXEAnKcQmNrz5D6inXke5/XT78vcMb03eSSfACOj1AY+lhGRUlJiB7ypwTxeAMYQ1L7lMBaU/L6DR7v8AcQJMJvTFLh4GBF6M0oL2R/66mU+9lzopsLv/MS//zcmUvWrd1WhF20NSWAYypKZ6CipCREp++UIMGUQamEhANpicDzB0zCFEPh1FYUsn8IntLPNx6k2+NjzeuNtEVE1B21142mleafVh4MrDjepetKkagoKYqSNMHkqkkHOuSN7Kmu8fAHkveUAGZXFbG/tYd+X2ppkXYe66LETur6/Pbjp907etISnamRolSsnlIsVJSUhKinpATps49CT9ZTCp7qmo6TVn2B5AMdAC6YVoLXb9h9rDvpNsYYGppPccOCKdRWFPLijuiiFJyuC1KY66Ks0M3h9t6k+xovqCgpipI0PXYeu8Ik15TK7VDoyGmt0cCfoigtqC4B4MMjnUm3aT01QGefl9lVRVw7fxIb97WFDvUDOHKyj/JCd9QIu6FOF2Y7KkpKQjTQQQkSeWBdIirsUOh0ZC9IVZSmleZTVujmw8Mnk27T0GwFRsyuKuK6+ZPxBQwbdjefdn9mZWHUtipK0VFRUhKi03dKkEFRykmqflmhGxHLoxhtUl1TEhEuml7KlgPtSbdpaBkUpYXVpVRNyA1N4RljqD/ezbmTJkRtW1tRyImu/pD3qVioKCmKkjShU1Tzk/OUXE7HiJ5VFA9fwCR1bEU4l9dVcKCtN+m1nn3Npyh0O5lcnIfDIVwzbxKv1rfg8fpp6uijy+Nj7pTiqG1DG3bb1FsKR0VJUZSk6bZFKVlPCawpvLSIkj+Ay5maKF1RZ50S8Ie9rUnVb2g+xayqolDS1+vmT6bP6+f1PS28tc+ycWmMDBFBUWpsUVEKR0VJiYnO2imRdPX5cAgUJhl9B1awQ1sapu+8foPbmdpH3KzKQqaV5vPKrhNJ1W9oPsXsyqLQ60tnljO5OI9/+/1efvH2Iaon5jO7qihq25mVhbgcwq5jXSmNMdtRUVISomtKSpBuj5cJeTlJHQcRpGJCblo8pQF/ALcrtY84EeGGBVN4fU9LwvOOuj1ejnd5mBUmOm6Xg7+74Tx2HO1i25FOvnDlrJjPKtfl5NxJE9h+VEUpnOQmhpVxSfBPSaPvlCBdHl/S60lBKorctIzAkRCJGPAFko4SDGfVwmn85PVGfrftGLdfOiNmvWDkXV2EJ3TDgqmUF+Zyqt/H1edVxe1r/tRi1u9uxhiTktBnM+opKTFRKVIi6fZ4mZCb/HoSWBtHewb8o55qyOsPpDx9B3DelAnMnTyBX2w8GDeR7N4TlihFi667bFY518yblFBozp9WQlvPACcSnK3U0+/j+y/Wc2wcHHehoqQkRKfvlCBdHl/K3se00gJgZM4qiod3CNN3YE3h/dkVM6k/0c3rcQIe9pzoJtflYHpZwZDHeP40a8Pue4c64tb71btN/HBDA/f9LvvPKVVRUmKiYqRE0tXnTSnyDmDaRCvv22iL0oAvQM4QPCWAT1441QpYeGVPTG9pT/MpZlcVpbRBN5ILppWQn+Nk0/74e6PeO2Rt6N2WQraJsYqKkpIQ1SYlSEfvAGWFKYqSnYz0yMnR9pTMkEXJ7XLw1WvqePfQSX774bEz7gcChg+bTjIvxh6kVPpZVDORtxvb4tYLnvN0qL0Xjze1hLFjDRUlJSHpOKBNyTyMMbT3nHm0dyIqitzkuhyjLkpDib4L56ZLpjN/ajH/9OzOM3L37Ws5xcleLx8ZhlNqL51Zzu7j3THzAxpj2N9yitKCHIwh61MTqSgpMdGoOyWcLo8Pr99QHnFgXSJEhGml+WmZvnOnuHk2HKdD+JebLqSz18tfP/0h/rDTZV+tbwHgspnlZx8AmiQAACAASURBVD3Oj86ybLy+tyXq/faeAbo8Pq4+bxJgCWI2o6KkJESlSQFC+3bKi1ITJYDqsoJRT6fj9Q99TSnIvKnF/P0N57F+dzN/+5tt+AMGf8Dw1NbDLKguOasghyAX2jnzXtoRfcNu0DNaPscKLz+gnhKIyEoRqReRBhG5J8r9XBF50r6/SURqwu59yy6vF5HrEtkUkVrbxl7bpvss+igVkadFZLeI7BKRy1J7POMbnbVTwmnvsaaXIo/2Toa6qiL2tZwiEBi9X6qhRt9FcvtlNdy9fDZrtxzm5off4stPvMfe5lP87ytmDsMoOSNnXiRBUZo3tZhJxbnsb83uM5gS/o+JiBN4CLgemAfcKiLzIqrdCXQYY2YDDwL3223nAauB+cBK4Eci4kxg837gQWNMHdBh2065D7vNvwEvGGPmAhcC2R9POQKoOClAKFVQeYprSmCJkscbGLV1pUDAnFWgQyRfv/Zc/vXmCznW6eGlncf5wpWzuGHBlGGxDbDyfCtnXnBaMJxD7b04HdYUaE15YdYncE3mf2wx0GCMaTTGDABrgVURdVYBj9vXTwNXibVrbBWw1hjTb4zZDzTY9qLatNussG1g27xxKH2ISDHwMeBRAGPMgDEm+YNSlDBUlZTB6buyIUzf1U2ysh7sbU7+VNezwRsIAAyLpwTWutinL6nmrXtWsPufruee6+cOawaGy2aWUzUhl//ZeviMewfbeplamofb5WBmZaFO3wHTgPAn1WSXRa1jjPEBnUB5nLaxysuBk7aNyL5S7WMm0AL8l4i8JyKPiEjU07ZE5C4R2SoiW1taoi82jkdUipRw2oJrSkOYvptdZWU92HNidBbpB3y2KA2TpxRERM5qX1IsXE4HNy+qZkN98xlZGw629TCjzProqikvpK1n4LTTbbONZP7Hov0PRH5exaozXOVD6cMFXAz82BhzEdADnLEeBmCMWWOMWWSMWVRZWRmtyrhGp+8UgOOdHkryc6Ie7Z2IkvwcppTksWOUko/22WszeSlkM083n1k0nYCBJzYPfsc2xtDY2kNNhRVQURM8gymLvaVkRKkJmB72uho4GquOiLiAEqA9TttY5a1AqW0jsq+h9NFkjNlklz+NJVJKkqgYKeEc6/QwpSRvyO0vOqeU9w/HT6czXHgGLE8pfwgCmi5mlBdy9XmTePytA6Fzqw6399Ht8TFvipWOaOY4OBgwGVHaAtTZUXFurKCCdRF11gF32Nc3AeuNteNyHbDajpyrBeqAzbFs2m022DawbT4zlD6MMceBwyIyx25zFbAziferRKDapAAc6+w7O1GaPpHD7X2jkjG812utABSMIU8J4C9XzKazz8vPNh4E4MMj1jL4/KlW5ojpZQWIZPfBgAlFyV6/uRt4ESt67SljzA4R+Y6IfNKu9ihQLiINwNewp8mMMTuAp7DE4AXgS8YYfyybtq1vAl+zbZXbtlPuw27zl8AvReRDYCHwz6k+IEU9JsXieKeHKXbKoKFw8YxSAN5NkHx0OOgbsD4CxpKnBHDh9FKuPq+KH65v4HB7L6/VtzAhz8U8W5TycpxMLcnPak8pqXS/xpjngOciyr4ddu0Bbo7R9j7gvmRs2uWNWNF5keVD6eN9YFG0NkoyqBopFh6vn7aeAaaehac0f2oJeTkONu5r47r5k4dxdGcSEqUx5ikB/OOq87n2gde4/dFNHO308MkLp54W2p7tEXia0UFJiOa+U453egCYXDJ0Tykvx8lHZ1Xwan3zcA0rJsFAh7HmKYGVwHbNZxfR7fExuTiPr15z7mn3a8oLaWztydq/Sz15VomDFdiYnb/6SiocareyCFRPHLooASyfU8n63c00tpxiZmVR4gZDpNf2lMbamlKQpbMr2Pp3VwOcsR+qpqKQbo+P9p4ByotS38ic6ainpMRB5UixCKa6mVkZdatf0iyfa+Vv+12U4yCGk1BI+Bj0lIKISNQNutkegaeipCQkS2cJlBTY39pDUa6LyrP8Zl49sYBLZ5bx9LtNIzr9NJbXlBIR3KuUrTnwVJSUmKgYKUH2tZxiZmXhsKTWufmS6Rxs6+WtffEPtjsbuuyMB8UpnpI7FqiemI/b6WDvidFJ2TTaqCgpCdFzlZT9rT3UVpzd1F2QTyyYQkVRLg9taBgWe9Ho7POSn+Mcttx3mUSO08HcKRP4sCk7j0bPvv8xZfhRTRrXdHm8NHX0UVc1PIEJeTlOvnDlTN7a18Zb+1qHxWYknX1eSguyz0sKsqC6hG1HOkf1KJDRQkVJiUn2/borQ2Gnna/u/Gklw2bzfy2ZwfSyfP7uN9ujniF0tpzs81KSn82iVMqpfh+Nrdl3Cq2KkpIQFafxzfYj1jTR/KnDJ0r5bif//KkLaGzt4R9/u3PYgx46+7wUZ7EoXVhtZcf44HD2TeGpKCkxydbNeUpqbD/SyeTiPConDO+emCvqKvnislk8sfkQP3m9cVhtd/Z6Kc1iUZpdVUSh2zkqKZtGG908qyREtWl8s/VgBxdOHz4vKZy/vnYOh9p7+e7zu+n2ePnaNXOG5byi410elswsG4YRZiZOh7CopoxN+9vTPZRhRz0lJSEafTd+OdzeS1NHH5fNLB8R+w6H8G+3LOSWRdN5aMM+bn90E4fbz27/Te+Aj84+L5PPIk/fWOCyWeU0NJ+iuduT7qEMKypKSkxUipSN9l6ij86uGLE+XE4H3/30BXzv0wt4//BJrnrgNb77/G7aTg3tiItgnr6zOWZjLHCp/UXh7cbs8pZUlJSE6PTd+OW1vS1UFOUOWzh4LESEz3xkOuu/vowbLpjCw6/t46PfXc/f/X/baGhOLcLsYJvlaU0rLRiJoWYM508tpijXxduNI7cJOR3ompISExWj8U3fgJ8Nu5v51EXThiWTQzJMLsnjgVsW8hfLZ/Gfr+/nqS1N/OLtQ1w6s4zbLp3BtfMmJ9wQu/u4lelgzqQJozHktOFyOlhcW8ZbDSOz1ytdqKekJES1aXzy2p5megf8fPyCKaPe9+yqCdx/0wLevGcFf33dHA6393H3f7/HR7+7nu+/WM+Rk30x224/2snUkjxKsnjzbJCP1VVwoK03q85XUlFSEqKh4eOTp7Y2UVGUy5La9EWxVU7I5UvLZ/P6N5bz2J8u4sLqEh56tYEr7l/Pnz2+hQ27m/GHZTXw+QO82dDKpbNGJjAj01gxdxLAqJxRNVro9J0SExWj8cvh9l421Dfzl8tn43Km/7ur0yGsmDuJFXMn0dTRyxObD/HklsO8squZ6on5/MmSc/jMoum8tOMEJ3u9fCIN3l06OKe8gJmVhayvb+FPl9amezjDgoqSkhCVpvHHI39oxCHCrUvOSfdQzqB6YgF/fd1c/uqqc3lxx3F+uekg33uhnn95sR5jYHFNGcvnVKV7mKPG8jlV/Pztg/QO+Chwj/2P9LH/DpQRQ8VofHK4vZf/3nyIWz4ynSlncfz5SON2OfijC6fyRxdOpaG5m1+/e4QJeTncftkMHMOwAXessHxOFY++sZ+N+9q46rxJ6R7OWZOUXy4iK0WkXkQaROSeKPdzReRJ+/4mEakJu/ctu7xeRK5LZFNEam0be22b7qH2Yd9zish7IvJs8o9FOQ1Vp3GDMYZ7f7cThwhfXlGX7uEkzeyqCXxj5Vy+uGwWRbnj67v2R2onUuB2siFL1pUSipKIOIGHgOuBecCtIjIvotqdQIcxZjbwIHC/3XYesBqYD6wEfmSLRDyb9wMPGmPqgA7bdsp9hI3tr4BdyT0OJRqa0WH88OyHx3hxxwm+es25WZ8RIVvIdTm5fHYFG3a3ZMU6cDKe0mKgwRjTaIwZANYCqyLqrAIet6+fBq4Sa2PDKmCtMabfGLMfaLDtRbVpt1lh28C2eeMQ+0BEqoFPAI8k9ziU0xj7v99KCjQ0d/M3v97GhdUl/Nnl2bFoPl5YMbeKIyf72HNi7B9lkYwoTQMOh71ussui1jHG+IBOoDxO21jl5cBJ20ZkX6n2AfAD4BtAIN4bFJG7RGSriGxtaWmJV3VckgVfvpQEtHT3879/9g5ul4Mf3XZJRkTcKcmzfK4V2PH73SfSPJKzJ5nfvGgrhpEfU7HqDFd5yn2IyA1AszHmnSj3T69szBpjzCJjzKLKyspE1ccNqkXjg+ZuD7f+59sc7/Twk9svYVpp5gY3KNGZVJzH+dOK2bB77K8rJSNKTcD0sNfVwNFYdUTEBZQA7XHaxipvBUptG5F9pdrHUuCTInIAa3pwhYj8Ion3q9gE56fVU8pedhzt5FMPvcWRjj7+63MfYVFN9h73kO2smFPFOwc76OgZSPdQzopkRGkLUGdHxbmxggrWRdRZB9xhX98ErDfWJ9o6YLUdOVcL1AGbY9m022ywbWDbfGYofRhjvmWMqTbG1Nj21xtjbkvyuShAcKO8alL2YYzhf7Ye5qYfb8QfMKy969JQ1mllbLJ8bhUBA6/vHdtLEAljJ40xPhG5G3gRcAKPGWN2iMh3gK3GmHXAo8DPRaQBy3tZbbfdISJPATsBH/AlY4wfIJpNu8tvAmtF5F7gPds2Q+lDOTsC6iJlJcc7PfzNb7axfnczi2vL+OGfXETVBI20G+tcWF1KeaGb3+9qZtXCyGX/sUNSAf3GmOeA5yLKvh127QFujtH2PuC+ZGza5Y3Y0XMR5Sn3EXb/VeDVWPeV6AQ1KRvCTBUr6/cjf2jkx6/tI2AM375hHn/60ZpxtdE0m3E4hGVzqnhl1wl8/sCYDVYZX7vMlJRQTyk78PoD/Oa9Izz48h6OdXq4bv4k/ubj5zGjvDDdQ1OGmavOq+JX7zbx7qGTLE5jIt2zQUVJiUlQlFSaxiYer5+nth7mJ681cuRkHwuqS/jBLQtZomtHWcvldRW4HBKamh2LqCgpMQkFOqgqjSlauvtZu/kQj288SOupfhbNmMi9nzqfZedWjtphfUp6KM7LYXFtGRt2N3PP9XPTPZwhoaKkKFmAMYZ3D53kZxsP8Ny2Y3j9ho+dW8lfLJvFktoyFaNxxIq5Vdz7u100dfRSPXHsHQmvoqQkgbpKmcqpfh/PfnCUn799kB1Hu5iQ6+K2S2dw+6UzmFlZlO7hKWkgKEobdjdz+2U16R5OyqgoKcoYIxAwbNrfzv+8c5jntx2nz+tnzqQJ3Pep87lx4TQKx1mWbOV0ZlYWUVNewO9VlJRsRdeUMoPD7b386t0mfvVuE4fb+5iQ6+LGi6Zy0yXTuficUp2iU0Isn1vFLzcdGpMH/42t0SppQTUpfZzq9/Hi9uM8/U4TGxvbEIGlsyr4+jVzuG7+ZPLdzsRGlHHHVXMn8V9vHuCthjaunje2Dv5TUVKUDMPj9fNqfQu//eAor+w6Qb8vwDllBXztmnP544unjcnFa2V0WVxbRqHbyfr6ZhUlJfvQ6buRxx8wbNzXxroPjvD89uN0e3yUF7pZ/ZHpfHLhVC4+Z6JOzylJ43Y5uKKukg27mzHGjKnfHRUlJSF68uzIYIzh/cMneeb9o/xu2zFauvspynVx3fzJrFo4lY/OKh+zqWKU9LNibhUv7DjOrmPdzJtanO7hJI2KkqKMIsYYdh7r4rltx/jtB8c41N6L2+VgxZwqVi2cyvK5VeTl6DqRcvZcOcc6G+6NhhYVJSW70Om7s8MYw/YjXfxu2zGe336Mg229OB3CR2eV85crZnPd+ZMpzstJ9zCVLGNScR6zq4p4o6GNuz42K93DSRoVJUUZAYwxfNDUyXPbjvHctmM0dfThcggfnV3BF6+cxbXzJ1NW6E73MJUs5/LZFazdcoh+n59c19jwwFWUlISoo5QcgYDhvcMneW7bMV7YfpwjJ/vIcQpLZ1fw5avquOa8SUxUIVJGkaWzK/jpWwd49+BJLps1NhLxqigpCdHzlGLjDxjeOdgREqLjXR7cTgdX1FXwtWvO5erzJlFSoFNzSnpYMrMMp0N4a1+ripIytlEhio3H6+fNhlZe2nGCV3adoK1nALfLwZXnVnLPBXNZcV6VrhEpGUFxXg4XVpfwRkMrX792TrqHkxQqSkpU/AEVpXA6e72srz/BSztO8NqeFnoH/BTlulg+t4pr5k1ixdwqijTnnJKBLJ1dwUMbGujyeMfElyX9K1Ki4vWrKB092cfLO0/w0s7jvN3Yjj9gqJqQy6cumsa18ydz6cyyMbN4rIxfls6u4D/WN/D2vjaunT853cNJiIqSEpUBXyB0PV5m8owx7Dlxipd2HOelnSfYdqQTgFmVhdz1sZlcO28SF1aX4nCMnd3xinLROaXk5zh5s6E1e0RJRFYC/wY4gUeMMd+NuJ8L/Ay4BGgDbjHGHLDvfQu4E/ADXzbGvBjPpojUAmuBMuBd4HZjzECqfYjIdLv+ZCAArDHG/FuqD2iss2F3MzMrC5lRXphSuwF/mChlcfzdgC/AlgPtrN/dzCu7TnCwrReAi88p5Z7r53LNvEnM0nOJlDFMrsvJ4toy3tzXlu6hJEVCURIRJ/AQcA3QBGwRkXXGmJ1h1e4EOowxs0VkNXA/cIuIzANWA/OBqcArInKu3SaWzfuBB40xa0XkYdv2j4fQhw/4ujHmXRGZALwjIi9HjDvr+dxPt+ByCA3//PGU2nnDRCnbONHl4dX6ZtbvbuaNva30DPhxOx1cNqucP//YLK4+r4qq4rx0D1NRho3LZpXz3ed303qqn4qi3HQPJy7JeEqLgQZjTCOAiKwFVgHhH+6rgP9rXz8N/FCsDICrgLXGmH5gv4g02PaIZlNEdgErgD+x6zxu2/1xqn0YYzYCxwCMMd227WkR485qgsLiG0LQQn8WTd/5A1aOuQ27m9lQ38yOo10ATCnJY9VF01g+p4qls8vH3LkzipIsi2vLANi8v52PXzAlzaOJTzJ/hdOAw2Gvm4AlseoYY3wi0gmU2+VvR7SdZl9Hs1kOnDTG+KLUH0ofAIhIDXARsCnaGxSRu4C7AM4555xoVcYkpzy+xJVi0NM/9LaZwMneAV7b08KG3c28tqeFjl4vDoFLZkzkGyvnsHxOFXMnTxhT2ZMVZahcMK2E/BwnmxrbskKUov3VRn53jlUnVnm01Mfx6g+lD6uRSBHwK+ArxpiuKHUxxqwB1gAsWrRojPsFg5w6C2EJbzsWPCVjDLuOdbOhvpkNu5t591AHAQNlhW6Wz6li2dwqrqyr1I2syrgkx+ngkhkT2bS/Pd1DSUgyotQETA97XQ0cjVGnSURcQAnQnqBttPJWoFREXLa3FF4/5T5EJAdLkH5pjPl1Eu81q+gd8A+5bbiXlama1NPv482GVluIWjje5QHg/GnF3L18NsvnVrGguhSnRsspCktqy3jglT2c7B2gtCBz010lI0pbgDo7Ku4IVlDBn0TUWQfcAWwEbgLWG2OMiKwD/ltEHsAKQqgDNmN5N2fYtNtssG2stW0+M5Q+7PWmR4FdxpgHUn0w2UDPwPB4SpnE/tae0NrQpsZ2BvwBinJdXFFXYXlEcyo1SEFRorC4tgxjYMuBDq7J4NNoE4qSvX5zN/AiVvj2Y8aYHSLyHWCrMWYd1of/z+0gg3YskcGu9xRWcIEP+JIxxg8Qzabd5TeBtSJyL/CebZtU+xCRy4HbgW0i8r5t42+MMc8N7VGNPYLrQkPxFLo83tB1OlMO9fv8bN5vhWy/Wt/C/tYeAGZXFXHHR2ewfG4Vi2aU4XbpYXiKEo8Lp5fidjnY1Ng2tkUJwP4gfy6i7Nth1x7g5hht7wPuS8amXd7IYIReeHlKfRhj3iD6etO4oaffmr5zD+H00hP2VFg6ONbZx4bdLWyob+bNhlZ6B/zkuqyQ7c8trWHZuVWcU16QtvEpylgkL8fJwumlbD6Q2etKGgObxQSn4HJzhiJK/aHrkfaTfP4A7x8+yfrdzWyob2HXMSseZVppPp++uJrlcyu5bGYF+W5N6aMoZ8OltWX8cEMD3R4vEzI0D56KUhbTesoSlpL81H/5jnT0UTUhl+bu/hFRpfaeAV7f08J6O2S7s8+L0yEsmjGRb10/l+Vzq6irKtKQbUUZRhbXlhNY38A7BztYNqcq3cOJiopSFtPabYlSTorTd8YYdh/v4vxpJZYoDQOWzW7W77YyKbxnh2xXFLm5Zt4kls+p4vK6iiEJqKIoyXHxjFJcDmHT/nYVJWX0OWavC/lSTBl0sK2Xjl4v508r4Q97W4ec+87j9bNxXxu/332C9buaOdppjWdBdQl/uaKOFXOruGBaiSY4VZRRosDtYkF1CZsaMzcPnopSFtNw4hSQ+jEUL+44DsCycyv58av7Umrb3jPASzuO8/LOE7y5rxWPN0CB28kVdRV85epzNWRbUdLM4tpyHvlDI30D/oxcp1VRylI6e700tARFKXlPqXfAx0/fOsDimjJqKqzM4oly57V09/PijuM8v/1Y6Nyh6on5rP7IOayYW8USPXdIUTKGJTPLePi1fbx7qIOlsyvSPZwzUFHKUn6x6SD+gOHC6hIOtfcm1cbnD/DNX23jeJeHH9yyMLQWFX62UhCvP8D63c08teUwG+qbCRiYWVHIF6+cxfUXTGbelGINUlCUDGTRjIk4BDY1tqkoKSOLMYY3G9p49I1GNtS3cP35k5lckkdjS0/CtjuPdnHv73by1r42vrFyDktmlodCysNFyeP18+SWwzz82j6OdXqonJDLn185i1ULpzJnkiY4VZRMZ0JeDvOnlvB2hubBU1HKAgZ8AZ798ChrXm9k9/FuKorcfOXqOr5w5SweeHnPaQf2RXK4vZd/fameZz44SnFeDv/vjy/g1sVWpnR3hKe0eX8733j6Aw609bJoxkT+8ZPzWTG3CtcQNucqipI+ltSW8bO3D+Lx+snLyaypdRWlMcypfh9rNx/ikT/s53iXh7qqIr736QWsumhqaA0nxylR14TaTvXzH+sb+OWmgzgdwhevnMWfXznrtJDsHKfl9Qz4Azzz/hG+/tQHTC3N52efX8wVdRXqFSnKGGXJzHIeeWM/Hxw+yZKZ5ekezmmoKI1BTnR5+MXbB3n8rQN0eXxcOrOM//fpC1h2buUZQuFyOPAHDIGAweEQegd8PPKH/ax5vZE+r5/PLJrOV66uY1KUiDgRwe1y8M7BDn7yWiMXz5jII3csojhDd4IripIci2vKEIFN+9tVlJSh4fMH2FDfwpNbDrF+dzMGuHbeJL5w5SwuOmdizHbBRKXeQIBXd7Xwf9ft4Finh5XzJ/N/rpvD7KqiuP3mOh28ta+Nygm5rLn9EhUkRckCSgpymDNpApv2t2EdrJA5qChlMF5/gLcb23hh+3Fe2nmClu5+Kifk8oUrZ/GZRdNDIdvxcNkbUx99Yz/fe6GeeVOK+Y9bL2JRTVlSY+i2gx3+YtmsjD6DRVGU1Lh0ZjlrtxxiwBfIqCz7KkoZRt+An9f2tPDSjuO8susEXR4fBW4ny+ZUsmrhNFbMrUopbVCw7vdeqOfKcytZ89lLUtozdPV5k9hQ3xwKflAUJTtYUlvGT986wLYjnVwyI/Zsy2ijopQBdPZ5Wb/7BC9sP85re1rweAOUFuRw7fzJXDd/MlfUVQw5QiYYrABw743np7yJ9QerF+LzBzIuQkdRlLNjca01W7Jpf5uKkmJlQXhp53Fe2H6cjfva8AUMk4pz+cyi6Vw3fzKLa8tSTqQajell1rlDn19aG7pOhaJc/RVRlGykvCiX2VVFbGps5y+WpXs0g+gnzihy9GQfL2y3hGjLwXaMgZryAu68opaV8ydzYXXpsCcnvfLcSn5x5xKWzExuDUlRlPHDktoynnn/KD5/IGP2G6oojTAer5/ntx/jqS1NbLQz886ZNIEvr6jj+gsmj3gWBBHh8rrMSyWiKEr6WTKznF9uOsTOY10sqC5N93AAFaURo6NngP966wCPv3WAzj4v55QV8PVrzuUTC6YwszJ+GLaiKMpocKm9rrRxX5uKUrbiDxh+8fZBvv9iPd39Pq6dN4nPLa1lSW2ZnhukKEpGUVWcx7wpxby88wR/fuWsdA8HgKQmEUVkpYjUi0iDiNwT5X6uiDxp398kIjVh975ll9eLyHWJbIpIrW1jr23TPdx9jBQnewe447HN/MO6HSw8p5QXv/Ix1nx2EZfNKldBUhQlI1l5/mTeOdRBc7cn3UMBkhAlEXECDwHXA/OAW0VkXkS1O4EOY8xs4EHgfrvtPGA1MB9YCfxIRJwJbN4PPGiMqQM6bNvD3cew09EzwM0Pb2Tz/na++8cX8LPPL2bO5Akj1Z2iKMqwcN38yRgDz35wLN1DAZLzlBYDDcaYRmPMALAWWBVRZxXwuH39NHCVWKv3q4C1xph+Y8x+oMG2F9Wm3WaFbQPb5o3D2UdyjyU1BnwBPvfTLRxs7+Wnn/8Iqxefo8lKFUUZE5w7qYiP1Ezk39fv5ejJvnQPJ6k1pWnA4bDXTcCSWHWMMT4R6QTK7fK3I9pOs6+j2SwHThpjfFHqD1cfZyAidwF32S9PiUgb0BqtbiKW3jeUVhlLBUN8DlmIPotB9FlYZN1zmPYPQ25aAcwYjjEkI0rRvvJHnoUQq06s8mgeWrz6w9nHmYXGrAHWBF+LyFZjzKJodccT+hwG0WcxiD4LC30Og9jPomY4bCUzfdcETA97XQ0cjVVHRFxACdAep22s8lag1LYR2ddw9aEoiqJkKMmI0hagzo6Kc2MFFayLqLMOuMO+vglYb4wxdvlqO3KuFitH+uZYNu02G2wb2DafGc4+knssiqIoSjpIOH1nr9/cDbwIOIHHjDE7ROQ7wFZjzDrgUeDnItKA5b2sttvuEJGngJ2AD/iSMcYPEM2m3eU3gbUici/wnm2bYe4jEWsSVxkX6HMYRJ/FIPosLPQ5DDJsz0IsZ0NRFEVR0k9mZOBTFEVRFFSUFEVRlAxCRSmM0U5LlA5E5DERaRaR7WFlZSLysp3a6WURmWiXi4j8u/08PhSRi8PaeI7xQgAAA3BJREFU3GHX3ysid0TrK5MRkekiskFEdonIDhH5K7t8PD6LPBHZLCIf2M/iH+3yYUv5NZawM8K8JyLP2q/H63M4ICLbROR9Edlql43834cxRn+sdTUnsA+YCbiBD4B56R7XCLzPjwEXA9vDyr4H3GNf3wPcb19/HHgeay/YpcAmu7wMaLT/nWhfT0z3e0vxOUwBLravJwB7sNJRjcdnIUCRfZ0DbLLf41PAarv8YeCL9vVfAA/b16uBJ+3refbfTS5Qa/89OdP9/obwPL4G/DfwrP16vD6HA0BFRNmI/32opzTIqKUlSifGmNexohfDCU/hFJna6WfG4m2sPWRTgOuAl40x7caYDuBlrLyDYwZjzDFjzLv2dTewCysTyHh8FsYYc8p+mWP/GIYv5deYQUSqgU8Aj9ivhzP1WTYw4n8fKkqDREunNC1G3WxjkjHmGFgf1kCVXR7rmWTVs7KnXS7C8hDG5bOwp6zeB5qxPjj2kWTKLyA85ddYfxY/AL4BBOzXSac+I7ueA1hfTF4SkXfESsUGo/D3oecpDZJMOqXxRqqpncYcIlIE/Ar4ijGmS2In0s3qZ2GsvX0LRaQU+A1wXrRq9r9Z+SxE5Aag2RjzjogsCxZHqZrVzyGMpcaYoyJSBbwsIrvj1B22Z6Ge0iDjOS3RCdvVxv632S7P6hROIpKDJUi/NMb82i4el88iiDHmJPAq1rrAcKX8GissBT4pIgewpu9XYHlO4+05AGCMOWr/24z1RWUxo/D3oaI0yHhOSxSewikytdNn7ciaS4FO22V/EbhWRCba0TfX2mVjBnvu/1FglzHmgbBb4/FZVNoeEiKSD1yNtcY2XCm/xgTGmG8ZY6qNlVh0Ndb7+l+Ms+cAICKFIjIheI31e72d0fj7SHeERyb9YEWQ7MGaT//bdI9nhN7jE8AxwIv1LeZOrHnw3wN77X/L7LqCdVDiPmAbsCjMzuexFnAbgM+l+30N4TlcjjWN8CHwvv3z8XH6LBZgpfT60P7g+bZdPhPrw7QB+B8g1y7Ps1832Pdnhtn6W/sZ1QPXp/u9ncUzWcZg9N24ew72e/7A/tkR/Dwcjb8PTTOkKIqiZAw6facoiqJkDCpKiqIoSsagoqQoiqJkDCpKiqIoSsagoqQoiqJkDCpKiqIoSsagoqQoiqJkDP8/oHrNcmlroVQAAAAASUVORK5CYII=\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", @@ -799,7 +737,7 @@ "# plt.plot(test_q, fplus_y, label = '+')\n", "# plt.plot(test_q, res_y, label = 'res')\n", "plt.legend()\n", - "plt.ylim(0.0, 1.5e-6)\n", + "# plt.ylim(0.0, 1.5e-6)\n", "# plt.yscale('log')\n", "# plt.xlim(770, 785)\n", "plt.savefig('test.png')\n", @@ -808,7 +746,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -824,7 +762,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -840,7 +778,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -853,7 +791,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -910,7 +848,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -994,16 +932,16 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ - "total_f._sample_and_weights = UniformSampleAndWeights" + "# total_f._sample_and_weights = UniformSampleAndWeights" ] }, { "cell_type": "code", - "execution_count": 20, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1012,7 +950,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1021,7 +959,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": null, "metadata": { "scrolled": false }, @@ -1069,7 +1007,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1086,7 +1024,7 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1110,7 +1048,7 @@ }, { "cell_type": "code", - "execution_count": 25, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1137,7 +1075,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1160,7 +1098,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1169,7 +1107,7 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1185,7 +1123,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1215,7 +1153,7 @@ }, { "cell_type": "code", - "execution_count": 30, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1229,7 +1167,7 @@ }, { "cell_type": "code", - "execution_count": 31, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1247,7 +1185,7 @@ }, { "cell_type": "code", - "execution_count": 32, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1261,7 +1199,7 @@ }, { "cell_type": "code", - "execution_count": 33, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1282,7 +1220,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1292,7 +1230,7 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1323,7 +1261,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1340,7 +1278,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1396,9 +1334,9 @@ " mean1 = ztf.constant(0.466)\n", " mean2 = ztf.constant(-0.885)\n", " mean3 = ztf.constant(-0.213)\n", - " sigma1 = ztf.constant(0.014)\n", - " sigma2 = ztf.constant(0.128)\n", - " sigma3 = ztf.constant(0.548)\n", + " sigma1 = ztf.constant(0.014/3.)\n", + " sigma2 = ztf.constant(0.128/3.)\n", + " sigma3 = ztf.constant(0.548/3.)\n", " x1 = (val1-mean1)/sigma1\n", " x2 = (val2-mean2)/sigma2\n", " x3 = (val3-mean3)/sigma3\n", @@ -1453,9 +1391,9 @@ "\n", "# 7. Constraint on Ctt with higher limits\n", "\n", - "# Ctt_abs = tf.pow(tf.pow(Ctt, 2.), 0.5)\n", + "constraint7 = tf.cond(tf.greater_equal(Ctt*Ctt, 0.25), lambda: 100000., lambda: 0.)\n", "\n", - "# constraint7 = tf.cond(tf.greater_equal(Ctt_abs, 0.5), lambda: 100000., lambda: 0.)\n", + "constraint7dtype = tf.float64\n", "\n", "# zfit.run(constraint6_0)\n", "\n", @@ -1464,7 +1402,7 @@ "#List of all constraints\n", "\n", "constraints = [constraint1, constraint2, constraint3_0, constraint3_1, constraint4, #constraint4_0, constraint4_1, constraint4_2,\n", - " constraint6_0, constraint6_1]" + " constraint6_0, constraint6_1]#, constraint7]" ] }, { @@ -1476,7 +1414,7 @@ }, { "cell_type": "code", - "execution_count": 38, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -1546,199 +1484,29 @@ }, { "cell_type": "code", - "execution_count": 39, + "execution_count": null, "metadata": { "scrolled": false }, - "outputs": [ - { - "name": "stdout", - "output_type": "stream", - "text": [ - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n", - "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow_probability\\python\\distributions\\categorical.py:263: multinomial (from tensorflow.python.ops.random_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.random.categorical instead.\n", - "Toy 0: Generating data...\n", - "Toy 0: Data generation finished\n", - "Toy 0: Loading data...\n", - "Toy 0: Loading data finished\n", - "Toy 0: Fitting pdf...\n", - "------------------------------------------------------------------\n", - "| FCN = 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", - "| True | True | False | False |\n", - "------------------------------------------------------------------\n", - "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", - "------------------------------------------------------------------\n", - "| False | True | True | True | False |\n", - "------------------------------------------------------------------\n", - "Function minimum: 703225.2607413743\n", - "----------------------------------------------------------------------------------------------\n", - "| | Name | Value | Hesse Err | Minos Err- | Minos Err+ | Limit- | Limit+ | Fixed |\n", - "----------------------------------------------------------------------------------------------\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", - "| | 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 0.165 -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': 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 2/2\n", - "Time taken: 9 min, 6 s\n", - "Projected time left: \n" - ] - }, - { - "data": { - "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": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ "# zfit.run.numeric_checks = False \n", "\n", "fitting_range = 'cut'\n", + "total_BR = 1.7e-10 + 4.9e-10 + 2.5e-9 + 6.02e-5 + 4.97e-6 + 1.38e-9 + 4.2e-10 + 2.6e-9 + 6.1e-10 + 4.37e-7\n", + "cut_BR = 1.0 - (6.02e-5 + 4.97e-6)/total_BR\n", "\n", "Ctt_list = []\n", "Ctt_error_list = []\n", "\n", - "nr_of_toys = 25\n", - "nevents = int(pdg[\"number_of_decays\"])\n", - "nevents = pdg[\"number_of_decays\"]\n", + "nr_of_toys = 50\n", + "if fitting_range == 'cut':\n", + " nevents = int(pdg[\"number_of_decays\"]*cut_BR)\n", + "else:\n", + " nevents = int(pdg[\"number_of_decays\"])\n", + "# nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", + "nevents *= 41\n", "# zfit.settings.set_verbosity(10)\n", "calls = int(nevents/event_stack + 1)\n", "\n", @@ -1764,29 +1532,44 @@ " \n", " reset_param_values()\n", " \n", - " for call in range(calls):\n", - "\n", - " sampler.resample(n=event_stack)\n", + " if fitting_range == 'cut':\n", + " \n", + " sampler.resample(n=nevents)\n", " s = sampler.unstack_x()\n", " sam = zfit.run(s)\n", - "\n", - " c = call + 1\n", + " calls = 0\n", + " c = 1\n", " \n", - " with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n", - " pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)\n", + " else: \n", + " for call in range(calls):\n", + "\n", + " sampler.resample(n=event_stack)\n", + " s = sampler.unstack_x()\n", + " sam = zfit.run(s)\n", + "\n", + " c = call + 1\n", + "\n", + " with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n", + " pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)\n", " \n", " print(\"Toy {}: Data generation finished\".format(toy))\n", " \n", " ### Load data\n", " \n", " print(\"Toy {}: Loading data...\".format(toy))\n", + " \n", + " if fitting_range == 'cut':\n", + " \n", + " total_samp = sam\n", + " \n", + " else:\n", + " \n", + " for call in range(calls):\n", + " with open(r\"data/zfit_toys/toy_0/{}.pkl\".format(call), \"rb\") as input_file:\n", + " sam = pkl.load(input_file)\n", + " total_samp = np.append(total_samp, sam)\n", "\n", - " for call in range(calls):\n", - " with open(r\"data/zfit_toys/toy_0/{}.pkl\".format(call), \"rb\") as input_file:\n", - " sam = pkl.load(input_file)\n", - " total_samp = np.append(total_samp, sam)\n", - "\n", - " total_samp = total_samp.astype('float64')\n", + " total_samp = total_samp.astype('float64')\n", " \n", " if fitting_range == 'full':\n", "\n", @@ -1838,11 +1621,11 @@ " \n", " if fitting_range == 'cut':\n", " \n", - " _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + " _1 = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 60.)))\n", " \n", " tot_sam_1 = total_samp[_1]\n", " \n", - " _2 = np.where((total_samp >= (jpsi_mass + 50.)) & (total_samp <= (psi2s_mass - 50.)))\n", + " _2 = np.where((total_samp >= (jpsi_mass + 70.)) & (total_samp <= (psi2s_mass - 50.)))\n", " \n", " tot_sam_2 = total_samp[_2]\n", "\n", @@ -1887,17 +1670,22 @@ " os.mkdir(plotdirName)\n", " # print(\"Directory \" , dirName , \" Created \")\n", " \n", + " plt.clf()\n", + " plt.hist(tot_sam, bins = int((x_max-x_min)/7.), label = 'toy data')\n", + " plt.savefig(plotdirName + '/toy_histo_cut_region{}.png'.format(toy))\n", + "\n", + " \n", " probs = total_f_fit.pdf(test_q, norm_range=False)\n", " calcs_test = zfit.run(probs)\n", " plt.clf()\n", " plt.plot(test_q, calcs_test, label = 'pdf')\n", " plt.legend()\n", - " plt.ylim(0.0, 1.5e-6)\n", + "# plt.ylim(0.0, 1.5e-6)\n", " plt.savefig(plotdirName + '/toy_fit_cut_region{}.png'.format(toy))\n", - "\n", + " \n", " print(\"Toy {0}/{1}\".format(toy+1, nr_of_toys))\n", " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", - " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(toy+1))*((nr_of_toys-toy-1)))))\n", " " ] }, @@ -1906,66 +1694,76 @@ "execution_count": null, "metadata": {}, "outputs": [], + "source": [ + "with open(\"data/results/Ctt_list.pkl\", \"wb\") as f:\n", + " pkl.dump(Ctt_list, f, pkl.HIGHEST_PROTOCOL)\n", + "with open(\"data/results/Ctt_error_list.pkl\", \"wb\") as f:\n", + " pkl.dump(Ctt_error_list, f, pkl.HIGHEST_PROTOCOL)" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "print('{0}/{1} fits converged'.format(len(Ctt_list), nr_of_toys))\n", + "print('Mean Ctt value = {}'.format(np.mean(Ctt_list)))\n", + "print('Mean Ctt error = {}'.format(np.mean(Ctt_error_list)))\n", + "print('95 Sensitivy = {}'.format((2*np.mean(Ctt_error_list)**2)*4.2/1000))" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "plt.hist(tot_sam, bins = int((x_max-x_min)/7.))\n", + "\n", + "plt.show()\n", + "\n", + "# _ = np.where((total_samp >= x_min) & (total_samp <= (jpsi_mass - 50.)))\n", + "\n", + "tot_sam.shape" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [ + "# sample from original values" + ] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], "source": [] }, { "cell_type": "code", - "execution_count": 40, - "metadata": {}, - "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)))\n", - "print('Sensitivy = {}'.format(np.mean(Ctt_error_list)**2*4.2/1000))" - ] - }, - { - "cell_type": "code", - "execution_count": 41, - "metadata": {}, - "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": 42, + "execution_count": null, "metadata": {}, "outputs": [], - "source": [ - "# sample from original values" - ] + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] + }, + { + "cell_type": "code", + "execution_count": null, + "metadata": {}, + "outputs": [], + "source": [] } ], "metadata": { diff --git a/run.job b/run.job new file mode 100644 index 0000000..005217c --- /dev/null +++ b/run.job @@ -0,0 +1,42 @@ +#!/bin/bash +#SBATCH --ntasks=1 +#SBATCH --time=24:00:00 +#SBATCH --gres gpu:Tesla-K80:1 --mem=16G + +#/net/cephfs/home/saslie + +mkdir -p scratch/$SLURM_JOB_ID +mkdir scratch/$SLURM_JOB_ID/data +mkdir scratch/$SLURM_JOB_ID/data/zfit_toys + + +cp raremodel-nb.py scratch/$SLURM_JOB_ID +cp pdg_const.py scratch/$SLURM_JOB_ID +cp helperfunctions.py scratch/$SLURM_JOB_ID + +cd scratch/$SLURM_JOB_ID + +module load vesta cuda +. '/net/cephfs/apps/vendor/Anaconda/5.2.0/anaconda3/etc/profile.d/conda.sh' +echo "Simulation starting" +conda activate rmd +python raremodel-nb.py +conda deactivate +echo "Simulation ended" + +cd + +#mkdir -p data/$SLURM_JOB_ID +#mkdir data/$SLURM_JOB_ID/data +#mkdir data/$SLURM_JOB_ID/data/zfit_toys + +cp -R scratch/$SLURM_JOB_ID data/finished/ +cp slurm-$SLURM_JOB_ID.out data/ +# Remove scratch directory at the end of your job +# Do not forget to copy any output first to a safe place +rm -rf scratch/$SLURM_JOB_ID +rm slurm-$SLURM_JOB_ID.out + +echo 'Stronger constraint on ff' + +# source ~/ml/bin/activate diff --git "a/scratch/3572424/data/set_20000_range\050211-4781\051.pkl" "b/scratch/3572424/data/set_20000_range\050211-4781\051.pkl" deleted file mode 100644 index f806b7f..0000000 --- "a/scratch/3572424/data/set_20000_range\050211-4781\051.pkl" +++ /dev/null @@ -1,20014 +0,0 @@ -(dp0 -S'counter_x' -p1 -I20000 -sS'x_part' -p2 -(lp3 -F3095.0193284273146 -aF3685.7519557118417 -aF3690.1134345293044 -aF3095.778249168396 -aF3675.1701053500174 -aF3095.8607879281044 -aF2579.4021481752397 -aF2871.290256690979 -aF3684.4348768234254 -aF3719.6514142990113 -aF3537.816619825363 -aF2267.4053640723228 -aF2175.566420221329 -aF2089.41093736887 -aF1564.1160194396973 -aF3096.324693894386 -aF3686.1349573493003 -aF3096.8561563372614 -aF3095.762994480133 -aF3686.0739385962484 -aF3095.66874229908 -aF3097.4080491662025 -aF3687.8873396635054 -aF3098.241064107418 -aF3691.967151558399 -aF3636.7190291523934 -aF3119.8896459937096 -aF3588.5245656371117 -aF3704.811599075794 -aF3257.1545998454094 -aF3702.8116004824637 -aF3100.483230876923 -aF3682.3231921195984 -aF3685.5402969121933 -aF3665.80181992054 -aF3093.600370013714 -aF1744.693391752243 -aF3120.7485394239425 -aF3095.781790435314 -aF3728.0581095576285 -aF3096.2143698096274 -aF3728.7246849536896 -aF3683.994125294685 -aF3093.8855782032015 -aF3727.1507280111314 -aF3094.0152430534363 -aF3096.440466082096 -aF3095.8948385715485 -aF3651.571647417545 -aF4501.722506844997 -aF3096.5704033374786 -aF3774.4669573307037 -aF3094.6987075686457 -aF3131.271550273895 -aF3097.5894709944723 -aF3093.712328529358 -aF2715.7174976825713 -aF3685.964159321785 -aF3108.1914793372152 -aF1851.8864517450334 -aF2911.11289280653 -aF2801.2295595765113 -aF3098.6335999250414 -aF3964.643342232704 -aF2230.678067648411 -aF2426.986564826965 -aF2873.5645672678947 -aF3086.270764708519 -aF2797.3477862238883 -aF3685.7356114029885 -aF3032.922395801544 -aF3096.3519344091414 -aF3481.239160299301 -aF3092.3255139231683 -aF3686.3531538724897 -aF3669.2183252811433 -aF2261.1670137882234 -aF3685.956259572506 -aF3096.122296869755 -aF3015.089665222168 -aF3095.7420192837717 -aF3114.0160462021827 -aF2867.7977502942085 -aF3112.7992124080656 -aF1908.7523883223535 -aF3093.5061178326605 -aF3034.8758131146433 -aF3090.1639790773393 -aF3101.1601576685907 -aF3095.4480941295624 -aF3610.660752737522 -aF3709.562889659405 -aF3694.523401463032 -aF3152.3603395819664 -aF3695.4177075624466 -aF3686.1316884875296 -aF4535.513275778293 -aF4447.029001319408 -aF2565.63887809515 -aF3094.784787595272 -aF4574.4334336400025 -aF3685.916488420963 -aF3096.2930948972703 -aF3654.0238385558127 -aF3956.5406511187552 -aF2452.4545392870905 -aF3100.5575974822045 -aF3686.27524600029 -aF3668.496996450424 -aF3076.7681835412977 -aF646.6454034924507 -aF3640.6969615221024 -aF3685.8925167679786 -aF3095.9185378193856 -aF4346.86426653862 -aF3094.758636701107 -aF3043.1893458127975 -aF2937.3092786312104 -aF4101.22973486185 -aF3935.153850579262 -aF3098.4355613827706 -aF3095.830006146431 -aF3099.2674867033957 -aF3686.294586765766 -aF3713.1417484879494 -aF3684.5669933199883 -aF3095.726492190361 -aF3097.485684633255 -aF3207.022790920734 -aF3717.3830966353416 -aF3083.063466501236 -aF3096.651580071449 -aF3685.820329403877 -aF2643.9945843577384 -aF3093.496583652496 -aF3096.059916090965 -aF4126.9742005467415 -aF3096.2587718486784 -aF3094.7115106105803 -aF2337.5110080361364 -aF3094.5712219595907 -aF3096.043026971817 -aF3685.860645365715 -aF3095.752098274231 -aF3084.463084149361 -aF3694.499429810047 -aF3101.385164320469 -aF3097.0863386869432 -aF3707.4877072453496 -aF3640.513360452652 -aF3096.2838331222533 -aF969.7546831846237 -aF3685.8524732112883 -aF3460.08581097126 -aF3096.2544133663177 -aF3096.341310608387 -aF984.2621641278266 -aF3685.684126830101 -aF3095.8180203199386 -aF3098.196117258072 -aF1687.1646934509279 -aF3610.733757317066 -aF3440.4843537688257 -aF2505.221050798893 -aF3103.589739179611 -aF2979.3874293684958 -aF3837.5151287317276 -aF3096.1879465103148 -aF3098.7973154187202 -aF1276.5714110016822 -aF1382.1000755429268 -aF3685.996303129196 -aF3570.129590833187 -aF3106.9059994459153 -aF3685.973693501949 -aF1900.1231380581855 -aF1905.9291813731195 -aF3096.122569274902 -aF3096.201294362545 -aF3096.4875921726225 -aF3685.3586026787757 -aF3376.630680346489 -aF2887.263549733162 -aF3095.7899625897408 -aF4097.1123310565945 -aF3030.81152831316 -aF3096.33204883337 -aF435.23040645122524 -aF3096.143544471264 -aF3095.9514988422393 -aF2877.790115916729 -aF3687.001205718517 -aF3230.2129135370255 -aF3790.4555050611493 -aF3095.895928192139 -aF1432.8690401077272 -aF3689.96252207756 -aF3665.0595158934593 -aF3096.321152627468 -aF2915.4817265629767 -aF3670.5566517710686 -aF3675.3210178017616 -aF3005.939031505585 -aF3945.6675996541976 -aF4583.006296038627 -aF3685.9900378108023 -aF3685.9984823703767 -aF3688.301123082638 -aF3186.8449244260787 -aF3249.0480950593947 -aF3096.7967720150946 -aF3688.262169146538 -aF2519.9900406837464 -aF3114.1258254766462 -aF1460.1890971660614 -aF2251.2375737547873 -aF3051.695196545124 -aF3086.794054996967 -aF3866.205383682251 -aF3663.7890182852743 -aF3698.013728618622 -aF3089.8752296209336 -aF3688.027628314495 -aF3718.790886437893 -aF3684.6574318289754 -aF3091.907372021675 -aF3095.521371114254 -aF3095.0122458934784 -aF3247.6694526076317 -aF3095.9629398584366 -aF3719.6811064600943 -aF3065.1035227179527 -aF3096.1822260022163 -aF3096.273209321499 -aF3142.5583851575852 -aF2824.891760313511 -aF2267.9142168879507 -aF3105.9773702979087 -aF3096.104590535164 -aF3097.542344903946 -aF3688.4435909748076 -aF3175.639538681507 -aF3713.0003702163694 -aF3060.219843232632 -aF3710.1297647714614 -aF3687.75658519268 -aF447.8770878314972 -aF3043.4361448764803 -aF3095.701430916786 -aF3694.7421427965164 -aF3228.6408634305 -aF3115.435277020931 -aF3681.6833124279974 -aF3048.43014844656 -aF3162.627834403515 -aF3930.5924263834954 -aF3097.015785753727 -aF2028.4406724333762 -aF3086.9899142980576 -aF3095.286557877064 -aF3096.0245034217833 -aF3095.8182927250864 -aF3553.9078642964364 -aF3685.7604002714156 -aF3069.847185957432 -aF3082.833284151554 -aF3987.982742869854 -aF3095.620526587963 -aF2995.3040621399878 -aF3639.5174472332 -aF3098.5461578726768 -aF3095.8218339920045 -aF418.70331374406817 -aF3667.5057141184807 -aF3323.8401971817016 -aF3102.0509225010874 -aF3685.91131272316 -aF3894.483762049675 -aF3690.280963695049 -aF3073.6924570202827 -aF3693.5509150862695 -aF3091.1353758335113 -aF3682.5610018134116 -aF3019.7028463959696 -aF3690.405725252628 -aF3680.8584696412086 -aF3554.3954695105554 -aF3101.633597815037 -aF3096.3355901002883 -aF4444.729901874065 -aF3063.8918646216393 -aF3743.543524980545 -aF2204.279284799099 -aF3074.9891055226326 -aF3027.977697563171 -aF3084.9288969516756 -aF3362.653299820423 -aF3685.760672676563 -aF3096.17623308897 -aF3042.050147485733 -aF3682.504341542721 -aF3095.978466951847 -aF3845.464728152752 -aF1436.41357588768 -aF3095.6878106594086 -aF3096.113852310181 -aF3003.7461700677873 -aF3685.838035738468 -aF3686.078569483757 -aF2782.722626256943 -aF3579.273142015934 -aF1943.4815929889678 -aF3765.929507601261 -aF3685.8968752503392 -aF1280.8552543520927 -aF3701.744317114353 -aF3091.5170154452326 -aF3095.9021935105325 -aF3146.9727105736733 -aF3092.3276931643486 -aF3102.58592621088 -aF3095.9021935105325 -aF3092.6848163127897 -aF3264.716294336319 -aF3095.813661837578 -aF3687.7015593528745 -aF3121.698416173458 -aF2510.115626490116 -aF1264.919553220272 -aF3093.6902637124062 -aF1052.277098584175 -aF3655.571099793911 -aF3004.450609779358 -aF1296.0494686722757 -aF3798.6410073399543 -aF2068.909181153774 -aF3096.7926859378813 -aF3666.5313209056853 -aF3083.7455689907074 -aF3686.049966943264 -aF3669.1308832287787 -aF3080.9185483694077 -aF3096.6916236281395 -aF3096.0975080013277 -aF3105.2429660201074 -aF2018.5117772102356 -aF3090.3268773555756 -aF1303.1458951711654 -aF3094.688083767891 -aF345.598761510849 -aF3095.08552287817 -aF3069.925093829632 -aF3521.5172578215597 -aF3698.099536240101 -aF3096.4358351945875 -aF3049.309199857712 -aF1006.8510885834694 -aF2368.0854893922806 -aF1128.3996274471283 -aF4596.114976549148 -aF3676.432158398628 -aF3452.0280667066572 -aF3086.4145946264266 -aF3096.358199727535 -aF2499.6304799556733 -aF4089.58904569149 -aF2197.276020860672 -aF3689.7565837860107 -aF3686.1044479727743 -aF3061.234280002117 -aF3094.802493929863 -aF3108.426020169258 -aF3096.343762254715 -aF3685.7465076088906 -aF1675.5493379592897 -aF2850.774335408211 -aF3685.9186676621434 -aF2113.862840628624 -aF3865.487051308155 -aF3690.884341096878 -aF2736.898087525368 -aF3690.5236766815183 -aF3099.3603768587113 -aF3095.831095767021 -aF3097.0669979214667 -aF3102.2026521682737 -aF3099.7978595256805 -aF3096.104590535164 -aF2492.8132687330244 -aF3096.467434191704 -aF1882.9112188100814 -aF3153.379679644108 -aF3103.9139013051986 -aF3955.5213110566137 -aF2802.9606942892074 -aF3651.2354994654656 -aF3089.075720512867 -aF3687.951082468033 -aF3696.690384411812 -aF2265.2441016316416 -aF3095.273754835129 -aF3685.7805582523347 -aF3095.3753619551658 -aF3496.545877945423 -aF3137.7365416407583 -aF3631.636493909359 -aF3639.677621459961 -aF3097.7343905329703 -aF3369.041200530529 -aF2393.165558922291 -aF1569.2658387541771 -aF3769.4127522230146 -aF3077.358757901192 -aF3100.6428602933884 -aF3265.73563439846 -aF3096.470975458622 -aF3615.9533123493193 -aF3096.9223507881165 -aF2475.5806466937065 -aF3102.5597753167153 -aF3685.839397764206 -aF3089.2323534727097 -aF3070.711255085468 -aF4126.31770414114 -aF3095.7733458757402 -aF3674.8663736104963 -aF2897.976154565811 -aF3934.2573652386664 -aF3096.586747646332 -aF3106.3530169963838 -aF3076.06674028635 -aF3685.2381996035574 -aF3020.0071229457853 -aF3684.124607360363 -aF3623.135818874836 -aF2473.1409861922266 -aF2604.5179027795793 -aF3696.7350588560103 -aF4421.1930075049395 -aF3095.813661837578 -aF3641.5327005147933 -aF3162.4733806848526 -aF3686.157566976547 -aF3096.0694502711294 -aF3096.560324347019 -aF3091.0928806304933 -aF3095.9016487002373 -aF3686.057321882248 -aF3685.164105403423 -aF3659.5882585048676 -aF3331.8723353624346 -aF3714.182063746452 -aF3686.1256955742833 -aF3096.1999323368073 -aF3090.768446099758 -aF3686.178814578056 -aF2927.6568746328353 -aF3674.6435461997985 -aF970.7740232467651 -aF3667.796915221214 -aF3685.7816478729246 -aF3097.8084847331047 -aF3414.7654941678047 -aF2304.9218182086943 -aF3095.8109377861024 -aF2941.788436472416 -aF3683.830409801006 -aF3684.056506073475 -aF3095.9713844180105 -aF3685.6696893572807 -aF3690.96088694334 -aF3096.928343701363 -aF3686.208234333992 -aF1349.6740563988685 -aF3097.6564826607705 -aF3647.7748644709586 -aF3683.14994174242 -aF841.208873295784 -aF2079.6277788996695 -aF3685.768572425842 -aF3256.15841422081 -aF3687.446860539913 -aF3095.5886551856993 -aF3490.2764734745024 -aF2238.1160902023316 -aF219.08209756612777 -aF3207.290292775631 -aF3087.5883884072305 -aF3120.908986055851 -aF3489.848797392845 -aF3693.2433696746825 -aF3025.978788590431 -aF3096.411863541603 -aF3096.3037186980246 -aF2562.7859789848326 -aF3685.7426939368247 -aF3095.229352796078 -aF3705.066570293903 -aF3096.0610057115555 -aF3100.509654176235 -aF3684.056506073475 -aF3598.148639500141 -aF3685.7051020264626 -aF3433.99511834383 -aF3096.4663445711135 -aF3686.4757361888883 -aF3098.357925915718 -aF3829.2993894815445 -aF3686.1210646867753 -aF3096.089335846901 -aF3685.0134653568266 -aF3683.8148827075956 -aF2311.839002120495 -aF3421.0068409085275 -aF3077.431762480736 -aF3726.13138794899 -aF3687.0175500273704 -aF2943.5345534682274 -aF2766.2181431770323 -aF3360.2351593256 -aF3861.1198519825934 -aF1471.8235210180283 -aF485.3480503082276 -aF3686.362960457802 -aF3087.5434415578843 -aF1044.9131702303887 -aF3109.2108193993568 -aF3095.8161134839056 -aF1200.8027364253999 -aF3421.1207062602043 -aF3688.664239144325 -aF3687.695294034481 -aF3085.2282702088355 -aF3031.9030557394026 -aF3094.182772219181 -aF3319.777274405956 -aF1262.9329024791718 -aF3686.109078860283 -aF3914.735722744465 -aF3067.4660925626754 -aF3096.10840420723 -aF3058.338885688782 -aF3099.2511423945425 -aF3090.0912469029427 -aF3093.3680084228517 -aF514.3978800535202 -aF2981.335126173496 -aF3046.6047615528105 -aF3681.363508784771 -aF3095.3165224432946 -aF3328.0020030260084 -aF3095.0038013339044 -aF3324.859537243843 -aF3688.1537518978116 -aF2448.5951031565664 -aF3525.839510297775 -aF3072.6731169581412 -aF3681.286145722866 -aF3666.314214003086 -aF3096.011427974701 -aF3096.131831049919 -aF3094.340494799614 -aF3095.8041276574136 -aF986.1651864886284 -aF3686.5604541897774 -aF3019.086938357353 -aF4115.341138720512 -aF3196.2837627887725 -aF3102.447816801071 -aF3096.222541964054 -aF3476.334778022766 -aF4736.881970977783 -aF2696.192586326599 -aF2915.809157550335 -aF3079.8605267763137 -aF3062.872524559498 -aF1384.6969138145448 -aF3095.90464515686 -aF2956.967123699188 -aF4136.992989468574 -aF3713.444935417175 -aF1917.2724041223526 -aF3030.986140012741 -aF3096.1304690241814 -aF2361.047902405262 -aF3096.419218480587 -aF3686.161108243465 -aF3100.4268430113793 -aF3085.372100126743 -aF3680.5718994259832 -aF3114.2827308416367 -aF2055.7571882247926 -aF3846.1942291378973 -aF3094.906007885933 -aF3523.0075863838197 -aF3096.5418007969856 -aF3692.643533539772 -aF3096.4968539476395 -aF3102.776609814167 -aF3093.6810019373893 -aF3683.9118589401246 -aF3199.8547218680383 -aF3757.1384486794473 -aF3082.1969457268715 -aF3099.9781917333603 -aF3355.8597878456117 -aF3683.547653257847 -aF3685.9818656563757 -aF3099.1639727473257 -aF3095.304536616802 -aF2134.761763548851 -aF3096.0669986248017 -aF3686.377942740917 -aF1444.5881819605827 -aF3551.489723801613 -aF2065.7114171266553 -aF2827.229813694954 -aF3678.8715464949605 -aF3685.91349196434 -aF2179.6721106052396 -aF3689.1597441077233 -aF3866.38135740757 -aF3089.4456467032433 -aF3685.7530453324316 -aF3686.6291002869607 -aF3338.3920801639556 -aF3091.7975927472116 -aF4636.730856454372 -aF3095.7074238300324 -aF3669.728540122509 -aF3710.8271219491958 -aF3125.1522410392763 -aF2981.335398578644 -aF2384.5463876485824 -aF3414.7349847912787 -aF495.72014870643613 -aF3113.914711487293 -aF3724.661762177944 -aF2730.5055559277534 -aF2286.2007744431494 -aF3281.534315741062 -aF3095.8997418642043 -aF3742.1597068309784 -aF3686.7266213297844 -aF3097.1767771959303 -aF3056.1343108296396 -aF2927.903128886223 -aF2397.7092767834665 -aF3094.468797624111 -aF1318.9399456262588 -aF3160.574989211559 -aF3685.727439248562 -aF3153.0476177692412 -aF2994.020761489868 -aF3117.6847987294195 -aF3094.7706225275992 -aF3096.017420887947 -aF3096.038668489456 -aF2949.2381724476813 -aF3118.656740295887 -aF3099.2982684850695 -aF3107.925339508057 -aF3102.335858285427 -aF3095.8204719662667 -aF3096.2753885626794 -aF3685.824143075943 -aF3583.411248612404 -aF2185.5337245702744 -aF356.4364003062248 -aF3126.528431844711 -aF3684.9546258449554 -aF3091.7254053831102 -aF3686.1055375933647 -aF3682.6438129782678 -aF3091.878769481182 -aF3683.5179610967634 -aF1165.4426414370537 -aF3060.037059378624 -aF3095.6000962018966 -aF3753.246323931217 -aF3006.9352171301844 -aF3192.9566063165666 -aF2974.88157582283 -aF3688.056230854988 -aF3080.4037026405335 -aF3685.469199168682 -aF3096.0196001291274 -aF3096.4829612851145 -aF3052.3255420565606 -aF3077.6485969781875 -aF3092.262860739231 -aF3095.3018125653266 -aF3684.5454733133315 -aF3082.0800839185713 -aF3674.5868859291077 -aF707.5012582659721 -aF3132.2677358984947 -aF3096.5701309323313 -aF3013.9215919494627 -aF3667.3079479813573 -aF237.1809679746628 -aF3100.6235195279123 -aF2219.9733625650406 -aF3686.144763934612 -aF3091.3568412184713 -aF817.7820306062698 -aF678.4304533243179 -aF3689.350972521305 -aF3096.03158595562 -aF3073.992919898033 -aF3095.9738360643387 -aF3103.625151848793 -aF3174.6201986193655 -aF1097.0117443203926 -aF3672.9818747997283 -aF2837.8282807707787 -aF426.1157302141189 -aF3623.8707679629324 -aF3829.850465095043 -aF2904.358607172966 -aF3143.6008796572687 -aF3683.978598201275 -aF2222.946392345428 -aF4213.777735245228 -aF3096.5461592793463 -aF4717.331181132793 -aF3096.142454850674 -aF3114.4390913963316 -aF3161.6316487789154 -aF3095.4540870428086 -aF3686.1799041986465 -aF3098.742017173767 -aF3094.3298709988594 -aF2260.170828163624 -aF3400.1430582523344 -aF3041.432877421379 -aF2154.0875467419623 -aF3097.5314486980437 -aF3685.8206018090245 -aF2610.5195329904554 -aF3903.7199309825896 -aF3523.9353983163833 -aF2885.224869608879 -aF3091.8869416356088 -aF3111.83489818573 -aF3555.391655135155 -aF3069.1852414488794 -aF3096.2827435016634 -aF3715.0390503406525 -aF3695.7909026145935 -aF3095.457900714874 -aF4167.796291148662 -aF4632.474253618717 -aF3687.9006875157356 -aF3095.141910743713 -aF3663.0208357691763 -aF3448.4276878714563 -aF2440.291921854019 -aF3073.8387385845185 -aF3096.3791749238967 -aF3691.7514066815374 -aF3145.3042290449143 -aF3683.9282032489778 -aF3685.6094878196714 -aF3089.0108880877497 -aF3651.90725055933 -aF3092.936246263981 -aF2780.7297101974486 -aF3098.356836295128 -aF3093.532541131973 -aF3096.267216408253 -aF3707.4879796504974 -aF3096.4993055939676 -aF3110.1416277885437 -aF3474.2960978984834 -aF3679.924937200546 -aF3063.535831093788 -aF3228.1973878502845 -aF3095.316250038147 -aF3678.7917317867277 -aF3096.294456923008 -aF4131.580299186706 -aF3064.877154040337 -aF3694.3722166061402 -aF3284.8985193133353 -aF3703.929006397724 -aF3094.777432656288 -aF2384.267444777489 -aF3695.7511314630506 -aF2008.1636504650116 -aF3093.34812284708 -aF3095.774707901478 -aF3453.1383900880815 -aF2530.7544024944305 -aF3081.6398772001266 -aF3684.8260506153106 -aF3096.1860396742823 -aF3287.4130912303926 -aF3087.5167458534243 -aF3561.156020462513 -aF3054.1675456643106 -aF3448.068657886982 -aF3095.9716568231584 -aF3682.2616285562513 -aF3077.4434759020805 -aF3234.1663294434547 -aF3685.2150451660154 -aF3094.5031206727026 -aF3722.8592573165893 -aF1065.3293912291526 -aF3685.6081257939336 -aF3685.8440286517143 -aF3097.1239305973054 -aF3685.9211193084716 -aF1428.1945677757265 -aF3226.6253377437592 -aF3458.687010538578 -aF3095.7608152389525 -aF3099.077620315552 -aF3685.992761862278 -aF3096.128017377853 -aF3093.8063083052634 -aF3245.799663674831 -aF1437.8352583527565 -aF2980.292631673813 -aF3093.2832904219626 -aF3087.415138733387 -aF3692.0769308328627 -aF2573.3207032561304 -aF3686.447406053543 -aF2458.69288957119 -aF3686.0349846601484 -aF3683.2305736660956 -aF3071.6387946128843 -aF3100.0122423768044 -aF3259.0445467591285 -aF3034.1877177119254 -aF3685.932560324669 -aF3095.896200597286 -aF3691.0927310347556 -aF3685.859555745125 -aF3096.4322939276694 -aF3670.686044216156 -aF3104.8144727230074 -aF3741.504844856262 -aF3551.8691841721534 -aF2834.1565317869185 -aF3585.123587369919 -aF1938.1220217108726 -aF3095.817203104496 -aF3734.450641155243 -aF2945.956235229969 -aF3096.307259964943 -aF3578.022802388668 -aF3690.00038639307 -aF3685.920846903324 -aF3096.114941930771 -aF3093.403693497181 -aF2512.5106125473976 -aF3611.101504266262 -aF3095.817203104496 -aF3685.8475699186324 -aF3125.365806674957 -aF3188.0636650562287 -aF3686.3588743805885 -aF3077.276219141483 -aF3662.795829117298 -aF3219.030682229996 -aF3684.7056475400923 -aF3099.0765306949615 -aF3682.6871253967283 -aF3310.657150065899 -aF3095.903010725975 -aF3092.745017850399 -aF3138.4292679309847 -aF554.3430984854698 -aF3854.8872222065925 -aF3624.9750984311104 -aF3660.996593117714 -aF2901.9404666781425 -aF3467.7431196689604 -aF2980.34193700552 -aF1883.1424907803537 -aF3704.2417275071143 -aF3096.2718472957613 -aF3122.8871922373773 -aF2262.1754576444628 -aF3271.3188503026963 -aF3589.1540939331053 -aF3092.6905368208886 -aF2849.1456250309943 -aF3687.7544059515 -aF3686.111530506611 -aF3089.346491229534 -aF2023.376933145523 -aF3652.288072955608 -aF4549.854317176341 -aF3686.479277455807 -aF3681.2352059602736 -aF2415.928277862072 -aF3846.863528585434 -aF3098.3402195811273 -aF3489.273750126362 -aF3685.676771891117 -aF3667.069048666954 -aF3094.3195196032525 -aF3555.6120308995246 -aF2497.5917998313903 -aF2583.6424067020416 -aF3682.1003647089005 -aF3686.0407051682473 -aF3686.9998436927795 -aF3085.970574235916 -aF3095.6733731865884 -aF3096.807123410702 -aF3685.9941238880156 -aF2346.026665353775 -aF261.08397486209867 -aF3707.7080830097198 -aF2799.214306294918 -aF3094.414861404896 -aF3684.853291130066 -aF3680.9420980215073 -aF3100.876039099693 -aF3685.8960580348967 -aF3089.2312638521194 -aF1332.4847467780114 -aF3095.9700223922728 -aF3677.0600522637365 -aF3719.977755665779 -aF2911.9933062434197 -aF3602.2001212596892 -aF3097.0580085515976 -aF2855.195743358135 -aF3685.922208929062 -aF3686.2109583854676 -aF3687.7827360868455 -aF3105.268844509125 -aF3185.6654101371764 -aF3097.3794466257095 -aF3096.5194635748862 -aF3685.766120779514 -aF3097.505842614174 -aF3687.320736956596 -aF3096.6842686891555 -aF3473.28029910326 -aF3177.908128750324 -aF3148.7967354416846 -aF3079.1887756824494 -aF3075.838464772701 -aF2842.907002341747 -aF3682.6604296922683 -aF3614.3621938824654 -aF3092.402876985073 -aF3728.5598798394203 -aF3691.3141964197157 -aF3682.016736328602 -aF2725.9098086833956 -aF3096.3293247818947 -aF3098.888026332855 -aF2940.973945081234 -aF3021.8978870749474 -aF2969.9295226454733 -aF3080.831378722191 -aF3690.305480158329 -aF3102.20755546093 -aF3686.1142545580865 -aF2851.0935942411425 -aF3083.521651959419 -aF3103.966747903824 -aF1657.188413798809 -aF3681.704287624359 -aF3687.3370812654493 -aF3736.671287918091 -aF3636.6220529198645 -aF3685.767482805252 -aF3095.7308506727218 -aF4374.519926738739 -aF3096.1824984073637 -aF3094.7071521282196 -aF4176.466946995258 -aF3792.6107745885847 -aF3686.3261857628822 -aF3087.6504967808723 -aF2221.7518957734105 -aF3686.2128652215 -aF3096.086884200573 -aF3096.2094665169716 -aF3685.606218957901 -aF3097.8994680523874 -aF3096.842263674736 -aF3090.7845180034637 -aF1666.2020277261734 -aF3096.6878099560736 -aF2915.4307868003843 -aF3078.305093383789 -aF3367.250136685371 -aF4108.684374129772 -aF3066.0553063035013 -aF2944.434580075741 -aF3685.4542168855664 -aF3088.9422419905663 -aF3487.430656898022 -aF3090.318705201149 -aF2948.7726320505144 -aF3087.7215945243834 -aF3091.7044301867486 -aF3096.47342710495 -aF3686.1128925323487 -aF3684.2910469055173 -aF3096.846622157097 -aF3096.9471396565436 -aF3092.6918988466264 -aF3120.519991505146 -aF3077.24761660099 -aF3097.0531052589417 -aF3098.427389228344 -aF3685.9053198099136 -aF3097.0686323523523 -aF3089.8874878525735 -aF3735.2629533052445 -aF3526.944930386543 -aF3685.141768181324 -aF3686.558274948597 -aF250.54652653932573 -aF3532.2946950793266 -aF3090.396613073349 -aF3723.1403794288635 -aF3133.3799661159514 -aF3701.7691059827803 -aF4068.161656785011 -aF3958.6024856805802 -aF3127.626769399643 -aF2648.586517930031 -aF2804.1636354207994 -aF3098.107857990265 -aF3097.07108399868 -aF3099.840082323551 -aF1379.8633568763732 -aF2968.498033595085 -aF3124.2388665795324 -aF2714.69815762043 -aF3810.4293401002883 -aF3365.1891193389893 -aF3697.7543989181518 -aF3095.944416308403 -aF3096.558145105839 -aF3096.069177865982 -aF3251.2703762531282 -aF3292.905051410198 -aF3702.2896722197534 -aF3096.0245034217833 -aF2067.3409447193144 -aF3705.425872683525 -aF3086.98855227232 -aF3658.807000541687 -aF3083.739576077461 -aF2903.9200348854065 -aF3901.392773807049 -aF3188.6705837249756 -aF3088.2669496297835 -aF3096.129379403591 -aF4106.121041691303 -aF3102.582384943962 -aF3095.951226437092 -aF3686.365139698982 -aF3161.5943292737006 -aF3033.168377649784 -aF3215.9672139406202 -aF3988.978928494453 -aF3743.17904689312 -aF3096.1342826962473 -aF1630.466013634205 -aF4624.8232102394095 -aF3693.9292858362196 -aF3048.6845748543737 -aF3090.8594294190407 -aF3704.2673335909844 -aF3140.8474084258078 -aF3533.138333821297 -aF3686.2624429583548 -aF3686.7443276643753 -aF3112.373443162441 -aF3806.8553845643996 -aF2922.9339141845703 -aF3690.2008765816686 -aF3070.3519526958466 -aF2636.4688473463057 -aF3119.0893196702004 -aF3686.0725765705106 -aF3095.5766693592072 -aF3096.055285203457 -aF3679.0611404776573 -aF3675.1973458647726 -aF3672.1698350548745 -aF3091.6654762506487 -aF3067.106790173054 -aF3089.7493784427643 -aF2137.79145359993 -aF3654.345549035072 -aF564.5847148180007 -aF3690.861731469631 -aF3051.918023955822 -aF3137.386773431301 -aF4284.828080260753 -aF3693.837212896347 -aF2067.8898410916327 -aF3007.798469042778 -aF3055.5156787395476 -aF3094.1838618397715 -aF3107.607170295715 -aF3974.1674434065817 -aF3090.320612037182 -aF3127.9498419046404 -aF3012.9022518873217 -aF3320.31554697752 -aF3082.0672808766367 -aF3091.7292190551757 -aF3320.6914660811426 -aF3685.0709428429604 -aF3095.9185378193856 -aF3250.2796387314797 -aF3336.330245602131 -aF3699.1186038970945 -aF3692.5879628896714 -aF3192.7160725712774 -aF903.5449776411056 -aF3093.6687437057494 -aF3686.718449175358 -aF3260.832069337368 -aF3101.381623053551 -aF3097.148447060585 -aF3488.2544100642203 -aF1973.135617351532 -aF3116.3214109659193 -aF2449.463530766964 -aF3101.9991655230524 -aF3328.131940281391 -aF3095.867598056793 -aF1038.1338233232498 -aF3685.980503630638 -aF3094.4679804086686 -aF3084.615086221695 -aF2447.5757630944254 -aF1280.5275509595872 -aF3696.3836562156675 -aF3153.345356595516 -aF3095.4682521104814 -aF3151.0320920825006 -aF3106.2576751947404 -aF3681.9845925211907 -aF3685.970152235031 -aF3097.9820068120957 -aF3097.3252380013464 -aF3685.693388605118 -aF3732.075268268585 -aF3696.810242676735 -aF3560.5213164687157 -aF3095.4328394412996 -aF3029.7077426552773 -aF1392.225919687748 -aF3339.4422020077704 -aF3093.131560754776 -aF3739.718411898613 -aF3096.502029645443 -aF3102.9498594880106 -aF1791.8916696429253 -aF3088.01470246315 -aF3686.477915430069 -aF3334.466994392872 -aF3091.6581213116647 -aF2695.1732462644577 -aF3151.53631401062 -aF3667.1981687068937 -aF644.7230403661728 -aF3666.8211599826814 -aF3693.0382485985756 -aF3790.3825004816053 -aF1878.9063183307649 -aF3606.644683647156 -aF2779.687215697765 -aF3125.856680750847 -aF3092.7600001335145 -aF3113.325499153137 -aF3044.088827610016 -aF3680.9442772626876 -aF2920.9810416817663 -aF3006.466135466099 -aF3386.723291063309 -aF3088.4922286868095 -aF2653.100543630123 -aF3112.2819150328637 -aF2946.7399448394776 -aF3482.2067433834077 -aF3662.8979810476303 -aF3877.195024549961 -aF3650.7361808300016 -aF3098.5616849660873 -aF3096.91417863369 -aF3097.729759645462 -aF3685.91349196434 -aF3686.127057600021 -aF3690.568351125717 -aF3447.0493178248407 -aF3098.9174460887907 -aF3251.4923864483835 -aF3095.522460734844 -aF3112.526807260513 -aF2906.397287297249 -aF3097.177049601078 -aF3086.5205602288247 -aF3562.1522060871125 -aF3685.936101591587 -aF3095.7180476307867 -aF3797.1337896585464 -aF3096.3685511231424 -aF3058.5524513244627 -aF3095.6237954497337 -aF3352.1204823851585 -aF2988.9641047358514 -aF3057.888872385025 -aF3685.69202657938 -aF3728.159444272518 -aF3124.6997760891913 -aF3097.5810264348984 -aF3095.6897174954415 -aF3096.9155406594277 -aF3683.7571328163144 -aF3686.128419625759 -aF3095.8229236125944 -aF3653.4250920414925 -aF627.0295088171958 -aF3097.492767167091 -aF3094.927255487442 -aF3540.5956971406936 -aF3095.9090036392213 -aF2905.244468712807 -aF3704.5312941789625 -aF3658.511168551445 -aF3897.185748708248 -aF3693.830402767658 -aF3733.557697081566 -aF3158.8882565379145 -aF3095.5001235127447 -aF3079.7316791415215 -aF2124.471931505203 -aF2804.9993744134904 -aF2835.377451658249 -aF3096.841174054146 -aF3096.2059252500535 -aF3685.303032028675 -aF2985.489032268524 -aF3692.946175658703 -aF3139.757243025303 -aF3097.7343905329703 -aF3121.562486004829 -aF3117.3295824170114 -aF3046.386565029621 -aF3095.878221857548 -aF3096.310801231861 -aF3940.3083007812497 -aF3052.4650134921076 -aF3126.4083011746407 -aF3684.643266761303 -aF3090.929982352257 -aF3687.557729434967 -aF3689.412536084652 -aF3686.5182313919067 -aF3215.1960349678993 -aF3181.1993277430533 -aF3096.3993329048158 -aF3096.354658460617 -aF3120.299615740776 -aF4241.948785984516 -aF3680.1785463929177 -aF2857.2543090581894 -aF4022.88982809782 -aF3028.0237340331078 -aF3095.7485570073127 -aF2537.7285190820694 -aF3685.3989186406134 -aF3698.7522189736364 -aF3609.9053732633593 -aF3686.49671138525 -aF4145.928967928886 -aF3096.0539231777193 -aF3685.975055527687 -aF3670.5531105041505 -aF3668.249380171299 -aF3094.506661939621 -aF3057.374299061298 -aF3095.5156506061553 -aF3643.7337341070174 -aF3096.2576822280885 -aF3681.440054631233 -aF3685.7993542075155 -aF2746.8226242661476 -aF3086.3276973843576 -aF3210.3050005435944 -aF4077.0382509231567 -aF3691.161921942234 -aF3686.1246059536934 -aF1805.3054439187051 -aF3176.21240670681 -aF3104.200471520424 -aF3097.326600027084 -aF3888.940317296982 -aF2240.532868671417 -aF2215.810194694996 -aF3686.05595985651 -aF2989.7657930850983 -aF3686.2071447134017 -aF382.5581471204758 -aF3691.8625479817388 -aF4612.658413565158 -aF3104.3644594192506 -aF3098.93760406971 -aF3096.4344731688498 -aF3095.672283565998 -aF3042.925930035114 -aF3096.40995670557 -aF3096.2094665169716 -aF3095.6842693924905 -aF715.4484060406685 -aF3686.140133047104 -aF3249.2107209324836 -aF4051.152134561539 -aF3694.2722439169884 -aF1052.18529804945 -aF3082.5450795054435 -aF3068.293114590645 -aF3642.3550916552545 -aF3123.7983874559404 -aF3095.678004074097 -aF3090.3020884871485 -aF3685.909950697422 -aF3095.9713844180105 -aF3687.767208993435 -aF2967.2899167656897 -aF2136.826594567299 -aF3113.8689474225043 -aF3097.672009754181 -aF596.611388015747 -aF2675.350323677063 -aF3685.92575019598 -aF3685.153754007816 -aF3085.861884582043 -aF3096.336952126026 -aF3684.046154677868 -aF2601.243865311146 -aF3099.8166554808618 -aF3695.640262567997 -aF3096.3355901002883 -aF3096.1844052433967 -aF3725.1496397972105 -aF2624.2607382535934 -aF3879.1974747896193 -aF3094.321698844433 -aF3054.3372540712357 -aF3098.1228402733805 -aF3094.9250762462616 -aF3852.85181094408 -aF3686.5536440610886 -aF3093.5731294989587 -aF3083.4568195343018 -aF3663.6549949526784 -aF3209.328972899914 -aF3681.5985944271088 -aF3089.650222969055 -aF3681.814884114265 -aF3020.855392575264 -aF3096.0517439365385 -aF3680.6849475622175 -aF3691.1815351128575 -aF4240.14301226139 -aF3097.3606506705282 -aF2920.324545276165 -aF3058.5807814598083 -aF2656.691660690308 -aF3095.947685170174 -aF3097.544524145126 -aF3401.051529419422 -aF3635.8078339338304 -aF3686.0374363064766 -aF3737.713782417774 -aF1899.0046425223352 -aF3031.135962843895 -aF2964.041212975979 -aF3686.048060107231 -aF888.1080503344535 -aF3685.094914495945 -aF3095.166972017288 -aF3091.901379108429 -aF2027.0418720006942 -aF3010.4312647938727 -aF2924.5541800022124 -aF3090.188223135471 -aF3723.9017518162727 -aF1449.2735504984855 -aF2123.2098784565924 -aF3054.1866140246393 -aF3702.2343739748 -aF2938.948340404034 -aF3679.1425896167752 -aF3099.514558172226 -aF3961.5948562264443 -aF3709.11042470932 -aF3481.708514368534 -aF3848.9632274627684 -aF3096.049292290211 -aF3384.1942816734313 -aF3679.301674222946 -aF3095.7286714315414 -aF3228.860694384575 -aF2061.0669093608853 -aF3014.0703251600266 -aF3096.244606781006 -aF3076.502860927582 -aF4541.279275536536 -aF3064.41488250494 -aF3669.144231081009 -aF3095.8005863904955 -aF3517.5589386224747 -aF3458.260696482658 -aF3893.370987021923 -aF2368.6297548770904 -aF3096.5546038389207 -aF3096.190942966938 -aF3686.854106938839 -aF3830.396637415886 -aF3804.119074857235 -aF3335.972850048542 -aF4536.677262973785 -aF3685.5353936195374 -aF3095.2892819285394 -aF3093.2209096431734 -aF3098.2772939920424 -aF3096.0337651968002 -aF3933.617485547066 -aF3684.7067371606827 -aF2833.936156022549 -aF3685.679223537445 -aF3094.8106660842896 -aF2625.4792064785956 -aF4182.943379378318 -aF3112.503380417824 -aF3094.5009414315223 -aF3095.5126541495324 -aF2746.484569478035 -aF3685.9382808327673 -aF4489.362123274803 -aF3694.9617013454435 -aF2517.551742208004 -aF3131.156595301628 -aF3685.5846989512443 -aF3685.5389348864555 -aF3645.1093801021575 -aF3095.9787393569945 -aF3096.142454850674 -aF3686.115071773529 -aF1759.6073011755943 -aF3127.149515581131 -aF3095.972474038601 -aF3685.973693501949 -aF3687.2450083255767 -aF3052.550548708439 -aF3159.0536064624785 -aF3686.6345483899117 -aF3094.845806348324 -aF3965.0228026032446 -aF3096.1808639764786 -aF542.4392659425736 -aF2121.938563632965 -aF3207.271224415302 -aF3686.1082616448402 -aF3678.720906448364 -aF3284.4594022154806 -aF3095.885304391384 -aF3397.3040518045427 -aF2261.569628596306 -aF3095.8466228604316 -aF3092.4845985293387 -aF2767.238572859764 -aF3064.800335788727 -aF3685.9734210968018 -aF3395.0744156718256 -aF3182.694559597969 -aF3691.175269794464 -aF3092.6684720039366 -aF2296.644787800312 -aF3096.0610057115555 -aF3651.1921870470046 -aF3686.028991746902 -aF3712.842647635937 -aF2809.2979276418687 -aF1347.0164717793466 -aF4022.732650327682 -aF3736.580304598808 -aF2727.8975490450857 -aF3211.8824987530706 -aF3102.252502310276 -aF4057.953001475334 -aF1435.4370034337044 -aF2144.6323640704154 -aF3095.4505457758905 -aF3689.4664723038672 -aF3085.536632835865 -aF3102.7940437436105 -aF3123.502555465698 -aF3097.366371178627 -aF3431.097272384167 -aF3106.999979221821 -aF3685.1194309592247 -aF3350.3484869003296 -aF2425.600839841366 -aF3682.4822767257688 -aF3369.3983236789704 -aF3095.879311478138 -aF3150.357889342308 -aF3685.5601824879645 -aF3096.0999596476554 -aF3058.8717101573943 -aF2690.5527101516723 -aF3100.1898505330087 -aF3537.5717275977136 -aF3095.904372751713 -aF3093.8855782032015 -aF3699.7304258584977 -aF3003.7235604405405 -aF3084.4535499691965 -aF3825.1476626276967 -aF3734.379271006584 -aF3353.6138074040414 -aF3094.9275278925898 -aF3108.4941214561463 -aF3095.078167939186 -aF3642.2022723674772 -aF3091.6616625785828 -aF3095.7251301646234 -aF3691.547375226021 -aF3251.7468128561973 -aF3594.027694427967 -aF1001.744854092598 -aF3433.7581258654595 -aF2935.9042128801348 -aF3685.7805582523347 -aF4492.301102411747 -aF3302.939095020294 -aF2146.5729783415795 -aF3686.1937968611714 -aF2997.4920202851295 -aF3287.5125191092493 -aF3684.1188868522645 -aF3098.2832869052886 -aF3023.971979868412 -aF3687.556367409229 -aF3270.385590267181 -aF3091.9120029091837 -aF3135.8438706755637 -aF3084.184141278267 -aF3095.8899352788926 -aF4073.249640130997 -aF3335.6609461545945 -aF3817.6271013140677 -aF3093.16452177763 -aF3095.075988698006 -aF3095.8972902178766 -aF3087.1391923189162 -aF3095.663566601276 -aF3096.439376461506 -aF3093.7891467809677 -aF3679.979145824909 -aF3685.919757282734 -aF3389.632850444317 -aF3691.094910275936 -aF3186.6319036006926 -aF3685.919757282734 -aF3733.142824041843 -aF4042.487471628189 -aF3685.8312256097793 -aF3147.2933314323427 -aF3686.2744287848473 -aF3096.1059525609016 -aF3095.068633759022 -aF3569.7738297104834 -aF3688.2586278796193 -aF2984.8388011813163 -aF3753.1120281934736 -aF3417.0433460116387 -aF3008.595254099369 -aF3680.1063590288163 -aF3095.9185378193856 -aF3685.4847262620924 -aF3378.741820240021 -aF3701.8538239836694 -aF3717.2839411616324 -aF3041.4527629971503 -aF3093.6690161108972 -aF3717.522295665741 -aF3094.1247499227525 -aF3049.864089143276 -aF3688.2166774868965 -aF3095.6049994945524 -aF3653.274179589748 -aF3684.259175503254 -aF836.1135350108146 -aF3689.01427975893 -aF3586.1197729945184 -aF3674.62556746006 -aF3095.308350288868 -aF3069.470177233219 -aF4707.880629348754 -aF3096.502029645443 -aF3096.1931222081184 -aF3056.402902305126 -aF3092.562506401539 -aF1686.7852330803871 -aF3164.7675768375398 -aF3062.293663620949 -aF3096.108131802082 -aF1900.5674308538437 -aF3172.5643569707872 -aF1573.627862381935 -aF1312.6441178560258 -aF3115.631953537464 -aF3570.5090512037277 -aF3768.716757071018 -aF3717.0695583105085 -aF3729.7053434848785 -aF3099.7496438145636 -aF3684.3003086805343 -aF2939.855721950531 -aF3097.9218052744864 -aF3686.1943416714666 -aF3690.1297788381576 -aF2612.9058020830153 -aF3907.8008324980733 -aF3096.197480690479 -aF3095.9525884628297 -aF3095.7662633419036 -aF3100.0413897275926 -aF1516.7831734061242 -aF3095.898107433319 -aF3686.139860641956 -aF3689.2687061667443 -aF2634.0354521632194 -aF3764.626048970222 -aF2880.681968963146 -aF3686.0513289690016 -aF2440.5983776450157 -aF3096.794865179062 -aF3095.961850237846 -aF3681.3708637237546 -aF3684.649259674549 -aF2031.194688475132 -aF3099.9604853987694 -aF3095.8084861397742 -aF3686.0728489756584 -aF3229.8568800091743 -aF3689.6007680416105 -aF3685.936101591587 -aF4668.034293985366 -aF1058.5119076013566 -aF3101.692709732056 -aF3095.756184351444 -aF3685.6190219998357 -aF3096.1694229602813 -aF3686.1384986162184 -aF3128.4080273628233 -aF3686.122426712513 -aF4437.387493526935 -aF3682.6577056407928 -aF3096.5556934595106 -aF3095.9008314847947 -aF3093.743110311031 -aF3095.9465955495834 -aF3183.004829061031 -aF3681.6163007616997 -aF3094.705790102482 -aF3649.8366990327836 -aF4435.453961789607 -aF3150.789651501179 -aF2832.3864431381226 -aF3004.7464417696 -aF3685.746235203743 -aF400.4491724014282 -aF3098.725400459766 -aF3096.4132255673408 -aF4606.747766673564 -aF1428.2637586832047 -aF2373.197171986103 -aF3095.802765631676 -aF3639.8263546705243 -aF3096.6363253831864 -aF3685.892244362831 -aF3054.250084424019 -aF3096.715050470829 -aF3094.4807834506037 -aF4263.069174289703 -aF3211.9307144641875 -aF3686.80834287405 -aF3208.242348766327 -aF3682.70810059309 -aF3686.095186197758 -aF3096.5003952145576 -aF3685.2101418733596 -aF4520.873950743675 -aF3746.237067079544 -aF3094.427664446831 -aF3097.6079945445063 -aF3096.335317695141 -aF3688.159744811058 -aF3160.5194185614587 -aF3685.5280386805534 -aF3099.6515779614447 -aF3095.6660182476044 -aF1148.9564095020294 -aF3096.281653881073 -aF3095.1465416312217 -aF3684.1967947244643 -aF1778.5568928599357 -aF3696.4512126922605 -aF4712.496262168884 -aF1860.1411449313164 -aF3113.607438480854 -aF3093.014698946476 -aF2675.130765128136 -aF3687.7813740611077 -aF3096.0577368497848 -aF3096.281653881073 -aF3104.7302995324135 -aF2911.4291551828383 -aF2675.5976675510406 -aF4397.307434546947 -aF3092.5012152433396 -aF3095.516467821598 -aF3095.494130599499 -aF3084.20048558712 -aF2602.5533168554307 -aF3097.0604601979257 -aF3583.275046038628 -aF3094.758636701107 -aF3095.9220790863037 -aF3687.595321345329 -aF3096.0231413960455 -aF3062.8136850476267 -aF3685.9243881702423 -aF3087.752376306057 -aF3095.2386145710943 -aF3096.201294362545 -aF3683.9788706064223 -aF3060.0749236941338 -aF3094.0928785204887 -aF3478.2190044283866 -aF3689.3877472162244 -aF1558.9490386009218 -aF4042.8323365449905 -aF3048.6434416770935 -aF1073.2337713956833 -aF3097.9182640075683 -aF3662.4716669917107 -aF1208.3657929420472 -aF3325.940168464184 -aF3096.803309738636 -aF3686.140133047104 -aF3095.739295232296 -aF3720.505949246883 -aF3702.9134800076486 -aF3686.0671284675595 -aF3681.2117791175842 -aF3727.90992115736 -aF3098.2584980368615 -aF3268.623673772812 -aF3428.5919622421266 -aF3684.100090897083 -aF3686.1869867324826 -aF3090.3306910276415 -aF2756.6836906075478 -aF3096.3508447885515 -aF3093.0844346642493 -aF3096.2072872757913 -aF3094.575308036804 -aF3683.177454662323 -aF3096.4110463261604 -aF688.6491876244545 -aF3686.0069269299506 -aF2713.7354778289796 -aF3686.161108243465 -aF3253.077239596844 -aF3832.3805641055105 -aF3692.16627972126 -aF3096.634690952301 -aF2630.057247388363 -aF3685.9889481902123 -aF3669.6435497164725 -aF3681.2872353434564 -aF3097.6777302622795 -aF3689.031986093521 -aF3096.8542495012284 -aF3097.080345773697 -aF1855.6292984724046 -aF3098.359832751751 -aF918.6779008030891 -aF370.06128857135775 -aF3733.3830853819845 -aF3096.405053412914 -aF3699.6043022751805 -aF3095.2879199028016 -aF3095.4516353964805 -aF2713.6861724972723 -aF3103.6297827363014 -aF3096.190398156643 -aF2034.3559502124785 -aF4198.523319387436 -aF1093.6660642981528 -aF3051.8036137938498 -aF3094.7978630423545 -aF3416.5650025725363 -aF2908.10336073637 -aF3092.2053832530974 -aF3095.3938855051993 -aF3221.4559052586555 -aF2447.9402411818505 -aF882.1077821493149 -aF3096.0879738211634 -aF3092.9313429713247 -aF3104.6090792417526 -aF3686.1480327963827 -aF3095.7548223257063 -aF3094.1637038588524 -aF3095.2786581277846 -aF3149.574724543095 -aF3588.3055518984793 -aF3705.14148170948 -aF3096.2094665169716 -aF3082.1702500224114 -aF2408.6191029429438 -aF3100.36555185318 -aF2901.298135340214 -aF3683.620657837391 -aF3096.281109070778 -aF3685.2422856807707 -aF3098.1879451036452 -aF3095.1819543004035 -aF3686.084562397003 -aF3723.074729788303 -aF2189.1866775989533 -aF3116.671179175377 -aF3498.6856203794478 -aF3427.8273209929466 -aF3095.5990065813066 -aF3685.617659974098 -aF3096.9343366146086 -aF2049.2170130372047 -aF3684.1341415405273 -aF3006.4506083726883 -aF3686.1962485074996 -aF3097.99263061285 -aF3096.1103110432623 -aF3686.483908343315 -aF3095.9643018841743 -aF3684.7832830071447 -aF3685.985406923294 -aF3094.781246328354 -aF3097.6564826607705 -aF3187.674398100376 -aF3685.304121649265 -aF3256.5378745913504 -aF4043.5068116903303 -aF3773.2296931505202 -aF3367.9428629755976 -aF3209.7389426469804 -aF3663.3422738432882 -aF3293.042616009712 -aF3685.602405285835 -aF3095.681545341015 -aF3678.0254561066627 -aF3096.0324031710625 -aF3795.201892352104 -aF3096.4878645777703 -aF3095.60418227911 -aF3677.157845711708 -aF3686.0962758183477 -aF3685.2207656741143 -aF3091.2718508124353 -aF430.1127309441566 -aF2228.141158509254 -aF3095.5322673201563 -aF3370.149072265625 -aF3976.8740609526635 -aF4137.0128750443455 -aF3095.65648406744 -aF3102.7471900582314 -aF2529.1096202135086 -aF3686.2727943539617 -aF3091.36038248539 -aF3125.669810819626 -aF4601.233741676807 -aF2643.39338619709 -aF3430.2555404782297 -aF3097.225265312195 -aF3727.404064798355 -aF3095.70987547636 -aF3747.182312941551 -aF3689.162740564346 -aF3634.6945140957832 -aF3141.677154505253 -aF3091.268309545517 -aF2475.4863945126535 -aF3096.548883330822 -aF3687.9254763841627 -aF3098.3423988223076 -aF3685.9889481902123 -aF1615.9419159770011 -aF3533.0666912674906 -aF4388.023322308063 -aF3686.202513825893 -aF3685.8475699186324 -aF3096.3919779658318 -aF3680.0366233110426 -aF3209.8710591435433 -aF3685.7064640522003 -aF4203.558728539943 -aF3065.896494102478 -aF3095.9681155562403 -aF2008.1059005737304 -aF3058.059942817688 -aF853.5278512835503 -aF2302.3988017320635 -aF3104.81637955904 -aF2983.000338840485 -aF3743.0507440686224 -aF3685.748414444923 -aF3095.980918598175 -aF3685.845390677452 -aF3653.55557410717 -aF4709.901330733299 -aF3430.675316810608 -aF3099.478600692749 -aF2983.892465698719 -aF4066.3300045728683 -aF2642.363422334194 -aF4071.011831843853 -aF3096.942781174183 -aF3686.0491497278213 -aF3685.9619800806045 -aF3081.6126366853714 -aF3110.999704003334 -aF3691.398097205162 -aF2221.032746183872 -aF3102.854517686367 -aF3160.949546289444 -aF3636.630497479439 -aF3379.2997059822083 -aF2994.6184183835985 -aF2242.167299556732 -aF1790.442474257946 -aF3088.7984120726587 -aF3095.2999057292936 -aF3085.3304221391677 -aF3095.4576283097267 -aF2969.8565180659293 -aF3096.509929394722 -aF3836.108156144619 -aF3679.398378050327 -aF3196.0146265029907 -aF3477.02886633873 -aF3079.2642319083216 -aF3680.8832585096357 -aF3771.5277057886124 -aF3687.3877486228944 -aF3684.4040950417516 -aF3692.7198069810865 -aF3166.086017751694 -aF2158.7413162827493 -aF3318.276866853237 -aF3142.2295921444893 -aF3096.469613432884 -aF3087.980651819706 -aF2318.1637048363687 -aF3677.9110459446906 -aF3095.2854682564735 -aF2023.6621413350106 -aF3068.0000066518783 -aF3078.62326259613 -aF1327.838059771061 -aF4599.53829203844 -aF3093.4649846553802 -aF3096.183588027954 -aF3285.3512566685677 -aF3428.28550645113 -aF3095.441011595726 -aF2980.431830704212 -aF3685.1513023614884 -aF2707.366100668907 -aF3091.328511083126 -aF3726.6628503918646 -aF3069.184151828289 -aF3086.9128236413003 -aF4400.732112061977 -aF3680.1867185473443 -aF3094.8850326895713 -aF3682.3430776953696 -aF3691.4969802737237 -aF3743.1360068798062 -aF595.0137318253517 -aF1074.2531114578246 -aF3076.06674028635 -aF3687.114526259899 -aF3059.0324291944503 -aF3055.4628321409227 -aF3127.820177054405 -aF3388.636664819717 -aF4000.3145238995553 -aF3075.568511271477 -aF3427.5960490226744 -aF3094.8730468630793 -aF3096.0411201357842 -aF3096.6251567721365 -aF3095.59192404747 -aF3616.820377933979 -aF3095.984732270241 -aF3097.734118127823 -aF3686.043156814575 -aF3686.2036034464836 -aF3730.449281942844 -aF1698.5362463355066 -aF269.25286042690277 -aF3685.1439474225044 -aF3845.8379232048987 -aF2108.343095123768 -aF3703.9559745073316 -aF3095.924258327484 -aF2363.7300034880636 -aF3092.2582298517227 -aF2491.164672780037 -aF373.14872851371763 -aF3958.3940957427026 -aF3623.5866493940352 -aF3097.080073368549 -aF4601.475637447834 -aF3672.1744659423825 -aF3096.07053989172 -aF3684.465386199951 -aF3096.1035009145735 -aF3096.0915150880815 -aF4412.975906229019 -aF3042.472103059292 -aF3687.550374495983 -aF1396.5756850838661 -aF4571.647818601131 -aF3070.1528245329855 -aF3685.702922785282 -aF3688.588782918453 -aF3685.0957317113875 -aF3107.4420927762985 -aF3050.7314271330833 -aF3096.1413652300835 -aF3096.7973168253898 -aF3016.7368991494177 -aF4172.618407070637 -aF4279.705501461029 -aF3203.7623737096787 -aF4389.1472659468645 -aF3095.895928192139 -aF3103.0326706528663 -aF3687.772384691238 -aF3096.3968812584876 -aF3067.0130828022957 -aF4279.2756461381905 -aF3753.973645675182 -aF3096.9256196498873 -aF3096.5450696587563 -aF3113.8468826055528 -aF3093.4611709833143 -aF3686.077479863167 -aF2970.9875442385674 -aF3097.021233856678 -aF3180.3943705320357 -aF2921.9377285599708 -aF1293.6822679400445 -aF3119.138897407055 -aF3686.7574031114577 -aF3396.3261173248293 -aF3630.833715939522 -aF3685.3338138103486 -aF3139.3143122553824 -aF3110.347838485241 -aF3135.354903435707 -aF3095.8722289443017 -aF3095.833547413349 -aF3906.7583379983903 -aF3678.041800415516 -aF3097.1274718642235 -aF3686.180993819237 -aF3702.447667205334 -aF3836.6183709859847 -aF2964.3441275000573 -aF3688.272520542145 -aF3011.9150556325912 -aF3104.1269221305847 -aF3281.9137761116026 -aF3399.763597881794 -aF1437.2869067907334 -aF3097.476422858238 -aF2980.4517162799834 -aF2813.6253558158874 -aF3162.164473247528 -aF3095.154986190796 -aF3686.125967979431 -aF4717.710641503333 -aF3095.956402134895 -aF3119.143800699711 -aF3046.9357338070868 -aF3092.603639578819 -aF3686.347160959244 -aF2977.017504584789 -aF3095.979556572437 -aF3097.305624830723 -aF3100.6733696699143 -aF3096.7986788511275 -aF3095.87685983181 -aF3686.0703973293303 -aF3095.4023300647737 -aF3683.624199104309 -aF3700.3081971764564 -aF3685.821691429615 -aF3754.0804284930227 -aF562.9407497525215 -aF3113.6875255942346 -aF3107.9356909036637 -aF3685.5236801981923 -aF3694.3817507863046 -aF3685.922208929062 -aF4434.32729409933 -aF3062.582685482502 -aF401.8722168922424 -aF3095.5131989598276 -aF3051.902769267559 -aF3686.9268391132355 -aF3708.34469383955 -aF3783.321759057045 -aF2655.3514273643495 -aF3385.749987471104 -aF3685.8957856297493 -aF1305.8762119650842 -aF3097.1650637745856 -aF3500.646392631531 -aF3071.48570291996 -aF3189.7476736783983 -aF3686.5533716559407 -aF1936.5077488064767 -aF986.0731135487556 -aF3096.565500044823 -aF3103.0781623125076 -aF3679.29786055088 -aF3690.2229413986206 -aF3094.6139895677566 -aF724.5821506381035 -aF3721.425861430168 -aF3183.260617494583 -aF3090.419767510891 -aF3747.2564071416855 -aF2675.211124646664 -aF3686.822235536575 -aF4387.003982245921 -aF3097.1002313494682 -aF3167.1857173323633 -aF3103.437737107277 -aF3095.7485570073127 -aF3684.7955412387846 -aF3212.298733818531 -aF3118.3527361512183 -aF3688.967970883846 -aF3687.8380343317986 -aF3420.6859476447107 -aF3716.814859497547 -aF3689.851925587654 -aF3496.528171610832 -aF3685.3667748332023 -aF3688.6677804112433 -aF2158.2251085281373 -aF3095.9087312340735 -aF4386.057646763325 -aF3092.5420760154725 -aF2862.768061649799 -aF3096.5679516911505 -aF3074.994826030731 -aF2968.621705532074 -aF3095.404509305954 -aF3097.4796917200088 -aF3081.1318415999413 -aF3685.9137643694876 -aF3167.967247700691 -aF2530.5201340675353 -aF3687.7661193728445 -aF3096.4791476130486 -aF2781.109170567989 -aF3088.108954644203 -aF3096.1198452234266 -aF3056.661142385006 -aF3494.767344737053 -aF3685.9875861644746 -aF1020.9221764802933 -aF3083.7338555693627 -aF3096.359016942978 -aF3096.9220783829687 -aF3082.631976747513 -aF3727.6666633605955 -aF3252.0578995347023 -aF3721.779443311691 -aF3096.543980038166 -aF3685.9889481902123 -aF3096.5619587779047 -aF3094.5526984095573 -aF3189.3976330637934 -aF3142.24620885849 -aF3690.5552756786346 -aF3092.333958482742 -aF3105.265575647354 -aF3660.053526496887 -aF3684.2485517024993 -aF2546.379016947746 -aF3095.7354815602303 -aF3915.4415244817733 -aF3685.594505536556 -aF3680.646266031265 -aF3664.8366884827615 -aF3094.9106387734414 -aF3075.516754293442 -aF3193.3289841532705 -aF2930.0551295518876 -aF3104.638771402836 -aF3095.7286714315414 -aF3126.7041331648825 -aF3681.009109687805 -aF3681.614666330814 -aF3190.825580847263 -aF3096.6867203354836 -aF3094.2805656671526 -aF3687.0156431913374 -aF3686.002296042442 -aF3096.1773227095605 -aF3077.029692482948 -aF3707.0129050731657 -aF4042.6855101704596 -aF3684.5430216670034 -aF3684.70591994524 -aF3639.1513347148893 -aF3097.2097382187844 -aF2934.0567611694337 -aF2993.800385725498 -aF3096.2756609678268 -aF3493.1138454914094 -aF3085.3598418951033 -aF3048.999202799797 -aF3096.052833557129 -aF3315.1728101968765 -aF3090.9199033617974 -aF2500.4588640093802 -aF2610.5772828817367 -aF3476.3609289169312 -aF3338.537272107601 -aF2596.343024301529 -aF3053.851828098297 -aF1751.0505106806756 -aF3030.37077678442 -aF3686.042067193985 -aF3720.9063848137853 -aF409.39631947278974 -aF3095.0784403443336 -aF3265.4536950707434 -aF3487.123111486435 -aF3214.3278797626494 -aF2922.4411332726477 -aF4602.4309623003 -aF3096.1966634750365 -aF4368.160900974273 -aF3685.686033666134 -aF3096.2576822280885 -aF4136.353109776974 -aF3686.3594191908837 -aF3686.1834454655645 -aF3255.5185345292093 -aF3093.4154069185256 -aF3003.2087147116663 -aF3700.8979543209075 -aF3687.145035636425 -aF3095.157437837124 -aF2724.506377363205 -aF2097.10502076149 -aF2941.3534054517745 -aF2712.989360129833 -aF3096.1966634750365 -aF3097.8098467588425 -aF2086.159781932831 -aF3682.1801794171333 -aF3094.563049805164 -aF3255.592901134491 -aF3095.8923869252203 -aF3685.1676466703416 -aF3089.633878660202 -aF3107.7599895834924 -aF3095.236435329914 -aF3278.751152348518 -aF3096.1103110432623 -aF3095.8338198184965 -aF2854.9105351686476 -aF3686.146943175793 -aF3208.696448147297 -aF3103.6570232510567 -aF3067.6510556578637 -aF3096.3238766789436 -aF3683.5201403379438 -aF3074.166441977024 -aF2798.5038736701013 -aF3111.4777750372887 -aF3098.5932839632032 -aF4555.157228183746 -aF3691.5713468790054 -aF3102.135640501976 -aF3045.804707634449 -aF3677.2087854743004 -aF3097.0672703266146 -aF3646.43245190382 -aF3095.926709973812 -aF3120.833529829979 -aF2685.526835179329 -aF3281.694489967823 -aF3694.1507512211797 -aF3682.0605935573576 -aF3112.49602547884 -aF3685.997120344639 -aF3095.0887917399405 -aF3134.3287532448767 -aF3726.220736837387 -aF3093.771440446377 -aF1408.6102720975875 -aF3096.8493462085726 -aF3095.058282363415 -aF3685.9467253923417 -aF3646.568109667301 -aF3090.8286476373673 -aF3668.0998297452925 -aF3688.5661732912063 -aF3265.0061334133147 -aF3095.7945934772492 -aF3021.013115155697 -aF3096.1786847352982 -aF3737.485506904125 -aF3434.4533038020136 -aF3094.082527124882 -aF3686.569171154499 -aF3684.1224281191826 -aF3403.9962290644644 -aF3092.6578482031823 -aF3173.980318927765 -aF3685.8546524524686 -aF3687.318557715416 -aF3677.4337921261786 -aF3093.071359217167 -aF3086.829467666149 -aF3868.2064718961715 -aF1057.7576177477836 -aF3698.372758603096 -aF1512.9158375263214 -aF3699.1011699676515 -aF3693.237104356289 -aF3837.1629088759423 -aF3691.3215513586997 -aF3054.56607439518 -aF1198.2317766427993 -aF3093.539351260662 -aF3102.878216934204 -aF3714.9044821977614 -aF3103.773885059357 -aF3241.3826142072676 -aF3667.202799594402 -aF1349.390210235119 -aF3185.6125635385515 -aF3074.89703258276 -aF4714.771934771537 -aF3081.8967552542686 -aF3094.4734285116197 -aF3685.7922716736794 -aF3683.9186690688134 -aF3094.0492936968803 -aF3695.1311373472213 -aF3064.0577593564985 -aF3882.210820531845 -aF3110.9596604466437 -aF3802.0204656004903 -aF2944.8140404462815 -aF3101.857514846325 -aF1734.4989015102387 -aF3108.0171400427816 -aF2600.0055115103723 -aF3060.362855935097 -aF288.8126396417618 -aF3103.2219922304153 -aF3767.5465045571327 -aF3115.5526836395265 -aF3989.901292324066 -aF3751.8053007006642 -aF3095.9534056782722 -aF3095.1607066988945 -aF3095.830006146431 -aF3095.438559949398 -aF3366.369723248482 -aF3685.8347668766974 -aF383.1980268120766 -aF1988.7141953349114 -aF354.94961301088335 -aF3475.9863718390466 -aF3770.485211288929 -aF3683.4095438480376 -aF3087.575312960148 -aF3693.715992605686 -aF3092.2522369384765 -aF3094.9920879125593 -aF3337.0055379629134 -aF3054.480266773701 -aF3687.1793586850167 -aF3095.830006146431 -aF3684.574893069267 -aF3094.5551500558854 -aF3095.9664811253547 -aF3680.443596601486 -aF3097.072990834713 -aF3685.9856793284416 -aF3096.6137157559397 -aF3108.651844036579 -aF3105.5951858758926 -aF4171.291521596908 -aF2938.958964204788 -aF3682.476283812523 -aF3477.6311541199684 -aF3684.195705103874 -aF492.7280505657196 -aF3333.6454204678535 -aF3102.5848365902903 -aF3943.775473499298 -aF3100.2903680324553 -aF3127.636303579807 -aF2945.3550370693206 -aF3105.7864142894746 -aF3096.508839774132 -aF3658.293244433403 -aF3934.492723286152 -aF3026.010659992695 -aF2968.8241025567054 -aF3294.382576930523 -aF3673.2447457671165 -aF3096.347303521633 -aF3073.0599322676658 -aF3097.1473574399947 -aF3031.8472126841543 -aF3096.3494827628138 -aF3631.707319247723 -aF3095.6897174954415 -aF3747.5734867334363 -aF3074.5260167717934 -aF3142.8825472831727 -aF2255.6001421928404 -aF4523.489857375621 -aF3690.148574793339 -aF3153.6964868307114 -aF3096.2165490508078 -aF3097.728397619724 -aF3096.9542221903803 -aF3166.9146742105486 -aF3095.04765856266 -aF3095.9149965524675 -aF3686.284780180454 -aF3687.8911533355713 -aF3269.215337753296 -aF3126.4862090468405 -aF2920.476819753647 -aF3098.9280698895454 -aF3671.3853082299233 -aF3073.262601697445 -aF3735.642413675785 -aF2928.928189456463 -aF3095.4371979236603 -aF3036.4584870219232 -aF3096.7147780656815 -aF3084.6714740872385 -aF3098.3990590929984 -aF3607.2603192806246 -aF3096.1895809412003 -aF2566.997362565994 -aF3096.037578868866 -aF2950.580040204525 -aF4443.707292950153 -aF3098.4851391196253 -aF3095.906824398041 -aF3685.9619800806045 -aF3096.0032558202743 -aF2185.2280859947205 -aF3096.724857056141 -aF3686.191890025139 -aF3773.2768192410467 -aF3141.2268687963488 -aF3093.3764529824257 -aF3062.503415584564 -aF3703.8878732204435 -aF4281.251673078536 -aF3703.410619401932 -aF3708.032245135307 -aF3094.923986625671 -aF3144.8054552197455 -aF1705.1390747070313 -aF3124.0124979019165 -aF3787.3244802951813 -aF3240.3820701003074 -aF2773.310483598709 -aF3120.21680457592 -aF3094.8765881299973 -aF3687.510330939293 -aF3685.807526361942 -aF3688.0668546557426 -aF4423.363531720638 -aF3683.6865798830986 -aF3695.278508532047 -aF3107.3075246334074 -aF3640.193829214573 -aF3098.5031178593636 -aF3095.7548223257063 -aF3092.10159689188 -aF2718.2170873165132 -aF2992.757891225815 -aF3475.3647432923317 -aF3099.972198820114 -aF3091.4287561774254 -aF3107.757537937164 -aF2956.0270535349846 -aF3256.0456384897234 -aF1486.2247639536859 -aF3664.1401485204697 -aF3686.5961392641066 -aF3095.883125150204 -aF3690.6675065994264 -aF3073.0256092190743 -aF2458.5136469841004 -aF3093.9692065834997 -aF3092.8863961219786 -aF3685.6958402514456 -aF421.96536538600924 -aF3686.3368095636365 -aF3094.847440779209 -aF3012.9575501322747 -aF3029.351436722279 -aF3879.3837999105453 -aF3681.532127571106 -aF3097.739021420479 -aF929.3866919636725 -aF3095.6553944468496 -aF2284.4789015054703 -aF3101.142178928852 -aF3687.999570584297 -aF3096.120934844017 -aF3698.0216283679006 -aF3426.246826326847 -aF3264.4112005710604 -aF3684.291319310665 -aF2985.7543548822405 -aF3090.4816034793853 -aF3094.563322210312 -aF4041.789842045307 -aF3229.0175997495653 -aF3098.742017173767 -aF3095.672283565998 -aF3715.8851407289503 -aF1796.9112792968751 -aF3686.670233464241 -aF3095.804944872856 -aF3701.917294383049 -aF3096.421670126915 -aF3108.756175208092 -aF3090.1770545244217 -aF2978.416305017471 -aF3102.4420962929726 -aF3095.3767239809035 -aF3583.253526031971 -aF2832.0235994815826 -aF3687.923024737835 -aF3630.6182434678076 -aF4104.356945955753 -aF3683.687397098541 -aF3686.200879395008 -aF3049.369401395321 -aF3688.49562035799 -aF3152.8283316254615 -aF3048.6927470088003 -aF3089.205930173397 -aF3882.9945301413536 -aF3096.044388997555 -aF3686.075300621986 -aF3095.5461599826813 -aF3094.4151338100432 -aF3101.0171449661257 -aF1859.7960076093675 -aF3103.153890943527 -aF3098.941145336628 -aF3686.0714869499207 -aF3614.7664431214334 -aF3115.9087171673773 -aF3096.118483197689 -aF3670.2455650925635 -aF3350.0728128910064 -aF2919.8758939981462 -aF1171.7880469441413 -aF3448.158551585674 -aF3096.100776863098 -aF3211.123305606842 -aF3685.839397764206 -aF3180.552093112469 -aF1904.5140366315843 -aF3317.2575267910956 -aF3715.795519435406 -aF3132.1990898013114 -aF3684.519594824314 -aF2994.545413804054 -aF3095.0887917399405 -aF3267.358079457283 -aF3126.552403497696 -aF3093.764085507393 -aF3121.8724830627443 -aF2984.820822441578 -aF3686.0407051682473 -aF3079.999180996418 -aF3134.757246541977 -aF3676.6255660533902 -aF3106.142447817326 -aF3098.7654440164565 -aF2978.413036155701 -aF3686.059773528576 -aF3098.42003428936 -aF3680.648717677593 -aF2844.9271589159966 -aF2981.7687951683997 -aF3097.872227537632 -aF3099.109764122963 -aF3151.721821916103 -aF2319.5625052690507 -aF2783.9743279099466 -aF3087.818025946617 -aF1935.4126801133157 -aF3096.3238766789436 -aF3283.2079729676248 -aF2780.066676068306 -aF3729.971755719185 -aF3095.018511211872 -aF3056.1980536341666 -aF1775.6710327267647 -aF3430.4375071167947 -aF2107.3006006240844 -aF3095.9081864237787 -aF270.1419908285141 -aF3095.9019211053846 -aF3095.8795838832857 -aF3109.4854037880896 -aF3096.215459430218 -aF3091.744473743439 -aF2887.5768156528475 -aF3101.908454608917 -aF3262.4114743828773 -aF376.82973927259445 -aF3666.736169576645 -aF3095.597644555569 -aF3227.2665794610975 -aF3094.2672178149223 -aF3073.5690574884416 -aF2929.058943927288 -aF3104.218177855015 -aF3752.8246407628058 -aF3097.4325656294823 -aF4420.080504882335 -aF3096.180046761036 -aF3724.4825195908547 -aF2799.6645920038222 -aF2692.658674347401 -aF3102.6529378771784 -aF3096.0304963350295 -aF2965.8290079593658 -aF3586.0227967619894 -aF3094.136463344097 -aF3090.92072057724 -aF3328.154005098343 -aF3086.348672580719 -aF3064.779088187218 -aF3702.2455425858498 -aF3075.889132130146 -aF3108.204554784298 -aF3418.6036826968193 -aF3310.9625162363054 -aF3091.890482902527 -aF3096.0574644446374 -aF3108.794856739044 -aF3086.4464660286903 -aF3015.744527196884 -aF3070.0081773996353 -aF3096.2023839831354 -aF1665.5621480345726 -aF3078.75129301548 -aF3097.5565099716187 -aF3683.7053758382795 -aF3099.295544433594 -aF3284.975065159798 -aF3095.6698319196703 -aF3096.0975080013277 -aF3269.2202410459518 -aF3068.4334032416346 -aF3295.425071430206 -aF3627.3373958706857 -aF3088.9068293213845 -aF3685.682492399216 -aF3095.9751980900764 -aF3091.651311182976 -aF3662.3281094789504 -aF4035.9543789744375 -aF3399.3427319288253 -aF3685.0227271318436 -aF3078.0977930665017 -aF3095.826464879513 -aF3093.7491032242774 -aF3048.3037524580955 -aF3094.1026851058004 -aF3094.918810927868 -aF3699.508960473537 -aF3094.2462426185607 -aF3686.1728216648103 -aF2963.738570857048 -aF3721.17633831501 -aF1517.3936333417894 -aF3115.9098067879677 -aF3097.0520156383513 -aF3665.0606055140493 -aF3764.3195931792256 -aF4689.816626799106 -aF3743.768531632423 -aF3709.055126464367 -aF3243.7841379880906 -aF2857.63376942873 -aF3671.8977023124694 -aF3096.128017377853 -aF3094.792142534256 -aF3126.3115973472595 -aF3095.9561297297478 -aF3095.7698046088217 -aF2725.5311655282976 -aF3685.5955951571464 -aF3141.886361658573 -aF3770.8020184755323 -aF3095.8795838832857 -aF3429.8891555547716 -aF3098.113850903511 -aF3095.5036647796633 -aF3686.138771021366 -aF3689.254813504219 -aF3685.827684342861 -aF4142.583015501499 -aF3096.43338354826 -aF4214.432869625091 -aF3693.565080153942 -aF3094.4984897851946 -aF3703.1028015851975 -aF2980.374080812931 -aF3833.8760683655737 -aF3703.2834061980247 -aF3990.472798323631 -aF617.1158682823182 -aF3128.111105751991 -aF2985.7589857697485 -aF3351.61053994894 -aF3843.112509703636 -aF3681.064407932758 -aF3125.4807616472244 -aF3774.0572599887846 -aF3094.8376341938974 -aF3685.7356114029885 -aF3685.921936523914 -aF3095.592196452618 -aF3701.7252487540245 -aF3095.539077448845 -aF3095.924258327484 -aF3675.9867759823796 -aF3095.1819543004035 -aF3392.342736852169 -aF3095.834909439087 -aF3098.549699139595 -aF3095.304536616802 -aF3685.695567846298 -aF3639.5871829509733 -aF3086.1650715112687 -aF3313.357229888439 -aF3686.1147993683812 -aF3182.2644318699836 -aF3690.795537018776 -aF3740.988909506798 -aF3660.6795135259626 -aF3086.011435008049 -aF3687.8862500429154 -aF3104.7858701825144 -aF3686.666692197323 -aF3578.0753765821455 -aF3096.047930264473 -aF3612.5980981469156 -aF3685.657975935936 -aF3554.6994736552238 -aF4748.5163948297495 -aF3043.305390405655 -aF917.455074095726 -aF2307.543990159035 -aF3684.8121579527856 -aF3095.9806461930275 -aF1409.629612159729 -aF3690.825229179859 -aF3702.2262018203733 -aF900.5223701238632 -aF4345.739778089523 -aF3681.2766115427016 -aF3112.4491717934607 -aF4470.348788785934 -aF3596.574682557583 -aF4002.110491037369 -aF3101.512105119228 -aF3467.9362549185753 -aF3094.259318065643 -aF3097.959124779701 -aF3097.900830078125 -aF3098.1280159711837 -aF3685.618204784393 -aF3685.0973661422727 -aF2391.08438359499 -aF3040.0150086283684 -aF2765.4543191432954 -aF3686.1172510147094 -aF3685.8535628318787 -aF2989.3059731960298 -aF3095.841991972923 -aF3096.108131802082 -aF1623.1892549276351 -aF3721.309544432163 -aF3687.3877486228944 -aF3686.628827881813 -aF3305.3719453930853 -aF3092.344309878349 -aF3599.042945599556 -aF3058.1541949987413 -aF2432.319167995453 -aF3685.8404873847962 -aF3447.9953809022904 -aF3070.0691961526873 -aF3683.7407885074613 -aF3095.5486116290094 -aF3685.468109548092 -aF2102.10120357275 -aF3752.030307352543 -aF3677.974788749218 -aF1190.6412072062492 -aF3104.89319781065 -aF3339.649502325058 -aF3113.125553774834 -aF3106.4132185339927 -aF3686.5195934176445 -aF3925.5861645817754 -aF3667.7462478637694 -aF3088.555699086189 -aF4063.6844057798385 -aF3104.174320626259 -aF3093.223633694649 -aF2296.328797829151 -aF3096.32986959219 -aF3095.6180749416353 -aF4504.397252988815 -aF2914.6685971975326 -aF3095.534174156189 -aF2608.0142228484156 -aF3685.9170332312583 -aF3060.657325899601 -aF3096.331776428223 -aF3209.859073317051 -aF3286.3117572188376 -aF1795.1016919016838 -aF3097.4739712119103 -aF3565.33117415905 -aF3103.90927041769 -aF2802.7002749681474 -aF3682.0510593771933 -aF4667.2268851280205 -aF2853.681443142891 -aF3094.431205713749 -aF3686.0069269299506 -aF3126.093945634365 -aF3096.2636751413347 -aF3107.2958112120627 -aF3055.430960738659 -aF3096.996444988251 -aF3696.2125857830047 -aF2948.006356370449 -aF3527.7817589998244 -aF3112.0934106707573 -aF3617.102862071991 -aF3694.3122874736787 -aF3094.1484491705896 -aF3028.735256278515 -aF3546.682862567902 -aF1361.407363319397 -aF3686.3485229849816 -aF4022.011593902111 -aF3144.9985904693604 -aF3095.300995349884 -aF1865.2650857567787 -aF3655.791203153133 -aF3429.276516377926 -aF3089.065913927555 -aF3090.913638043404 -aF3686.579250144958 -aF2396.2178586006166 -aF3686.4179862976075 -aF3122.2157135486605 -aF3101.475875234604 -aF3681.4531300783156 -aF3437.4816318273542 -aF3685.918395256996 -aF3685.837490928173 -aF3706.884329843521 -aF3046.8393023848535 -aF3018.372419655323 -aF2393.449132680893 -aF3094.861605846882 -aF3686.364867293835 -aF3623.186213827133 -aF4243.44864872694 -aF3094.393613803387 -aF3108.686439490318 -aF3093.549702656269 -aF3601.106686997414 -aF3096.190398156643 -aF3775.975264632702 -aF3092.6352385759355 -aF3101.9637528538706 -aF3717.4416637420654 -aF3098.0819795012476 -aF3689.9320127010346 -aF863.0930856347084 -aF3277.4643104314805 -aF3113.6605574846267 -aF3096.2070148706434 -aF3095.4576283097267 -aF2562.5255596637726 -aF3685.4591201782227 -aF3655.715746927261 -aF3095.5142885804175 -aF3099.3925206661224 -aF3096.276750588417 -aF3686.4215275645256 -aF3095.8005863904955 -aF3215.9375217795373 -aF3738.371368443966 -aF3096.3919779658318 -aF3095.5249123811723 -aF3106.2966291308403 -aF3490.3671843886377 -aF3761.4350950717926 -aF2607.3046074390413 -aF3685.101724624634 -aF3095.006252980232 -aF3216.702163028717 -aF3114.7518125057222 -aF3090.6071822524073 -aF3260.635665225983 -aF3311.9241064071657 -aF3685.9679729938507 -aF2296.7297782063483 -aF3435.6962884902955 -aF3478.5606004834176 -aF3685.5116943717003 -aF2118.17501411438 -aF3096.2802918553352 -aF3685.709732913971 -aF3136.4191903471947 -aF3695.1545641899106 -aF3093.7668095588683 -aF3116.8362566947935 -aF4002.833999109268 -aF3691.970692825317 -aF3658.1679380655287 -aF3096.0727191329 -aF3686.21068598032 -aF3097.6978882431986 -aF4073.2471884846686 -aF3099.775522303581 -aF3093.432840847969 -aF3094.3511186003684 -aF1498.597950565815 -aF3689.8023478507994 -aF3057.51322568655 -aF1973.6180468678474 -aF4011.362187063694 -aF3371.597178030014 -aF3587.9432530522345 -aF3098.038394677639 -aF3685.8227810502053 -aF3779.127809405327 -aF2999.883465075493 -aF3687.31474404335 -aF3095.7828800559046 -aF3685.889792716503 -aF3097.188763022423 -aF3056.440766620636 -aF1477.0697717547416 -aF3685.5163252592088 -aF395.4655202269554 -aF3099.0977782964706 -aF3107.8013951659204 -aF2411.781726706028 -aF3095.5788486003876 -aF3685.256178343296 -aF4006.8288205981253 -aF3097.551606678963 -aF3091.676372456551 -aF3080.144100534916 -aF2537.1766262531282 -aF3835.450842523575 -aF3095.8120274066923 -aF3089.3887140274046 -aF3112.4690573692324 -aF3099.7395648241045 -aF1312.287811923027 -aF2146.88787869215 -aF3685.8949684143067 -aF3699.413618671894 -aF3060.591403853893 -aF3444.680755066872 -aF3101.8114783763885 -aF3089.729220461845 -aF3687.268707573414 -aF3131.6425660848618 -aF3096.0610057115555 -aF3083.946059179306 -aF3682.647081840038 -aF3683.9257516026496 -aF3333.650596165657 -aF3095.9550401091574 -aF3675.3575200915334 -aF834.1628417491912 -aF3074.2781280875206 -aF3593.6871879935265 -aF3099.4497257471085 -aF3105.6153438568117 -aF2495.619041752815 -aF2943.6898244023323 -aF3101.9702905774116 -aF3070.534464144707 -aF3092.0095239520074 -aF3086.467441225052 -aF3096.269395649433 -aF3083.5690504550935 -aF3127.7460828542708 -aF3096.2718472957613 -aF4628.253608262538 -aF3095.945505928993 -aF3080.279758298397 -aF3157.438243937492 -aF3881.7485489964483 -aF3997.3017229676248 -aF3093.6796399116515 -aF3085.190133488178 -aF3105.663831973076 -aF3685.1523919820784 -aF3097.8117535948754 -aF1080.2168773531914 -aF3666.9674415469167 -aF3653.472218132019 -aF3072.5055877923965 -aF3931.223589110374 -aF3096.9351538300516 -aF3095.3846237301827 -aF3095.9220790863037 -aF3098.868140757084 -aF3118.572022294998 -aF2791.110525560379 -aF4138.24332909584 -aF2935.671851289272 -aF3441.1642770171165 -aF3688.876170349121 -aF3093.846896672249 -aF3145.533866584301 -aF1880.8270470261575 -aF3177.5106896400453 -aF3095.961577832699 -aF3108.1348190665244 -aF3237.044834637642 -aF3710.543548190594 -aF3027.271895825863 -aF3897.5297964096067 -aF3095.7286714315414 -aF3095.6878106594086 -aF3095.7379332065584 -aF1488.9678837895394 -aF3686.21068598032 -aF3712.343873810768 -aF3077.558975684643 -aF3095.5823898673057 -aF3832.01336196661 -aF3092.6235251545904 -aF3096.7572732686995 -aF3668.936658358574 -aF2511.829054868221 -aF3375.2258870005608 -aF3096.9046444535256 -aF3687.225395154953 -aF3099.3195160865785 -aF2320.0928780913355 -aF3068.664402806759 -aF3686.175273311138 -aF3690.6271906375882 -aF3154.7313539862635 -aF3094.048476481438 -aF3481.7401133656504 -aF3670.784110069275 -aF3011.0863991737365 -aF3685.9698798298837 -aF3686.219130539894 -aF3686.084562397003 -aF3847.8237567305564 -aF2979.231613624096 -aF2157.171445417404 -aF3299.303575921059 -aF3644.8002002596854 -aF3683.436784362793 -aF3583.484798002243 -aF3688.9576194882393 -aF3096.710419583321 -aF3094.5464330911636 -aF3686.725804114342 -aF3095.7346643447877 -aF927.549319243431 -aF3724.5416315078733 -aF3093.4510919928553 -aF3096.937877881527 -aF3095.542618715763 -aF3096.256865012646 -aF3093.5213725209237 -aF2632.109820175171 -aF3097.1402749061585 -aF2679.217659556866 -aF3095.6624769806863 -aF3095.5186470627787 -aF3097.755910539627 -aF3581.161999309063 -aF3408.8003662467004 -aF3686.700742840767 -aF3666.9848754763602 -aF3685.3667748332023 -aF3097.217910373211 -aF2990.524441421032 -aF332.70609828233717 -aF3096.1846776485445 -aF3685.987858569622 -aF3743.820288610458 -aF3667.6639815092085 -aF3770.5840943574904 -aF2697.5112996459006 -aF3644.3499145507812 -aF4758.991462373733 -aF1431.0237676382064 -aF3096.0577368497848 -aF3096.014969241619 -aF2741.315954208374 -aF3680.6672412276266 -aF3287.604319643974 -aF3792.183370912075 -aF3508.863766312599 -aF3689.939095234871 -aF3097.9100918531417 -aF3677.2994963884353 -aF3096.4603516578672 -aF3763.3002531170846 -aF3180.40281509161 -aF3490.436920106411 -aF3718.39835062027 -aF3095.8820355296134 -aF3685.214772760868 -aF3095.9890907526014 -aF3105.259582734108 -aF3686.4179862976075 -aF3095.9610330224036 -aF3432.713996934891 -aF3061.114421737194 -aF3060.955609536171 -aF3769.9278703570367 -aF3683.24010784626 -aF3099.2037438988687 -aF3410.1615747690203 -aF3122.245133304596 -aF2910.817060816288 -aF2260.408093047142 -aF3736.5320888876913 -aF3686.6557959914207 -aF3662.3128547906877 -aF3096.8011304974557 -aF3099.0046157360075 -aF3095.3549315690993 -aF3060.323629593849 -aF3068.3470508098603 -aF3057.5532692432403 -aF3096.222541964054 -aF3667.34662951231 -aF3681.4580333709714 -aF1956.1999169230462 -aF3684.8189680814744 -aF3108.2042823791503 -aF3093.7333037257195 -aF3685.593143510818 -aF3685.9017785429955 -aF3686.125967979431 -aF2918.1834408164023 -aF3095.8735909700395 -aF3095.821016776562 -aF3685.884344613552 -aF4270.080337977409 -aF3096.045478618145 -aF3030.73253082037 -aF2891.380953538418 -aF3096.8537046909332 -aF3697.2567147135733 -aF3095.6014582276343 -aF3538.4611304044724 -aF3043.7401490211487 -aF3686.1009067058562 -aF3635.5950855135916 -aF3414.8172511458397 -aF3689.6868480682374 -aF2797.0873669028283 -aF3662.155949425697 -aF2835.7332127809523 -aF3096.0255930423737 -aF2921.9483523607255 -aF2883.163852262497 -aF3095.667380273342 -aF991.2697865486144 -aF3127.4439855456353 -aF3095.5766693592072 -aF3068.283852815628 -aF3096.56577244997 -aF3686.057321882248 -aF3097.2996319174767 -aF3409.7965518713 -aF3095.5178298473356 -aF3101.453265607357 -aF3561.2519070744515 -aF3081.57831363678 -aF3686.1330505132673 -aF3619.0320353269576 -aF3687.4642944693564 -aF3089.5878421902657 -aF3685.807526361942 -aF3094.264766168594 -aF3096.469613432884 -aF2119.2175086140633 -aF3686.074211001396 -aF3686.718449175358 -aF2967.247693967819 -aF3096.5619587779047 -aF3695.5789714097978 -aF3086.4761581897737 -aF3096.863511276245 -aF2151.781909573078 -aF3095.952316057682 -aF3096.924530029297 -aF3458.403436779976 -aF3159.186812579632 -aF3185.0563122272492 -aF3115.190112388134 -aF3123.800839102268 -aF3124.124728822708 -aF3770.9472104191777 -aF3097.0920591950417 -aF3095.9713844180105 -aF3157.105637252331 -aF2496.612775731087 -aF2604.2373254776003 -aF3685.6059465527533 -aF3517.245400297642 -aF3685.816788136959 -aF4495.440571737289 -aF3096.4731546998023 -aF3097.9607592105867 -aF3098.9242562174795 -aF3687.440867626667 -aF2579.1185744166373 -aF3093.602004444599 -aF3095.980918598175 -aF2985.406493508816 -aF3096.575034224987 -aF3686.2245786428452 -aF3651.334382534027 -aF3517.3924990773203 -aF3127.2671946048736 -aF3781.9248654603957 -aF3097.748283195496 -aF3709.1082454681396 -aF3126.761065840721 -aF3096.6374150037764 -aF3543.304766333103 -aF3095.821016776562 -aF3685.6094878196714 -aF3683.250459241867 -aF3626.25730946064 -aF3099.4630735993387 -aF3102.004886031151 -aF3086.6221673488617 -aF3686.203875851631 -aF3770.9591962456702 -aF3092.1639776706697 -aF2905.4651168823243 -aF3082.1664363503455 -aF3099.9070939898493 -aF3686.2493675112723 -aF3110.081698656082 -aF3691.4158035397527 -aF3685.960890460014 -aF3087.6398729801176 -aF3687.022725725174 -aF2978.950219106674 -aF3094.184679055214 -aF3732.3817240595818 -aF3086.863790714741 -aF4117.466716086864 -aF3092.080349290371 -aF3686.064949226379 -aF3060.1152396559714 -aF3685.997120344639 -aF3681.2188616514204 -aF3702.8862394928933 -aF3134.208077764511 -aF583.9235734581947 -aF3693.316101849079 -aF3095.1677892327307 -aF1959.2413203954698 -aF3744.838266646862 -aF2742.1149185061454 -aF3686.2000621795655 -aF3096.3203354120255 -aF3153.4164543390275 -aF3094.925893461704 -aF4479.89985806942 -aF3025.0575143814085 -aF3095.9836426496504 -aF3684.053237211704 -aF3094.475062942505 -aF3115.978180480003 -aF3682.0377115249635 -aF3098.226899039745 -aF3658.9862431287766 -aF3467.9362549185753 -aF3686.8799854278564 -aF3686.192979645729 -aF3360.3691826581953 -aF3099.014149916172 -aF3808.869275820255 -aF2622.323665249348 -aF3028.3775883197786 -aF3684.131689894199 -aF3666.9865099072454 -aF3095.9713844180105 -aF3075.2928372621536 -aF3096.347575926781 -aF4236.578318500518 -aF963.5868858337402 -aF3088.1539014935493 -aF2220.7322833061216 -aF3693.837212896347 -aF3094.4935864925383 -aF3065.5521739959718 -aF3095.6910795211793 -aF3686.104720377922 -aF3092.115761959553 -aF3846.804416668415 -aF3063.5527202129365 -aF3086.275668001175 -aF1829.1171950817109 -aF3094.781246328354 -aF3686.2060550928113 -aF3745.720859324932 -aF3754.0052446722984 -aF3168.2320255041122 -aF3692.576521873474 -aF3725.5609715700148 -aF512.9824629068374 -aF3161.8051708579064 -aF3096.037578868866 -aF2900.5844338536263 -aF3686.160018622875 -aF3095.7379332065584 -aF3672.187813794613 -aF2423.6792939305305 -aF3685.9031405687333 -aF3715.069832122326 -aF3679.812978684902 -aF3126.31377658844 -aF3115.5592213630675 -aF3836.780179643631 -aF2309.807404530048 -aF3114.1517039656637 -aF230.8154044866562 -aF3097.2274445533753 -aF3691.2943108439445 -aF2359.642019438744 -aF3691.9309216737747 -aF3093.844445025921 -aF548.4278207063675 -aF1332.2243274569512 -aF1382.360494863987 -aF2823.2597810745237 -aF3697.4326884388925 -aF3096.870866215229 -aF3273.1818291068075 -aF3688.0431554079055 -aF3686.0513289690016 -aF3688.9799567103387 -aF2659.3990954518317 -aF3676.94836615324 -aF3096.204835629463 -aF3090.724044060707 -aF3034.383577013016 -aF3095.5295432686808 -aF3685.6190219998357 -aF3744.8396286725997 -aF3095.8370886802672 -aF3645.247217106819 -aF3096.07162951231 -aF3125.3453762888907 -aF3102.4750573158262 -aF3687.3193749308584 -aF3686.14721558094 -aF4642.616986882686 -aF3801.362607169151 -aF3096.913906228542 -aF2578.6119008421897 -aF3365.9090861439704 -aF3095.0887917399405 -aF3089.7139657735825 -aF3095.44727691412 -aF3059.9594239115713 -aF3085.3056332707406 -aF3250.755530524254 -aF3085.8158481121063 -aF3228.9244371891023 -aF3815.995122075081 -aF3091.4652584671976 -aF3089.0828030467032 -aF4079.5400197982785 -aF3084.9768402576447 -aF2009.0135545253754 -aF2004.7239906668663 -aF3095.647222292423 -aF3741.400786089897 -aF3685.6936610102653 -aF3869.7858769416807 -aF3686.3746738791465 -aF3095.660297739506 -aF3097.080345773697 -aF3111.259033703804 -aF2138.7162690758705 -aF3173.0307145833967 -aF3688.137135183811 -aF3090.45436296463 -aF3098.1457223057746 -aF3081.7856139540672 -aF3097.3241483807565 -aF2900.6620693206787 -aF2514.281790816784 -aF3078.3565779566766 -aF3688.2128638148306 -aF3106.782055103779 -aF3431.514052259922 -aF3097.5529687047006 -aF2470.121919941902 -aF3094.9907258868216 -aF3422.997032916546 -aF3125.1203696370126 -aF3095.904372751713 -aF3095.8112101912498 -aF3095.2149153232576 -aF3679.7233573913572 -aF3120.240776228905 -aF3097.305080020428 -aF1926.8256526470184 -aF2107.543041205406 -aF3639.4338188529014 -aF3861.130475783348 -aF2219.8943650722504 -aF3074.7518406391146 -aF3687.0140087604523 -aF3095.628426337242 -aF4639.913910603523 -aF3172.8751712441444 -aF3097.174870359898 -aF3487.7063309073446 -aF3687.1453080415727 -aF4716.463843142986 -aF4038.82117074728 -aF3681.627741777897 -aF3095.022869694233 -aF3684.875628352165 -aF3095.761904859543 -aF3096.251689314842 -aF3622.4695158839227 -aF3684.540842425823 -aF3684.0036594748494 -aF3153.0299114346503 -aF2986.4579773783685 -aF3988.812761354446 -aF3685.6636964440345 -aF3096.0787120461464 -aF3688.717358148098 -aF3094.6927146553994 -aF3096.2130077838897 -aF3685.9976651549337 -aF3098.806577193737 -aF3675.510611784458 -aF3477.84689899683 -aF2455.4087731122972 -aF773.4260280251502 -aF3097.966207313538 -aF3053.0844627976417 -aF3134.862122523785 -aF2441.5414442658425 -aF3096.769259095192 -aF3689.340621125698 -aF3068.508859467506 -aF3427.9564410328867 -aF3096.5322666168213 -aF3094.926165866852 -aF3095.8986522436144 -aF3825.570707821846 -aF3093.2990899205206 -aF3303.2815082907678 -aF3522.816902780533 -aF4203.367772531509 -aF3682.480914700031 -aF3685.8285015583037 -aF4274.96401746273 -aF3095.892659330368 -aF3683.7557707905767 -aF3089.3786350369455 -aF3694.605395412445 -aF1881.8695415258408 -aF3732.075268268585 -aF3723.4007987499235 -aF3786.058885979652 -aF1732.7170994400979 -aF3093.4532712340356 -aF3083.7006221413612 -aF3059.211126971245 -aF3675.2076972603795 -aF3685.967700588703 -aF1506.5816006302834 -aF3095.664928627014 -aF3103.747461760044 -aF3026.2757102012633 -aF3095.664928627014 -aF3095.5344465613366 -aF3685.9761451482773 -aF3170.861279988289 -aF3095.8278269052507 -aF3082.0800839185713 -aF3685.1913459181783 -aF2699.1811432003974 -aF3776.971450257301 -aF3685.0883767724035 -aF3081.8681527137755 -aF3081.794058513641 -aF3685.914853990078 -aF3677.1782760977744 -aF3095.8095757603646 -aF3689.2545410990715 -aF3094.955858027935 -aF3685.623925292492 -aF3102.760265505314 -aF3041.8215995669366 -aF3141.107827746868 -aF3100.4824136614798 -aF3096.165881693363 -aF2723.0590888142588 -aF3119.4404499053953 -aF1954.8011164903642 -aF4319.543937075137 -aF3175.438231277466 -aF3686.081021130085 -aF3101.72784999609 -aF3751.4468155264854 -aF3095.9185378193856 -aF2520.14994250536 -aF3093.830552363396 -aF3907.275362968445 -aF2692.960226845741 -aF3106.505019068718 -aF3686.2011518001555 -aF1409.12756947279 -aF3023.31221460104 -aF3686.1507568478582 -aF3096.18849132061 -aF3371.8118332862855 -aF3676.683043539524 -aF3685.8492043495175 -aF3684.7988101005553 -aF3669.0061216711997 -aF4655.2012874841685 -aF3826.8518292307854 -aF3088.762999403477 -aF3097.3878911852835 -aF3589.8906774520874 -aF3076.2852092146873 -aF1122.3824701428414 -aF2860.8217268705366 -aF2440.522104203701 -aF3250.1486118555067 -aF3443.2029571413996 -aF2153.499696433544 -aF3090.913910448551 -aF847.1729115962981 -aF3692.8252277731895 -aF3095.979556572437 -aF3161.290325129032 -aF3096.2021115779876 -aF3694.892782843113 -aF3686.4580298542974 -aF3095.658935713768 -aF3997.730488669872 -aF3444.8068786501885 -aF3685.2583575844765 -aF1800.0390352010727 -aF321.90223772525786 -aF4644.850981497764 -aF3688.209050142765 -aF3252.460514342785 -aF3686.2177685141564 -aF3095.8180203199386 -aF3685.398646235466 -aF3096.0482026696204 -aF2887.7361726641657 -aF3659.9219548106194 -aF1934.7373877525329 -aF3675.9006959557532 -aF3688.5228608727452 -aF3120.9901627898216 -aF4369.944065070152 -aF3117.955569446087 -aF2588.2667564868925 -aF3689.264075279236 -aF3686.5743468523024 -aF3696.2964865684507 -aF3070.7262373685835 -aF3096.2650371670725 -aF3686.104720377922 -aF3919.310222387314 -aF3095.8158410787582 -aF3019.8123532652853 -aF3710.193235170841 -aF2942.656591677666 -aF3094.858064579964 -aF3685.1510299563406 -aF3684.021093404293 -aF3024.0844831943514 -aF1880.4168048739434 -aF3623.4193926334383 -aF3094.638778436184 -aF3251.1349908947946 -aF2533.4223385095597 -aF2695.539903593063 -aF1469.9771589279176 -aF3094.335591506958 -aF3095.9021935105325 -aF3043.710184454918 -aF3686.0423395991324 -aF3065.3775622963904 -aF3685.6863060712813 -aF701.0487975358963 -aF3685.921664118767 -aF3698.009914946556 -aF3098.261222088337 -aF1198.5049990057946 -aF3701.157556426525 -aF3986.36710793972 -aF3678.765580892563 -aF3100.036758840084 -aF3684.5727138280868 -aF1873.3427155971528 -aF3722.5748663425443 -aF3102.2827392816544 -aF3690.8960545182226 -aF3012.6301191449165 -aF3088.1740594744683 -aF680.3168589711189 -aF3095.5368982076643 -aF3685.4329692840574 -aF3823.4143486738203 -aF2990.0730660915374 -aF3096.0503819108008 -aF3615.0410275101663 -aF3096.108131802082 -aF3686.2864146113393 -aF3669.7647700071334 -aF3092.633876550198 -aF3379.4876655340195 -aF3016.517613005638 -aF3101.683992767334 -aF2828.9762030959128 -aF3700.06848064661 -aF3684.775383257866 -aF3685.826594722271 -aF3285.5656395196916 -aF3074.0059953451155 -aF3339.527192413807 -aF3164.750960123539 -aF3098.9975332021713 -aF3373.0463734149935 -aF3124.0786923527717 -aF3080.8493574619292 -aF3682.7802879571914 -aF3095.6733731865884 -aF3110.2429625034333 -aF3215.949235200882 -aF3096.845804941654 -aF3693.477910506725 -aF3685.297039115429 -aF3092.566047668457 -aF3096.442917728424 -aF3687.0946406841276 -aF3097.17459795475 -aF2609.8227206230163 -aF3690.307931804657 -aF3108.7123179793357 -aF2962.7004348397254 -aF3703.4885272741317 -aF3095.760542833805 -aF3539.675240147114 -aF3679.3150220751763 -aF3101.4401901602746 -aF2366.3113146662713 -aF3096.785603404045 -aF3103.2102788090706 -aF3114.30125439167 -aF3013.732815182209 -aF3095.885304391384 -aF3099.6717359423637 -aF3673.593696761131 -aF4144.066806340217 -aF4363.537095999717 -aF2983.6298671364784 -aF3095.9678431510924 -aF3084.0473938941955 -aF3257.6067923903465 -aF3393.770139825344 -aF2804.73895509243 -aF3686.109078860283 -aF2782.7286191701887 -aF2737.0620754241945 -aF4211.523310244083 -aF3091.208108007908 -aF3234.086242330074 -aF2876.008313846588 -aF2594.100585126877 -aF3685.1616537570953 -aF3098.625155365467 -aF3097.2582263350487 -aF4419.961463832855 -aF3672.8404965281484 -aF1959.308604466915 -aF4371.218376350403 -aF3355.5909239649773 -aF3096.060188496113 -aF3086.4181358933447 -aF3097.232347846031 -aF3732.7331266999245 -aF2904.9608949542044 -aF3685.377126228809 -aF3145.0187484502794 -aF3106.4884023547174 -aF3088.5379927515983 -aF3681.546565043926 -aF3098.634417140484 -aF3684.4809132933615 -aF3079.4712598204615 -aF3708.4460285544396 -aF3688.072847568989 -aF3028.008206939697 -aF3096.4897714138033 -aF3098.3023552656173 -aF2985.228612947464 -aF3641.2592057466504 -aF3773.077691078186 -aF4267.002432215213 -aF3686.276880431175 -aF3099.904097533226 -aF3092.0130652189255 -aF3840.3407873272895 -aF3090.489775633812 -aF3686.194886481762 -aF3094.937062072754 -aF3096.0432993769646 -aF3096.1882189154626 -aF3684.294315767288 -aF3637.835073041916 -aF3826.158013319969 -aF2179.955684363842 -aF3096.6597522258758 -aF3096.6935304641725 -aF3686.3686809659002 -aF3097.678002667427 -aF3642.6155109763145 -aF3095.8700497031214 -aF3095.985821890831 -aF385.2873742938042 -aF3685.900688922405 -aF3093.4557228803633 -aF3022.6336533784865 -aF3095.7992243647577 -aF3685.9734210968018 -aF3096.1694229602813 -aF3681.156480872631 -aF3688.8690878152847 -aF3098.136460530758 -aF3719.457189428806 -aF3096.828098607063 -aF3089.8556164503098 -aF4648.454629194736 -aF3086.6112711429596 -aF3483.3968814730642 -aF3025.380586886406 -aF3147.982788860798 -aF2242.585441458225 -aF3226.1892171025274 -aF2118.05924192667 -aF3529.672795534134 -aF3697.7898115873336 -aF3685.153209197521 -aF3096.0520163416863 -aF3094.4832350969314 -aF3684.5174155831337 -aF3603.1685215592383 -aF3095.020418047905 -aF4018.255944132805 -aF3094.9931775331497 -aF3065.311367845535 -aF3181.0367018699644 -aF3096.2990878105165 -aF3095.760542833805 -aF3080.609913337231 -aF3235.920618593693 -aF3704.443034911156 -aF3097.839266514778 -aF3095.1854955673216 -aF3169.085470831394 -aF3093.4862322568893 -aF2713.662745654583 -aF3922.9353900909423 -aF3239.634862780571 -aF3105.19257106781 -aF3095.3105295300484 -aF3096.335317695141 -aF3092.8251049637793 -aF3061.8357505679132 -aF3102.966476202011 -aF2926.2602534413336 -aF3658.376600408554 -aF3753.207642400265 -aF3686.812701356411 -aF3096.208104491234 -aF3687.6176585674284 -aF3096.470975458622 -aF3095.9539504885674 -aF3849.9871984124184 -aF3698.307108962536 -aF3096.231803739071 -aF1145.5347284436225 -aF3095.793503856659 -aF3087.5080288887025 -aF3084.530368220806 -aF767.1405516505241 -aF3684.1175248265267 -aF3123.4303681015967 -aF3024.4505957126617 -aF3093.911456692219 -aF3686.704284107685 -aF3883.121470940113 -aF3091.522735953331 -aF2904.178274965286 -aF3776.23922522068 -aF3695.468374919891 -aF2657.5696224808694 -aF3686.214227247238 -aF3143.956095969677 -aF4027.4155672192574 -aF3029.531768929958 -aF3687.5430195569993 -aF3094.582118165493 -aF3095.129924917221 -aF2208.3814339160917 -aF3664.5468494057654 -aF1071.571010375023 -aF3484.824012041092 -aF3686.561816215515 -aF3095.837361085415 -aF2965.508114695549 -aF3096.3227870583532 -aF3124.3260362267492 -aF3661.993051147461 -aF3707.8805154681204 -aF3860.432301390171 -aF2856.407129049301 -aF3104.479414391518 -aF3079.7578300356863 -aF3682.251004755497 -aF3098.292821085453 -aF3623.5738463521 -aF3686.213137626648 -aF3580.7013622045515 -aF3770.6960528731347 -aF3122.8779304623604 -aF3686.2978556275366 -aF1698.1641409039498 -aF3095.831095767021 -aF3685.5672650218007 -aF3687.708641886711 -aF3132.648013484478 -aF3095.620526587963 -aF4125.894931352138 -aF1602.0538843393326 -aF3712.039052450657 -aF3079.368018269539 -aF3113.164235305786 -aF3061.3677585244177 -aF3096.2342553853987 -aF1986.2810725569725 -aF3686.2046930670735 -aF1820.9722811698914 -aF3663.1303426384925 -aF3275.2205092310905 -aF3687.3161060690877 -aF3114.9604748487473 -aF3096.0348548173906 -aF3674.0883845090866 -aF3624.131187283993 -aF3096.2625855207443 -aF3093.293097007275 -aF3092.2737569451333 -aF2528.456665074825 -aF3095.1656099915504 -aF3682.1229743361473 -aF3688.446042621136 -aF3785.2931551098823 -aF3037.36477894783 -aF3679.8080753922463 -aF515.735389328003 -aF3095.43610830307 -aF1673.2409767389297 -aF2354.7354579210282 -aF3096.042209756374 -aF3095.9536780834196 -aF3124.825899672508 -aF3715.2711395263673 -aF3690.537841749191 -aF3685.690119743347 -aF3787.4222737431523 -aF3736.206564736366 -aF3259.967182993889 -aF3648.4942864656446 -aF3674.9096860289574 -aF3096.1212072491644 -aF3099.2920031666754 -aF3011.610779082775 -aF3136.5602962136268 -aF2557.1744329452513 -aF3199.204218375683 -aF3102.048198449612 -aF3086.6398736834526 -aF3065.614554774761 -aF3028.493632912636 -aF3670.1616643071175 -aF3097.1274718642235 -aF3685.939642858505 -aF3094.261497306824 -aF3559.993395292759 -aF3690.938004910946 -aF3226.8626026272773 -aF3648.525613057613 -aF3685.817877757549 -aF2727.2484075784682 -aF3686.450947320461 -aF3095.727581810951 -aF3096.0915150880815 -aF3712.6053827524183 -aF3687.981591844559 -aF2191.3051724314687 -aF3100.4363771915437 -aF3686.665330171585 -aF3104.8929254055024 -aF754.013075184822 -aF2629.6696148633955 -aF3686.216678893566 -aF3695.6247354745865 -aF4038.2534784197805 -aF3708.3327080130575 -aF3072.9866552829744 -aF3110.5020197987556 -aF3689.117793715 -aF3684.5029781103135 -aF4474.332986474036 -aF3686.5833362221715 -aF3095.7662633419036 -aF3095.5142885804175 -aF3686.0693077087403 -aF3691.9066776156424 -aF3248.693695962429 -aF2961.704249215126 -aF3084.6066416621206 -aF3091.808216547966 -aF3685.909950697422 -aF3097.25359544754 -aF3093.6085421681405 -aF3024.424444818497 -aF3096.180046761036 -aF3096.1321034550665 -aF3621.4256593585014 -aF3670.23929977417 -aF3685.426976370811 -aF3826.4780893683433 -aF3158.6921248316767 -aF3095.7414744734765 -aF3303.544379258156 -aF3688.23520103693 -aF733.9354537844657 -aF3088.2141030311586 -aF3097.1852217555047 -aF4503.354758489131 -aF3705.483077764511 -aF4593.346795439719 -aF3095.831368172169 -aF3686.9374629139897 -aF3097.327962052822 -aF3091.5017607569694 -aF3321.6824760079385 -aF3095.9751980900764 -aF3096.3884366989137 -aF3315.679483771324 -aF2851.3161492466925 -aF3678.5520152568815 -aF3986.7972356677055 -aF3679.3161116957663 -aF3686.075300621986 -aF3685.7642139434815 -aF2707.488955390453 -aF3113.4908490777016 -aF426.1119165420532 -aF3095.7390228271483 -aF4410.899634194374 -aF3458.835471343994 -aF3210.8552589416504 -aF3704.912933790684 -aF3096.179774355888 -aF3094.7945941805838 -aF3095.0640028715134 -aF3096.6551213383673 -aF3111.546148729324 -aF2320.1950300216677 -aF3398.364797449112 -aF1577.0302026748657 -aF3685.5917814850804 -aF3149.7545119404795 -aF534.9671927452088 -aF3081.9365264058115 -aF2875.67979323864 -aF3606.36928204298 -aF3095.8746805906294 -aF2862.353188610077 -aF3091.44046959877 -aF3673.6536258935926 -aF2833.4330237150193 -aF3401.825432443619 -aF4139.262669157982 -aF3714.680565166473 -aF3099.6175273180006 -aF2950.7581931710242 -aF2900.8927964806558 -aF3685.9140367746354 -aF2930.6402558088303 -aF3695.3441581726074 -aF3095.674190402031 -aF3030.4666633963584 -aF3096.9174474954607 -aF3145.399843251705 -aF2384.553470182419 -aF3047.881796884537 -aF3651.98624805212 -aF2184.437566256523 -aF3187.748764705658 -aF3097.090697169304 -aF3542.3987468123437 -aF1973.8945380926132 -aF3102.4952152967453 -aF3688.488537824154 -aF3099.3369500160215 -aF3723.8170338153836 -aF3084.0956096053123 -aF3096.83736038208 -aF2515.3011308789255 -aF3962.036969780922 -aF714.6330974340439 -aF1889.6437120318412 -aF3910.981707406044 -aF1118.2582562088967 -aF3018.7930132031443 -aF3101.3026255607606 -aF3709.1507406711576 -aF3150.673879313469 -aF3338.6832812666894 -aF3095.245697104931 -aF2697.992094731331 -aF3745.511652171612 -aF3817.3435275554657 -aF3112.2783737659456 -aF3092.5742198228836 -aF3556.3412594795227 -aF3689.021089887619 -aF3036.6638805031776 -aF3096.558417510986 -aF3238.5923682808875 -aF3688.726619923115 -aF3096.476968371868 -aF222.033607339859 -aF2892.1123613595964 -aF3941.3202859044072 -aF3095.819109940529 -aF3616.970200765133 -aF3685.7064640522003 -aF3456.0084507226943 -aF3177.0029264450072 -aF3764.6990535497666 -aF3685.7702068567273 -aF3685.5517379283906 -aF3624.5931864142417 -aF3777.2585652828216 -aF903.7184997200966 -aF3092.8014057159426 -aF3782.596888959408 -aF3677.0911064505576 -aF3095.2124636769295 -aF3083.511028158665 -aF2491.4240024805067 -aF3101.156616401672 -aF3102.2688466191294 -aF3004.267281115055 -aF3095.888573253155 -aF2074.766981446743 -aF3685.973693501949 -aF3303.2548125863077 -aF3686.0033856630325 -aF3077.878506922722 -aF2658.612116980553 -aF3095.6755524277687 -aF3091.4426488399504 -aF3094.9392413139344 -aF3655.423728609085 -aF3095.584569108486 -aF382.26939766407014 -aF3920.251654577255 -aF3023.502080988884 -aF2801.2044983029364 -aF3096.1261105418207 -aF1070.5285158753395 -aF3080.9490577459337 -aF4322.083297860622 -aF3094.877950155735 -aF3035.248190951347 -aF2856.492664265633 -aF3188.1058878540994 -aF3684.803713393211 -aF4184.705295872688 -aF3686.180993819237 -aF965.3449886560439 -aF3765.1670455932617 -aF3091.712602341175 -aF3685.853835237026 -aF4339.594317960738 -aF3093.537989234924 -aF3121.858590400219 -aF457.0571413040161 -aF3685.735883808136 -aF2161.569426524639 -aF3095.9220790863037 -aF4425.372792088985 -aF3039.1408605098723 -aF3943.2540900468825 -aF3095.9771049261094 -aF3097.0697219729423 -aF3097.5954639077186 -aF3686.5866050839422 -aF3096.5358078837394 -aF3688.7511363863946 -aF1800.2572317242623 -aF3100.0392104864122 -aF3684.5846996545793 -aF2604.2182571172716 -aF3095.6202541828156 -aF3256.564297890663 -aF3282.5855272054673 -aF3687.4800939679144 -aF3089.006257200241 -aF3658.8843636035917 -aF3140.0884876847267 -aF3700.5517273783685 -aF3712.462097644806 -aF4485.558530199527 -aF3109.672546124458 -aF3685.312293803692 -aF3094.4984897851946 -aF2920.905857861042 -aF3023.018834257126 -aF3685.818150162697 -aF3118.8749368190765 -aF3683.2575417757034 -aF3047.6589694738386 -aF2028.2104900836944 -aF3704.924919617176 -aF3094.168334746361 -aF3641.0023276925085 -aF2566.9505088806154 -aF674.2730059623718 -aF3091.9648495078086 -aF418.44289442300794 -aF3687.026266992092 -aF2964.4887746334075 -aF3232.6659218907357 -aF1780.4612772464752 -aF3680.5517414450646 -aF3097.5919226408005 -aF654.495847439766 -aF3726.6721121668816 -aF3699.26270622015 -aF4342.601670789718 -aF3095.1691512584684 -aF3730.3430439352987 -aF3096.4312043070795 -aF3095.023959314823 -aF3074.7638264656066 -aF3686.0044752836225 -aF3686.9412765860557 -aF3662.4869216799734 -aF3073.501773416996 -aF3674.946188318729 -aF3841.360127389431 -aF3099.9732884407044 -aF3095.7640841007233 -aF3469.9749350428583 -aF2905.3602409005166 -aF3158.1674725174903 -aF3225.7029739141462 -aF3112.143533217907 -aF3245.256215405464 -aF3157.8035392403604 -aF3746.876946771145 -aF3122.1083859205246 -aF3687.861461174488 -aF3700.478722798824 -aF3091.4252149105073 -aF3998.7498287320136 -aF3095.3846237301827 -aF3096.097780406475 -aF2974.5241802692412 -aF2813.078366279602 -aF3096.3355901002883 -aF3686.115071773529 -aF3707.7824496150015 -aF2901.1041828751563 -aF3096.1054077506064 -aF3101.114121198654 -aF4540.510820615291 -aF1565.3895135045052 -aF3096.283560717106 -aF3686.638362061977 -aF3806.8305956959725 -aF4143.698242175578 -aF1489.874448120594 -aF3155.478288900852 -aF3575.0258009552954 -aF2624.3854998111724 -aF3095.525729596615 -aF3095.808758544922 -aF3092.2489680767058 -aF3094.49004522562 -aF1855.9128722310068 -aF3685.4482239723206 -aF1179.675265586376 -aF2954.304090976715 -aF4290.8367930054665 -aF3114.6570155143736 -aF3686.4335133910176 -aF3093.6687437057494 -aF3685.9690626144406 -aF3545.450501680374 -aF3097.9169019818305 -aF3105.3159705996513 -aF3098.34212641716 -aF3096.2015667676924 -aF3686.797446668148 -aF3686.2226718068123 -aF3094.5605981588365 -aF3542.39248149395 -aF1800.6424126029015 -aF2854.0609035134316 -aF3687.359690892696 -aF3089.0013539075853 -aF3079.1697073221208 -aF3684.360510218143 -aF3095.952316057682 -aF3096.490861034393 -aF3685.8995993018148 -aF1049.951848244667 -aF2989.093769586086 -aF3685.7465076088906 -aF3009.963545155525 -aF3550.722630906105 -aF2716.776881301403 -aF3137.1331642389296 -aF2236.664170765877 -aF3109.3960548996924 -aF3091.2930984139443 -aF3755.790588009357 -aF3091.29582246542 -aF3099.168331229687 -aF3095.8594259023666 -aF3664.288609325886 -aF3688.163286077976 -aF3352.3397685289383 -aF3097.217910373211 -aF3705.4302311658857 -aF3095.004346144199 -aF3105.1252869963646 -aF3433.2536315321922 -aF4391.605449998378 -aF3114.063172292709 -aF3170.134230649471 -aF3553.366867673397 -aF3665.9456498384475 -aF3088.4742499470713 -aF3096.4039637923242 -aF3681.725535225868 -aF3685.9584388136864 -aF4099.855723297595 -aF3090.5766728758813 -aF881.8705172657966 -aF3207.6629430174826 -aF3433.529577946663 -aF3149.835143864155 -aF2883.3605287790297 -aF3328.7421278119086 -aF3119.7234788537025 -aF3095.539077448845 -aF3095.2410662174225 -aF3178.998021745682 -aF3096.6311496853828 -aF3620.9331508517266 -aF3095.9735636591913 -aF3219.7601832151413 -aF3096.020689749718 -aF3685.0050207972527 -aF3685.955987167358 -aF3601.4706202745438 -aF3161.2500091671945 -aF3116.931598496437 -aF3096.61698461771 -aF4181.297235071659 -aF4331.93237837553 -aF2792.8217746973037 -aF3063.8975851297378 -aF3684.741332614422 -aF3094.730578970909 -aF3097.75781737566 -aF3077.9795692324637 -aF3088.5665952920913 -aF4437.276897037029 -aF3686.87399251461 -aF3042.2302072882653 -aF3685.5672650218007 -aF3096.8093026518823 -aF3602.2259997487067 -aF3682.996032834053 -aF2942.5342817664146 -aF2990.971185863018 -aF3685.8957856297493 -aF3686.202241420746 -aF3096.185494863987 -aF3777.824350774288 -aF3095.349211061001 -aF3096.924530029297 -aF3095.1380970716477 -aF3227.852522933483 -aF3095.827554500103 -aF3693.4528492331506 -aF3095.7428364992143 -aF3685.839670169353 -aF3638.029025506973 -aF3094.553515625 -aF3095.366917395592 -aF243.39126052856446 -aF3685.752228116989 -aF3095.7308506727218 -aF3097.3135245800017 -aF3686.231933581829 -aF3682.796904671192 -aF3687.327819490433 -aF3097.7447419285772 -aF3627.3153310537336 -aF1774.5871326446534 -aF3737.324787867069 -aF1751.8176035761833 -aF3686.709187400341 -aF3021.3293775320053 -aF3738.0956944346426 -aF3096.731394779682 -aF3689.0962737083432 -aF3686.042067193985 -aF3095.893748950958 -aF4431.970717167854 -aF3685.33027254343 -aF3685.547107040882 -aF2753.23095536232 -aF3416.780747449398 -aF3686.078569483757 -aF3247.8802941918375 -aF4263.291456890106 -aF3549.244832980633 -aF774.0953274726867 -aF3071.9357162237166 -aF4572.007120990753 -aF2982.751905345917 -aF3095.9702947974206 -aF3686.2223994016645 -aF3671.0870245933534 -aF4198.262900066376 -aF3691.600766634941 -aF3096.10840420723 -aF3095.9501368165015 -aF3095.9136345267298 -aF3094.1367357492445 -aF3095.8370886802672 -aF3685.891154742241 -aF3748.9156268954275 -aF3549.9062326788903 -aF4022.3910542726517 -aF4164.423915421962 -aF3066.1569134235383 -aF3100.0631821393968 -aF3398.8782811522483 -aF3098.191213965416 -aF3096.5145602822304 -aF3044.033256959915 -aF3355.7644460439683 -aF3093.6030940651895 -aF3090.331508243084 -aF3096.2413379192353 -aF2853.267932128906 -aF3686.105809998512 -aF3095.5251847863196 -aF2945.2065762639045 -aF3091.3854437589644 -aF3683.0279042363168 -aF3101.215183508396 -aF3098.623793339729 -aF3686.281238913536 -aF3093.255232691765 -aF489.56433718204494 -aF3916.8920818924903 -aF3046.616202569008 -aF270.04174573421477 -aF3095.7640841007233 -aF3097.6869920372965 -aF3990.464898574352 -aF3099.2476011276244 -aF3777.4070260882377 -aF3321.2313730835913 -aF4439.173926484585 -aF2658.504789352417 -aF3095.1645203709604 -aF1628.2407359838487 -aF3092.98418956995 -aF3095.900014269352 -aF3111.435552239418 -aF2432.9590476870535 -aF2969.309528529644 -aF3692.0733895659446 -aF3687.0377080082894 -aF3089.950958251953 -aF2241.906880235672 -aF3673.747878074646 -aF3053.149295222759 -aF3114.2001920819284 -aF3094.5502467632296 -aF3683.890338933468 -aF3064.8474618792534 -aF3687.6250135064124 -aF3097.8885718464853 -aF3685.2504578351973 -aF3760.24223293066 -aF3096.6382322192194 -aF3096.5674068808557 -aF3076.3040051698686 -aF588.9023223400116 -aF3430.2511819958686 -aF3688.278513455391 -aF3111.83489818573 -aF3095.899469459057 -aF3407.259370326996 -aF3690.714360284805 -aF3096.4840509057044 -aF3090.41540902853 -aF3275.8402309417725 -aF3440.8638141393662 -aF3097.994537448883 -aF3097.539076042175 -aF2961.654943883419 -aF3686.8690892219543 -aF4720.979503273963 -aF888.4801557660103 -aF3678.7843768477437 -aF3112.678809332848 -aF4117.650861966609 -aF2775.7651263833045 -aF3212.8620676636697 -aF3787.053709578514 -aF3692.830948281288 -aF3685.6083981990814 -aF3096.561686372757 -aF3685.388294839859 -aF2925.6181945085527 -aF3589.870247066021 -aF3111.5006570696833 -aF3095.701430916786 -aF3091.678551697731 -aF3095.73439193964 -aF275.83362398147585 -aF3026.109815466404 -aF3682.151032066345 -aF1957.3004337191583 -aF485.9383522629738 -aF3686.059773528576 -aF3095.5660455584525 -aF2769.6774161458015 -aF3084.2250020504 -aF3096.7006129980086 -aF3095.583479487896 -aF1057.6704481005668 -aF2558.845093715191 -aF3066.365303361416 -aF3110.373989379406 -aF3081.0408582806585 -aF3095.1454520106317 -aF3096.715867686272 -aF2743.0375547409058 -aF3123.68043602705 -aF3095.7643565058706 -aF3071.520843183994 -aF3686.1221543073652 -aF3100.277292585373 -aF1557.5167323350906 -aF3035.4759216547013 -aF3286.682773029804 -aF3684.0012078285217 -aF3684.478189241886 -aF3094.479421424866 -aF1801.151537823677 -aF1541.0462998986245 -aF3096.324693894386 -aF3096.261495900154 -aF3057.1618230462072 -aF1055.5979897379875 -aF3138.9394827723504 -aF2912.477642595768 -aF3191.1712629795074 -aF4596.41407740116 -aF4252.926713430881 -aF3095.1631583452227 -aF4079.2278434991836 -aF3058.3367064476015 -aF2187.0382182002068 -aF3763.679713487625 -aF3705.4836225748063 -aF3095.81012057066 -aF3100.501209616661 -aF3084.291196501255 -aF3095.8430815935135 -aF3683.4084542274472 -aF540.4607873558998 -aF3095.604727089405 -aF3099.8392651081085 -aF3096.3685511231424 -aF3096.421670126915 -aF3685.8312256097793 -aF3093.330688917637 -aF2711.7218589782715 -aF3090.3938890218733 -aF3066.0070905923844 -aF3795.788108229637 -aF3469.626256453991 -aF3090.6968035459518 -aF2891.7604139089585 -aF1307.620149719715 -aF3096.33095921278 -aF4109.404068529606 -aF3687.0752999186516 -aF2983.9207958340644 -aF3667.7260898828504 -aF3810.907955944538 -aF3193.0448655843734 -aF3128.7155727744102 -aF3068.7009050965307 -aF3686.14721558094 -aF3679.174461019039 -aF3453.7379538178443 -aF3095.573128092289 -aF3678.4035544514654 -aF3019.533410394192 -aF3084.5336370825767 -aF3683.519050717354 -aF381.428210568428 -aF3685.531852352619 -aF3254.755255305767 -aF3844.2517080307007 -aF3518.877924346924 -aF2632.4892805457116 -aF3095.900559079647 -aF2154.786538350582 -aF2551.976397919655 -aF3495.6899809718134 -aF3686.409541738033 -aF3096.114124715328 -aF4686.212161886691 -aF3663.1505006194116 -aF3685.4656579017637 -aF3678.71355150938 -aF3929.4540452718734 -aF3558.4932601451874 -aF3774.0403708696363 -aF3720.7625548958777 -aF3372.8641343712807 -aF3683.375220799446 -aF3685.682492399216 -aF2689.323890531063 -aF3075.3233466386796 -aF725.8575515389442 -aF3673.8631054520606 -aF3096.1955738544466 -aF3096.4249389886854 -aF3096.357110106945 -aF2603.8578651070593 -aF3581.891227889061 -aF1563.9969783902168 -aF3689.2687061667443 -aF3082.1827806591987 -aF3685.8314980149266 -aF3190.151922917366 -aF3129.724561440945 -aF3691.3874734044075 -aF3149.075950717926 -aF3096.2710300803183 -aF3054.6262759327888 -aF3103.8501585006716 -aF3682.2172265172003 -aF3691.2578085541722 -aF3096.8468945622444 -aF3686.9377353191376 -aF3684.413629221916 -aF1369.6089375019073 -aF3095.5497012495994 -aF3335.7124307274817 -aF3096.4840509057044 -aF3094.5622325897216 -aF3094.4230335593224 -aF3076.3168082118036 -aF3096.829460632801 -aF3628.334671115875 -aF3097.8275530934334 -aF3107.8261840343475 -aF3723.159447789192 -aF3738.3441279292106 -aF3081.769269645214 -aF3098.255229175091 -aF2890.3341005563734 -aF2671.593856692314 -aF2630.839322566986 -aF3234.7375630378724 -aF4433.013211667537 -aF3095.9561297297478 -aF3107.98063775301 -aF913.4885827422141 -aF3685.8451182723043 -aF4238.445383381843 -aF3096.1931222081184 -aF3929.1617545485497 -aF3097.150898706913 -aF3721.519023990631 -aF3098.2835593104364 -aF3090.2775720238687 -aF1711.7217450976373 -aF4651.415128338336 -aF3432.651616156101 -aF3685.29022898674 -aF2508.9448291659355 -aF3128.3715250730515 -aF3065.1607277989388 -aF3191.3477815151214 -aF3699.8516461491586 -aF3098.113850903511 -aF3096.129651808739 -aF4173.974984705448 -aF3097.2808359622954 -aF3685.9889481902123 -aF2229.7164774775506 -aF3692.8734434843063 -aF3106.7041472315786 -aF2219.9545666098593 -aF3687.498617517948 -aF3095.221997857094 -aF4715.51505601406 -aF3095.8972902178766 -aF2454.691257953644 -aF3678.1842683076857 -aF3169.574438071251 -aF3092.745017850399 -aF4177.315489029884 -aF3097.1756875753404 -aF3115.653201138973 -aF3096.360378968716 -aF3099.806031680107 -aF1519.6175489664079 -aF4284.278911483288 -aF3095.579938220978 -aF3806.650808298588 -aF3080.8455437898638 -aF3686.1079892396924 -aF3686.340350830555 -aF3628.8506064653398 -aF3092.987458431721 -aF3145.453507065773 -aF3129.2157086253164 -aF2520.6225654363634 -aF3096.2070148706434 -aF2998.7589766263964 -aF3685.8135192751884 -aF3104.5325333952906 -aF3071.840374422073 -aF514.9162670493126 -aF3686.628827881813 -aF3098.0228675842286 -aF1024.5209208846093 -aF1086.3367313981057 -aF2937.106336796284 -aF3659.793107175827 -aF1386.8020607948304 -aF3100.642315483093 -aF3097.2309858202934 -aF3047.318735444546 -aF3848.8509965419767 -aF3097.398514986038 -aF3687.2537252902985 -aF3095.202929496765 -aF3685.8206018090245 -aF3180.625642502308 -aF3248.8996342539785 -aF3686.9189393639563 -aF3096.0468406438827 -aF3086.529822003841 -aF3996.6879941701886 -aF3697.4844454169274 -aF3687.143946015835 -aF3681.4343341231347 -aF3686.207417118549 -aF2767.6730590701104 -aF4547.95238443613 -aF3637.7323763012887 -aF3686.0398879528043 -aF3097.221451640129 -aF3642.4858461260797 -aF3002.0210282683374 -aF938.8195374131202 -aF3684.843484544754 -aF3095.752098274231 -aF3085.960222840309 -aF3095.166972017288 -aF3081.576134395599 -aF3685.9102231025695 -aF3686.0066545248033 -aF3864.211650407314 -aF3097.0168753743174 -aF1258.1851531624795 -aF3100.951222920418 -aF3690.2834153413773 -aF3685.909133481979 -aF3636.9497563123705 -aF3677.378766286373 -aF3097.3704572558404 -aF3807.143044400215 -aF4072.9892208099363 -aF3851.3020980596543 -aF2250.3122134685514 -aF3755.0692591786383 -aF3686.1221543073652 -aF1187.186292719841 -aF3679.396198809147 -aF3171.280511510372 -aF3095.59192404747 -aF3093.207016980648 -aF891.1505434274673 -aF3684.791727566719 -aF3096.056647229195 -aF3056.1193285465242 -aF3096.086884200573 -aF3096.2647647619247 -aF3680.422348999977 -aF1612.7180010557174 -aF3095.9090036392213 -aF3096.3685511231424 -aF3735.892209196091 -aF3103.7605372071266 -aF3687.2512736439703 -aF644.872590792179 -aF3092.922898411751 -aF3095.2821993947027 -aF3097.757000160217 -aF3098.1830418109894 -aF3743.5718551158902 -aF3095.6839969873427 -aF3098.658116388321 -aF3095.8430815935135 -aF3287.7021130919456 -aF3096.117393577099 -aF3115.0991290688517 -aF3676.8647377729417 -aF623.9445205211639 -aF3686.5626334309577 -aF3101.985817670822 -aF3092.087431824207 -aF3699.872076535225 -aF3106.2917258381844 -aF3093.864330601692 -aF3095.913906931877 -aF3097.7507348418235 -aF3685.6849440455435 -aF3106.2130007505416 -aF3685.5882402181624 -aF1030.9020114660264 -aF3686.58796710968 -aF3772.2634720921515 -aF3062.269147157669 -aF3109.6927041053773 -aF3092.0272302865983 -aF2729.6758098483087 -aF3686.4849979639052 -aF4726.526216888427 -aF3663.527509343624 -aF3415.7382529497145 -aF3096.0397581100465 -aF3090.688631391525 -aF3686.282600939274 -aF3749.5146458148956 -aF3716.636978936195 -aF3685.9984823703767 -aF3095.5167402267457 -aF3090.0980570316315 -aF2174.029782783985 -aF2538.8489214539527 -aF3685.995213508606 -aF3279.485284221172 -aF2779.4292480230333 -aF3143.1462354660034 -aF3085.958588409424 -aF3015.2931518673895 -aF3686.375763499737 -aF3095.8267372846603 -aF3072.956963121891 -aF3079.4151443600654 -aF3689.1929775357244 -aF3051.5006992697718 -aF3096.440466082096 -aF3930.473385334015 -aF2996.376521205902 -aF1489.6241077899933 -aF3107.6259662508965 -aF3095.3116191506388 -aF3687.1897100806236 -aF3680.1938010811805 -aF2996.2277879953385 -aF3096.5842960000036 -aF3095.6088131666183 -aF3092.8417216777802 -aF3096.026682662964 -aF3094.6243409633635 -aF3095.692169141769 -aF4276.150614285469 -aF3082.544807100296 -aF4596.939546930789 -aF548.8304355144501 -aF3098.4271168231962 -aF3096.5693137168882 -aF3241.1221948862076 -aF3975.9606864929196 -aF3686.10935126543 -aF3631.14997831583 -aF3045.5968625068663 -aF3093.073810863495 -aF3686.024360859394 -aF3101.4322904109954 -aF3724.2280931830405 -aF3122.385694360733 -aF3088.0386741161346 -aF2926.3474230885504 -aF3727.217739677429 -aF3671.764496195316 -aF3685.464840686321 -aF3095.8204719662667 -aF3093.275118267536 -aF3686.078569483757 -aF3685.261081635952 -aF3096.1906705617903 -aF3741.9257108092306 -aF4321.247831273078 -aF3084.46090490818 -aF3095.2337112784385 -aF3759.1997384309766 -aF3088.9316181898116 -aF3684.2970398187636 -aF3685.9570767879486 -aF2755.3380091786385 -aF3098.5638642072677 -aF3095.59192404747 -aF3095.406960952282 -aF3110.438549399376 -aF3686.152663683891 -aF3713.8617152929305 -aF3170.7702966690063 -aF2312.159622979164 -aF3859.712879395485 -aF3098.331775021553 -aF3686.355605518818 -aF3023.780206644535 -aF3269.855217444897 -aF2780.358694386482 -aF3136.2658262491227 -aF2986.130001580715 -aF3100.8784907460213 -aF3104.105946934223 -aF2654.5715314269064 -aF3095.9558573246004 -aF3602.4226762652397 -aF3133.1723933935164 -aF1615.321104645729 -aF3096.193394613266 -aF3096.1985703110695 -aF3164.9326543569564 -aF3024.7595031499864 -aF1050.0618999242784 -aF3685.9028681635855 -aF1746.3594216346742 -aF3096.044388997555 -aF3096.044388997555 -aF3095.1536241650583 -aF2747.791569375992 -aF4048.9333946347238 -aF3092.3829914093017 -aF476.50877567529676 -aF2978.2773783922194 -aF685.9954166769982 -aF3527.5589315891266 -aF3095.5423463106154 -aF3094.9896362662316 -aF3179.445855808258 -aF3058.4094386219977 -aF2948.0218834638595 -aF2372.913598227501 -aF2871.9385809421537 -aF3096.1824984073637 -aF3685.936101591587 -aF3180.0173618078234 -aF3648.720655143261 -aF3350.731488537788 -aF3099.0694481611254 -aF4262.798403573036 -aF3102.4900395989416 -aF3686.0703973293303 -aF3716.535371816158 -aF3096.6077228426934 -aF3067.3846434235575 -aF3094.8482579946517 -aF3096.0397581100465 -aF3124.8931837439536 -aF3686.066856062412 -aF3685.230027449131 -aF3685.7781066060065 -aF1547.207287120819 -aF3096.427663040161 -aF3373.425833785534 -aF3165.1304204940798 -aF3095.8267372846603 -aF3051.241641974449 -aF3106.356830668449 -aF1945.0626324653626 -aF3093.5300894856455 -aF3358.888115870953 -aF3195.126858127117 -aF3992.729402565956 -aF1118.0920890688897 -aF3056.1555584311486 -aF3686.205782687664 -aF3096.2824710965156 -aF3520.847685968876 -aF3155.3456275939943 -aF3084.689452826977 -aF2402.141580939293 -aF3095.6000962018966 -aF3686.4841807484627 -aF3112.1615119576454 -aF3102.24759901762 -aF3631.5174528598786 -aF2960.0439398407934 -aF4398.155431771278 -aF767.6801862478255 -aF3775.3451915264127 -aF3688.065220224857 -aF1940.7461004972458 -aF3094.8588817954064 -aF3682.8015355587004 -aF3096.174326252937 -aF3196.3477779984473 -aF3090.4175882697104 -aF3686.0033856630325 -aF3098.261222088337 -aF3677.676505112648 -aF3686.200334584713 -aF3097.7003398895263 -aF3091.2320796608924 -aF3686.697201573849 -aF3232.7154996275904 -aF3691.8301317691803 -aF3116.8327154278754 -aF3097.9613040208815 -aF3497.399868083 -aF3695.2490887761114 -aF3687.2463703513145 -aF3099.3301398873327 -aF3690.6838509082795 -aF3095.9395130157473 -aF3095.0947846531867 -aF3094.259318065643 -aF3038.004658639431 -aF3093.8833989620207 -aF3095.9558573246004 -aF3096.1953014492988 -aF3097.0509260177614 -aF3049.0594043374062 -aF2221.899811768532 -aF3683.6416330337524 -aF3755.263756453991 -aF3678.183451092243 -aF3686.0442464351654 -aF1778.0916248679162 -aF3721.2185611128807 -aF3688.6756801605225 -aF3686.3722222328183 -aF3686.9023226499557 -aF3686.1044479727743 -aF3685.8876134753227 -aF3717.5737802386284 -aF3096.0383960843087 -aF3097.586746942997 -aF3097.224992907047 -aF3076.298829472065 -aF3141.4872881174088 -aF551.692051589489 -aF3823.03488830328 -aF3097.455447661877 -aF1739.4659369707108 -aF3095.822651207447 -aF3002.976897931099 -aF3688.4474046468736 -aF2779.0198230862616 -aF3096.0915150880815 -aF3690.2940391421316 -aF3725.150457012653 -aF1922.7649091124536 -aF3686.6356380105017 -aF4288.4472550511355 -aF3739.2011145234105 -aF3102.7945885539057 -aF3253.9655527830123 -aF3095.8065793037413 -aF3685.9892205953597 -aF3594.0535729169846 -aF2965.341947555542 -aF3071.3228046417235 -aF3091.149540901184 -aF3094.7308513760568 -aF4092.316093623638 -aF3456.293931317329 -aF3095.3609244823456 -aF2565.6751079797746 -aF3688.209050142765 -aF3686.099817085266 -aF3687.3005789756776 -aF3685.444682705402 -aF3128.7052213788033 -aF3689.5419285297394 -aF3095.67092154026 -aF3990.4951355457306 -aF3168.5447466135024 -aF3097.5341727495193 -aF3096.052833557129 -aF3692.342798256874 -aF3691.834762656689 -aF3686.597228884697 -aF3096.4061430335046 -aF3472.7066138625146 -aF3685.9173056364057 -aF2095.1513310432433 -aF3471.3219784975054 -aF2645.090470266342 -aF3634.9549334168432 -aF3102.9193501114846 -aF3109.413488829136 -aF768.9209916949271 -aF3092.718866956234 -aF3528.357895886898 -aF3778.8058265209197 -aF3126.5227113366127 -aF3697.6211928009984 -aF2876.248030376434 -aF3094.616168808937 -aF2701.3342334866525 -aF3096.0634573578836 -aF3685.301942408085 -aF3655.4743959665298 -aF3154.7512395620347 -aF3066.662224972248 -aF3687.5530985474584 -aF2378.8408618330955 -aF3092.3459443092347 -aF2941.000913190842 -aF3096.1400032043457 -aF3098.5554196476937 -aF3032.586520254612 -aF3063.0220749855043 -aF3093.3693704485895 -aF2731.6815289497376 -aF3116.3824297189713 -aF3093.24460889101 -aF3693.678128290176 -aF3086.638784062862 -aF3686.0635872006415 -aF3096.585385620594 -aF3102.1073103666304 -aF3096.4875921726225 -aF3721.2953793644906 -aF3665.189180743694 -aF3683.403823339939 -aF3802.8393154740334 -aF3681.236295580864 -aF3096.4944023013113 -aF3096.281653881073 -aF3097.6118082165717 -aF3041.3546971440314 -aF3684.2145010590552 -aF3685.81106762886 -aF3678.847030031681 -aF3180.050322830677 -aF3106.047650825977 -aF3096.2802918553352 -aF3108.464974105358 -aF3682.359966814518 -aF3122.30370041132 -aF4299.216792559623 -aF3097.780154597759 -aF3095.7899625897408 -aF3096.6477663993837 -aF3689.2485481858253 -aF3097.0792561531066 -aF3357.1632464766503 -aF2984.9635627388952 -aF1016.1144980311393 -aF3686.2804216980935 -aF3567.97677295208 -aF1022.6470458745956 -aF3088.245974433422 -aF3095.663839006424 -aF3654.1499621391295 -aF3095.7074238300324 -aF3948.3096571803094 -aF2407.943810582161 -aF3685.7064640522003 -aF3692.8271346092224 -aF1324.0271117568016 -aF3626.2959909915926 -aF3742.0630030035973 -aF4133.290458703041 -aF2294.1620872855187 -aF2118.6250274181366 -aF3093.537989234924 -aF3174.040520465374 -aF3683.9009627342225 -aF3718.2523414611815 -aF3095.3693690419195 -aF4202.819420969486 -aF3096.249510073662 -aF3686.1518464684486 -aF3091.6605729579924 -aF802.6975955605507 -aF2204.09105284214 -aF3476.7684470176696 -aF3089.7905116200445 -aF3551.3050331115724 -aF3095.8065793037413 -aF3378.986984872818 -aF3098.7049700736998 -aF3094.3225160598754 -aF3685.309024941921 -aF3264.2472126722337 -aF3510.467143011093 -aF3096.1400032043457 -aF3095.695710408688 -aF3541.773849403858 -aF3095.1334661841393 -aF2973.693616974354 -aF2936.106065094471 -aF3096.1729642271994 -aF3662.2499292016028 -aF4214.172450304031 -aF3096.4628033041954 -aF2637.4315271377563 -aF3681.5724435329435 -aF3093.6889016866685 -aF3510.839793252945 -aF3039.4810945391655 -aF3731.9197249293325 -aF3351.7739830374717 -aF3178.426515746117 -aF3879.3317705273626 -aF2293.0792768239976 -aF3190.3720262765883 -aF3504.8229083538054 -aF3096.567679286003 -aF3754.391787576675 -aF3150.5164291381834 -aF3072.0215238451956 -aF3146.0394505381582 -aF1250.7708298563957 -aF3685.8560144782064 -aF3095.588382780552 -aF3280.9124147892 -aF3096.29091565609 -aF2591.2288900613785 -aF3101.376719760895 -aF3100.7248542428015 -aF3088.403697013855 -aF3687.6059451460837 -aF3097.957217943668 -aF2948.8279302954675 -aF3094.846895968914 -aF3096.3884366989137 -aF4047.9083340644834 -aF3095.5093852877617 -aF3096.0231413960455 -aF3690.2052350640297 -aF2996.6971420645714 -aF1171.869768488407 -aF3686.8031671762465 -aF4565.663894724846 -aF2722.0419279932976 -aF3685.8334048509596 -aF3095.9196274399756 -aF2002.4216223597527 -aF4424.196546661853 -aF2307.827563917637 -aF3090.612085545063 -aF3308.1453022003175 -aF3055.9942945837975 -aF3103.703876936436 -aF3099.2465115070345 -aF3687.0837444782255 -aF3015.894350028038 -aF3096.2179110765455 -aF3104.379169297218 -aF3068.229644191265 -aF3685.919757282734 -aF3712.9132005691527 -aF2786.518591988087 -aF3686.1823558449746 -aF3084.2514253497125 -aF1110.6636006951333 -aF4396.132823550701 -aF3371.258850836754 -aF3126.18574616909 -aF3432.2898621201516 -aF3097.085249066353 -aF3094.9177213072776 -aF3093.6690161108972 -aF3075.5510773420333 -aF3729.112862288952 -aF3686.345254123211 -aF3689.3896540522574 -aF3095.854795014858 -aF3095.8286441206933 -aF3097.561140859127 -aF3091.5644139409064 -aF3144.924768674374 -aF3685.2381996035574 -aF3685.929019057751 -aF4636.284384417533 -aF3111.771155381203 -aF3095.9147241473197 -aF3094.5055723190308 -aF3097.287373685837 -aF3161.2652638554573 -aF3082.850990486145 -aF3685.918395256996 -aF2202.024042582512 -aF2812.445841526985 -aF3547.8675525546073 -aF3685.2632608771323 -aF3395.9984139323233 -aF3690.715722310543 -aF1546.0898812055589 -aF3221.6610263347625 -aF3069.897036099434 -aF3092.757820892334 -aF2980.713225221634 -aF3096.642045891285 -aF2996.019125652313 -aF3106.633594298363 -aF2582.914267742634 -aF3685.0347129583356 -aF3726.120764148235 -aF1911.1705288171768 -aF3099.334770774841 -aF3626.108848655224 -aF3032.238931286335 -aF3096.846077346802 -aF3256.1270876288413 -aF2281.93463742733 -aF3803.208152043819 -aF3269.5582958340647 -aF3685.9205744981764 -aF3684.8058926343915 -aF3087.8240188598634 -aF3108.1500737547876 -aF3750.9543070197105 -aF3684.2790610790253 -aF3095.574490118027 -aF3672.9927710056304 -aF3650.214252567291 -aF3095.5649559378626 -aF3095.8090309500694 -aF3102.119296193123 -aF4020.3523741483687 -aF3094.931069159508 -aF3096.2399758934976 -aF3784.1466018438337 -aF3603.311261856556 -aF3095.9561297297478 -aF3636.7187567472456 -aF3090.4235811829567 -aF3095.7499190330504 -aF2674.9507053256034 -aF3095.7501914381983 -aF3685.278515565395 -aF3097.0367609500886 -aF3080.2326322078707 -aF3095.193667721748 -aF3095.8820355296134 -aF3689.228390204906 -aF3238.680899953842 -aF3685.8829825878142 -aF2906.735614490509 -aF3685.971514260769 -aF3686.3387163996695 -aF3796.629295325279 -aF3685.916488420963 -aF3095.688900279999 -aF3108.2748353123666 -aF3137.6646266818047 -aF2499.368971014023 -aF3975.065290772915 -aF3312.598309147358 -aF3095.6251574754715 -aF2821.935347247124 -aF4095.7263336658475 -aF3670.5956057071685 -aF1350.6696972131729 -aF3095.362014102936 -aF2880.765597343445 -aF3685.6010432600974 -aF3097.425483095646 -aF3923.743888568878 -aF3088.022057402134 -aF1736.4724768042565 -aF3665.6827788710593 -aF3635.9742734789847 -aF3142.41428283453 -aF3228.258679008484 -aF3019.9608140707014 -aF3684.4182601094244 -aF3648.928227865696 -aF3684.4277942895887 -aF2530.8037078261377 -aF4449.122434878349 -aF3620.3142463564873 -aF3096.7689866900446 -aF3096.2470584273337 -aF3106.653479874134 -aF3692.195971882343 -aF3100.102680885792 -aF3095.6123544335364 -aF3685.704012405872 -aF4131.878855228424 -aF3095.775797522068 -aF3095.985821890831 -aF3686.1529360890386 -aF3101.5309010744095 -aF3053.5162249565124 -aF3685.825232696533 -aF4235.051487648487 -aF3692.286137986183 -aF3033.547838020325 -aF2963.049930644035 -aF3688.1216080904005 -aF3092.137009561062 -aF3685.8372185230255 -aF3099.066996514797 -aF3137.0073130607607 -aF3583.928818392754 -aF3691.4495817780494 -aF3644.6541911005975 -aF3050.128322136402 -aF3691.409538221359 -aF3095.4519078016283 -aF3115.339935219288 -aF3070.22691873312 -aF3686.143674314022 -aF3526.539591526985 -aF2470.5618542551993 -aF1878.0329874277115 -aF1615.774659216404 -aF3097.7673515558245 -aF3684.4231634020803 -aF3177.1304120540617 -aF3686.2858698010446 -aF3096.107042181492 -aF3682.2556356430055 -aF2655.590871489048 -aF3098.078438234329 -aF3095.741746878624 -aF3123.114922940731 -aF3666.32156894207 -aF3117.1192856431007 -aF3686.19733812809 -aF533.6907022237777 -aF3099.9817330002784 -aF3397.9853370785713 -aF3095.8005863904955 -aF4640.9577671289435 -aF3652.3657084226606 -aF3094.1898547530172 -aF3738.4797856926916 -aF3093.077352130413 -aF3372.3833392858505 -aF2615.110376942158 -aF2401.1222408771514 -aF3096.928343701363 -aF2953.6437808990477 -aF426.98252339363097 -aF795.3056094765662 -aF3695.700464105606 -aF3413.8009075403215 -aF1188.0582615971566 -aF3685.3798502802847 -aF1792.9322573065758 -aF3271.8938975691794 -aF3097.5102010965347 -aF2961.063279902935 -aF3097.485684633255 -aF3676.6340106129646 -aF3231.696159565449 -aF3685.8549248576164 -aF1832.7856752038003 -aF3095.143817579746 -aF4361.647149085998 -aF3708.342242193222 -aF3798.5415794610976 -aF3674.0330862641335 -aF3096.145723712444 -aF3097.6235216379164 -aF3095.595737719536 -aF754.3925355553627 -aF3197.367118060589 -aF1467.0637858748437 -aF3093.2102858424187 -aF3090.8569777727125 -aF3095.893748950958 -aF3698.324542891979 -aF3671.49699434042 -aF2958.4955889821053 -aF2977.5492394328116 -aF3094.4279368519783 -aF3219.207473170757 -aF470.3445195913315 -aF3094.098599028587 -aF1770.1512872219087 -aF3094.936517262459 -aF2757.756149673462 -aF3089.341860342026 -aF3623.1069439291955 -aF4098.859537672996 -aF3168.3290017366407 -aF3385.21389414072 -aF3096.1528062462808 -aF3092.7082431554795 -aF3094.7850600004194 -aF3094.7234964370728 -aF3095.244335079193 -aF1182.627047765255 -aF3090.3375011563303 -aF3177.9786816835403 -aF3090.2350768208503 -aF3685.69311619997 -aF2023.2578920960427 -aF3008.6091467618944 -aF3149.855029439926 -aF3106.5202737569807 -aF3486.862692165375 -aF681.6554578661918 -aF3687.394013941288 -aF587.70047082901 -aF3095.359562456608 -aF3081.5254670381546 -aF3686.443864786625 -aF3082.317621207237 -aF3728.655766451359 -aF3095.349211061001 -aF3625.6738176345825 -aF3553.391928946972 -aF4451.3291889786715 -aF3749.4081354022023 -aF2501.821434557438 -aF3687.448767375946 -aF3095.2574105262756 -aF3275.8958015918734 -aF3096.133193075657 -aF3466.1579941153527 -aF3092.1688809633256 -aF3772.9502054691316 -aF3096.6630210876465 -aF3685.623925292492 -aF3685.8851618289946 -aF1890.4922540664672 -aF3014.2482057213783 -aF3655.8031889796257 -aF1555.1165705800056 -aF3077.362026762962 -aF3426.0961862802505 -aF3685.4743748664855 -aF3687.484724855423 -aF3098.250325882435 -aF3134.2704585433007 -aF3677.0826618909837 -aF3685.760672676563 -aF3705.4833501696585 -aF3686.852744913101 -aF3691.3234581947327 -aF3096.143544471264 -aF3574.5864114522933 -aF3677.470566821098 -aF3664.19462954998 -aF3687.010467493534 -aF2718.8155614256857 -aF3095.143817579746 -aF3102.780151081085 -aF3096.187129294872 -aF3067.233458566666 -aF3103.9174425721167 -aF3067.3906363368033 -aF3096.938967502117 -aF1432.3116991758347 -aF3096.1991151213647 -aF2961.0575593948365 -aF3006.841509759426 -aF2874.984615302086 -aF3115.9147100806235 -aF4451.958444869518 -aF3096.0659090042113 -aF3090.69925519228 -aF3120.106208086014 -aF3723.557704114914 -aF3096.2448791861534 -aF3109.122287726402 -aF3686.2071447134017 -aF3143.9054286122323 -aF1353.4779218792917 -aF3096.0550127983092 -aF3701.2765974760055 -aF3701.890326273441 -aF3095.6404121637343 -aF3686.748958551884 -aF3084.7104280233384 -aF2653.322281420231 -aF2662.5519126296044 -aF3089.2884689331054 -aF3679.609219634533 -aF3680.1997939944267 -aF3096.9198991417884 -aF3686.8530173182485 -aF3680.931201815605 -aF3097.3690952301026 -aF3686.02926415205 -aF2508.646273124218 -aF3533.118720650673 -aF2990.064893937111 -aF3095.59192404747 -aF3215.0004480719567 -aF2943.0267902731894 -aF3095.964029479027 -aF3093.774981713295 -aF3095.498761487007 -aF3217.1935819149016 -aF3101.778517353535 -aF3686.065766441822 -aF3299.437871658802 -aF3107.107579255104 -aF3074.577501344681 -aF3694.912123608589 -aF3687.2354741454124 -aF3659.2638239741323 -aF3147.469032752514 -aF3094.330143404007 -aF3094.2745727539063 -aF3117.830807888508 -aF3091.585389137268 -aF3096.0517439365385 -aF1521.9705846309662 -aF3023.3982946276665 -aF3834.4546568989754 -aF3685.0815666437147 -aF3080.8479954361915 -aF3148.0566106557844 -aF3096.619436264038 -aF3473.668204033375 -aF3686.1256955742833 -aF2768.136692631245 -aF3694.662055683136 -aF3092.7379353165625 -aF1365.9371885180474 -aF3092.4954947352408 -aF3688.0785680770873 -aF3095.4211260199545 -aF3095.878221857548 -aF3096.997534608841 -aF2581.506205534935 -aF3051.5374739646913 -aF3101.1609748840333 -aF2408.9631506443025 -aF4143.318781805038 -aF1703.722840344906 -aF3475.749106955528 -aF2138.7802842855453 -aF3686.203875851631 -aF3096.163430047035 -aF3095.5695868253706 -aF3098.5804809212686 -aF3090.6180784583094 -aF3095.6829073667527 -aF3145.455141496658 -aF3096.484868121147 -aF3714.323986828327 -aF3678.0254561066627 -aF3595.009714984894 -aF1922.4069687485696 -aF3687.763667726517 -aF3106.9612976908684 -aF3118.800570213795 -aF3662.7013045310973 -aF3096.1754158735275 -aF3094.3121646642685 -aF3658.394306743145 -aF3095.574217712879 -aF499.5087594985962 -aF3095.999714553356 -aF3096.4287526607513 -aF3792.7842966675757 -aF3321.5555352091787 -aF616.3569475412369 -aF715.4606642723083 -aF3092.8073986291884 -aF4085.5195851922035 -aF3014.122899353504 -aF3679.440600848198 -aF492.4676312446594 -aF3145.020110476017 -aF1191.3756114840508 -aF3127.35218501091 -aF3004.05970839262 -aF4491.042318224906 -aF3097.110855150223 -aF3001.703948676586 -aF3793.2634573221208 -aF3686.7209008216855 -aF2194.8189264297484 -aF3097.735207748413 -aF3685.931198298931 -aF3745.818925178051 -aF3684.3686823725698 -aF2066.451269507408 -aF3079.4317610740663 -aF2228.9507466077803 -aF3687.8617335796357 -aF3695.4689197301864 -aF3095.0013496875763 -aF3101.32605240345 -aF3698.006646084785 -aF2843.964479124546 -aF3552.9416432380676 -aF3041.3762171506883 -aF3683.1395903468133 -aF3617.9492248654365 -aF3693.85137796402 -aF3673.9023317933083 -aF3685.842939031124 -aF4746.46736330986 -aF3096.1114006638527 -aF3686.0679456830026 -aF3099.977919328213 -aF2186.781340146065 -aF3692.3749420642853 -aF3107.4739641785623 -aF3684.4218013763425 -aF3096.0245034217833 -aF3233.6454908013343 -aF3684.9532638192177 -aF3730.672926568985 -aF3096.492495465279 -aF3094.3347742915153 -aF3676.933111464977 -aF3685.8851618289946 -aF3095.8850319862368 -aF3192.1312187194826 -aF3097.3151590108873 -aF3653.0159395098685 -aF3748.5996369242666 -aF2990.2920798301698 -aF3100.065361380577 -aF3094.7139622569084 -aF2054.037766933441 -aF2686.041680908203 -aF3655.2657336235047 -aF3096.2862847685815 -aF1036.8717702746392 -aF3095.8150238633157 -aF2775.060686671734 -aF3095.023959314823 -aF3095.369096636772 -aF3732.210653626919 -aF3121.7327392220495 -aF3231.335767555237 -aF3686.341440451145 -aF3069.2380880475043 -aF3322.794706225395 -aF3695.0687565684316 -aF2664.9112136125564 -aF3095.845260834694 -aF3095.6227058291433 -aF3101.330955696106 -aF3100.5562354564668 -aF3686.468653655052 -aF3118.9700062155725 -aF3685.257267963886 -aF1644.4466630220413 -aF3096.329597187042 -aF2442.7808876872064 -aF721.2803278446197 -aF3117.2900836706162 -aF3097.035671329498 -aF3089.829465556145 -aF2354.7518022298814 -aF3095.3197913050653 -aF3682.1314188957213 -aF1713.9876111149788 -aF1222.7896455049515 -aF3686.0763902425765 -aF3103.4153998851775 -aF3142.8800956368445 -aF3337.444110250473 -aF3094.916904091835 -aF3501.2274328112603 -aF3686.577070903778 -aF3686.337899184227 -aF3105.5660385251044 -aF3683.9810498476027 -aF3685.838035738468 -aF3095.897017812729 -aF3943.1091705083845 -aF3095.579938220978 -aF3486.9566719412805 -aF3686.054053020477 -aF3089.89674962759 -aF841.2903224349021 -aF3133.447250187397 -aF3704.0919046759604 -aF2061.236617767811 -aF3686.0551426410675 -aF3088.693536090851 -aF3673.710013759136 -aF3095.231532037258 -aF757.7875209093094 -aF3688.045334649086 -aF3687.188892865181 -aF3095.6191645622253 -aF3680.7241739034653 -aF2730.6417585015297 -aF3684.038799738884 -aF3682.424526834488 -aF3247.1709511876106 -aF3685.4602097988127 -aF3109.686711192131 -aF3120.291171181202 -aF3686.648713457584 -aF965.4877289533615 -aF2808.9435285449026 -aF3686.2068723082543 -aF3384.039827954769 -aF3677.7873740077016 -aF2909.555007767677 -aF3103.525996375084 -aF3088.825652587414 -aF3685.757131409645 -aF2769.320565402508 -aF3010.1038338065146 -aF3685.9584388136864 -aF3685.662606823444 -aF3095.701430916786 -aF3091.7540079236032 -aF3096.4001501202583 -aF3653.6514607191084 -aF2515.964165008068 -aF3683.5307641386985 -aF3692.7625745892524 -aF3699.4547518491745 -aF3602.6692029237747 -aF3686.555823302269 -aF3246.655015838146 -aF3686.5615438103673 -aF3094.863512682915 -aF3469.6344286084177 -aF3686.1305988669396 -aF3686.4119933843613 -aF3095.2857406616213 -aF3066.3288010716437 -aF2048.3393236517904 -aF3215.0170647859572 -aF3707.797976708412 -aF3687.0249049663544 -aF2265.910677027702 -aF3095.647222292423 -aF3178.4867172837257 -aF2217.82980645895 -aF3684.555007493496 -aF3116.9531185030937 -aF3102.9596660733223 -aF3095.911455285549 -aF3635.946760559082 -aF1520.9667716622353 -aF3699.809423351288 -aF3665.3575271248815 -aF3524.4360789775847 -aF3707.699910855293 -aF3087.2950080633163 -aF3096.663293492794 -aF4309.172655892372 -aF3686.1384986162184 -aF3064.7246071577074 -aF3686.0325330138207 -aF3439.1482065200807 -aF3094.777705061436 -aF3097.444279050827 -aF3096.249510073662 -aF3048.7962609648703 -aF3081.29228823185 -aF4744.408525204658 -aF3096.204835629463 -aF3142.4180965065957 -aF3895.2451344370843 -aF3685.222127699852 -aF3099.0797995567323 -aF3068.208396589756 -aF3043.0340748786925 -aF3654.4365323543548 -aF3761.1689552426337 -aF3677.2913242340087 -aF3095.8003139853477 -aF3578.7027256369593 -aF3106.9634769320487 -aF3095.751008653641 -aF3095.81229981184 -aF1293.3779913902283 -aF3095.0950570583345 -aF3095.579121005535 -aF3095.1631583452227 -aF3085.2160119771956 -aF3097.737659394741 -aF3094.1824998140337 -aF3093.0781693458557 -aF3046.5410187482835 -aF3678.987046277523 -aF3085.2729446530343 -aF3592.024426972866 -aF3097.6709201335907 -aF3832.460651218891 -aF3662.845134449005 -aF2329.755633485317 -aF3124.34265294075 -aF2063.3826255202293 -aF3097.0403022170067 -aF3084.896753144264 -aF3127.643386113644 -aF3685.80888838768 -aF661.1218302488327 -aF3096.4968539476395 -aF3092.153353869915 -aF3672.699118256569 -aF3000.6200485944746 -aF3146.0100307822227 -aF3684.265985631943 -aF3096.4884093880655 -aF3676.032812452316 -aF2551.316632652283 -aF3083.9081948637963 -aF3754.63722461462 -aF3095.6191645622253 -aF3101.8436221837997 -aF3095.826464879513 -aF2927.290489709377 -aF3689.5315771341325 -aF3096.5415283918383 -aF3686.5699883699417 -aF3080.176244342327 -aF3096.2165490508078 -aF3094.7659916400908 -aF3725.1425572633743 -aF3610.0012598752974 -aF3695.6519759893417 -aF3733.984283542633 -aF3095.2304424166678 -aF3096.719953763485 -aF3682.701018059254 -aF3689.6350910902024 -aF3096.2059252500535 -aF3076.6821035146713 -aF3017.5369530677795 -aF4035.2404050827026 -aF3685.967700588703 -aF3680.0276339411735 -aF3698.4351393818856 -aF1571.4115741014482 -aF3096.138641178608 -aF3088.710425209999 -aF3067.628446030617 -aF3334.409516906738 -aF564.1513182282448 -aF2849.5888282060623 -aF3425.441051900387 -aF4619.109239864349 -aF3530.4990003466605 -aF3094.912000799179 -aF3686.1172510147094 -aF3095.4058713316917 -aF3637.9764513134955 -aF3098.5404373645783 -aF3080.545898127556 -aF3096.851253044605 -aF3121.4815816760065 -aF3149.317574083805 -aF3164.5510147452355 -aF3360.5593214511873 -aF2725.0588150024414 -aF3107.616432070732 -aF3726.563422513008 -aF267.0951392531395 -aF3689.029534447193 -aF3110.377803051472 -aF3388.9126112341883 -aF3079.358211684227 -aF3096.2470584273337 -aF3129.2838099122046 -aF3096.7915963172914 -aF2919.7917208075523 -aF3683.295406091213 -aF3124.182751119137 -aF3059.7687403082846 -aF368.6731119394302 -aF3447.913931763172 -aF3097.9795551657676 -aF3095.075716292858 -aF3685.8546524524686 -aF3095.7390228271483 -aF2803.642796778679 -aF3008.3217593312265 -aF3550.8585610747336 -aF3096.2165490508078 -aF3079.512665402889 -aF3685.246644163132 -aF3022.045530664921 -aF2170.5560723423955 -aF2837.5526067614555 -aF3122.5856397390367 -aF3096.170784986019 -aF3169.858556640148 -aF664.084236228466 -aF3610.0549236893653 -aF3103.126650428772 -aF2990.6573751330375 -aF3082.749928176403 -aF3677.6143967390058 -aF3710.990020227432 -aF3095.5916516423226 -aF3096.043571782112 -aF3686.1976105332374 -aF3095.688627874851 -aF3686.127057600021 -aF3095.3020849704744 -aF3094.3840796232225 -aF3093.170514690876 -aF3699.2817745804787 -aF1910.4622754335403 -aF3104.409951078892 -aF3684.0456098675727 -aF3730.4370237112043 -aF3671.39348038435 -aF3096.7738899827004 -aF3686.156477355957 -aF3687.459663581848 -aF2524.1485776662826 -aF3092.171060204506 -aF3684.8118855476378 -aF2434.632432508469 -aF3702.092178487778 -aF3686.443047571182 -aF3057.599033308029 -aF1298.5654026150703 -aF3680.424800646305 -aF4317.391936409473 -aF3096.4698858380316 -aF3113.8384380459784 -aF3094.8711400270463 -aF3160.305035710335 -aF3687.079113590717 -aF3701.227019739151 -aF3811.091557013988 -aF3642.2965245485307 -aF3591.4450212240217 -aF3096.1078593969346 -aF3654.469220972061 -aF2652.42416164875 -aF3093.302086377144 -aF3679.5364874601364 -aF3108.107850956917 -aF3124.919062232971 -aF3054.8687165141105 -aF3055.6712220788004 -aF3767.693603336811 -aF3682.7369755387303 -aF3787.9183235168457 -aF2985.9790891289713 -aF3555.3115680217743 -aF3685.606218957901 -aF3785.5543916463853 -aF3092.7787960886953 -aF3719.6031985878944 -aF3096.0950563549995 -aF828.5093453168869 -aF3672.6906736969945 -aF3101.426297497749 -aF3073.484067082405 -aF3096.17623308897 -aF2329.1476251959803 -aF3110.6828968167306 -aF1761.6116582512857 -aF3097.821560180187 -aF3207.594569325447 -aF3686.6724127054213 -aF2704.4581757187843 -aF2662.4503055095674 -aF3086.5216498494146 -aF3092.8583383917808 -aF3699.9295540213584 -aF2982.9602952837945 -aF3684.8505670785903 -aF3083.3456782341004 -aF3096.0482026696204 -aF3378.3141441583634 -aF3096.260133874416 -aF3325.86825350523 -aF3095.882852745056 -aF1655.3194420814514 -aF2747.7430812597277 -aF2117.434616923332 -aF3095.608540761471 -aF3093.4440094590186 -aF3097.7422902822495 -aF3097.2241756916046 -aF3523.4167389154436 -aF3685.8440286517143 -aF3089.2119230866433 -aF3095.9811910033227 -aF3649.543591094017 -aF3658.683328604698 -aF3161.07349063158 -aF3708.0357864022253 -aF3177.4673772215842 -aF3685.8312256097793 -aF2574.89002931118 -aF3831.1326761245728 -aF3224.240430676937 -aF3027.9624428749084 -aF3090.633333146572 -aF3477.8526195049285 -aF4444.428621780872 -aF3040.7831911444664 -aF3095.6404121637343 -aF2459.0876046299936 -aF2325.204560685158 -aF3180.8596385240553 -aF3654.522339975834 -aF3655.530783832073 -aF3112.8332630515097 -aF3677.8581993460652 -aF3193.85881216526 -aF3095.956402134895 -aF3373.5051036834716 -aF3685.9570767879486 -aF3429.214135599136 -aF3088.355481302738 -aF3089.307809698582 -aF2481.1167365074157 -aF3686.063314795494 -aF3211.055204319954 -aF3092.278387832642 -aF3099.579663002491 -aF3346.7704452872276 -aF3633.954389309883 -aF3686.1248783588408 -aF3095.9776497364046 -aF3683.4700177907944 -aF3091.162343943119 -aF3062.569337630272 -aF1975.2900696635247 -aF3095.454359447956 -aF3662.353987967968 -aF3685.898237276077 -aF2815.409609532356 -aF3095.9593985915185 -aF3095.73330231905 -aF4221.073289906978 -aF3049.380842411518 -aF3687.89905308485 -aF2107.4915566325185 -aF3094.407506465912 -aF3096.7183193325995 -aF2247.9322096943856 -aF3797.377592265606 -aF3095.941964662075 -aF3090.774711418152 -aF3685.6863060712813 -aF3437.392282938957 -aF3607.7631791830063 -aF3685.7331597566604 -aF3070.9588713645935 -aF3692.449308669567 -aF3686.2956763863563 -aF3095.584569108486 -aF3095.1029568076133 -aF3096.6265187978743 -aF3095.6237954497337 -aF3688.7827353835105 -aF3199.356220448017 -aF3043.334537756443 -aF3095.9539504885674 -aF3686.28968347311 -aF3584.15736631155 -aF2278.6072085499763 -aF3100.8569707393644 -aF3731.1681591272354 -aF3146.2761706113815 -aF2722.439367103577 -aF3387.439444196224 -aF3102.854517686367 -aF3748.2498687148095 -aF3686.036346685886 -aF3095.9593985915185 -aF2164.8502741217612 -aF2662.958068704605 -aF4067.7110986709595 -aF3094.2007509589193 -aF3096.48677495718 -aF418.69241753816607 -aF3685.9595284342763 -aF2993.066253852844 -aF3094.6033657670023 -aF3090.848805618286 -aF601.5326594114304 -aF3682.5988661289216 -aF3631.4436310648916 -aF3043.9392771840094 -aF1795.265407395363 -aF3099.0844304442408 -aF3685.3221003890035 -aF3195.120047998428 -aF2918.710272371769 -aF3116.2938980460167 -aF3686.4193483233453 -aF3685.9734210968018 -aF3182.3164612531664 -aF3464.5570690631866 -aF3141.860755574703 -aF3023.62602533102 -aF3608.7084250450134 -aF3702.693921458721 -aF3685.753590142727 -aF2736.281907081604 -aF3110.760532283783 -aF2698.7488362312315 -aF2720.5020216941834 -aF3099.37944521904 -aF3684.480640888214 -aF3094.4336573600767 -aF3180.0081000328064 -aF2780.767846918106 -aF2713.6741866707803 -aF1367.4359616398813 -aF3685.035802578926 -aF3054.3631325602532 -aF3686.0033856630325 -aF3676.330551278591 -aF3691.195155370235 -aF3871.3505721092224 -aF2900.883534705639 -aF2996.1632279753685 -aF3095.724040544033 -aF3978.8138580083846 -aF3058.7725546836855 -aF2570.207384824753 -aF3119.261207318306 -aF3500.5308928489685 -aF3551.901055574417 -aF3370.5418804883957 -aF3686.038253521919 -aF3399.61268543005 -aF3175.69156806469 -aF3226.4804182052612 -aF3096.6575729846954 -aF3096.335317695141 -aF2421.5305621266366 -aF3097.8474386692046 -aF2179.5250118255613 -aF3051.095632815361 -aF3093.373456525803 -aF3127.338292348385 -aF3089.2225468873976 -aF668.4056714892387 -aF3441.9264666199683 -aF3217.608454954624 -aF3095.890480089188 -aF3095.88911806345 -aF3011.5173441171646 -aF3104.8896565437317 -aF3095.5284536480904 -aF3095.2124636769295 -aF3102.1073103666304 -aF3684.4844545602796 -aF2702.375910770893 -aF2832.637600684166 -aF3225.239885163307 -aF3094.2664005994798 -aF3094.2958203554153 -aF2969.8260086894034 -aF3134.4970996260645 -aF3096.3214250326155 -aF3102.8063019752503 -aF3333.4133312821386 -aF3681.6141215205193 -aF3683.003115367889 -aF3684.5727138280868 -aF2664.2558068275453 -aF3095.9890907526014 -aF3685.7356114029885 -aF483.9135648012161 -aF3683.003115367889 -aF3089.150087118149 -aF3087.2688571691515 -aF3395.0049523591997 -aF1277.0467579841613 -aF3686.115071773529 -aF3687.4504018068315 -aF3681.4441407084464 -aF3685.084018290043 -aF3099.88584638834 -aF3116.879841518402 -aF3661.335737526417 -aF2656.382753252983 -aF3685.5975019931793 -aF750.880688393116 -aF3054.398545229435 -aF3739.4307520627976 -aF3095.3573832154275 -aF3084.927534925938 -aF3096.651580071449 -aF3087.645865893364 -aF3685.164105403423 -aF568.742162179947 -aF3676.0208266258237 -aF3685.9243881702423 -aF1579.9773539662363 -aF3031.310029733181 -aF3095.6839969873427 -aF3096.5687689065935 -aF3097.440737783909 -aF3114.795124924183 -aF1049.2961690545083 -aF1395.7802620530128 -aF3068.5015045285227 -aF3254.177483987808 -aF1684.0347583055498 -aF3093.830552363396 -aF2695.202938425541 -aF1195.8643035054206 -aF3680.587426519394 -aF4705.267991578578 -aF3431.400186908245 -aF3685.8876134753227 -aF3113.493028318882 -aF3907.1446084976196 -aF3681.3746773958205 -aF3053.3653125047686 -aF3092.4222177505494 -aF4225.759748065471 -aF3103.9656582832336 -aF3685.6271941542623 -aF3689.491533577442 -aF3096.1931222081184 -aF3496.0095122098924 -aF4064.605135178566 -aF2939.469179046154 -aF3098.3350438833236 -aF2985.9829028010367 -aF3686.5195934176445 -aF526.857146692276 -aF3079.812311065197 -aF3096.1999323368073 -aF3688.71599612236 -aF1344.8639263033867 -aF3095.7237681388856 -aF3069.4211443066597 -aF3133.364439022541 -aF3760.659830021858 -aF3687.3395329117775 -aF3064.6259964942933 -aF3055.3119196891785 -aF3230.3395819306375 -aF3036.477555382252 -aF3032.8888899683952 -aF2851.5188186764717 -aF3122.115468454361 -aF3556.277789080143 -aF2325.6300575256346 -aF3673.1575761198997 -aF3099.75781596899 -aF3051.9735946059227 -aF3096.119028007984 -aF3747.4522664427755 -aF3096.4252113938333 -aF3095.803038036823 -aF3100.775794005394 -aF3095.2323492527007 -aF3062.147926867008 -aF938.9230513691901 -aF3685.580068063736 -aF1671.264404988289 -aF3043.047967541218 -aF3682.8064388513562 -aF3128.7714158296585 -aF3092.0909730911253 -aF3095.526002001762 -aF3031.4516804099085 -aF3695.1194239258766 -aF3706.548999106884 -aF3766.4764971375466 -aF3095.445914888382 -aF423.2838063001633 -aF4084.60730035305 -aF3079.5034036278726 -aF3688.0785680770873 -aF3708.489613378048 -aF3113.321957886219 -aF2179.601557672024 -aF3701.3999970078467 -aF3108.6357721328736 -aF3681.2956799030303 -aF3603.914366853237 -aF3080.8406404972075 -aF3064.623544847965 -aF3683.002842962742 -aF3369.53970195055 -aF3119.0163150906565 -aF3098.0653627872466 -aF2905.2188626289367 -aF3096.0975080013277 -aF3096.476968371868 -aF3512.9713635325434 -aF3685.7525005221364 -aF3080.5320054650306 -aF3665.18182580471 -aF3685.684671640396 -aF3533.4788402557374 -aF3481.7082419633866 -aF3060.564163339138 -aF1011.5301918029785 -aF3852.301824951172 -aF3104.0661757826806 -aF3096.025865447521 -aF2822.5229251503943 -aF3112.6472103357314 -aF3094.3249677062036 -aF3259.911884748936 -aF2166.0153509378433 -aF3086.618353676796 -aF1445.6513792514802 -aF3685.911040318012 -aF3587.778720343113 -aF3086.254692804813 -aF3098.112761282921 -aF2905.9519048810007 -aF3096.6112641096115 -aF3687.0788411855697 -aF3099.8531577706335 -aF3145.620219016075 -aF2957.7151482343675 -aF3095.831095767021 -aF2448.9530435204506 -aF3096.122296869755 -aF3718.3008295774457 -aF2913.4098130106927 -aF3687.1216087937355 -aF3093.5371720194817 -aF4134.4015992999075 -aF3684.287505638599 -aF3097.1378232598304 -aF3973.881145596504 -aF3920.86102489233 -aF3679.939102268219 -aF1394.9543296456338 -aF3711.9682271122933 -aF415.27400534152986 -aF3675.9009683609006 -aF3532.5376804709435 -aF3088.3097172379494 -aF3719.7680037021637 -aF3095.8923869252203 -aF3096.5668620705605 -aF3679.6563457250595 -aF3119.257666051388 -aF3685.334086215496 -aF3098.2846489310264 -aF3722.4136024951936 -aF1341.7996407985688 -aF3184.482354581356 -aF3102.65048623085 -aF3092.8354563593866 -aF3074.1405634880066 -aF3095.754549920559 -aF3893.3513738512993 -aF3766.8690329551696 -aF3086.709064590931 -aF3096.1895809412003 -aF3791.771766734123 -aF2512.84866733551 -aF2932.347963678837 -aF3685.394287753105 -aF3093.4246686935426 -aF2185.3977944016456 -aF3093.5766707658768 -aF3078.151729285717 -aF3781.0411831617353 -aF3686.059773528576 -aF3095.9880011320115 -aF2847.6277835488318 -aF3095.166427206993 -aF3094.7894184827805 -aF3059.5151311159134 -aF3207.521564745903 -aF3095.078167939186 -aF3095.0996879458426 -aF3092.4707058668137 -aF3690.682761287689 -aF3685.9835000872613 -aF3096.3257835149766 -aF3032.4639379382133 -aF3140.267730271816 -aF3089.9506858468058 -aF2236.0858546376226 -aF3218.8514396429064 -aF3017.5404943346975 -aF3095.0171491861342 -aF3119.274555170536 -aF3120.5469596147536 -aF4631.377550494671 -aF3095.469614136219 -aF3114.060993051529 -aF2825.3205260157583 -aF3096.020962154865 -aF3095.8523433685305 -aF3096.415677213669 -aF3101.7008818864824 -aF3614.975377869606 -aF2742.952836740017 -aF3592.160084736347 -aF2210.2727428555486 -aF3368.668822693825 -aF3064.001643896103 -aF3616.9546736717225 -aF3615.7999482512473 -aF3080.7477503418922 -aF3095.3176120638846 -aF446.57390160560607 -aF3093.7205006837844 -aF3096.585385620594 -aF3081.111683619022 -aF3096.1882189154626 -aF3528.7643243670464 -aF3685.313928234577 -aF3786.7635980963705 -aF3095.7379332065584 -aF3640.885738289356 -aF3686.190527999401 -aF3627.225709760189 -aF3686.356695139408 -aF3746.4680666446684 -aF3095.499033892155 -aF3697.2662488937376 -aF3686.87399251461 -aF3102.779061460495 -aF3452.1528282642366 -aF3323.9875683665277 -aF3685.526949059963 -aF2941.670212638378 -aF3103.3927902579308 -aF3096.626791203022 -aF3560.0391593575478 -aF3603.969937503338 -aF3685.629918205738 -aF3100.062909734249 -aF348.3996312379837 -aF3096.5077501535416 -aF3097.6071773290632 -aF3100.795679581165 -aF3050.6924731969834 -aF3679.7579528450965 -aF3097.3701848506926 -aF3683.2185878396035 -aF3076.052847623825 -aF3096.404781007767 -aF3095.3244221925734 -aF4200.010379087925 -aF3096.2729369163512 -aF3093.147905063629 -aF3094.8482579946517 -aF3685.9407324790955 -aF3158.24320114851 -aF3681.896605658531 -aF3556.6016788005827 -aF3686.4405959248543 -aF3685.590964269638 -aF3050.693835222721 -aF3097.1579812407495 -aF4012.468696773052 -aF2619.1863751649857 -aF3095.749101817608 -aF3111.8370774269106 -aF697.5748146891593 -aF3731.337595129013 -aF3095.9081864237787 -aF3258.7111228585245 -aF2341.824815952778 -aF3187.465190947056 -aF3116.6488419532775 -aF3176.640082788467 -aF2834.0633692264555 -aF2989.1765807509423 -aF2894.6342882156373 -aF3686.2049654722214 -aF3096.2508720993997 -aF3080.660308289528 -aF2400.7495906352997 -aF3674.199525809288 -aF2033.252164554596 -aF3088.9329802155494 -aF3096.014969241619 -aF3568.9666932582854 -aF3478.9471433877943 -aF3728.634246444702 -aF3107.3887013673784 -aF3352.0431193232534 -aF3097.7989505529404 -aF2759.061242735386 -aF3089.0814410209655 -aF3950.541472554207 -aF3687.074482703209 -aF3097.5870193481446 -aF3733.035768818855 -aF3091.267219924927 -aF3054.201868712902 -aF3093.738479423523 -aF3685.178270471096 -aF3222.000443148613 -aF3082.476705813408 -aF3095.693531167507 -aF3022.557924747467 -aF3092.2734845399855 -aF3096.8419912695886 -aF3675.2809742450713 -aF3598.198217236996 -aF3092.6521276950834 -aF3688.504882133007 -aF2467.0758855819704 -aF3082.0332302331926 -aF3096.6254291772843 -aF4077.1017213225364 -aF3096.1304690241814 -aF3099.156617808342 -aF1876.7673931121826 -aF2683.2822167634963 -aF2515.3940210342407 -aF3092.2960941672327 -aF3388.8526821017267 -aF3695.192700910568 -aF3222.0652755737306 -aF3674.894431340694 -aF1910.7586522340775 -aF1024.6236176252364 -aF3074.439936745167 -aF3395.998686337471 -aF3670.9832382321356 -aF1598.706842291355 -aF3305.2744243502616 -aF3094.363104426861 -aF3096.176505494118 -aF2797.285405445099 -aF3194.0582127332686 -aF3075.263417506218 -aF984.8061572074889 -aF3096.333955669403 -aF4076.295946896076 -aF3095.920717060566 -aF3095.7335747241973 -aF3095.425756907463 -aF3686.0537806153297 -aF3133.6589089870454 -aF3685.9241157650945 -aF3668.1267978549004 -aF3063.1672669291497 -aF3122.1190097212793 -aF3096.1143971204756 -aF3707.5914936065674 -aF3120.5575834155084 -aF2977.3059816360474 -aF3097.281653177738 -aF3117.248678088188 -aF3685.0131929516792 -aF2460.6125286459924 -aF3096.9307953476905 -aF2271.6159304380417 -aF4100.897400581836 -aF3724.0420404672623 -aF3090.300998866558 -aF2799.773009252548 -aF3283.717915403843 -aF1381.4234211564064 -aF3188.1227769732477 -aF3095.888845658302 -aF3712.0717410683633 -aF3698.4765449643132 -aF3686.0428844094276 -aF2686.9703100562097 -aF2473.6833448410034 -aF3686.322644495964 -aF3628.2060958862303 -aF3686.33980602026 -aF3123.564936244488 -aF3098.7782470583916 -aF3690.3812087893484 -aF3111.9683767080305 -aF3692.220488345623 -aF3210.556158089638 -aF2384.8555674910544 -aF3107.477233040333 -aF3686.3588743805885 -aF3108.9822714805605 -aF3691.3978248000144 -aF1716.4714012503625 -aF3093.909549856186 -aF3093.1528083562853 -aF3685.8974200606344 -aF3533.7893821239472 -aF2849.164693391323 -aF3095.9844598650934 -aF2676.118506193161 -aF1221.1410495519638 -aF3466.7237796068193 -aF3225.4610781431197 -aF3095.9395130157473 -aF3095.2928231954575 -aF3731.5331820249557 -aF3686.263532578945 -aF2810.5550773978234 -aF3685.972603881359 -aF3135.509084749222 -aF3095.4317498207092 -aF1105.0825640320777 -aF3095.4731554031373 -aF1484.0022103548051 -aF3671.6713336348535 -aF3686.8987813830377 -aF3689.2131355166434 -aF3095.8983798384666 -aF3129.19418861866 -aF3425.368047320843 -aF2658.242463195324 -aF3685.067401576042 -aF3682.78600846529 -aF3095.005163359642 -aF3685.8028954744336 -aF3675.887620508671 -aF3096.8517978549003 -aF1268.665668809414 -aF3096.0432993769646 -aF3096.2040184140205 -aF2115.1036460757255 -aF346.1196001529694 -aF1099.4050959467888 -aF2679.6938237547874 -aF1869.8039003252984 -aF3096.2824710965156 -aF3685.8889755010605 -aF3684.8083442807197 -aF2407.402813959122 -aF3094.3383155584334 -aF3801.507526707649 -aF2814.868068099022 -aF3105.4845893859865 -aF3095.719137251377 -aF3688.8903354167937 -aF3204.9124682426454 -aF3686.2283923149107 -aF2254.154215669632 -aF3103.593280446529 -aF3288.6130359053614 -aF3674.873183739185 -aF3095.9125449061394 -aF3685.7604002714156 -aF2220.7143045663834 -aF3095.540167069435 -aF3000.8496861338617 -aF3684.0012078285217 -aF3088.295279765129 -aF3097.014151322842 -aF3097.0225958824158 -aF4590.671776890754 -aF1604.3336430191994 -aF3260.378514766693 -aF3263.8454150795937 -aF3649.2017226338385 -aF3222.8724120259285 -aF3492.385978937149 -aF3106.946860218048 -aF3093.8975640296935 -aF3095.606089115143 -aF3783.988334453106 -aF4693.6806938171385 -aF3104.7104139566422 -aF4038.782761621475 -aF3066.6047474861143 -aF3095.397699177265 -aF783.840621626377 -aF3684.655252587795 -aF3095.756184351444 -aF3085.2794823765753 -aF3959.489981651306 -aF3684.0646782279014 -aF3582.3202659964563 -aF3030.2906896710397 -aF3095.8806735038756 -aF2019.059311556816 -aF3719.8856827259065 -aF4690.482384979724 -aF3095.5886551856993 -aF3062.6723067760468 -aF3095.9727464437483 -aF3374.535067546368 -aF3686.048877322674 -aF3708.5743313789367 -aF3686.930652785301 -aF3463.2862990498543 -aF3489.020413339138 -aF3084.339684617519 -aF2438.7509259343146 -aF1794.0327741026879 -aF3093.8975640296935 -aF3084.392258810997 -aF3087.8602487444878 -aF3095.1242044091223 -aF3148.0451696395876 -aF2718.4633415699004 -aF3096.0465682387353 -aF4118.533182239532 -aF3097.340765094757 -aF3688.4378704667092 -aF3054.4078070044516 -aF3686.0635872006415 -aF3104.961843907833 -aF3096.401512145996 -aF3756.6990591764447 -aF3141.2870703339577 -aF3686.0834727764127 -aF2962.98373619318 -aF3615.935333609581 -aF3813.8126120328902 -aF3096.6845410943033 -aF2863.1121093511583 -aF3096.360106563568 -aF1776.525567674637 -aF3219.647135078907 -aF3685.8421218156814 -aF3082.1615330576897 -aF3092.706063914299 -aF3685.991672241688 -aF3096.8716834306715 -aF3188.7414090633392 -aF840.6275607109069 -aF3096.261495900154 -aF2981.2291605710984 -aF3096.3345004796984 -aF3095.078167939186 -aF4165.8886379003525 -aF3686.5781605243683 -aF3684.7165437459944 -aF3646.8745654582976 -aF3691.673771214485 -aF3428.4859966397285 -aF3686.2155892729756 -aF3704.4558379530904 -aF3685.4444103002547 -aF3098.1421810388565 -aF3685.5422037482263 -aF4224.360947632789 -aF4421.737000584601 -aF3095.357655620575 -aF3561.2385592222213 -aF3198.0328762412073 -aF3685.5364832401274 -aF3121.276733005047 -aF3143.8168969392777 -aF3137.1636736154555 -aF3559.0198192954063 -aF2851.2820986032484 -aF3685.8208742141724 -aF4196.431792664527 -aF3095.3622865080833 -aF2991.761978006363 -aF3681.810253226757 -aF3261.3978548288346 -aF3722.7576501965523 -aF533.0767010211945 -aF2654.3440731287 -aF2881.197087097168 -aF3096.0762603998182 -aF1430.9483114123345 -aF3096.027772283554 -aF3095.9465955495834 -aF3272.6661661624908 -aF3686.700742840767 -aF3686.0600459337234 -aF3095.814751458168 -aF3689.33707985878 -aF2927.002829873562 -aF3089.0966957092287 -aF2781.2050571799277 -aF3094.9994428515433 -aF3686.1079892396924 -aF2869.8394268751144 -aF3686.3509746313093 -aF4439.432166564464 -aF3154.8631980776786 -aF3685.103359055519 -aF3096.143544471264 -aF3679.0044802069665 -aF3074.1473736166954 -aF3097.065908300877 -aF2340.7823214530945 -aF3095.8395403265954 -aF3094.332595050335 -aF3082.2290895342826 -aF3096.2119181632997 -aF3657.6228553652763 -aF3685.616570353508 -aF3078.360391628742 -aF3095.799496769905 -aF3686.2978556275366 -aF3588.7980604052545 -aF4190.347623693942 -aF3096.273209321499 -aF3685.9889481902123 -aF3686.125423169136 -aF2813.4401203155517 -aF4557.372426843643 -aF2998.537511241436 -aF3678.789007735252 -aF2204.1700503349302 -aF3643.4370849013326 -aF3100.050923907757 -aF3735.158077323437 -aF1473.371054661274 -aF3742.3027195334435 -aF3086.3203424453736 -aF3686.0398879528043 -aF2036.9508816480636 -aF3106.895103240013 -aF691.9793405532837 -aF2900.3049461722376 -aF3150.6613486766814 -aF1760.098175251484 -aF2929.741318821907 -aF3643.279089915752 -aF3458.1710751891137 -aF3144.6240333914757 -aF3678.9459131002427 -aF3671.1188959956166 -aF3649.6002513647077 -aF3097.436379301548 -aF3096.1718746066094 -aF3096.6523972868918 -aF3678.9197622060774 -aF4574.5524746894835 -aF3082.2536059975623 -aF3078.618631708622 -aF4062.176370882988 -aF3087.0051689863203 -aF3096.4219425320625 -aF4133.3822592377655 -aF3692.8094282746315 -aF3096.1879465103148 -aF3680.675685787201 -aF3061.7551186442374 -aF3227.9418718218803 -aF2952.198126780987 -aF2959.280388212204 -aF3070.234273672104 -aF3691.4817255854605 -aF3686.13277810812 -aF3685.8500215649606 -aF3119.9661918401716 -aF3686.0611355543138 -aF3097.5881089687346 -aF3345.9774739027025 -aF3894.370713913441 -aF3246.0737032532693 -aF3685.7816478729246 -aF3701.765564715862 -aF3425.4584858298304 -aF3681.460757422447 -aF3035.283876025677 -aF1457.7862113595008 -aF3096.59028891325 -aF4100.56751794815 -aF3703.4155226945877 -aF3210.8963921189306 -aF3085.6665700912476 -aF3095.808758544922 -aF3686.1553877353667 -aF3095.660025334358 -aF3685.8995993018148 -aF3090.5014890551565 -aF3913.711751794815 -aF3095.7687149882317 -aF3050.9425411224365 -aF3685.000389909744 -aF3685.61302908659 -aF3702.510047984123 -aF1397.475984096527 -aF3125.808737444878 -aF3428.8063450932505 -aF3273.1804670810698 -aF3685.879441320896 -aF3685.8263223171234 -aF3102.90872631073 -aF3686.2692530870436 -aF3685.827684342861 -aF3104.141359603405 -aF3095.8324577927588 -aF3376.823543190956 -aF3103.039753186703 -aF3687.6680535197256 -aF3787.569917333126 -aF4191.173556101322 -aF3670.62883913517 -aF3103.9408694148065 -aF3095.675824832916 -aF3797.134606873989 -aF3683.9706984519958 -aF3094.6997971892356 -aF2805.706265771389 -aF3096.587837266922 -aF3094.85070964098 -aF3106.300442802906 -aF3017.248203611374 -aF3672.415272092819 -aF3067.116869163513 -aF3096.3203354120255 -aF3095.976015305519 -aF2680.713163816929 -aF3059.6611402750013 -aF1204.9541908740998 -aF3686.270887517929 -aF3667.681415438652 -aF3694.0826499342916 -aF3049.139219045639 -aF2018.443948328495 -aF3208.993369758129 -aF3200.6822887063026 -aF3659.9295821547507 -aF3099.4159475088118 -aF2406.0759284853934 -aF1782.6165467739106 -aF3663.440339696407 -aF3731.110136830807 -aF3095.819654750824 -aF2086.690427160263 -aF3089.3146198272707 -aF3678.761767220497 -aF3095.9716568231584 -aF3686.2493675112723 -aF3098.0419359445573 -aF3094.180320572853 -aF3722.325070822239 -aF3583.3396060585974 -aF3223.89175208807 -aF2864.536788272858 -aF3106.3543790221215 -aF3654.9748049259183 -aF3725.2899284482 -aF3678.0502449750898 -aF3019.6309314370155 -aF3067.624087548256 -aF3170.639814603329 -aF3095.885304391384 -aF3685.91131272316 -aF3475.813939380646 -aF3096.4486382365226 -aF2771.7471504569053 -aF3095.7597256183626 -aF3095.866508436203 -aF3279.480108523369 -aF3837.9607835531233 -aF3163.112170755863 -aF1338.4683982491495 -aF3835.0370591044425 -aF1119.272692978382 -aF3686.3722222328183 -aF2895.664252078533 -aF3041.062678825855 -aF3099.136459827423 -aF3097.305352425575 -aF3439.454117500782 -aF3096.5619587779047 -aF3220.9811030864716 -aF1744.0744872570037 -aF3096.049292290211 -aF3122.0225782990456 -aF3089.546709012985 -aF2945.245257794857 -aF3095.9041003465654 -aF3086.486781990528 -aF4605.012818288803 -aF3096.494674706459 -aF3090.8360025763513 -aF3687.7001973271367 -aF3685.691209363937 -aF3597.1788771748543 -aF3094.475062942505 -aF3693.5558183789253 -aF3687.61302767992 -aF3095.087974524498 -aF3118.2078166127203 -aF1568.5807398080826 -aF3095.974925684929 -aF3168.095278120041 -aF819.4167338967322 -aF3685.664786064625 -aF3096.540438771248 -aF3095.16343075037 -aF3699.5759721398354 -aF3096.0337651968002 -aF3832.1520161867143 -aF3118.824269461632 -aF3096.468523812294 -aF3095.978466951847 -aF3095.570676445961 -aF3096.5546038389207 -aF3094.9106387734414 -aF3686.039615547657 -aF3069.273500716686 -aF3095.2914611697197 -aF3245.256215405464 -aF3684.8704526543615 -aF3107.184125101566 -aF2687.989650118351 -aF3113.2023720264433 -aF3627.2126343131067 -aF3693.7383298277855 -aF3685.3033044338226 -aF3092.6387798428536 -aF3582.8904099702836 -aF3484.563592720032 -aF3096.120934844017 -aF3714.7582006335256 -aF3099.6725531578063 -aF3369.282823896408 -aF3111.421659576893 -aF3095.7017033219336 -aF2636.201617896557 -aF3205.555889201164 -aF1696.8688544273377 -aF275.88701539039613 -aF3660.8064543247224 -aF2763.6809616327287 -aF3685.316379880905 -aF1959.2045457005502 -aF3139.990694236755 -aF3095.2277183651922 -aF3095.806306898594 -aF3105.175954353809 -aF3610.7471051692964 -aF3058.244905912876 -aF1281.3537557721138 -aF3104.739833712578 -aF3095.806306898594 -aF2734.243226957321 -aF3112.7992124080656 -aF3096.554876244068 -aF3799.57862585783 -aF3121.358182144165 -aF3056.263703274727 -aF3577.328441667557 -aF3704.006914269924 -aF3684.3343593239783 -aF3250.2289713740347 -aF3095.458717930317 -aF3122.7637927055357 -aF3713.8677082061768 -aF1579.771143269539 -aF3593.1004273056983 -aF3098.289552223682 -aF3095.482417178154 -aF3122.1121995925905 -aF3090.8913008213044 -aF3841.8352019667623 -aF3070.5603426337243 -aF4127.594194662571 -aF3643.0069571733475 -aF2798.0524983406067 -aF3214.7449320435526 -aF3098.6864465236663 -aF385.73302911520005 -aF726.1179708600043 -aF3634.3845170378686 -aF2838.564047074318 -aF3661.069597697258 -aF3105.7414674401284 -aF3713.583862042427 -aF920.2439579963684 -aF3686.648713457584 -aF3684.6528009414674 -aF3096.0253206372263 -aF4719.539297258853 -aF3682.4844559669496 -aF3096.179774355888 -aF3095.897017812729 -aF3095.6118096232412 -aF3727.9875566244123 -aF2800.97377114296 -aF3077.5614273309707 -aF3139.360076320171 -aF3687.0308978796006 -aF3712.4770799279213 -aF3097.0648186802864 -aF3095.5131989598276 -aF3082.621352946758 -aF2691.0291467547418 -aF3556.758584165573 -aF3677.7424271583554 -aF2714.2339792490006 -aF3690.034437036514 -aF3096.13618953228 -aF3685.9028681635855 -aF3339.756012737751 -aF3686.740786397457 -aF3685.921936523914 -aF3095.9593985915185 -aF3644.945392203331 -aF544.3730700850487 -aF3095.8144790530205 -aF3462.6453297376634 -aF1122.3070139169693 -aF3688.2902268767357 -aF3171.1440365314484 -aF3096.037578868866 -aF3687.5915076732635 -aF3035.0528764605524 -aF3095.9501368165015 -aF2899.913772380352 -aF2787.7378774285316 -aF3898.933227729797 -aF3682.6176620841024 -aF2232.215522301197 -aF3686.958710515499 -aF3725.365384674072 -aF3990.9595863223076 -aF3103.9054567456246 -aF3104.288730788231 -aF3096.0577368497848 -aF2986.451984465122 -aF3092.7038846731184 -aF3075.5731421589853 -aF3220.1630704283716 -aF3686.760944378376 -aF4565.924314045906 -aF3086.9929107546805 -aF3056.720254302025 -aF3094.3157059311866 -aF3527.493009543419 -aF3093.668471300602 -aF3666.325382614136 -aF3686.0660388469696 -aF4630.468806922435 -aF3685.586060976982 -aF2265.240560364723 -aF3095.958581376076 -aF3102.2783807992937 -aF3687.442229652405 -aF3669.197622489929 -aF3095.665201032162 -aF2473.632677483559 -aF3685.346889257431 -aF3071.178974723816 -aF3652.0745073199273 -aF3113.7618921995163 -aF2674.079826068878 -aF3374.402133834362 -aF3096.2647647619247 -aF3086.838729441166 -aF3665.774851810932 -aF3647.4839357733727 -aF3096.2533237457274 -aF3091.8163887023925 -aF3880.010331749916 -aF2175.6890025377274 -aF3685.22675858736 -aF2158.1071570992467 -aF3368.4745978236197 -aF3471.2816625356672 -aF3142.7975568771362 -aF3046.851015806198 -aF3673.451228868961 -aF3099.5548741340635 -aF3689.76448353529 -aF3686.573802042007 -aF3686.1365917801854 -aF3677.7699400782585 -aF3686.1976105332374 -aF3085.652405023575 -aF3635.5441457509996 -aF1240.9179356694221 -aF3512.8561361551283 -aF3110.501202583313 -aF3684.411177575588 -aF3095.9893631577493 -aF3685.6884853124616 -aF3686.9352836728094 -aF3091.4145911097526 -aF4728.209953105449 -aF3095.998624932766 -aF2068.2412437319754 -aF3467.8210275411607 -aF3914.920685839653 -aF3690.7015572428704 -aF3098.414041376114 -aF3078.1046031951905 -aF3322.4471172571184 -aF3096.726763892174 -aF3688.5580011367797 -aF3098.0492908835413 -aF3380.199732589722 -aF3088.3296028137206 -aF2696.6518614053725 -aF3107.3197828650473 -aF2857.0551808953287 -aF3103.066448891163 -aF3006.3100473165514 -aF3948.2271184206006 -aF3081.209749472141 -aF3099.518371844292 -aF3087.9370669960977 -aF3677.7625851392745 -aF3676.191897058487 -aF3096.026682662964 -aF1729.1771945476532 -aF3685.629918205738 -aF3094.0533797740936 -aF3723.3490417718886 -aF3095.5319949150085 -aF3597.911919426918 -aF2262.8425778508185 -aF2654.6685076594354 -aF2128.5389403581617 -aF4003.8582424640654 -aF3372.473232984543 -aF3857.5665992379186 -aF3847.004906857014 -aF3112.097769153118 -aF3688.423977804184 -aF3265.960368645191 -aF3686.0551426410675 -aF3895.1920154333116 -aF3010.7987393379212 -aF3096.7112367987634 -aF3667.2335813760756 -aF3687.8393963575363 -aF3686.375491094589 -aF3007.416829431057 -aF2853.405769133568 -aF1778.812953698635 -aF3900.2320554733274 -aF4066.7979966163634 -aF3106.496574509144 -aF3096.1389135837553 -aF3967.901580202579 -aF3695.932280886173 -aF3068.9615968227386 -aF3685.4294280171393 -aF4555.6118723750105 -aF3573.462467813492 -aF3110.7804178595543 -aF1954.6199670672418 -aF303.44760619401933 -aF3269.3439129829408 -aF3675.742700970173 -aF772.3690960526466 -aF3094.404237604141 -aF3089.113040018082 -aF3098.4203066945074 -aF3079.542085158825 -aF3295.7742948293685 -aF3738.973656225204 -aF3687.1123470187185 -aF3824.000019741058 -aF3686.0325330138207 -aF3649.272547972202 -aF3685.6261045336723 -aF3096.180046761036 -aF1348.0170158863068 -aF3098.2328919529914 -aF3747.8668670773504 -aF3094.389800131321 -aF3148.459770274162 -aF3214.833463716507 -aF2951.1787867188455 -aF3091.0106142759323 -aF3072.1040626049044 -aF3096.117393577099 -aF3679.8968794703483 -aF3783.722194623947 -aF3097.207558977604 -aF715.7265316963195 -aF3095.998624932766 -aF3101.1906670451162 -aF3095.8370886802672 -aF3137.372335958481 -aF3102.1359129071234 -aF3018.5731822490693 -aF3685.714636206627 -aF3684.5081538081167 -aF3172.5006141662598 -aF2980.2781942009924 -aF3098.4227583408356 -aF3095.826464879513 -aF3096.4949471116065 -aF3681.4544921040533 -aF3090.9490507125856 -aF3703.7949830651282 -aF3095.971112012863 -aF3699.4920713543893 -aF4245.0397671937935 -aF3796.1318835258485 -aF3096.3345004796984 -aF3085.276213514805 -aF3095.945505928993 -aF3686.6089423060416 -aF3696.7998912811277 -aF3092.9030128359796 -aF3660.8701971292494 -aF4170.614049994945 -aF3682.127605223656 -aF3763.2087249875067 -aF3642.2788182139398 -aF3092.5505205750464 -aF2550.56452203989 -aF1553.2642155766487 -aF3096.099687242508 -aF3531.7798493504524 -aF2131.444686067104 -aF3644.1450658798217 -aF3096.041937351227 -aF3717.5865832805634 -aF362.4309479832649 -aF3686.130326461792 -aF1754.0025652647018 -aF3427.761398947239 -aF3687.1052644848824 -aF3098.210554730892 -aF3690.4495824813844 -aF3685.6135738968846 -aF3160.73407381773 -aF3073.266142964363 -aF3040.9755091786383 -aF3587.6512347340586 -aF3688.4351464152337 -aF3384.800110721588 -aF3114.2178984165193 -aF3057.61673964262 -aF3104.2909100294114 -aF3007.9422989606855 -aF2013.622377216816 -aF3127.007047688961 -aF3094.9335208058355 -aF3738.9676633119584 -aF1087.2740775108336 -aF2906.2319373726846 -aF3085.6769214868546 -aF3739.542438173294 -aF3685.975055527687 -aF3095.731940293312 -aF3075.85971237421 -aF3685.972603881359 -aF3660.8170781254767 -aF3060.7030899643896 -aF3712.5669736266136 -aF3080.7395781874657 -aF3665.637832021713 -aF3664.3076776862144 -aF3095.6641114115714 -aF2995.1460671544073 -aF3686.2109583854676 -aF3121.1424372673036 -aF3110.3669068455697 -aF3847.2004937529564 -aF3686.506517970562 -aF3850.263144826889 -aF3769.58137100935 -aF3097.3701848506926 -aF3697.9287382125854 -aF3109.822368955612 -aF3183.453207933903 -aF3687.3052098631856 -aF1591.5799064159394 -aF2836.1020493507385 -aF3095.359562456608 -aF2993.1994599699974 -aF3095.273754835129 -aF2765.7207313776016 -aF3099.382986485958 -aF3430.196700966358 -aF3686.0338950395585 -aF3096.1683333396913 -aF3029.3577020406724 -aF3421.8104360938073 -aF3687.0180948376656 -aF3095.016876780987 -aF3093.770078420639 -aF3032.0975530147552 -aF948.7296366810798 -aF2729.786406338215 -aF2696.733310544491 -aF3022.033817243576 -aF3496.6292339205743 -aF2970.4890428185463 -aF3090.355207490921 -aF3354.601276063919 -aF3202.4373950719832 -aF2472.348014807701 -aF3682.7070109724996 -aF3095.6000962018966 -aF3572.8716210484504 -aF3685.908043861389 -aF3013.4323523044586 -aF2549.545181977749 -aF3097.001075875759 -aF1706.8143663644792 -aF3095.9880011320115 -aF3699.550093650818 -aF3685.4517652392387 -aF3685.2006076931953 -aF3112.742552137375 -aF3065.0781890392304 -aF3426.7676649689674 -aF3174.9113997220993 -aF3103.327140617371 -aF3588.643606686592 -aF3095.7180476307867 -aF3096.3908883452414 -aF498.01597929000854 -aF607.5579888701438 -aF3665.642735314369 -aF3718.939892053604 -aF3108.412944722176 -aF2105.833426499367 -aF3175.6161118388177 -aF3685.7015607595445 -aF3113.2127234220507 -aF3103.3966039299967 -aF3125.063981771469 -aF3678.9148589134215 -aF3687.310113155842 -aF3102.723490810394 -aF3686.3297270298003 -aF3096.0032558202743 -aF3784.078228151798 -aF3062.8076921343804 -aF3698.4258776068687 -aF4361.874607384204 -aF239.86170703172684 -aF1766.2000505566598 -aF3138.802735388279 -aF3090.395251047611 -aF3696.2894040346146 -aF3069.6671261548995 -aF3336.471351468563 -aF3182.196058177948 -aF3059.1146955490112 -aF3843.740675973892 -aF3686.7386071562764 -aF3828.323361837864 -aF2978.658200788498 -aF3084.475069975853 -aF3097.160705292225 -aF2818.4907841563227 -aF3687.012919139862 -aF3093.451909208298 -aF3145.842501616478 -aF3689.4664723038672 -aF3095.985821890831 -aF3686.1213370919227 -aF3097.514831984043 -aF3105.5434288978577 -aF3746.229984545708 -aF3118.8280831336974 -aF3130.2437656521797 -aF1530.4786146044733 -aF3113.1171092152595 -aF3082.1522712826727 -aF3015.22859184742 -aF3852.280304944515 -aF3649.633212387562 -aF3009.7562448382378 -aF3091.449731373787 -aF3098.805759978294 -aF3096.2470584273337 -aF1873.810707640648 -aF3117.277008223534 -aF3099.8283689022064 -aF3069.541002571583 -aF2989.249312925339 -aF3685.330544948578 -aF3631.8699451208113 -aF3045.054776263237 -aF3685.9162160158157 -aF3087.6376937389373 -aF3163.797814512253 -aF3095.964029479027 -aF3095.0073426008225 -aF3094.8640574932097 -aF3055.175444710255 -aF3406.761958527565 -aF3120.2464967370033 -aF3100.608537244797 -aF2816.295471072197 -aF2089.961468172073 -aF2872.2510296463965 -aF3095.5856587290764 -aF3096.383805811405 -aF3095.137279856205 -aF2834.9236246824266 -aF3802.924033474922 -aF3095.6213438034056 -aF3085.0678235769274 -aF2854.0644447803497 -aF3093.226902556419 -aF3711.434585428238 -aF3176.191159105301 -aF3686.1330505132673 -aF3089.426305937767 -aF3097.8991956472396 -aF3138.34073625803 -aF3686.011557817459 -aF3715.525565934181 -aF3236.6081691861154 -aF3094.9792848706247 -aF3638.2662903904916 -aF3099.1724173069 -aF2512.033086323738 -aF4201.3950144529335 -aF3207.9971841335296 -aF3106.046561205387 -aF3928.7703083515166 -aF3685.5438381791114 -aF3728.37518914938 -aF3093.0689075708387 -aF2977.407861161232 -aF3684.9025964617726 -aF3096.955584216118 -aF4550.255297553539 -aF3123.491659259796 -aF3782.7028545618055 -aF3086.9346160531045 -aF3663.876460337639 -aF3686.277697646618 -aF3685.9856793284416 -aF3095.509657692909 -aF3828.2187582612037 -aF3093.5924702644347 -aF4375.621805560589 -aF3503.679623949528 -aF1907.2775868535043 -aF3703.8867835998535 -aF3707.9450754880904 -aF3203.9225479364395 -aF3608.3382264494894 -aF3096.338858962059 -aF3034.8030809402467 -aF3303.882706451416 -aF902.2407017946243 -aF3686.110168480873 -aF3105.778242135048 -aF3685.0404334664345 -aF372.3034553408623 -aF3177.7784639000893 -aF3640.2578444242477 -aF3208.968580889702 -aF2958.4721621394156 -aF4501.931986403465 -aF3095.5766693592072 -aF3096.0596436858177 -aF3684.04479265213 -aF874.9901080489158 -aF3224.8604247927665 -aF3224.740021717548 -aF3787.630936086178 -aF3105.6216091752053 -aF4620.001366722583 -aF3775.7832190036775 -aF3729.21991751194 -aF3106.126920723915 -aF3096.3966088533402 -aF3095.736843585968 -aF3686.279332077503 -aF3083.9542313337324 -aF3693.712451338768 -aF3096.0457510232927 -aF642.6669263124465 -aF3096.2838331222533 -aF1661.7157873511314 -aF1771.3299842953681 -aF4396.832904779911 -aF3097.0612774133683 -aF3131.9046198368073 -aF3096.4984883785246 -aF2742.753980982304 -aF3688.585241651535 -aF3722.056479346752 -aF3094.879039776325 -aF3109.4535323858263 -aF2823.42158973217 -aF3688.0017498254774 -aF3967.667311775684 -aF3096.699795782566 -aF3685.8851618289946 -aF3482.1337388038637 -aF3135.506905508041 -aF3095.5840242981913 -aF3098.8610582232477 -aF3096.4742443203927 -aF3685.891427147388 -aF3511.1931027293203 -aF3690.502156674862 -aF3686.116161394119 -aF2794.857730770111 -aF3087.7861545443534 -aF3667.72391064167 -aF3079.9970017552378 -aF2034.0554873347282 -aF3065.2375460505486 -aF3096.944688010216 -aF3096.014969241619 -aF3096.4170392394067 -aF3096.278112614155 -aF3096.0634573578836 -aF3374.367538380623 -aF3686.584425842762 -aF4724.353513431549 -aF3699.5473695993423 -aF3836.1138766527174 -aF3096.7008854031565 -aF3684.564269268513 -aF3557.099090600014 -aF3686.800987935066 -aF4187.006846964359 -aF247.59120309352875 -aF3323.4664573192595 -aF3096.1035009145735 -aF3094.6232513427735 -aF2201.928428375721 -aF2994.1035726547243 -aF3669.4215395212173 -aF3097.230168604851 -aF4082.7546729445457 -aF1728.7081128835678 -aF3128.2601113677024 -aF2341.881476223469 -aF3096.6916236281395 -aF543.5177179217338 -aF400.00814846754076 -aF3121.9506633400915 -aF3846.181153690815 -aF3096.0503819108008 -aF3095.830006146431 -aF3169.3744926929476 -aF3039.5175968289377 -aF3065.989656662941 -aF3091.1658852100372 -aF3093.7529168963433 -aF3095.813661837578 -aF3683.6890315294263 -aF3685.7356114029885 -aF3095.609630382061 -aF3955.563806259632 -aF3687.0175500273704 -aF3094.6894457936287 -aF3676.997671484947 -aF575.2316700100898 -aF1039.2831006407737 -aF3685.3329965949056 -aF3685.998754775524 -aF2867.1246371746065 -aF3095.6878106594086 -aF3081.656493914127 -aF3767.682707130909 -aF559.3071374893188 -aF2783.6539794564246 -aF3177.9163009047506 -aF877.5956632852553 -aF3213.725591981411 -aF3685.953807926178 -aF3102.887478709221 -aF3016.8703776717184 -aF3095.21464291811 -aF3110.3788926720617 -aF3684.4986196279524 -aF3685.953807926178 -aF3096.7986788511275 -aF3098.4167654275893 -aF3685.9889481902123 -aF3097.9109090685843 -aF644.4991233348846 -aF3096.0975080013277 -aF3093.507207453251 -aF3671.146953725815 -aF3088.7115148305893 -aF3098.491132032871 -aF3683.04533816576 -aF4286.882015073299 -aF3095.9125449061394 -aF3697.559901642799 -aF3737.954316163063 -aF3341.2842056155205 -aF2129.7876455545425 -aF3135.3006948113443 -aF3549.19062435627 -aF3672.4904559135434 -aF3113.2571254611016 -aF3173.959071326256 -aF3815.1689172625543 -aF2789.7923570513726 -aF3686.4427751660346 -aF2045.943247973919 -aF4009.6700062870977 -aF1168.456804394722 -aF2917.669957113266 -aF3748.886207139492 -aF3685.5966847777368 -aF3674.419356763363 -aF3681.5157832622526 -aF3095.8253752589226 -aF3096.8564287424088 -aF1772.1842468380928 -aF2287.150651192665 -aF3744.2716639399528 -aF3683.4803691864013 -aF3121.57692347765 -aF3672.249104952812 -aF4030.073969054222 -aF3686.0671284675595 -aF3689.173091959953 -aF3095.905462372303 -aF3698.985670185089 -aF3683.363507378101 -aF3084.453822374344 -aF3015.3111306071282 -aF3017.5769966244698 -aF3685.7416043162343 -aF2216.3272196650505 -aF3108.787774205208 -aF3355.03412784338 -aF4615.402078211307 -aF3095.692441546917 -aF1812.1357305884362 -aF3356.1414547681807 -aF3055.328536403179 -aF3041.4492217302322 -aF3110.7509981036187 -aF3097.752369272709 -aF3108.5121001958846 -aF3100.056916821003 -aF3778.755159163475 -aF3096.442917728424 -aF3094.467708003521 -aF3088.389259541035 -aF1978.3137668013574 -aF3640.0328377723695 -aF1824.197558116913 -aF3687.0355287671086 -aF3096.735208451748 -aF3689.1741815805435 -aF2799.2954830288886 -aF3097.235616707802 -aF3686.1104408860206 -aF3686.060863149166 -aF3023.146047461033 -aF3096.0253206372263 -aF3093.78778475523 -aF2915.4874470710756 -aF3763.075246465206 -aF3735.907191479206 -aF2832.399518585205 -aF3119.060989534855 -aF3095.168878853321 -aF3632.3812495827674 -aF3094.42548520565 -aF3095.923168706894 -aF3097.3936116933824 -aF3100.847708964348 -aF3095.81012057066 -aF3150.498450398445 -aF3115.194470870495 -aF3661.185914695263 -aF3443.8286717653273 -aF3096.804671764374 -aF3096.29200527668 -aF3044.7701128840445 -aF3108.755902802944 -aF3092.1841356515883 -aF3602.614721894264 -aF3686.1207922816275 -aF3072.2075765609743 -aF3095.300995349884 -aF3685.5315799474715 -aF3115.62350897789 -aF1864.5211472988128 -aF3096.5017572402953 -aF3135.35681027174 -aF3296.566448998451 -aF3686.1940692663193 -aF3187.9029460191728 -aF3683.081840455532 -aF3258.3136837482452 -aF3091.524370384216 -aF3095.3584728360174 -aF3095.7180476307867 -aF3108.830269408226 -aF3434.1915224552154 -aF3686.5797949552534 -aF3060.439401781559 -aF2658.4306951522826 -aF4270.041656446457 -aF3393.2727280259132 -aF4067.1161658287047 -aF3097.0062515735626 -aF2994.218800032139 -aF3713.2218356013295 -aF2723.3829785346984 -aF3902.650195968151 -aF3477.0054394960403 -aF2058.0347676634788 -aF3662.9088772535324 -aF3095.906551992893 -aF3097.2108278393744 -aF3107.0898729205132 -aF3092.014154839516 -aF3096.0362168431284 -aF3620.0126938581466 -aF3104.1282841563225 -aF3117.573929834366 -aF3096.405325818062 -aF3677.0717656850816 -aF3321.0371482133864 -aF3096.208649301529 -aF3685.1055382966993 -aF3687.01537078619 -aF3685.99031021595 -aF3685.9064094305036 -aF3319.523665213585 -aF3705.5163111925126 -aF2911.251002216339 -aF3739.0265028238296 -aF3100.7038790464403 -aF3686.060863149166 -aF3684.682220697403 -aF3096.128289783001 -aF3530.397665631771 -aF2202.385524213314 -aF2057.8143918991086 -aF3054.510776150227 -aF3095.4363807082177 -aF3295.3923828125 -aF3078.5227450966836 -aF3059.655419766903 -aF3519.0212094545363 -aF3096.422759747505 -aF3736.9289831876754 -aF3842.780175423622 -aF3625.4983887195585 -aF4000.7898708820344 -aF3748.1673299551007 -aF3096.1285621881484 -aF3092.617804646492 -aF3087.3007285714148 -aF2995.7682405114174 -aF3685.310386967659 -aF3676.7620410323143 -aF3095.8722289443017 -aF3112.193383359909 -aF3686.3733118534087 -aF3093.9561311364173 -aF3098.618072831631 -aF3666.346357810497 -aF3090.4954961419107 -aF3137.7602408885955 -aF3099.5409814715385 -aF3367.4882187843323 -aF357.0092683315277 -aF2904.233845615387 -aF3825.0193598031997 -aF3686.0409775733947 -aF2712.6398643255234 -aF3096.208649301529 -aF3686.081021130085 -aF3103.67554680109 -aF1472.2067950606347 -aF3891.650748515129 -aF3689.0777501583098 -aF3096.8011304974557 -aF3095.8934765458107 -aF3098.0631835460663 -aF2929.049682152271 -aF2652.0095610141752 -aF3685.9704246401784 -aF3685.9301086783407 -aF4013.530804443359 -aF3686.0325330138207 -aF2450.1104929924013 -aF4217.5203095674515 -aF2914.688482773304 -aF3627.214813554287 -aF3686.676226377487 -aF3088.479153239727 -aF3723.8440019249915 -aF3737.480603611469 -aF3095.808758544922 -aF3683.815972328186 -aF4091.270875072479 -aF530.7212137103081 -aF3697.645709264278 -aF2337.824273955822 -aF3247.131180036068 -aF3680.4705647110936 -aF3097.312434959412 -aF3095.7038825631143 -aF3685.309024941921 -aF4039.854948282242 -aF449.25573028326033 -aF3684.444138598442 -aF3685.8323152303697 -aF1318.0856830835344 -aF3845.945523238182 -aF3712.923007154465 -aF3676.3406302690505 -aF3617.6040875434874 -aF3506.1862961173056 -aF3690.650617480278 -aF3125.9359506487845 -aF3090.189040350914 -aF3096.4826888799666 -aF4204.51187415123 -aF3648.9364000201226 -aF3093.441557812691 -aF3183.89096300602 -aF4011.673273742199 -aF3881.0430196642874 -aF3079.4064273953436 -aF3688.747867524624 -aF3079.875509059429 -aF3095.808758544922 -aF3727.858981394768 -aF3686.139860641956 -aF3095.758091187477 -aF3684.9965762376783 -aF3330.0668340444563 -aF3095.9149965524675 -aF1664.1633476018906 -aF3097.221451640129 -aF3095.222270262241 -aF3686.0453360557553 -aF3169.1053564071653 -aF3092.7733479857443 -aF3096.2770229935645 -aF3096.2683060288427 -aF332.74968310594556 -aF2974.9281571030615 -aF3460.22718924284 -aF3107.0092409968374 -aF3680.8761759757995 -aF3683.178544282913 -aF3095.9790117621424 -aF3096.39442961216 -aF3111.271019530296 -aF3574.8468307733538 -aF3686.7021048665047 -aF3094.7657192349434 -aF3131.455423748493 -aF3683.3882962465286 -aF3686.005564904213 -aF3096.09614597559 -aF3091.131834566593 -aF3096.131831049919 -aF780.7556333303451 -aF3685.918395256996 -aF3095.6804557204246 -aF4203.046879267692 -aF3686.6391792774198 -aF3681.030357289314 -aF3096.422759747505 -aF723.3925573587418 -aF3069.4644567251207 -aF3685.84893194437 -aF2884.7241889476777 -aF2457.0333974123 -aF622.0213401794433 -aF1574.0642554283143 -aF2814.4886077284814 -aF3098.5543300271033 -aF3095.7984071493147 -aF4528.043381822109 -aF3679.730439925194 -aF2515.0145606637 -aF3069.532830417156 -aF3095.8003139853477 -aF1689.4768683433533 -aF2466.6964252114294 -aF814.1481459379196 -aF3096.245968806744 -aF3727.404064798355 -aF2777.8678217172624 -aF2975.660109734535 -aF3769.2607501506805 -aF3685.977234768867 -aF3095.5368982076643 -aF3432.067307114601 -aF3103.4815943360327 -aF3588.1004308223723 -aF3690.7083673715592 -aF3083.289835178852 -aF3107.624876630306 -aF3645.4757650256156 -aF4038.4033012509344 -aF3650.5792754650115 -aF3685.76993445158 -aF3472.6526776432993 -aF1869.0264560341836 -aF3097.6295145511626 -aF1629.716627073288 -aF2362.8937196850775 -aF3801.1049118995666 -aF3797.861656212807 -aF795.8011144399643 -aF3840.839016342163 -aF3020.5325924754143 -aF3756.5824697732924 -aF3096.606633222103 -aF2379.753146672249 -aF2664.4418595433235 -aF3686.028991746902 -aF3624.075616633892 -aF3130.908434212208 -aF3096.367461502552 -aF3099.08225120306 -aF3792.1125455737115 -aF3095.903010725975 -aF3685.9151263952253 -aF3716.045587360859 -aF4217.371848762035 -aF4746.401441264152 -aF462.6659632921219 -aF3103.3459365725516 -aF3096.210556137562 -aF3685.8840722084046 -aF2822.4254041075706 -aF3064.290120947361 -aF3096.2802918553352 -aF3164.8571981310843 -aF3144.1890023708343 -aF3690.7475937128065 -aF3473.2971882224083 -aF3116.2810950040816 -aF3864.3118955016134 -aF3267.373334145546 -aF3117.94521805048 -aF3085.4461943268775 -aF3096.045478618145 -aF3095.9681155562403 -aF1020.4236750602722 -aF3651.612780594826 -aF3096.277840209007 -aF3687.39047267437 -aF3685.9584388136864 -aF3096.3391313672064 -aF3096.0985976219176 -aF3686.5193210124967 -aF2605.8069239377974 -aF3687.247732377052 -aF1736.9589923977853 -aF1255.7506683588028 -aF1761.9911186218262 -aF1170.6997883796691 -aF3701.1818004846573 -aF3097.57857478857 -aF3095.672283565998 -aF2796.9860321879387 -aF3686.189438378811 -aF3285.8029044032096 -aF2751.785846054554 -aF3059.9880264520643 -aF3096.3522068142893 -aF3687.5220443606377 -aF257.3634653568268 -aF3096.4954919219017 -aF3462.3778278827667 -aF3094.210829949379 -aF3094.7177759289743 -aF3053.0043756842615 -aF3702.4604702472684 -aF3095.6553944468496 -aF3645.0186691880226 -aF3095.6216162085534 -aF3098.726762485504 -aF3092.4690714359285 -aF1841.4846611857415 -aF1901.0882694959641 -aF3576.6640455126762 -aF3684.3368109703065 -aF3097.1002313494682 -aF3081.4758893013 -aF4564.904973983764 -aF3680.769937968254 -aF3704.7116263866424 -aF3682.0270877242087 -aF3158.080030465126 -aF3086.0160658955574 -aF3111.8008475422857 -aF4396.740287029743 -aF3096.245968806744 -aF3685.9960307240485 -aF3587.7847132563593 -aF3094.482962691784 -aF3024.573450434208 -aF3690.5247663021087 -aF2986.3155094861986 -aF3213.8293783426284 -aF3685.8238706707953 -aF3722.4013442635537 -aF3017.912872171402 -aF3683.04424854517 -aF3679.3594241142273 -aF3032.372137403488 -aF3384.7717805862426 -aF3059.7510339736937 -aF2669.392278289795 -aF3488.0588231682777 -aF3686.3520642518997 -aF3078.154180932045 -aF3759.754900121689 -aF3692.3054787516594 -aF3095.7371159911154 -aF3685.8238706707953 -aF3315.1294977784155 -aF3685.7533177375794 -aF3227.301719725132 -aF3882.4369168043136 -aF3095.3481214404105 -aF3096.4134979724886 -aF3685.806436741352 -aF3685.4152629494665 -aF3101.692437326908 -aF3624.4790486574175 -aF3686.977778875828 -aF3686.4860875844956 -aF3092.913636636734 -aF3094.781246328354 -aF3095.9806461930275 -aF3831.2105839967726 -aF2689.561972630024 -aF3097.397425365448 -aF3104.0552795767785 -aF3303.7473210930825 -aF3686.1902555942534 -aF3094.032949388027 -aF4132.774523353576 -aF3686.3711326122284 -aF3132.4053004980087 -aF3096.0220517754556 -aF3884.9928943037985 -aF3096.613443350792 -aF2880.817626726627 -aF3087.8676036834718 -aF3682.9301107883452 -aF3685.680585563183 -aF3218.092518901825 -aF3684.7470531225204 -aF3082.608549904823 -aF2411.2976627588273 -aF2346.709040248394 -aF3552.338265836239 -aF3202.49786901474 -aF2907.4945352315904 -aF3685.859283339977 -aF2308.7845232009886 -aF3089.499310517311 -aF3100.113304686546 -aF1545.5123822927476 -aF3682.729620599747 -aF3685.915943610668 -aF3656.944838953018 -aF3091.2056563615797 -aF3107.7093222260473 -aF3082.6829165101053 -aF3685.096004116535 -aF3096.194211828709 -aF1530.607189834118 -aF3688.0584100961682 -aF3184.9476225733756 -aF3686.4155346512794 -aF3064.063752269745 -aF4080.9587058067323 -aF3685.9892205953597 -aF3981.9612270832063 -aF3154.501444041729 -aF3096.038668489456 -aF3690.395101451874 -aF2807.7201570272446 -aF3691.026808989048 -aF2918.2861375570296 -aF3141.6025154948234 -aF3028.979331290722 -aF3674.935836923122 -aF3695.2820497989655 -aF2512.8391331553457 -aF3665.6620760798455 -aF3687.0802032113074 -aF3559.5403855323793 -aF3096.640956270695 -aF3668.704569172859 -aF3095.9781945466993 -aF3685.9930342674256 -aF3626.1723190546036 -aF2698.058289182186 -aF3080.6946313381195 -aF3686.1118029117583 -aF3096.912816607952 -aF3354.0379422187807 -aF3686.066856062412 -aF1199.7921133279801 -aF3279.9083294153215 -aF3658.9813398361207 -aF3685.6040397167203 -aF3210.391897785664 -aF3085.586482977867 -aF3647.594532263279 -aF3092.3227898716927 -aF2955.4402928471563 -aF3685.3185591220854 -aF3096.042482161522 -aF3684.1414964795113 -aF3123.806287205219 -aF3096.219000697136 -aF4257.871139264106 -aF3555.961799108982 -aF3096.7561836481095 -aF3048.9417253136635 -aF3093.429299581051 -aF3884.0228595733643 -aF3097.9512250304224 -aF3699.621736204624 -aF3132.9157877445223 -aF2916.5299415707586 -aF3346.356934273243 -aF3096.0163312673567 -aF3685.978596794605 -aF815.201536643505 -aF3095.8084861397742 -aF3566.751222193241 -aF3098.1361881256103 -aF1472.9915942907335 -aF321.20379092693327 -aF3084.848265028 -aF3685.966610968113 -aF3074.543450701237 -aF3097.4420998096466 -aF3067.478078389168 -aF3100.329049563408 -aF3088.528458571434 -aF3096.1481753587723 -aF3095.815568673611 -aF3097.436379301548 -aF3020.8099009156226 -aF3107.7365627408026 -aF3093.20347571373 -aF3551.229304480553 -aF3096.1789571404456 -aF2908.690666234493 -aF3096.2108285427094 -aF3747.4116780757904 -aF3848.2764940857887 -aF3086.492502498627 -aF3685.8840722084046 -aF2838.1638839125635 -aF3100.341852605343 -aF3096.1882189154626 -aF3096.354658460617 -aF3080.1018777370455 -aF3787.4034777879715 -aF3685.0183686494825 -aF3394.2920680880547 -aF3685.9924894571304 -aF3095.986911511421 -aF2770.660798728466 -aF3104.4303814649584 -aF3093.9847336769103 -aF2461.8901087880135 -aF3684.051602780819 -aF3105.822644174099 -aF2774.80517064333 -aF3152.5406717896462 -aF3686.0516013741494 -aF3100.4693382143973 -aF3095.894566166401 -aF3064.7341413378717 -aF3036.0948261499407 -aF3671.4983563661576 -aF3988.382361221313 -aF3663.2145158290864 -aF3068.5134903550147 -aF3685.898509681225 -aF3685.775927364826 -aF3685.8263223171234 -aF3021.551932537556 -aF3108.6442166924476 -aF3095.67092154026 -aF3664.6825071692465 -aF3318.5043251514435 -aF3134.058527338505 -aF3089.1637073755264 -aF3100.283285498619 -aF3649.399488770962 -aF3185.761296749115 -aF3080.992642569542 -aF3684.7342500805853 -aF3470.2072966337205 -aF2058.810577523708 -aF3675.060598480701 -aF3686.3730394482614 -aF3104.272386479378 -aF3065.4565597891806 -aF1508.0335200667382 -aF3686.174183690548 -aF3023.447872364521 -aF3693.480362153053 -aF3096.055285203457 -aF3685.446861946583 -aF3838.340243923664 -aF3682.64354057312 -aF3083.2353541493417 -aF3686.109078860283 -aF3092.9169054985045 -aF3971.842465472221 -aF3686.043429219723 -aF3095.8253752589226 -aF3094.543164229393 -aF3685.676771891117 -aF3680.958714735508 -aF4654.004884076118 -aF3096.1043181300165 -aF3562.863455927372 -aF3086.5772204995155 -aF3673.5732663750646 -aF3730.9701205849647 -aF4616.999734401702 -aF3096.4061430335046 -aF2251.009025835991 -aF2934.5729689240457 -aF3266.3539940834044 -aF3686.0197299718857 -aF3019.837142133713 -aF3845.6731180906295 -aF3685.1700983166693 -aF3694.3760302782057 -aF2984.79276471138 -aF3096.328507566452 -aF3426.316289639473 -aF3064.846372258663 -aF3685.891427147388 -aF3086.2710371136664 -aF3095.9890907526014 -aF3651.605425655842 -aF3589.7266895532607 -aF3083.158808302879 -aF3096.3323212385176 -aF3945.771386015415 -aF3095.6118096232412 -aF3094.3402223944663 -aF2346.8814727067947 -aF3668.5983311653135 -aF3685.4871779084206 -aF3672.1853621482846 -aF3098.2857385516168 -aF3686.257539665699 -aF3708.1700821399686 -aF3675.128972172737 -aF3683.2324805021285 -aF3097.362829911709 -aF3099.2225398540495 -aF3437.912031960487 -aF430.41292141675945 -aF2278.6813027501107 -aF3092.9171779036524 -aF2194.0758051872253 -aF3096.180046761036 -aF3690.2657090067864 -aF3686.070669734478 -aF3685.8170605421064 -aF2091.8819244623182 -aF3097.0215062618254 -aF3096.3543860554696 -aF3842.34187554121 -aF3089.5028517842293 -aF3667.813531935215 -aF3683.4016440987584 -aF3097.980644786358 -aF4094.159186851978 -aF635.9889141201973 -aF3719.116410589218 -aF2949.280395245552 -aF2286.3653071522713 -aF3686.4392338991165 -aF3096.847984182835 -aF3686.9505383610726 -aF3081.5630589485168 -aF3687.7516819000243 -aF3096.351117193699 -aF3685.786278760433 -aF3097.9972615003585 -aF3279.2106998324393 -aF3642.348009121418 -aF3686.6468066215516 -aF3880.023679602146 -aF3111.7798723459246 -aF3689.92138890028 -aF3095.7640841007233 -aF3096.1857672691344 -aF3072.357399392128 -aF571.2098804116249 -aF3096.1857672691344 -aF3087.3644713759422 -aF3096.1882189154626 -aF2475.831259429455 -aF3096.283560717106 -aF3685.992761862278 -aF1877.926749420166 -aF3118.0184950351713 -aF3112.206186401844 -aF3294.5558266043663 -aF3100.6507600426676 -aF3188.2894889235495 -aF3312.6664104342462 -aF3727.45582177639 -aF3685.682764804363 -aF3174.596771776676 -aF2832.8849445581436 -aF3585.0873574852944 -aF3100.052013528347 -aF3463.960774195194 -aF1042.9951655864716 -aF2402.5204964995382 -aF3121.4333659648896 -aF3094.8482579946517 -aF3686.0442464351654 -aF3094.940058529377 -aF3095.6450430512427 -aF3096.4287526607513 -aF3106.8869310855866 -aF1794.9910954117775 -aF4269.004610049724 -aF3096.1378239631654 -aF3099.7659881234167 -aF4078.3133794188498 -aF3683.637002146244 -aF3686.248277890682 -aF2949.0390442848206 -aF3095.761904859543 -aF3205.935349571705 -aF552.1810188293457 -aF3770.98371270895 -aF3142.091755139828 -aF3096.1152143359186 -aF2904.015921497345 -aF3686.0385259270665 -aF3685.9930342674256 -aF3096.2764781832693 -aF3101.959121966362 -aF3686.209323954582 -aF3687.320736956596 -aF3144.8231615543364 -aF2960.5339967012405 -aF3118.593269896507 -aF3103.5979113340377 -aF3097.809574353695 -aF3687.108533346653 -aF3093.1563496232034 -aF3129.2012711524962 -aF3096.3579273223877 -aF3096.2211799383163 -aF3095.307805478573 -aF3098.4982145667077 -aF3691.264618682861 -aF2704.2209108352663 -aF3596.1358378648756 -aF3080.3622970581055 -aF2931.0801901221275 -aF3970.2006796479222 -aF3102.8654138922693 -aF2468.505467796326 -aF3102.8640518665316 -aF3662.7827536702157 -aF3095.1380970716477 -aF2912.3117478609083 -aF3496.308613061905 -aF3099.7022453188897 -aF3747.1090359568593 -aF3139.739536690712 -aF3096.2612234950066 -aF2322.311890423298 -aF3095.599278986454 -aF3220.7650858044626 -aF2958.2408901691438 -aF3095.9681155562403 -aF3489.347844326496 -aF3095.868960082531 -aF2310.1694309711456 -aF3090.4944065213203 -aF3096.338858962059 -aF1166.1672391295433 -aF2783.5807024717333 -aF3099.157707428932 -aF4369.423226428032 -aF3681.0208231091497 -aF2790.244277191162 -aF3685.5939607262612 -aF3215.002082502842 -aF2680.5491759181023 -aF3107.3004420995712 -aF3685.9254777908322 -aF3093.050111615658 -aF4542.09812541008 -aF3095.570676445961 -aF3686.890609228611 -aF3686.0916449308393 -aF3683.80997941494 -aF1490.0520562767983 -aF3391.5571204066277 -aF2692.9673093795777 -aF3103.304258584976 -aF3186.120599138737 -aF3096.074081158638 -aF3428.953716278076 -aF3093.575853550434 -aF3109.3192366480826 -aF3435.969783258438 -aF3128.7016801118853 -aF1077.9850619792937 -aF3833.8409281015397 -aF3685.9655213475226 -aF3108.94113830328 -aF3828.1149718999864 -aF3096.128017377853 -aF3685.773748123646 -aF2497.353990137577 -aF3130.242948436737 -aF3056.1016222119333 -aF3095.65648406744 -aF3097.8651450037955 -aF3951.8035256028174 -aF3095.5273640275 -aF3436.557905972004 -aF2827.0718187093735 -aF3684.92520608902 -aF2892.436523485184 -aF3677.4316128849982 -aF3025.632561647892 -aF3092.849893832207 -aF3920.0435370445252 -aF3095.892659330368 -aF3466.9327143549917 -aF3023.7799342393873 -aF3223.7476497650146 -aF3073.767640841007 -aF3099.2189985871314 -aF4561.917779135703 -aF3002.6124198436737 -aF3115.188750362396 -aF3096.090425467491 -aF2974.629056251049 -aF1864.3808586478233 -aF3686.1943416714666 -aF3104.9498580813406 -aF3048.047964024544 -aF3475.4263068556784 -aF3721.5860356569287 -aF3702.9543407797814 -aF3693.6721353769303 -aF3684.741060209274 -aF3659.3452731132506 -aF3099.451087772846 -aF3096.037578868866 -aF3683.1033604621884 -aF3095.675824832916 -aF3478.9120031237603 -aF3685.583609330654 -aF3423.6723252773286 -aF3685.728528869152 -aF3684.6574318289754 -aF3686.0976378440855 -aF3686.573802042007 -aF3127.5976220488546 -aF3095.9504092216494 -aF3127.1941900253296 -aF676.4729499340057 -aF3095.0838884472846 -aF4201.274883782863 -aF2088.3798838853836 -aF2739.198276591301 -aF3095.0182388067246 -aF3101.4772372603416 -aF3767.74154664278 -aF3799.326651096344 -aF3657.028739738464 -aF3085.511843967438 -aF3686.182083439827 -aF2048.253243625164 -aF3099.6750048041345 -aF3103.9019154787065 -aF3610.6800935029983 -aF3687.434874713421 -aF3081.589209842682 -aF4755.193317401408 -aF3685.606218957901 -aF3685.7794686317443 -aF4179.745342946052 -aF3095.345669794083 -aF3802.7872860908506 -aF3265.2935208439826 -aF3095.911455285549 -aF3965.9729517579076 -aF3095.761632454395 -aF3685.818150162697 -aF2770.023098278046 -aF3097.013334107399 -aF3680.011017227173 -aF3095.990452778339 -aF3998.819564449787 -aF3089.2895585536958 -aF3013.150957787037 -aF3272.3853164553643 -aF3771.082323372364 -aF2662.674494946003 -aF3092.478333210945 -aF3093.5439821481705 -aF3685.309024941921 -aF3686.1586565971375 -aF2803.1609120726584 -aF3158.756957256794 -aF1528.693271267414 -aF3686.1256955742833 -aF3110.956936395168 -aF3082.2397133350373 -aF3724.297828900814 -aF3119.4369086384772 -aF3718.125400662422 -aF2389.0563272714617 -aF3115.4222015738487 -aF3892.499290549755 -aF2987.613520014286 -aF2548.712167036533 -aF3956.931007695198 -aF3093.224723315239 -aF3688.116977202892 -aF1467.1523175477982 -aF4709.568996453285 -aF3679.7446049928662 -aF3096.571220552921 -aF3067.4257766008377 -aF3095.6178025364875 -aF3096.3274179458617 -aF3687.9358277797696 -aF3685.9028681635855 -aF3156.448051226139 -aF4342.570889008045 -aF3096.172691822052 -aF3668.145593810081 -aF3685.3351758360864 -aF3672.0260051369664 -aF3913.7022176146506 -aF3552.3320005178452 -aF3122.6068873405457 -aF3457.368569624424 -aF3680.2420167922974 -aF3065.552446401119 -aF4378.544167983531 -aF3685.922208929062 -aF3140.578544545174 -aF3095.8054896831513 -aF3006.1114639639854 -aF3588.9830235004424 -aF3095.166427206993 -aF3095.22799077034 -aF3619.19874727726 -aF3095.9844598650934 -aF3109.698969423771 -aF3110.760532283783 -aF3684.3665031313894 -aF3090.406964468956 -aF3078.424951648712 -aF3025.9245799660684 -aF3095.166427206993 -aF3608.2731216192246 -aF3110.204280972481 -aF3096.8024925231935 -aF3096.5807547330855 -aF3280.024918818474 -aF3118.5341579794886 -aF3757.5277156352995 -aF3735.5587852954864 -aF3095.310257124901 -aF3094.344036066532 -aF3529.317851626873 -aF2983.109028494358 -aF3679.0273622393606 -aF2950.02188205719 -aF3686.1175234198568 -aF3099.210826432705 -aF3040.462842690945 -aF3096.1269277572633 -aF1172.3906071305275 -aF2239.3247518420217 -aF4735.720163023471 -aF3094.9920879125593 -aF3118.915252780914 -aF2910.9178507208826 -aF2045.227912056446 -aF3354.6388679742813 -aF3092.5720405817033 -aF3095.2157325387 -aF3018.295873808861 -aF1709.7748655080795 -aF3175.5771579027178 -aF3687.5520089268684 -aF3677.2711662530896 -aF3938.9577160596846 -aF3164.2361143946646 -aF3685.631007826328 -aF2863.839975905418 -aF4072.290501606464 -aF4059.537582218647 -aF3095.9558573246004 -aF2983.2384209394454 -aF3203.4861548900603 -aF3095.658118498325 -aF3097.579936814308 -aF3091.492226576805 -aF3049.010099005699 -aF3679.6988409280775 -aF3685.9747831225395 -aF3372.103034389019 -aF4403.1480733156195 -aF3089.09097520113 -aF2378.636830377579 -aF3066.196684575081 -aF3701.3662187695504 -aF3690.0510537505147 -aF3682.64354057312 -aF3686.4405959248543 -aF3130.042730653286 -aF2391.2764292240145 -aF3023.4865538954737 -aF416.9887957453728 -aF3696.734514045715 -aF3096.307259964943 -aF2183.481696593761 -aF3685.8993268966674 -aF3189.4188806653024 -aF3283.4623993754385 -aF3095.0590995788575 -aF3151.7101084947585 -aF3686.443047571182 -aF3063.9882960438727 -aF3684.953536224365 -aF3096.852615070343 -aF3025.1708349227906 -aF2986.243322122097 -aF3096.1645196676254 -aF1803.0687252521516 -aF3135.5197085499763 -aF3823.324454975128 -aF3501.205095589161 -aF3011.2337703585627 -aF3075.1852372288704 -aF3685.1030866503716 -aF3096.6995233774187 -aF3907.251936125755 -aF3088.6725608944894 -aF3075.046855413914 -aF3108.7654369831084 -aF3685.9194848775865 -aF3719.586309468746 -aF3112.3652710080146 -aF3682.65688842535 -aF3687.096819925308 -aF3095.7501914381983 -aF3095.1430003643036 -aF3064.4996005058288 -aF3572.1628228545187 -aF3017.4579555749892 -aF3095.2726652145384 -aF3085.0512068629264 -aF3697.2572595238685 -aF3118.404765534401 -aF3007.2392212748528 -aF3093.3674636125565 -aF3096.4026017665865 -aF3095.8346370339395 -aF3449.1043422579764 -aF3508.707950568199 -aF3669.9655326008797 -aF3123.0691588759423 -aF3673.358066308498 -aF1557.7586281061174 -aF3685.6225632667542 -aF4342.103169369697 -aF3646.71629806757 -aF3095.9929044246674 -aF3168.548015475273 -aF2842.6645617604254 -aF3660.8020958423613 -aF3105.109759902954 -aF3792.8477670669554 -aF3686.174183690548 -aF3103.9305180191996 -aF820.8367819309234 -aF3018.870921075344 -aF2917.4871732592583 -aF3135.2827160716056 -aF3684.145854961872 -aF2742.3905925154686 -aF3687.6895735263824 -aF3109.8471578240396 -aF3096.0926047086714 -aF3096.5698585271834 -aF3095.9147241473197 -aF3098.4085932731628 -aF3101.142178928852 -aF3094.802493929863 -aF3088.172969853878 -aF3100.793500339985 -aF3076.810678744316 -aF3131.0620707154276 -aF3040.7831911444664 -aF3255.606248986721 -aF3681.5841569542886 -aF3693.082923042774 -aF1409.2466105222702 -aF3685.760672676563 -aF3152.7294485569 -aF3082.3020941138266 -aF3684.682220697403 -aF2801.9448954939844 -aF1959.1230965614318 -aF3096.2849227428437 -aF3102.510469985008 -aF3686.5797949552534 -aF3094.85288888216 -aF501.5686872243881 -aF3186.7719198465347 -aF2771.042438340187 -aF3665.1469579458235 -aF2778.73733894825 -aF3094.355749487877 -aF3082.2522439718246 -aF3095.867598056793 -aF2585.0717165112496 -aF3666.289697539806 -aF3686.0409775733947 -aF4017.600809752941 -aF3684.5868788957596 -aF3181.6637785196303 -aF3096.0985976219176 -aF3098.482687473297 -aF3051.5963134765625 -aF3120.247858762741 -aF3100.5551458358764 -aF3687.901232326031 -aF3133.893449819088 -aF263.27901554107666 -aF3093.8409037590027 -aF1225.330095911026 -aF3686.081021130085 -aF4225.58731560707 -aF3167.579615175724 -aF3474.9904586195944 -aF1251.780635738373 -aF3095.5802106261253 -aF3096.1342826962473 -aF2397.916577100754 -aF3335.345773398876 -aF3012.1316177248955 -aF3546.642001795769 -aF3096.474789130688 -aF3095.0748990774155 -aF3100.4930374622345 -aF4538.010413765906 -aF3096.8539770960806 -aF3689.360506701469 -aF3745.5713088989255 -aF3288.4692059874533 -aF3096.2552305817603 -aF3138.7201966285706 -aF3053.5772437095643 -aF3670.232489645481 -aF3096.122569274902 -aF3690.5152321219443 -aF3692.2839587450026 -aF3595.0933433651926 -aF3685.893333983421 -aF3603.56677788496 -aF3683.7680290222165 -aF520.2777451634407 -aF3685.9069542407988 -aF3090.561418187618 -aF226.06302428245544 -aF3686.645717000961 -aF3095.8395403265954 -aF3066.742584490776 -aF3868.7191383838654 -aF3686.1128925323487 -aF3709.2507133603094 -aF2962.529092001915 -aF1455.0030479669572 -aF3095.386802971363 -aF3088.2565982341766 -aF3143.4731216430664 -aF601.0657569885254 -aF4385.361106801032 -aF3984.587757515907 -aF3096.942508769035 -aF3096.442917728424 -aF3110.420843064785 -aF3099.673915183544 -aF2076.371992576122 -aF3264.985975432396 -aF3111.823184764385 -aF3150.1298862338067 -aF3463.1920468688013 -aF3095.806306898594 -aF2693.878232192993 -aF4119.796597313881 -aF3668.82469984293 -aF2042.4398453712463 -aF3685.8944236040115 -aF3108.2998965859415 -aF3685.9230261445045 -aF3779.6824262857435 -aF3096.03267557621 -aF3096.807123410702 -aF2243.3443621993065 -aF483.32190082073214 -aF3097.4396481633185 -aF3095.9561297297478 -aF4533.784592711925 -aF3094.8648747086527 -aF3095.0547410964964 -aF3095.759453213215 -aF3434.950443196297 -aF3815.416533541679 -aF3183.3878306984902 -aF3685.243375301361 -aF3489.2010179519652 -aF3099.4502705574037 -aF3048.4170729994776 -aF2833.8029499053955 -aF3107.271294748783 -aF2372.886357712746 -aF3685.9230261445045 -aF3030.781018936634 -aF3686.375763499737 -aF3095.74773979187 -aF3326.404619240761 -aF2331.0460166692733 -aF3095.9893631577493 -aF4124.551701569557 -aF3108.791315472126 -aF3096.9416915535926 -aF3094.691625034809 -aF3638.566753268242 -aF3684.490992283821 -aF3116.616153335571 -aF3500.760530388355 -aF3095.988273537159 -aF3341.2768506765365 -aF3662.100651180744 -aF3685.923298549652 -aF3685.8513835906983 -aF2968.875042319298 -aF1332.976165664196 -aF3687.366501021385 -aF2850.2649377822877 -aF3095.3835341095923 -aF3112.5870087981225 -aF3698.1502035975454 -aF2870.2935262560845 -aF3098.039756703377 -aF3073.0242471933366 -aF3096.4826888799666 -aF3094.932158780098 -aF3089.885853421688 -aF3108.6333204865455 -aF3076.9425228357313 -aF227.40053355693817 -aF3686.2049654722214 -aF3095.67092154026 -aF3095.9643018841743 -aF3096.2132801890375 -aF3763.825177836418 -aF3092.345671904087 -aF1601.5442143082619 -aF3809.9926746487617 -aF3083.574770963192 -aF3022.44405939579 -aF2970.849707233906 -aF3087.8651520371436 -aF3091.6077263593675 -aF2530.7745604753495 -aF3686.2798768877983 -aF2985.2239820599557 -aF3061.9073931217195 -aF3686.659609663486 -aF3031.207877802849 -aF3062.9689559817316 -aF2638.7965493321417 -aF766.2506040334702 -aF3154.957450258732 -aF3091.0160623788834 -aF3686.69148106575 -aF3099.300175321102 -aF3149.390578663349 -aF1191.1190058350562 -aF3036.0060220718383 -aF3089.715055394173 -aF4475.471639990806 -aF3688.383661842346 -aF3686.583063817024 -aF3096.0359444379806 -aF3649.3915890216826 -aF2820.2099330425262 -aF3102.2868253588676 -aF3094.7308513760568 -aF1550.1021366238595 -aF3095.2751168608665 -aF3099.7480093836784 -aF3224.480964422226 -aF3021.9406546831133 -aF3687.674046432972 -aF3113.384611070156 -aF3050.933006942272 -aF3095.2124636769295 -aF673.1449762463569 -aF3334.149097585678 -aF805.0707892060279 -aF3687.3855693817136 -aF2462.866681241989 -aF3095.067271733284 -aF3096.399060499668 -aF3095.9939940452578 -aF3094.625430583954 -aF1949.0917770028113 -aF3088.1152199625967 -aF2668.8447439432143 -aF2109.6135927319524 -aF3087.4611752033234 -aF3095.804944872856 -aF3772.0785089969636 -aF3084.031866800785 -aF3126.0443678975107 -aF2983.2476827144624 -aF3087.4282141804697 -aF2365.7169266343117 -aF3685.97696236372 -aF3435.5388383150103 -aF3687.074482703209 -aF3451.880423116684 -aF3681.727986872196 -aF3674.5680899739264 -aF3095.6052718997003 -aF2776.151941692829 -aF3697.756578159332 -aF3686.6936603069303 -aF3100.4126779437065 -aF3096.0220517754556 -aF3095.824285638332 -aF3686.9164877176286 -aF3083.257691371441 -aF3696.2354678153993 -aF3663.926855289936 -aF4004.7536381840705 -aF3685.5942331314086 -aF3656.519342112541 -aF3676.0892003178596 -aF3546.5461151838304 -aF3685.8513835906983 -aF3091.9460535526277 -aF3698.774828600883 -aF3091.4263045310972 -aF3139.1821957588195 -aF3094.2086507081985 -aF3095.584569108486 -aF3019.151225972176 -aF3219.8707797050474 -aF3096.5668620705605 -aF3757.6056235075 -aF3532.0672367811203 -aF3685.385843193531 -aF3117.491663479805 -aF399.56957617998125 -aF2820.003722345829 -aF2939.405436241627 -aF3095.724040544033 -aF3104.6444919109344 -aF3686.063314795494 -aF3101.758359372616 -aF3073.6407000422478 -aF3363.7786054849626 -aF3096.2873743891714 -aF1257.420239508152 -aF4322.983596873283 -aF3117.2797322750093 -aF3682.5803425788877 -aF3096.2563202023507 -aF3432.11525042057 -aF3686.7173595547674 -aF4512.24769693613 -aF3095.6123544335364 -aF2901.0224613308906 -aF3097.6850852012635 -aF3827.3288106441496 -aF4195.4228039979935 -aF3095.5856587290764 -aF852.3603228211402 -aF417.1348049044609 -aF2776.6068582892417 -aF3079.6420578479765 -aF3684.5795239567756 -aF3090.623798966408 -aF3608.110768151283 -aF3678.267351877689 -aF3047.1149763941767 -aF3687.176089823246 -aF3106.2969015359877 -aF3109.470149099827 -aF4018.186208415031 -aF3683.7620361089707 -aF3645.124362385273 -aF3691.5536405444145 -aF3689.776469361782 -aF3081.472348034382 -aF3099.428750550747 -aF3682.770753777027 -aF3683.971243262291 -aF3693.4719175934792 -aF3686.3332682967184 -aF631.0749976634979 -aF3687.0259945869443 -aF1298.3049832940103 -aF3094.638778436184 -aF3076.9825663924216 -aF3096.024231016636 -aF2924.857094526291 -aF3095.315432822704 -aF3097.5845677018165 -aF3686.0728489756584 -aF3691.413351893425 -aF3684.8508394837377 -aF3748.300536072254 -aF1770.5040518879891 -aF3528.1549540519713 -aF3097.5845677018165 -aF3095.5483392238616 -aF3263.575461578369 -aF2919.0063767671586 -aF3689.8579185009003 -aF3097.2898253321646 -aF3681.5560992240903 -aF3094.5325404286386 -aF3624.588555526733 -aF3686.1137097477913 -aF3089.442105436325 -aF3095.22908039093 -aF3679.2417450904845 -aF3075.917189860344 -aF3109.1122087359427 -aF3710.270053422451 -aF3688.6026755809785 -aF3675.979965853691 -aF3559.3033930540087 -aF3095.8370886802672 -aF3102.934877204895 -aF2392.2957692861555 -aF3634.8595916152 -aF3089.559784460068 -aF1555.5818385720254 -aF3097.3489372491836 -aF3316.361858665943 -aF3090.4742485404013 -aF1456.5631122469902 -aF3812.0545092105863 -aF3096.099687242508 -aF3096.365009856224 -aF3164.7664872169494 -aF3229.838084053993 -aF2499.9353013157843 -aF3095.6567564725874 -aF3100.027497065067 -aF3096.0751707792283 -aF2626.714019012451 -aF3093.8738647818564 -aF3189.7582974791526 -aF3017.3590725064278 -aF3096.2399758934976 -aF3685.9783243894576 -aF3647.918421983719 -aF3068.418965768814 -aF3098.871682024002 -aF3091.798409962654 -aF3704.9657803893087 -aF3104.6578397631647 -aF3095.8523433685305 -aF3138.41864413023 -aF3094.616168808937 -aF2373.928852212429 -aF3096.912816607952 -aF1238.3630477905274 -aF3674.534856545925 -aF3657.9064291238783 -aF1833.664726614952 -aF3108.373173570633 -aF3087.347582256794 -aF3686.1044479727743 -aF3100.4055954098703 -aF3838.397993814945 -aF3152.427351248264 -aF3097.008703219891 -aF3732.0670961141586 -aF3132.715025150776 -aF2971.166786825657 -aF3694.876710939407 -aF3693.7797354102136 -aF2291.0098149180412 -aF3685.9478150129316 -aF3703.3150051951407 -aF3095.6227058291433 -aF2297.2737712860107 -aF3685.932560324669 -aF1430.6878920912743 -aF3685.893333983421 -aF3527.256017065048 -aF3094.7632675886152 -aF3095.9008314847947 -aF3099.6891698718073 -aF2893.455863547325 -aF4022.874028599262 -aF3112.5938189268113 -aF4283.735735619068 -aF3068.235364699364 -aF3681.354247009754 -aF3100.2963609457015 -aF3685.6898473381993 -aF3686.0352570652963 -aF3587.963683438301 -aF3072.89812361002 -aF3686.0371639013288 -aF3673.512247622013 -aF3685.7015607595445 -aF3097.589198589325 -aF4148.489303910732 -aF2969.611353433132 -aF3058.0340643286704 -aF3095.5082956671713 -aF3651.84486978054 -aF3686.3814840078353 -aF3126.0315648555757 -aF3134.240221571922 -aF3078.961317384243 -aF3066.5717864632607 -aF3046.5538217902185 -aF1704.926871097088 -aF3096.059916090965 -aF3097.105134642124 -aF3082.2383513092996 -aF3652.796925771236 -aF3686.116161394119 -aF3685.181811738014 -aF3909.6932310581205 -aF3110.8790285229684 -aF404.86404262781144 -aF1102.1822664260865 -aF2867.4850291848184 -aF3120.2500380039214 -aF2935.0265234947205 -aF3056.961605262756 -aF3714.4321316719056 -aF3794.9665343046186 -aF3686.3447093129157 -aF3095.217094564438 -aF1687.9661093950272 -aF3096.4756063461305 -aF3094.8637850880623 -aF1646.33443069458 -aF3451.1430223822595 -aF3679.8377675533293 -aF3687.2981273293494 -aF2337.296080374718 -aF3086.1574441671373 -aF3456.55652987957 -aF3686.0031132578847 -aF2398.0663999319077 -aF2460.9775515437127 -aF4074.7402410984037 -aF3187.3802005410193 -aF3686.1763629317284 -aF4651.619977009296 -aF2131.3433513522145 -aF3094.993449938297 -aF3629.1946541666985 -aF3108.1184747576713 -aF3097.6837231755258 -aF3095.4342014670374 -aF3095.8910248994825 -aF3100.4693382143973 -aF3712.5231163978574 -aF3095.8523433685305 -aF3098.4982145667077 -aF3096.251689314842 -aF3685.5212285518646 -aF3106.508560335636 -aF3097.230168604851 -aF2136.8241429209706 -aF3096.4252113938333 -aF3683.146945285797 -aF3148.9667162537576 -aF3698.3166431427003 -aF3093.3552053809167 -aF3096.6112641096115 -aF2998.920512878895 -aF3096.201294362545 -aF3844.1318497657776 -aF3760.0845103502274 -aF3685.6729582190515 -aF3196.694277346134 -aF3684.851929104328 -aF3091.451093399525 -aF3672.698845851421 -aF3090.6464085936545 -aF3800.7562333106994 -aF3095.962122642994 -aF3096.6477663993837 -aF3096.059916090965 -aF2765.5474817037584 -aF3103.107037258148 -aF3018.276260638237 -aF2381.050339984894 -aF3682.930928003788 -aF3694.366223692894 -aF3709.4097979664803 -aF2378.4496880412103 -aF3089.473704433441 -aF3096.179774355888 -aF3665.200894165039 -aF3685.909133481979 -aF3101.7409254431723 -aF3095.157437837124 -aF1220.8615618705749 -aF3096.103228509426 -aF3686.139860641956 -aF3096.402874171734 -aF3685.109079563618 -aF3096.2132801890375 -aF3687.5348474025727 -aF3109.8531507372854 -aF3096.0517439365385 -aF3161.79917794466 -aF3103.323326945305 -aF3685.772386097908 -aF3096.0645469784736 -aF3095.944416308403 -aF2889.6789661765097 -aF3083.4083314180375 -aF3098.0405739188195 -aF3131.953925168514 -aF3262.5621144294737 -aF3686.1848074913023 -aF3688.047513890266 -aF3096.1740538477898 -aF2096.063343477249 -aF3080.661397910118 -aF3097.169694662094 -aF3684.880531644821 -aF3100.694344866276 -aF2466.316964840889 -aF3096.1906705617903 -aF3691.6898431181908 -aF3100.8218304753304 -aF3686.4016419887544 -aF3686.2049654722214 -aF3097.780427002907 -aF2904.598051297665 -aF625.3313351273537 -aF3392.6023389577867 -aF3098.905732667446 -aF3076.2982846617697 -aF3094.042211163044 -aF3681.005568420887 -aF3735.024598801136 -aF3770.997605371475 -aF3650.958735835552 -aF3103.257677304745 -aF804.1399808168411 -aF3687.1782690644263 -aF3685.475192081928 -aF2131.8797170877456 -aF3082.0130722522736 -aF2086.453162276745 -aF3093.8158424854278 -aF3134.8234409928323 -aF3427.6137553572653 -aF3096.4203081011774 -aF3399.7777629494667 -aF3666.546847999096 -aF3095.5368982076643 -aF2959.639690601826 -aF3679.4702930092812 -aF3685.1864426255224 -aF3667.8819056272505 -aF2587.110396635532 -aF1504.3274480342866 -aF3089.715055394173 -aF3720.6320728302003 -aF3431.9033192157744 -aF3199.5387318968774 -aF3088.5592403531073 -aF3690.424793612957 -aF1961.6679054498672 -aF3096.347575926781 -aF3684.6939341187476 -aF3689.369768476486 -aF3095.9057347774506 -aF3109.452442765236 -aF3688.289409661293 -aF2464.3760781645774 -aF3095.959670996666 -aF3064.789711987972 -aF3687.075572323799 -aF3095.883125150204 -aF3078.0021788597105 -aF2920.040971517563 -aF3090.1337421059607 -aF3095.9514988422393 -aF3663.026556277275 -aF465.204234457016 -aF3683.031445503235 -aF3107.613980424404 -aF3097.065091085434 -aF3142.171842253208 -aF3686.1118029117583 -aF3685.6263769388197 -aF2781.998845779896 -aF3686.1079892396924 -aF3092.627066421509 -aF3602.4861466646194 -aF3109.6809906840326 -aF2191.967389345169 -aF3105.9204376220705 -aF3096.2552305817603 -aF4191.202975857257 -aF3081.059926640987 -aF3095.288737118244 -aF3683.547380852699 -aF3284.5947875738143 -aF3096.309711611271 -aF3685.9017785429955 -aF3661.763413608074 -aF3096.0348548173906 -aF3083.0607424497603 -aF3265.0696038126944 -aF3103.9283387780188 -aF3095.1449072003365 -aF1224.2154140472412 -aF4554.930859506129 -aF3650.00068693161 -aF3067.629535651207 -aF3099.673915183544 -aF3563.113796257973 -aF3083.2481571912767 -aF1577.3047870635987 -aF4476.069841694832 -aF3685.8854342341424 -aF3095.097236299515 -aF1826.1455273270608 -aF1735.1071822047234 -aF3687.159473109245 -aF3683.0644065260885 -aF3092.642593514919 -aF3084.098878467083 -aF2603.1907449007035 -aF1392.2754974246025 -aF2742.4047575831414 -aF3095.2843786358835 -aF1699.265747320652 -aF3096.294456923008 -aF2724.2587610840797 -aF3094.8659643292426 -aF3095.08552287817 -aF3800.725451529026 -aF2989.6445727944374 -aF3094.64368172884 -aF3055.9858500242235 -aF3685.7026503801344 -aF3125.510726213455 -aF2984.267022776604 -aF3678.186992359161 -aF3080.172703075409 -aF3802.86192510128 -aF3096.340220987797 -aF3687.8568302869794 -aF3094.638778436184 -aF3094.997808420658 -aF3686.0551426410675 -aF3467.975208854675 -aF3103.8433483719828 -aF3096.2710300803183 -aF933.9952422499656 -aF3416.0101132869722 -aF3684.7955412387846 -aF3092.8261945843697 -aF3096.0634573578836 -aF3673.835047721863 -aF3687.0333495259283 -aF1122.8809715628624 -aF3086.017427921295 -aF2377.6174903154374 -aF3685.3259140610694 -aF3071.8556291103364 -aF3373.5217203974726 -aF3115.589458334446 -aF2175.6067361831665 -aF3093.2999071359636 -aF3095.080619585514 -aF3096.2108285427094 -aF3107.6848057627676 -aF3685.897147655487 -aF3022.053975224495 -aF3714.726874041557 -aF3026.8545711398124 -aF3093.434202873707 -aF3048.8706275701525 -aF3095.6088131666183 -aF3104.6553881168365 -aF3686.036074280739 -aF3063.946073246002 -aF3202.2292775392534 -aF2936.045863556862 -aF2872.6855158567428 -aF3099.4491809368133 -aF3083.257691371441 -aF3096.9024652123453 -aF3096.1729642271994 -aF3072.989106929302 -aF2885.2393070816993 -aF563.0140267372132 -aF3096.3486655473707 -aF3707.552812075615 -aF3094.870050406456 -aF3723.6639421224595 -aF371.5540687799454 -aF3534.745796597004 -aF3244.449078953266 -aF2822.2486131668093 -aF3686.122426712513 -aF3067.168626141548 -aF3686.0750282168387 -aF3110.8523328185083 -aF3128.161773109436 -aF3096.1165763616564 -aF3795.978791832924 -aF3477.2647691965103 -aF3052.971687066555 -aF3693.4839034199713 -aF3111.190387606621 -aF3685.157022869587 -aF3078.5336413025857 -aF4181.0842142462725 -aF2732.851781463623 -aF3094.543164229393 -aF3132.9672723174094 -aF3096.382716190815 -aF3105.6744557738302 -aF3093.9457797408104 -aF3101.488678276539 -aF3089.1849549770354 -aF3092.7542796254156 -aF3082.687274992466 -aF3686.2670738458633 -aF3111.276740038395 -aF2839.8871188759804 -aF3686.1799041986465 -aF3106.567672252655 -aF3096.2862847685815 -aF3095.9487747907638 -aF2884.031190252304 -aF3685.9835000872613 -aF3696.452302312851 -aF3226.9222593545915 -aF2744.9313153266908 -aF3637.0889553427696 -aF1009.0036340594291 -aF3687.0695794105527 -aF3046.500975191593 -aF3713.4460250377656 -aF4720.40118714571 -aF1819.0150501847268 -aF3090.41540902853 -aF3124.820723974705 -aF3096.567679286003 -aF3686.2188581347464 -aF2886.4139180779457 -aF4407.612793684005 -aF3105.325777184963 -aF3093.0476599693297 -aF3062.4246904969214 -aF3094.5799389243125 -aF3534.462222838402 -aF2064.650126671791 -aF3697.194333934784 -aF3094.7945941805838 -aF3096.5017572402953 -aF3096.2473308324816 -aF3685.7470524191854 -aF4147.180124771595 -aF3096.3957916378977 -aF3104.5055652856827 -aF3097.97819314003 -aF3097.369912445545 -aF3094.209740328789 -aF3093.8087599515916 -aF3685.81106762886 -aF3108.1160231113436 -aF2994.3980426192284 -aF3683.37031750679 -aF2075.1769511938096 -aF3096.1718746066094 -aF3639.2406836032865 -aF3098.493311274052 -aF3095.9196274399756 -aF3096.2590442538262 -aF3096.2813814759256 -aF1323.4746741175652 -aF3104.3546528339384 -aF3096.4181288599966 -aF3084.0765412449837 -aF3681.103634274006 -aF3685.698019492626 -aF3613.427571821213 -aF3687.6269203424454 -aF2738.1557820916178 -aF3686.203331041336 -aF3582.02933729887 -aF3059.4729083180428 -aF3449.768738412857 -aF3685.9867689490316 -aF3116.7232085585592 -aF2457.1725964426996 -aF3705.9619660139083 -aF4212.077382314204 -aF3686.617114460468 -aF3099.0694481611254 -aF2774.568178164959 -aF3095.8934765458107 -aF3097.1702394723893 -aF4059.358612036705 -aF3096.1917601823807 -aF3917.3467260837556 -aF3697.795532095432 -aF3095.055830717087 -aF3093.8891194701196 -aF3708.6190058231355 -aF3099.386255347729 -aF3685.967700588703 -aF3081.9942762970923 -aF3163.0059327483177 -aF3714.4509276270865 -aF3620.9846354246138 -aF2705.9286187052726 -aF3684.9344678640364 -aF3839.4404883146285 -aF3093.876044023037 -aF3096.2538685560226 -aF4277.799210238456 -aF3066.51512619257 -aF3692.783549785614 -aF2546.7868074536323 -aF3154.4308911085127 -aF3094.041393947601 -aF3685.9186676621434 -aF2613.480576944351 -aF3671.3474439144134 -aF3616.67109991312 -aF3095.6897174954415 -aF3675.0205549240113 -aF3718.6048337221146 -aF3096.3453966856005 -aF4179.81126499176 -aF3095.883125150204 -aF3095.826464879513 -aF3105.3290460467338 -aF3945.6384523034094 -aF3078.928356361389 -aF3034.8700926065444 -aF3694.26979227066 -aF3098.8665063261988 -aF3686.0562322616574 -aF3143.302868425846 -aF3685.8549248576164 -aF3201.1742524027823 -aF3091.153082168102 -aF3696.6879327654838 -aF1024.8840369462966 -aF3707.063844835758 -aF3667.711924815178 -aF3095.235890519619 -aF3095.945505928993 -aF3678.7146411299705 -aF1981.4480604290961 -aF3684.7219918489454 -aF3689.759035432339 -aF710.1645633935929 -aF3102.5208213806154 -aF2853.634317052364 -aF3658.465404486656 -aF4279.26011904478 -aF3096.181136381626 -aF2432.1339324951173 -aF3101.037575352192 -aF3682.3692285895345 -aF3097.431476008892 -aF3534.4052901625632 -aF3682.2016994237897 -aF3686.645717000961 -aF3061.9711359262465 -aF3264.0502637505533 -aF3722.049396812916 -aF2149.161916863918 -aF3110.5739347577096 -aF3095.7357539653776 -aF1659.1066908478738 -aF3038.7445110201834 -aF3096.2590442538262 -aF3750.5598643660546 -aF3097.9062781810762 -aF3685.0886491775514 -aF3022.274350988865 -aF1989.716918683052 -aF2392.4109966635706 -aF3096.2805642604826 -aF2014.036977851391 -aF3135.1331656455995 -aF2399.085739994049 -aF3096.365009856224 -aF3066.5870411515234 -aF3686.1232439279556 -aF3096.2143698096274 -aF3686.3436196923253 -aF3667.2087925076485 -aF3097.516194009781 -aF3258.1052938103676 -aF2578.83336622715 -aF3095.826192474365 -aF644.3285977125167 -aF3686.128419625759 -aF3113.3137857317924 -aF2783.22712059021 -aF3085.1182185292246 -aF3686.075300621986 -aF3092.892389035225 -aF4073.6977465987206 -aF3042.9177578806875 -aF3803.8812651634216 -aF3263.4305420398714 -aF3095.681272935867 -aF2617.052625644207 -aF3646.501915216446 -aF1060.7698738694191 -aF3681.8355869054794 -aF3373.4299198627473 -aF2313.9989025354384 -aF4496.82384507656 -aF3096.397970879078 -aF2997.8780183792114 -aF3687.177996659279 -aF3095.9681155562403 -aF3093.5415305018423 -aF3096.4347455739976 -aF3095.447004508972 -aF3118.032387697697 -aF3139.842233431339 -aF3414.9907732248307 -aF3670.25155800581 -aF3675.3624233841897 -aF3096.0503819108008 -aF3793.4486928224565 -aF3081.6951754450797 -aF3413.986687850952 -aF3685.8688175201414 -aF3685.9881309747693 -aF3262.924140870571 -aF3801.798727810383 -aF3401.5334141254425 -aF3096.4875921726225 -aF3685.897147655487 -aF2946.662309372425 -aF3137.3903146982193 -aF1397.0812690377236 -aF3518.2121661663055 -aF1748.82959151268 -aF3684.630463719368 -aF3365.38824750185 -aF3685.819239783287 -aF3686.2493675112723 -aF3683.8325890421866 -aF3095.9490471959116 -aF1869.145497083664 -aF3689.6220156431195 -aF3100.1228388667105 -aF3039.6295553445816 -aF3685.889792716503 -aF3196.3984453558924 -aF482.5869517326355 -aF486.28103793859486 -aF3685.8298635840415 -aF3685.9679729938507 -aF3204.3940812468527 -aF3685.606218957901 -aF3898.3513703346252 -aF2713.277019965649 -aF3095.8005863904955 -aF3060.332891368866 -aF3097.844987022877 -aF3685.839397764206 -aF1553.524634897709 -aF3094.468797624111 -aF3682.112078130245 -aF3405.2879742741584 -aF3095.0324038743975 -aF3456.7344104409217 -aF2586.4833199858667 -aF3688.2883200407027 -aF3686.0031132578847 -aF3684.178815984726 -aF3894.567118024826 -aF3095.758363592625 -aF2330.697338080406 -aF2375.8781834483148 -aF3096.181953597069 -aF3095.3835341095923 -aF4283.183297979831 -aF3688.4765519976613 -aF3189.6275430083274 -aF3092.1713326096533 -aF3093.090155172348 -aF3096.7610869407654 -aF2903.6018656730653 -aF3041.482182753086 -aF3693.63209182024 -aF2688.6722974181175 -aF2835.8416300296785 -aF530.2450495123863 -aF3649.9393957734105 -aF3639.4673246860502 -aF3134.9127898812294 -aF3393.621679019928 -aF3101.194208312035 -aF1325.0407313108444 -aF3095.217094564438 -aF3218.784427976608 -aF3095.543708336353 -aF3842.1767980217933 -aF3682.401917207241 -aF3096.0340376019476 -aF2974.137909770012 -aF4460.09300738573 -aF3095.758363592625 -aF3099.386527752876 -aF3095.754549920559 -aF3186.2698771595956 -aF3153.363062930107 -aF3100.5202779769897 -aF3054.04850461483 -aF3096.1413652300835 -aF3166.5602751135825 -aF3131.6725306510925 -aF3685.9976651549337 -aF3159.019283413887 -aF3096.81066467762 -aF3685.8524732112883 -aF3673.6890385627744 -aF3166.523500418663 -aF3077.7191499114037 -aF772.9051893830299 -aF3686.205782687664 -aF3685.246916568279 -aF3098.8458035349845 -aF3095.377813601494 -aF3206.0200675725937 -aF4071.3384456157683 -aF4035.4327231168745 -aF3684.955443060398 -aF3099.09559905529 -aF3096.730305159092 -aF3372.48249475956 -aF3068.447568309307 -aF3447.3307123422624 -aF3084.408058309555 -aF3043.729525220394 -aF3118.3856971740724 -aF3470.124757874012 -aF3094.6420472979544 -aF3686.9069535374642 -aF3095.7485570073127 -aF3686.0020236372948 -aF3095.3718206882477 -aF3394.537232720852 -aF3098.125564324856 -aF3052.044147539139 -aF3686.8481140255926 -aF3660.276353907585 -aF3874.151441836357 -aF3685.696929872036 -aF2680.1697155475617 -aF2487.987611544132 -aF2627.823252773285 -aF3130.2522102117537 -aF3692.261621522903 -aF1668.755825984478 -aF3041.07330262661 -aF3686.2223994016645 -aF3702.9159316539763 -aF3096.2721197009087 -aF2620.4380768179894 -aF3681.248553812504 -aF3359.624426984787 -aF2968.7818797588347 -aF2895.0796706318856 -aF3096.8719558358193 -aF3094.9283451080323 -aF4241.505582809448 -aF1577.1852012038232 -aF4729.626732277869 -aF3595.074002599716 -aF3306.3891062140465 -aF3118.9825368523598 -aF3180.7408698797226 -aF3091.5949233174324 -aF3095.2198186159135 -aF3084.2781210541725 -aF3099.0716274023057 -aF3102.6224285006524 -aF4069.5024349212645 -aF3096.347575926781 -aF3095.881763124466 -aF3096.543980038166 -aF3095.906824398041 -aF3033.0566915392874 -aF3685.0202754855154 -aF4181.398569786548 -aF3727.405426824093 -aF2762.761594259739 -aF3103.6570232510567 -aF3096.2827435016634 -aF3112.442906475067 -aF2719.1786774873735 -aF4670.680437588691 -aF2918.0848301529886 -aF3276.4446979641916 -aF3099.5159201979636 -aF3094.2696694612505 -aF3096.7324844002724 -aF2959.5231011986734 -aF3713.2128462314604 -aF3686.0551426410675 -aF3681.7538653612137 -aF3116.072705066204 -aF4341.75231153965 -aF3943.9857702732083 -aF3685.900688922405 -aF3096.8670525431635 -aF3685.838035738468 -aF3685.8230534553527 -aF3098.2211785316467 -aF3686.344981718063 -aF3710.021619927883 -aF3732.12811486721 -aF3619.0584586262703 -aF3182.3268126487733 -aF635.0828945994377 -aF3198.6152784466744 -aF1946.6793570160867 -aF1490.2539084911348 -aF3686.1079892396924 -aF3095.834909439087 -aF3685.0368921995164 -aF2739.4091181755066 -aF3096.5842960000036 -aF3625.720943725109 -aF3203.6348881006243 -aF2579.508386182785 -aF2958.898203790188 -aF3261.453425478935 -aF3429.864639091492 -aF3106.06181589365 -aF3095.6946207880974 -aF3154.2304009199142 -aF3114.9482166171074 -aF3869.106498503685 -aF3098.342671227455 -aF3095.5941032886503 -aF3685.759038245678 -aF3884.833809697628 -aF904.5714002370834 -aF3103.659202492237 -aF2852.543062031269 -aF3687.0322599053384 -aF3694.0848291754724 -aF3096.0127900004386 -aF3698.4435839414596 -aF3599.4662631988526 -aF3684.411177575588 -aF3094.6997971892356 -aF3163.5142407536505 -aF3687.480911183357 -aF3787.48710616827 -aF3090.5698627471925 -aF546.7566151261329 -aF3706.447391986847 -aF3697.338436257839 -aF3682.400555181503 -aF3084.179237985611 -aF3684.9873144626617 -aF2687.9705817580225 -aF3096.055285203457 -aF2607.0866833209993 -aF958.6127678394317 -aF3475.8057672262194 -aF3085.2102914690972 -aF3096.1917601823807 -aF3297.740242779255 -aF3095.6109924077987 -aF3083.3636569738387 -aF1167.8877500414849 -aF3094.1944856405257 -aF3576.5463664889335 -aF3094.7836979746817 -aF3607.527276325226 -aF3106.8776693105697 -aF3691.704280591011 -aF3099.3192436814306 -aF3762.330763196945 -aF3090.9678466677665 -aF3095.5319949150085 -aF3689.3708580970765 -aF3100.8485261797905 -aF3615.6209780693052 -aF3685.920846903324 -aF3090.7875144600866 -aF3686.5615438103673 -aF3097.065908300877 -aF3705.2853116273877 -aF3092.1334682941438 -aF3129.5687456965447 -aF2530.6533401846887 -aF2624.776673603058 -aF3096.055285203457 -aF3424.051785647869 -aF3683.207964038849 -aF3303.881616830826 -aF2498.9023409962656 -aF1601.7106538534165 -aF3685.6508934020994 -aF3095.6099027872087 -aF3072.279491519928 -aF3091.942512285709 -aF3844.321443748474 -aF2907.100909793377 -aF2915.5601792454718 -aF2989.3244967460632 -aF3347.891392469406 -aF3686.849203646183 -aF3090.0250524520875 -aF3684.626650047302 -aF3686.1341401338577 -aF4157.611335086822 -aF2846.5790237307547 -aF3096.2753885626794 -aF3690.498615407944 -aF3085.301002383232 -aF2863.4373610973357 -aF3769.6094287395476 -aF3096.3200630068777 -aF2851.252678847313 -aF3182.215398943424 -aF3655.9832487821577 -aF3156.3513473987578 -aF3033.3416273236276 -aF3096.1089490175245 -aF3096.184950053692 -aF3899.3938648343087 -aF4023.630770099163 -aF3677.2087854743004 -aF3696.47572915554 -aF3095.7180476307867 -aF3096.6338737368583 -aF3688.8186928629875 -aF3265.9219595193863 -aF3686.088103663921 -aF3275.9028841257095 -aF3685.9856793284416 -aF3585.649329304695 -aF3094.1282911896706 -aF2968.6097197055815 -aF3096.777703654766 -aF3095.7700770139695 -aF3095.974108469486 -aF2199.5012985110284 -aF3268.5849922418593 -aF3271.5463086009026 -aF1353.596962928772 -aF3095.867598056793 -aF3686.130326461792 -aF3683.515781855583 -aF3094.766808855534 -aF2519.973151564598 -aF1891.3631333231926 -aF3088.047663486004 -aF3102.3630988001823 -aF3636.2507647037505 -aF3093.3633775353433 -aF3725.17197701931 -aF3061.7355054736136 -aF3096.333955669403 -aF3064.977126729488 -aF3094.795683801174 -aF3679.0025733709335 -aF3097.6188907504084 -aF3050.627640771866 -aF3685.9102231025695 -aF3585.142928135395 -aF2923.4967032194136 -aF3098.049835693836 -aF3088.0302295565607 -aF3688.4247950196263 -aF3685.9960307240485 -aF2787.5790652275086 -aF3723.7331330299376 -aF3089.472614812851 -aF3682.729893004894 -aF3076.3593034148216 -aF3914.297422862053 -aF1974.3260278463363 -aF3098.9833681344985 -aF3653.4689492702482 -aF1536.5235572338104 -aF3106.387340044975 -aF3098.8313660621643 -aF3685.693388605118 -aF3138.058252120018 -aF3185.3744814395905 -aF3096.363920235634 -aF3090.8646051168444 -aF2524.799898374081 -aF3684.2613547444344 -aF4247.0544756650925 -aF2884.1289837002755 -aF3096.4731546998023 -aF3011.9313999414444 -aF3685.9835000872613 -aF3685.4081804156303 -aF2984.5710269212723 -aF3015.0738657236097 -aF3113.5475093483924 -aF3755.4326476454735 -aF3096.090425467491 -aF3068.6336210250856 -aF2809.482073521614 -aF3184.188974237442 -aF1509.5287519216538 -aF3082.306997406483 -aF3095.6521255850794 -aF3095.7474673867227 -aF3837.652965736389 -aF3093.4388337612154 -aF3268.800737118721 -aF3093.278659534454 -aF3686.4969837903977 -aF3097.0934212207794 -aF3099.3660973668098 -aF3068.2933869957924 -aF3355.6879001975058 -aF3671.2036139965057 -aF4450.022461485863 -aF3581.8702526926995 -aF3680.410635578632 -aF3089.960764837265 -aF3069.6908254027367 -aF3097.0958728671076 -aF3111.4134874224665 -aF3095.7591808080674 -aF3098.624065744877 -aF3096.118483197689 -aF1828.5331584453584 -aF3098.210554730892 -aF2143.32073328495 -aF3692.2605319023132 -aF3907.631396496296 -aF3073.321168804169 -aF3095.4519078016283 -aF3091.4677101135253 -aF3687.0851065039633 -aF3206.521020638943 -aF2863.836707043648 -aF3093.223361289501 -aF3683.6004998564717 -aF3095.90464515686 -aF3686.820056295395 -aF3107.410221374035 -aF3097.236161518097 -aF3682.4727425456044 -aF3092.926439678669 -aF3259.657185935974 -aF1740.4449610710144 -aF3109.048193526268 -aF3153.4679389119146 -aF4019.9740033984185 -aF3095.16343075037 -aF3033.9003302812575 -aF3688.5983170986174 -aF3686.0502393484117 -aF3094.763539993763 -aF3108.257401382923 -aF3095.905462372303 -aF3686.7031944870946 -aF2762.4001126289368 -aF3686.130326461792 -aF3645.4594207167625 -aF1446.0681591272355 -aF3683.8127034664153 -aF2938.806962132454 -aF3686.0635872006415 -aF836.3077598810196 -aF2863.21099241972 -aF3692.001202201843 -aF3690.4386862754823 -aF2837.583388543129 -aF3052.1879774570466 -aF3684.433787202835 -aF3035.565542948246 -aF3152.382949209213 -aF2281.629271256924 -aF2964.192125427723 -aF3682.978326499462 -aF3619.793407714367 -aF3077.8640694499018 -aF3106.180039727688 -aF3181.28431814909 -aF3686.253998398781 -aF3061.9071207165716 -aF3237.489672243595 -aF4132.399693870544 -aF3094.5516087889673 -aF3095.4649832487107 -aF4347.77246530056 -aF2217.053179383278 -aF257.5181914806366 -aF4182.55247799158 -aF3148.82397595644 -aF3634.778142476082 -aF3094.695438706875 -aF3096.427663040161 -aF3112.58946044445 -aF3096.47451672554 -aF3097.7744340896606 -aF3095.719137251377 -aF2676.250622689724 -aF3105.241876399517 -aF3687.588238811493 -aF3092.3829914093017 -aF3092.6578482031823 -aF3718.030058860779 -aF3690.679220020771 -aF3097.129651105404 -aF3748.3125218987466 -aF3291.7288059830666 -aF3749.365095388889 -aF3189.4352249741555 -aF3440.6584206581115 -aF523.4046838521957 -aF3096.210556137562 -aF2998.3193147182465 -aF3119.9555680394174 -aF3686.1044479727743 -aF3104.189575314522 -aF3096.3214250326155 -aF3095.741746878624 -aF3839.544274675846 -aF3275.191089475155 -aF3678.795273053646 -aF3685.7993542075155 -aF3085.1985780477526 -aF3684.6198399186133 -aF3070.6581360816954 -aF3095.8204719662667 -aF3685.768572425842 -aF524.2739286780358 -aF3437.177627682686 -aF3097.0882455229757 -aF3686.4604815006255 -aF2958.6889966368676 -aF3094.614806783199 -aF3097.1402749061585 -aF3685.9570767879486 -aF3096.5630483984946 -aF3686.2341128230096 -aF2988.7393704891206 -aF3020.1964445233343 -aF3687.0886477708814 -aF3686.140133047104 -aF3152.506621146202 -aF3102.742559170723 -aF1880.7714763760566 -aF4361.126310443878 -aF3091.806854522228 -aF3095.601730632782 -aF3510.6311309099196 -aF2939.8263021945954 -aF3739.7854235649106 -aF2924.4713688373567 -aF3138.971354174614 -aF3094.7352098584174 -aF3871.7006127238274 -aF3399.6750662088393 -aF4293.561389291286 -aF3096.767897069454 -aF2921.289949119091 -aF3371.4863091349603 -aF3044.7488652825355 -aF2507.450142121315 -aF3686.4580298542974 -aF3096.412135946751 -aF1297.2668472766877 -aF3177.8468375921248 -aF3445.3015663981437 -aF3065.1067915797234 -aF3094.425757610798 -aF3047.756218111515 -aF1035.5530569553375 -aF3096.1844052433967 -aF3485.4072314620016 -aF3025.7575956106184 -aF3095.6088131666183 -aF1707.801835024357 -aF3685.828773963451 -aF3281.391575443745 -aF3111.0882356762886 -aF3105.108670282364 -aF3722.8140380620957 -aF3096.219000697136 -aF3685.6228356719016 -aF2157.2602494955063 -aF3686.3259133577344 -aF3711.306010198593 -aF3095.3007229447367 -aF3685.661517202854 -aF3095.0498378038405 -aF2986.5023794174194 -aF3104.086061358452 -aF2906.534307086468 -aF3374.164868950844 -aF2894.060330569744 -aF3096.502029645443 -aF3276.922224187851 -aF2803.2434508323668 -aF3096.926164460182 -aF3087.676647675037 -aF2365.9043413758277 -aF3095.7662633419036 -aF3631.2921738028526 -aF1731.0390837311745 -aF3009.7349972367288 -aF3100.512105822563 -aF3514.202907204628 -aF3097.919626033306 -aF2128.822514116764 -aF3684.150758254528 -aF3515.449433159828 -aF3688.707551562786 -aF3094.85070964098 -aF3685.794723320007 -aF3685.7565865993497 -aF3662.0012233018874 -aF3117.1151995658875 -aF3686.25072953701 -aF3601.615267407894 -aF3685.9796864151954 -aF3096.1857672691344 -aF3271.373058927059 -aF3108.8664992928507 -aF3547.411273932457 -aF3095.424667286873 -aF2960.5424412608145 -aF3094.8411754608155 -aF3689.266254520416 -aF3096.4663445711135 -aF3094.8624230623245 -aF3686.112620127201 -aF3686.9200289845467 -aF3685.4907191753387 -aF3088.613176572323 -aF3143.093661272526 -aF3087.199121451378 -aF3684.388840353489 -aF2916.037977874279 -aF2497.7462535500526 -aF2680.6423384785653 -aF3668.084302651882 -aF3095.654577231407 -aF3096.778520870209 -aF3351.305173778534 -aF3685.257540369034 -aF4246.0351356029505 -aF3136.1241755723954 -aF3092.3252415180204 -aF3459.96676992178 -aF3096.074081158638 -aF1307.752266216278 -aF3095.534174156189 -aF2632.3340096116067 -aF414.89835864305496 -aF3095.907914018631 -aF2923.9826740026474 -aF4053.420724630356 -aF3096.766807448864 -aF3095.2304424166678 -aF3114.4336432933806 -aF3685.900416517258 -aF2190.794957590103 -aF3047.1160660147666 -aF3836.6336256742475 -aF4142.7810540437695 -aF3675.6672447443007 -aF3686.115071773529 -aF3122.9947922706606 -aF3097.060187792778 -aF3689.2613512277603 -aF3100.5872896432875 -aF3093.307262074947 -aF3676.1033653855325 -aF1835.1850197434426 -aF2635.811261320114 -aF1209.7741275548935 -aF1863.2037960052492 -aF3685.825505101681 -aF3936.069676685333 -aF3061.0258900642393 -aF1506.1269564390184 -aF3120.2524896502496 -aF3687.1496665239333 -aF3097.214369106293 -aF3137.655909717083 -aF3096.1272001624106 -aF3215.6449586510657 -aF3481.374818062782 -aF3686.3332682967184 -aF3094.736571884155 -aF3156.3976562738417 -aF3707.491248512268 -aF3090.7286749482155 -aF3148.4338917851446 -aF3686.065766441822 -aF3683.758222436905 -aF3090.636602008343 -aF2111.1257137060165 -aF3097.211100244522 -aF3096.058826470375 -aF3093.569860637188 -aF3177.606576251984 -aF3119.4875759959223 -aF3685.8238706707953 -aF3097.1416369318963 -aF1936.292003929615 -aF3095.1656099915504 -aF3095.984732270241 -aF3094.6646569252016 -aF3075.5437224030493 -aF3260.6533715605738 -aF3701.847831070423 -aF3676.096282851696 -aF3094.5033930778504 -aF3686.291045498848 -aF3683.9701536417006 -aF3964.172898542881 -aF4586.624108803272 -aF3091.390074646473 -aF3101.072715616226 -aF3095.8125722169875 -aF3686.0695801138877 -aF3271.1823753237722 -aF1098.6802258491516 -aF2989.3106040835382 -aF3096.392250370979 -aF3030.2754349827765 -aF3097.074625265598 -aF2907.7301656842233 -aF3697.7636606931687 -aF3095.8338198184965 -aF3670.830418944359 -aF3784.91941524744 -aF3687.6375441431996 -aF3070.216567337513 -aF3640.6713554382322 -aF3145.5504832983015 -aF3105.715588951111 -aF2920.5261250853537 -aF3084.538267970085 -aF1440.5214455127716 -aF2928.7432263612745 -aF3687.2428290843964 -aF3087.4233108878134 -aF643.8077590703964 -aF3380.5342461109162 -aF3687.1534801959992 -aF3092.25850225687 -aF2928.9115727424623 -aF3032.299132823944 -aF3094.4407398939134 -aF3683.5686284542085 -aF3096.1999323368073 -aF3686.03035377264 -aF3707.626906275749 -aF3038.6164806008337 -aF3793.7842959642408 -aF3116.108117735386 -aF3565.865088248253 -aF3043.5666269421577 -aF3091.0446649193764 -aF3685.9595284342763 -aF2884.8914457082747 -aF3095.7395676374435 -aF3697.2888585209844 -aF3644.4054852008817 -aF3092.9062816977503 -aF3096.454631149769 -aF3094.9817365169524 -aF3094.2427013516426 -aF3099.9073663949966 -aF2728.3298560142516 -aF3075.6570429444314 -aF3252.0815987825395 -aF3096.103228509426 -aF3722.713792967796 -aF3750.8507930636406 -aF367.1767904639244 -aF3095.2914611697197 -aF2967.5762145757676 -aF4254.8226532578465 -aF3050.41570956707 -aF3684.1414964795113 -aF4150.009597039222 -aF3554.438237118721 -aF3724.1294825196264 -aF3676.561823248863 -aF3682.702652490139 -aF3095.762994480133 -aF2622.3353786706925 -aF1221.661343383789 -aF3213.430849611759 -aF3009.761965346336 -aF3685.7121845602987 -aF3095.941964662075 -aF2722.2527695775034 -aF3095.232894062996 -aF3095.3116191506388 -aF3061.314639520645 -aF4061.7734836697578 -aF3788.77040681839 -aF2335.2527693629263 -aF3105.154434347153 -aF3094.1661555051805 -aF3645.085408449173 -aF3129.524343657494 -aF416.75534453392027 -aF3220.9198119282723 -aF3201.4951456665995 -aF3685.973693501949 -aF3685.9818656563757 -aF3680.7225394725797 -aF3096.203746008873 -aF3095.689989900589 -aF3145.9991345763206 -aF3678.5661803245544 -aF3105.5521458625794 -aF819.5513020396232 -aF3095.940875041485 -aF3094.7869668364524 -aF3679.0671333909036 -aF3637.841883170605 -aF3096.6970717310905 -aF3686.0679456830026 -aF2340.911713898182 -aF3837.3786537528035 -aF3687.477369916439 -aF3093.1740559577943 -aF3698.2123119711873 -aF3094.4976725697516 -aF3108.497935128212 -aF2016.7427781820297 -aF3104.3156988978385 -aF3686.427520477772 -aF1759.3923735141755 -aF3096.4132255673408 -aF2730.163687467575 -aF3123.604979801178 -aF3688.7827353835105 -aF3947.662694954872 -aF3687.0921890378 -aF3095.3927958846093 -aF3672.751964855194 -aF3110.5251742362975 -aF3843.1759801030157 -aF3692.3735800385475 -aF3600.911917316914 -aF2966.3035377264023 -aF3686.2888662576675 -aF3685.8440286517143 -aF3686.7843712210656 -aF998.5914921045303 -aF3686.191617619991 -aF3686.452309346199 -aF3733.5432596087453 -aF3752.621698927879 -aF3689.764755940437 -aF3685.8206018090245 -aF3555.0835649132728 -aF3856.739304804802 -aF2412.3273542165757 -aF2368.465222167969 -aF3376.8232707858087 -aF3647.5389616131783 -aF3531.0195665836336 -aF3685.8854342341424 -aF3685.3940153479575 -aF4068.576257419586 -aF3095.608540761471 -aF3686.2727943539617 -aF3689.7495012521745 -aF3724.3637509465216 -aF3154.761863362789 -aF3689.7037371873853 -aF3097.5832056760787 -aF2931.968503308296 -aF4325.644722759723 -aF4097.528293716907 -aF2740.0105887413024 -aF3701.069297158718 -aF2171.3675672769546 -aF3684.0597749352455 -aF3845.5785935044287 -aF3045.857554233074 -aF3686.2766080260276 -aF3494.8771240115166 -aF3700.5528169989584 -aF3096.4036913871764 -aF2932.258342385292 -aF3900.1023906230926 -aF4084.2997549414636 -aF3088.574767446518 -aF3042.4236149430276 -aF3093.2481501579286 -aF3686.4166242718698 -aF3648.204992198944 -aF3687.6176585674284 -aF4624.248162972926 -aF3134.619681942463 -aF2902.8906158328055 -aF3097.161794912815 -aF3124.0844128608705 -aF3687.669687950611 -aF3183.3358013153074 -aF532.8941895723343 -aF3711.372204649448 -aF214.560444521904 -aF2846.86559394598 -aF3094.2367084383964 -aF3691.4580263376233 -aF3107.4489029049873 -aF3688.3419838547707 -aF885.2161972880363 -aF3095.837361085415 -aF3688.1276010036468 -aF3912.25874273777 -aF3685.84675270319 -aF3777.762787210941 -aF3704.2992049932477 -aF3096.18740170002 -aF3091.8398155450823 -aF2661.268339574337 -aF3677.992495083809 -aF4236.0528489708895 -aF3096.2704852700235 -aF3686.276880431175 -aF3094.52899916172 -aF2774.1132615685465 -aF3686.227030289173 -aF2581.711871421337 -aF3096.4843233108522 -aF3096.5535142183303 -aF1275.6051899433137 -aF1442.4342744588853 -aF3075.3443218350412 -aF4182.645640552044 -aF1990.9838750243186 -aF3084.3208886623383 -aF3686.0020236372948 -aF3096.208649301529 -aF3406.359071314335 -aF3635.7334673285486 -aF1268.5044049620628 -aF3095.970567202568 -aF3686.584425842762 -aF2985.171952676773 -aF3094.509113585949 -aF3095.147631251812 -aF3688.4484942674635 -aF3107.537434577942 -aF3094.6987075686457 -aF3685.623652887344 -aF1218.2658132195472 -aF3679.0682230114935 -aF3071.7295055270197 -aF3685.9690626144406 -aF3584.146742510796 -aF3680.194073486328 -aF3092.773075580597 -aF3843.752389395237 -aF3095.6970724344255 -aF3076.2966502308846 -aF3686.49562176466 -aF1796.705613410473 -aF2987.1128393530844 -aF3095.7559119462967 -aF3096.439376461506 -aF3363.4759633660315 -aF3097.1816804885866 -aF3686.02926415205 -aF3096.0457510232927 -aF1182.3595459103585 -aF3686.3351751327514 -aF3098.118754196167 -aF3085.220642864704 -aF3094.359563159943 -aF2909.790910625458 -aF3338.9347112178802 -aF3675.447958600521 -aF3685.965248942375 -aF3095.6415017843246 -aF3062.777999973297 -aF3620.771614599228 -aF3686.0693077087403 -aF3054.629272389412 -aF3257.5953513741492 -aF3672.027367162704 -aF3639.432729232311 -aF3095.8877560377123 -aF3656.6138666987417 -aF3078.398255944252 -aF3686.0513289690016 -aF3495.882026600838 -aF3686.0186403512953 -aF3121.5720201849936 -aF3738.7660835027696 -aF3097.146267819405 -aF3556.222218430042 -aF3375.324497663975 -aF3096.117393577099 -aF4627.880958020686 -aF2628.3154888749123 -aF4119.972026228905 -aF3095.90464515686 -aF3150.222776389122 -aF3096.4312043070795 -aF3529.630300331116 -aF2762.052251255512 -aF3089.5764011740685 -aF3685.825505101681 -aF3095.4728829979895 -aF3708.430501461029 -aF3692.3392569899556 -aF3680.698295414448 -aF3688.3188294172287 -aF1685.8391700029374 -aF3685.218314027786 -aF3687.3583288669583 -aF3683.910224509239 -aF3686.28859385252 -aF3642.7547100067136 -aF3685.7604002714156 -aF3806.943643832207 -aF3686.059501123428 -aF3095.9607606172563 -aF4608.281680059433 -aF3095.0182388067246 -aF2394.813882470131 -aF3153.791011416912 -aF3013.3607097506524 -aF232.9390750169754 -aF1130.253344476223 -aF3096.249510073662 -aF3685.909950697422 -aF3107.097772669792 -aF803.2712808012961 -aF3096.086884200573 -aF3702.227563846111 -aF3782.6519147992135 -aF3729.480336833 -aF3118.537971651554 -aF2803.131492316723 -aF3066.4088881850244 -aF931.8410623431205 -aF1057.1496094584465 -aF3096.439376461506 -aF3686.434603011608 -aF3683.3103883743283 -aF2852.747093486786 -aF3096.9929037213324 -aF3683.9633435130118 -aF3102.226351416111 -aF2182.8679677963255 -aF2248.466940999031 -aF3598.5068522691727 -aF3689.529942703247 -aF3430.9817726016045 -aF2723.536615037918 -aF3435.138947558403 -aF3686.122426712513 -aF3097.75890699625 -aF3685.22675858736 -aF3095.797045123577 -aF3764.4846706986427 -aF3096.285195147991 -aF3686.1518464684486 -aF3094.722134411335 -aF3736.8758641839026 -aF2499.2899735212327 -aF3096.3415830135345 -aF2033.6316249251365 -aF1402.5018590688705 -aF3690.5261283278464 -aF3686.942366206646 -aF3397.3217581391336 -aF3674.3289182543754 -aF3096.090425467491 -aF3099.157707428932 -aF3095.8112101912498 -aF3089.7450199604036 -aF3096.444007349014 -aF3002.6197747826577 -aF669.0455511808395 -aF3686.488811635971 -aF3100.072171509266 -aF3095.751008653641 -aF3696.97777184248 -aF3100.1231112718583 -aF3096.0234138011933 -aF3095.803038036823 -aF3044.092368876934 -aF3095.7237681388856 -aF3096.61589499712 -aF882.6381549715995 -aF3025.044438934326 -aF3095.5932860732078 -aF3096.391160750389 -aF3652.6392031908035 -aF3135.081681072712 -aF3112.8863820552824 -aF3095.9395130157473 -aF2959.504305243492 -aF3100.8880249261856 -aF1566.8795696616173 -aF3195.5321969866754 -aF3113.4143032312395 -aF2979.090235352516 -aF3088.848807024956 -aF3685.966338562965 -aF3289.273073577881 -aF3641.2793637275695 -aF4531.478138327598 -aF3096.2718472957613 -aF3128.890184473991 -aF3937.9656165122983 -aF910.9113576412201 -aF3686.077479863167 -aF2960.913729476929 -aF3097.8359976530073 -aF3083.171611344814 -aF3096.093966734409 -aF3052.168909096718 -aF390.78205852508546 -aF3108.0797932267187 -aF2969.8012198209763 -aF1680.2772017002105 -aF3091.1577130556107 -aF3097.7433799028395 -aF3095.87577021122 -aF4545.126725840568 -aF513.5496104240417 -aF3104.862143623829 -aF3096.0408477306364 -aF3495.6970635056496 -aF2760.0043093562126 -aF3988.575496470928 -aF3091.081439614296 -aF1522.774179816246 -aF2167.4356713771817 -aF2923.906945371628 -aF3096.638777029514 -aF3631.2899945616723 -aF3095.5036647796633 -aF3061.6693110227584 -aF3447.711807143688 -aF3516.264741766453 -aF4566.080947005748 -aF3686.189438378811 -aF3686.631007122993 -aF3095.286557877064 -aF3112.609346020222 -aF3677.7663988113404 -aF3095.6839969873427 -aF3657.547126734257 -aF3095.3513903021812 -aF3056.7766421675683 -aF3726.4092411994934 -aF3963.1304040431974 -aF3096.6856307148933 -aF3697.7094520688056 -aF498.3913535833359 -aF3145.0952942967415 -aF3095.9855494856834 -aF3096.158799159527 -aF4413.999877178669 -aF3561.986856162548 -aF3115.1073012232782 -aF3686.2673462510106 -aF3096.363920235634 -aF3683.694752037525 -aF3687.3643217802046 -aF3095.663839006424 -aF3096.634690952301 -aF3854.267228090763 -aF3164.759404683113 -aF3685.8571040987968 -aF3065.7714601397515 -aF3095.8003139853477 -aF3084.714241695404 -aF3095.847712481022 -aF3106.3816195368768 -aF3748.3721786260603 -aF3201.636523938179 -aF3070.9624126315116 -aF3099.9566717267035 -aF3215.8258356690408 -aF3109.818827688694 -aF3096.1669713139536 -aF3096.713960850239 -aF667.7124003887176 -aF3049.1547461390496 -aF3095.4352910876273 -aF3082.3737366676332 -aF3099.030766630173 -aF4065.991677379608 -aF3686.082110750675 -aF3095.229352796078 -aF4515.8303694367405 -aF3194.655597221851 -aF3194.512856924534 -aF3674.714371538162 -aF3387.0169438123703 -aF3017.0621508955956 -aF3096.5524245977404 -aF3428.9948494553564 -aF3259.889275121689 -aF4469.6124776721 -aF2633.7494267582892 -aF3116.3955051660537 -aF3693.863363790512 -aF2843.727214241028 -aF3688.751408791542 -aF3695.6187425613402 -aF1754.3923770308495 -aF3686.362960457802 -aF3135.594075155258 -aF3088.626524424553 -aF1823.9371387958527 -aF3686.269797897339 -aF3685.911040318012 -aF3687.781646466255 -aF3223.082163989544 -aF3089.5902938365934 -aF2972.8652329206466 -aF3688.7723839879036 -aF3096.321152627468 -aF3095.831368172169 -aF3096.3282351613043 -aF4209.437504029273 -aF3082.1773325562476 -aF3128.6145104646685 -aF3373.901180768013 -aF3478.65049418211 -aF3226.519644546509 -aF2791.8152376770972 -aF2936.4253239274026 -aF3688.510875046253 -aF3685.9350119709966 -aF3096.07053989172 -aF3036.8769013285637 -aF3884.2339735627174 -aF3686.077479863167 -aF3755.045559930801 -aF3619.7291200995446 -aF3685.9856793284416 -aF3120.9792665839195 -aF2101.1091040253636 -aF3097.1168480634688 -aF3103.8447103977205 -aF3088.6899948239325 -aF3252.818999516964 -aF3159.3557037711143 -aF3104.01169475317 -aF3685.70537443161 -aF3095.172420120239 -aF3096.7809725165366 -aF3661.2888838410377 -aF1445.025664627552 -aF3681.8197874069215 -aF3096.1056801557543 -aF3624.7983074903486 -aF3258.6378458738327 -aF3093.287376499176 -aF2581.9714735269545 -aF3079.4407504439355 -aF3111.0419268012047 -aF3686.5018870830536 -aF3263.3036012411117 -aF3685.8500215649606 -aF3685.8876134753227 -aF4501.060017526149 -aF3096.775252008438 -aF3092.8354563593866 -aF3097.3832602977755 -aF3129.040824520588 -aF3096.944688010216 -aF3130.1628613233565 -aF3555.2028783679007 -aF3678.175006532669 -aF3675.6566209435464 -aF1919.4865131616593 -aF3685.936101591587 -aF4126.537807500362 -aF2926.8497381806374 -aF3681.870454764366 -aF3103.080613958836 -aF3688.111529099941 -aF3624.4896724581718 -aF3099.763536477089 -aF1617.837038588524 -aF3090.046572458744 -aF3090.5979204773903 -aF2883.7222828149797 -aF3095.9855494856834 -aF2717.4369189739227 -aF3686.2376540899277 -aF3058.373481142521 -aF3086.5042159199716 -aF2347.7613413333893 -aF2941.4672708034514 -aF3147.5017213702204 -aF3216.6950804948806 -aF1615.7773832678795 -aF4339.948172247409 -aF3127.117916584015 -aF3097.7493728160857 -aF3070.3552215576174 -aF3095.9890907526014 -aF4581.569631290436 -aF3121.271829712391 -aF3807.6638830423353 -aF3099.604451870918 -aF2624.1122774481773 -aF3685.313928234577 -aF3074.828658890724 -aF3092.02259939909 -aF3045.5821526288987 -aF3093.675826239586 -aF3094.2555043935777 -aF3649.247486698627 -aF3673.662070453167 -aF3096.122296869755 -aF3686.1033583521844 -aF3095.815568673611 -aF1052.4857609272003 -aF3087.998358154297 -aF3096.069177865982 -aF3063.37647408247 -aF3654.6484635591505 -aF3686.598318505287 -aF4182.417909848689 -aF2742.189285111427 -aF3247.41475379467 -aF3095.85125374794 -aF3093.058556175232 -aF3096.2729369163512 -aF3686.7317970275876 -aF3095.81229981184 -aF3085.937068402767 -aF3698.660690844059 -aF3960.3033834218977 -aF3099.1342805862428 -aF3125.580189526081 -aF3133.6003418803216 -aF3093.2225440740585 -aF2875.005862903595 -aF3820.675859725475 -aF3686.6503478884697 -aF3100.0983224034308 -aF736.4524773478507 -aF3703.2798649311067 -aF3097.229896199703 -aF3680.248826920986 -aF3095.8678704619406 -aF2880.97317006588 -aF3466.66248844862 -aF3107.1699600338934 -aF3092.1274753808975 -aF3242.2262529492377 -aF2391.2325719952582 -aF3104.569852900505 -aF3248.3787956118585 -aF3097.1367336392404 -aF3771.742633450031 -aF3689.8151508927344 -aF3722.4288571834563 -aF2945.775903022289 -aF2129.379310238361 -aF3096.560596752167 -aF3095.1972089886667 -aF2962.15344530344 -aF3571.4071709752084 -aF3097.377267384529 -aF3939.008111011982 -aF3099.6199789643288 -aF2772.409094965458 -aF2273.3914671897887 -aF3096.1340102910995 -aF2834.63051674366 -aF3634.690972828865 -aF3122.396862971783 -aF3119.4369086384772 -aF3094.8564301490783 -aF3685.8865238547323 -aF3096.347575926781 -aF2238.9409329891205 -aF2253.7028403401373 -aF3097.5330831289293 -aF2206.8164663434027 -aF3833.7559376955032 -aF3678.048882949352 -aF3095.3067158579825 -aF4748.015714168548 -aF1790.629888999462 -aF3097.1133067965507 -aF3096.069177865982 -aF3094.0563762307165 -aF4088.6816641449927 -aF2294.0362361073494 -aF3089.505575835705 -aF3646.1720325827596 -aF3096.086884200573 -aF3688.0796576976777 -aF451.64445102214813 -aF3676.9282081723213 -aF3096.0185105085375 -aF3098.1372777462007 -aF3684.3430762887 -aF3096.789417076111 -aF702.5301367282867 -aF3096.162340426445 -aF3109.1459869742393 -aF3646.8813755869865 -aF3095.5660455584525 -aF3095.026410961151 -aF3712.2991993665696 -aF2513.3823090195656 -aF3128.058259153366 -aF250.40078978538514 -aF3015.617586398125 -aF3729.7124260187147 -aF3096.2059252500535 -aF3841.582954800129 -aF3686.4356926321984 -aF3094.3320502400397 -aF3730.2111998438836 -aF3643.951930630207 -aF4040.115367603302 -aF3560.9675161004066 -aF3958.267154943943 -aF3096.1824984073637 -aF3704.2221143364904 -aF3091.98364546299 -aF3122.591360247135 -aF3102.5066563129426 -aF2888.3357363939285 -aF3173.518864607811 -aF2198.9096345305443 -aF3065.788076853752 -aF558.560747385025 -aF3686.0693077087403 -aF3685.489357149601 -aF3096.9471396565436 -aF3245.920883965492 -aF3687.972602474689 -aF3993.126841676235 -aF3686.178814578056 -aF3095.61535089016 -aF4142.232157671451 -aF3676.940466403961 -aF3093.170514690876 -aF3685.608670604229 -aF3101.2233556628225 -aF601.5500933408737 -aF3688.6002239346503 -aF3095.9501368165015 -aF3095.1642479658126 -aF2873.4090239286425 -aF3840.4693625569344 -aF2763.0715913176537 -aF3096.3271455407144 -aF609.4749038934708 -aF3072.6829235434534 -aF3092.1778703331947 -aF3210.1799665808676 -aF3090.618895673752 -aF3243.261392509937 -aF4090.313098573685 -aF3097.140547311306 -aF3137.457326364517 -aF4131.380353808403 -aF3667.4267166256905 -aF3308.446854698658 -aF2990.320682370663 -aF1684.819829940796 -aF2701.138374185562 -aF3686.2188581347464 -aF2910.30929762125 -aF3091.044392514229 -aF2833.9552243828775 -aF2795.8852429866793 -aF4311.826426839828 -aF3081.95559476614 -aF2449.9089131832125 -aF3688.7454158782957 -aF3679.959260249138 -aF3095.5695868253706 -aF2247.4244464993476 -aF3695.456389093399 -aF1868.7660367131234 -aF3092.631697309017 -aF3366.606988132 -aF3687.844299650192 -aF3012.341369688511 -aF3095.8150238633157 -aF3095.243245458603 -aF3096.1340102910995 -aF3685.4152629494665 -aF3685.964159321785 -aF3685.3351758360864 -aF2951.7009873867037 -aF3418.382217311859 -aF3094.2111023545267 -aF3096.8637836813928 -aF3684.8121579527856 -aF656.351743710041 -aF3084.0803549170496 -aF3140.6267602562903 -aF3686.2692530870436 -aF3473.7670871019363 -aF3011.7968317985533 -aF3066.508860874176 -aF3096.2119181632997 -aF3689.7140885829926 -aF3095.5246399760244 -aF3113.8997292041777 -aF2255.3628773093224 -aF3153.719368863106 -aF3147.9070602297784 -aF3118.959382414818 -aF3620.8857523560523 -aF3096.5407111763952 -aF3101.8199229359625 -aF3687.961706268787 -aF3484.9430530905724 -aF3095.7308506727218 -aF3096.2625855207443 -aF3090.7926901578903 -aF3305.97913646698 -aF3095.96920517683 -aF4206.454122853279 -aF3095.5965549349785 -aF2968.1185732245444 -aF4094.1853377461434 -aF1172.9433171749115 -aF3685.707281267643 -aF998.6233635067939 -aF3096.622705125809 -aF3686.2894110679626 -aF3184.355141377449 -aF1905.203221654892 -aF3096.2484204530715 -aF3105.770887196064 -aF3126.6842475891112 -aF3282.514974272251 -aF3095.43501868248 -aF3106.8046647310257 -aF4154.917520582676 -aF3101.165605771542 -aF3095.0193284273146 -aF3685.7519557118417 -aF3690.1134345293044 -aF3095.778249168396 -aF3675.1701053500174 -aF3095.8607879281044 -aF2579.4021481752397 -aF2871.290256690979 -aF3684.4348768234254 -aF3719.6514142990113 -aF3537.816619825363 -aF2267.4053640723228 -aF2175.566420221329 -aF2089.41093736887 -aF1564.1160194396973 -aF3096.324693894386 -aF3686.1349573493003 -aF3096.8561563372614 -aF3095.762994480133 -aF3686.0739385962484 -aF3095.66874229908 -aF3097.4080491662025 -aF3687.8873396635054 -aF3098.241064107418 -aF3691.967151558399 -aF3636.7190291523934 -aF3119.8896459937096 -aF3588.5245656371117 -aF3704.811599075794 -aF3257.1545998454094 -aF3702.8116004824637 -aF3100.483230876923 -aF3682.3231921195984 -aF3685.5402969121933 -aF3665.80181992054 -aF3093.600370013714 -aF1744.693391752243 -aF3120.7485394239425 -aF3095.781790435314 -aF3728.0581095576285 -aF3096.2143698096274 -aF3728.7246849536896 -aF3683.994125294685 -aF3093.8855782032015 -aF3727.1507280111314 -aF3094.0152430534363 -aF3096.440466082096 -aF3095.8948385715485 -aF3651.571647417545 -aF4501.722506844997 -aF3096.5704033374786 -aF3774.4669573307037 -aF3094.6987075686457 -aF3131.271550273895 -aF3097.5894709944723 -aF3093.712328529358 -aF2715.7174976825713 -aF3685.964159321785 -aF3108.1914793372152 -aF1851.8864517450334 -aF2911.11289280653 -aF2801.2295595765113 -aF3098.6335999250414 -aF3964.643342232704 -aF2230.678067648411 -aF2426.986564826965 -aF2873.5645672678947 -aF3086.270764708519 -aF2797.3477862238883 -aF3685.7356114029885 -aF3032.922395801544 -aF3096.3519344091414 -aF3481.239160299301 -aF3092.3255139231683 -aF3686.3531538724897 -aF3669.2183252811433 -aF2261.1670137882234 -aF3685.956259572506 -aF3096.122296869755 -aF3015.089665222168 -aF3095.7420192837717 -aF3114.0160462021827 -aF2867.7977502942085 -aF3112.7992124080656 -aF1908.7523883223535 -aF3093.5061178326605 -aF3034.8758131146433 -aF3090.1639790773393 -aF3101.1601576685907 -aF3095.4480941295624 -aF3610.660752737522 -aF3709.562889659405 -aF3694.523401463032 -aF3152.3603395819664 -aF3695.4177075624466 -aF3686.1316884875296 -aF4535.513275778293 -aF4447.029001319408 -aF2565.63887809515 -aF3094.784787595272 -aF4574.4334336400025 -aF3685.916488420963 -aF3096.2930948972703 -aF3654.0238385558127 -aF3956.5406511187552 -aF2452.4545392870905 -aF3100.5575974822045 -aF3686.27524600029 -aF3668.496996450424 -aF3076.7681835412977 -aF646.6454034924507 -aF3640.6969615221024 -aF3685.8925167679786 -aF3095.9185378193856 -aF4346.86426653862 -aF3094.758636701107 -aF3043.1893458127975 -aF2937.3092786312104 -aF4101.22973486185 -aF3935.153850579262 -aF3098.4355613827706 -aF3095.830006146431 -aF3099.2674867033957 -aF3686.294586765766 -aF3713.1417484879494 -aF3684.5669933199883 -aF3095.726492190361 -aF3097.485684633255 -aF3207.022790920734 -aF3717.3830966353416 -aF3083.063466501236 -aF3096.651580071449 -aF3685.820329403877 -aF2643.9945843577384 -aF3093.496583652496 -aF3096.059916090965 -aF4126.9742005467415 -aF3096.2587718486784 -aF3094.7115106105803 -aF2337.5110080361364 -aF3094.5712219595907 -aF3096.043026971817 -aF3685.860645365715 -aF3095.752098274231 -aF3084.463084149361 -aF3694.499429810047 -aF3101.385164320469 -aF3097.0863386869432 -aF3707.4877072453496 -aF3640.513360452652 -aF3096.2838331222533 -aF969.7546831846237 -aF3685.8524732112883 -aF3460.08581097126 -aF3096.2544133663177 -aF3096.341310608387 -aF984.2621641278266 -aF3685.684126830101 -aF3095.8180203199386 -aF3098.196117258072 -aF1687.1646934509279 -aF3610.733757317066 -aF3440.4843537688257 -aF2505.221050798893 -aF3103.589739179611 -aF2979.3874293684958 -aF3837.5151287317276 -aF3096.1879465103148 -aF3098.7973154187202 -aF1276.5714110016822 -aF1382.1000755429268 -aF3685.996303129196 -aF3570.129590833187 -aF3106.9059994459153 -aF3685.973693501949 -aF1900.1231380581855 -aF1905.9291813731195 -aF3096.122569274902 -aF3096.201294362545 -aF3096.4875921726225 -aF3685.3586026787757 -aF3376.630680346489 -aF2887.263549733162 -aF3095.7899625897408 -aF4097.1123310565945 -aF3030.81152831316 -aF3096.33204883337 -aF435.23040645122524 -aF3096.143544471264 -aF3095.9514988422393 -aF2877.790115916729 -aF3687.001205718517 -aF3230.2129135370255 -aF3790.4555050611493 -aF3095.895928192139 -aF1432.8690401077272 -aF3689.96252207756 -aF3665.0595158934593 -aF3096.321152627468 -aF2915.4817265629767 -aF3670.5566517710686 -aF3675.3210178017616 -aF3005.939031505585 -aF3945.6675996541976 -aF4583.006296038627 -aF3685.9900378108023 -aF3685.9984823703767 -aF3688.301123082638 -aF3186.8449244260787 -aF3249.0480950593947 -aF3096.7967720150946 -aF3688.262169146538 -aF2519.9900406837464 -aF3114.1258254766462 -aF1460.1890971660614 -aF2251.2375737547873 -aF3051.695196545124 -aF3086.794054996967 -aF3866.205383682251 -aF3663.7890182852743 -aF3698.013728618622 -aF3089.8752296209336 -aF3688.027628314495 -aF3718.790886437893 -aF3684.6574318289754 -aF3091.907372021675 -aF3095.521371114254 -aF3095.0122458934784 -aF3247.6694526076317 -aF3095.9629398584366 -aF3719.6811064600943 -aF3065.1035227179527 -aF3096.1822260022163 -aF3096.273209321499 -aF3142.5583851575852 -aF2824.891760313511 -aF2267.9142168879507 -aF3105.9773702979087 -aF3096.104590535164 -aF3097.542344903946 -aF3688.4435909748076 -aF3175.639538681507 -aF3713.0003702163694 -aF3060.219843232632 -aF3710.1297647714614 -aF3687.75658519268 -aF447.8770878314972 -aF3043.4361448764803 -aF3095.701430916786 -aF3694.7421427965164 -aF3228.6408634305 -aF3115.435277020931 -aF3681.6833124279974 -aF3048.43014844656 -aF3162.627834403515 -aF3930.5924263834954 -aF3097.015785753727 -aF2028.4406724333762 -aF3086.9899142980576 -aF3095.286557877064 -aF3096.0245034217833 -aF3095.8182927250864 -aF3553.9078642964364 -aF3685.7604002714156 -aF3069.847185957432 -aF3082.833284151554 -aF3987.982742869854 -aF3095.620526587963 -aF2995.3040621399878 -aF3639.5174472332 -aF3098.5461578726768 -aF3095.8218339920045 -aF418.70331374406817 -aF3667.5057141184807 -aF3323.8401971817016 -aF3102.0509225010874 -aF3685.91131272316 -aF3894.483762049675 -aF3690.280963695049 -aF3073.6924570202827 -aF3693.5509150862695 -aF3091.1353758335113 -aF3682.5610018134116 -aF3019.7028463959696 -aF3690.405725252628 -aF3680.8584696412086 -aF3554.3954695105554 -aF3101.633597815037 -aF3096.3355901002883 -aF4444.729901874065 -aF3063.8918646216393 -aF3743.543524980545 -aF2204.279284799099 -aF3074.9891055226326 -aF3027.977697563171 -aF3084.9288969516756 -aF3362.653299820423 -aF3685.760672676563 -aF3096.17623308897 -aF3042.050147485733 -aF3682.504341542721 -aF3095.978466951847 -aF3845.464728152752 -aF1436.41357588768 -aF3095.6878106594086 -aF3096.113852310181 -aF3003.7461700677873 -aF3685.838035738468 -aF3686.078569483757 -aF2782.722626256943 -aF3579.273142015934 -aF1943.4815929889678 -aF3765.929507601261 -aF3685.8968752503392 -aF1280.8552543520927 -aF3701.744317114353 -aF3091.5170154452326 -aF3095.9021935105325 -aF3146.9727105736733 -aF3092.3276931643486 -aF3102.58592621088 -aF3095.9021935105325 -aF3092.6848163127897 -aF3264.716294336319 -aF3095.813661837578 -aF3687.7015593528745 -aF3121.698416173458 -aF2510.115626490116 -aF1264.919553220272 -aF3093.6902637124062 -aF1052.277098584175 -aF3655.571099793911 -aF3004.450609779358 -aF1296.0494686722757 -aF3798.6410073399543 -aF2068.909181153774 -aF3096.7926859378813 -aF3666.5313209056853 -aF3083.7455689907074 -aF3686.049966943264 -aF3669.1308832287787 -aF3080.9185483694077 -aF3096.6916236281395 -aF3096.0975080013277 -aF3105.2429660201074 -aF2018.5117772102356 -aF3090.3268773555756 -aF1303.1458951711654 -aF3094.688083767891 -aF345.598761510849 -aF3095.08552287817 -aF3069.925093829632 -aF3521.5172578215597 -aF3698.099536240101 -aF3096.4358351945875 -aF3049.309199857712 -aF1006.8510885834694 -aF2368.0854893922806 -aF1128.3996274471283 -aF4596.114976549148 -aF3676.432158398628 -aF3452.0280667066572 -aF3086.4145946264266 -aF3096.358199727535 -aF2499.6304799556733 -aF4089.58904569149 -aF2197.276020860672 -aF3689.7565837860107 -aF3686.1044479727743 -aF3061.234280002117 -aF3094.802493929863 -aF3108.426020169258 -aF3096.343762254715 -aF3685.7465076088906 -aF1675.5493379592897 -aF2850.774335408211 -aF3685.9186676621434 -aF2113.862840628624 -aF3865.487051308155 -aF3690.884341096878 -aF2736.898087525368 -aF3690.5236766815183 -aF3099.3603768587113 -aF3095.831095767021 -aF3097.0669979214667 -aF3102.2026521682737 -aF3099.7978595256805 -aF3096.104590535164 -aF2492.8132687330244 -aF3096.467434191704 -aF1882.9112188100814 -aF3153.379679644108 -aF3103.9139013051986 -aF3955.5213110566137 -aF2802.9606942892074 -aF3651.2354994654656 -aF3089.075720512867 -aF3687.951082468033 -aF3696.690384411812 -aF2265.2441016316416 -aF3095.273754835129 -aF3685.7805582523347 -aF3095.3753619551658 -aF3496.545877945423 -aF3137.7365416407583 -aF3631.636493909359 -aF3639.677621459961 -aF3097.7343905329703 -aF3369.041200530529 -aF2393.165558922291 -aF1569.2658387541771 -aF3769.4127522230146 -aF3077.358757901192 -aF3100.6428602933884 -aF3265.73563439846 -aF3096.470975458622 -aF3615.9533123493193 -aF3096.9223507881165 -aF2475.5806466937065 -aF3102.5597753167153 -aF3685.839397764206 -aF3089.2323534727097 -aF3070.711255085468 -aF4126.31770414114 -aF3095.7733458757402 -aF3674.8663736104963 -aF2897.976154565811 -aF3934.2573652386664 -aF3096.586747646332 -aF3106.3530169963838 -aF3076.06674028635 -aF3685.2381996035574 -aF3020.0071229457853 -aF3684.124607360363 -aF3623.135818874836 -aF2473.1409861922266 -aF2604.5179027795793 -aF3696.7350588560103 -aF4421.1930075049395 -aF3095.813661837578 -aF3641.5327005147933 -aF3162.4733806848526 -aF3686.157566976547 -aF3096.0694502711294 -aF3096.560324347019 -aF3091.0928806304933 -aF3095.9016487002373 -aF3686.057321882248 -aF3685.164105403423 -aF3659.5882585048676 -aF3331.8723353624346 -aF3714.182063746452 -aF3686.1256955742833 -aF3096.1999323368073 -aF3090.768446099758 -aF3686.178814578056 -aF2927.6568746328353 -aF3674.6435461997985 -aF970.7740232467651 -aF3667.796915221214 -aF3685.7816478729246 -aF3097.8084847331047 -aF3414.7654941678047 -aF2304.9218182086943 -aF3095.8109377861024 -aF2941.788436472416 -aF3683.830409801006 -aF3684.056506073475 -aF3095.9713844180105 -aF3685.6696893572807 -aF3690.96088694334 -aF3096.928343701363 -aF3686.208234333992 -aF1349.6740563988685 -aF3097.6564826607705 -aF3647.7748644709586 -aF3683.14994174242 -aF841.208873295784 -aF2079.6277788996695 -aF3685.768572425842 -aF3256.15841422081 -aF3687.446860539913 -aF3095.5886551856993 -aF3490.2764734745024 -aF2238.1160902023316 -aF219.08209756612777 -aF3207.290292775631 -aF3087.5883884072305 -aF3120.908986055851 -aF3489.848797392845 -aF3693.2433696746825 -aF3025.978788590431 -aF3096.411863541603 -aF3096.3037186980246 -aF2562.7859789848326 -aF3685.7426939368247 -aF3095.229352796078 -aF3705.066570293903 -aF3096.0610057115555 -aF3100.509654176235 -aF3684.056506073475 -aF3598.148639500141 -aF3685.7051020264626 -aF3433.99511834383 -aF3096.4663445711135 -aF3686.4757361888883 -aF3098.357925915718 -aF3829.2993894815445 -aF3686.1210646867753 -aF3096.089335846901 -aF3685.0134653568266 -aF3683.8148827075956 -aF2311.839002120495 -aF3421.0068409085275 -aF3077.431762480736 -aF3726.13138794899 -aF3687.0175500273704 -aF2943.5345534682274 -aF2766.2181431770323 -aF3360.2351593256 -aF3861.1198519825934 -aF1471.8235210180283 -aF485.3480503082276 -aF3686.362960457802 -aF3087.5434415578843 -aF1044.9131702303887 -aF3109.2108193993568 -aF3095.8161134839056 -aF1200.8027364253999 -aF3421.1207062602043 -aF3688.664239144325 -aF3687.695294034481 -aF3085.2282702088355 -aF3031.9030557394026 -aF3094.182772219181 -aF3319.777274405956 -aF1262.9329024791718 -aF3686.109078860283 -aF3914.735722744465 -aF3067.4660925626754 -aF3096.10840420723 -aF3058.338885688782 -aF3099.2511423945425 -aF3090.0912469029427 -aF3093.3680084228517 -aF514.3978800535202 -aF2981.335126173496 -aF3046.6047615528105 -aF3681.363508784771 -aF3095.3165224432946 -aF3328.0020030260084 -aF3095.0038013339044 -aF3324.859537243843 -aF3688.1537518978116 -aF2448.5951031565664 -aF3525.839510297775 -aF3072.6731169581412 -aF3681.286145722866 -aF3666.314214003086 -aF3096.011427974701 -aF3096.131831049919 -aF3094.340494799614 -aF3095.8041276574136 -aF986.1651864886284 -aF3686.5604541897774 -aF3019.086938357353 -aF4115.341138720512 -aF3196.2837627887725 -aF3102.447816801071 -aF3096.222541964054 -aF3476.334778022766 -aF4736.881970977783 -aF2696.192586326599 -aF2915.809157550335 -aF3079.8605267763137 -aF3062.872524559498 -aF1384.6969138145448 -aF3095.90464515686 -aF2956.967123699188 -aF4136.992989468574 -aF3713.444935417175 -aF1917.2724041223526 -aF3030.986140012741 -aF3096.1304690241814 -aF2361.047902405262 -aF3096.419218480587 -aF3686.161108243465 -aF3100.4268430113793 -aF3085.372100126743 -aF3680.5718994259832 -aF3114.2827308416367 -aF2055.7571882247926 -aF3846.1942291378973 -aF3094.906007885933 -aF3523.0075863838197 -aF3096.5418007969856 -aF3692.643533539772 -aF3096.4968539476395 -aF3102.776609814167 -aF3093.6810019373893 -aF3683.9118589401246 -aF3199.8547218680383 -aF3757.1384486794473 -aF3082.1969457268715 -aF3099.9781917333603 -aF3355.8597878456117 -aF3683.547653257847 -aF3685.9818656563757 -aF3099.1639727473257 -aF3095.304536616802 -aF2134.761763548851 -aF3096.0669986248017 -aF3686.377942740917 -aF1444.5881819605827 -aF3551.489723801613 -aF2065.7114171266553 -aF2827.229813694954 -aF3678.8715464949605 -aF3685.91349196434 -aF2179.6721106052396 -aF3689.1597441077233 -aF3866.38135740757 -aF3089.4456467032433 -aF3685.7530453324316 -aF3686.6291002869607 -aF3338.3920801639556 -aF3091.7975927472116 -aF4636.730856454372 -aF3095.7074238300324 -aF3669.728540122509 -aF3710.8271219491958 -aF3125.1522410392763 -aF2981.335398578644 -aF2384.5463876485824 -aF3414.7349847912787 -aF495.72014870643613 -aF3113.914711487293 -aF3724.661762177944 -aF2730.5055559277534 -aF2286.2007744431494 -aF3281.534315741062 -aF3095.8997418642043 -aF3742.1597068309784 -aF3686.7266213297844 -aF3097.1767771959303 -aF3056.1343108296396 -aF2927.903128886223 -aF2397.7092767834665 -aF3094.468797624111 -aF1318.9399456262588 -aF3160.574989211559 -aF3685.727439248562 -aF3153.0476177692412 -aF2994.020761489868 -aF3117.6847987294195 -aF3094.7706225275992 -aF3096.017420887947 -aF3096.038668489456 -aF2949.2381724476813 -aF3118.656740295887 -aF3099.2982684850695 -aF3107.925339508057 -aF3102.335858285427 -aF3095.8204719662667 -aF3096.2753885626794 -aF3685.824143075943 -aF3583.411248612404 -aF2185.5337245702744 -aF356.4364003062248 -aF3126.528431844711 -aF3684.9546258449554 -aF3091.7254053831102 -aF3686.1055375933647 -aF3682.6438129782678 -aF3091.878769481182 -aF3683.5179610967634 -aF1165.4426414370537 -aF3060.037059378624 -aF3095.6000962018966 -aF3753.246323931217 -aF3006.9352171301844 -aF3192.9566063165666 -aF2974.88157582283 -aF3688.056230854988 -aF3080.4037026405335 -aF3685.469199168682 -aF3096.0196001291274 -aF3096.4829612851145 -aF3052.3255420565606 -aF3077.6485969781875 -aF3092.262860739231 -aF3095.3018125653266 -aF3684.5454733133315 -aF3082.0800839185713 -aF3674.5868859291077 -aF707.5012582659721 -aF3132.2677358984947 -aF3096.5701309323313 -aF3013.9215919494627 -aF3667.3079479813573 -aF237.1809679746628 -aF3100.6235195279123 -aF2219.9733625650406 -aF3686.144763934612 -aF3091.3568412184713 -aF817.7820306062698 -aF678.4304533243179 -aF3689.350972521305 -aF3096.03158595562 -aF3073.992919898033 -aF3095.9738360643387 -aF3103.625151848793 -aF3174.6201986193655 -aF1097.0117443203926 -aF3672.9818747997283 -aF2837.8282807707787 -aF426.1157302141189 -aF3623.8707679629324 -aF3829.850465095043 -aF2904.358607172966 -aF3143.6008796572687 -aF3683.978598201275 -aF2222.946392345428 -aF4213.777735245228 -aF3096.5461592793463 -aF4717.331181132793 -aF3096.142454850674 -aF3114.4390913963316 -aF3161.6316487789154 -aF3095.4540870428086 -aF3686.1799041986465 -aF3098.742017173767 -aF3094.3298709988594 -aF2260.170828163624 -aF3400.1430582523344 -aF3041.432877421379 -aF2154.0875467419623 -aF3097.5314486980437 -aF3685.8206018090245 -aF2610.5195329904554 -aF3903.7199309825896 -aF3523.9353983163833 -aF2885.224869608879 -aF3091.8869416356088 -aF3111.83489818573 -aF3555.391655135155 -aF3069.1852414488794 -aF3096.2827435016634 -aF3715.0390503406525 -aF3695.7909026145935 -aF3095.457900714874 -aF4167.796291148662 -aF4632.474253618717 -aF3687.9006875157356 -aF3095.141910743713 -aF3663.0208357691763 -aF3448.4276878714563 -aF2440.291921854019 -aF3073.8387385845185 -aF3096.3791749238967 -aF3691.7514066815374 -aF3145.3042290449143 -aF3683.9282032489778 -aF3685.6094878196714 -aF3089.0108880877497 -aF3651.90725055933 -aF3092.936246263981 -aF2780.7297101974486 -aF3098.356836295128 -aF3093.532541131973 -aF3096.267216408253 -aF3707.4879796504974 -aF3096.4993055939676 -aF3110.1416277885437 -aF3474.2960978984834 -aF3679.924937200546 -aF3063.535831093788 -aF3228.1973878502845 -aF3095.316250038147 -aF3678.7917317867277 -aF3096.294456923008 -aF4131.580299186706 -aF3064.877154040337 -aF3694.3722166061402 -aF3284.8985193133353 -aF3703.929006397724 -aF3094.777432656288 -aF2384.267444777489 -aF3695.7511314630506 -aF2008.1636504650116 -aF3093.34812284708 -aF3095.774707901478 -aF3453.1383900880815 -aF2530.7544024944305 -aF3081.6398772001266 -aF3684.8260506153106 -aF3096.1860396742823 -aF3287.4130912303926 -aF3087.5167458534243 -aF3561.156020462513 -aF3054.1675456643106 -aF3448.068657886982 -aF3095.9716568231584 -aF3682.2616285562513 -aF3077.4434759020805 -aF3234.1663294434547 -aF3685.2150451660154 -aF3094.5031206727026 -aF3722.8592573165893 -aF1065.3293912291526 -aF3685.6081257939336 -aF3685.8440286517143 -aF3097.1239305973054 -aF3685.9211193084716 -aF1428.1945677757265 -aF3226.6253377437592 -aF3458.687010538578 -aF3095.7608152389525 -aF3099.077620315552 -aF3685.992761862278 -aF3096.128017377853 -aF3093.8063083052634 -aF3245.799663674831 -aF1437.8352583527565 -aF2980.292631673813 -aF3093.2832904219626 -aF3087.415138733387 -aF3692.0769308328627 -aF2573.3207032561304 -aF3686.447406053543 -aF2458.69288957119 -aF3686.0349846601484 -aF3683.2305736660956 -aF3071.6387946128843 -aF3100.0122423768044 -aF3259.0445467591285 -aF3034.1877177119254 -aF3685.932560324669 -aF3095.896200597286 -aF3691.0927310347556 -aF3685.859555745125 -aF3096.4322939276694 -aF3670.686044216156 -aF3104.8144727230074 -aF3741.504844856262 -aF3551.8691841721534 -aF2834.1565317869185 -aF3585.123587369919 -aF1938.1220217108726 -aF3095.817203104496 -aF3734.450641155243 -aF2945.956235229969 -aF3096.307259964943 -aF3578.022802388668 -aF3690.00038639307 -aF3685.920846903324 -aF3096.114941930771 -aF3093.403693497181 -aF2512.5106125473976 -aF3611.101504266262 -aF3095.817203104496 -aF3685.8475699186324 -aF3125.365806674957 -aF3188.0636650562287 -aF3686.3588743805885 -aF3077.276219141483 -aF3662.795829117298 -aF3219.030682229996 -aF3684.7056475400923 -aF3099.0765306949615 -aF3682.6871253967283 -aF3310.657150065899 -aF3095.903010725975 -aF3092.745017850399 -aF3138.4292679309847 -aF554.3430984854698 -aF3854.8872222065925 -aF3624.9750984311104 -aF3660.996593117714 -aF2901.9404666781425 -aF3467.7431196689604 -aF2980.34193700552 -aF1883.1424907803537 -aF3704.2417275071143 -aF3096.2718472957613 -aF3122.8871922373773 -aF2262.1754576444628 -aF3271.3188503026963 -aF3589.1540939331053 -aF3092.6905368208886 -aF2849.1456250309943 -aF3687.7544059515 -aF3686.111530506611 -aF3089.346491229534 -aF2023.376933145523 -aF3652.288072955608 -aF4549.854317176341 -aF3686.479277455807 -aF3681.2352059602736 -aF2415.928277862072 -aF3846.863528585434 -aF3098.3402195811273 -aF3489.273750126362 -aF3685.676771891117 -aF3667.069048666954 -aF3094.3195196032525 -aF3555.6120308995246 -aF2497.5917998313903 -aF2583.6424067020416 -aF3682.1003647089005 -aF3686.0407051682473 -aF3686.9998436927795 -aF3085.970574235916 -aF3095.6733731865884 -aF3096.807123410702 -aF3685.9941238880156 -aF2346.026665353775 -aF261.08397486209867 -aF3707.7080830097198 -aF2799.214306294918 -aF3094.414861404896 -aF3684.853291130066 -aF3680.9420980215073 -aF3100.876039099693 -aF3685.8960580348967 -aF3089.2312638521194 -aF1332.4847467780114 -aF3095.9700223922728 -aF3677.0600522637365 -aF3719.977755665779 -aF2911.9933062434197 -aF3602.2001212596892 -aF3097.0580085515976 -aF2855.195743358135 -aF3685.922208929062 -aF3686.2109583854676 -aF3687.7827360868455 -aF3105.268844509125 -aF3185.6654101371764 -aF3097.3794466257095 -aF3096.5194635748862 -aF3685.766120779514 -aF3097.505842614174 -aF3687.320736956596 -aF3096.6842686891555 -aF3473.28029910326 -aF3177.908128750324 -aF3148.7967354416846 -aF3079.1887756824494 -aF3075.838464772701 -aF2842.907002341747 -aF3682.6604296922683 -aF3614.3621938824654 -aF3092.402876985073 -aF3728.5598798394203 -aF3691.3141964197157 -aF3682.016736328602 -aF2725.9098086833956 -aF3096.3293247818947 -aF3098.888026332855 -aF2940.973945081234 -aF3021.8978870749474 -aF2969.9295226454733 -aF3080.831378722191 -aF3690.305480158329 -aF3102.20755546093 -aF3686.1142545580865 -aF2851.0935942411425 -aF3083.521651959419 -aF3103.966747903824 -aF1657.188413798809 -aF3681.704287624359 -aF3687.3370812654493 -aF3736.671287918091 -aF3636.6220529198645 -aF3685.767482805252 -aF3095.7308506727218 -aF4374.519926738739 -aF3096.1824984073637 -aF3094.7071521282196 -aF4176.466946995258 -aF3792.6107745885847 -aF3686.3261857628822 -aF3087.6504967808723 -aF2221.7518957734105 -aF3686.2128652215 -aF3096.086884200573 -aF3096.2094665169716 -aF3685.606218957901 -aF3097.8994680523874 -aF3096.842263674736 -aF3090.7845180034637 -aF1666.2020277261734 -aF3096.6878099560736 -aF2915.4307868003843 -aF3078.305093383789 -aF3367.250136685371 -aF4108.684374129772 -aF3066.0553063035013 -aF2944.434580075741 -aF3685.4542168855664 -aF3088.9422419905663 -aF3487.430656898022 -aF3090.318705201149 -aF2948.7726320505144 -aF3087.7215945243834 -aF3091.7044301867486 -aF3096.47342710495 -aF3686.1128925323487 -aF3684.2910469055173 -aF3096.846622157097 -aF3096.9471396565436 -aF3092.6918988466264 -aF3120.519991505146 -aF3077.24761660099 -aF3097.0531052589417 -aF3098.427389228344 -aF3685.9053198099136 -aF3097.0686323523523 -aF3089.8874878525735 -aF3735.2629533052445 -aF3526.944930386543 -aF3685.141768181324 -aF3686.558274948597 -aF250.54652653932573 -aF3532.2946950793266 -aF3090.396613073349 -aF3723.1403794288635 -aF3133.3799661159514 -aF3701.7691059827803 -aF4068.161656785011 -aF3958.6024856805802 -aF3127.626769399643 -aF2648.586517930031 -aF2804.1636354207994 -aF3098.107857990265 -aF3097.07108399868 -aF3099.840082323551 -aF1379.8633568763732 -aF2968.498033595085 -aF3124.2388665795324 -aF2714.69815762043 -aF3810.4293401002883 -aF3365.1891193389893 -aF3697.7543989181518 -aF3095.944416308403 -aF3096.558145105839 -aF3096.069177865982 -aF3251.2703762531282 -aF3292.905051410198 -aF3702.2896722197534 -aF3096.0245034217833 -aF2067.3409447193144 -aF3705.425872683525 -aF3086.98855227232 -aF3658.807000541687 -aF3083.739576077461 -aF2903.9200348854065 -aF3901.392773807049 -aF3188.6705837249756 -aF3088.2669496297835 -aF3096.129379403591 -aF4106.121041691303 -aF3102.582384943962 -aF3095.951226437092 -aF3686.365139698982 -aF3161.5943292737006 -aF3033.168377649784 -aF3215.9672139406202 -aF3988.978928494453 -aF3743.17904689312 -aF3096.1342826962473 -aF1630.466013634205 -aF4624.8232102394095 -aF3693.9292858362196 -aF3048.6845748543737 -aF3090.8594294190407 -aF3704.2673335909844 -aF3140.8474084258078 -aF3533.138333821297 -aF3686.2624429583548 -aF3686.7443276643753 -aF3112.373443162441 -aF3806.8553845643996 -aF2922.9339141845703 -aF3690.2008765816686 -aF3070.3519526958466 -aF2636.4688473463057 -aF3119.0893196702004 -aF3686.0725765705106 -aF3095.5766693592072 -aF3096.055285203457 -aF3679.0611404776573 -aF3675.1973458647726 -aF3672.1698350548745 -aF3091.6654762506487 -aF3067.106790173054 -aF3089.7493784427643 -aF2137.79145359993 -aF3654.345549035072 -aF564.5847148180007 -aF3690.861731469631 -aF3051.918023955822 -aF3137.386773431301 -aF4284.828080260753 -aF3693.837212896347 -aF2067.8898410916327 -aF3007.798469042778 -aF3055.5156787395476 -aF3094.1838618397715 -aF3107.607170295715 -aF3974.1674434065817 -aF3090.320612037182 -aF3127.9498419046404 -aF3012.9022518873217 -aF3320.31554697752 -aF3082.0672808766367 -aF3091.7292190551757 -aF3320.6914660811426 -aF3685.0709428429604 -aF3095.9185378193856 -aF3250.2796387314797 -aF3336.330245602131 -aF3699.1186038970945 -aF3692.5879628896714 -aF3192.7160725712774 -aF903.5449776411056 -aF3093.6687437057494 -aF3686.718449175358 -aF3260.832069337368 -aF3101.381623053551 -aF3097.148447060585 -aF3488.2544100642203 -aF1973.135617351532 -aF3116.3214109659193 -aF2449.463530766964 -aF3101.9991655230524 -aF3328.131940281391 -aF3095.867598056793 -aF1038.1338233232498 -aF3685.980503630638 -aF3094.4679804086686 -aF3084.615086221695 -aF2447.5757630944254 -aF1280.5275509595872 -aF3696.3836562156675 -aF3153.345356595516 -aF3095.4682521104814 -aF3151.0320920825006 -aF3106.2576751947404 -aF3681.9845925211907 -aF3685.970152235031 -aF3097.9820068120957 -aF3097.3252380013464 -aF3685.693388605118 -aF3732.075268268585 -aF3696.810242676735 -aF3560.5213164687157 -aF3095.4328394412996 -aF3029.7077426552773 -aF1392.225919687748 -aF3339.4422020077704 -aF3093.131560754776 -aF3739.718411898613 -aF3096.502029645443 -aF3102.9498594880106 -aF1791.8916696429253 -aF3088.01470246315 -aF3686.477915430069 -aF3334.466994392872 -aF3091.6581213116647 -aF2695.1732462644577 -aF3151.53631401062 -aF3667.1981687068937 -aF644.7230403661728 -aF3666.8211599826814 -aF3693.0382485985756 -aF3790.3825004816053 -aF1878.9063183307649 -aF3606.644683647156 -aF2779.687215697765 -aF3125.856680750847 -aF3092.7600001335145 -aF3113.325499153137 -aF3044.088827610016 -aF3680.9442772626876 -aF2920.9810416817663 -aF3006.466135466099 -aF3386.723291063309 -aF3088.4922286868095 -aF2653.100543630123 -aF3112.2819150328637 -aF2946.7399448394776 -aF3482.2067433834077 -aF3662.8979810476303 -aF3877.195024549961 -aF3650.7361808300016 -aF3098.5616849660873 -aF3096.91417863369 -aF3097.729759645462 -aF3685.91349196434 -aF3686.127057600021 -aF3690.568351125717 -aF3447.0493178248407 -aF3098.9174460887907 -aF3251.4923864483835 -aF3095.522460734844 -aF3112.526807260513 -aF2906.397287297249 -aF3097.177049601078 -aF3086.5205602288247 -aF3562.1522060871125 -aF3685.936101591587 -aF3095.7180476307867 -aF3797.1337896585464 -aF3096.3685511231424 -aF3058.5524513244627 -aF3095.6237954497337 -aF3352.1204823851585 -aF2988.9641047358514 -aF3057.888872385025 -aF3685.69202657938 -aF3728.159444272518 -aF3124.6997760891913 -aF3097.5810264348984 -aF3095.6897174954415 -aF3096.9155406594277 -aF3683.7571328163144 -aF3686.128419625759 -aF3095.8229236125944 -aF3653.4250920414925 -aF627.0295088171958 -aF3097.492767167091 -aF3094.927255487442 -aF3540.5956971406936 -aF3095.9090036392213 -aF2905.244468712807 -aF3704.5312941789625 -aF3658.511168551445 -aF3897.185748708248 -aF3693.830402767658 -aF3733.557697081566 -aF3158.8882565379145 -aF3095.5001235127447 -aF3079.7316791415215 -aF2124.471931505203 -aF2804.9993744134904 -aF2835.377451658249 -aF3096.841174054146 -aF3096.2059252500535 -aF3685.303032028675 -aF2985.489032268524 -aF3692.946175658703 -aF3139.757243025303 -aF3097.7343905329703 -aF3121.562486004829 -aF3117.3295824170114 -aF3046.386565029621 -aF3095.878221857548 -aF3096.310801231861 -aF3940.3083007812497 -aF3052.4650134921076 -aF3126.4083011746407 -aF3684.643266761303 -aF3090.929982352257 -aF3687.557729434967 -aF3689.412536084652 -aF3686.5182313919067 -aF3215.1960349678993 -aF3181.1993277430533 -aF3096.3993329048158 -aF3096.354658460617 -aF3120.299615740776 -aF4241.948785984516 -aF3680.1785463929177 -aF2857.2543090581894 -aF4022.88982809782 -aF3028.0237340331078 -aF3095.7485570073127 -aF2537.7285190820694 -aF3685.3989186406134 -aF3698.7522189736364 -aF3609.9053732633593 -aF3686.49671138525 -aF4145.928967928886 -aF3096.0539231777193 -aF3685.975055527687 -aF3670.5531105041505 -aF3668.249380171299 -aF3094.506661939621 -aF3057.374299061298 -aF3095.5156506061553 -aF3643.7337341070174 -aF3096.2576822280885 -aF3681.440054631233 -aF3685.7993542075155 -aF2746.8226242661476 -aF3086.3276973843576 -aF3210.3050005435944 -aF4077.0382509231567 -aF3691.161921942234 -aF3686.1246059536934 -aF1805.3054439187051 -aF3176.21240670681 -aF3104.200471520424 -aF3097.326600027084 -aF3888.940317296982 -aF2240.532868671417 -aF2215.810194694996 -aF3686.05595985651 -aF2989.7657930850983 -aF3686.2071447134017 -aF382.5581471204758 -aF3691.8625479817388 -aF4612.658413565158 -aF3104.3644594192506 -aF3098.93760406971 -aF3096.4344731688498 -aF3095.672283565998 -aF3042.925930035114 -aF3096.40995670557 -aF3096.2094665169716 -aF3095.6842693924905 -aF715.4484060406685 -aF3686.140133047104 -aF3249.2107209324836 -aF4051.152134561539 -aF3694.2722439169884 -aF1052.18529804945 -aF3082.5450795054435 -aF3068.293114590645 -aF3642.3550916552545 -aF3123.7983874559404 -aF3095.678004074097 -aF3090.3020884871485 -aF3685.909950697422 -aF3095.9713844180105 -aF3687.767208993435 -aF2967.2899167656897 -aF2136.826594567299 -aF3113.8689474225043 -aF3097.672009754181 -aF596.611388015747 -aF2675.350323677063 -aF3685.92575019598 -aF3685.153754007816 -aF3085.861884582043 -aF3096.336952126026 -aF3684.046154677868 -aF2601.243865311146 -aF3099.8166554808618 -aF3695.640262567997 -aF3096.3355901002883 -aF3096.1844052433967 -aF3725.1496397972105 -aF2624.2607382535934 -aF3879.1974747896193 -aF3094.321698844433 -aF3054.3372540712357 -aF3098.1228402733805 -aF3094.9250762462616 -aF3852.85181094408 -aF3686.5536440610886 -aF3093.5731294989587 -aF3083.4568195343018 -aF3663.6549949526784 -aF3209.328972899914 -aF3681.5985944271088 -aF3089.650222969055 -aF3681.814884114265 -aF3020.855392575264 -aF3096.0517439365385 -aF3680.6849475622175 -aF3691.1815351128575 -aF4240.14301226139 -aF3097.3606506705282 -aF2920.324545276165 -aF3058.5807814598083 -aF2656.691660690308 -aF3095.947685170174 -aF3097.544524145126 -aF3401.051529419422 -aF3635.8078339338304 -aF3686.0374363064766 -aF3737.713782417774 -aF1899.0046425223352 -aF3031.135962843895 -aF2964.041212975979 -aF3686.048060107231 -aF888.1080503344535 -aF3685.094914495945 -aF3095.166972017288 -aF3091.901379108429 -aF2027.0418720006942 -aF3010.4312647938727 -aF2924.5541800022124 -aF3090.188223135471 -aF3723.9017518162727 -aF1449.2735504984855 -aF2123.2098784565924 -aF3054.1866140246393 -aF3702.2343739748 -aF2938.948340404034 -aF3679.1425896167752 -aF3099.514558172226 -aF3961.5948562264443 -aF3709.11042470932 -aF3481.708514368534 -aF3848.9632274627684 -aF3096.049292290211 -aF3384.1942816734313 -aF3679.301674222946 -aF3095.7286714315414 -aF3228.860694384575 -aF2061.0669093608853 -aF3014.0703251600266 -aF3096.244606781006 -aF3076.502860927582 -aF4541.279275536536 -aF3064.41488250494 -aF3669.144231081009 -aF3095.8005863904955 -aF3517.5589386224747 -aF3458.260696482658 -aF3893.370987021923 -aF2368.6297548770904 -aF3096.5546038389207 -aF3096.190942966938 -aF3686.854106938839 -aF3830.396637415886 -aF3804.119074857235 -aF3335.972850048542 -aF4536.677262973785 -aF3685.5353936195374 -aF3095.2892819285394 -aF3093.2209096431734 -aF3098.2772939920424 -aF3096.0337651968002 -aF3933.617485547066 -aF3684.7067371606827 -aF2833.936156022549 -aF3685.679223537445 -aF3094.8106660842896 -aF2625.4792064785956 -aF4182.943379378318 -aF3112.503380417824 -aF3094.5009414315223 -aF3095.5126541495324 -aF2746.484569478035 -aF3685.9382808327673 -aF4489.362123274803 -aF3694.9617013454435 -aF2517.551742208004 -aF3131.156595301628 -aF3685.5846989512443 -aF3685.5389348864555 -aF3645.1093801021575 -aF3095.9787393569945 -aF3096.142454850674 -aF3686.115071773529 -aF1759.6073011755943 -aF3127.149515581131 -aF3095.972474038601 -aF3685.973693501949 -aF3687.2450083255767 -aF3052.550548708439 -aF3159.0536064624785 -aF3686.6345483899117 -aF3094.845806348324 -aF3965.0228026032446 -aF3096.1808639764786 -aF542.4392659425736 -aF2121.938563632965 -aF3207.271224415302 -aF3686.1082616448402 -aF3678.720906448364 -aF3284.4594022154806 -aF3095.885304391384 -aF3397.3040518045427 -aF2261.569628596306 -aF3095.8466228604316 -aF3092.4845985293387 -aF2767.238572859764 -aF3064.800335788727 -aF3685.9734210968018 -aF3395.0744156718256 -aF3182.694559597969 -aF3691.175269794464 -aF3092.6684720039366 -aF2296.644787800312 -aF3096.0610057115555 -aF3651.1921870470046 -aF3686.028991746902 -aF3712.842647635937 -aF2809.2979276418687 -aF1347.0164717793466 -aF4022.732650327682 -aF3736.580304598808 -aF2727.8975490450857 -aF3211.8824987530706 -aF3102.252502310276 -aF4057.953001475334 -aF1435.4370034337044 -aF2144.6323640704154 -aF3095.4505457758905 -aF3689.4664723038672 -aF3085.536632835865 -aF3102.7940437436105 -aF3123.502555465698 -aF3097.366371178627 -aF3431.097272384167 -aF3106.999979221821 -aF3685.1194309592247 -aF3350.3484869003296 -aF2425.600839841366 -aF3682.4822767257688 -aF3369.3983236789704 -aF3095.879311478138 -aF3150.357889342308 -aF3685.5601824879645 -aF3096.0999596476554 -aF3058.8717101573943 -aF2690.5527101516723 -aF3100.1898505330087 -aF3537.5717275977136 -aF3095.904372751713 -aF3093.8855782032015 -aF3699.7304258584977 -aF3003.7235604405405 -aF3084.4535499691965 -aF3825.1476626276967 -aF3734.379271006584 -aF3353.6138074040414 -aF3094.9275278925898 -aF3108.4941214561463 -aF3095.078167939186 -aF3642.2022723674772 -aF3091.6616625785828 -aF3095.7251301646234 -aF3691.547375226021 -aF3251.7468128561973 -aF3594.027694427967 -aF1001.744854092598 -aF3433.7581258654595 -aF2935.9042128801348 -aF3685.7805582523347 -aF4492.301102411747 -aF3302.939095020294 -aF2146.5729783415795 -aF3686.1937968611714 -aF2997.4920202851295 -aF3287.5125191092493 -aF3684.1188868522645 -aF3098.2832869052886 -aF3023.971979868412 -aF3687.556367409229 -aF3270.385590267181 -aF3091.9120029091837 -aF3135.8438706755637 -aF3084.184141278267 -aF3095.8899352788926 -aF4073.249640130997 -aF3335.6609461545945 -aF3817.6271013140677 -aF3093.16452177763 -aF3095.075988698006 -aF3095.8972902178766 -aF3087.1391923189162 -aF3095.663566601276 -aF3096.439376461506 -aF3093.7891467809677 -aF3679.979145824909 -aF3685.919757282734 -aF3389.632850444317 -aF3691.094910275936 -aF3186.6319036006926 -aF3685.919757282734 -aF3733.142824041843 -aF4042.487471628189 -aF3685.8312256097793 -aF3147.2933314323427 -aF3686.2744287848473 -aF3096.1059525609016 -aF3095.068633759022 -aF3569.7738297104834 -aF3688.2586278796193 -aF2984.8388011813163 -aF3753.1120281934736 -aF3417.0433460116387 -aF3008.595254099369 -aF3680.1063590288163 -aF3095.9185378193856 -aF3685.4847262620924 -aF3378.741820240021 -aF3701.8538239836694 -aF3717.2839411616324 -aF3041.4527629971503 -aF3093.6690161108972 -aF3717.522295665741 -aF3094.1247499227525 -aF3049.864089143276 -aF3688.2166774868965 -aF3095.6049994945524 -aF3653.274179589748 -aF3684.259175503254 -aF836.1135350108146 -aF3689.01427975893 -aF3586.1197729945184 -aF3674.62556746006 -aF3095.308350288868 -aF3069.470177233219 -aF4707.880629348754 -aF3096.502029645443 -aF3096.1931222081184 -aF3056.402902305126 -aF3092.562506401539 -aF1686.7852330803871 -aF3164.7675768375398 -aF3062.293663620949 -aF3096.108131802082 -aF1900.5674308538437 -aF3172.5643569707872 -aF1573.627862381935 -aF1312.6441178560258 -aF3115.631953537464 -aF3570.5090512037277 -aF3768.716757071018 -aF3717.0695583105085 -aF3729.7053434848785 -aF3099.7496438145636 -aF3684.3003086805343 -aF2939.855721950531 -aF3097.9218052744864 -aF3686.1943416714666 -aF3690.1297788381576 -aF2612.9058020830153 -aF3907.8008324980733 -aF3096.197480690479 -aF3095.9525884628297 -aF3095.7662633419036 -aF3100.0413897275926 -aF1516.7831734061242 -aF3095.898107433319 -aF3686.139860641956 -aF3689.2687061667443 -aF2634.0354521632194 -aF3764.626048970222 -aF2880.681968963146 -aF3686.0513289690016 -aF2440.5983776450157 -aF3096.794865179062 -aF3095.961850237846 -aF3681.3708637237546 -aF3684.649259674549 -aF2031.194688475132 -aF3099.9604853987694 -aF3095.8084861397742 -aF3686.0728489756584 -aF3229.8568800091743 -aF3689.6007680416105 -aF3685.936101591587 -aF4668.034293985366 -aF1058.5119076013566 -aF3101.692709732056 -aF3095.756184351444 -aF3685.6190219998357 -aF3096.1694229602813 -aF3686.1384986162184 -aF3128.4080273628233 -aF3686.122426712513 -aF4437.387493526935 -aF3682.6577056407928 -aF3096.5556934595106 -aF3095.9008314847947 -aF3093.743110311031 -aF3095.9465955495834 -aF3183.004829061031 -aF3681.6163007616997 -aF3094.705790102482 -aF3649.8366990327836 -aF4435.453961789607 -aF3150.789651501179 -aF2832.3864431381226 -aF3004.7464417696 -aF3685.746235203743 -aF400.4491724014282 -aF3098.725400459766 -aF3096.4132255673408 -aF4606.747766673564 -aF1428.2637586832047 -aF2373.197171986103 -aF3095.802765631676 -aF3639.8263546705243 -aF3096.6363253831864 -aF3685.892244362831 -aF3054.250084424019 -aF3096.715050470829 -aF3094.4807834506037 -aF4263.069174289703 -aF3211.9307144641875 -aF3686.80834287405 -aF3208.242348766327 -aF3682.70810059309 -aF3686.095186197758 -aF3096.5003952145576 -aF3685.2101418733596 -aF4520.873950743675 -aF3746.237067079544 -aF3094.427664446831 -aF3097.6079945445063 -aF3096.335317695141 -aF3688.159744811058 -aF3160.5194185614587 -aF3685.5280386805534 -aF3099.6515779614447 -aF3095.6660182476044 -aF1148.9564095020294 -aF3096.281653881073 -aF3095.1465416312217 -aF3684.1967947244643 -aF1778.5568928599357 -aF3696.4512126922605 -aF4712.496262168884 -aF1860.1411449313164 -aF3113.607438480854 -aF3093.014698946476 -aF2675.130765128136 -aF3687.7813740611077 -aF3096.0577368497848 -aF3096.281653881073 -aF3104.7302995324135 -aF2911.4291551828383 -aF2675.5976675510406 -aF4397.307434546947 -aF3092.5012152433396 -aF3095.516467821598 -aF3095.494130599499 -aF3084.20048558712 -aF2602.5533168554307 -aF3097.0604601979257 -aF3583.275046038628 -aF3094.758636701107 -aF3095.9220790863037 -aF3687.595321345329 -aF3096.0231413960455 -aF3062.8136850476267 -aF3685.9243881702423 -aF3087.752376306057 -aF3095.2386145710943 -aF3096.201294362545 -aF3683.9788706064223 -aF3060.0749236941338 -aF3094.0928785204887 -aF3478.2190044283866 -aF3689.3877472162244 -aF1558.9490386009218 -aF4042.8323365449905 -aF3048.6434416770935 -aF1073.2337713956833 -aF3097.9182640075683 -aF3662.4716669917107 -aF1208.3657929420472 -aF3325.940168464184 -aF3096.803309738636 -aF3686.140133047104 -aF3095.739295232296 -aF3720.505949246883 -aF3702.9134800076486 -aF3686.0671284675595 -aF3681.2117791175842 -aF3727.90992115736 -aF3098.2584980368615 -aF3268.623673772812 -aF3428.5919622421266 -aF3684.100090897083 -aF3686.1869867324826 -aF3090.3306910276415 -aF2756.6836906075478 -aF3096.3508447885515 -aF3093.0844346642493 -aF3096.2072872757913 -aF3094.575308036804 -aF3683.177454662323 -aF3096.4110463261604 -aF688.6491876244545 -aF3686.0069269299506 -aF2713.7354778289796 -aF3686.161108243465 -aF3253.077239596844 -aF3832.3805641055105 -aF3692.16627972126 -aF3096.634690952301 -aF2630.057247388363 -aF3685.9889481902123 -aF3669.6435497164725 -aF3681.2872353434564 -aF3097.6777302622795 -aF3689.031986093521 -aF3096.8542495012284 -aF3097.080345773697 -aF1855.6292984724046 -aF3098.359832751751 -aF918.6779008030891 -aF370.06128857135775 -aF3733.3830853819845 -aF3096.405053412914 -aF3699.6043022751805 -aF3095.2879199028016 -aF3095.4516353964805 -aF2713.6861724972723 -aF3103.6297827363014 -aF3096.190398156643 -aF2034.3559502124785 -aF4198.523319387436 -aF1093.6660642981528 -aF3051.8036137938498 -aF3094.7978630423545 -aF3416.5650025725363 -aF2908.10336073637 -aF3092.2053832530974 -aF3095.3938855051993 -aF3221.4559052586555 -aF2447.9402411818505 -aF882.1077821493149 -aF3096.0879738211634 -aF3092.9313429713247 -aF3104.6090792417526 -aF3686.1480327963827 -aF3095.7548223257063 -aF3094.1637038588524 -aF3095.2786581277846 -aF3149.574724543095 -aF3588.3055518984793 -aF3705.14148170948 -aF3096.2094665169716 -aF3082.1702500224114 -aF2408.6191029429438 -aF3100.36555185318 -aF2901.298135340214 -aF3683.620657837391 -aF3096.281109070778 -aF3685.2422856807707 -aF3098.1879451036452 -aF3095.1819543004035 -aF3686.084562397003 -aF3723.074729788303 -aF2189.1866775989533 -aF3116.671179175377 -aF3498.6856203794478 -aF3427.8273209929466 -aF3095.5990065813066 -aF3685.617659974098 -aF3096.9343366146086 -aF2049.2170130372047 -aF3684.1341415405273 -aF3006.4506083726883 -aF3686.1962485074996 -aF3097.99263061285 -aF3096.1103110432623 -aF3686.483908343315 -aF3095.9643018841743 -aF3684.7832830071447 -aF3685.985406923294 -aF3094.781246328354 -aF3097.6564826607705 -aF3187.674398100376 -aF3685.304121649265 -aF3256.5378745913504 -aF4043.5068116903303 -aF3773.2296931505202 -aF3367.9428629755976 -aF3209.7389426469804 -aF3663.3422738432882 -aF3293.042616009712 -aF3685.602405285835 -aF3095.681545341015 -aF3678.0254561066627 -aF3096.0324031710625 -aF3795.201892352104 -aF3096.4878645777703 -aF3095.60418227911 -aF3677.157845711708 -aF3686.0962758183477 -aF3685.2207656741143 -aF3091.2718508124353 -aF430.1127309441566 -aF2228.141158509254 -aF3095.5322673201563 -aF3370.149072265625 -aF3976.8740609526635 -aF4137.0128750443455 -aF3095.65648406744 -aF3102.7471900582314 -aF2529.1096202135086 -aF3686.2727943539617 -aF3091.36038248539 -aF3125.669810819626 -aF4601.233741676807 -aF2643.39338619709 -aF3430.2555404782297 -aF3097.225265312195 -aF3727.404064798355 -aF3095.70987547636 -aF3747.182312941551 -aF3689.162740564346 -aF3634.6945140957832 -aF3141.677154505253 -aF3091.268309545517 -aF2475.4863945126535 -aF3096.548883330822 -aF3687.9254763841627 -aF3098.3423988223076 -aF3685.9889481902123 -aF1615.9419159770011 -aF3533.0666912674906 -aF4388.023322308063 -aF3686.202513825893 -aF3685.8475699186324 -aF3096.3919779658318 -aF3680.0366233110426 -aF3209.8710591435433 -aF3685.7064640522003 -aF4203.558728539943 -aF3065.896494102478 -aF3095.9681155562403 -aF2008.1059005737304 -aF3058.059942817688 -aF853.5278512835503 -aF2302.3988017320635 -aF3104.81637955904 -aF2983.000338840485 -aF3743.0507440686224 -aF3685.748414444923 -aF3095.980918598175 -aF3685.845390677452 -aF3653.55557410717 -aF4709.901330733299 -aF3430.675316810608 -aF3099.478600692749 -aF2983.892465698719 -aF4066.3300045728683 -aF2642.363422334194 -aF4071.011831843853 -aF3096.942781174183 -aF3686.0491497278213 -aF3685.9619800806045 -aF3081.6126366853714 -aF3110.999704003334 -aF3691.398097205162 -aF2221.032746183872 -aF3102.854517686367 -aF3160.949546289444 -aF3636.630497479439 -aF3379.2997059822083 -aF2994.6184183835985 -aF2242.167299556732 -aF1790.442474257946 -aF3088.7984120726587 -aF3095.2999057292936 -aF3085.3304221391677 -aF3095.4576283097267 -aF2969.8565180659293 -aF3096.509929394722 -aF3836.108156144619 -aF3679.398378050327 -aF3196.0146265029907 -aF3477.02886633873 -aF3079.2642319083216 -aF3680.8832585096357 -aF3771.5277057886124 -aF3687.3877486228944 -aF3684.4040950417516 -aF3692.7198069810865 -aF3166.086017751694 -aF2158.7413162827493 -aF3318.276866853237 -aF3142.2295921444893 -aF3096.469613432884 -aF3087.980651819706 -aF2318.1637048363687 -aF3677.9110459446906 -aF3095.2854682564735 -aF2023.6621413350106 -aF3068.0000066518783 -aF3078.62326259613 -aF1327.838059771061 -aF4599.53829203844 -aF3093.4649846553802 -aF3096.183588027954 -aF3285.3512566685677 -aF3428.28550645113 -aF3095.441011595726 -aF2980.431830704212 -aF3685.1513023614884 -aF2707.366100668907 -aF3091.328511083126 -aF3726.6628503918646 -aF3069.184151828289 -aF3086.9128236413003 -aF4400.732112061977 -aF3680.1867185473443 -aF3094.8850326895713 -aF3682.3430776953696 -aF3691.4969802737237 -aF3743.1360068798062 -aF595.0137318253517 -aF1074.2531114578246 -aF3076.06674028635 -aF3687.114526259899 -aF3059.0324291944503 -aF3055.4628321409227 -aF3127.820177054405 -aF3388.636664819717 -aF4000.3145238995553 -aF3075.568511271477 -aF3427.5960490226744 -aF3094.8730468630793 -aF3096.0411201357842 -aF3096.6251567721365 -aF3095.59192404747 -aF3616.820377933979 -aF3095.984732270241 -aF3097.734118127823 -aF3686.043156814575 -aF3686.2036034464836 -aF3730.449281942844 -aF1698.5362463355066 -aF269.25286042690277 -aF3685.1439474225044 -aF3845.8379232048987 -aF2108.343095123768 -aF3703.9559745073316 -aF3095.924258327484 -aF2363.7300034880636 -aF3092.2582298517227 -aF2491.164672780037 -aF373.14872851371763 -aF3958.3940957427026 -aF3623.5866493940352 -aF3097.080073368549 -aF4601.475637447834 -aF3672.1744659423825 -aF3096.07053989172 -aF3684.465386199951 -aF3096.1035009145735 -aF3096.0915150880815 -aF4412.975906229019 -aF3042.472103059292 -aF3687.550374495983 -aF1396.5756850838661 -aF4571.647818601131 -aF3070.1528245329855 -aF3685.702922785282 -aF3688.588782918453 -aF3685.0957317113875 -aF3107.4420927762985 -aF3050.7314271330833 -aF3096.1413652300835 -aF3096.7973168253898 -aF3016.7368991494177 -aF4172.618407070637 -aF4279.705501461029 -aF3203.7623737096787 -aF4389.1472659468645 -aF3095.895928192139 -aF3103.0326706528663 -aF3687.772384691238 -aF3096.3968812584876 -aF3067.0130828022957 -aF4279.2756461381905 -aF3753.973645675182 -aF3096.9256196498873 -aF3096.5450696587563 -aF3113.8468826055528 -aF3093.4611709833143 -aF3686.077479863167 -aF2970.9875442385674 -aF3097.021233856678 -aF3180.3943705320357 -aF2921.9377285599708 -aF1293.6822679400445 -aF3119.138897407055 -aF3686.7574031114577 -aF3396.3261173248293 -aF3630.833715939522 -aF3685.3338138103486 -aF3139.3143122553824 -aF3110.347838485241 -aF3135.354903435707 -aF3095.8722289443017 -aF3095.833547413349 -aF3906.7583379983903 -aF3678.041800415516 -aF3097.1274718642235 -aF3686.180993819237 -aF3702.447667205334 -aF3836.6183709859847 -aF2964.3441275000573 -aF3688.272520542145 -aF3011.9150556325912 -aF3104.1269221305847 -aF3281.9137761116026 -aF3399.763597881794 -aF1437.2869067907334 -aF3097.476422858238 -aF2980.4517162799834 -aF2813.6253558158874 -aF3162.164473247528 -aF3095.154986190796 -aF3686.125967979431 -aF4717.710641503333 -aF3095.956402134895 -aF3119.143800699711 -aF3046.9357338070868 -aF3092.603639578819 -aF3686.347160959244 -aF2977.017504584789 -aF3095.979556572437 -aF3097.305624830723 -aF3100.6733696699143 -aF3096.7986788511275 -aF3095.87685983181 -aF3686.0703973293303 -aF3095.4023300647737 -aF3683.624199104309 -aF3700.3081971764564 -aF3685.821691429615 -aF3754.0804284930227 -aF562.9407497525215 -aF3113.6875255942346 -aF3107.9356909036637 -aF3685.5236801981923 -aF3694.3817507863046 -aF3685.922208929062 -aF4434.32729409933 -aF3062.582685482502 -aF401.8722168922424 -aF3095.5131989598276 -aF3051.902769267559 -aF3686.9268391132355 -aF3708.34469383955 -aF3783.321759057045 -aF2655.3514273643495 -aF3385.749987471104 -aF3685.8957856297493 -aF1305.8762119650842 -aF3097.1650637745856 -aF3500.646392631531 -aF3071.48570291996 -aF3189.7476736783983 -aF3686.5533716559407 -aF1936.5077488064767 -aF986.0731135487556 -aF3096.565500044823 -aF3103.0781623125076 -aF3679.29786055088 -aF3690.2229413986206 -aF3094.6139895677566 -aF724.5821506381035 -aF3721.425861430168 -aF3183.260617494583 -aF3090.419767510891 -aF3747.2564071416855 -aF2675.211124646664 -aF3686.822235536575 -aF4387.003982245921 -aF3097.1002313494682 -aF3167.1857173323633 -aF3103.437737107277 -aF3095.7485570073127 -aF3684.7955412387846 -aF3212.298733818531 -aF3118.3527361512183 -aF3688.967970883846 -aF3687.8380343317986 -aF3420.6859476447107 -aF3716.814859497547 -aF3689.851925587654 -aF3496.528171610832 -aF3685.3667748332023 -aF3688.6677804112433 -aF2158.2251085281373 -aF3095.9087312340735 -aF4386.057646763325 -aF3092.5420760154725 -aF2862.768061649799 -aF3096.5679516911505 -aF3074.994826030731 -aF2968.621705532074 -aF3095.404509305954 -aF3097.4796917200088 -aF3081.1318415999413 -aF3685.9137643694876 -aF3167.967247700691 -aF2530.5201340675353 -aF3687.7661193728445 -aF3096.4791476130486 -aF2781.109170567989 -aF3088.108954644203 -aF3096.1198452234266 -aF3056.661142385006 -aF3494.767344737053 -aF3685.9875861644746 -aF1020.9221764802933 -aF3083.7338555693627 -aF3096.359016942978 -aF3096.9220783829687 -aF3082.631976747513 -aF3727.6666633605955 -aF3252.0578995347023 -aF3721.779443311691 -aF3096.543980038166 -aF3685.9889481902123 -aF3096.5619587779047 -aF3094.5526984095573 -aF3189.3976330637934 -aF3142.24620885849 -aF3690.5552756786346 -aF3092.333958482742 -aF3105.265575647354 -aF3660.053526496887 -aF3684.2485517024993 -aF2546.379016947746 -aF3095.7354815602303 -aF3915.4415244817733 -aF3685.594505536556 -aF3680.646266031265 -aF3664.8366884827615 -aF3094.9106387734414 -aF3075.516754293442 -aF3193.3289841532705 -aF2930.0551295518876 -aF3104.638771402836 -aF3095.7286714315414 -aF3126.7041331648825 -aF3681.009109687805 -aF3681.614666330814 -aF3190.825580847263 -aF3096.6867203354836 -aF3094.2805656671526 -aF3687.0156431913374 -aF3686.002296042442 -aF3096.1773227095605 -aF3077.029692482948 -aF3707.0129050731657 -aF4042.6855101704596 -aF3684.5430216670034 -aF3684.70591994524 -aF3639.1513347148893 -aF3097.2097382187844 -aF2934.0567611694337 -aF2993.800385725498 -aF3096.2756609678268 -aF3493.1138454914094 -aF3085.3598418951033 -aF3048.999202799797 -aF3096.052833557129 -aF3315.1728101968765 -aF3090.9199033617974 -aF2500.4588640093802 -aF2610.5772828817367 -aF3476.3609289169312 -aF3338.537272107601 -aF2596.343024301529 -aF3053.851828098297 -aF1751.0505106806756 -aF3030.37077678442 -aF3686.042067193985 -aF3720.9063848137853 -aF409.39631947278974 -aF3095.0784403443336 -aF3265.4536950707434 -aF3487.123111486435 -aF3214.3278797626494 -aF2922.4411332726477 -aF4602.4309623003 -aF3096.1966634750365 -aF4368.160900974273 -aF3685.686033666134 -aF3096.2576822280885 -aF4136.353109776974 -aF3686.3594191908837 -aF3686.1834454655645 -aF3255.5185345292093 -aF3093.4154069185256 -aF3003.2087147116663 -aF3700.8979543209075 -aF3687.145035636425 -aF3095.157437837124 -aF2724.506377363205 -aF2097.10502076149 -aF2941.3534054517745 -aF2712.989360129833 -aF3096.1966634750365 -aF3097.8098467588425 -aF2086.159781932831 -aF3682.1801794171333 -aF3094.563049805164 -aF3255.592901134491 -aF3095.8923869252203 -aF3685.1676466703416 -aF3089.633878660202 -aF3107.7599895834924 -aF3095.236435329914 -aF3278.751152348518 -aF3096.1103110432623 -aF3095.8338198184965 -aF2854.9105351686476 -aF3686.146943175793 -aF3208.696448147297 -aF3103.6570232510567 -aF3067.6510556578637 -aF3096.3238766789436 -aF3683.5201403379438 -aF3074.166441977024 -aF2798.5038736701013 -aF3111.4777750372887 -aF3098.5932839632032 -aF4555.157228183746 -aF3691.5713468790054 -aF3102.135640501976 -aF3045.804707634449 -aF3677.2087854743004 -aF3097.0672703266146 -aF3646.43245190382 -aF3095.926709973812 -aF3120.833529829979 -aF2685.526835179329 -aF3281.694489967823 -aF3694.1507512211797 -aF3682.0605935573576 -aF3112.49602547884 -aF3685.997120344639 -aF3095.0887917399405 -aF3134.3287532448767 -aF3726.220736837387 -aF3093.771440446377 -aF1408.6102720975875 -aF3096.8493462085726 -aF3095.058282363415 -aF3685.9467253923417 -aF3646.568109667301 -aF3090.8286476373673 -aF3668.0998297452925 -aF3688.5661732912063 -aF3265.0061334133147 -aF3095.7945934772492 -aF3021.013115155697 -aF3096.1786847352982 -aF3737.485506904125 -aF3434.4533038020136 -aF3094.082527124882 -aF3686.569171154499 -aF3684.1224281191826 -aF3403.9962290644644 -aF3092.6578482031823 -aF3173.980318927765 -aF3685.8546524524686 -aF3687.318557715416 -aF3677.4337921261786 -aF3093.071359217167 -aF3086.829467666149 -aF3868.2064718961715 -aF1057.7576177477836 -aF3698.372758603096 -aF1512.9158375263214 -aF3699.1011699676515 -aF3693.237104356289 -aF3837.1629088759423 -aF3691.3215513586997 -aF3054.56607439518 -aF1198.2317766427993 -aF3093.539351260662 -aF3102.878216934204 -aF3714.9044821977614 -aF3103.773885059357 -aF3241.3826142072676 -aF3667.202799594402 -aF1349.390210235119 -aF3185.6125635385515 -aF3074.89703258276 -aF4714.771934771537 -aF3081.8967552542686 -aF3094.4734285116197 -aF3685.7922716736794 -aF3683.9186690688134 -aF3094.0492936968803 -aF3695.1311373472213 -aF3064.0577593564985 -aF3882.210820531845 -aF3110.9596604466437 -aF3802.0204656004903 -aF2944.8140404462815 -aF3101.857514846325 -aF1734.4989015102387 -aF3108.0171400427816 -aF2600.0055115103723 -aF3060.362855935097 -aF288.8126396417618 -aF3103.2219922304153 -aF3767.5465045571327 -aF3115.5526836395265 -aF3989.901292324066 -aF3751.8053007006642 -aF3095.9534056782722 -aF3095.1607066988945 -aF3095.830006146431 -aF3095.438559949398 -aF3366.369723248482 -aF3685.8347668766974 -aF383.1980268120766 -aF1988.7141953349114 -aF354.94961301088335 -aF3475.9863718390466 -aF3770.485211288929 -aF3683.4095438480376 -aF3087.575312960148 -aF3693.715992605686 -aF3092.2522369384765 -aF3094.9920879125593 -aF3337.0055379629134 -aF3054.480266773701 -aF3687.1793586850167 -aF3095.830006146431 -aF3684.574893069267 -aF3094.5551500558854 -aF3095.9664811253547 -aF3680.443596601486 -aF3097.072990834713 -aF3685.9856793284416 -aF3096.6137157559397 -aF3108.651844036579 -aF3105.5951858758926 -aF4171.291521596908 -aF2938.958964204788 -aF3682.476283812523 -aF3477.6311541199684 -aF3684.195705103874 -aF492.7280505657196 -aF3333.6454204678535 -aF3102.5848365902903 -aF3943.775473499298 -aF3100.2903680324553 -aF3127.636303579807 -aF2945.3550370693206 -aF3105.7864142894746 -aF3096.508839774132 -aF3658.293244433403 -aF3934.492723286152 -aF3026.010659992695 -aF2968.8241025567054 -aF3294.382576930523 -aF3673.2447457671165 -aF3096.347303521633 -aF3073.0599322676658 -aF3097.1473574399947 -aF3031.8472126841543 -aF3096.3494827628138 -aF3631.707319247723 -aF3095.6897174954415 -aF3747.5734867334363 -aF3074.5260167717934 -aF3142.8825472831727 -aF2255.6001421928404 -aF4523.489857375621 -aF3690.148574793339 -aF3153.6964868307114 -aF3096.2165490508078 -aF3097.728397619724 -aF3096.9542221903803 -aF3166.9146742105486 -aF3095.04765856266 -aF3095.9149965524675 -aF3686.284780180454 -aF3687.8911533355713 -aF3269.215337753296 -aF3126.4862090468405 -aF2920.476819753647 -aF3098.9280698895454 -aF3671.3853082299233 -aF3073.262601697445 -aF3735.642413675785 -aF2928.928189456463 -aF3095.4371979236603 -aF3036.4584870219232 -aF3096.7147780656815 -aF3084.6714740872385 -aF3098.3990590929984 -aF3607.2603192806246 -aF3096.1895809412003 -aF2566.997362565994 -aF3096.037578868866 -aF2950.580040204525 -aF4443.707292950153 -aF3098.4851391196253 -aF3095.906824398041 -aF3685.9619800806045 -aF3096.0032558202743 -aF2185.2280859947205 -aF3096.724857056141 -aF3686.191890025139 -aF3773.2768192410467 -aF3141.2268687963488 -aF3093.3764529824257 -aF3062.503415584564 -aF3703.8878732204435 -aF4281.251673078536 -aF3703.410619401932 -aF3708.032245135307 -aF3094.923986625671 -aF3144.8054552197455 -aF1705.1390747070313 -aF3124.0124979019165 -aF3787.3244802951813 -aF3240.3820701003074 -aF2773.310483598709 -aF3120.21680457592 -aF3094.8765881299973 -aF3687.510330939293 -aF3685.807526361942 -aF3688.0668546557426 -aF4423.363531720638 -aF3683.6865798830986 -aF3695.278508532047 -aF3107.3075246334074 -aF3640.193829214573 -aF3098.5031178593636 -aF3095.7548223257063 -aF3092.10159689188 -aF2718.2170873165132 -aF2992.757891225815 -aF3475.3647432923317 -aF3099.972198820114 -aF3091.4287561774254 -aF3107.757537937164 -aF2956.0270535349846 -aF3256.0456384897234 -aF1486.2247639536859 -aF3664.1401485204697 -aF3686.5961392641066 -aF3095.883125150204 -aF3690.6675065994264 -aF3073.0256092190743 -aF2458.5136469841004 -aF3093.9692065834997 -aF3092.8863961219786 -aF3685.6958402514456 -aF421.96536538600924 -aF3686.3368095636365 -aF3094.847440779209 -aF3012.9575501322747 -aF3029.351436722279 -aF3879.3837999105453 -aF3681.532127571106 -aF3097.739021420479 -aF929.3866919636725 -aF3095.6553944468496 -aF2284.4789015054703 -aF3101.142178928852 -aF3687.999570584297 -aF3096.120934844017 -aF3698.0216283679006 -aF3426.246826326847 -aF3264.4112005710604 -aF3684.291319310665 -aF2985.7543548822405 -aF3090.4816034793853 -aF3094.563322210312 -aF4041.789842045307 -aF3229.0175997495653 -aF3098.742017173767 -aF3095.672283565998 -aF3715.8851407289503 -aF1796.9112792968751 -aF3686.670233464241 -aF3095.804944872856 -aF3701.917294383049 -aF3096.421670126915 -aF3108.756175208092 -aF3090.1770545244217 -aF2978.416305017471 -aF3102.4420962929726 -aF3095.3767239809035 -aF3583.253526031971 -aF2832.0235994815826 -aF3687.923024737835 -aF3630.6182434678076 -aF4104.356945955753 -aF3683.687397098541 -aF3686.200879395008 -aF3049.369401395321 -aF3688.49562035799 -aF3152.8283316254615 -aF3048.6927470088003 -aF3089.205930173397 -aF3882.9945301413536 -aF3096.044388997555 -aF3686.075300621986 -aF3095.5461599826813 -aF3094.4151338100432 -aF3101.0171449661257 -aF1859.7960076093675 -aF3103.153890943527 -aF3098.941145336628 -aF3686.0714869499207 -aF3614.7664431214334 -aF3115.9087171673773 -aF3096.118483197689 -aF3670.2455650925635 -aF3350.0728128910064 -aF2919.8758939981462 -aF1171.7880469441413 -aF3448.158551585674 -aF3096.100776863098 -aF3211.123305606842 -aF3685.839397764206 -aF3180.552093112469 -aF1904.5140366315843 -aF3317.2575267910956 -aF3715.795519435406 -aF3132.1990898013114 -aF3684.519594824314 -aF2994.545413804054 -aF3095.0887917399405 -aF3267.358079457283 -aF3126.552403497696 -aF3093.764085507393 -aF3121.8724830627443 -aF2984.820822441578 -aF3686.0407051682473 -aF3079.999180996418 -aF3134.757246541977 -aF3676.6255660533902 -aF3106.142447817326 -aF3098.7654440164565 -aF2978.413036155701 -aF3686.059773528576 -aF3098.42003428936 -aF3680.648717677593 -aF2844.9271589159966 -aF2981.7687951683997 -aF3097.872227537632 -aF3099.109764122963 -aF3151.721821916103 -aF2319.5625052690507 -aF2783.9743279099466 -aF3087.818025946617 -aF1935.4126801133157 -aF3096.3238766789436 -aF3283.2079729676248 -aF2780.066676068306 -aF3729.971755719185 -aF3095.018511211872 -aF3056.1980536341666 -aF1775.6710327267647 -aF3430.4375071167947 -aF2107.3006006240844 -aF3095.9081864237787 -aF270.1419908285141 -aF3095.9019211053846 -aF3095.8795838832857 -aF3109.4854037880896 -aF3096.215459430218 -aF3091.744473743439 -aF2887.5768156528475 -aF3101.908454608917 -aF3262.4114743828773 -aF376.82973927259445 -aF3666.736169576645 -aF3095.597644555569 -aF3227.2665794610975 -aF3094.2672178149223 -aF3073.5690574884416 -aF2929.058943927288 -aF3104.218177855015 -aF3752.8246407628058 -aF3097.4325656294823 -aF4420.080504882335 -aF3096.180046761036 -aF3724.4825195908547 -aF2799.6645920038222 -aF2692.658674347401 -aF3102.6529378771784 -aF3096.0304963350295 -aF2965.8290079593658 -aF3586.0227967619894 -aF3094.136463344097 -aF3090.92072057724 -aF3328.154005098343 -aF3086.348672580719 -aF3064.779088187218 -aF3702.2455425858498 -aF3075.889132130146 -aF3108.204554784298 -aF3418.6036826968193 -aF3310.9625162363054 -aF3091.890482902527 -aF3096.0574644446374 -aF3108.794856739044 -aF3086.4464660286903 -aF3015.744527196884 -aF3070.0081773996353 -aF3096.2023839831354 -aF1665.5621480345726 -aF3078.75129301548 -aF3097.5565099716187 -aF3683.7053758382795 -aF3099.295544433594 -aF3284.975065159798 -aF3095.6698319196703 -aF3096.0975080013277 -aF3269.2202410459518 -aF3068.4334032416346 -aF3295.425071430206 -aF3627.3373958706857 -aF3088.9068293213845 -aF3685.682492399216 -aF3095.9751980900764 -aF3091.651311182976 -aF3662.3281094789504 -aF4035.9543789744375 -aF3399.3427319288253 -aF3685.0227271318436 -aF3078.0977930665017 -aF3095.826464879513 -aF3093.7491032242774 -aF3048.3037524580955 -aF3094.1026851058004 -aF3094.918810927868 -aF3699.508960473537 -aF3094.2462426185607 -aF3686.1728216648103 -aF2963.738570857048 -aF3721.17633831501 -aF1517.3936333417894 -aF3115.9098067879677 -aF3097.0520156383513 -aF3665.0606055140493 -aF3764.3195931792256 -aF4689.816626799106 -aF3743.768531632423 -aF3709.055126464367 -aF3243.7841379880906 -aF2857.63376942873 -aF3671.8977023124694 -aF3096.128017377853 -aF3094.792142534256 -aF3126.3115973472595 -aF3095.9561297297478 -aF3095.7698046088217 -aF2725.5311655282976 -aF3685.5955951571464 -aF3141.886361658573 -aF3770.8020184755323 -aF3095.8795838832857 -aF3429.8891555547716 -aF3098.113850903511 -aF3095.5036647796633 -aF3686.138771021366 -aF3689.254813504219 -aF3685.827684342861 -aF4142.583015501499 -aF3096.43338354826 -aF4214.432869625091 -aF3693.565080153942 -aF3094.4984897851946 -aF3703.1028015851975 -aF2980.374080812931 -aF3833.8760683655737 -aF3703.2834061980247 -aF3990.472798323631 -aF617.1158682823182 -aF3128.111105751991 -aF2985.7589857697485 -aF3351.61053994894 -aF3843.112509703636 -aF3681.064407932758 -aF3125.4807616472244 -aF3774.0572599887846 -aF3094.8376341938974 -aF3685.7356114029885 -aF3685.921936523914 -aF3095.592196452618 -aF3701.7252487540245 -aF3095.539077448845 -aF3095.924258327484 -aF3675.9867759823796 -aF3095.1819543004035 -aF3392.342736852169 -aF3095.834909439087 -aF3098.549699139595 -aF3095.304536616802 -aF3685.695567846298 -aF3639.5871829509733 -aF3086.1650715112687 -aF3313.357229888439 -aF3686.1147993683812 -aF3182.2644318699836 -aF3690.795537018776 -aF3740.988909506798 -aF3660.6795135259626 -aF3086.011435008049 -aF3687.8862500429154 -aF3104.7858701825144 -aF3686.666692197323 -aF3578.0753765821455 -aF3096.047930264473 -aF3612.5980981469156 -aF3685.657975935936 -aF3554.6994736552238 -aF4748.5163948297495 -aF3043.305390405655 -aF917.455074095726 -aF2307.543990159035 -aF3684.8121579527856 -aF3095.9806461930275 -aF1409.629612159729 -aF3690.825229179859 -aF3702.2262018203733 -aF900.5223701238632 -aF4345.739778089523 -aF3681.2766115427016 -aF3112.4491717934607 -aF4470.348788785934 -aF3596.574682557583 -aF4002.110491037369 -aF3101.512105119228 -aF3467.9362549185753 -aF3094.259318065643 -aF3097.959124779701 -aF3097.900830078125 -aF3098.1280159711837 -aF3685.618204784393 -aF3685.0973661422727 -aF2391.08438359499 -aF3040.0150086283684 -aF2765.4543191432954 -aF3686.1172510147094 -aF3685.8535628318787 -aF2989.3059731960298 -aF3095.841991972923 -aF3096.108131802082 -aF1623.1892549276351 -aF3721.309544432163 -aF3687.3877486228944 -aF3686.628827881813 -aF3305.3719453930853 -aF3092.344309878349 -aF3599.042945599556 -aF3058.1541949987413 -aF2432.319167995453 -aF3685.8404873847962 -aF3447.9953809022904 -aF3070.0691961526873 -aF3683.7407885074613 -aF3095.5486116290094 -aF3685.468109548092 -aF2102.10120357275 -aF3752.030307352543 -aF3677.974788749218 -aF1190.6412072062492 -aF3104.89319781065 -aF3339.649502325058 -aF3113.125553774834 -aF3106.4132185339927 -aF3686.5195934176445 -aF3925.5861645817754 -aF3667.7462478637694 -aF3088.555699086189 -aF4063.6844057798385 -aF3104.174320626259 -aF3093.223633694649 -aF2296.328797829151 -aF3096.32986959219 -aF3095.6180749416353 -aF4504.397252988815 -aF2914.6685971975326 -aF3095.534174156189 -aF2608.0142228484156 -aF3685.9170332312583 -aF3060.657325899601 -aF3096.331776428223 -aF3209.859073317051 -aF3286.3117572188376 -aF1795.1016919016838 -aF3097.4739712119103 -aF3565.33117415905 -aF3103.90927041769 -aF2802.7002749681474 -aF3682.0510593771933 -aF4667.2268851280205 -aF2853.681443142891 -aF3094.431205713749 -aF3686.0069269299506 -aF3126.093945634365 -aF3096.2636751413347 -aF3107.2958112120627 -aF3055.430960738659 -aF3096.996444988251 -aF3696.2125857830047 -aF2948.006356370449 -aF3527.7817589998244 -aF3112.0934106707573 -aF3617.102862071991 -aF3694.3122874736787 -aF3094.1484491705896 -aF3028.735256278515 -aF3546.682862567902 -aF1361.407363319397 -aF3686.3485229849816 -aF4022.011593902111 -aF3144.9985904693604 -aF3095.300995349884 -aF1865.2650857567787 -aF3655.791203153133 -aF3429.276516377926 -aF3089.065913927555 -aF3090.913638043404 -aF3686.579250144958 -aF2396.2178586006166 -aF3686.4179862976075 -aF3122.2157135486605 -aF3101.475875234604 -aF3681.4531300783156 -aF3437.4816318273542 -aF3685.918395256996 -aF3685.837490928173 -aF3706.884329843521 -aF3046.8393023848535 -aF3018.372419655323 -aF2393.449132680893 -aF3094.861605846882 -aF3686.364867293835 -aF3623.186213827133 -aF4243.44864872694 -aF3094.393613803387 -aF3108.686439490318 -aF3093.549702656269 -aF3601.106686997414 -aF3096.190398156643 -aF3775.975264632702 -aF3092.6352385759355 -aF3101.9637528538706 -aF3717.4416637420654 -aF3098.0819795012476 -aF3689.9320127010346 -aF863.0930856347084 -aF3277.4643104314805 -aF3113.6605574846267 -aF3096.2070148706434 -aF3095.4576283097267 -aF2562.5255596637726 -aF3685.4591201782227 -aF3655.715746927261 -aF3095.5142885804175 -aF3099.3925206661224 -aF3096.276750588417 -aF3686.4215275645256 -aF3095.8005863904955 -aF3215.9375217795373 -aF3738.371368443966 -aF3096.3919779658318 -aF3095.5249123811723 -aF3106.2966291308403 -aF3490.3671843886377 -aF3761.4350950717926 -aF2607.3046074390413 -aF3685.101724624634 -aF3095.006252980232 -aF3216.702163028717 -aF3114.7518125057222 -aF3090.6071822524073 -aF3260.635665225983 -aF3311.9241064071657 -aF3685.9679729938507 -aF2296.7297782063483 -aF3435.6962884902955 -aF3478.5606004834176 -aF3685.5116943717003 -aF2118.17501411438 -aF3096.2802918553352 -aF3685.709732913971 -aF3136.4191903471947 -aF3695.1545641899106 -aF3093.7668095588683 -aF3116.8362566947935 -aF4002.833999109268 -aF3691.970692825317 -aF3658.1679380655287 -aF3096.0727191329 -aF3686.21068598032 -aF3097.6978882431986 -aF4073.2471884846686 -aF3099.775522303581 -aF3093.432840847969 -aF3094.3511186003684 -aF1498.597950565815 -aF3689.8023478507994 -aF3057.51322568655 -aF1973.6180468678474 -aF4011.362187063694 -aF3371.597178030014 -aF3587.9432530522345 -aF3098.038394677639 -aF3685.8227810502053 -aF3779.127809405327 -aF2999.883465075493 -aF3687.31474404335 -aF3095.7828800559046 -aF3685.889792716503 -aF3097.188763022423 -aF3056.440766620636 -aF1477.0697717547416 -aF3685.5163252592088 -aF395.4655202269554 -aF3099.0977782964706 -aF3107.8013951659204 -aF2411.781726706028 -aF3095.5788486003876 -aF3685.256178343296 -aF4006.8288205981253 -aF3097.551606678963 -aF3091.676372456551 -aF3080.144100534916 -aF2537.1766262531282 -aF3835.450842523575 -aF3095.8120274066923 -aF3089.3887140274046 -aF3112.4690573692324 -aF3099.7395648241045 -aF1312.287811923027 -aF2146.88787869215 -aF3685.8949684143067 -aF3699.413618671894 -aF3060.591403853893 -aF3444.680755066872 -aF3101.8114783763885 -aF3089.729220461845 -aF3687.268707573414 -aF3131.6425660848618 -aF3096.0610057115555 -aF3083.946059179306 -aF3682.647081840038 -aF3683.9257516026496 -aF3333.650596165657 -aF3095.9550401091574 -aF3675.3575200915334 -aF834.1628417491912 -aF3074.2781280875206 -aF3593.6871879935265 -aF3099.4497257471085 -aF3105.6153438568117 -aF2495.619041752815 -aF2943.6898244023323 -aF3101.9702905774116 -aF3070.534464144707 -aF3092.0095239520074 -aF3086.467441225052 -aF3096.269395649433 -aF3083.5690504550935 -aF3127.7460828542708 -aF3096.2718472957613 -aF4628.253608262538 -aF3095.945505928993 -aF3080.279758298397 -aF3157.438243937492 -aF3881.7485489964483 -aF3997.3017229676248 -aF3093.6796399116515 -aF3085.190133488178 -aF3105.663831973076 -aF3685.1523919820784 -aF3097.8117535948754 -aF1080.2168773531914 -aF3666.9674415469167 -aF3653.472218132019 -aF3072.5055877923965 -aF3931.223589110374 -aF3096.9351538300516 -aF3095.3846237301827 -aF3095.9220790863037 -aF3098.868140757084 -aF3118.572022294998 -aF2791.110525560379 -aF4138.24332909584 -aF2935.671851289272 -aF3441.1642770171165 -aF3688.876170349121 -aF3093.846896672249 -aF3145.533866584301 -aF1880.8270470261575 -aF3177.5106896400453 -aF3095.961577832699 -aF3108.1348190665244 -aF3237.044834637642 -aF3710.543548190594 -aF3027.271895825863 -aF3897.5297964096067 -aF3095.7286714315414 -aF3095.6878106594086 -aF3095.7379332065584 -aF1488.9678837895394 -aF3686.21068598032 -aF3712.343873810768 -aF3077.558975684643 -aF3095.5823898673057 -aF3832.01336196661 -aF3092.6235251545904 -aF3096.7572732686995 -aF3668.936658358574 -aF2511.829054868221 -aF3375.2258870005608 -aF3096.9046444535256 -aF3687.225395154953 -aF3099.3195160865785 -aF2320.0928780913355 -aF3068.664402806759 -aF3686.175273311138 -aF3690.6271906375882 -aF3154.7313539862635 -aF3094.048476481438 -aF3481.7401133656504 -aF3670.784110069275 -aF3011.0863991737365 -aF3685.9698798298837 -aF3686.219130539894 -aF3686.084562397003 -aF3847.8237567305564 -aF2979.231613624096 -aF2157.171445417404 -aF3299.303575921059 -aF3644.8002002596854 -aF3683.436784362793 -aF3583.484798002243 -aF3688.9576194882393 -aF3096.710419583321 -aF3094.5464330911636 -aF3686.725804114342 -aF3095.7346643447877 -aF927.549319243431 -aF3724.5416315078733 -aF3093.4510919928553 -aF3096.937877881527 -aF3095.542618715763 -aF3096.256865012646 -aF3093.5213725209237 -aF2632.109820175171 -aF3097.1402749061585 -aF2679.217659556866 -aF3095.6624769806863 -aF3095.5186470627787 -aF3097.755910539627 -aF3581.161999309063 -aF3408.8003662467004 -aF3686.700742840767 -aF3666.9848754763602 -aF3685.3667748332023 -aF3097.217910373211 -aF2990.524441421032 -aF332.70609828233717 -aF3096.1846776485445 -aF3685.987858569622 -aF3743.820288610458 -aF3667.6639815092085 -aF3770.5840943574904 -aF2697.5112996459006 -aF3644.3499145507812 -aF4758.991462373733 -aF1431.0237676382064 -aF3096.0577368497848 -aF3096.014969241619 -aF2741.315954208374 -aF3680.6672412276266 -aF3287.604319643974 -aF3792.183370912075 -aF3508.863766312599 -aF3689.939095234871 -aF3097.9100918531417 -aF3677.2994963884353 -aF3096.4603516578672 -aF3763.3002531170846 -aF3180.40281509161 -aF3490.436920106411 -aF3718.39835062027 -aF3095.8820355296134 -aF3685.214772760868 -aF3095.9890907526014 -aF3105.259582734108 -aF3686.4179862976075 -aF3095.9610330224036 -aF3432.713996934891 -aF3061.114421737194 -aF3060.955609536171 -aF3769.9278703570367 -aF3683.24010784626 -aF3099.2037438988687 -aF3410.1615747690203 -aF3122.245133304596 -aF2910.817060816288 -aF2260.408093047142 -aF3736.5320888876913 -aF3686.6557959914207 -aF3662.3128547906877 -aF3096.8011304974557 -aF3099.0046157360075 -aF3095.3549315690993 -aF3060.323629593849 -aF3068.3470508098603 -aF3057.5532692432403 -aF3096.222541964054 -aF3667.34662951231 -aF3681.4580333709714 -aF1956.1999169230462 -aF3684.8189680814744 -aF3108.2042823791503 -aF3093.7333037257195 -aF3685.593143510818 -aF3685.9017785429955 -aF3686.125967979431 -aF2918.1834408164023 -aF3095.8735909700395 -aF3095.821016776562 -aF3685.884344613552 -aF4270.080337977409 -aF3096.045478618145 -aF3030.73253082037 -aF2891.380953538418 -aF3096.8537046909332 -aF3697.2567147135733 -aF3095.6014582276343 -aF3538.4611304044724 -aF3043.7401490211487 -aF3686.1009067058562 -aF3635.5950855135916 -aF3414.8172511458397 -aF3689.6868480682374 -aF2797.0873669028283 -aF3662.155949425697 -aF2835.7332127809523 -aF3096.0255930423737 -aF2921.9483523607255 -aF2883.163852262497 -aF3095.667380273342 -aF991.2697865486144 -aF3127.4439855456353 -aF3095.5766693592072 -aF3068.283852815628 -aF3096.56577244997 -aF3686.057321882248 -aF3097.2996319174767 -aF3409.7965518713 -aF3095.5178298473356 -aF3101.453265607357 -aF3561.2519070744515 -aF3081.57831363678 -aF3686.1330505132673 -aF3619.0320353269576 -aF3687.4642944693564 -aF3089.5878421902657 -aF3685.807526361942 -aF3094.264766168594 -aF3096.469613432884 -aF2119.2175086140633 -aF3686.074211001396 -aF3686.718449175358 -aF2967.247693967819 -aF3096.5619587779047 -aF3695.5789714097978 -aF3086.4761581897737 -aF3096.863511276245 -aF2151.781909573078 -aF3095.952316057682 -aF3096.924530029297 -aF3458.403436779976 -aF3159.186812579632 -aF3185.0563122272492 -aF3115.190112388134 -aF3123.800839102268 -aF3124.124728822708 -aF3770.9472104191777 -aF3097.0920591950417 -aF3095.9713844180105 -aF3157.105637252331 -aF2496.612775731087 -aF2604.2373254776003 -aF3685.6059465527533 -aF3517.245400297642 -aF3685.816788136959 -aF4495.440571737289 -aF3096.4731546998023 -aF3097.9607592105867 -aF3098.9242562174795 -aF3687.440867626667 -aF2579.1185744166373 -aF3093.602004444599 -aF3095.980918598175 -aF2985.406493508816 -aF3096.575034224987 -aF3686.2245786428452 -aF3651.334382534027 -aF3517.3924990773203 -aF3127.2671946048736 -aF3781.9248654603957 -aF3097.748283195496 -aF3709.1082454681396 -aF3126.761065840721 -aF3096.6374150037764 -aF3543.304766333103 -aF3095.821016776562 -aF3685.6094878196714 -aF3683.250459241867 -aF3626.25730946064 -aF3099.4630735993387 -aF3102.004886031151 -aF3086.6221673488617 -aF3686.203875851631 -aF3770.9591962456702 -aF3092.1639776706697 -aF2905.4651168823243 -aF3082.1664363503455 -aF3099.9070939898493 -aF3686.2493675112723 -aF3110.081698656082 -aF3691.4158035397527 -aF3685.960890460014 -aF3087.6398729801176 -aF3687.022725725174 -aF2978.950219106674 -aF3094.184679055214 -aF3732.3817240595818 -aF3086.863790714741 -aF4117.466716086864 -aF3092.080349290371 -aF3686.064949226379 -aF3060.1152396559714 -aF3685.997120344639 -aF3681.2188616514204 -aF3702.8862394928933 -aF3134.208077764511 -aF583.9235734581947 -aF3693.316101849079 -aF3095.1677892327307 -aF1959.2413203954698 -aF3744.838266646862 -aF2742.1149185061454 -aF3686.2000621795655 -aF3096.3203354120255 -aF3153.4164543390275 -aF3094.925893461704 -aF4479.89985806942 -aF3025.0575143814085 -aF3095.9836426496504 -aF3684.053237211704 -aF3094.475062942505 -aF3115.978180480003 -aF3682.0377115249635 -aF3098.226899039745 -aF3658.9862431287766 -aF3467.9362549185753 -aF3686.8799854278564 -aF3686.192979645729 -aF3360.3691826581953 -aF3099.014149916172 -aF3808.869275820255 -aF2622.323665249348 -aF3028.3775883197786 -aF3684.131689894199 -aF3666.9865099072454 -aF3095.9713844180105 -aF3075.2928372621536 -aF3096.347575926781 -aF4236.578318500518 -aF963.5868858337402 -aF3088.1539014935493 -aF2220.7322833061216 -aF3693.837212896347 -aF3094.4935864925383 -aF3065.5521739959718 -aF3095.6910795211793 -aF3686.104720377922 -aF3092.115761959553 -aF3846.804416668415 -aF3063.5527202129365 -aF3086.275668001175 -aF1829.1171950817109 -aF3094.781246328354 -aF3686.2060550928113 -aF3745.720859324932 -aF3754.0052446722984 -aF3168.2320255041122 -aF3692.576521873474 -aF3725.5609715700148 -aF512.9824629068374 -aF3161.8051708579064 -aF3096.037578868866 -aF2900.5844338536263 -aF3686.160018622875 -aF3095.7379332065584 -aF3672.187813794613 -aF2423.6792939305305 -aF3685.9031405687333 -aF3715.069832122326 -aF3679.812978684902 -aF3126.31377658844 -aF3115.5592213630675 -aF3836.780179643631 -aF2309.807404530048 -aF3114.1517039656637 -aF230.8154044866562 -aF3097.2274445533753 -aF3691.2943108439445 -aF2359.642019438744 -aF3691.9309216737747 -aF3093.844445025921 -aF548.4278207063675 -aF1332.2243274569512 -aF1382.360494863987 -aF2823.2597810745237 -aF3697.4326884388925 -aF3096.870866215229 -aF3273.1818291068075 -aF3688.0431554079055 -aF3686.0513289690016 -aF3688.9799567103387 -aF2659.3990954518317 -aF3676.94836615324 -aF3096.204835629463 -aF3090.724044060707 -aF3034.383577013016 -aF3095.5295432686808 -aF3685.6190219998357 -aF3744.8396286725997 -aF3095.8370886802672 -aF3645.247217106819 -aF3096.07162951231 -aF3125.3453762888907 -aF3102.4750573158262 -aF3687.3193749308584 -aF3686.14721558094 -aF4642.616986882686 -aF3801.362607169151 -aF3096.913906228542 -aF2578.6119008421897 -aF3365.9090861439704 -aF3095.0887917399405 -aF3089.7139657735825 -aF3095.44727691412 -aF3059.9594239115713 -aF3085.3056332707406 -aF3250.755530524254 -aF3085.8158481121063 -aF3228.9244371891023 -aF3815.995122075081 -aF3091.4652584671976 -aF3089.0828030467032 -aF4079.5400197982785 -aF3084.9768402576447 -aF2009.0135545253754 -aF2004.7239906668663 -aF3095.647222292423 -aF3741.400786089897 -aF3685.6936610102653 -aF3869.7858769416807 -aF3686.3746738791465 -aF3095.660297739506 -aF3097.080345773697 -aF3111.259033703804 -aF2138.7162690758705 -aF3173.0307145833967 -aF3688.137135183811 -aF3090.45436296463 -aF3098.1457223057746 -aF3081.7856139540672 -aF3097.3241483807565 -aF2900.6620693206787 -aF2514.281790816784 -aF3078.3565779566766 -aF3688.2128638148306 -aF3106.782055103779 -aF3431.514052259922 -aF3097.5529687047006 -aF2470.121919941902 -aF3094.9907258868216 -aF3422.997032916546 -aF3125.1203696370126 -aF3095.904372751713 -aF3095.8112101912498 -aF3095.2149153232576 -aF3679.7233573913572 -aF3120.240776228905 -aF3097.305080020428 -aF1926.8256526470184 -aF2107.543041205406 -aF3639.4338188529014 -aF3861.130475783348 -aF2219.8943650722504 -aF3074.7518406391146 -aF3687.0140087604523 -aF3095.628426337242 -aF4639.913910603523 -aF3172.8751712441444 -aF3097.174870359898 -aF3487.7063309073446 -aF3687.1453080415727 -aF4716.463843142986 -aF4038.82117074728 -aF3681.627741777897 -aF3095.022869694233 -aF3684.875628352165 -aF3095.761904859543 -aF3096.251689314842 -aF3622.4695158839227 -aF3684.540842425823 -aF3684.0036594748494 -aF3153.0299114346503 -aF2986.4579773783685 -aF3988.812761354446 -aF3685.6636964440345 -aF3096.0787120461464 -aF3688.717358148098 -aF3094.6927146553994 -aF3096.2130077838897 -aF3685.9976651549337 -aF3098.806577193737 -aF3675.510611784458 -aF3477.84689899683 -aF2455.4087731122972 -aF773.4260280251502 -aF3097.966207313538 -aF3053.0844627976417 -aF3134.862122523785 -aF2441.5414442658425 -aF3096.769259095192 -aF3689.340621125698 -aF3068.508859467506 -aF3427.9564410328867 -aF3096.5322666168213 -aF3094.926165866852 -aF3095.8986522436144 -aF3825.570707821846 -aF3093.2990899205206 -aF3303.2815082907678 -aF3522.816902780533 -aF4203.367772531509 -aF3682.480914700031 -aF3685.8285015583037 -aF4274.96401746273 -aF3095.892659330368 -aF3683.7557707905767 -aF3089.3786350369455 -aF3694.605395412445 -aF1881.8695415258408 -aF3732.075268268585 -aF3723.4007987499235 -aF3786.058885979652 -aF1732.7170994400979 -aF3093.4532712340356 -aF3083.7006221413612 -aF3059.211126971245 -aF3675.2076972603795 -aF3685.967700588703 -aF1506.5816006302834 -aF3095.664928627014 -aF3103.747461760044 -aF3026.2757102012633 -aF3095.664928627014 -aF3095.5344465613366 -aF3685.9761451482773 -aF3170.861279988289 -aF3095.8278269052507 -aF3082.0800839185713 -aF3685.1913459181783 -aF2699.1811432003974 -aF3776.971450257301 -aF3685.0883767724035 -aF3081.8681527137755 -aF3081.794058513641 -aF3685.914853990078 -aF3677.1782760977744 -aF3095.8095757603646 -aF3689.2545410990715 -aF3094.955858027935 -aF3685.623925292492 -aF3102.760265505314 -aF3041.8215995669366 -aF3141.107827746868 -aF3100.4824136614798 -aF3096.165881693363 -aF2723.0590888142588 -aF3119.4404499053953 -aF1954.8011164903642 -aF4319.543937075137 -aF3175.438231277466 -aF3686.081021130085 -aF3101.72784999609 -aF3751.4468155264854 -aF3095.9185378193856 -aF2520.14994250536 -aF3093.830552363396 -aF3907.275362968445 -aF2692.960226845741 -aF3106.505019068718 -aF3686.2011518001555 -aF1409.12756947279 -aF3023.31221460104 -aF3686.1507568478582 -aF3096.18849132061 -aF3371.8118332862855 -aF3676.683043539524 -aF3685.8492043495175 -aF3684.7988101005553 -aF3669.0061216711997 -aF4655.2012874841685 -aF3826.8518292307854 -aF3088.762999403477 -aF3097.3878911852835 -aF3589.8906774520874 -aF3076.2852092146873 -aF1122.3824701428414 -aF2860.8217268705366 -aF2440.522104203701 -aF3250.1486118555067 -aF3443.2029571413996 -aF2153.499696433544 -aF3090.913910448551 -aF847.1729115962981 -aF3692.8252277731895 -aF3095.979556572437 -aF3161.290325129032 -aF3096.2021115779876 -aF3694.892782843113 -aF3686.4580298542974 -aF3095.658935713768 -aF3997.730488669872 -aF3444.8068786501885 -aF3685.2583575844765 -aF1800.0390352010727 -aF321.90223772525786 -aF4644.850981497764 -aF3688.209050142765 -aF3252.460514342785 -aF3686.2177685141564 -aF3095.8180203199386 -aF3685.398646235466 -aF3096.0482026696204 -aF2887.7361726641657 -aF3659.9219548106194 -aF1934.7373877525329 -aF3675.9006959557532 -aF3688.5228608727452 -aF3120.9901627898216 -aF4369.944065070152 -aF3117.955569446087 -aF2588.2667564868925 -aF3689.264075279236 -aF3686.5743468523024 -aF3696.2964865684507 -aF3070.7262373685835 -aF3096.2650371670725 -aF3686.104720377922 -aF3919.310222387314 -aF3095.8158410787582 -aF3019.8123532652853 -aF3710.193235170841 -aF2942.656591677666 -aF3094.858064579964 -aF3685.1510299563406 -aF3684.021093404293 -aF3024.0844831943514 -aF1880.4168048739434 -aF3623.4193926334383 -aF3094.638778436184 -aF3251.1349908947946 -aF2533.4223385095597 -aF2695.539903593063 -aF1469.9771589279176 -aF3094.335591506958 -aF3095.9021935105325 -aF3043.710184454918 -aF3686.0423395991324 -aF3065.3775622963904 -aF3685.6863060712813 -aF701.0487975358963 -aF3685.921664118767 -aF3698.009914946556 -aF3098.261222088337 -aF1198.5049990057946 -aF3701.157556426525 -aF3986.36710793972 -aF3678.765580892563 -aF3100.036758840084 -aF3684.5727138280868 -aF1873.3427155971528 -aF3722.5748663425443 -aF3102.2827392816544 -aF3690.8960545182226 -aF3012.6301191449165 -aF3088.1740594744683 -aF680.3168589711189 -aF3095.5368982076643 -aF3685.4329692840574 -aF3823.4143486738203 -aF2990.0730660915374 -aF3096.0503819108008 -aF3615.0410275101663 -aF3096.108131802082 -aF3686.2864146113393 -aF3669.7647700071334 -aF3092.633876550198 -aF3379.4876655340195 -aF3016.517613005638 -aF3101.683992767334 -aF2828.9762030959128 -aF3700.06848064661 -aF3684.775383257866 -aF3685.826594722271 -aF3285.5656395196916 -aF3074.0059953451155 -aF3339.527192413807 -aF3164.750960123539 -aF3098.9975332021713 -aF3373.0463734149935 -aF3124.0786923527717 -aF3080.8493574619292 -aF3682.7802879571914 -aF3095.6733731865884 -aF3110.2429625034333 -aF3215.949235200882 -aF3096.845804941654 -aF3693.477910506725 -aF3685.297039115429 -aF3092.566047668457 -aF3096.442917728424 -aF3687.0946406841276 -aF3097.17459795475 -aF2609.8227206230163 -aF3690.307931804657 -aF3108.7123179793357 -aF2962.7004348397254 -aF3703.4885272741317 -aF3095.760542833805 -aF3539.675240147114 -aF3679.3150220751763 -aF3101.4401901602746 -aF2366.3113146662713 -aF3096.785603404045 -aF3103.2102788090706 -aF3114.30125439167 -aF3013.732815182209 -aF3095.885304391384 -aF3099.6717359423637 -aF3673.593696761131 -aF4144.066806340217 -aF4363.537095999717 -aF2983.6298671364784 -aF3095.9678431510924 -aF3084.0473938941955 -aF3257.6067923903465 -aF3393.770139825344 -aF2804.73895509243 -aF3686.109078860283 -aF2782.7286191701887 -aF2737.0620754241945 -aF4211.523310244083 -aF3091.208108007908 -aF3234.086242330074 -aF2876.008313846588 -aF2594.100585126877 -aF3685.1616537570953 -aF3098.625155365467 -aF3097.2582263350487 -aF4419.961463832855 -aF3672.8404965281484 -aF1959.308604466915 -aF4371.218376350403 -aF3355.5909239649773 -aF3096.060188496113 -aF3086.4181358933447 -aF3097.232347846031 -aF3732.7331266999245 -aF2904.9608949542044 -aF3685.377126228809 -aF3145.0187484502794 -aF3106.4884023547174 -aF3088.5379927515983 -aF3681.546565043926 -aF3098.634417140484 -aF3684.4809132933615 -aF3079.4712598204615 -aF3708.4460285544396 -aF3688.072847568989 -aF3028.008206939697 -aF3096.4897714138033 -aF3098.3023552656173 -aF2985.228612947464 -aF3641.2592057466504 -aF3773.077691078186 -aF4267.002432215213 -aF3686.276880431175 -aF3099.904097533226 -aF3092.0130652189255 -aF3840.3407873272895 -aF3090.489775633812 -aF3686.194886481762 -aF3094.937062072754 -aF3096.0432993769646 -aF3096.1882189154626 -aF3684.294315767288 -aF3637.835073041916 -aF3826.158013319969 -aF2179.955684363842 -aF3096.6597522258758 -aF3096.6935304641725 -aF3686.3686809659002 -aF3097.678002667427 -aF3642.6155109763145 -aF3095.8700497031214 -aF3095.985821890831 -aF385.2873742938042 -aF3685.900688922405 -aF3093.4557228803633 -aF3022.6336533784865 -aF3095.7992243647577 -aF3685.9734210968018 -aF3096.1694229602813 -aF3681.156480872631 -aF3688.8690878152847 -aF3098.136460530758 -aF3719.457189428806 -aF3096.828098607063 -aF3089.8556164503098 -aF4648.454629194736 -aF3086.6112711429596 -aF3483.3968814730642 -aF3025.380586886406 -aF3147.982788860798 -aF2242.585441458225 -aF3226.1892171025274 -aF2118.05924192667 -aF3529.672795534134 -aF3697.7898115873336 -aF3685.153209197521 -aF3096.0520163416863 -aF3094.4832350969314 -aF3684.5174155831337 -aF3603.1685215592383 -aF3095.020418047905 -aF4018.255944132805 -aF3094.9931775331497 -aF3065.311367845535 -aF3181.0367018699644 -aF3096.2990878105165 -aF3095.760542833805 -aF3080.609913337231 -aF3235.920618593693 -aF3704.443034911156 -aF3097.839266514778 -aF3095.1854955673216 -aF3169.085470831394 -aF3093.4862322568893 -aF2713.662745654583 -aF3922.9353900909423 -aF3239.634862780571 -aF3105.19257106781 -aF3095.3105295300484 -aF3096.335317695141 -aF3092.8251049637793 -aF3061.8357505679132 -aF3102.966476202011 -aF2926.2602534413336 -aF3658.376600408554 -aF3753.207642400265 -aF3686.812701356411 -aF3096.208104491234 -aF3687.6176585674284 -aF3096.470975458622 -aF3095.9539504885674 -aF3849.9871984124184 -aF3698.307108962536 -aF3096.231803739071 -aF1145.5347284436225 -aF3095.793503856659 -aF3087.5080288887025 -aF3084.530368220806 -aF767.1405516505241 -aF3684.1175248265267 -aF3123.4303681015967 -aF3024.4505957126617 -aF3093.911456692219 -aF3686.704284107685 -aF3883.121470940113 -aF3091.522735953331 -aF2904.178274965286 -aF3776.23922522068 -aF3695.468374919891 -aF2657.5696224808694 -aF3686.214227247238 -aF3143.956095969677 -aF4027.4155672192574 -aF3029.531768929958 -aF3687.5430195569993 -aF3094.582118165493 -aF3095.129924917221 -aF2208.3814339160917 -aF3664.5468494057654 -aF1071.571010375023 -aF3484.824012041092 -aF3686.561816215515 -aF3095.837361085415 -aF2965.508114695549 -aF3096.3227870583532 -aF3124.3260362267492 -aF3661.993051147461 -aF3707.8805154681204 -aF3860.432301390171 -aF2856.407129049301 -aF3104.479414391518 -aF3079.7578300356863 -aF3682.251004755497 -aF3098.292821085453 -aF3623.5738463521 -aF3686.213137626648 -aF3580.7013622045515 -aF3770.6960528731347 -aF3122.8779304623604 -aF3686.2978556275366 -aF1698.1641409039498 -aF3095.831095767021 -aF3685.5672650218007 -aF3687.708641886711 -aF3132.648013484478 -aF3095.620526587963 -aF4125.894931352138 -aF1602.0538843393326 -aF3712.039052450657 -aF3079.368018269539 -aF3113.164235305786 -aF3061.3677585244177 -aF3096.2342553853987 -aF1986.2810725569725 -aF3686.2046930670735 -aF1820.9722811698914 -aF3663.1303426384925 -aF3275.2205092310905 -aF3687.3161060690877 -aF3114.9604748487473 -aF3096.0348548173906 -aF3674.0883845090866 -aF3624.131187283993 -aF3096.2625855207443 -aF3093.293097007275 -aF3092.2737569451333 -aF2528.456665074825 -aF3095.1656099915504 -aF3682.1229743361473 -aF3688.446042621136 -aF3785.2931551098823 -aF3037.36477894783 -aF3679.8080753922463 -aF515.735389328003 -aF3095.43610830307 -aF1673.2409767389297 -aF2354.7354579210282 -aF3096.042209756374 -aF3095.9536780834196 -aF3124.825899672508 -aF3715.2711395263673 -aF3690.537841749191 -aF3685.690119743347 -aF3787.4222737431523 -aF3736.206564736366 -aF3259.967182993889 -aF3648.4942864656446 -aF3674.9096860289574 -aF3096.1212072491644 -aF3099.2920031666754 -aF3011.610779082775 -aF3136.5602962136268 -aF2557.1744329452513 -aF3199.204218375683 -aF3102.048198449612 -aF3086.6398736834526 -aF3065.614554774761 -aF3028.493632912636 -aF3670.1616643071175 -aF3097.1274718642235 -aF3685.939642858505 -aF3094.261497306824 -aF3559.993395292759 -aF3690.938004910946 -aF3226.8626026272773 -aF3648.525613057613 -aF3685.817877757549 -aF2727.2484075784682 -aF3686.450947320461 -aF3095.727581810951 -aF3096.0915150880815 -aF3712.6053827524183 -aF3687.981591844559 -aF2191.3051724314687 -aF3100.4363771915437 -aF3686.665330171585 -aF3104.8929254055024 -aF754.013075184822 -aF2629.6696148633955 -aF3686.216678893566 -aF3695.6247354745865 -aF4038.2534784197805 -aF3708.3327080130575 -aF3072.9866552829744 -aF3110.5020197987556 -aF3689.117793715 -aF3684.5029781103135 -aF4474.332986474036 -aF3686.5833362221715 -aF3095.7662633419036 -aF3095.5142885804175 -aF3686.0693077087403 -aF3691.9066776156424 -aF3248.693695962429 -aF2961.704249215126 -aF3084.6066416621206 -aF3091.808216547966 -aF3685.909950697422 -aF3097.25359544754 -aF3093.6085421681405 -aF3024.424444818497 -aF3096.180046761036 -aF3096.1321034550665 -aF3621.4256593585014 -aF3670.23929977417 -aF3685.426976370811 -aF3826.4780893683433 -aF3158.6921248316767 -aF3095.7414744734765 -aF3303.544379258156 -aF3688.23520103693 -aF733.9354537844657 -aF3088.2141030311586 -aF3097.1852217555047 -aF4503.354758489131 -aF3705.483077764511 -aF4593.346795439719 -aF3095.831368172169 -aF3686.9374629139897 -aF3097.327962052822 -aF3091.5017607569694 -aF3321.6824760079385 -aF3095.9751980900764 -aF3096.3884366989137 -aF3315.679483771324 -aF2851.3161492466925 -aF3678.5520152568815 -aF3986.7972356677055 -aF3679.3161116957663 -aF3686.075300621986 -aF3685.7642139434815 -aF2707.488955390453 -aF3113.4908490777016 -aF426.1119165420532 -aF3095.7390228271483 -aF4410.899634194374 -aF3458.835471343994 -aF3210.8552589416504 -aF3704.912933790684 -aF3096.179774355888 -aF3094.7945941805838 -aF3095.0640028715134 -aF3096.6551213383673 -aF3111.546148729324 -aF2320.1950300216677 -aF3398.364797449112 -aF1577.0302026748657 -aF3685.5917814850804 -aF3149.7545119404795 -aF534.9671927452088 -aF3081.9365264058115 -aF2875.67979323864 -aF3606.36928204298 -aF3095.8746805906294 -aF2862.353188610077 -aF3091.44046959877 -aF3673.6536258935926 -aF2833.4330237150193 -aF3401.825432443619 -aF4139.262669157982 -aF3714.680565166473 -aF3099.6175273180006 -aF2950.7581931710242 -aF2900.8927964806558 -aF3685.9140367746354 -aF2930.6402558088303 -aF3695.3441581726074 -aF3095.674190402031 -aF3030.4666633963584 -aF3096.9174474954607 -aF3145.399843251705 -aF2384.553470182419 -aF3047.881796884537 -aF3651.98624805212 -aF2184.437566256523 -aF3187.748764705658 -aF3097.090697169304 -aF3542.3987468123437 -aF1973.8945380926132 -aF3102.4952152967453 -aF3688.488537824154 -aF3099.3369500160215 -aF3723.8170338153836 -aF3084.0956096053123 -aF3096.83736038208 -aF2515.3011308789255 -aF3962.036969780922 -aF714.6330974340439 -aF1889.6437120318412 -aF3910.981707406044 -aF1118.2582562088967 -aF3018.7930132031443 -aF3101.3026255607606 -aF3709.1507406711576 -aF3150.673879313469 -aF3338.6832812666894 -aF3095.245697104931 -aF2697.992094731331 -aF3745.511652171612 -aF3817.3435275554657 -aF3112.2783737659456 -aF3092.5742198228836 -aF3556.3412594795227 -aF3689.021089887619 -aF3036.6638805031776 -aF3096.558417510986 -aF3238.5923682808875 -aF3688.726619923115 -aF3096.476968371868 -aF222.033607339859 -aF2892.1123613595964 -aF3941.3202859044072 -aF3095.819109940529 -aF3616.970200765133 -aF3685.7064640522003 -aF3456.0084507226943 -aF3177.0029264450072 -aF3764.6990535497666 -aF3685.7702068567273 -aF3685.5517379283906 -aF3624.5931864142417 -aF3777.2585652828216 -aF903.7184997200966 -aF3092.8014057159426 -aF3782.596888959408 -aF3677.0911064505576 -aF3095.2124636769295 -aF3083.511028158665 -aF2491.4240024805067 -aF3101.156616401672 -aF3102.2688466191294 -aF3004.267281115055 -aF3095.888573253155 -aF2074.766981446743 -aF3685.973693501949 -aF3303.2548125863077 -aF3686.0033856630325 -aF3077.878506922722 -aF2658.612116980553 -aF3095.6755524277687 -aF3091.4426488399504 -aF3094.9392413139344 -aF3655.423728609085 -aF3095.584569108486 -aF382.26939766407014 -aF3920.251654577255 -aF3023.502080988884 -aF2801.2044983029364 -aF3096.1261105418207 -aF1070.5285158753395 -aF3080.9490577459337 -aF4322.083297860622 -aF3094.877950155735 -aF3035.248190951347 -aF2856.492664265633 -aF3188.1058878540994 -aF3684.803713393211 -aF4184.705295872688 -aF3686.180993819237 -aF965.3449886560439 -aF3765.1670455932617 -aF3091.712602341175 -aF3685.853835237026 -aF4339.594317960738 -aF3093.537989234924 -aF3121.858590400219 -aF457.0571413040161 -aF3685.735883808136 -aF2161.569426524639 -aF3095.9220790863037 -aF4425.372792088985 -aF3039.1408605098723 -aF3943.2540900468825 -aF3095.9771049261094 -aF3097.0697219729423 -aF3097.5954639077186 -aF3686.5866050839422 -aF3096.5358078837394 -aF3688.7511363863946 -aF1800.2572317242623 -aF3100.0392104864122 -aF3684.5846996545793 -aF2604.2182571172716 -aF3095.6202541828156 -aF3256.564297890663 -aF3282.5855272054673 -aF3687.4800939679144 -aF3089.006257200241 -aF3658.8843636035917 -aF3140.0884876847267 -aF3700.5517273783685 -aF3712.462097644806 -aF4485.558530199527 -aF3109.672546124458 -aF3685.312293803692 -aF3094.4984897851946 -aF2920.905857861042 -aF3023.018834257126 -aF3685.818150162697 -aF3118.8749368190765 -aF3683.2575417757034 -aF3047.6589694738386 -aF2028.2104900836944 -aF3704.924919617176 -aF3094.168334746361 -aF3641.0023276925085 -aF2566.9505088806154 -aF674.2730059623718 -aF3091.9648495078086 -aF418.44289442300794 -aF3687.026266992092 -aF2964.4887746334075 -aF3232.6659218907357 -aF1780.4612772464752 -aF3680.5517414450646 -aF3097.5919226408005 -aF654.495847439766 -aF3726.6721121668816 -aF3699.26270622015 -aF4342.601670789718 -aF3095.1691512584684 -aF3730.3430439352987 -aF3096.4312043070795 -aF3095.023959314823 -aF3074.7638264656066 -aF3686.0044752836225 -aF3686.9412765860557 -aF3662.4869216799734 -aF3073.501773416996 -aF3674.946188318729 -aF3841.360127389431 -aF3099.9732884407044 -aF3095.7640841007233 -aF3469.9749350428583 -aF2905.3602409005166 -aF3158.1674725174903 -aF3225.7029739141462 -aF3112.143533217907 -aF3245.256215405464 -aF3157.8035392403604 -aF3746.876946771145 -aF3122.1083859205246 -aF3687.861461174488 -aF3700.478722798824 -aF3091.4252149105073 -aF3998.7498287320136 -aF3095.3846237301827 -aF3096.097780406475 -aF2974.5241802692412 -aF2813.078366279602 -aF3096.3355901002883 -aF3686.115071773529 -aF3707.7824496150015 -aF2901.1041828751563 -aF3096.1054077506064 -aF3101.114121198654 -aF4540.510820615291 -aF1565.3895135045052 -aF3096.283560717106 -aF3686.638362061977 -aF3806.8305956959725 -aF4143.698242175578 -aF1489.874448120594 -aF3155.478288900852 -aF3575.0258009552954 -aF2624.3854998111724 -aF3095.525729596615 -aF3095.808758544922 -aF3092.2489680767058 -aF3094.49004522562 -aF1855.9128722310068 -aF3685.4482239723206 -aF1179.675265586376 -aF2954.304090976715 -aF4290.8367930054665 -aF3114.6570155143736 -aF3686.4335133910176 -aF3093.6687437057494 -aF3685.9690626144406 -aF3545.450501680374 -aF3097.9169019818305 -aF3105.3159705996513 -aF3098.34212641716 -aF3096.2015667676924 -aF3686.797446668148 -aF3686.2226718068123 -aF3094.5605981588365 -aF3542.39248149395 -aF1800.6424126029015 -aF2854.0609035134316 -aF3687.359690892696 -aF3089.0013539075853 -aF3079.1697073221208 -aF3684.360510218143 -aF3095.952316057682 -aF3096.490861034393 -aF3685.8995993018148 -aF1049.951848244667 -aF2989.093769586086 -aF3685.7465076088906 -aF3009.963545155525 -aF3550.722630906105 -aF2716.776881301403 -aF3137.1331642389296 -aF2236.664170765877 -aF3109.3960548996924 -aF3091.2930984139443 -aF3755.790588009357 -aF3091.29582246542 -aF3099.168331229687 -aF3095.8594259023666 -aF3664.288609325886 -aF3688.163286077976 -aF3352.3397685289383 -aF3097.217910373211 -aF3705.4302311658857 -aF3095.004346144199 -aF3105.1252869963646 -aF3433.2536315321922 -aF4391.605449998378 -aF3114.063172292709 -aF3170.134230649471 -aF3553.366867673397 -aF3665.9456498384475 -aF3088.4742499470713 -aF3096.4039637923242 -aF3681.725535225868 -aF3685.9584388136864 -aF4099.855723297595 -aF3090.5766728758813 -aF881.8705172657966 -aF3207.6629430174826 -aF3433.529577946663 -aF3149.835143864155 -aF2883.3605287790297 -aF3328.7421278119086 -aF3119.7234788537025 -aF3095.539077448845 -aF3095.2410662174225 -aF3178.998021745682 -aF3096.6311496853828 -aF3620.9331508517266 -aF3095.9735636591913 -aF3219.7601832151413 -aF3096.020689749718 -aF3685.0050207972527 -aF3685.955987167358 -aF3601.4706202745438 -aF3161.2500091671945 -aF3116.931598496437 -aF3096.61698461771 -aF4181.297235071659 -aF4331.93237837553 -aF2792.8217746973037 -aF3063.8975851297378 -aF3684.741332614422 -aF3094.730578970909 -aF3097.75781737566 -aF3077.9795692324637 -aF3088.5665952920913 -aF4437.276897037029 -aF3686.87399251461 -aF3042.2302072882653 -aF3685.5672650218007 -aF3096.8093026518823 -aF3602.2259997487067 -aF3682.996032834053 -aF2942.5342817664146 -aF2990.971185863018 -aF3685.8957856297493 -aF3686.202241420746 -aF3096.185494863987 -aF3777.824350774288 -aF3095.349211061001 -aF3096.924530029297 -aF3095.1380970716477 -aF3227.852522933483 -aF3095.827554500103 -aF3693.4528492331506 -aF3095.7428364992143 -aF3685.839670169353 -aF3638.029025506973 -aF3094.553515625 -aF3095.366917395592 -aF243.39126052856446 -aF3685.752228116989 -aF3095.7308506727218 -aF3097.3135245800017 -aF3686.231933581829 -aF3682.796904671192 -aF3687.327819490433 -aF3097.7447419285772 -aF3627.3153310537336 -aF1774.5871326446534 -aF3737.324787867069 -aF1751.8176035761833 -aF3686.709187400341 -aF3021.3293775320053 -aF3738.0956944346426 -aF3096.731394779682 -aF3689.0962737083432 -aF3686.042067193985 -aF3095.893748950958 -aF4431.970717167854 -aF3685.33027254343 -aF3685.547107040882 -aF2753.23095536232 -aF3416.780747449398 -aF3686.078569483757 -aF3247.8802941918375 -aF4263.291456890106 -aF3549.244832980633 -aF774.0953274726867 -aF3071.9357162237166 -aF4572.007120990753 -aF2982.751905345917 -aF3095.9702947974206 -aF3686.2223994016645 -aF3671.0870245933534 -aF4198.262900066376 -aF3691.600766634941 -aF3096.10840420723 -aF3095.9501368165015 -aF3095.9136345267298 -aF3094.1367357492445 -aF3095.8370886802672 -aF3685.891154742241 -aF3748.9156268954275 -aF3549.9062326788903 -aF4022.3910542726517 -aF4164.423915421962 -aF3066.1569134235383 -aF3100.0631821393968 -aF3398.8782811522483 -aF3098.191213965416 -aF3096.5145602822304 -aF3044.033256959915 -aF3355.7644460439683 -aF3093.6030940651895 -aF3090.331508243084 -aF3096.2413379192353 -aF2853.267932128906 -aF3686.105809998512 -aF3095.5251847863196 -aF2945.2065762639045 -aF3091.3854437589644 -aF3683.0279042363168 -aF3101.215183508396 -aF3098.623793339729 -aF3686.281238913536 -aF3093.255232691765 -aF489.56433718204494 -aF3916.8920818924903 -aF3046.616202569008 -aF270.04174573421477 -aF3095.7640841007233 -aF3097.6869920372965 -aF3990.464898574352 -aF3099.2476011276244 -aF3777.4070260882377 -aF3321.2313730835913 -aF4439.173926484585 -aF2658.504789352417 -aF3095.1645203709604 -aF1628.2407359838487 -aF3092.98418956995 -aF3095.900014269352 -aF3111.435552239418 -aF2432.9590476870535 -aF2969.309528529644 -aF3692.0733895659446 -aF3687.0377080082894 -aF3089.950958251953 -aF2241.906880235672 -aF3673.747878074646 -aF3053.149295222759 -aF3114.2001920819284 -aF3094.5502467632296 -aF3683.890338933468 -aF3064.8474618792534 -aF3687.6250135064124 -aF3097.8885718464853 -aF3685.2504578351973 -aF3760.24223293066 -aF3096.6382322192194 -aF3096.5674068808557 -aF3076.3040051698686 -aF588.9023223400116 -aF3430.2511819958686 -aF3688.278513455391 -aF3111.83489818573 -aF3095.899469459057 -aF3407.259370326996 -aF3690.714360284805 -aF3096.4840509057044 -aF3090.41540902853 -aF3275.8402309417725 -aF3440.8638141393662 -aF3097.994537448883 -aF3097.539076042175 -aF2961.654943883419 -aF3686.8690892219543 -aF4720.979503273963 -aF888.4801557660103 -aF3678.7843768477437 -aF3112.678809332848 -aF4117.650861966609 -aF2775.7651263833045 -aF3212.8620676636697 -aF3787.053709578514 -aF3692.830948281288 -aF3685.6083981990814 -aF3096.561686372757 -aF3685.388294839859 -aF2925.6181945085527 -aF3589.870247066021 -aF3111.5006570696833 -aF3095.701430916786 -aF3091.678551697731 -aF3095.73439193964 -aF275.83362398147585 -aF3026.109815466404 -aF3682.151032066345 -aF1957.3004337191583 -aF485.9383522629738 -aF3686.059773528576 -aF3095.5660455584525 -aF2769.6774161458015 -aF3084.2250020504 -aF3096.7006129980086 -aF3095.583479487896 -aF1057.6704481005668 -aF2558.845093715191 -aF3066.365303361416 -aF3110.373989379406 -aF3081.0408582806585 -aF3095.1454520106317 -aF3096.715867686272 -aF2743.0375547409058 -aF3123.68043602705 -aF3095.7643565058706 -aF3071.520843183994 -aF3686.1221543073652 -aF3100.277292585373 -aF1557.5167323350906 -aF3035.4759216547013 -aF3286.682773029804 -aF3684.0012078285217 -aF3684.478189241886 -aF3094.479421424866 -aF1801.151537823677 -aF1541.0462998986245 -aF3096.324693894386 -aF3096.261495900154 -aF3057.1618230462072 -aF1055.5979897379875 -aF3138.9394827723504 -aF2912.477642595768 -aF3191.1712629795074 -aF4596.41407740116 -aF4252.926713430881 -aF3095.1631583452227 -aF4079.2278434991836 -aF3058.3367064476015 -aF2187.0382182002068 -aF3763.679713487625 -aF3705.4836225748063 -aF3095.81012057066 -aF3100.501209616661 -aF3084.291196501255 -aF3095.8430815935135 -aF3683.4084542274472 -aF540.4607873558998 -aF3095.604727089405 -aF3099.8392651081085 -aF3096.3685511231424 -aF3096.421670126915 -aF3685.8312256097793 -aF3093.330688917637 -aF2711.7218589782715 -aF3090.3938890218733 -aF3066.0070905923844 -aF3795.788108229637 -aF3469.626256453991 -aF3090.6968035459518 -aF2891.7604139089585 -aF1307.620149719715 -aF3096.33095921278 -aF4109.404068529606 -aF3687.0752999186516 -aF2983.9207958340644 -aF3667.7260898828504 -aF3810.907955944538 -aF3193.0448655843734 -aF3128.7155727744102 -aF3068.7009050965307 -aF3686.14721558094 -aF3679.174461019039 -aF3453.7379538178443 -aF3095.573128092289 -aF3678.4035544514654 -aF3019.533410394192 -aF3084.5336370825767 -aF3683.519050717354 -aF381.428210568428 -aF3685.531852352619 -aF3254.755255305767 -aF3844.2517080307007 -aF3518.877924346924 -aF2632.4892805457116 -aF3095.900559079647 -aF2154.786538350582 -aF2551.976397919655 -aF3495.6899809718134 -aF3686.409541738033 -aF3096.114124715328 -aF4686.212161886691 -aF3663.1505006194116 -aF3685.4656579017637 -aF3678.71355150938 -aF3929.4540452718734 -aF3558.4932601451874 -aF3774.0403708696363 -aF3720.7625548958777 -aF3372.8641343712807 -aF3683.375220799446 -aF3685.682492399216 -aF2689.323890531063 -aF3075.3233466386796 -aF725.8575515389442 -aF3673.8631054520606 -aF3096.1955738544466 -aF3096.4249389886854 -aF3096.357110106945 -aF2603.8578651070593 -aF3581.891227889061 -aF1563.9969783902168 -aF3689.2687061667443 -aF3082.1827806591987 -aF3685.8314980149266 -aF3190.151922917366 -aF3129.724561440945 -aF3691.3874734044075 -aF3149.075950717926 -aF3096.2710300803183 -aF3054.6262759327888 -aF3103.8501585006716 -aF3682.2172265172003 -aF3691.2578085541722 -aF3096.8468945622444 -aF3686.9377353191376 -aF3684.413629221916 -aF1369.6089375019073 -aF3095.5497012495994 -aF3335.7124307274817 -aF3096.4840509057044 -aF3094.5622325897216 -aF3094.4230335593224 -aF3076.3168082118036 -aF3096.829460632801 -aF3628.334671115875 -aF3097.8275530934334 -aF3107.8261840343475 -aF3723.159447789192 -aF3738.3441279292106 -aF3081.769269645214 -aF3098.255229175091 -aF2890.3341005563734 -aF2671.593856692314 -aF2630.839322566986 -aF3234.7375630378724 -aF4433.013211667537 -aF3095.9561297297478 -aF3107.98063775301 -aF913.4885827422141 -aF3685.8451182723043 -aF4238.445383381843 -aF3096.1931222081184 -aF3929.1617545485497 -aF3097.150898706913 -aF3721.519023990631 -aF3098.2835593104364 -aF3090.2775720238687 -aF1711.7217450976373 -aF4651.415128338336 -aF3432.651616156101 -aF3685.29022898674 -aF2508.9448291659355 -aF3128.3715250730515 -aF3065.1607277989388 -aF3191.3477815151214 -aF3699.8516461491586 -aF3098.113850903511 -aF3096.129651808739 -aF4173.974984705448 -aF3097.2808359622954 -aF3685.9889481902123 -aF2229.7164774775506 -aF3692.8734434843063 -aF3106.7041472315786 -aF2219.9545666098593 -aF3687.498617517948 -aF3095.221997857094 -aF4715.51505601406 -aF3095.8972902178766 -aF2454.691257953644 -aF3678.1842683076857 -aF3169.574438071251 -aF3092.745017850399 -aF4177.315489029884 -aF3097.1756875753404 -aF3115.653201138973 -aF3096.360378968716 -aF3099.806031680107 -aF1519.6175489664079 -aF4284.278911483288 -aF3095.579938220978 -aF3806.650808298588 -aF3080.8455437898638 -aF3686.1079892396924 -aF3686.340350830555 -aF3628.8506064653398 -aF3092.987458431721 -aF3145.453507065773 -aF3129.2157086253164 -aF2520.6225654363634 -aF3096.2070148706434 -aF2998.7589766263964 -aF3685.8135192751884 -aF3104.5325333952906 -aF3071.840374422073 -aF514.9162670493126 -aF3686.628827881813 -aF3098.0228675842286 -aF1024.5209208846093 -aF1086.3367313981057 -aF2937.106336796284 -aF3659.793107175827 -aF1386.8020607948304 -aF3100.642315483093 -aF3097.2309858202934 -aF3047.318735444546 -aF3848.8509965419767 -aF3097.398514986038 -aF3687.2537252902985 -aF3095.202929496765 -aF3685.8206018090245 -aF3180.625642502308 -aF3248.8996342539785 -aF3686.9189393639563 -aF3096.0468406438827 -aF3086.529822003841 -aF3996.6879941701886 -aF3697.4844454169274 -aF3687.143946015835 -aF3681.4343341231347 -aF3686.207417118549 -aF2767.6730590701104 -aF4547.95238443613 -aF3637.7323763012887 -aF3686.0398879528043 -aF3097.221451640129 -aF3642.4858461260797 -aF3002.0210282683374 -aF938.8195374131202 -aF3684.843484544754 -aF3095.752098274231 -aF3085.960222840309 -aF3095.166972017288 -aF3081.576134395599 -aF3685.9102231025695 -aF3686.0066545248033 -aF3864.211650407314 -aF3097.0168753743174 -aF1258.1851531624795 -aF3100.951222920418 -aF3690.2834153413773 -aF3685.909133481979 -aF3636.9497563123705 -aF3677.378766286373 -aF3097.3704572558404 -aF3807.143044400215 -aF4072.9892208099363 -aF3851.3020980596543 -aF2250.3122134685514 -aF3755.0692591786383 -aF3686.1221543073652 -aF1187.186292719841 -aF3679.396198809147 -aF3171.280511510372 -aF3095.59192404747 -aF3093.207016980648 -aF891.1505434274673 -aF3684.791727566719 -aF3096.056647229195 -aF3056.1193285465242 -aF3096.086884200573 -aF3096.2647647619247 -aF3680.422348999977 -aF1612.7180010557174 -aF3095.9090036392213 -aF3096.3685511231424 -aF3735.892209196091 -aF3103.7605372071266 -aF3687.2512736439703 -aF644.872590792179 -aF3092.922898411751 -aF3095.2821993947027 -aF3097.757000160217 -aF3098.1830418109894 -aF3743.5718551158902 -aF3095.6839969873427 -aF3098.658116388321 -aF3095.8430815935135 -aF3287.7021130919456 -aF3096.117393577099 -aF3115.0991290688517 -aF3676.8647377729417 -aF623.9445205211639 -aF3686.5626334309577 -aF3101.985817670822 -aF3092.087431824207 -aF3699.872076535225 -aF3106.2917258381844 -aF3093.864330601692 -aF3095.913906931877 -aF3097.7507348418235 -aF3685.6849440455435 -aF3106.2130007505416 -aF3685.5882402181624 -aF1030.9020114660264 -aF3686.58796710968 -aF3772.2634720921515 -aF3062.269147157669 -aF3109.6927041053773 -aF3092.0272302865983 -aF2729.6758098483087 -aF3686.4849979639052 -aF4726.526216888427 -aF3663.527509343624 -aF3415.7382529497145 -aF3096.0397581100465 -aF3090.688631391525 -aF3686.282600939274 -aF3749.5146458148956 -aF3716.636978936195 -aF3685.9984823703767 -aF3095.5167402267457 -aF3090.0980570316315 -aF2174.029782783985 -aF2538.8489214539527 -aF3685.995213508606 -aF3279.485284221172 -aF2779.4292480230333 -aF3143.1462354660034 -aF3085.958588409424 -aF3015.2931518673895 -aF3686.375763499737 -aF3095.8267372846603 -aF3072.956963121891 -aF3079.4151443600654 -aF3689.1929775357244 -aF3051.5006992697718 -aF3096.440466082096 -aF3930.473385334015 -aF2996.376521205902 -aF1489.6241077899933 -aF3107.6259662508965 -aF3095.3116191506388 -aF3687.1897100806236 -aF3680.1938010811805 -aF2996.2277879953385 -aF3096.5842960000036 -aF3095.6088131666183 -aF3092.8417216777802 -aF3096.026682662964 -aF3094.6243409633635 -aF3095.692169141769 -aF4276.150614285469 -aF3082.544807100296 -aF4596.939546930789 -aF548.8304355144501 -aF3098.4271168231962 -aF3096.5693137168882 -aF3241.1221948862076 -aF3975.9606864929196 -aF3686.10935126543 -aF3631.14997831583 -aF3045.5968625068663 -aF3093.073810863495 -aF3686.024360859394 -aF3101.4322904109954 -aF3724.2280931830405 -aF3122.385694360733 -aF3088.0386741161346 -aF2926.3474230885504 -aF3727.217739677429 -aF3671.764496195316 -aF3685.464840686321 -aF3095.8204719662667 -aF3093.275118267536 -aF3686.078569483757 -aF3685.261081635952 -aF3096.1906705617903 -aF3741.9257108092306 -aF4321.247831273078 -aF3084.46090490818 -aF3095.2337112784385 -aF3759.1997384309766 -aF3088.9316181898116 -aF3684.2970398187636 -aF3685.9570767879486 -aF2755.3380091786385 -aF3098.5638642072677 -aF3095.59192404747 -aF3095.406960952282 -aF3110.438549399376 -aF3686.152663683891 -aF3713.8617152929305 -aF3170.7702966690063 -aF2312.159622979164 -aF3859.712879395485 -aF3098.331775021553 -aF3686.355605518818 -aF3023.780206644535 -aF3269.855217444897 -aF2780.358694386482 -aF3136.2658262491227 -aF2986.130001580715 -aF3100.8784907460213 -aF3104.105946934223 -aF2654.5715314269064 -aF3095.9558573246004 -aF3602.4226762652397 -aF3133.1723933935164 -aF1615.321104645729 -aF3096.193394613266 -aF3096.1985703110695 -aF3164.9326543569564 -aF3024.7595031499864 -aF1050.0618999242784 -aF3685.9028681635855 -aF1746.3594216346742 -aF3096.044388997555 -aF3096.044388997555 -aF3095.1536241650583 -aF2747.791569375992 -aF4048.9333946347238 -aF3092.3829914093017 -aF476.50877567529676 -aF2978.2773783922194 -aF685.9954166769982 -aF3527.5589315891266 -aF3095.5423463106154 -aF3094.9896362662316 -aF3179.445855808258 -aF3058.4094386219977 -aF2948.0218834638595 -aF2372.913598227501 -aF2871.9385809421537 -aF3096.1824984073637 -aF3685.936101591587 -aF3180.0173618078234 -aF3648.720655143261 -aF3350.731488537788 -aF3099.0694481611254 -aF4262.798403573036 -aF3102.4900395989416 -aF3686.0703973293303 -aF3716.535371816158 -aF3096.6077228426934 -aF3067.3846434235575 -aF3094.8482579946517 -aF3096.0397581100465 -aF3124.8931837439536 -aF3686.066856062412 -aF3685.230027449131 -aF3685.7781066060065 -aF1547.207287120819 -aF3096.427663040161 -aF3373.425833785534 -aF3165.1304204940798 -aF3095.8267372846603 -aF3051.241641974449 -aF3106.356830668449 -aF1945.0626324653626 -aF3093.5300894856455 -aF3358.888115870953 -aF3195.126858127117 -aF3992.729402565956 -aF1118.0920890688897 -aF3056.1555584311486 -aF3686.205782687664 -aF3096.2824710965156 -aF3520.847685968876 -aF3155.3456275939943 -aF3084.689452826977 -aF2402.141580939293 -aF3095.6000962018966 -aF3686.4841807484627 -aF3112.1615119576454 -aF3102.24759901762 -aF3631.5174528598786 -aF2960.0439398407934 -aF4398.155431771278 -aF767.6801862478255 -aF3775.3451915264127 -aF3688.065220224857 -aF1940.7461004972458 -aF3094.8588817954064 -aF3682.8015355587004 -aF3096.174326252937 -aF3196.3477779984473 -aF3090.4175882697104 -aF3686.0033856630325 -aF3098.261222088337 -aF3677.676505112648 -aF3686.200334584713 -aF3097.7003398895263 -aF3091.2320796608924 -aF3686.697201573849 -aF3232.7154996275904 -aF3691.8301317691803 -aF3116.8327154278754 -aF3097.9613040208815 -aF3497.399868083 -aF3695.2490887761114 -aF3687.2463703513145 -aF3099.3301398873327 -aF3690.6838509082795 -aF3095.9395130157473 -aF3095.0947846531867 -aF3094.259318065643 -aF3038.004658639431 -aF3093.8833989620207 -aF3095.9558573246004 -aF3096.1953014492988 -aF3097.0509260177614 -aF3049.0594043374062 -aF2221.899811768532 -aF3683.6416330337524 -aF3755.263756453991 -aF3678.183451092243 -aF3686.0442464351654 -aF1778.0916248679162 -aF3721.2185611128807 -aF3688.6756801605225 -aF3686.3722222328183 -aF3686.9023226499557 -aF3686.1044479727743 -aF3685.8876134753227 -aF3717.5737802386284 -aF3096.0383960843087 -aF3097.586746942997 -aF3097.224992907047 -aF3076.298829472065 -aF3141.4872881174088 -aF551.692051589489 -aF3823.03488830328 -aF3097.455447661877 -aF1739.4659369707108 -aF3095.822651207447 -aF3002.976897931099 -aF3688.4474046468736 -aF2779.0198230862616 -aF3096.0915150880815 -aF3690.2940391421316 -aF3725.150457012653 -aF1922.7649091124536 -aF3686.6356380105017 -aF4288.4472550511355 -aF3739.2011145234105 -aF3102.7945885539057 -aF3253.9655527830123 -aF3095.8065793037413 -aF3685.9892205953597 -aF3594.0535729169846 -aF2965.341947555542 -aF3071.3228046417235 -aF3091.149540901184 -aF3094.7308513760568 -aF4092.316093623638 -aF3456.293931317329 -aF3095.3609244823456 -aF2565.6751079797746 -aF3688.209050142765 -aF3686.099817085266 -aF3687.3005789756776 -aF3685.444682705402 -aF3128.7052213788033 -aF3689.5419285297394 -aF3095.67092154026 -aF3990.4951355457306 -aF3168.5447466135024 -aF3097.5341727495193 -aF3096.052833557129 -aF3692.342798256874 -aF3691.834762656689 -aF3686.597228884697 -aF3096.4061430335046 -aF3472.7066138625146 -aF3685.9173056364057 -aF2095.1513310432433 -aF3471.3219784975054 -aF2645.090470266342 -aF3634.9549334168432 -aF3102.9193501114846 -aF3109.413488829136 -aF768.9209916949271 -aF3092.718866956234 -aF3528.357895886898 -aF3778.8058265209197 -aF3126.5227113366127 -aF3697.6211928009984 -aF2876.248030376434 -aF3094.616168808937 -aF2701.3342334866525 -aF3096.0634573578836 -aF3685.301942408085 -aF3655.4743959665298 -aF3154.7512395620347 -aF3066.662224972248 -aF3687.5530985474584 -aF2378.8408618330955 -aF3092.3459443092347 -aF2941.000913190842 -aF3096.1400032043457 -aF3098.5554196476937 -aF3032.586520254612 -aF3063.0220749855043 -aF3093.3693704485895 -aF2731.6815289497376 -aF3116.3824297189713 -aF3093.24460889101 -aF3693.678128290176 -aF3086.638784062862 -aF3686.0635872006415 -aF3096.585385620594 -aF3102.1073103666304 -aF3096.4875921726225 -aF3721.2953793644906 -aF3665.189180743694 -aF3683.403823339939 -aF3802.8393154740334 -aF3681.236295580864 -aF3096.4944023013113 -aF3096.281653881073 -aF3097.6118082165717 -aF3041.3546971440314 -aF3684.2145010590552 -aF3685.81106762886 -aF3678.847030031681 -aF3180.050322830677 -aF3106.047650825977 -aF3096.2802918553352 -aF3108.464974105358 -aF3682.359966814518 -aF3122.30370041132 -aF4299.216792559623 -aF3097.780154597759 -aF3095.7899625897408 -aF3096.6477663993837 -aF3689.2485481858253 -aF3097.0792561531066 -aF3357.1632464766503 -aF2984.9635627388952 -aF1016.1144980311393 -aF3686.2804216980935 -aF3567.97677295208 -aF1022.6470458745956 -aF3088.245974433422 -aF3095.663839006424 -aF3654.1499621391295 -aF3095.7074238300324 -aF3948.3096571803094 -aF2407.943810582161 -aF3685.7064640522003 -aF3692.8271346092224 -aF1324.0271117568016 -aF3626.2959909915926 -aF3742.0630030035973 -aF4133.290458703041 -aF2294.1620872855187 -aF2118.6250274181366 -aF3093.537989234924 -aF3174.040520465374 -aF3683.9009627342225 -aF3718.2523414611815 -aF3095.3693690419195 -aF4202.819420969486 -aF3096.249510073662 -aF3686.1518464684486 -aF3091.6605729579924 -aF802.6975955605507 -aF2204.09105284214 -aF3476.7684470176696 -aF3089.7905116200445 -aF3551.3050331115724 -aF3095.8065793037413 -aF3378.986984872818 -aF3098.7049700736998 -aF3094.3225160598754 -aF3685.309024941921 -aF3264.2472126722337 -aF3510.467143011093 -aF3096.1400032043457 -aF3095.695710408688 -aF3541.773849403858 -aF3095.1334661841393 -aF2973.693616974354 -aF2936.106065094471 -aF3096.1729642271994 -aF3662.2499292016028 -aF4214.172450304031 -aF3096.4628033041954 -aF2637.4315271377563 -aF3681.5724435329435 -aF3093.6889016866685 -aF3510.839793252945 -aF3039.4810945391655 -aF3731.9197249293325 -aF3351.7739830374717 -aF3178.426515746117 -aF3879.3317705273626 -aF2293.0792768239976 -aF3190.3720262765883 -aF3504.8229083538054 -aF3096.567679286003 -aF3754.391787576675 -aF3150.5164291381834 -aF3072.0215238451956 -aF3146.0394505381582 -aF1250.7708298563957 -aF3685.8560144782064 -aF3095.588382780552 -aF3280.9124147892 -aF3096.29091565609 -aF2591.2288900613785 -aF3101.376719760895 -aF3100.7248542428015 -aF3088.403697013855 -aF3687.6059451460837 -aF3097.957217943668 -aF2948.8279302954675 -aF3094.846895968914 -aF3096.3884366989137 -aF4047.9083340644834 -aF3095.5093852877617 -aF3096.0231413960455 -aF3690.2052350640297 -aF2996.6971420645714 -aF1171.869768488407 -aF3686.8031671762465 -aF4565.663894724846 -aF2722.0419279932976 -aF3685.8334048509596 -aF3095.9196274399756 -aF2002.4216223597527 -aF4424.196546661853 -aF2307.827563917637 -aF3090.612085545063 -aF3308.1453022003175 -aF3055.9942945837975 -aF3103.703876936436 -aF3099.2465115070345 -aF3687.0837444782255 -aF3015.894350028038 -aF3096.2179110765455 -aF3104.379169297218 -aF3068.229644191265 -aF3685.919757282734 -aF3712.9132005691527 -aF2786.518591988087 -aF3686.1823558449746 -aF3084.2514253497125 -aF1110.6636006951333 -aF4396.132823550701 -aF3371.258850836754 -aF3126.18574616909 -aF3432.2898621201516 -aF3097.085249066353 -aF3094.9177213072776 -aF3093.6690161108972 -aF3075.5510773420333 -aF3729.112862288952 -aF3686.345254123211 -aF3689.3896540522574 -aF3095.854795014858 -aF3095.8286441206933 -aF3097.561140859127 -aF3091.5644139409064 -aF3144.924768674374 -aF3685.2381996035574 -aF3685.929019057751 -aF4636.284384417533 -aF3111.771155381203 -aF3095.9147241473197 -aF3094.5055723190308 -aF3097.287373685837 -aF3161.2652638554573 -aF3082.850990486145 -aF3685.918395256996 -aF2202.024042582512 -aF2812.445841526985 -aF3547.8675525546073 -aF3685.2632608771323 -aF3395.9984139323233 -aF3690.715722310543 -aF1546.0898812055589 -aF3221.6610263347625 -aF3069.897036099434 -aF3092.757820892334 -aF2980.713225221634 -aF3096.642045891285 -aF2996.019125652313 -aF3106.633594298363 -aF2582.914267742634 -aF3685.0347129583356 -aF3726.120764148235 -aF1911.1705288171768 -aF3099.334770774841 -aF3626.108848655224 -aF3032.238931286335 -aF3096.846077346802 -aF3256.1270876288413 -aF2281.93463742733 -aF3803.208152043819 -aF3269.5582958340647 -aF3685.9205744981764 -aF3684.8058926343915 -aF3087.8240188598634 -aF3108.1500737547876 -aF3750.9543070197105 -aF3684.2790610790253 -aF3095.574490118027 -aF3672.9927710056304 -aF3650.214252567291 -aF3095.5649559378626 -aF3095.8090309500694 -aF3102.119296193123 -aF4020.3523741483687 -aF3094.931069159508 -aF3096.2399758934976 -aF3784.1466018438337 -aF3603.311261856556 -aF3095.9561297297478 -aF3636.7187567472456 -aF3090.4235811829567 -aF3095.7499190330504 -aF2674.9507053256034 -aF3095.7501914381983 -aF3685.278515565395 -aF3097.0367609500886 -aF3080.2326322078707 -aF3095.193667721748 -aF3095.8820355296134 -aF3689.228390204906 -aF3238.680899953842 -aF3685.8829825878142 -aF2906.735614490509 -aF3685.971514260769 -aF3686.3387163996695 -aF3796.629295325279 -aF3685.916488420963 -aF3095.688900279999 -aF3108.2748353123666 -aF3137.6646266818047 -aF2499.368971014023 -aF3975.065290772915 -aF3312.598309147358 -aF3095.6251574754715 -aF2821.935347247124 -aF4095.7263336658475 -aF3670.5956057071685 -aF1350.6696972131729 -aF3095.362014102936 -aF2880.765597343445 -aF3685.6010432600974 -aF3097.425483095646 -aF3923.743888568878 -aF3088.022057402134 -aF1736.4724768042565 -aF3665.6827788710593 -aF3635.9742734789847 -aF3142.41428283453 -aF3228.258679008484 -aF3019.9608140707014 -aF3684.4182601094244 -aF3648.928227865696 -aF3684.4277942895887 -aF2530.8037078261377 -aF4449.122434878349 -aF3620.3142463564873 -aF3096.7689866900446 -aF3096.2470584273337 -aF3106.653479874134 -aF3692.195971882343 -aF3100.102680885792 -aF3095.6123544335364 -aF3685.704012405872 -aF4131.878855228424 -aF3095.775797522068 -aF3095.985821890831 -aF3686.1529360890386 -aF3101.5309010744095 -aF3053.5162249565124 -aF3685.825232696533 -aF4235.051487648487 -aF3692.286137986183 -aF3033.547838020325 -aF2963.049930644035 -aF3688.1216080904005 -aF3092.137009561062 -aF3685.8372185230255 -aF3099.066996514797 -aF3137.0073130607607 -aF3583.928818392754 -aF3691.4495817780494 -aF3644.6541911005975 -aF3050.128322136402 -aF3691.409538221359 -aF3095.4519078016283 -aF3115.339935219288 -aF3070.22691873312 -aF3686.143674314022 -aF3526.539591526985 -aF2470.5618542551993 -aF1878.0329874277115 -aF1615.774659216404 -aF3097.7673515558245 -aF3684.4231634020803 -aF3177.1304120540617 -aF3686.2858698010446 -aF3096.107042181492 -aF3682.2556356430055 -aF2655.590871489048 -aF3098.078438234329 -aF3095.741746878624 -aF3123.114922940731 -aF3666.32156894207 -aF3117.1192856431007 -aF3686.19733812809 -aF533.6907022237777 -aF3099.9817330002784 -aF3397.9853370785713 -aF3095.8005863904955 -aF4640.9577671289435 -aF3652.3657084226606 -aF3094.1898547530172 -aF3738.4797856926916 -aF3093.077352130413 -aF3372.3833392858505 -aF2615.110376942158 -aF2401.1222408771514 -aF3096.928343701363 -aF2953.6437808990477 -aF426.98252339363097 -aF795.3056094765662 -aF3695.700464105606 -aF3413.8009075403215 -aF1188.0582615971566 -aF3685.3798502802847 -aF1792.9322573065758 -aF3271.8938975691794 -aF3097.5102010965347 -aF2961.063279902935 -aF3097.485684633255 -aF3676.6340106129646 -aF3231.696159565449 -aF3685.8549248576164 -aF1832.7856752038003 -aF3095.143817579746 -aF4361.647149085998 -aF3708.342242193222 -aF3798.5415794610976 -aF3674.0330862641335 -aF3096.145723712444 -aF3097.6235216379164 -aF3095.595737719536 -aF754.3925355553627 -aF3197.367118060589 -aF1467.0637858748437 -aF3093.2102858424187 -aF3090.8569777727125 -aF3095.893748950958 -aF3698.324542891979 -aF3671.49699434042 -aF2958.4955889821053 -aF2977.5492394328116 -aF3094.4279368519783 -aF3219.207473170757 -aF470.3445195913315 -aF3094.098599028587 -aF1770.1512872219087 -aF3094.936517262459 -aF2757.756149673462 -aF3089.341860342026 -aF3623.1069439291955 -aF4098.859537672996 -aF3168.3290017366407 -aF3385.21389414072 -aF3096.1528062462808 -aF3092.7082431554795 -aF3094.7850600004194 -aF3094.7234964370728 -aF3095.244335079193 -aF1182.627047765255 -aF3090.3375011563303 -aF3177.9786816835403 -aF3090.2350768208503 -aF3685.69311619997 -aF2023.2578920960427 -aF3008.6091467618944 -aF3149.855029439926 -aF3106.5202737569807 -aF3486.862692165375 -aF681.6554578661918 -aF3687.394013941288 -aF587.70047082901 -aF3095.359562456608 -aF3081.5254670381546 -aF3686.443864786625 -aF3082.317621207237 -aF3728.655766451359 -aF3095.349211061001 -aF3625.6738176345825 -aF3553.391928946972 -aF4451.3291889786715 -aF3749.4081354022023 -aF2501.821434557438 -aF3687.448767375946 -aF3095.2574105262756 -aF3275.8958015918734 -aF3096.133193075657 -aF3466.1579941153527 -aF3092.1688809633256 -aF3772.9502054691316 -aF3096.6630210876465 -aF3685.623925292492 -aF3685.8851618289946 -aF1890.4922540664672 -aF3014.2482057213783 -aF3655.8031889796257 -aF1555.1165705800056 -aF3077.362026762962 -aF3426.0961862802505 -aF3685.4743748664855 -aF3687.484724855423 -aF3098.250325882435 -aF3134.2704585433007 -aF3677.0826618909837 -aF3685.760672676563 -aF3705.4833501696585 -aF3686.852744913101 -aF3691.3234581947327 -aF3096.143544471264 -aF3574.5864114522933 -aF3677.470566821098 -aF3664.19462954998 -aF3687.010467493534 -aF2718.8155614256857 -aF3095.143817579746 -aF3102.780151081085 -aF3096.187129294872 -aF3067.233458566666 -aF3103.9174425721167 -aF3067.3906363368033 -aF3096.938967502117 -aF1432.3116991758347 -aF3096.1991151213647 -aF2961.0575593948365 -aF3006.841509759426 -aF2874.984615302086 -aF3115.9147100806235 -aF4451.958444869518 -aF3096.0659090042113 -aF3090.69925519228 -aF3120.106208086014 -aF3723.557704114914 -aF3096.2448791861534 -aF3109.122287726402 -aF3686.2071447134017 -aF3143.9054286122323 -aF1353.4779218792917 -aF3096.0550127983092 -aF3701.2765974760055 -aF3701.890326273441 -aF3095.6404121637343 -aF3686.748958551884 -aF3084.7104280233384 -aF2653.322281420231 -aF2662.5519126296044 -aF3089.2884689331054 -aF3679.609219634533 -aF3680.1997939944267 -aF3096.9198991417884 -aF3686.8530173182485 -aF3680.931201815605 -aF3097.3690952301026 -aF3686.02926415205 -aF2508.646273124218 -aF3533.118720650673 -aF2990.064893937111 -aF3095.59192404747 -aF3215.0004480719567 -aF2943.0267902731894 -aF3095.964029479027 -aF3093.774981713295 -aF3095.498761487007 -aF3217.1935819149016 -aF3101.778517353535 -aF3686.065766441822 -aF3299.437871658802 -aF3107.107579255104 -aF3074.577501344681 -aF3694.912123608589 -aF3687.2354741454124 -aF3659.2638239741323 -aF3147.469032752514 -aF3094.330143404007 -aF3094.2745727539063 -aF3117.830807888508 -aF3091.585389137268 -aF3096.0517439365385 -aF1521.9705846309662 -aF3023.3982946276665 -aF3834.4546568989754 -aF3685.0815666437147 -aF3080.8479954361915 -aF3148.0566106557844 -aF3096.619436264038 -aF3473.668204033375 -aF3686.1256955742833 -aF2768.136692631245 -aF3694.662055683136 -aF3092.7379353165625 -aF1365.9371885180474 -aF3092.4954947352408 -aF3688.0785680770873 -aF3095.4211260199545 -aF3095.878221857548 -aF3096.997534608841 -aF2581.506205534935 -aF3051.5374739646913 -aF3101.1609748840333 -aF2408.9631506443025 -aF4143.318781805038 -aF1703.722840344906 -aF3475.749106955528 -aF2138.7802842855453 -aF3686.203875851631 -aF3096.163430047035 -aF3095.5695868253706 -aF3098.5804809212686 -aF3090.6180784583094 -aF3095.6829073667527 -aF3145.455141496658 -aF3096.484868121147 -aF3714.323986828327 -aF3678.0254561066627 -aF3595.009714984894 -aF1922.4069687485696 -aF3687.763667726517 -aF3106.9612976908684 -aF3118.800570213795 -aF3662.7013045310973 -aF3096.1754158735275 -aF3094.3121646642685 -aF3658.394306743145 -aF3095.574217712879 -aF499.5087594985962 -aF3095.999714553356 -aF3096.4287526607513 -aF3792.7842966675757 -aF3321.5555352091787 -aF616.3569475412369 -aF715.4606642723083 -aF3092.8073986291884 -aF4085.5195851922035 -aF3014.122899353504 -aF3679.440600848198 -aF492.4676312446594 -aF3145.020110476017 -aF1191.3756114840508 -aF3127.35218501091 -aF3004.05970839262 -aF4491.042318224906 -aF3097.110855150223 -aF3001.703948676586 -aF3793.2634573221208 -aF3686.7209008216855 -aF2194.8189264297484 -aF3097.735207748413 -aF3685.931198298931 -aF3745.818925178051 -aF3684.3686823725698 -aF2066.451269507408 -aF3079.4317610740663 -aF2228.9507466077803 -aF3687.8617335796357 -aF3695.4689197301864 -aF3095.0013496875763 -aF3101.32605240345 -aF3698.006646084785 -aF2843.964479124546 -aF3552.9416432380676 -aF3041.3762171506883 -aF3683.1395903468133 -aF3617.9492248654365 -aF3693.85137796402 -aF3673.9023317933083 -aF3685.842939031124 -aF4746.46736330986 -aF3096.1114006638527 -aF3686.0679456830026 -aF3099.977919328213 -aF2186.781340146065 -aF3692.3749420642853 -aF3107.4739641785623 -aF3684.4218013763425 -aF3096.0245034217833 -aF3233.6454908013343 -aF3684.9532638192177 -aF3730.672926568985 -aF3096.492495465279 -aF3094.3347742915153 -aF3676.933111464977 -aF3685.8851618289946 -aF3095.8850319862368 -aF3192.1312187194826 -aF3097.3151590108873 -aF3653.0159395098685 -aF3748.5996369242666 -aF2990.2920798301698 -aF3100.065361380577 -aF3094.7139622569084 -aF2054.037766933441 -aF2686.041680908203 -aF3655.2657336235047 -aF3096.2862847685815 -aF1036.8717702746392 -aF3095.8150238633157 -aF2775.060686671734 -aF3095.023959314823 -aF3095.369096636772 -aF3732.210653626919 -aF3121.7327392220495 -aF3231.335767555237 -aF3686.341440451145 -aF3069.2380880475043 -aF3322.794706225395 -aF3695.0687565684316 -aF2664.9112136125564 -aF3095.845260834694 -aF3095.6227058291433 -aF3101.330955696106 -aF3100.5562354564668 -aF3686.468653655052 -aF3118.9700062155725 -aF3685.257267963886 -aF1644.4466630220413 -aF3096.329597187042 -aF2442.7808876872064 -aF721.2803278446197 -aF3117.2900836706162 -aF3097.035671329498 -aF3089.829465556145 -aF2354.7518022298814 -aF3095.3197913050653 -aF3682.1314188957213 -aF1713.9876111149788 -aF1222.7896455049515 -aF3686.0763902425765 -aF3103.4153998851775 -aF3142.8800956368445 -aF3337.444110250473 -aF3094.916904091835 -aF3501.2274328112603 -aF3686.577070903778 -aF3686.337899184227 -aF3105.5660385251044 -aF3683.9810498476027 -aF3685.838035738468 -aF3095.897017812729 -aF3943.1091705083845 -aF3095.579938220978 -aF3486.9566719412805 -aF3686.054053020477 -aF3089.89674962759 -aF841.2903224349021 -aF3133.447250187397 -aF3704.0919046759604 -aF2061.236617767811 -aF3686.0551426410675 -aF3088.693536090851 -aF3673.710013759136 -aF3095.231532037258 -aF757.7875209093094 -aF3688.045334649086 -aF3687.188892865181 -aF3095.6191645622253 -aF3680.7241739034653 -aF2730.6417585015297 -aF3684.038799738884 -aF3682.424526834488 -aF3247.1709511876106 -aF3685.4602097988127 -aF3109.686711192131 -aF3120.291171181202 -aF3686.648713457584 -aF965.4877289533615 -aF2808.9435285449026 -aF3686.2068723082543 -aF3384.039827954769 -aF3677.7873740077016 -aF2909.555007767677 -aF3103.525996375084 -aF3088.825652587414 -aF3685.757131409645 -aF2769.320565402508 -aF3010.1038338065146 -aF3685.9584388136864 -aF3685.662606823444 -aF3095.701430916786 -aF3091.7540079236032 -aF3096.4001501202583 -aF3653.6514607191084 -aF2515.964165008068 -aF3683.5307641386985 -aF3692.7625745892524 -aF3699.4547518491745 -aF3602.6692029237747 -aF3686.555823302269 -aF3246.655015838146 -aF3686.5615438103673 -aF3094.863512682915 -aF3469.6344286084177 -aF3686.1305988669396 -aF3686.4119933843613 -aF3095.2857406616213 -aF3066.3288010716437 -aF2048.3393236517904 -aF3215.0170647859572 -aF3707.797976708412 -aF3687.0249049663544 -aF2265.910677027702 -aF3095.647222292423 -aF3178.4867172837257 -aF2217.82980645895 -aF3684.555007493496 -aF3116.9531185030937 -aF3102.9596660733223 -aF3095.911455285549 -aF3635.946760559082 -aF1520.9667716622353 -aF3699.809423351288 -aF3665.3575271248815 -aF3524.4360789775847 -aF3707.699910855293 -aF3087.2950080633163 -aF3096.663293492794 -aF4309.172655892372 -aF3686.1384986162184 -aF3064.7246071577074 -aF3686.0325330138207 -aF3439.1482065200807 -aF3094.777705061436 -aF3097.444279050827 -aF3096.249510073662 -aF3048.7962609648703 -aF3081.29228823185 -aF4744.408525204658 -aF3096.204835629463 -aF3142.4180965065957 -aF3895.2451344370843 -aF3685.222127699852 -aF3099.0797995567323 -aF3068.208396589756 -aF3043.0340748786925 -aF3654.4365323543548 -aF3761.1689552426337 -aF3677.2913242340087 -aF3095.8003139853477 -aF3578.7027256369593 -aF3106.9634769320487 -aF3095.751008653641 -aF3095.81229981184 -aF1293.3779913902283 -aF3095.0950570583345 -aF3095.579121005535 -aF3095.1631583452227 -aF3085.2160119771956 -aF3097.737659394741 -aF3094.1824998140337 -aF3093.0781693458557 -aF3046.5410187482835 -aF3678.987046277523 -aF3085.2729446530343 -aF3592.024426972866 -aF3097.6709201335907 -aF3832.460651218891 -aF3662.845134449005 -aF2329.755633485317 -aF3124.34265294075 -aF2063.3826255202293 -aF3097.0403022170067 -aF3084.896753144264 -aF3127.643386113644 -aF3685.80888838768 -aF661.1218302488327 -aF3096.4968539476395 -aF3092.153353869915 -aF3672.699118256569 -aF3000.6200485944746 -aF3146.0100307822227 -aF3684.265985631943 -aF3096.4884093880655 -aF3676.032812452316 -aF2551.316632652283 -aF3083.9081948637963 -aF3754.63722461462 -aF3095.6191645622253 -aF3101.8436221837997 -aF3095.826464879513 -aF2927.290489709377 -aF3689.5315771341325 -aF3096.5415283918383 -aF3686.5699883699417 -aF3080.176244342327 -aF3096.2165490508078 -aF3094.7659916400908 -aF3725.1425572633743 -aF3610.0012598752974 -aF3695.6519759893417 -aF3733.984283542633 -aF3095.2304424166678 -aF3096.719953763485 -aF3682.701018059254 -aF3689.6350910902024 -aF3096.2059252500535 -aF3076.6821035146713 -aF3017.5369530677795 -aF4035.2404050827026 -aF3685.967700588703 -aF3680.0276339411735 -aF3698.4351393818856 -aF1571.4115741014482 -aF3096.138641178608 -aF3088.710425209999 -aF3067.628446030617 -aF3334.409516906738 -aF564.1513182282448 -aF2849.5888282060623 -aF3425.441051900387 -aF4619.109239864349 -aF3530.4990003466605 -aF3094.912000799179 -aF3686.1172510147094 -aF3095.4058713316917 -aF3637.9764513134955 -aF3098.5404373645783 -aF3080.545898127556 -aF3096.851253044605 -aF3121.4815816760065 -aF3149.317574083805 -aF3164.5510147452355 -aF3360.5593214511873 -aF2725.0588150024414 -aF3107.616432070732 -aF3726.563422513008 -aF267.0951392531395 -aF3689.029534447193 -aF3110.377803051472 -aF3388.9126112341883 -aF3079.358211684227 -aF3096.2470584273337 -aF3129.2838099122046 -aF3096.7915963172914 -aF2919.7917208075523 -aF3683.295406091213 -aF3124.182751119137 -aF3059.7687403082846 -aF368.6731119394302 -aF3447.913931763172 -aF3097.9795551657676 -aF3095.075716292858 -aF3685.8546524524686 -aF3095.7390228271483 -aF2803.642796778679 -aF3008.3217593312265 -aF3550.8585610747336 -aF3096.2165490508078 -aF3079.512665402889 -aF3685.246644163132 -aF3022.045530664921 -aF2170.5560723423955 -aF2837.5526067614555 -aF3122.5856397390367 -aF3096.170784986019 -aF3169.858556640148 -aF664.084236228466 -aF3610.0549236893653 -aF3103.126650428772 -aF2990.6573751330375 -aF3082.749928176403 -aF3677.6143967390058 -aF3710.990020227432 -aF3095.5916516423226 -aF3096.043571782112 -aF3686.1976105332374 -aF3095.688627874851 -aF3686.127057600021 -aF3095.3020849704744 -aF3094.3840796232225 -aF3093.170514690876 -aF3699.2817745804787 -aF1910.4622754335403 -aF3104.409951078892 -aF3684.0456098675727 -aF3730.4370237112043 -aF3671.39348038435 -aF3096.7738899827004 -aF3686.156477355957 -aF3687.459663581848 -aF2524.1485776662826 -aF3092.171060204506 -aF3684.8118855476378 -aF2434.632432508469 -aF3702.092178487778 -aF3686.443047571182 -aF3057.599033308029 -aF1298.5654026150703 -aF3680.424800646305 -aF4317.391936409473 -aF3096.4698858380316 -aF3113.8384380459784 -aF3094.8711400270463 -aF3160.305035710335 -aF3687.079113590717 -aF3701.227019739151 -aF3811.091557013988 -aF3642.2965245485307 -aF3591.4450212240217 -aF3096.1078593969346 -aF3654.469220972061 -aF2652.42416164875 -aF3093.302086377144 -aF3679.5364874601364 -aF3108.107850956917 -aF3124.919062232971 -aF3054.8687165141105 -aF3055.6712220788004 -aF3767.693603336811 -aF3682.7369755387303 -aF3787.9183235168457 -aF2985.9790891289713 -aF3555.3115680217743 -aF3685.606218957901 -aF3785.5543916463853 -aF3092.7787960886953 -aF3719.6031985878944 -aF3096.0950563549995 -aF828.5093453168869 -aF3672.6906736969945 -aF3101.426297497749 -aF3073.484067082405 -aF3096.17623308897 -aF2329.1476251959803 -aF3110.6828968167306 -aF1761.6116582512857 -aF3097.821560180187 -aF3207.594569325447 -aF3686.6724127054213 -aF2704.4581757187843 -aF2662.4503055095674 -aF3086.5216498494146 -aF3092.8583383917808 -aF3699.9295540213584 -aF2982.9602952837945 -aF3684.8505670785903 -aF3083.3456782341004 -aF3096.0482026696204 -aF3378.3141441583634 -aF3096.260133874416 -aF3325.86825350523 -aF3095.882852745056 -aF1655.3194420814514 -aF2747.7430812597277 -aF2117.434616923332 -aF3095.608540761471 -aF3093.4440094590186 -aF3097.7422902822495 -aF3097.2241756916046 -aF3523.4167389154436 -aF3685.8440286517143 -aF3089.2119230866433 -aF3095.9811910033227 -aF3649.543591094017 -aF3658.683328604698 -aF3161.07349063158 -aF3708.0357864022253 -aF3177.4673772215842 -aF3685.8312256097793 -aF2574.89002931118 -aF3831.1326761245728 -aF3224.240430676937 -aF3027.9624428749084 -aF3090.633333146572 -aF3477.8526195049285 -aF4444.428621780872 -aF3040.7831911444664 -aF3095.6404121637343 -aF2459.0876046299936 -aF2325.204560685158 -aF3180.8596385240553 -aF3654.522339975834 -aF3655.530783832073 -aF3112.8332630515097 -aF3677.8581993460652 -aF3193.85881216526 -aF3095.956402134895 -aF3373.5051036834716 -aF3685.9570767879486 -aF3429.214135599136 -aF3088.355481302738 -aF3089.307809698582 -aF2481.1167365074157 -aF3686.063314795494 -aF3211.055204319954 -aF3092.278387832642 -aF3099.579663002491 -aF3346.7704452872276 -aF3633.954389309883 -aF3686.1248783588408 -aF3095.9776497364046 -aF3683.4700177907944 -aF3091.162343943119 -aF3062.569337630272 -aF1975.2900696635247 -aF3095.454359447956 -aF3662.353987967968 -aF3685.898237276077 -aF2815.409609532356 -aF3095.9593985915185 -aF3095.73330231905 -aF4221.073289906978 -aF3049.380842411518 -aF3687.89905308485 -aF2107.4915566325185 -aF3094.407506465912 -aF3096.7183193325995 -aF2247.9322096943856 -aF3797.377592265606 -aF3095.941964662075 -aF3090.774711418152 -aF3685.6863060712813 -aF3437.392282938957 -aF3607.7631791830063 -aF3685.7331597566604 -aF3070.9588713645935 -aF3692.449308669567 -aF3686.2956763863563 -aF3095.584569108486 -aF3095.1029568076133 -aF3096.6265187978743 -aF3095.6237954497337 -aF3688.7827353835105 -aF3199.356220448017 -aF3043.334537756443 -aF3095.9539504885674 -aF3686.28968347311 -aF3584.15736631155 -aF2278.6072085499763 -aF3100.8569707393644 -aF3731.1681591272354 -aF3146.2761706113815 -aF2722.439367103577 -aF3387.439444196224 -aF3102.854517686367 -aF3748.2498687148095 -aF3686.036346685886 -aF3095.9593985915185 -aF2164.8502741217612 -aF2662.958068704605 -aF4067.7110986709595 -aF3094.2007509589193 -aF3096.48677495718 -aF418.69241753816607 -aF3685.9595284342763 -aF2993.066253852844 -aF3094.6033657670023 -aF3090.848805618286 -aF601.5326594114304 -aF3682.5988661289216 -aF3631.4436310648916 -aF3043.9392771840094 -aF1795.265407395363 -aF3099.0844304442408 -aF3685.3221003890035 -aF3195.120047998428 -aF2918.710272371769 -aF3116.2938980460167 -aF3686.4193483233453 -aF3685.9734210968018 -aF3182.3164612531664 -aF3464.5570690631866 -aF3141.860755574703 -aF3023.62602533102 -aF3608.7084250450134 -aF3702.693921458721 -aF3685.753590142727 -aF2736.281907081604 -aF3110.760532283783 -aF2698.7488362312315 -aF2720.5020216941834 -aF3099.37944521904 -aF3684.480640888214 -aF3094.4336573600767 -aF3180.0081000328064 -aF2780.767846918106 -aF2713.6741866707803 -aF1367.4359616398813 -aF3685.035802578926 -aF3054.3631325602532 -aF3686.0033856630325 -aF3676.330551278591 -aF3691.195155370235 -aF3871.3505721092224 -aF2900.883534705639 -aF2996.1632279753685 -aF3095.724040544033 -aF3978.8138580083846 -aF3058.7725546836855 -aF2570.207384824753 -aF3119.261207318306 -aF3500.5308928489685 -aF3551.901055574417 -aF3370.5418804883957 -aF3686.038253521919 -aF3399.61268543005 -aF3175.69156806469 -aF3226.4804182052612 -aF3096.6575729846954 -aF3096.335317695141 -aF2421.5305621266366 -aF3097.8474386692046 -aF2179.5250118255613 -aF3051.095632815361 -aF3093.373456525803 -aF3127.338292348385 -aF3089.2225468873976 -aF668.4056714892387 -aF3441.9264666199683 -aF3217.608454954624 -aF3095.890480089188 -aF3095.88911806345 -aF3011.5173441171646 -aF3104.8896565437317 -aF3095.5284536480904 -aF3095.2124636769295 -aF3102.1073103666304 -aF3684.4844545602796 -aF2702.375910770893 -aF2832.637600684166 -aF3225.239885163307 -aF3094.2664005994798 -aF3094.2958203554153 -aF2969.8260086894034 -aF3134.4970996260645 -aF3096.3214250326155 -aF3102.8063019752503 -aF3333.4133312821386 -aF3681.6141215205193 -aF3683.003115367889 -aF3684.5727138280868 -aF2664.2558068275453 -aF3095.9890907526014 -aF3685.7356114029885 -aF483.9135648012161 -aF3683.003115367889 -aF3089.150087118149 -aF3087.2688571691515 -aF3395.0049523591997 -aF1277.0467579841613 -aF3686.115071773529 -aF3687.4504018068315 -aF3681.4441407084464 -aF3685.084018290043 -aF3099.88584638834 -aF3116.879841518402 -aF3661.335737526417 -aF2656.382753252983 -aF3685.5975019931793 -aF750.880688393116 -aF3054.398545229435 -aF3739.4307520627976 -aF3095.3573832154275 -aF3084.927534925938 -aF3096.651580071449 -aF3087.645865893364 -aF3685.164105403423 -aF568.742162179947 -aF3676.0208266258237 -aF3685.9243881702423 -aF1579.9773539662363 -aF3031.310029733181 -aF3095.6839969873427 -aF3096.5687689065935 -aF3097.440737783909 -aF3114.795124924183 -aF1049.2961690545083 -aF1395.7802620530128 -aF3068.5015045285227 -aF3254.177483987808 -aF1684.0347583055498 -aF3093.830552363396 -aF2695.202938425541 -aF1195.8643035054206 -aF3680.587426519394 -aF4705.267991578578 -aF3431.400186908245 -aF3685.8876134753227 -aF3113.493028318882 -aF3907.1446084976196 -aF3681.3746773958205 -aF3053.3653125047686 -aF3092.4222177505494 -aF4225.759748065471 -aF3103.9656582832336 -aF3685.6271941542623 -aF3689.491533577442 -aF3096.1931222081184 -aF3496.0095122098924 -aF4064.605135178566 -aF2939.469179046154 -aF3098.3350438833236 -aF2985.9829028010367 -aF3686.5195934176445 -aF526.857146692276 -aF3079.812311065197 -aF3096.1999323368073 -aF3688.71599612236 -aF1344.8639263033867 -aF3095.7237681388856 -aF3069.4211443066597 -aF3133.364439022541 -aF3760.659830021858 -aF3687.3395329117775 -aF3064.6259964942933 -aF3055.3119196891785 -aF3230.3395819306375 -aF3036.477555382252 -aF3032.8888899683952 -aF2851.5188186764717 -aF3122.115468454361 -aF3556.277789080143 -aF2325.6300575256346 -aF3673.1575761198997 -aF3099.75781596899 -aF3051.9735946059227 -aF3096.119028007984 -aF3747.4522664427755 -aF3096.4252113938333 -aF3095.803038036823 -aF3100.775794005394 -aF3095.2323492527007 -aF3062.147926867008 -aF938.9230513691901 -aF3685.580068063736 -aF1671.264404988289 -aF3043.047967541218 -aF3682.8064388513562 -aF3128.7714158296585 -aF3092.0909730911253 -aF3095.526002001762 -aF3031.4516804099085 -aF3695.1194239258766 -aF3706.548999106884 -aF3766.4764971375466 -aF3095.445914888382 -aF423.2838063001633 -aF4084.60730035305 -aF3079.5034036278726 -aF3688.0785680770873 -aF3708.489613378048 -aF3113.321957886219 -aF2179.601557672024 -aF3701.3999970078467 -aF3108.6357721328736 -aF3681.2956799030303 -aF3603.914366853237 -aF3080.8406404972075 -aF3064.623544847965 -aF3683.002842962742 -aF3369.53970195055 -aF3119.0163150906565 -aF3098.0653627872466 -aF2905.2188626289367 -aF3096.0975080013277 -aF3096.476968371868 -aF3512.9713635325434 -aF3685.7525005221364 -aF3080.5320054650306 -aF3665.18182580471 -aF3685.684671640396 -aF3533.4788402557374 -aF3481.7082419633866 -aF3060.564163339138 -aF1011.5301918029785 -aF3852.301824951172 -aF3104.0661757826806 -aF3096.025865447521 -aF2822.5229251503943 -aF3112.6472103357314 -aF3094.3249677062036 -aF3259.911884748936 -aF2166.0153509378433 -aF3086.618353676796 -aF1445.6513792514802 -aF3685.911040318012 -aF3587.778720343113 -aF3086.254692804813 -aF3098.112761282921 -aF2905.9519048810007 -aF3096.6112641096115 -aF3687.0788411855697 -aF3099.8531577706335 -aF3145.620219016075 -aF2957.7151482343675 -aF3095.831095767021 -aF2448.9530435204506 -aF3096.122296869755 -aF3718.3008295774457 -aF2913.4098130106927 -aF3687.1216087937355 -aF3093.5371720194817 -aF4134.4015992999075 -aF3684.287505638599 -aF3097.1378232598304 -aF3973.881145596504 -aF3920.86102489233 -aF3679.939102268219 -aF1394.9543296456338 -aF3711.9682271122933 -aF415.27400534152986 -aF3675.9009683609006 -aF3532.5376804709435 -aF3088.3097172379494 -aF3719.7680037021637 -aF3095.8923869252203 -aF3096.5668620705605 -aF3679.6563457250595 -aF3119.257666051388 -aF3685.334086215496 -aF3098.2846489310264 -aF3722.4136024951936 -aF1341.7996407985688 -aF3184.482354581356 -aF3102.65048623085 -aF3092.8354563593866 -aF3074.1405634880066 -aF3095.754549920559 -aF3893.3513738512993 -aF3766.8690329551696 -aF3086.709064590931 -aF3096.1895809412003 -aF3791.771766734123 -aF2512.84866733551 -aF2932.347963678837 -aF3685.394287753105 -aF3093.4246686935426 -aF2185.3977944016456 -aF3093.5766707658768 -aF3078.151729285717 -aF3781.0411831617353 -aF3686.059773528576 -aF3095.9880011320115 -aF2847.6277835488318 -aF3095.166427206993 -aF3094.7894184827805 -aF3059.5151311159134 -aF3207.521564745903 -aF3095.078167939186 -aF3095.0996879458426 -aF3092.4707058668137 -aF3690.682761287689 -aF3685.9835000872613 -aF3096.3257835149766 -aF3032.4639379382133 -aF3140.267730271816 -aF3089.9506858468058 -aF2236.0858546376226 -aF3218.8514396429064 -aF3017.5404943346975 -aF3095.0171491861342 -aF3119.274555170536 -aF3120.5469596147536 -aF4631.377550494671 -aF3095.469614136219 -aF3114.060993051529 -aF2825.3205260157583 -aF3096.020962154865 -aF3095.8523433685305 -aF3096.415677213669 -aF3101.7008818864824 -aF3614.975377869606 -aF2742.952836740017 -aF3592.160084736347 -aF2210.2727428555486 -aF3368.668822693825 -aF3064.001643896103 -aF3616.9546736717225 -aF3615.7999482512473 -aF3080.7477503418922 -aF3095.3176120638846 -aF446.57390160560607 -aF3093.7205006837844 -aF3096.585385620594 -aF3081.111683619022 -aF3096.1882189154626 -aF3528.7643243670464 -aF3685.313928234577 -aF3786.7635980963705 -aF3095.7379332065584 -aF3640.885738289356 -aF3686.190527999401 -aF3627.225709760189 -aF3686.356695139408 -aF3746.4680666446684 -aF3095.499033892155 -aF3697.2662488937376 -aF3686.87399251461 -aF3102.779061460495 -aF3452.1528282642366 -aF3323.9875683665277 -aF3685.526949059963 -aF2941.670212638378 -aF3103.3927902579308 -aF3096.626791203022 -aF3560.0391593575478 -aF3603.969937503338 -aF3685.629918205738 -aF3100.062909734249 -aF348.3996312379837 -aF3096.5077501535416 -aF3097.6071773290632 -aF3100.795679581165 -aF3050.6924731969834 -aF3679.7579528450965 -aF3097.3701848506926 -aF3683.2185878396035 -aF3076.052847623825 -aF3096.404781007767 -aF3095.3244221925734 -aF4200.010379087925 -aF3096.2729369163512 -aF3093.147905063629 -aF3094.8482579946517 -aF3685.9407324790955 -aF3158.24320114851 -aF3681.896605658531 -aF3556.6016788005827 -aF3686.4405959248543 -aF3685.590964269638 -aF3050.693835222721 -aF3097.1579812407495 -aF4012.468696773052 -aF2619.1863751649857 -aF3095.749101817608 -aF3111.8370774269106 -aF697.5748146891593 -aF3731.337595129013 -aF3095.9081864237787 -aF3258.7111228585245 -aF2341.824815952778 -aF3187.465190947056 -aF3116.6488419532775 -aF3176.640082788467 -aF2834.0633692264555 -aF2989.1765807509423 -aF2894.6342882156373 -aF3686.2049654722214 -aF3096.2508720993997 -aF3080.660308289528 -aF2400.7495906352997 -aF3674.199525809288 -aF2033.252164554596 -aF3088.9329802155494 -aF3096.014969241619 -aF3568.9666932582854 -aF3478.9471433877943 -aF3728.634246444702 -aF3107.3887013673784 -aF3352.0431193232534 -aF3097.7989505529404 -aF2759.061242735386 -aF3089.0814410209655 -aF3950.541472554207 -aF3687.074482703209 -aF3097.5870193481446 -aF3733.035768818855 -aF3091.267219924927 -aF3054.201868712902 -aF3093.738479423523 -aF3685.178270471096 -aF3222.000443148613 -aF3082.476705813408 -aF3095.693531167507 -aF3022.557924747467 -aF3092.2734845399855 -aF3096.8419912695886 -aF3675.2809742450713 -aF3598.198217236996 -aF3092.6521276950834 -aF3688.504882133007 -aF2467.0758855819704 -aF3082.0332302331926 -aF3096.6254291772843 -aF4077.1017213225364 -aF3096.1304690241814 -aF3099.156617808342 -aF1876.7673931121826 -aF2683.2822167634963 -aF2515.3940210342407 -aF3092.2960941672327 -aF3388.8526821017267 -aF3695.192700910568 -aF3222.0652755737306 -aF3674.894431340694 -aF1910.7586522340775 -aF1024.6236176252364 -aF3074.439936745167 -aF3395.998686337471 -aF3670.9832382321356 -aF1598.706842291355 -aF3305.2744243502616 -aF3094.363104426861 -aF3096.176505494118 -aF2797.285405445099 -aF3194.0582127332686 -aF3075.263417506218 -aF984.8061572074889 -aF3096.333955669403 -aF4076.295946896076 -aF3095.920717060566 -aF3095.7335747241973 -aF3095.425756907463 -aF3686.0537806153297 -aF3133.6589089870454 -aF3685.9241157650945 -aF3668.1267978549004 -aF3063.1672669291497 -aF3122.1190097212793 -aF3096.1143971204756 -aF3707.5914936065674 -aF3120.5575834155084 -aF2977.3059816360474 -aF3097.281653177738 -aF3117.248678088188 -aF3685.0131929516792 -aF2460.6125286459924 -aF3096.9307953476905 -aF2271.6159304380417 -aF4100.897400581836 -aF3724.0420404672623 -aF3090.300998866558 -aF2799.773009252548 -aF3283.717915403843 -aF1381.4234211564064 -aF3188.1227769732477 -aF3095.888845658302 -aF3712.0717410683633 -aF3698.4765449643132 -aF3686.0428844094276 -aF2686.9703100562097 -aF2473.6833448410034 -aF3686.322644495964 -aF3628.2060958862303 -aF3686.33980602026 -aF3123.564936244488 -aF3098.7782470583916 -aF3690.3812087893484 -aF3111.9683767080305 -aF3692.220488345623 -aF3210.556158089638 -aF2384.8555674910544 -aF3107.477233040333 -aF3686.3588743805885 -aF3108.9822714805605 -aF3691.3978248000144 -aF1716.4714012503625 -aF3093.909549856186 -aF3093.1528083562853 -aF3685.8974200606344 -aF3533.7893821239472 -aF2849.164693391323 -aF3095.9844598650934 -aF2676.118506193161 -aF1221.1410495519638 -aF3466.7237796068193 -aF3225.4610781431197 -aF3095.9395130157473 -aF3095.2928231954575 -aF3731.5331820249557 -aF3686.263532578945 -aF2810.5550773978234 -aF3685.972603881359 -aF3135.509084749222 -aF3095.4317498207092 -aF1105.0825640320777 -aF3095.4731554031373 -aF1484.0022103548051 -aF3671.6713336348535 -aF3686.8987813830377 -aF3689.2131355166434 -aF3095.8983798384666 -aF3129.19418861866 -aF3425.368047320843 -aF2658.242463195324 -aF3685.067401576042 -aF3682.78600846529 -aF3095.005163359642 -aF3685.8028954744336 -aF3675.887620508671 -aF3096.8517978549003 -aF1268.665668809414 -aF3096.0432993769646 -aF3096.2040184140205 -aF2115.1036460757255 -aF346.1196001529694 -aF1099.4050959467888 -aF2679.6938237547874 -aF1869.8039003252984 -aF3096.2824710965156 -aF3685.8889755010605 -aF3684.8083442807197 -aF2407.402813959122 -aF3094.3383155584334 -aF3801.507526707649 -aF2814.868068099022 -aF3105.4845893859865 -aF3095.719137251377 -aF3688.8903354167937 -aF3204.9124682426454 -aF3686.2283923149107 -aF2254.154215669632 -aF3103.593280446529 -aF3288.6130359053614 -aF3674.873183739185 -aF3095.9125449061394 -aF3685.7604002714156 -aF2220.7143045663834 -aF3095.540167069435 -aF3000.8496861338617 -aF3684.0012078285217 -aF3088.295279765129 -aF3097.014151322842 -aF3097.0225958824158 -aF4590.671776890754 -aF1604.3336430191994 -aF3260.378514766693 -aF3263.8454150795937 -aF3649.2017226338385 -aF3222.8724120259285 -aF3492.385978937149 -aF3106.946860218048 -aF3093.8975640296935 -aF3095.606089115143 -aF3783.988334453106 -aF4693.6806938171385 -aF3104.7104139566422 -aF4038.782761621475 -aF3066.6047474861143 -aF3095.397699177265 -aF783.840621626377 -aF3684.655252587795 -aF3095.756184351444 -aF3085.2794823765753 -aF3959.489981651306 -aF3684.0646782279014 -aF3582.3202659964563 -aF3030.2906896710397 -aF3095.8806735038756 -aF2019.059311556816 -aF3719.8856827259065 -aF4690.482384979724 -aF3095.5886551856993 -aF3062.6723067760468 -aF3095.9727464437483 -aF3374.535067546368 -aF3686.048877322674 -aF3708.5743313789367 -aF3686.930652785301 -aF3463.2862990498543 -aF3489.020413339138 -aF3084.339684617519 -aF2438.7509259343146 -aF1794.0327741026879 -aF3093.8975640296935 -aF3084.392258810997 -aF3087.8602487444878 -aF3095.1242044091223 -aF3148.0451696395876 -aF2718.4633415699004 -aF3096.0465682387353 -aF4118.533182239532 -aF3097.340765094757 -aF3688.4378704667092 -aF3054.4078070044516 -aF3686.0635872006415 -aF3104.961843907833 -aF3096.401512145996 -aF3756.6990591764447 -aF3141.2870703339577 -aF3686.0834727764127 -aF2962.98373619318 -aF3615.935333609581 -aF3813.8126120328902 -aF3096.6845410943033 -aF2863.1121093511583 -aF3096.360106563568 -aF1776.525567674637 -aF3219.647135078907 -aF3685.8421218156814 -aF3082.1615330576897 -aF3092.706063914299 -aF3685.991672241688 -aF3096.8716834306715 -aF3188.7414090633392 -aF840.6275607109069 -aF3096.261495900154 -aF2981.2291605710984 -aF3096.3345004796984 -aF3095.078167939186 -aF4165.8886379003525 -aF3686.5781605243683 -aF3684.7165437459944 -aF3646.8745654582976 -aF3691.673771214485 -aF3428.4859966397285 -aF3686.2155892729756 -aF3704.4558379530904 -aF3685.4444103002547 -aF3098.1421810388565 -aF3685.5422037482263 -aF4224.360947632789 -aF4421.737000584601 -aF3095.357655620575 -aF3561.2385592222213 -aF3198.0328762412073 -aF3685.5364832401274 -aF3121.276733005047 -aF3143.8168969392777 -aF3137.1636736154555 -aF3559.0198192954063 -aF2851.2820986032484 -aF3685.8208742141724 -aF4196.431792664527 -aF3095.3622865080833 -aF2991.761978006363 -aF3681.810253226757 -aF3261.3978548288346 -aF3722.7576501965523 -aF533.0767010211945 -aF2654.3440731287 -aF2881.197087097168 -aF3096.0762603998182 -aF1430.9483114123345 -aF3096.027772283554 -aF3095.9465955495834 -aF3272.6661661624908 -aF3686.700742840767 -aF3686.0600459337234 -aF3095.814751458168 -aF3689.33707985878 -aF2927.002829873562 -aF3089.0966957092287 -aF2781.2050571799277 -aF3094.9994428515433 -aF3686.1079892396924 -aF2869.8394268751144 -aF3686.3509746313093 -aF4439.432166564464 -aF3154.8631980776786 -aF3685.103359055519 -aF3096.143544471264 -aF3679.0044802069665 -aF3074.1473736166954 -aF3097.065908300877 -aF2340.7823214530945 -aF3095.8395403265954 -aF3094.332595050335 -aF3082.2290895342826 -aF3096.2119181632997 -aF3657.6228553652763 -aF3685.616570353508 -aF3078.360391628742 -aF3095.799496769905 -aF3686.2978556275366 -aF3588.7980604052545 -aF4190.347623693942 -aF3096.273209321499 -aF3685.9889481902123 -aF3686.125423169136 -aF2813.4401203155517 -aF4557.372426843643 -aF2998.537511241436 -aF3678.789007735252 -aF2204.1700503349302 -aF3643.4370849013326 -aF3100.050923907757 -aF3735.158077323437 -aF1473.371054661274 -aF3742.3027195334435 -aF3086.3203424453736 -aF3686.0398879528043 -aF2036.9508816480636 -aF3106.895103240013 -aF691.9793405532837 -aF2900.3049461722376 -aF3150.6613486766814 -aF1760.098175251484 -aF2929.741318821907 -aF3643.279089915752 -aF3458.1710751891137 -aF3144.6240333914757 -aF3678.9459131002427 -aF3671.1188959956166 -aF3649.6002513647077 -aF3097.436379301548 -aF3096.1718746066094 -aF3096.6523972868918 -aF3678.9197622060774 -aF4574.5524746894835 -aF3082.2536059975623 -aF3078.618631708622 -aF4062.176370882988 -aF3087.0051689863203 -aF3096.4219425320625 -aF4133.3822592377655 -aF3692.8094282746315 -aF3096.1879465103148 -aF3680.675685787201 -aF3061.7551186442374 -aF3227.9418718218803 -aF2952.198126780987 -aF2959.280388212204 -aF3070.234273672104 -aF3691.4817255854605 -aF3686.13277810812 -aF3685.8500215649606 -aF3119.9661918401716 -aF3686.0611355543138 -aF3097.5881089687346 -aF3345.9774739027025 -aF3894.370713913441 -aF3246.0737032532693 -aF3685.7816478729246 -aF3701.765564715862 -aF3425.4584858298304 -aF3681.460757422447 -aF3035.283876025677 -aF1457.7862113595008 -aF3096.59028891325 -aF4100.56751794815 -aF3703.4155226945877 -aF3210.8963921189306 -aF3085.6665700912476 -aF3095.808758544922 -aF3686.1553877353667 -aF3095.660025334358 -aF3685.8995993018148 -aF3090.5014890551565 -aF3913.711751794815 -aF3095.7687149882317 -aF3050.9425411224365 -aF3685.000389909744 -aF3685.61302908659 -aF3702.510047984123 -aF1397.475984096527 -aF3125.808737444878 -aF3428.8063450932505 -aF3273.1804670810698 -aF3685.879441320896 -aF3685.8263223171234 -aF3102.90872631073 -aF3686.2692530870436 -aF3685.827684342861 -aF3104.141359603405 -aF3095.8324577927588 -aF3376.823543190956 -aF3103.039753186703 -aF3687.6680535197256 -aF3787.569917333126 -aF4191.173556101322 -aF3670.62883913517 -aF3103.9408694148065 -aF3095.675824832916 -aF3797.134606873989 -aF3683.9706984519958 -aF3094.6997971892356 -aF2805.706265771389 -aF3096.587837266922 -aF3094.85070964098 -aF3106.300442802906 -aF3017.248203611374 -aF3672.415272092819 -aF3067.116869163513 -aF3096.3203354120255 -aF3095.976015305519 -aF2680.713163816929 -aF3059.6611402750013 -aF1204.9541908740998 -aF3686.270887517929 -aF3667.681415438652 -aF3694.0826499342916 -aF3049.139219045639 -aF2018.443948328495 -aF3208.993369758129 -aF3200.6822887063026 -aF3659.9295821547507 -aF3099.4159475088118 -aF2406.0759284853934 -aF1782.6165467739106 -aF3663.440339696407 -aF3731.110136830807 -aF3095.819654750824 -aF2086.690427160263 -aF3089.3146198272707 -aF3678.761767220497 -aF3095.9716568231584 -aF3686.2493675112723 -aF3098.0419359445573 -aF3094.180320572853 -aF3722.325070822239 -aF3583.3396060585974 -aF3223.89175208807 -aF2864.536788272858 -aF3106.3543790221215 -aF3654.9748049259183 -aF3725.2899284482 -aF3678.0502449750898 -aF3019.6309314370155 -aF3067.624087548256 -aF3170.639814603329 -aF3095.885304391384 -aF3685.91131272316 -aF3475.813939380646 -aF3096.4486382365226 -aF2771.7471504569053 -aF3095.7597256183626 -aF3095.866508436203 -aF3279.480108523369 -aF3837.9607835531233 -aF3163.112170755863 -aF1338.4683982491495 -aF3835.0370591044425 -aF1119.272692978382 -aF3686.3722222328183 -aF2895.664252078533 -aF3041.062678825855 -aF3099.136459827423 -aF3097.305352425575 -aF3439.454117500782 -aF3096.5619587779047 -aF3220.9811030864716 -aF1744.0744872570037 -aF3096.049292290211 -aF3122.0225782990456 -aF3089.546709012985 -aF2945.245257794857 -aF3095.9041003465654 -aF3086.486781990528 -aF4605.012818288803 -aF3096.494674706459 -aF3090.8360025763513 -aF3687.7001973271367 -aF3685.691209363937 -aF3597.1788771748543 -aF3094.475062942505 -aF3693.5558183789253 -aF3687.61302767992 -aF3095.087974524498 -aF3118.2078166127203 -aF1568.5807398080826 -aF3095.974925684929 -aF3168.095278120041 -aF819.4167338967322 -aF3685.664786064625 -aF3096.540438771248 -aF3095.16343075037 -aF3699.5759721398354 -aF3096.0337651968002 -aF3832.1520161867143 -aF3118.824269461632 -aF3096.468523812294 -aF3095.978466951847 -aF3095.570676445961 -aF3096.5546038389207 -aF3094.9106387734414 -aF3686.039615547657 -aF3069.273500716686 -aF3095.2914611697197 -aF3245.256215405464 -aF3684.8704526543615 -aF3107.184125101566 -aF2687.989650118351 -aF3113.2023720264433 -aF3627.2126343131067 -aF3693.7383298277855 -aF3685.3033044338226 -aF3092.6387798428536 -aF3582.8904099702836 -aF3484.563592720032 -aF3096.120934844017 -aF3714.7582006335256 -aF3099.6725531578063 -aF3369.282823896408 -aF3111.421659576893 -aF3095.7017033219336 -aF2636.201617896557 -aF3205.555889201164 -aF1696.8688544273377 -aF275.88701539039613 -aF3660.8064543247224 -aF2763.6809616327287 -aF3685.316379880905 -aF1959.2045457005502 -aF3139.990694236755 -aF3095.2277183651922 -aF3095.806306898594 -aF3105.175954353809 -aF3610.7471051692964 -aF3058.244905912876 -aF1281.3537557721138 -aF3104.739833712578 -aF3095.806306898594 -aF2734.243226957321 -aF3112.7992124080656 -aF3096.554876244068 -aF3799.57862585783 -aF3121.358182144165 -aF3056.263703274727 -aF3577.328441667557 -aF3704.006914269924 -aF3684.3343593239783 -aF3250.2289713740347 -aF3095.458717930317 -aF3122.7637927055357 -aF3713.8677082061768 -aF1579.771143269539 -aF3593.1004273056983 -aF3098.289552223682 -aF3095.482417178154 -aF3122.1121995925905 -aF3090.8913008213044 -aF3841.8352019667623 -aF3070.5603426337243 -aF4127.594194662571 -aF3643.0069571733475 -aF2798.0524983406067 -aF3214.7449320435526 -aF3098.6864465236663 -aF385.73302911520005 -aF726.1179708600043 -aF3634.3845170378686 -aF2838.564047074318 -aF3661.069597697258 -aF3105.7414674401284 -aF3713.583862042427 -aF920.2439579963684 -aF3686.648713457584 -aF3684.6528009414674 -aF3096.0253206372263 -aF4719.539297258853 -aF3682.4844559669496 -aF3096.179774355888 -aF3095.897017812729 -aF3095.6118096232412 -aF3727.9875566244123 -aF2800.97377114296 -aF3077.5614273309707 -aF3139.360076320171 -aF3687.0308978796006 -aF3712.4770799279213 -aF3097.0648186802864 -aF3095.5131989598276 -aF3082.621352946758 -aF2691.0291467547418 -aF3556.758584165573 -aF3677.7424271583554 -aF2714.2339792490006 -aF3690.034437036514 -aF3096.13618953228 -aF3685.9028681635855 -aF3339.756012737751 -aF3686.740786397457 -aF3685.921936523914 -aF3095.9593985915185 -aF3644.945392203331 -aF544.3730700850487 -aF3095.8144790530205 -aF3462.6453297376634 -aF1122.3070139169693 -aF3688.2902268767357 -aF3171.1440365314484 -aF3096.037578868866 -aF3687.5915076732635 -aF3035.0528764605524 -aF3095.9501368165015 -aF2899.913772380352 -aF2787.7378774285316 -aF3898.933227729797 -aF3682.6176620841024 -aF2232.215522301197 -aF3686.958710515499 -aF3725.365384674072 -aF3990.9595863223076 -aF3103.9054567456246 -aF3104.288730788231 -aF3096.0577368497848 -aF2986.451984465122 -aF3092.7038846731184 -aF3075.5731421589853 -aF3220.1630704283716 -aF3686.760944378376 -aF4565.924314045906 -aF3086.9929107546805 -aF3056.720254302025 -aF3094.3157059311866 -aF3527.493009543419 -aF3093.668471300602 -aF3666.325382614136 -aF3686.0660388469696 -aF4630.468806922435 -aF3685.586060976982 -aF2265.240560364723 -aF3095.958581376076 -aF3102.2783807992937 -aF3687.442229652405 -aF3669.197622489929 -aF3095.665201032162 -aF2473.632677483559 -aF3685.346889257431 -aF3071.178974723816 -aF3652.0745073199273 -aF3113.7618921995163 -aF2674.079826068878 -aF3374.402133834362 -aF3096.2647647619247 -aF3086.838729441166 -aF3665.774851810932 -aF3647.4839357733727 -aF3096.2533237457274 -aF3091.8163887023925 -aF3880.010331749916 -aF2175.6890025377274 -aF3685.22675858736 -aF2158.1071570992467 -aF3368.4745978236197 -aF3471.2816625356672 -aF3142.7975568771362 -aF3046.851015806198 -aF3673.451228868961 -aF3099.5548741340635 -aF3689.76448353529 -aF3686.573802042007 -aF3686.1365917801854 -aF3677.7699400782585 -aF3686.1976105332374 -aF3085.652405023575 -aF3635.5441457509996 -aF1240.9179356694221 -aF3512.8561361551283 -aF3110.501202583313 -aF3684.411177575588 -aF3095.9893631577493 -aF3685.6884853124616 -aF3686.9352836728094 -aF3091.4145911097526 -aF4728.209953105449 -aF3095.998624932766 -aF2068.2412437319754 -aF3467.8210275411607 -aF3914.920685839653 -aF3690.7015572428704 -aF3098.414041376114 -aF3078.1046031951905 -aF3322.4471172571184 -aF3096.726763892174 -aF3688.5580011367797 -aF3098.0492908835413 -aF3380.199732589722 -aF3088.3296028137206 -aF2696.6518614053725 -aF3107.3197828650473 -aF2857.0551808953287 -aF3103.066448891163 -aF3006.3100473165514 -aF3948.2271184206006 -aF3081.209749472141 -aF3099.518371844292 -aF3087.9370669960977 -aF3677.7625851392745 -aF3676.191897058487 -aF3096.026682662964 -aF1729.1771945476532 -aF3685.629918205738 -aF3094.0533797740936 -aF3723.3490417718886 -aF3095.5319949150085 -aF3597.911919426918 -aF2262.8425778508185 -aF2654.6685076594354 -aF2128.5389403581617 -aF4003.8582424640654 -aF3372.473232984543 -aF3857.5665992379186 -aF3847.004906857014 -aF3112.097769153118 -aF3688.423977804184 -aF3265.960368645191 -aF3686.0551426410675 -aF3895.1920154333116 -aF3010.7987393379212 -aF3096.7112367987634 -aF3667.2335813760756 -aF3687.8393963575363 -aF3686.375491094589 -aF3007.416829431057 -aF2853.405769133568 -aF1778.812953698635 -aF3900.2320554733274 -aF4066.7979966163634 -aF3106.496574509144 -aF3096.1389135837553 -aF3967.901580202579 -aF3695.932280886173 -aF3068.9615968227386 -aF3685.4294280171393 -aF4555.6118723750105 -aF3573.462467813492 -aF3110.7804178595543 -aF1954.6199670672418 -aF303.44760619401933 -aF3269.3439129829408 -aF3675.742700970173 -aF772.3690960526466 -aF3094.404237604141 -aF3089.113040018082 -aF3098.4203066945074 -aF3079.542085158825 -aF3295.7742948293685 -aF3738.973656225204 -aF3687.1123470187185 -aF3824.000019741058 -aF3686.0325330138207 -aF3649.272547972202 -aF3685.6261045336723 -aF3096.180046761036 -aF1348.0170158863068 -aF3098.2328919529914 -aF3747.8668670773504 -aF3094.389800131321 -aF3148.459770274162 -aF3214.833463716507 -aF2951.1787867188455 -aF3091.0106142759323 -aF3072.1040626049044 -aF3096.117393577099 -aF3679.8968794703483 -aF3783.722194623947 -aF3097.207558977604 -aF715.7265316963195 -aF3095.998624932766 -aF3101.1906670451162 -aF3095.8370886802672 -aF3137.372335958481 -aF3102.1359129071234 -aF3018.5731822490693 -aF3685.714636206627 -aF3684.5081538081167 -aF3172.5006141662598 -aF2980.2781942009924 -aF3098.4227583408356 -aF3095.826464879513 -aF3096.4949471116065 -aF3681.4544921040533 -aF3090.9490507125856 -aF3703.7949830651282 -aF3095.971112012863 -aF3699.4920713543893 -aF4245.0397671937935 -aF3796.1318835258485 -aF3096.3345004796984 -aF3085.276213514805 -aF3095.945505928993 -aF3686.6089423060416 -aF3696.7998912811277 -aF3092.9030128359796 -aF3660.8701971292494 -aF4170.614049994945 -aF3682.127605223656 -aF3763.2087249875067 -aF3642.2788182139398 -aF3092.5505205750464 -aF2550.56452203989 -aF1553.2642155766487 -aF3096.099687242508 -aF3531.7798493504524 -aF2131.444686067104 -aF3644.1450658798217 -aF3096.041937351227 -aF3717.5865832805634 -aF362.4309479832649 -aF3686.130326461792 -aF1754.0025652647018 -aF3427.761398947239 -aF3687.1052644848824 -aF3098.210554730892 -aF3690.4495824813844 -aF3685.6135738968846 -aF3160.73407381773 -aF3073.266142964363 -aF3040.9755091786383 -aF3587.6512347340586 -aF3688.4351464152337 -aF3384.800110721588 -aF3114.2178984165193 -aF3057.61673964262 -aF3104.2909100294114 -aF3007.9422989606855 -aF2013.622377216816 -aF3127.007047688961 -aF3094.9335208058355 -aF3738.9676633119584 -aF1087.2740775108336 -aF2906.2319373726846 -aF3085.6769214868546 -aF3739.542438173294 -aF3685.975055527687 -aF3095.731940293312 -aF3075.85971237421 -aF3685.972603881359 -aF3660.8170781254767 -aF3060.7030899643896 -aF3712.5669736266136 -aF3080.7395781874657 -aF3665.637832021713 -aF3664.3076776862144 -aF3095.6641114115714 -aF2995.1460671544073 -aF3686.2109583854676 -aF3121.1424372673036 -aF3110.3669068455697 -aF3847.2004937529564 -aF3686.506517970562 -aF3850.263144826889 -aF3769.58137100935 -aF3097.3701848506926 -aF3697.9287382125854 -aF3109.822368955612 -aF3183.453207933903 -aF3687.3052098631856 -aF1591.5799064159394 -aF2836.1020493507385 -aF3095.359562456608 -aF2993.1994599699974 -aF3095.273754835129 -aF2765.7207313776016 -aF3099.382986485958 -aF3430.196700966358 -aF3686.0338950395585 -aF3096.1683333396913 -aF3029.3577020406724 -aF3421.8104360938073 -aF3687.0180948376656 -aF3095.016876780987 -aF3093.770078420639 -aF3032.0975530147552 -aF948.7296366810798 -aF2729.786406338215 -aF2696.733310544491 -aF3022.033817243576 -aF3496.6292339205743 -aF2970.4890428185463 -aF3090.355207490921 -aF3354.601276063919 -aF3202.4373950719832 -aF2472.348014807701 -aF3682.7070109724996 -aF3095.6000962018966 -aF3572.8716210484504 -aF3685.908043861389 -aF3013.4323523044586 -aF2549.545181977749 -aF3097.001075875759 -aF1706.8143663644792 -aF3095.9880011320115 -aF3699.550093650818 -aF3685.4517652392387 -aF3685.2006076931953 -aF3112.742552137375 -aF3065.0781890392304 -aF3426.7676649689674 -aF3174.9113997220993 -aF3103.327140617371 -aF3588.643606686592 -aF3095.7180476307867 -aF3096.3908883452414 -aF498.01597929000854 -aF607.5579888701438 -aF3665.642735314369 -aF3718.939892053604 -aF3108.412944722176 -aF2105.833426499367 -aF3175.6161118388177 -aF3685.7015607595445 -aF3113.2127234220507 -aF3103.3966039299967 -aF3125.063981771469 -aF3678.9148589134215 -aF3687.310113155842 -aF3102.723490810394 -aF3686.3297270298003 -aF3096.0032558202743 -aF3784.078228151798 -aF3062.8076921343804 -aF3698.4258776068687 -aF4361.874607384204 -aF239.86170703172684 -aF1766.2000505566598 -aF3138.802735388279 -aF3090.395251047611 -aF3696.2894040346146 -aF3069.6671261548995 -aF3336.471351468563 -aF3182.196058177948 -aF3059.1146955490112 -aF3843.740675973892 -aF3686.7386071562764 -aF3828.323361837864 -aF2978.658200788498 -aF3084.475069975853 -aF3097.160705292225 -aF2818.4907841563227 -aF3687.012919139862 -aF3093.451909208298 -aF3145.842501616478 -aF3689.4664723038672 -aF3095.985821890831 -aF3686.1213370919227 -aF3097.514831984043 -aF3105.5434288978577 -aF3746.229984545708 -aF3118.8280831336974 -aF3130.2437656521797 -aF1530.4786146044733 -aF3113.1171092152595 -aF3082.1522712826727 -aF3015.22859184742 -aF3852.280304944515 -aF3649.633212387562 -aF3009.7562448382378 -aF3091.449731373787 -aF3098.805759978294 -aF3096.2470584273337 -aF1873.810707640648 -aF3117.277008223534 -aF3099.8283689022064 -aF3069.541002571583 -aF2989.249312925339 -aF3685.330544948578 -aF3631.8699451208113 -aF3045.054776263237 -aF3685.9162160158157 -aF3087.6376937389373 -aF3163.797814512253 -aF3095.964029479027 -aF3095.0073426008225 -aF3094.8640574932097 -aF3055.175444710255 -aF3406.761958527565 -aF3120.2464967370033 -aF3100.608537244797 -aF2816.295471072197 -aF2089.961468172073 -aF2872.2510296463965 -aF3095.5856587290764 -aF3096.383805811405 -aF3095.137279856205 -aF2834.9236246824266 -aF3802.924033474922 -aF3095.6213438034056 -aF3085.0678235769274 -aF2854.0644447803497 -aF3093.226902556419 -aF3711.434585428238 -aF3176.191159105301 -aF3686.1330505132673 -aF3089.426305937767 -aF3097.8991956472396 -aF3138.34073625803 -aF3686.011557817459 -aF3715.525565934181 -aF3236.6081691861154 -aF3094.9792848706247 -aF3638.2662903904916 -aF3099.1724173069 -aF2512.033086323738 -aF4201.3950144529335 -aF3207.9971841335296 -aF3106.046561205387 -aF3928.7703083515166 -aF3685.5438381791114 -aF3728.37518914938 -aF3093.0689075708387 -aF2977.407861161232 -aF3684.9025964617726 -aF3096.955584216118 -aF4550.255297553539 -aF3123.491659259796 -aF3782.7028545618055 -aF3086.9346160531045 -aF3663.876460337639 -aF3686.277697646618 -aF3685.9856793284416 -aF3095.509657692909 -aF3828.2187582612037 -aF3093.5924702644347 -aF4375.621805560589 -aF3503.679623949528 -aF1907.2775868535043 -aF3703.8867835998535 -aF3707.9450754880904 -aF3203.9225479364395 -aF3608.3382264494894 -aF3096.338858962059 -aF3034.8030809402467 -aF3303.882706451416 -aF902.2407017946243 -aF3686.110168480873 -aF3105.778242135048 -aF3685.0404334664345 -aF372.3034553408623 -aF3177.7784639000893 -aF3640.2578444242477 -aF3208.968580889702 -aF2958.4721621394156 -aF4501.931986403465 -aF3095.5766693592072 -aF3096.0596436858177 -aF3684.04479265213 -aF874.9901080489158 -aF3224.8604247927665 -aF3224.740021717548 -aF3787.630936086178 -aF3105.6216091752053 -aF4620.001366722583 -aF3775.7832190036775 -aF3729.21991751194 -aF3106.126920723915 -aF3096.3966088533402 -aF3095.736843585968 -aF3686.279332077503 -aF3083.9542313337324 -aF3693.712451338768 -aF3096.0457510232927 -aF642.6669263124465 -aF3096.2838331222533 -aF1661.7157873511314 -aF1771.3299842953681 -aF4396.832904779911 -aF3097.0612774133683 -aF3131.9046198368073 -aF3096.4984883785246 -aF2742.753980982304 -aF3688.585241651535 -aF3722.056479346752 -aF3094.879039776325 -aF3109.4535323858263 -aF2823.42158973217 -aF3688.0017498254774 -aF3967.667311775684 -aF3096.699795782566 -aF3685.8851618289946 -aF3482.1337388038637 -aF3135.506905508041 -aF3095.5840242981913 -aF3098.8610582232477 -aF3096.4742443203927 -aF3685.891427147388 -aF3511.1931027293203 -aF3690.502156674862 -aF3686.116161394119 -aF2794.857730770111 -aF3087.7861545443534 -aF3667.72391064167 -aF3079.9970017552378 -aF2034.0554873347282 -aF3065.2375460505486 -aF3096.944688010216 -aF3096.014969241619 -aF3096.4170392394067 -aF3096.278112614155 -aF3096.0634573578836 -aF3374.367538380623 -aF3686.584425842762 -aF4724.353513431549 -aF3699.5473695993423 -aF3836.1138766527174 -aF3096.7008854031565 -aF3684.564269268513 -aF3557.099090600014 -aF3686.800987935066 -aF4187.006846964359 -aF247.59120309352875 -aF3323.4664573192595 -aF3096.1035009145735 -aF3094.6232513427735 -aF2201.928428375721 -aF2994.1035726547243 -aF3669.4215395212173 -aF3097.230168604851 -aF4082.7546729445457 -aF1728.7081128835678 -aF3128.2601113677024 -aF2341.881476223469 -aF3096.6916236281395 -aF543.5177179217338 -aF400.00814846754076 -aF3121.9506633400915 -aF3846.181153690815 -aF3096.0503819108008 -aF3095.830006146431 -aF3169.3744926929476 -aF3039.5175968289377 -aF3065.989656662941 -aF3091.1658852100372 -aF3093.7529168963433 -aF3095.813661837578 -aF3683.6890315294263 -aF3685.7356114029885 -aF3095.609630382061 -aF3955.563806259632 -aF3687.0175500273704 -aF3094.6894457936287 -aF3676.997671484947 -aF575.2316700100898 -aF1039.2831006407737 -aF3685.3329965949056 -aF3685.998754775524 -aF2867.1246371746065 -aF3095.6878106594086 -aF3081.656493914127 -aF3767.682707130909 -aF559.3071374893188 -aF2783.6539794564246 -aF3177.9163009047506 -aF877.5956632852553 -aF3213.725591981411 -aF3685.953807926178 -aF3102.887478709221 -aF3016.8703776717184 -aF3095.21464291811 -aF3110.3788926720617 -aF3684.4986196279524 -aF3685.953807926178 -aF3096.7986788511275 -aF3098.4167654275893 -aF3685.9889481902123 -aF3097.9109090685843 -aF644.4991233348846 -aF3096.0975080013277 -aF3093.507207453251 -aF3671.146953725815 -aF3088.7115148305893 -aF3098.491132032871 -aF3683.04533816576 -aF4286.882015073299 -aF3095.9125449061394 -aF3697.559901642799 -aF3737.954316163063 -aF3341.2842056155205 -aF2129.7876455545425 -aF3135.3006948113443 -aF3549.19062435627 -aF3672.4904559135434 -aF3113.2571254611016 -aF3173.959071326256 -aF3815.1689172625543 -aF2789.7923570513726 -aF3686.4427751660346 -aF2045.943247973919 -aF4009.6700062870977 -aF1168.456804394722 -aF2917.669957113266 -aF3748.886207139492 -aF3685.5966847777368 -aF3674.419356763363 -aF3681.5157832622526 -aF3095.8253752589226 -aF3096.8564287424088 -aF1772.1842468380928 -aF2287.150651192665 -aF3744.2716639399528 -aF3683.4803691864013 -aF3121.57692347765 -aF3672.249104952812 -aF4030.073969054222 -aF3686.0671284675595 -aF3689.173091959953 -aF3095.905462372303 -aF3698.985670185089 -aF3683.363507378101 -aF3084.453822374344 -aF3015.3111306071282 -aF3017.5769966244698 -aF3685.7416043162343 -aF2216.3272196650505 -aF3108.787774205208 -aF3355.03412784338 -aF4615.402078211307 -aF3095.692441546917 -aF1812.1357305884362 -aF3356.1414547681807 -aF3055.328536403179 -aF3041.4492217302322 -aF3110.7509981036187 -aF3097.752369272709 -aF3108.5121001958846 -aF3100.056916821003 -aF3778.755159163475 -aF3096.442917728424 -aF3094.467708003521 -aF3088.389259541035 -aF1978.3137668013574 -aF3640.0328377723695 -aF1824.197558116913 -aF3687.0355287671086 -aF3096.735208451748 -aF3689.1741815805435 -aF2799.2954830288886 -aF3097.235616707802 -aF3686.1104408860206 -aF3686.060863149166 -aF3023.146047461033 -aF3096.0253206372263 -aF3093.78778475523 -aF2915.4874470710756 -aF3763.075246465206 -aF3735.907191479206 -aF2832.399518585205 -aF3119.060989534855 -aF3095.168878853321 -aF3632.3812495827674 -aF3094.42548520565 -aF3095.923168706894 -aF3097.3936116933824 -aF3100.847708964348 -aF3095.81012057066 -aF3150.498450398445 -aF3115.194470870495 -aF3661.185914695263 -aF3443.8286717653273 -aF3096.804671764374 -aF3096.29200527668 -aF3044.7701128840445 -aF3108.755902802944 -aF3092.1841356515883 -aF3602.614721894264 -aF3686.1207922816275 -aF3072.2075765609743 -aF3095.300995349884 -aF3685.5315799474715 -aF3115.62350897789 -aF1864.5211472988128 -aF3096.5017572402953 -aF3135.35681027174 -aF3296.566448998451 -aF3686.1940692663193 -aF3187.9029460191728 -aF3683.081840455532 -aF3258.3136837482452 -aF3091.524370384216 -aF3095.3584728360174 -aF3095.7180476307867 -aF3108.830269408226 -aF3434.1915224552154 -aF3686.5797949552534 -aF3060.439401781559 -aF2658.4306951522826 -aF4270.041656446457 -aF3393.2727280259132 -aF4067.1161658287047 -aF3097.0062515735626 -aF2994.218800032139 -aF3713.2218356013295 -aF2723.3829785346984 -aF3902.650195968151 -aF3477.0054394960403 -aF2058.0347676634788 -aF3662.9088772535324 -aF3095.906551992893 -aF3097.2108278393744 -aF3107.0898729205132 -aF3092.014154839516 -aF3096.0362168431284 -aF3620.0126938581466 -aF3104.1282841563225 -aF3117.573929834366 -aF3096.405325818062 -aF3677.0717656850816 -aF3321.0371482133864 -aF3096.208649301529 -aF3685.1055382966993 -aF3687.01537078619 -aF3685.99031021595 -aF3685.9064094305036 -aF3319.523665213585 -aF3705.5163111925126 -aF2911.251002216339 -aF3739.0265028238296 -aF3100.7038790464403 -aF3686.060863149166 -aF3684.682220697403 -aF3096.128289783001 -aF3530.397665631771 -aF2202.385524213314 -aF2057.8143918991086 -aF3054.510776150227 -aF3095.4363807082177 -aF3295.3923828125 -aF3078.5227450966836 -aF3059.655419766903 -aF3519.0212094545363 -aF3096.422759747505 -aF3736.9289831876754 -aF3842.780175423622 -aF3625.4983887195585 -aF4000.7898708820344 -aF3748.1673299551007 -aF3096.1285621881484 -aF3092.617804646492 -aF3087.3007285714148 -aF2995.7682405114174 -aF3685.310386967659 -aF3676.7620410323143 -aF3095.8722289443017 -aF3112.193383359909 -aF3686.3733118534087 -aF3093.9561311364173 -aF3098.618072831631 -aF3666.346357810497 -aF3090.4954961419107 -aF3137.7602408885955 -aF3099.5409814715385 -aF3367.4882187843323 -aF357.0092683315277 -aF2904.233845615387 -aF3825.0193598031997 -aF3686.0409775733947 -aF2712.6398643255234 -aF3096.208649301529 -aF3686.081021130085 -aF3103.67554680109 -aF1472.2067950606347 -aF3891.650748515129 -aF3689.0777501583098 -aF3096.8011304974557 -aF3095.8934765458107 -aF3098.0631835460663 -aF2929.049682152271 -aF2652.0095610141752 -aF3685.9704246401784 -aF3685.9301086783407 -aF4013.530804443359 -aF3686.0325330138207 -aF2450.1104929924013 -aF4217.5203095674515 -aF2914.688482773304 -aF3627.214813554287 -aF3686.676226377487 -aF3088.479153239727 -aF3723.8440019249915 -aF3737.480603611469 -aF3095.808758544922 -aF3683.815972328186 -aF4091.270875072479 -aF530.7212137103081 -aF3697.645709264278 -aF2337.824273955822 -aF3247.131180036068 -aF3680.4705647110936 -aF3097.312434959412 -aF3095.7038825631143 -aF3685.309024941921 -aF4039.854948282242 -aF449.25573028326033 -aF3684.444138598442 -aF3685.8323152303697 -aF1318.0856830835344 -aF3845.945523238182 -aF3712.923007154465 -aF3676.3406302690505 -aF3617.6040875434874 -aF3506.1862961173056 -aF3690.650617480278 -aF3125.9359506487845 -aF3090.189040350914 -aF3096.4826888799666 -aF4204.51187415123 -aF3648.9364000201226 -aF3093.441557812691 -aF3183.89096300602 -aF4011.673273742199 -aF3881.0430196642874 -aF3079.4064273953436 -aF3688.747867524624 -aF3079.875509059429 -aF3095.808758544922 -aF3727.858981394768 -aF3686.139860641956 -aF3095.758091187477 -aF3684.9965762376783 -aF3330.0668340444563 -aF3095.9149965524675 -aF1664.1633476018906 -aF3097.221451640129 -aF3095.222270262241 -aF3686.0453360557553 -aF3169.1053564071653 -aF3092.7733479857443 -aF3096.2770229935645 -aF3096.2683060288427 -aF332.74968310594556 -aF2974.9281571030615 -aF3460.22718924284 -aF3107.0092409968374 -aF3680.8761759757995 -aF3683.178544282913 -aF3095.9790117621424 -aF3096.39442961216 -aF3111.271019530296 -aF3574.8468307733538 -aF3686.7021048665047 -aF3094.7657192349434 -aF3131.455423748493 -aF3683.3882962465286 -aF3686.005564904213 -aF3096.09614597559 -aF3091.131834566593 -aF3096.131831049919 -aF780.7556333303451 -aF3685.918395256996 -aF3095.6804557204246 -aF4203.046879267692 -aF3686.6391792774198 -aF3681.030357289314 -aF3096.422759747505 -aF723.3925573587418 -aF3069.4644567251207 -aF3685.84893194437 -aF2884.7241889476777 -aF2457.0333974123 -aF622.0213401794433 -aF1574.0642554283143 -aF2814.4886077284814 -aF3098.5543300271033 -aF3095.7984071493147 -aF4528.043381822109 -aF3679.730439925194 -aF2515.0145606637 -aF3069.532830417156 -aF3095.8003139853477 -aF1689.4768683433533 -aF2466.6964252114294 -aF814.1481459379196 -aF3096.245968806744 -aF3727.404064798355 -aF2777.8678217172624 -aF2975.660109734535 -aF3769.2607501506805 -aF3685.977234768867 -aF3095.5368982076643 -aF3432.067307114601 -aF3103.4815943360327 -aF3588.1004308223723 -aF3690.7083673715592 -aF3083.289835178852 -aF3107.624876630306 -aF3645.4757650256156 -aF4038.4033012509344 -aF3650.5792754650115 -aF3685.76993445158 -aF3472.6526776432993 -aF1869.0264560341836 -aF3097.6295145511626 -aF1629.716627073288 -aF2362.8937196850775 -aF3801.1049118995666 -aF3797.861656212807 -aF795.8011144399643 -aF3840.839016342163 -aF3020.5325924754143 -aF3756.5824697732924 -aF3096.606633222103 -aF2379.753146672249 -aF2664.4418595433235 -aF3686.028991746902 -aF3624.075616633892 -aF3130.908434212208 -aF3096.367461502552 -aF3099.08225120306 -aF3792.1125455737115 -aF3095.903010725975 -aF3685.9151263952253 -aF3716.045587360859 -aF4217.371848762035 -aF4746.401441264152 -aF462.6659632921219 -aF3103.3459365725516 -aF3096.210556137562 -aF3685.8840722084046 -aF2822.4254041075706 -aF3064.290120947361 -aF3096.2802918553352 -aF3164.8571981310843 -aF3144.1890023708343 -aF3690.7475937128065 -aF3473.2971882224083 -aF3116.2810950040816 -aF3864.3118955016134 -aF3267.373334145546 -aF3117.94521805048 -aF3085.4461943268775 -aF3096.045478618145 -aF3095.9681155562403 -aF1020.4236750602722 -aF3651.612780594826 -aF3096.277840209007 -aF3687.39047267437 -aF3685.9584388136864 -aF3096.3391313672064 -aF3096.0985976219176 -aF3686.5193210124967 -aF2605.8069239377974 -aF3687.247732377052 -aF1736.9589923977853 -aF1255.7506683588028 -aF1761.9911186218262 -aF1170.6997883796691 -aF3701.1818004846573 -aF3097.57857478857 -aF3095.672283565998 -aF2796.9860321879387 -aF3686.189438378811 -aF3285.8029044032096 -aF2751.785846054554 -aF3059.9880264520643 -aF3096.3522068142893 -aF3687.5220443606377 -aF257.3634653568268 -aF3096.4954919219017 -aF3462.3778278827667 -aF3094.210829949379 -aF3094.7177759289743 -aF3053.0043756842615 -aF3702.4604702472684 -aF3095.6553944468496 -aF3645.0186691880226 -aF3095.6216162085534 -aF3098.726762485504 -aF3092.4690714359285 -aF1841.4846611857415 -aF1901.0882694959641 -aF3576.6640455126762 -aF3684.3368109703065 -aF3097.1002313494682 -aF3081.4758893013 -aF4564.904973983764 -aF3680.769937968254 -aF3704.7116263866424 -aF3682.0270877242087 -aF3158.080030465126 -aF3086.0160658955574 -aF3111.8008475422857 -aF4396.740287029743 -aF3096.245968806744 -aF3685.9960307240485 -aF3587.7847132563593 -aF3094.482962691784 -aF3024.573450434208 -aF3690.5247663021087 -aF2986.3155094861986 -aF3213.8293783426284 -aF3685.8238706707953 -aF3722.4013442635537 -aF3017.912872171402 -aF3683.04424854517 -aF3679.3594241142273 -aF3032.372137403488 -aF3384.7717805862426 -aF3059.7510339736937 -aF2669.392278289795 -aF3488.0588231682777 -aF3686.3520642518997 -aF3078.154180932045 -aF3759.754900121689 -aF3692.3054787516594 -aF3095.7371159911154 -aF3685.8238706707953 -aF3315.1294977784155 -aF3685.7533177375794 -aF3227.301719725132 -aF3882.4369168043136 -aF3095.3481214404105 -aF3096.4134979724886 -aF3685.806436741352 -aF3685.4152629494665 -aF3101.692437326908 -aF3624.4790486574175 -aF3686.977778875828 -aF3686.4860875844956 -aF3092.913636636734 -aF3094.781246328354 -aF3095.9806461930275 -aF3831.2105839967726 -aF2689.561972630024 -aF3097.397425365448 -aF3104.0552795767785 -aF3303.7473210930825 -aF3686.1902555942534 -aF3094.032949388027 -aF4132.774523353576 -aF3686.3711326122284 -aF3132.4053004980087 -aF3096.0220517754556 -aF3884.9928943037985 -aF3096.613443350792 -aF2880.817626726627 -aF3087.8676036834718 -aF3682.9301107883452 -aF3685.680585563183 -aF3218.092518901825 -aF3684.7470531225204 -aF3082.608549904823 -aF2411.2976627588273 -aF2346.709040248394 -aF3552.338265836239 -aF3202.49786901474 -aF2907.4945352315904 -aF3685.859283339977 -aF2308.7845232009886 -aF3089.499310517311 -aF3100.113304686546 -aF1545.5123822927476 -aF3682.729620599747 -aF3685.915943610668 -aF3656.944838953018 -aF3091.2056563615797 -aF3107.7093222260473 -aF3082.6829165101053 -aF3685.096004116535 -aF3096.194211828709 -aF1530.607189834118 -aF3688.0584100961682 -aF3184.9476225733756 -aF3686.4155346512794 -aF3064.063752269745 -aF4080.9587058067323 -aF3685.9892205953597 -aF3981.9612270832063 -aF3154.501444041729 -aF3096.038668489456 -aF3690.395101451874 -aF2807.7201570272446 -aF3691.026808989048 -aF2918.2861375570296 -aF3141.6025154948234 -aF3028.979331290722 -aF3674.935836923122 -aF3695.2820497989655 -aF2512.8391331553457 -aF3665.6620760798455 -aF3687.0802032113074 -aF3559.5403855323793 -aF3096.640956270695 -aF3668.704569172859 -aF3095.9781945466993 -aF3685.9930342674256 -aF3626.1723190546036 -aF2698.058289182186 -aF3080.6946313381195 -aF3686.1118029117583 -aF3096.912816607952 -aF3354.0379422187807 -aF3686.066856062412 -aF1199.7921133279801 -aF3279.9083294153215 -aF3658.9813398361207 -aF3685.6040397167203 -aF3210.391897785664 -aF3085.586482977867 -aF3647.594532263279 -aF3092.3227898716927 -aF2955.4402928471563 -aF3685.3185591220854 -aF3096.042482161522 -aF3684.1414964795113 -aF3123.806287205219 -aF3096.219000697136 -aF4257.871139264106 -aF3555.961799108982 -aF3096.7561836481095 -aF3048.9417253136635 -aF3093.429299581051 -aF3884.0228595733643 -aF3097.9512250304224 -aF3699.621736204624 -aF3132.9157877445223 -aF2916.5299415707586 -aF3346.356934273243 -aF3096.0163312673567 -aF3685.978596794605 -aF815.201536643505 -aF3095.8084861397742 -aF3566.751222193241 -aF3098.1361881256103 -aF1472.9915942907335 -aF321.20379092693327 -aF3084.848265028 -aF3685.966610968113 -aF3074.543450701237 -aF3097.4420998096466 -aF3067.478078389168 -aF3100.329049563408 -aF3088.528458571434 -aF3096.1481753587723 -aF3095.815568673611 -aF3097.436379301548 -aF3020.8099009156226 -aF3107.7365627408026 -aF3093.20347571373 -aF3551.229304480553 -aF3096.1789571404456 -aF2908.690666234493 -aF3096.2108285427094 -aF3747.4116780757904 -aF3848.2764940857887 -aF3086.492502498627 -aF3685.8840722084046 -aF2838.1638839125635 -aF3100.341852605343 -aF3096.1882189154626 -aF3096.354658460617 -aF3080.1018777370455 -aF3787.4034777879715 -aF3685.0183686494825 -aF3394.2920680880547 -aF3685.9924894571304 -aF3095.986911511421 -aF2770.660798728466 -aF3104.4303814649584 -aF3093.9847336769103 -aF2461.8901087880135 -aF3684.051602780819 -aF3105.822644174099 -aF2774.80517064333 -aF3152.5406717896462 -aF3686.0516013741494 -aF3100.4693382143973 -aF3095.894566166401 -aF3064.7341413378717 -aF3036.0948261499407 -aF3671.4983563661576 -aF3988.382361221313 -aF3663.2145158290864 -aF3068.5134903550147 -aF3685.898509681225 -aF3685.775927364826 -aF3685.8263223171234 -aF3021.551932537556 -aF3108.6442166924476 -aF3095.67092154026 -aF3664.6825071692465 -aF3318.5043251514435 -aF3134.058527338505 -aF3089.1637073755264 -aF3100.283285498619 -aF3649.399488770962 -aF3185.761296749115 -aF3080.992642569542 -aF3684.7342500805853 -aF3470.2072966337205 -aF2058.810577523708 -aF3675.060598480701 -aF3686.3730394482614 -aF3104.272386479378 -aF3065.4565597891806 -aF1508.0335200667382 -aF3686.174183690548 -aF3023.447872364521 -aF3693.480362153053 -aF3096.055285203457 -aF3685.446861946583 -aF3838.340243923664 -aF3682.64354057312 -aF3083.2353541493417 -aF3686.109078860283 -aF3092.9169054985045 -aF3971.842465472221 -aF3686.043429219723 -aF3095.8253752589226 -aF3094.543164229393 -aF3685.676771891117 -aF3680.958714735508 -aF4654.004884076118 -aF3096.1043181300165 -aF3562.863455927372 -aF3086.5772204995155 -aF3673.5732663750646 -aF3730.9701205849647 -aF4616.999734401702 -aF3096.4061430335046 -aF2251.009025835991 -aF2934.5729689240457 -aF3266.3539940834044 -aF3686.0197299718857 -aF3019.837142133713 -aF3845.6731180906295 -aF3685.1700983166693 -aF3694.3760302782057 -aF2984.79276471138 -aF3096.328507566452 -aF3426.316289639473 -aF3064.846372258663 -aF3685.891427147388 -aF3086.2710371136664 -aF3095.9890907526014 -aF3651.605425655842 -aF3589.7266895532607 -aF3083.158808302879 -aF3096.3323212385176 -aF3945.771386015415 -aF3095.6118096232412 -aF3094.3402223944663 -aF2346.8814727067947 -aF3668.5983311653135 -aF3685.4871779084206 -aF3672.1853621482846 -aF3098.2857385516168 -aF3686.257539665699 -aF3708.1700821399686 -aF3675.128972172737 -aF3683.2324805021285 -aF3097.362829911709 -aF3099.2225398540495 -aF3437.912031960487 -aF430.41292141675945 -aF2278.6813027501107 -aF3092.9171779036524 -aF2194.0758051872253 -aF3096.180046761036 -aF3690.2657090067864 -aF3686.070669734478 -aF3685.8170605421064 -aF2091.8819244623182 -aF3097.0215062618254 -aF3096.3543860554696 -aF3842.34187554121 -aF3089.5028517842293 -aF3667.813531935215 -aF3683.4016440987584 -aF3097.980644786358 -aF4094.159186851978 -aF635.9889141201973 -aF3719.116410589218 -aF2949.280395245552 -aF2286.3653071522713 -aF3686.4392338991165 -aF3096.847984182835 -aF3686.9505383610726 -aF3081.5630589485168 -aF3687.7516819000243 -aF3096.351117193699 -aF3685.786278760433 -aF3097.9972615003585 -aF3279.2106998324393 -aF3642.348009121418 -aF3686.6468066215516 -aF3880.023679602146 -aF3111.7798723459246 -aF3689.92138890028 -aF3095.7640841007233 -aF3096.1857672691344 -aF3072.357399392128 -aF571.2098804116249 -aF3096.1857672691344 -aF3087.3644713759422 -aF3096.1882189154626 -aF2475.831259429455 -aF3096.283560717106 -aF3685.992761862278 -aF1877.926749420166 -aF3118.0184950351713 -aF3112.206186401844 -aF3294.5558266043663 -aF3100.6507600426676 -aF3188.2894889235495 -aF3312.6664104342462 -aF3727.45582177639 -aF3685.682764804363 -aF3174.596771776676 -aF2832.8849445581436 -aF3585.0873574852944 -aF3100.052013528347 -aF3463.960774195194 -aF1042.9951655864716 -aF2402.5204964995382 -aF3121.4333659648896 -aF3094.8482579946517 -aF3686.0442464351654 -aF3094.940058529377 -aF3095.6450430512427 -aF3096.4287526607513 -aF3106.8869310855866 -aF1794.9910954117775 -aF4269.004610049724 -aF3096.1378239631654 -aF3099.7659881234167 -aF4078.3133794188498 -aF3683.637002146244 -aF3686.248277890682 -aF2949.0390442848206 -aF3095.761904859543 -aF3205.935349571705 -aF552.1810188293457 -aF3770.98371270895 -aF3142.091755139828 -aF3096.1152143359186 -aF2904.015921497345 -aF3686.0385259270665 -aF3685.9930342674256 -aF3096.2764781832693 -aF3101.959121966362 -aF3686.209323954582 -asS'y_part' -p4 -(lp5 -sS'counter_tot' -p6 -I12319483 -s. \ No newline at end of file diff --git a/scratch/3572424/helperfunctions.py b/scratch/3572424/helperfunctions.py deleted file mode 100644 index dabb34c..0000000 --- a/scratch/3572424/helperfunctions.py +++ /dev/null @@ -1,23 +0,0 @@ -# some helperfunctions - -#Dislpay time (e.g. while generating points) - -display_intervals = ( - ('w', 604800), # 60 * 60 * 24 * 7 - ('d', 86400), # 60 * 60 * 24 - ('h', 3600), # 60 * 60 - ('min', 60), - ('s', 1), - ) - -def display_time(seconds, granularity=2): - result = [] - - for name, count in display_intervals: - value = seconds // count - if value: - seconds -= value * count - if value == 1: - name = name.rstrip('s') - result.append("{} {}".format(value, name)) - return ', '.join(result[:granularity]) diff --git a/scratch/3572424/helperfunctions.pyc b/scratch/3572424/helperfunctions.pyc deleted file mode 100644 index cf55c57..0000000 --- a/scratch/3572424/helperfunctions.pyc +++ /dev/null Binary files differ diff --git a/scratch/3572424/pdg_const.py b/scratch/3572424/pdg_const.py deleted file mode 100644 index 53ced55..0000000 --- a/scratch/3572424/pdg_const.py +++ /dev/null @@ -1,65 +0,0 @@ -pdg = { - -###Particle masses### - -"mbstar" : 5415.4, -"mbstar0" : 5711.0, -"B0_M" : 5279.5, -"Bs_M" : 5366.7, -"Bplus_M" : 5279.3, -"Lb_M" : 5619.4, -"D0_M" : 1864.8, -"Dst_M" : 2010, -"pi_M" : 139.6, -"Jpsi_M" : 3096.9, -"Psi2s_M" : 3685.6, -"kaon_M" : 493.7, -"Ks_M" : 497.6, -"phi_M" : 1019.5, -"rho_M" : 775.26, -"rho_width" : 149.1, -"omega_M" : 782.65, -"omega_width" : 8.49, - -"muon_M" : 105.7, - -"squark_M" : 95.0, -"bquark_M" : 4180.0, -"cquark_M" : 1275.0, - -"Bplus_tau" : 1.638e-12, - -###Wilson coefficients### - -"C1" : -0.257, -"C2" : 1.009, -"C3" : -0.005, -"C4" : -0.078, - -"C7eff" : -0.306, - -"C9eff" : 4.211, -"C10eff" : -4.103, - -###Other constants - -"GF" : 1.1663787e-5, -"alpha_ew" : 1.0/137.0, -"Vts" : 0.0394, -"Vtb" : 1.019, - -#Formfactor z coefficients - -#"b0" : [0.285, 0.19, -0.17], -#"bplus" : [0.437, -1.41, -2.5], -#"bT" : [0.440, -1.47, -2.7] - -"b0" : [0.292, 0.281, 0.150], -"bplus" : [0.466, -0.885, -0.213], -"bT" : [0.460, -1.089, -1.114], - -#Resonances format(mass, width, phase, scale) - -"jpsi": (3096, 0.09, -1.5, 2e-2), -"psi2s": (3686, 0.3, -1.5, 3.14e-3) -} diff --git a/scratch/3572424/pdg_const.pyc b/scratch/3572424/pdg_const.pyc deleted file mode 100644 index f1807c3..0000000 --- a/scratch/3572424/pdg_const.pyc +++ /dev/null Binary files differ diff --git a/scratch/3572424/raremodel.py b/scratch/3572424/raremodel.py deleted file mode 100644 index c2a8726..0000000 --- a/scratch/3572424/raremodel.py +++ /dev/null @@ -1,1047 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c - -mmu = pdg['muon_M'] -mb = pdg["bquark_M"] -ms = pdg["squark_M"] -mK = pdg["Ks_M"] -mB = pdg["Bplus_M"] - -class model: - - def __init__(self): - - - self.mmu = pdg['muon_M'] - self.mb = pdg["bquark_M"] - self.ms = pdg["squark_M"] - self.mK = pdg["Ks_M"] - self.mB = pdg["Bplus_M"] - - self.C7eff = pdg["C7eff"] - self.C9eff = pdg["C9eff"] - self.C10eff = pdg["C10eff"] - - #self.C1 = pdg["C1"] - #self.C2 = pdg["C2"] - #self.C3 = pdg["C3"] - #self.C4 = pdg["C4"] - - self.GF = pdg["GF"] #Fermi coupling const. - self.alpha_ew = pdg["alpha_ew"] - self.Vts = pdg["Vts"] - self.Vtb = pdg["Vtb"] - - self.x_min = 2*self.mmu - self.x_max = (self.mB - self.mK) - 0.1 - self.total_pdf_string = "self.total_nonres(q2)" - self.mode = "" - self.total_scale_amp = 1.0 - self._mean = 0.0 - - self.cusp_mean = 1 - self.cusp_sigma_L = 1 - self.cusp_sigma_R = 1 - self.cusp_amp = 0 - - - def formfactor(self, q2, subscript): - - #check if subscript is viable - - if subscript != "0" and subscript != "+" and subscript != "T": - raise ValueError('Wrong subscript entered, choose either 0, + or T') - - #get constants - - mh = self.mK - mbstar0 = pdg["mbstar0"] - mbstar = pdg["mbstar"] - b0 = pdg["b0"] - bplus = pdg["bplus"] - bT = pdg["bT"] - - N = 3 - - #some helperfunctions - - tpos = (self.mB - self.mK)**2 - tzero = (self.mB + self.mK)*(np.sqrt(self.mB)-np.sqrt(self.mK))**2 - - z_oben = np.sqrt(tpos - q2) - np.sqrt(tpos - tzero) - z_unten = np.sqrt(tpos - q2) + np.sqrt(tpos - tzero) - z = z_oben/z_unten - - #calculate f0 - - if subscript == "0": - prefactor = 1/(1 - q2/(mbstar0**2)) - _sum = 0 - - for i in range(N): - _sum += b0[i]*(z**i) - - return prefactor * _sum - - #calculate f+ or fT - - else: - prefactor = 1/(1 - q2/(mbstar**2)) - _sum = 0 - - if subscript == "T": - b = bT - else: - b = bplus - - for i in range(N): - _sum += b[i] * (z**i - ((-1)**(i-N)) * (i/N) * z**N) - - return prefactor * _sum - - def axiv_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #left term in bracket - - bracket_left = 2./3. * kabs**2 * beta**2 * abs(C10eff*self.formfactor(q2, "+"))**2 - - #middle term in bracket - - _top = 4. * mmu**2 * (mB**2 - mK**2) * (mB**2 - mK**2) - - _under = q2 * mB**2 - - bracket_middle = _top/_under * abs(C10eff * self.formfactor(q2, "0"))**2 - - return prefactor1 * (bracket_left + bracket_middle) * 2 * np.sqrt(q2) - - - def vec_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C7eff = self.C7eff - C9eff = self.C9eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #right term in bracket - - prefactor2 = kabs**2 * (1. - 1./3. * beta**2) - - abs_bracket = abs(C9eff * self.formfactor(q2, "+") + 2 * C7eff * (mb + ms)/(mB + mK) * self.formfactor(q2, "T"))**2 - - bracket_right = prefactor2 * abs_bracket - - return prefactor1 * bracket_right * 2 * np.sqrt(q2) - - def total_nonres(self, q2): - - #Get constants - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - C9eff = self.C9eff - C7eff = self.C7eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #vector nonresonant part - - vec_nonres_part = self.vec_nonres(q2) - - #axial verctor nonresonant part including C7 - - axiv_nonres_part = self.axiv_nonres(q2) - - #Complete term - - return self.total_scale_amp*complex(vec_nonres_part + axiv_nonres_part, 0.0) - - def generate_points(self, set_size = 10000, x_min = 2* mmu, x_max = (mB - mK) - 0.1, save = True, verbose = 1, mode = "slim_points", resolution = 7.0, min_bin_scaling = 100): - - #Setup contants and variables - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - - self.mode = mode - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - - x2 = 4000 - y2 = self.total_pdf(x2**2) - - #Translate to MeV**2 - - dgamma_tot = [] - - for i in dq: - dgamma_tot.append(self.total_pdf(i**2)) - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #Generate random points - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - _max = max(dgamma_tot) - - A1_x1 = (_max-y1)*x1 - A23_x1 = y1*x1 - - A1_x2 = (_max-y2)*x2 - A23_x2 = y2*x2 - - if mode == "slim_points": - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x = ROOT.TRandom1().Uniform(x_min, x_max) - y = ROOT.TRandom1().Uniform(0, _max) - - if y < self.total_pdf(x**2): - x_part.append(x) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - print(" {0} out of {1} particles chosen!".format(int(counter_x), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter_tot": counter, "counter_x": counter_x} - - pkl.dump( part_set, open("./data/set_{0}_range({1}-{2}).pkl".format(int(set_size),int(x_min), int(x_max)) , "wb" ) ) - - print(" Set saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - if mode == "full_points": - - x = [] - y = [] - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x.append(ROOT.TRandom1().Uniform(x_min, x_max)) - y.append(ROOT.TRandom1().Uniform(0, _max)) - - if y[-1] < self.total_pdf(x[-1]**2): - x_part.append(x) - y_part.append(y) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - - print(" {0} out of {1} particles chosen!".format(len(x_part), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter": counter} - - raw_set = {"x" : x, "y": y, "counter": counter} - - pkl.dump( part_set, open("./data/set_{0}.pkl".format(int(set_size)) , "wb" ) ) - - pkl.dump( part_set, open("./data/set_raw_{0}.pkl".format(int(set_size)) , "wb" ) ) - - print(" Sets saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - - if mode == "fast_binned": - - nbins = int((x_max-x_min)/resolution) - - print("Generating set with {} bins...".format(nbins)) - - dq = np.linspace(x_min, x_max ,nbins+1) - - bin_mean = [] - bin_true_height = [] - - for i in range(len(dq)-1): - a = dq[i] - b = dq[i+1] - c = (a+b)/2 - bin_mean.append(c) - - height = self.total_pdf(c**2) - bin_true_height.append(height) - - _min = min(bin_true_height) - - for i in range(len(bin_true_height)): - bin_true_height[i] = bin_true_height[i]/_min*min_bin_scaling - - start = time.time() - - bin_height = [] - - for i in range(len(bin_mean)): - bin_height.append(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - #print(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - - #progress calls - if verbose != 0: - end = time.time() - print(" Time to generate set: {0}s".format(end-start)) - - print(" {0} bins simulated".format(nbins)) - - print(" Set generated!") - - #Save the set - - if save: - - _ = 0 - - for i in bin_height: - _ += i - - part_set = {"bin_mean" : bin_mean, "bin_height": bin_height, "nbins": nbins} - - pkl.dump( part_set, open("./data/binned_set_{0}bins_{1}part.pkl".format(nbins, _) , "wb" ) ) - - print(" Set saved!") - - print - - return bin_mean, bin_height, nbins - - - - - def draw_plots(self, part_set, counter, mode,min_bin_scaling = 100, x_min = 2* mmu, x_max = (mB - mK) - 0.1, resolution = 7): - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - if mode != self.mode: - raise ValueError('self.mode and mode are different, set them to the same value') - #Resolution based on experiment chosen to be ~7MeV - - #Setup contants and variables - - print("Generating plots") - - if mode == "fast_binned": - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/fast_binned/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/fast_binned/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/fast_binned/tot.png") - - print(" tot.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 1e-5) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/fast_binned/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - #Create histo with pdf - - #Translate to MeV**2 - - plt.clf() - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - dgamma_tot = [] - - for i in dq2: - dgamma_tot.append(self.total_pdf(i)) - - _min = min(dgamma_tot) - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/_min*min_bin_scaling - - bin_mean, bin_height = part_set - - nbins = counter - - plt.hist(bin_mean, bins=nbins, range=(self.x_min, self.x_max), weights = bin_height, label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - _sum = 0 - - for i in bin_height: - _sum += i - - #print(_max) - - plt.grid() - - plt.ylim(0, 2000) - - plt.legend() - - plt.title("{0} random points generated according to pdf ({1} particles)".format(len(bin_mean), _sum)) - - plt.savefig("./plots/fast_binned/histo.png") - - print(" histo.png created") - - print(" All plots drawn \n") - - return - - else: - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/points/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/points/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/points/tot.png") - - print(" tot.png created") - - - #Particle set - - x_part, y_part = part_set - - set_size = len(x_part) - - #Plot generated generate_points - - #plt.clf() - - #plt.plot(x_part, y_part, label = "Random points generated", marker = ".", linewidth = 0) - - #plt.plot(dq, dgamma_tot, label = "pdf") - - #plt.grid() - - #plt.title("Random points generated and pdf") - - #plt.legend() - - #plt.savefig("./plots/points/points_raw.png") - - #print(" points_raw.png created") - - #Histo unnormalized - - bins = int((x_max-x_min)/resolution) - - plt.clf() - - wheights = np.ones_like(x_part) - - _max = max(dgamma_tot) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - x2 = 4000 - y2 = self.total_pdf(x2**2) - - for i in range(len(wheights)): - if x_part[i] < x1: - wheights[i] = x1*y1/(x1*_max) - elif x_part[i] > x2: - wheights[i] = x2*y2/(x2*_max) - - _y, _x, _ = plt.hist(x_part, bins=bins, weights = wheights, range=(x_min, x_max), label = "toy data binned ({0} points)".format(sum(wheights))) - - _mean_histo = float(np.mean(_y)) - - plt.legend() - - plt.title("Binned toy data") - - plt.savefig("./plots/points/histo_raw.png") - - print(" histo_raw.png created") - - - #Histo and pdf normailzed - - plt.clf() - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/(float(set_size)*_max * 2.0 * mmu / counter) - - _mean = np.mean(dgamma_tot) - - #Attempt for marked field of std-dev - - #dgamma_min = [] - #dgamma_plu = [] - #for i in range(len(dgamma_tot)): - #dgamma_min.append(dgamma_tot[i]-np.sqrt(dgamma_tot[i])) - #dgamma_plu.append(dgamma_tot[i]+np.sqrt(dgamma_tot[i])) - - #plt.plot(dq, dgamma_min, alpha = 0.5) - - #plt.plot(dq, dgamma_plu, alpha = 0.5) - - #plt.fill_between(dq, dgamma_min, dgamma_plu) - - #Plot histo - - plt.hist(x_part, bins=bins, range=(x_min, x_max), weights = wheights/(_mean_histo/_mean), label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - #print(_max) - - plt.grid() - - plt.legend() - - plt.ylim(0, 1e-5) - - plt.title("{0} random points generated according to pdf".format(sum(wheights))) - - plt.savefig("./plots/points/histo.png") - - print(" histo.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 2*self._mean) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/points/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - print(" All plots drawn \n") - - return - - - def total_pdf(self, q2): - - #Calculate the pdf with the added resonances - - exec("_sum = abs({0})".format(self.total_pdf_string)) - - return _sum - - - def resonance(self, q2, _mass, width, phase, scale): #returns [real, imaginary] - - #calculate the resonance ---------------------------------------------> Formula correct? - - #if abs(np.sqrt(q2) - _mass) < 300: - #return 0., 0. - - np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #print(q2) - - #Teiler erweitert mit kompl. konj. - - #p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - #p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - #gamma_j = p / p0 * _mass /q2 * width - - #_top_im = - _mass**2 * width * gamma_j - - #_top_re = _mass * width * (_mass**2 - q2) - - #_bottom = (_mass**2 - q2)**2 + _mass**2 * gamma_j**2 - - #real = _top_re/_bottom - - #imaginary = _top_im/_bottom - - #com = complex(real, imaginary) * scale - - #r = abs(com) - - #_phase = c.phase(com) - - #_phase += phase - - #x = c.cos(phase)*r - #y = c.sin(phase)*r - - #com = complex(x,y) - - - #Original formula - - p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - gamma_j = p / p0 * _mass /q2 * width - - _top = complex(_mass * width, 0.0) - - _bottom = complex((_mass**2 - q2), -_mass*gamma_j) - - com = _top/_bottom * scale - - r = abs(com) - - _phase = c.phase(com) - - _phase += phase - - x = c.cos(phase)*r - y = c.sin(phase)*r - - com = complex(x,y) - - return self.total_scale_amp*com - - - def add_resonance(self, _mass, width, phase, scale): - - #Adds the resonace to the pdf in form of a string (to be executed later) - - self.total_pdf_string += "+ self.resonance(q2,{0},{1},{2},{3})".format(_mass, width, phase, scale) - - def bifur_gauss(self, q2, mean, amp, sigma_L, sigma_R): - - q = np.sqrt(q2) - - if q < mean: - sigma = sigma_L - else: - sigma = sigma_R - - _exp = np.exp(- (q-mean)**2 / (2 * sigma**2)) - - dgamma = amp*_exp/(np.sqrt(2*np.pi))*2*(sigma_L*sigma_R)/(sigma_L+sigma_R) - - com = complex(dgamma, 0) - - return self.total_scale_amp*com - - def add_cusp(self, mean, amp, sigma_L, sigma_R): - - self.total_pdf_string += "+ self.bifur_gauss(q2,{0},{1},{2},{3})".format(mean, amp, sigma_L, sigma_R) - - self.cusp_mean = mean - self.cusp_sigma_L = sigma_L - self.cusp_sigma_R = sigma_R - self.cusp_amp = amp - - - def normalize_pdf(self): - x_scan = np.linspace(self.x_min, self.x_max, 10000) - - y_scan = [] - - for i in x_scan: - y_scan.append(self.total_pdf(i**2)) - - _mean = np.mean(y_scan) - - self.total_scale_amp = 1.0/(self.x_max-self.x_min)*_mean - - self._mean = _mean * self.total_scale_amp - diff --git a/scratch/3572424/raremodel.pyc b/scratch/3572424/raremodel.pyc deleted file mode 100644 index 6efd73a..0000000 --- a/scratch/3572424/raremodel.pyc +++ /dev/null Binary files differ diff --git a/scratch/3572424/test.py b/scratch/3572424/test.py deleted file mode 100644 index 5b1f243..0000000 --- a/scratch/3572424/test.py +++ /dev/null @@ -1,56 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c -import raremodel as rm - - -modl = rm.model() - -load_set = False - -draw = False - -mode = "slim_points" - -modl.mode = mode - -min_bin_scaling = 100 - -set_size = 2e4 - -jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] -modl.add_resonance(jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale) - -psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] -modl.add_resonance(psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale) - -modl.add_cusp(3525, 3e-7, 200, 7) - -modl.normalize_pdf() - -if load_set: - - with open(r"./data/set_1000.pkl", "rb") as input_file: - set_dic = pkl.load(input_file) - - part_set = (set_dic["x_part"], set_dic["y_part"]) - counter_tot = set_dic["counter_tot"] - -else: - x_part, y_part, counter = modl.generate_points(set_size, mode = mode, min_bin_scaling = min_bin_scaling, verbose = 0) - part_set = (x_part, y_part) - -if draw: - modl.draw_plots(part_set = part_set, counter = counter_tot, mode = mode, min_bin_scaling = min_bin_scaling) - - -print("Run finished") diff --git a/scratch/3583788/helperfunctions.py b/scratch/3583788/helperfunctions.py deleted file mode 100644 index dabb34c..0000000 --- a/scratch/3583788/helperfunctions.py +++ /dev/null @@ -1,23 +0,0 @@ -# some helperfunctions - -#Dislpay time (e.g. while generating points) - -display_intervals = ( - ('w', 604800), # 60 * 60 * 24 * 7 - ('d', 86400), # 60 * 60 * 24 - ('h', 3600), # 60 * 60 - ('min', 60), - ('s', 1), - ) - -def display_time(seconds, granularity=2): - result = [] - - for name, count in display_intervals: - value = seconds // count - if value: - seconds -= value * count - if value == 1: - name = name.rstrip('s') - result.append("{} {}".format(value, name)) - return ', '.join(result[:granularity]) diff --git a/scratch/3583788/pdg_const.py b/scratch/3583788/pdg_const.py deleted file mode 100644 index 53ced55..0000000 --- a/scratch/3583788/pdg_const.py +++ /dev/null @@ -1,65 +0,0 @@ -pdg = { - -###Particle masses### - -"mbstar" : 5415.4, -"mbstar0" : 5711.0, -"B0_M" : 5279.5, -"Bs_M" : 5366.7, -"Bplus_M" : 5279.3, -"Lb_M" : 5619.4, -"D0_M" : 1864.8, -"Dst_M" : 2010, -"pi_M" : 139.6, -"Jpsi_M" : 3096.9, -"Psi2s_M" : 3685.6, -"kaon_M" : 493.7, -"Ks_M" : 497.6, -"phi_M" : 1019.5, -"rho_M" : 775.26, -"rho_width" : 149.1, -"omega_M" : 782.65, -"omega_width" : 8.49, - -"muon_M" : 105.7, - -"squark_M" : 95.0, -"bquark_M" : 4180.0, -"cquark_M" : 1275.0, - -"Bplus_tau" : 1.638e-12, - -###Wilson coefficients### - -"C1" : -0.257, -"C2" : 1.009, -"C3" : -0.005, -"C4" : -0.078, - -"C7eff" : -0.306, - -"C9eff" : 4.211, -"C10eff" : -4.103, - -###Other constants - -"GF" : 1.1663787e-5, -"alpha_ew" : 1.0/137.0, -"Vts" : 0.0394, -"Vtb" : 1.019, - -#Formfactor z coefficients - -#"b0" : [0.285, 0.19, -0.17], -#"bplus" : [0.437, -1.41, -2.5], -#"bT" : [0.440, -1.47, -2.7] - -"b0" : [0.292, 0.281, 0.150], -"bplus" : [0.466, -0.885, -0.213], -"bT" : [0.460, -1.089, -1.114], - -#Resonances format(mass, width, phase, scale) - -"jpsi": (3096, 0.09, -1.5, 2e-2), -"psi2s": (3686, 0.3, -1.5, 3.14e-3) -} diff --git a/scratch/3583788/raremodel.py b/scratch/3583788/raremodel.py deleted file mode 100644 index 58aacbb..0000000 --- a/scratch/3583788/raremodel.py +++ /dev/null @@ -1,1068 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c - -mmu = pdg['muon_M'] -mb = pdg["bquark_M"] -ms = pdg["squark_M"] -mK = pdg["Ks_M"] -mB = pdg["Bplus_M"] - -class model: - - def __init__(self): - - - self.mmu = pdg['muon_M'] - self.mb = pdg["bquark_M"] - self.ms = pdg["squark_M"] - self.mK = pdg["Ks_M"] - self.mB = pdg["Bplus_M"] - - self.C7eff = pdg["C7eff"] - self.C9eff = pdg["C9eff"] - self.C10eff = pdg["C10eff"] - - #self.C1 = pdg["C1"] - #self.C2 = pdg["C2"] - #self.C3 = pdg["C3"] - #self.C4 = pdg["C4"] - - self.GF = pdg["GF"] #Fermi coupling const. - self.alpha_ew = pdg["alpha_ew"] - self.Vts = pdg["Vts"] - self.Vtb = pdg["Vtb"] - - self.x_min = 2*self.mmu - self.x_max = (self.mB - self.mK) - 0.1 - self.total_pdf_string = "self.total_nonres(q2)" - self.mode = "" - self.total_scale_amp = 1.0 - self._mean = 0.0 - - self.cusp_mean = 1 - self.cusp_sigma_L = 1 - self.cusp_sigma_R = 1 - self.cusp_amp = 0 - - - def formfactor(self, q2, subscript): - - #check if subscript is viable - - if subscript != "0" and subscript != "+" and subscript != "T": - raise ValueError('Wrong subscript entered, choose either 0, + or T') - - #get constants - - mh = self.mK - mbstar0 = pdg["mbstar0"] - mbstar = pdg["mbstar"] - b0 = pdg["b0"] - bplus = pdg["bplus"] - bT = pdg["bT"] - - N = 3 - - #some helperfunctions - - tpos = (self.mB - self.mK)**2 - tzero = (self.mB + self.mK)*(np.sqrt(self.mB)-np.sqrt(self.mK))**2 - - z_oben = np.sqrt(tpos - q2) - np.sqrt(tpos - tzero) - z_unten = np.sqrt(tpos - q2) + np.sqrt(tpos - tzero) - z = z_oben/z_unten - - #calculate f0 - - if subscript == "0": - prefactor = 1/(1 - q2/(mbstar0**2)) - _sum = 0 - - for i in range(N): - _sum += b0[i]*(z**i) - - return prefactor * _sum - - #calculate f+ or fT - - else: - prefactor = 1/(1 - q2/(mbstar**2)) - _sum = 0 - - if subscript == "T": - b = bT - else: - b = bplus - - for i in range(N): - _sum += b[i] * (z**i - ((-1)**(i-N)) * (i/N) * z**N) - - return prefactor * _sum - - def axiv_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #left term in bracket - - bracket_left = 2./3. * kabs**2 * beta**2 * abs(C10eff*self.formfactor(q2, "+"))**2 - - #middle term in bracket - - _top = 4. * mmu**2 * (mB**2 - mK**2) * (mB**2 - mK**2) - - _under = q2 * mB**2 - - bracket_middle = _top/_under * abs(C10eff * self.formfactor(q2, "0"))**2 - - return prefactor1 * (bracket_left + bracket_middle) * 2 * np.sqrt(q2) - - - def vec_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C7eff = self.C7eff - C9eff = self.C9eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #right term in bracket - - prefactor2 = kabs**2 * (1. - 1./3. * beta**2) - - abs_bracket = abs(C9eff * self.formfactor(q2, "+") + 2 * C7eff * (mb + ms)/(mB + mK) * self.formfactor(q2, "T"))**2 - - bracket_right = prefactor2 * abs_bracket - - return prefactor1 * bracket_right * 2 * np.sqrt(q2) - - def total_nonres(self, q2): - - #Get constants - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - C9eff = self.C9eff - C7eff = self.C7eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #vector nonresonant part - - vec_nonres_part = self.vec_nonres(q2) - - #axial verctor nonresonant part including C7 - - axiv_nonres_part = self.axiv_nonres(q2) - - #Complete term - - return self.total_scale_amp*complex(vec_nonres_part + axiv_nonres_part, 0.0) - - def generate_points(self, set_size = 10000, x_min = 2* mmu, x_max = (mB - mK) - 0.1, save = True, verbose = 1, mode = "slim_points", resolution = 7.0, min_bin_scaling = 100): - - #Setup contants and variables - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - - self.mode = mode - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - - x2 = 4000 - y2 = self.total_pdf(x2**2) - - #Translate to MeV**2 - - dgamma_tot = [] - - for i in dq: - dgamma_tot.append(self.total_pdf(i**2)) - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #Generate random points - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - _max = max(dgamma_tot) - - A1_x1 = (_max-y1)*x1 - A23_x1 = y1*x1 - - A1_x2 = (_max-y2)*x2 - A23_x2 = y2*x2 - - if mode == "slim_points": - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x = ROOT.TRandom1().Uniform(x_min, x_max) - y = ROOT.TRandom1().Uniform(0, _max) - - if y < self.total_pdf(x**2): - x_part.append(x) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - print(" {0} out of {1} particles chosen!".format(int(counter_x), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter_tot": counter, "counter_x": counter_x} - - pkl.dump( part_set, open("./data/set_{0}_range({1}-{2}).pkl".format(int(set_size),int(x_min), int(x_max)) , "wb" ) ) - - print(" Set saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - if mode == "full_points": - - x = [] - y = [] - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x.append(ROOT.TRandom1().Uniform(x_min, x_max)) - y.append(ROOT.TRandom1().Uniform(0, _max)) - - if y[-1] < self.total_pdf(x[-1]**2): - x_part.append(x) - y_part.append(y) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - - print(" {0} out of {1} particles chosen!".format(len(x_part), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter": counter} - - raw_set = {"x" : x, "y": y, "counter": counter} - - pkl.dump( part_set, open("./data/set_{0}.pkl".format(int(set_size)) , "wb" ) ) - - pkl.dump( part_set, open("./data/set_raw_{0}.pkl".format(int(set_size)) , "wb" ) ) - - print(" Sets saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - - if mode == "fast_binned": - - nbins = int((x_max-x_min)/resolution) - - print("Generating set with {} bins...".format(nbins)) - - dq = np.linspace(x_min, x_max ,nbins+1) - - bin_mean = [] - bin_true_height = [] - - for i in range(len(dq)-1): - a = dq[i] - b = dq[i+1] - c = (a+b)/2 - bin_mean.append(c) - - height = self.total_pdf(c**2) - bin_true_height.append(height) - - _min = min(bin_true_height) - - for i in range(len(bin_true_height)): - bin_true_height[i] = bin_true_height[i]/_min*min_bin_scaling - - start = time.time() - - bin_height = [] - - for i in range(len(bin_mean)): - bin_height.append(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - #print(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - - #progress calls - if verbose != 0: - end = time.time() - print(" Time to generate set: {0}s".format(end-start)) - - print(" {0} bins simulated".format(nbins)) - - print(" Set generated!") - - #Save the set - - if save: - - _ = 0 - - for i in bin_height: - _ += i - - part_set = {"bin_mean" : bin_mean, "bin_height": bin_height, "nbins": nbins} - - pkl.dump( part_set, open("./data/binned_set_{0}bins_{1}part.pkl".format(nbins, _) , "wb" ) ) - - print(" Set saved!") - - print - - return bin_mean, bin_height, nbins - - - - - def draw_plots(self, part_set, counter, mode,min_bin_scaling = 100, x_min = 2* mmu, x_max = (mB - mK) - 0.1, resolution = 7): - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - if mode != self.mode: - raise ValueError('self.mode and mode are different, set them to the same value') - #Resolution based on experiment chosen to be ~7MeV - - #Setup contants and variables - - print("Generating plots") - - if mode == "fast_binned": - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/fast_binned/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/fast_binned/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/fast_binned/tot.png") - - print(" tot.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 1e-5) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/fast_binned/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - #Create histo with pdf - - #Translate to MeV**2 - - plt.clf() - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - dgamma_tot = [] - - for i in dq2: - dgamma_tot.append(self.total_pdf(i)) - - _min = min(dgamma_tot) - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/_min*min_bin_scaling - - bin_mean, bin_height = part_set - - nbins = counter - - plt.hist(bin_mean, bins=nbins, range=(self.x_min, self.x_max), weights = bin_height, label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - _sum = 0 - - for i in bin_height: - _sum += i - - #print(_max) - - plt.grid() - - plt.ylim(0, 2000) - - plt.legend() - - plt.title("{0} random points generated according to pdf ({1} particles)".format(len(bin_mean), _sum)) - - plt.savefig("./plots/fast_binned/histo.png") - - print(" histo.png created") - - print(" All plots drawn \n") - - return - - else: - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/points/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/points/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/points/tot.png") - - print(" tot.png created") - - - #Particle set - - x_part, y_part = part_set - - set_size = len(x_part) - - #Plot generated generate_points - - #plt.clf() - - #plt.plot(x_part, y_part, label = "Random points generated", marker = ".", linewidth = 0) - - #plt.plot(dq, dgamma_tot, label = "pdf") - - #plt.grid() - - #plt.title("Random points generated and pdf") - - #plt.legend() - - #plt.savefig("./plots/points/points_raw.png") - - #print(" points_raw.png created") - - #Histo unnormalized - - bins = int((x_max-x_min)/resolution) - - plt.clf() - - wheights = np.ones_like(x_part) - - _max = max(dgamma_tot) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - x2 = 4000 - y2 = self.total_pdf(x2**2) - - for i in range(len(wheights)): - if x_part[i] < x1: - wheights[i] = x1*y1/(x1*_max) - elif x_part[i] > x2: - wheights[i] = x2*y2/(x2*_max) - - _y, _x, _ = plt.hist(x_part, bins=bins, weights = wheights, range=(x_min, x_max), label = "toy data binned ({0} points)".format(sum(wheights))) - - _mean_histo = float(np.mean(_y)) - - plt.legend() - - plt.title("Binned toy data") - - plt.savefig("./plots/points/histo_raw.png") - - print(" histo_raw.png created") - - - #Histo and pdf normailzed - - plt.clf() - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/(float(set_size)*_max * 2.0 * mmu / counter) - - _mean = np.mean(dgamma_tot) - - #Attempt for marked field of std-dev - - #dgamma_min = [] - #dgamma_plu = [] - #for i in range(len(dgamma_tot)): - #dgamma_min.append(dgamma_tot[i]-np.sqrt(dgamma_tot[i])) - #dgamma_plu.append(dgamma_tot[i]+np.sqrt(dgamma_tot[i])) - - #plt.plot(dq, dgamma_min, alpha = 0.5) - - #plt.plot(dq, dgamma_plu, alpha = 0.5) - - #plt.fill_between(dq, dgamma_min, dgamma_plu) - - #Plot histo - - plt.hist(x_part, bins=bins, range=(x_min, x_max), weights = wheights/(_mean_histo/_mean), label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - #print(_max) - - plt.grid() - - plt.legend() - - plt.ylim(0, 1e-5) - - plt.title("{0} random points generated according to pdf".format(sum(wheights))) - - plt.savefig("./plots/points/histo.png") - - print(" histo.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 2*self._mean) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/points/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - print(" All plots drawn \n") - - return - - - def total_pdf(self, q2, cusp_amp = -1.0, scan = False): - - if scan: - self.normalize_pdf() - - if cusp_amp == -1.0: - cusp_amp = cusp_amp - else: - cusp_amp = self.cusp_amp - - #Calculate the pdf with the added resonances - - exec("_sum = abs({0})".format(self.total_pdf_string)) - - return _sum - - - def resonance(self, q2, _mass, width, phase, scale): #returns [real, imaginary] - - #calculate the resonance ---------------------------------------------> Formula correct? - - #if abs(np.sqrt(q2) - _mass) < 300: - #return 0., 0. - - np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #print(q2) - - #Teiler erweitert mit kompl. konj. - - #p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - #p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - #gamma_j = p / p0 * _mass /q2 * width - - #_top_im = - _mass**2 * width * gamma_j - - #_top_re = _mass * width * (_mass**2 - q2) - - #_bottom = (_mass**2 - q2)**2 + _mass**2 * gamma_j**2 - - #real = _top_re/_bottom - - #imaginary = _top_im/_bottom - - #com = complex(real, imaginary) * scale - - #r = abs(com) - - #_phase = c.phase(com) - - #_phase += phase - - #x = c.cos(phase)*r - #y = c.sin(phase)*r - - #com = complex(x,y) - - - #Original formula - - p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - gamma_j = p / p0 * _mass /q2 * width - - _top = complex(_mass * width, 0.0) - - _bottom = complex((_mass**2 - q2), -_mass*gamma_j) - - com = _top/_bottom * scale - - r = abs(com) - - _phase = c.phase(com) - - _phase += phase - - x = c.cos(phase)*r - y = c.sin(phase)*r - - com = complex(x,y) - - return self.total_scale_amp*com - - - def add_resonance(self, _mass, width, phase, scale): - - #Adds the resonace to the pdf in form of a string (to be executed later) - - self.total_pdf_string += "+ self.resonance(q2,{0},{1},{2},{3})".format(_mass, width, phase, scale) - - def bifur_gauss(self, q2, mean, amp, sigma_L, sigma_R): - - q = np.sqrt(q2) - - if q < mean: - sigma = sigma_L - else: - sigma = sigma_R - - _exp = np.exp(- (q-mean)**2 / (2 * sigma**2)) - - dgamma = amp*_exp/(np.sqrt(2*np.pi))*2*(sigma_L*sigma_R)/(sigma_L+sigma_R) - - com = complex(dgamma, 0) - - return self.total_scale_amp*com - - def add_cusp(self, mean, amp, sigma_L, sigma_R): - - self.total_pdf_string += "+ self.bifur_gauss(q2,{0},{1},{2},{3})".format(mean, "cusp_amp", sigma_L, sigma_R) - - self.cusp_mean = mean - self.cusp_sigma_L = sigma_L - self.cusp_sigma_R = sigma_R - self.cusp_amp = amp - - - def normalize_pdf(self): - - self.total_scale_amp = 1.0 - - x_scan = np.linspace(self.x_min, self.x_max, 10000) - - y_scan = [] - - for i in x_scan: - y_scan.append(self.total_pdf(i**2)) - - _mean = np.mean(y_scan) - - self.total_scale_amp = 1.0/(self.x_max-self.x_min)*_mean - - self._mean = _mean * self.total_scale_amp - - def log_likelihood(self, x_part, cusp_amp): - - _sum = 0.0 - - for i in x_part: - - _sum += np.log(self.total_pdf(i**2, cusp_amp = cusp_amp, scan = True)) - - return _sum - diff --git a/scratch/3583788/test.py b/scratch/3583788/test.py deleted file mode 100644 index fbe0718..0000000 --- a/scratch/3583788/test.py +++ /dev/null @@ -1,94 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c -import raremodel as rm - - -modl = rm.model() - -load_set = True - -draw = False - -mode = "slim_points" - -modl.mode = mode - -min_bin_scaling = 100 - -set_size = 1e3 - -x_min = 211.0 - -x_max= 4781.0 - -jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] -modl.add_resonance(jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale) - -psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] -modl.add_resonance(psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale) - -modl.add_cusp(3525, 3e-7, 200, 7) - -modl.normalize_pdf() - -if load_set: - - with open(r"./data/set_{0}_range({1}-{2}).pkl".format(int(set_size), int(x_min), int(x_max)), "rb") as input_file: - set_dic = pkl.load(input_file) - - part_set = (set_dic["x_part"], set_dic["y_part"]) - counter_tot = set_dic["counter_tot"] - -else: - x_part, y_part, counter = modl.generate_points(set_size, mode = mode, min_bin_scaling = min_bin_scaling, verbose = 1) - part_set = (x_part, y_part) - -if draw: - modl.draw_plots(part_set = part_set, counter = counter_tot, mode = mode, min_bin_scaling = min_bin_scaling) - -cusp_amp_scan_y = [] -cusp_amp_scan_x = np.linspace(0, 2*modl.cusp_amp, 100) - -print("Likelihood scan starting") - -counter = 0 - -for i in cusp_amp_scan_x: - cusp_amp_scan_y.append(modl.log_likelihood(x_part = set_dic["x_part"], cusp_amp = i)) - counter +=1 - print("{0}{1}".format(counter, "%")) - -print("Scan finished") -print("Plotting...") - -plt.clf() - -plt.title("Cusp amp log_likelihood scan") - -#plt.yscale("log") - -#plt.ylim(0, 2*self._mean) - -plt.grid() - -plt.plot(cusp_amp_scan_x, cusp_amp_scan_y, label = "log_likelihood") - -plt.legend() - -plt.savefig("./cusp_amp_scan.png") - -print(" pdf_and_parts.png created") - -print(modl.log_likelihood(x_part = set_dic["x_part"], cusp_amp = modl.cusp_amp)) - -print("Run finished") diff --git a/scratch/3583789/data/set_1000_range211-4781.pkl b/scratch/3583789/data/set_1000_range211-4781.pkl deleted file mode 100644 index ac69bbe..0000000 --- a/scratch/3583789/data/set_1000_range211-4781.pkl +++ /dev/null @@ -1,1014 +0,0 @@ -(dp0 -S'counter_x' -p1 -I1000 -sS'x_part' -p2 -(lp3 -F3095.0193284273146 -aF3685.7519557118417 -aF3690.1134345293044 -aF3095.778249168396 -aF3675.1701053500174 -aF3095.8607879281044 -aF2579.4021481752397 -aF2871.290256690979 -aF3684.4348768234254 -aF3719.6514142990113 -aF3537.816619825363 -aF2267.4053640723228 -aF2175.566420221329 -aF2089.41093736887 -aF1564.1160194396973 -aF3096.324693894386 -aF3686.1349573493003 -aF3096.8561563372614 -aF3095.762994480133 -aF3686.0739385962484 -aF3095.66874229908 -aF3097.4080491662025 -aF3687.8873396635054 -aF3098.241064107418 -aF3691.967151558399 -aF3636.7190291523934 -aF3119.8896459937096 -aF3588.5245656371117 -aF3704.811599075794 -aF3257.1545998454094 -aF3702.8116004824637 -aF3100.483230876923 -aF3682.3231921195984 -aF3685.5402969121933 -aF3665.80181992054 -aF3093.600370013714 -aF1744.693391752243 -aF3120.7485394239425 -aF3095.781790435314 -aF3728.0581095576285 -aF3096.2143698096274 -aF3728.7246849536896 -aF3683.994125294685 -aF3093.8855782032015 -aF3727.1507280111314 -aF3094.0152430534363 -aF3096.440466082096 -aF3095.8948385715485 -aF3651.571647417545 -aF4501.722506844997 -aF3096.5704033374786 -aF3774.4669573307037 -aF3094.6987075686457 -aF3131.271550273895 -aF3097.5894709944723 -aF3093.712328529358 -aF2715.7174976825713 -aF3685.964159321785 -aF3108.1914793372152 -aF1851.8864517450334 -aF2911.11289280653 -aF2801.2295595765113 -aF3098.6335999250414 -aF3964.643342232704 -aF2230.678067648411 -aF2426.986564826965 -aF2873.5645672678947 -aF3086.270764708519 -aF2797.3477862238883 -aF3685.7356114029885 -aF3032.922395801544 -aF3096.3519344091414 -aF3481.239160299301 -aF3092.3255139231683 -aF3686.3531538724897 -aF3669.2183252811433 -aF2261.1670137882234 -aF3685.956259572506 -aF3096.122296869755 -aF3015.089665222168 -aF3095.7420192837717 -aF3114.0160462021827 -aF2867.7977502942085 -aF3112.7992124080656 -aF1908.7523883223535 -aF3093.5061178326605 -aF3034.8758131146433 -aF3090.1639790773393 -aF3101.1601576685907 -aF3095.4480941295624 -aF3610.660752737522 -aF3709.562889659405 -aF3694.523401463032 -aF3152.3603395819664 -aF3695.4177075624466 -aF3686.1316884875296 -aF4535.513275778293 -aF4447.029001319408 -aF2565.63887809515 -aF3094.784787595272 -aF4574.4334336400025 -aF3685.916488420963 -aF3096.2930948972703 -aF3654.0238385558127 -aF3956.5406511187552 -aF2452.4545392870905 -aF3100.5575974822045 -aF3686.27524600029 -aF3668.496996450424 -aF3076.7681835412977 -aF646.6454034924507 -aF3640.6969615221024 -aF3685.8925167679786 -aF3095.9185378193856 -aF4346.86426653862 -aF3094.758636701107 -aF3043.1893458127975 -aF2937.3092786312104 -aF4101.22973486185 -aF3935.153850579262 -aF3098.4355613827706 -aF3095.830006146431 -aF3099.2674867033957 -aF3686.294586765766 -aF3713.1417484879494 -aF3684.5669933199883 -aF3095.726492190361 -aF3097.485684633255 -aF3207.022790920734 -aF3717.3830966353416 -aF3083.063466501236 -aF3096.651580071449 -aF3685.820329403877 -aF2643.9945843577384 -aF3093.496583652496 -aF3096.059916090965 -aF4126.9742005467415 -aF3096.2587718486784 -aF3094.7115106105803 -aF2337.5110080361364 -aF3094.5712219595907 -aF3096.043026971817 -aF3685.860645365715 -aF3095.752098274231 -aF3084.463084149361 -aF3694.499429810047 -aF3101.385164320469 -aF3097.0863386869432 -aF3707.4877072453496 -aF3640.513360452652 -aF3096.2838331222533 -aF969.7546831846237 -aF3685.8524732112883 -aF3460.08581097126 -aF3096.2544133663177 -aF3096.341310608387 -aF984.2621641278266 -aF3685.684126830101 -aF3095.8180203199386 -aF3098.196117258072 -aF1687.1646934509279 -aF3610.733757317066 -aF3440.4843537688257 -aF2505.221050798893 -aF3103.589739179611 -aF2979.3874293684958 -aF3837.5151287317276 -aF3096.1879465103148 -aF3098.7973154187202 -aF1276.5714110016822 -aF1382.1000755429268 -aF3685.996303129196 -aF3570.129590833187 -aF3106.9059994459153 -aF3685.973693501949 -aF1900.1231380581855 -aF1905.9291813731195 -aF3096.122569274902 -aF3096.201294362545 -aF3096.4875921726225 -aF3685.3586026787757 -aF3376.630680346489 -aF2887.263549733162 -aF3095.7899625897408 -aF4097.1123310565945 -aF3030.81152831316 -aF3096.33204883337 -aF435.23040645122524 -aF3096.143544471264 -aF3095.9514988422393 -aF2877.790115916729 -aF3687.001205718517 -aF3230.2129135370255 -aF3790.4555050611493 -aF3095.895928192139 -aF1432.8690401077272 -aF3689.96252207756 -aF3665.0595158934593 -aF3096.321152627468 -aF2915.4817265629767 -aF3670.5566517710686 -aF3675.3210178017616 -aF3005.939031505585 -aF3945.6675996541976 -aF4583.006296038627 -aF3685.9900378108023 -aF3685.9984823703767 -aF3688.301123082638 -aF3186.8449244260787 -aF3249.0480950593947 -aF3096.7967720150946 -aF3688.262169146538 -aF2519.9900406837464 -aF3114.1258254766462 -aF1460.1890971660614 -aF2251.2375737547873 -aF3051.695196545124 -aF3086.794054996967 -aF3866.205383682251 -aF3663.7890182852743 -aF3698.013728618622 -aF3089.8752296209336 -aF3688.027628314495 -aF3718.790886437893 -aF3684.6574318289754 -aF3091.907372021675 -aF3095.521371114254 -aF3095.0122458934784 -aF3247.6694526076317 -aF3095.9629398584366 -aF3719.6811064600943 -aF3065.1035227179527 -aF3096.1822260022163 -aF3096.273209321499 -aF3142.5583851575852 -aF2824.891760313511 -aF2267.9142168879507 -aF3105.9773702979087 -aF3096.104590535164 -aF3097.542344903946 -aF3688.4435909748076 -aF3175.639538681507 -aF3713.0003702163694 -aF3060.219843232632 -aF3710.1297647714614 -aF3687.75658519268 -aF447.8770878314972 -aF3043.4361448764803 -aF3095.701430916786 -aF3694.7421427965164 -aF3228.6408634305 -aF3115.435277020931 -aF3681.6833124279974 -aF3048.43014844656 -aF3162.627834403515 -aF3930.5924263834954 -aF3097.015785753727 -aF2028.4406724333762 -aF3086.9899142980576 -aF3095.286557877064 -aF3096.0245034217833 -aF3095.8182927250864 -aF3553.9078642964364 -aF3685.7604002714156 -aF3069.847185957432 -aF3082.833284151554 -aF3987.982742869854 -aF3095.620526587963 -aF2995.3040621399878 -aF3639.5174472332 -aF3098.5461578726768 -aF3095.8218339920045 -aF418.70331374406817 -aF3667.5057141184807 -aF3323.8401971817016 -aF3102.0509225010874 -aF3685.91131272316 -aF3894.483762049675 -aF3690.280963695049 -aF3073.6924570202827 -aF3693.5509150862695 -aF3091.1353758335113 -aF3682.5610018134116 -aF3019.7028463959696 -aF3690.405725252628 -aF3680.8584696412086 -aF3554.3954695105554 -aF3101.633597815037 -aF3096.3355901002883 -aF4444.729901874065 -aF3063.8918646216393 -aF3743.543524980545 -aF2204.279284799099 -aF3074.9891055226326 -aF3027.977697563171 -aF3084.9288969516756 -aF3362.653299820423 -aF3685.760672676563 -aF3096.17623308897 -aF3042.050147485733 -aF3682.504341542721 -aF3095.978466951847 -aF3845.464728152752 -aF1436.41357588768 -aF3095.6878106594086 -aF3096.113852310181 -aF3003.7461700677873 -aF3685.838035738468 -aF3686.078569483757 -aF2782.722626256943 -aF3579.273142015934 -aF1943.4815929889678 -aF3765.929507601261 -aF3685.8968752503392 -aF1280.8552543520927 -aF3701.744317114353 -aF3091.5170154452326 -aF3095.9021935105325 -aF3146.9727105736733 -aF3092.3276931643486 -aF3102.58592621088 -aF3095.9021935105325 -aF3092.6848163127897 -aF3264.716294336319 -aF3095.813661837578 -aF3687.7015593528745 -aF3121.698416173458 -aF2510.115626490116 -aF1264.919553220272 -aF3093.6902637124062 -aF1052.277098584175 -aF3655.571099793911 -aF3004.450609779358 -aF1296.0494686722757 -aF3798.6410073399543 -aF2068.909181153774 -aF3096.7926859378813 -aF3666.5313209056853 -aF3083.7455689907074 -aF3686.049966943264 -aF3669.1308832287787 -aF3080.9185483694077 -aF3096.6916236281395 -aF3096.0975080013277 -aF3105.2429660201074 -aF2018.5117772102356 -aF3090.3268773555756 -aF1303.1458951711654 -aF3094.688083767891 -aF345.598761510849 -aF3095.08552287817 -aF3069.925093829632 -aF3521.5172578215597 -aF3698.099536240101 -aF3096.4358351945875 -aF3049.309199857712 -aF1006.8510885834694 -aF2368.0854893922806 -aF1128.3996274471283 -aF4596.114976549148 -aF3676.432158398628 -aF3452.0280667066572 -aF3086.4145946264266 -aF3096.358199727535 -aF2499.6304799556733 -aF4089.58904569149 -aF2197.276020860672 -aF3689.7565837860107 -aF3686.1044479727743 -aF3061.234280002117 -aF3094.802493929863 -aF3108.426020169258 -aF3096.343762254715 -aF3685.7465076088906 -aF1675.5493379592897 -aF2850.774335408211 -aF3685.9186676621434 -aF2113.862840628624 -aF3865.487051308155 -aF3690.884341096878 -aF2736.898087525368 -aF3690.5236766815183 -aF3099.3603768587113 -aF3095.831095767021 -aF3097.0669979214667 -aF3102.2026521682737 -aF3099.7978595256805 -aF3096.104590535164 -aF2492.8132687330244 -aF3096.467434191704 -aF1882.9112188100814 -aF3153.379679644108 -aF3103.9139013051986 -aF3955.5213110566137 -aF2802.9606942892074 -aF3651.2354994654656 -aF3089.075720512867 -aF3687.951082468033 -aF3696.690384411812 -aF2265.2441016316416 -aF3095.273754835129 -aF3685.7805582523347 -aF3095.3753619551658 -aF3496.545877945423 -aF3137.7365416407583 -aF3631.636493909359 -aF3639.677621459961 -aF3097.7343905329703 -aF3369.041200530529 -aF2393.165558922291 -aF1569.2658387541771 -aF3769.4127522230146 -aF3077.358757901192 -aF3100.6428602933884 -aF3265.73563439846 -aF3096.470975458622 -aF3615.9533123493193 -aF3096.9223507881165 -aF2475.5806466937065 -aF3102.5597753167153 -aF3685.839397764206 -aF3089.2323534727097 -aF3070.711255085468 -aF4126.31770414114 -aF3095.7733458757402 -aF3674.8663736104963 -aF2897.976154565811 -aF3934.2573652386664 -aF3096.586747646332 -aF3106.3530169963838 -aF3076.06674028635 -aF3685.2381996035574 -aF3020.0071229457853 -aF3684.124607360363 -aF3623.135818874836 -aF2473.1409861922266 -aF2604.5179027795793 -aF3696.7350588560103 -aF4421.1930075049395 -aF3095.813661837578 -aF3641.5327005147933 -aF3162.4733806848526 -aF3686.157566976547 -aF3096.0694502711294 -aF3096.560324347019 -aF3091.0928806304933 -aF3095.9016487002373 -aF3686.057321882248 -aF3685.164105403423 -aF3659.5882585048676 -aF3331.8723353624346 -aF3714.182063746452 -aF3686.1256955742833 -aF3096.1999323368073 -aF3090.768446099758 -aF3686.178814578056 -aF2927.6568746328353 -aF3674.6435461997985 -aF970.7740232467651 -aF3667.796915221214 -aF3685.7816478729246 -aF3097.8084847331047 -aF3414.7654941678047 -aF2304.9218182086943 -aF3095.8109377861024 -aF2941.788436472416 -aF3683.830409801006 -aF3684.056506073475 -aF3095.9713844180105 -aF3685.6696893572807 -aF3690.96088694334 -aF3096.928343701363 -aF3686.208234333992 -aF1349.6740563988685 -aF3097.6564826607705 -aF3647.7748644709586 -aF3683.14994174242 -aF841.208873295784 -aF2079.6277788996695 -aF3685.768572425842 -aF3256.15841422081 -aF3687.446860539913 -aF3095.5886551856993 -aF3490.2764734745024 -aF2238.1160902023316 -aF219.08209756612777 -aF3207.290292775631 -aF3087.5883884072305 -aF3120.908986055851 -aF3489.848797392845 -aF3693.2433696746825 -aF3025.978788590431 -aF3096.411863541603 -aF3096.3037186980246 -aF2562.7859789848326 -aF3685.7426939368247 -aF3095.229352796078 -aF3705.066570293903 -aF3096.0610057115555 -aF3100.509654176235 -aF3684.056506073475 -aF3598.148639500141 -aF3685.7051020264626 -aF3433.99511834383 -aF3096.4663445711135 -aF3686.4757361888883 -aF3098.357925915718 -aF3829.2993894815445 -aF3686.1210646867753 -aF3096.089335846901 -aF3685.0134653568266 -aF3683.8148827075956 -aF2311.839002120495 -aF3421.0068409085275 -aF3077.431762480736 -aF3726.13138794899 -aF3687.0175500273704 -aF2943.5345534682274 -aF2766.2181431770323 -aF3360.2351593256 -aF3861.1198519825934 -aF1471.8235210180283 -aF485.3480503082276 -aF3686.362960457802 -aF3087.5434415578843 -aF1044.9131702303887 -aF3109.2108193993568 -aF3095.8161134839056 -aF1200.8027364253999 -aF3421.1207062602043 -aF3688.664239144325 -aF3687.695294034481 -aF3085.2282702088355 -aF3031.9030557394026 -aF3094.182772219181 -aF3319.777274405956 -aF1262.9329024791718 -aF3686.109078860283 -aF3914.735722744465 -aF3067.4660925626754 -aF3096.10840420723 -aF3058.338885688782 -aF3099.2511423945425 -aF3090.0912469029427 -aF3093.3680084228517 -aF514.3978800535202 -aF2981.335126173496 -aF3046.6047615528105 -aF3681.363508784771 -aF3095.3165224432946 -aF3328.0020030260084 -aF3095.0038013339044 -aF3324.859537243843 -aF3688.1537518978116 -aF2448.5951031565664 -aF3525.839510297775 -aF3072.6731169581412 -aF3681.286145722866 -aF3666.314214003086 -aF3096.011427974701 -aF3096.131831049919 -aF3094.340494799614 -aF3095.8041276574136 -aF986.1651864886284 -aF3686.5604541897774 -aF3019.086938357353 -aF4115.341138720512 -aF3196.2837627887725 -aF3102.447816801071 -aF3096.222541964054 -aF3476.334778022766 -aF4736.881970977783 -aF2696.192586326599 -aF2915.809157550335 -aF3079.8605267763137 -aF3062.872524559498 -aF1384.6969138145448 -aF3095.90464515686 -aF2956.967123699188 -aF4136.992989468574 -aF3713.444935417175 -aF1917.2724041223526 -aF3030.986140012741 -aF3096.1304690241814 -aF2361.047902405262 -aF3096.419218480587 -aF3686.161108243465 -aF3100.4268430113793 -aF3085.372100126743 -aF3680.5718994259832 -aF3114.2827308416367 -aF2055.7571882247926 -aF3846.1942291378973 -aF3094.906007885933 -aF3523.0075863838197 -aF3096.5418007969856 -aF3692.643533539772 -aF3096.4968539476395 -aF3102.776609814167 -aF3093.6810019373893 -aF3683.9118589401246 -aF3199.8547218680383 -aF3757.1384486794473 -aF3082.1969457268715 -aF3099.9781917333603 -aF3355.8597878456117 -aF3683.547653257847 -aF3685.9818656563757 -aF3099.1639727473257 -aF3095.304536616802 -aF2134.761763548851 -aF3096.0669986248017 -aF3686.377942740917 -aF1444.5881819605827 -aF3551.489723801613 -aF2065.7114171266553 -aF2827.229813694954 -aF3678.8715464949605 -aF3685.91349196434 -aF2179.6721106052396 -aF3689.1597441077233 -aF3866.38135740757 -aF3089.4456467032433 -aF3685.7530453324316 -aF3686.6291002869607 -aF3338.3920801639556 -aF3091.7975927472116 -aF4636.730856454372 -aF3095.7074238300324 -aF3669.728540122509 -aF3710.8271219491958 -aF3125.1522410392763 -aF2981.335398578644 -aF2384.5463876485824 -aF3414.7349847912787 -aF495.72014870643613 -aF3113.914711487293 -aF3724.661762177944 -aF2730.5055559277534 -aF2286.2007744431494 -aF3281.534315741062 -aF3095.8997418642043 -aF3742.1597068309784 -aF3686.7266213297844 -aF3097.1767771959303 -aF3056.1343108296396 -aF2927.903128886223 -aF2397.7092767834665 -aF3094.468797624111 -aF1318.9399456262588 -aF3160.574989211559 -aF3685.727439248562 -aF3153.0476177692412 -aF2994.020761489868 -aF3117.6847987294195 -aF3094.7706225275992 -aF3096.017420887947 -aF3096.038668489456 -aF2949.2381724476813 -aF3118.656740295887 -aF3099.2982684850695 -aF3107.925339508057 -aF3102.335858285427 -aF3095.8204719662667 -aF3096.2753885626794 -aF3685.824143075943 -aF3583.411248612404 -aF2185.5337245702744 -aF356.4364003062248 -aF3126.528431844711 -aF3684.9546258449554 -aF3091.7254053831102 -aF3686.1055375933647 -aF3682.6438129782678 -aF3091.878769481182 -aF3683.5179610967634 -aF1165.4426414370537 -aF3060.037059378624 -aF3095.6000962018966 -aF3753.246323931217 -aF3006.9352171301844 -aF3192.9566063165666 -aF2974.88157582283 -aF3688.056230854988 -aF3080.4037026405335 -aF3685.469199168682 -aF3096.0196001291274 -aF3096.4829612851145 -aF3052.3255420565606 -aF3077.6485969781875 -aF3092.262860739231 -aF3095.3018125653266 -aF3684.5454733133315 -aF3082.0800839185713 -aF3674.5868859291077 -aF707.5012582659721 -aF3132.2677358984947 -aF3096.5701309323313 -aF3013.9215919494627 -aF3667.3079479813573 -aF237.1809679746628 -aF3100.6235195279123 -aF2219.9733625650406 -aF3686.144763934612 -aF3091.3568412184713 -aF817.7820306062698 -aF678.4304533243179 -aF3689.350972521305 -aF3096.03158595562 -aF3073.992919898033 -aF3095.9738360643387 -aF3103.625151848793 -aF3174.6201986193655 -aF1097.0117443203926 -aF3672.9818747997283 -aF2837.8282807707787 -aF426.1157302141189 -aF3623.8707679629324 -aF3829.850465095043 -aF2904.358607172966 -aF3143.6008796572687 -aF3683.978598201275 -aF2222.946392345428 -aF4213.777735245228 -aF3096.5461592793463 -aF4717.331181132793 -aF3096.142454850674 -aF3114.4390913963316 -aF3161.6316487789154 -aF3095.4540870428086 -aF3686.1799041986465 -aF3098.742017173767 -aF3094.3298709988594 -aF2260.170828163624 -aF3400.1430582523344 -aF3041.432877421379 -aF2154.0875467419623 -aF3097.5314486980437 -aF3685.8206018090245 -aF2610.5195329904554 -aF3903.7199309825896 -aF3523.9353983163833 -aF2885.224869608879 -aF3091.8869416356088 -aF3111.83489818573 -aF3555.391655135155 -aF3069.1852414488794 -aF3096.2827435016634 -aF3715.0390503406525 -aF3695.7909026145935 -aF3095.457900714874 -aF4167.796291148662 -aF4632.474253618717 -aF3687.9006875157356 -aF3095.141910743713 -aF3663.0208357691763 -aF3448.4276878714563 -aF2440.291921854019 -aF3073.8387385845185 -aF3096.3791749238967 -aF3691.7514066815374 -aF3145.3042290449143 -aF3683.9282032489778 -aF3685.6094878196714 -aF3089.0108880877497 -aF3651.90725055933 -aF3092.936246263981 -aF2780.7297101974486 -aF3098.356836295128 -aF3093.532541131973 -aF3096.267216408253 -aF3707.4879796504974 -aF3096.4993055939676 -aF3110.1416277885437 -aF3474.2960978984834 -aF3679.924937200546 -aF3063.535831093788 -aF3228.1973878502845 -aF3095.316250038147 -aF3678.7917317867277 -aF3096.294456923008 -aF4131.580299186706 -aF3064.877154040337 -aF3694.3722166061402 -aF3284.8985193133353 -aF3703.929006397724 -aF3094.777432656288 -aF2384.267444777489 -aF3695.7511314630506 -aF2008.1636504650116 -aF3093.34812284708 -aF3095.774707901478 -aF3453.1383900880815 -aF2530.7544024944305 -aF3081.6398772001266 -aF3684.8260506153106 -aF3096.1860396742823 -aF3287.4130912303926 -aF3087.5167458534243 -aF3561.156020462513 -aF3054.1675456643106 -aF3448.068657886982 -aF3095.9716568231584 -aF3682.2616285562513 -aF3077.4434759020805 -aF3234.1663294434547 -aF3685.2150451660154 -aF3094.5031206727026 -aF3722.8592573165893 -aF1065.3293912291526 -aF3685.6081257939336 -aF3685.8440286517143 -aF3097.1239305973054 -aF3685.9211193084716 -aF1428.1945677757265 -aF3226.6253377437592 -aF3458.687010538578 -aF3095.7608152389525 -aF3099.077620315552 -aF3685.992761862278 -aF3096.128017377853 -aF3093.8063083052634 -aF3245.799663674831 -aF1437.8352583527565 -aF2980.292631673813 -aF3093.2832904219626 -aF3087.415138733387 -aF3692.0769308328627 -aF2573.3207032561304 -aF3686.447406053543 -aF2458.69288957119 -aF3686.0349846601484 -aF3683.2305736660956 -aF3071.6387946128843 -aF3100.0122423768044 -aF3259.0445467591285 -aF3034.1877177119254 -aF3685.932560324669 -aF3095.896200597286 -aF3691.0927310347556 -aF3685.859555745125 -aF3096.4322939276694 -aF3670.686044216156 -aF3104.8144727230074 -aF3741.504844856262 -aF3551.8691841721534 -aF2834.1565317869185 -aF3585.123587369919 -aF1938.1220217108726 -aF3095.817203104496 -aF3734.450641155243 -aF2945.956235229969 -aF3096.307259964943 -aF3578.022802388668 -aF3690.00038639307 -aF3685.920846903324 -aF3096.114941930771 -aF3093.403693497181 -aF2512.5106125473976 -aF3611.101504266262 -aF3095.817203104496 -aF3685.8475699186324 -aF3125.365806674957 -aF3188.0636650562287 -aF3686.3588743805885 -aF3077.276219141483 -aF3662.795829117298 -aF3219.030682229996 -aF3684.7056475400923 -aF3099.0765306949615 -aF3682.6871253967283 -aF3310.657150065899 -aF3095.903010725975 -aF3092.745017850399 -aF3138.4292679309847 -aF554.3430984854698 -aF3854.8872222065925 -aF3624.9750984311104 -aF3660.996593117714 -aF2901.9404666781425 -aF3467.7431196689604 -aF2980.34193700552 -aF1883.1424907803537 -aF3704.2417275071143 -aF3096.2718472957613 -aF3122.8871922373773 -aF2262.1754576444628 -aF3271.3188503026963 -aF3589.1540939331053 -aF3092.6905368208886 -aF2849.1456250309943 -aF3687.7544059515 -aF3686.111530506611 -aF3089.346491229534 -aF2023.376933145523 -aF3652.288072955608 -aF4549.854317176341 -aF3686.479277455807 -aF3681.2352059602736 -aF2415.928277862072 -aF3846.863528585434 -aF3098.3402195811273 -aF3489.273750126362 -aF3685.676771891117 -aF3667.069048666954 -aF3094.3195196032525 -aF3555.6120308995246 -aF2497.5917998313903 -aF2583.6424067020416 -aF3682.1003647089005 -aF3686.0407051682473 -aF3686.9998436927795 -aF3085.970574235916 -aF3095.6733731865884 -aF3096.807123410702 -aF3685.9941238880156 -aF2346.026665353775 -aF261.08397486209867 -aF3707.7080830097198 -aF2799.214306294918 -aF3094.414861404896 -aF3684.853291130066 -aF3680.9420980215073 -aF3100.876039099693 -aF3685.8960580348967 -aF3089.2312638521194 -aF1332.4847467780114 -aF3095.9700223922728 -aF3677.0600522637365 -aF3719.977755665779 -aF2911.9933062434197 -aF3602.2001212596892 -aF3097.0580085515976 -aF2855.195743358135 -aF3685.922208929062 -aF3686.2109583854676 -aF3687.7827360868455 -aF3105.268844509125 -aF3185.6654101371764 -aF3097.3794466257095 -aF3096.5194635748862 -aF3685.766120779514 -aF3097.505842614174 -aF3687.320736956596 -aF3096.6842686891555 -aF3473.28029910326 -aF3177.908128750324 -aF3148.7967354416846 -aF3079.1887756824494 -aF3075.838464772701 -aF2842.907002341747 -aF3682.6604296922683 -aF3614.3621938824654 -aF3092.402876985073 -aF3728.5598798394203 -aF3691.3141964197157 -aF3682.016736328602 -aF2725.9098086833956 -aF3096.3293247818947 -aF3098.888026332855 -aF2940.973945081234 -aF3021.8978870749474 -aF2969.9295226454733 -aF3080.831378722191 -aF3690.305480158329 -aF3102.20755546093 -aF3686.1142545580865 -aF2851.0935942411425 -aF3083.521651959419 -aF3103.966747903824 -aF1657.188413798809 -aF3681.704287624359 -aF3687.3370812654493 -aF3736.671287918091 -aF3636.6220529198645 -aF3685.767482805252 -aF3095.7308506727218 -aF4374.519926738739 -aF3096.1824984073637 -aF3094.7071521282196 -aF4176.466946995258 -aF3792.6107745885847 -aF3686.3261857628822 -aF3087.6504967808723 -aF2221.7518957734105 -aF3686.2128652215 -aF3096.086884200573 -aF3096.2094665169716 -aF3685.606218957901 -aF3097.8994680523874 -aF3096.842263674736 -aF3090.7845180034637 -aF1666.2020277261734 -aF3096.6878099560736 -aF2915.4307868003843 -aF3078.305093383789 -aF3367.250136685371 -aF4108.684374129772 -aF3066.0553063035013 -aF2944.434580075741 -asS'y_part' -p4 -(lp5 -sS'counter_tot' -p6 -I607381 -s. \ No newline at end of file diff --git a/scratch/3583789/helperfunctions.py b/scratch/3583789/helperfunctions.py deleted file mode 100644 index dabb34c..0000000 --- a/scratch/3583789/helperfunctions.py +++ /dev/null @@ -1,23 +0,0 @@ -# some helperfunctions - -#Dislpay time (e.g. while generating points) - -display_intervals = ( - ('w', 604800), # 60 * 60 * 24 * 7 - ('d', 86400), # 60 * 60 * 24 - ('h', 3600), # 60 * 60 - ('min', 60), - ('s', 1), - ) - -def display_time(seconds, granularity=2): - result = [] - - for name, count in display_intervals: - value = seconds // count - if value: - seconds -= value * count - if value == 1: - name = name.rstrip('s') - result.append("{} {}".format(value, name)) - return ', '.join(result[:granularity]) diff --git a/scratch/3583789/helperfunctions.pyc b/scratch/3583789/helperfunctions.pyc deleted file mode 100644 index e504406..0000000 --- a/scratch/3583789/helperfunctions.pyc +++ /dev/null Binary files differ diff --git a/scratch/3583789/pdg_const.py b/scratch/3583789/pdg_const.py deleted file mode 100644 index 53ced55..0000000 --- a/scratch/3583789/pdg_const.py +++ /dev/null @@ -1,65 +0,0 @@ -pdg = { - -###Particle masses### - -"mbstar" : 5415.4, -"mbstar0" : 5711.0, -"B0_M" : 5279.5, -"Bs_M" : 5366.7, -"Bplus_M" : 5279.3, -"Lb_M" : 5619.4, -"D0_M" : 1864.8, -"Dst_M" : 2010, -"pi_M" : 139.6, -"Jpsi_M" : 3096.9, -"Psi2s_M" : 3685.6, -"kaon_M" : 493.7, -"Ks_M" : 497.6, -"phi_M" : 1019.5, -"rho_M" : 775.26, -"rho_width" : 149.1, -"omega_M" : 782.65, -"omega_width" : 8.49, - -"muon_M" : 105.7, - -"squark_M" : 95.0, -"bquark_M" : 4180.0, -"cquark_M" : 1275.0, - -"Bplus_tau" : 1.638e-12, - -###Wilson coefficients### - -"C1" : -0.257, -"C2" : 1.009, -"C3" : -0.005, -"C4" : -0.078, - -"C7eff" : -0.306, - -"C9eff" : 4.211, -"C10eff" : -4.103, - -###Other constants - -"GF" : 1.1663787e-5, -"alpha_ew" : 1.0/137.0, -"Vts" : 0.0394, -"Vtb" : 1.019, - -#Formfactor z coefficients - -#"b0" : [0.285, 0.19, -0.17], -#"bplus" : [0.437, -1.41, -2.5], -#"bT" : [0.440, -1.47, -2.7] - -"b0" : [0.292, 0.281, 0.150], -"bplus" : [0.466, -0.885, -0.213], -"bT" : [0.460, -1.089, -1.114], - -#Resonances format(mass, width, phase, scale) - -"jpsi": (3096, 0.09, -1.5, 2e-2), -"psi2s": (3686, 0.3, -1.5, 3.14e-3) -} diff --git a/scratch/3583789/pdg_const.pyc b/scratch/3583789/pdg_const.pyc deleted file mode 100644 index 5eb742e..0000000 --- a/scratch/3583789/pdg_const.pyc +++ /dev/null Binary files differ diff --git a/scratch/3583789/raremodel.py b/scratch/3583789/raremodel.py deleted file mode 100644 index 58aacbb..0000000 --- a/scratch/3583789/raremodel.py +++ /dev/null @@ -1,1068 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c - -mmu = pdg['muon_M'] -mb = pdg["bquark_M"] -ms = pdg["squark_M"] -mK = pdg["Ks_M"] -mB = pdg["Bplus_M"] - -class model: - - def __init__(self): - - - self.mmu = pdg['muon_M'] - self.mb = pdg["bquark_M"] - self.ms = pdg["squark_M"] - self.mK = pdg["Ks_M"] - self.mB = pdg["Bplus_M"] - - self.C7eff = pdg["C7eff"] - self.C9eff = pdg["C9eff"] - self.C10eff = pdg["C10eff"] - - #self.C1 = pdg["C1"] - #self.C2 = pdg["C2"] - #self.C3 = pdg["C3"] - #self.C4 = pdg["C4"] - - self.GF = pdg["GF"] #Fermi coupling const. - self.alpha_ew = pdg["alpha_ew"] - self.Vts = pdg["Vts"] - self.Vtb = pdg["Vtb"] - - self.x_min = 2*self.mmu - self.x_max = (self.mB - self.mK) - 0.1 - self.total_pdf_string = "self.total_nonres(q2)" - self.mode = "" - self.total_scale_amp = 1.0 - self._mean = 0.0 - - self.cusp_mean = 1 - self.cusp_sigma_L = 1 - self.cusp_sigma_R = 1 - self.cusp_amp = 0 - - - def formfactor(self, q2, subscript): - - #check if subscript is viable - - if subscript != "0" and subscript != "+" and subscript != "T": - raise ValueError('Wrong subscript entered, choose either 0, + or T') - - #get constants - - mh = self.mK - mbstar0 = pdg["mbstar0"] - mbstar = pdg["mbstar"] - b0 = pdg["b0"] - bplus = pdg["bplus"] - bT = pdg["bT"] - - N = 3 - - #some helperfunctions - - tpos = (self.mB - self.mK)**2 - tzero = (self.mB + self.mK)*(np.sqrt(self.mB)-np.sqrt(self.mK))**2 - - z_oben = np.sqrt(tpos - q2) - np.sqrt(tpos - tzero) - z_unten = np.sqrt(tpos - q2) + np.sqrt(tpos - tzero) - z = z_oben/z_unten - - #calculate f0 - - if subscript == "0": - prefactor = 1/(1 - q2/(mbstar0**2)) - _sum = 0 - - for i in range(N): - _sum += b0[i]*(z**i) - - return prefactor * _sum - - #calculate f+ or fT - - else: - prefactor = 1/(1 - q2/(mbstar**2)) - _sum = 0 - - if subscript == "T": - b = bT - else: - b = bplus - - for i in range(N): - _sum += b[i] * (z**i - ((-1)**(i-N)) * (i/N) * z**N) - - return prefactor * _sum - - def axiv_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #left term in bracket - - bracket_left = 2./3. * kabs**2 * beta**2 * abs(C10eff*self.formfactor(q2, "+"))**2 - - #middle term in bracket - - _top = 4. * mmu**2 * (mB**2 - mK**2) * (mB**2 - mK**2) - - _under = q2 * mB**2 - - bracket_middle = _top/_under * abs(C10eff * self.formfactor(q2, "0"))**2 - - return prefactor1 * (bracket_left + bracket_middle) * 2 * np.sqrt(q2) - - - def vec_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C7eff = self.C7eff - C9eff = self.C9eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #right term in bracket - - prefactor2 = kabs**2 * (1. - 1./3. * beta**2) - - abs_bracket = abs(C9eff * self.formfactor(q2, "+") + 2 * C7eff * (mb + ms)/(mB + mK) * self.formfactor(q2, "T"))**2 - - bracket_right = prefactor2 * abs_bracket - - return prefactor1 * bracket_right * 2 * np.sqrt(q2) - - def total_nonres(self, q2): - - #Get constants - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - C9eff = self.C9eff - C7eff = self.C7eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #vector nonresonant part - - vec_nonres_part = self.vec_nonres(q2) - - #axial verctor nonresonant part including C7 - - axiv_nonres_part = self.axiv_nonres(q2) - - #Complete term - - return self.total_scale_amp*complex(vec_nonres_part + axiv_nonres_part, 0.0) - - def generate_points(self, set_size = 10000, x_min = 2* mmu, x_max = (mB - mK) - 0.1, save = True, verbose = 1, mode = "slim_points", resolution = 7.0, min_bin_scaling = 100): - - #Setup contants and variables - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - - self.mode = mode - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - - x2 = 4000 - y2 = self.total_pdf(x2**2) - - #Translate to MeV**2 - - dgamma_tot = [] - - for i in dq: - dgamma_tot.append(self.total_pdf(i**2)) - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #Generate random points - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - _max = max(dgamma_tot) - - A1_x1 = (_max-y1)*x1 - A23_x1 = y1*x1 - - A1_x2 = (_max-y2)*x2 - A23_x2 = y2*x2 - - if mode == "slim_points": - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x = ROOT.TRandom1().Uniform(x_min, x_max) - y = ROOT.TRandom1().Uniform(0, _max) - - if y < self.total_pdf(x**2): - x_part.append(x) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - print(" {0} out of {1} particles chosen!".format(int(counter_x), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter_tot": counter, "counter_x": counter_x} - - pkl.dump( part_set, open("./data/set_{0}_range({1}-{2}).pkl".format(int(set_size),int(x_min), int(x_max)) , "wb" ) ) - - print(" Set saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - if mode == "full_points": - - x = [] - y = [] - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x.append(ROOT.TRandom1().Uniform(x_min, x_max)) - y.append(ROOT.TRandom1().Uniform(0, _max)) - - if y[-1] < self.total_pdf(x[-1]**2): - x_part.append(x) - y_part.append(y) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - - print(" {0} out of {1} particles chosen!".format(len(x_part), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter": counter} - - raw_set = {"x" : x, "y": y, "counter": counter} - - pkl.dump( part_set, open("./data/set_{0}.pkl".format(int(set_size)) , "wb" ) ) - - pkl.dump( part_set, open("./data/set_raw_{0}.pkl".format(int(set_size)) , "wb" ) ) - - print(" Sets saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - - if mode == "fast_binned": - - nbins = int((x_max-x_min)/resolution) - - print("Generating set with {} bins...".format(nbins)) - - dq = np.linspace(x_min, x_max ,nbins+1) - - bin_mean = [] - bin_true_height = [] - - for i in range(len(dq)-1): - a = dq[i] - b = dq[i+1] - c = (a+b)/2 - bin_mean.append(c) - - height = self.total_pdf(c**2) - bin_true_height.append(height) - - _min = min(bin_true_height) - - for i in range(len(bin_true_height)): - bin_true_height[i] = bin_true_height[i]/_min*min_bin_scaling - - start = time.time() - - bin_height = [] - - for i in range(len(bin_mean)): - bin_height.append(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - #print(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - - #progress calls - if verbose != 0: - end = time.time() - print(" Time to generate set: {0}s".format(end-start)) - - print(" {0} bins simulated".format(nbins)) - - print(" Set generated!") - - #Save the set - - if save: - - _ = 0 - - for i in bin_height: - _ += i - - part_set = {"bin_mean" : bin_mean, "bin_height": bin_height, "nbins": nbins} - - pkl.dump( part_set, open("./data/binned_set_{0}bins_{1}part.pkl".format(nbins, _) , "wb" ) ) - - print(" Set saved!") - - print - - return bin_mean, bin_height, nbins - - - - - def draw_plots(self, part_set, counter, mode,min_bin_scaling = 100, x_min = 2* mmu, x_max = (mB - mK) - 0.1, resolution = 7): - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - if mode != self.mode: - raise ValueError('self.mode and mode are different, set them to the same value') - #Resolution based on experiment chosen to be ~7MeV - - #Setup contants and variables - - print("Generating plots") - - if mode == "fast_binned": - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/fast_binned/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/fast_binned/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/fast_binned/tot.png") - - print(" tot.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 1e-5) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/fast_binned/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - #Create histo with pdf - - #Translate to MeV**2 - - plt.clf() - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - dgamma_tot = [] - - for i in dq2: - dgamma_tot.append(self.total_pdf(i)) - - _min = min(dgamma_tot) - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/_min*min_bin_scaling - - bin_mean, bin_height = part_set - - nbins = counter - - plt.hist(bin_mean, bins=nbins, range=(self.x_min, self.x_max), weights = bin_height, label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - _sum = 0 - - for i in bin_height: - _sum += i - - #print(_max) - - plt.grid() - - plt.ylim(0, 2000) - - plt.legend() - - plt.title("{0} random points generated according to pdf ({1} particles)".format(len(bin_mean), _sum)) - - plt.savefig("./plots/fast_binned/histo.png") - - print(" histo.png created") - - print(" All plots drawn \n") - - return - - else: - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/points/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/points/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/points/tot.png") - - print(" tot.png created") - - - #Particle set - - x_part, y_part = part_set - - set_size = len(x_part) - - #Plot generated generate_points - - #plt.clf() - - #plt.plot(x_part, y_part, label = "Random points generated", marker = ".", linewidth = 0) - - #plt.plot(dq, dgamma_tot, label = "pdf") - - #plt.grid() - - #plt.title("Random points generated and pdf") - - #plt.legend() - - #plt.savefig("./plots/points/points_raw.png") - - #print(" points_raw.png created") - - #Histo unnormalized - - bins = int((x_max-x_min)/resolution) - - plt.clf() - - wheights = np.ones_like(x_part) - - _max = max(dgamma_tot) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - x2 = 4000 - y2 = self.total_pdf(x2**2) - - for i in range(len(wheights)): - if x_part[i] < x1: - wheights[i] = x1*y1/(x1*_max) - elif x_part[i] > x2: - wheights[i] = x2*y2/(x2*_max) - - _y, _x, _ = plt.hist(x_part, bins=bins, weights = wheights, range=(x_min, x_max), label = "toy data binned ({0} points)".format(sum(wheights))) - - _mean_histo = float(np.mean(_y)) - - plt.legend() - - plt.title("Binned toy data") - - plt.savefig("./plots/points/histo_raw.png") - - print(" histo_raw.png created") - - - #Histo and pdf normailzed - - plt.clf() - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/(float(set_size)*_max * 2.0 * mmu / counter) - - _mean = np.mean(dgamma_tot) - - #Attempt for marked field of std-dev - - #dgamma_min = [] - #dgamma_plu = [] - #for i in range(len(dgamma_tot)): - #dgamma_min.append(dgamma_tot[i]-np.sqrt(dgamma_tot[i])) - #dgamma_plu.append(dgamma_tot[i]+np.sqrt(dgamma_tot[i])) - - #plt.plot(dq, dgamma_min, alpha = 0.5) - - #plt.plot(dq, dgamma_plu, alpha = 0.5) - - #plt.fill_between(dq, dgamma_min, dgamma_plu) - - #Plot histo - - plt.hist(x_part, bins=bins, range=(x_min, x_max), weights = wheights/(_mean_histo/_mean), label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - #print(_max) - - plt.grid() - - plt.legend() - - plt.ylim(0, 1e-5) - - plt.title("{0} random points generated according to pdf".format(sum(wheights))) - - plt.savefig("./plots/points/histo.png") - - print(" histo.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 2*self._mean) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/points/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - print(" All plots drawn \n") - - return - - - def total_pdf(self, q2, cusp_amp = -1.0, scan = False): - - if scan: - self.normalize_pdf() - - if cusp_amp == -1.0: - cusp_amp = cusp_amp - else: - cusp_amp = self.cusp_amp - - #Calculate the pdf with the added resonances - - exec("_sum = abs({0})".format(self.total_pdf_string)) - - return _sum - - - def resonance(self, q2, _mass, width, phase, scale): #returns [real, imaginary] - - #calculate the resonance ---------------------------------------------> Formula correct? - - #if abs(np.sqrt(q2) - _mass) < 300: - #return 0., 0. - - np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #print(q2) - - #Teiler erweitert mit kompl. konj. - - #p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - #p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - #gamma_j = p / p0 * _mass /q2 * width - - #_top_im = - _mass**2 * width * gamma_j - - #_top_re = _mass * width * (_mass**2 - q2) - - #_bottom = (_mass**2 - q2)**2 + _mass**2 * gamma_j**2 - - #real = _top_re/_bottom - - #imaginary = _top_im/_bottom - - #com = complex(real, imaginary) * scale - - #r = abs(com) - - #_phase = c.phase(com) - - #_phase += phase - - #x = c.cos(phase)*r - #y = c.sin(phase)*r - - #com = complex(x,y) - - - #Original formula - - p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - gamma_j = p / p0 * _mass /q2 * width - - _top = complex(_mass * width, 0.0) - - _bottom = complex((_mass**2 - q2), -_mass*gamma_j) - - com = _top/_bottom * scale - - r = abs(com) - - _phase = c.phase(com) - - _phase += phase - - x = c.cos(phase)*r - y = c.sin(phase)*r - - com = complex(x,y) - - return self.total_scale_amp*com - - - def add_resonance(self, _mass, width, phase, scale): - - #Adds the resonace to the pdf in form of a string (to be executed later) - - self.total_pdf_string += "+ self.resonance(q2,{0},{1},{2},{3})".format(_mass, width, phase, scale) - - def bifur_gauss(self, q2, mean, amp, sigma_L, sigma_R): - - q = np.sqrt(q2) - - if q < mean: - sigma = sigma_L - else: - sigma = sigma_R - - _exp = np.exp(- (q-mean)**2 / (2 * sigma**2)) - - dgamma = amp*_exp/(np.sqrt(2*np.pi))*2*(sigma_L*sigma_R)/(sigma_L+sigma_R) - - com = complex(dgamma, 0) - - return self.total_scale_amp*com - - def add_cusp(self, mean, amp, sigma_L, sigma_R): - - self.total_pdf_string += "+ self.bifur_gauss(q2,{0},{1},{2},{3})".format(mean, "cusp_amp", sigma_L, sigma_R) - - self.cusp_mean = mean - self.cusp_sigma_L = sigma_L - self.cusp_sigma_R = sigma_R - self.cusp_amp = amp - - - def normalize_pdf(self): - - self.total_scale_amp = 1.0 - - x_scan = np.linspace(self.x_min, self.x_max, 10000) - - y_scan = [] - - for i in x_scan: - y_scan.append(self.total_pdf(i**2)) - - _mean = np.mean(y_scan) - - self.total_scale_amp = 1.0/(self.x_max-self.x_min)*_mean - - self._mean = _mean * self.total_scale_amp - - def log_likelihood(self, x_part, cusp_amp): - - _sum = 0.0 - - for i in x_part: - - _sum += np.log(self.total_pdf(i**2, cusp_amp = cusp_amp, scan = True)) - - return _sum - diff --git a/scratch/3583789/raremodel.pyc b/scratch/3583789/raremodel.pyc deleted file mode 100644 index 27c5ac2..0000000 --- a/scratch/3583789/raremodel.pyc +++ /dev/null Binary files differ diff --git a/scratch/3583789/test.py b/scratch/3583789/test.py deleted file mode 100644 index 48c9357..0000000 --- a/scratch/3583789/test.py +++ /dev/null @@ -1,94 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c -import raremodel as rm - - -modl = rm.model() - -load_set = True - -draw = False - -mode = "slim_points" - -modl.mode = mode - -min_bin_scaling = 100 - -set_size = 1e3 - -x_min = 211.0 - -x_max= 4781.0 - -jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] -modl.add_resonance(jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale) - -psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] -modl.add_resonance(psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale) - -modl.add_cusp(3525, 3e-7, 200, 7) - -modl.normalize_pdf() - -if load_set: - - with open(r"./data/set_{0}_range{1}-{2}.pkl".format(int(set_size), int(x_min), int(x_max)), "rb") as input_file: - set_dic = pkl.load(input_file) - - part_set = (set_dic["x_part"], set_dic["y_part"]) - counter_tot = set_dic["counter_tot"] - -else: - x_part, y_part, counter = modl.generate_points(set_size, mode = mode, min_bin_scaling = min_bin_scaling, verbose = 1) - part_set = (x_part, y_part) - -if draw: - modl.draw_plots(part_set = part_set, counter = counter_tot, mode = mode, min_bin_scaling = min_bin_scaling) - -cusp_amp_scan_y = [] -cusp_amp_scan_x = np.linspace(0, 2*modl.cusp_amp, 100) - -print("Likelihood scan starting") - -counter = 0 - -for i in cusp_amp_scan_x: - cusp_amp_scan_y.append(modl.log_likelihood(x_part = set_dic["x_part"], cusp_amp = i)) - counter +=1 - print("{0}{1}".format(counter, "%")) - -print("Scan finished") -print("Plotting...") - -plt.clf() - -plt.title("Cusp amp log_likelihood scan") - -#plt.yscale("log") - -#plt.ylim(0, 2*self._mean) - -plt.grid() - -plt.plot(cusp_amp_scan_x, cusp_amp_scan_y, label = "log_likelihood") - -plt.legend() - -plt.savefig("./cusp_amp_scan.png") - -print(" pdf_and_parts.png created") - -print(modl.log_likelihood(x_part = set_dic["x_part"], cusp_amp = modl.cusp_amp)) - -print("Run finished") diff --git a/scratch/3583790/data/set_1000_range211-4781.pkl b/scratch/3583790/data/set_1000_range211-4781.pkl deleted file mode 100644 index ac69bbe..0000000 --- a/scratch/3583790/data/set_1000_range211-4781.pkl +++ /dev/null @@ -1,1014 +0,0 @@ -(dp0 -S'counter_x' -p1 -I1000 -sS'x_part' -p2 -(lp3 -F3095.0193284273146 -aF3685.7519557118417 -aF3690.1134345293044 -aF3095.778249168396 -aF3675.1701053500174 -aF3095.8607879281044 -aF2579.4021481752397 -aF2871.290256690979 -aF3684.4348768234254 -aF3719.6514142990113 -aF3537.816619825363 -aF2267.4053640723228 -aF2175.566420221329 -aF2089.41093736887 -aF1564.1160194396973 -aF3096.324693894386 -aF3686.1349573493003 -aF3096.8561563372614 -aF3095.762994480133 -aF3686.0739385962484 -aF3095.66874229908 -aF3097.4080491662025 -aF3687.8873396635054 -aF3098.241064107418 -aF3691.967151558399 -aF3636.7190291523934 -aF3119.8896459937096 -aF3588.5245656371117 -aF3704.811599075794 -aF3257.1545998454094 -aF3702.8116004824637 -aF3100.483230876923 -aF3682.3231921195984 -aF3685.5402969121933 -aF3665.80181992054 -aF3093.600370013714 -aF1744.693391752243 -aF3120.7485394239425 -aF3095.781790435314 -aF3728.0581095576285 -aF3096.2143698096274 -aF3728.7246849536896 -aF3683.994125294685 -aF3093.8855782032015 -aF3727.1507280111314 -aF3094.0152430534363 -aF3096.440466082096 -aF3095.8948385715485 -aF3651.571647417545 -aF4501.722506844997 -aF3096.5704033374786 -aF3774.4669573307037 -aF3094.6987075686457 -aF3131.271550273895 -aF3097.5894709944723 -aF3093.712328529358 -aF2715.7174976825713 -aF3685.964159321785 -aF3108.1914793372152 -aF1851.8864517450334 -aF2911.11289280653 -aF2801.2295595765113 -aF3098.6335999250414 -aF3964.643342232704 -aF2230.678067648411 -aF2426.986564826965 -aF2873.5645672678947 -aF3086.270764708519 -aF2797.3477862238883 -aF3685.7356114029885 -aF3032.922395801544 -aF3096.3519344091414 -aF3481.239160299301 -aF3092.3255139231683 -aF3686.3531538724897 -aF3669.2183252811433 -aF2261.1670137882234 -aF3685.956259572506 -aF3096.122296869755 -aF3015.089665222168 -aF3095.7420192837717 -aF3114.0160462021827 -aF2867.7977502942085 -aF3112.7992124080656 -aF1908.7523883223535 -aF3093.5061178326605 -aF3034.8758131146433 -aF3090.1639790773393 -aF3101.1601576685907 -aF3095.4480941295624 -aF3610.660752737522 -aF3709.562889659405 -aF3694.523401463032 -aF3152.3603395819664 -aF3695.4177075624466 -aF3686.1316884875296 -aF4535.513275778293 -aF4447.029001319408 -aF2565.63887809515 -aF3094.784787595272 -aF4574.4334336400025 -aF3685.916488420963 -aF3096.2930948972703 -aF3654.0238385558127 -aF3956.5406511187552 -aF2452.4545392870905 -aF3100.5575974822045 -aF3686.27524600029 -aF3668.496996450424 -aF3076.7681835412977 -aF646.6454034924507 -aF3640.6969615221024 -aF3685.8925167679786 -aF3095.9185378193856 -aF4346.86426653862 -aF3094.758636701107 -aF3043.1893458127975 -aF2937.3092786312104 -aF4101.22973486185 -aF3935.153850579262 -aF3098.4355613827706 -aF3095.830006146431 -aF3099.2674867033957 -aF3686.294586765766 -aF3713.1417484879494 -aF3684.5669933199883 -aF3095.726492190361 -aF3097.485684633255 -aF3207.022790920734 -aF3717.3830966353416 -aF3083.063466501236 -aF3096.651580071449 -aF3685.820329403877 -aF2643.9945843577384 -aF3093.496583652496 -aF3096.059916090965 -aF4126.9742005467415 -aF3096.2587718486784 -aF3094.7115106105803 -aF2337.5110080361364 -aF3094.5712219595907 -aF3096.043026971817 -aF3685.860645365715 -aF3095.752098274231 -aF3084.463084149361 -aF3694.499429810047 -aF3101.385164320469 -aF3097.0863386869432 -aF3707.4877072453496 -aF3640.513360452652 -aF3096.2838331222533 -aF969.7546831846237 -aF3685.8524732112883 -aF3460.08581097126 -aF3096.2544133663177 -aF3096.341310608387 -aF984.2621641278266 -aF3685.684126830101 -aF3095.8180203199386 -aF3098.196117258072 -aF1687.1646934509279 -aF3610.733757317066 -aF3440.4843537688257 -aF2505.221050798893 -aF3103.589739179611 -aF2979.3874293684958 -aF3837.5151287317276 -aF3096.1879465103148 -aF3098.7973154187202 -aF1276.5714110016822 -aF1382.1000755429268 -aF3685.996303129196 -aF3570.129590833187 -aF3106.9059994459153 -aF3685.973693501949 -aF1900.1231380581855 -aF1905.9291813731195 -aF3096.122569274902 -aF3096.201294362545 -aF3096.4875921726225 -aF3685.3586026787757 -aF3376.630680346489 -aF2887.263549733162 -aF3095.7899625897408 -aF4097.1123310565945 -aF3030.81152831316 -aF3096.33204883337 -aF435.23040645122524 -aF3096.143544471264 -aF3095.9514988422393 -aF2877.790115916729 -aF3687.001205718517 -aF3230.2129135370255 -aF3790.4555050611493 -aF3095.895928192139 -aF1432.8690401077272 -aF3689.96252207756 -aF3665.0595158934593 -aF3096.321152627468 -aF2915.4817265629767 -aF3670.5566517710686 -aF3675.3210178017616 -aF3005.939031505585 -aF3945.6675996541976 -aF4583.006296038627 -aF3685.9900378108023 -aF3685.9984823703767 -aF3688.301123082638 -aF3186.8449244260787 -aF3249.0480950593947 -aF3096.7967720150946 -aF3688.262169146538 -aF2519.9900406837464 -aF3114.1258254766462 -aF1460.1890971660614 -aF2251.2375737547873 -aF3051.695196545124 -aF3086.794054996967 -aF3866.205383682251 -aF3663.7890182852743 -aF3698.013728618622 -aF3089.8752296209336 -aF3688.027628314495 -aF3718.790886437893 -aF3684.6574318289754 -aF3091.907372021675 -aF3095.521371114254 -aF3095.0122458934784 -aF3247.6694526076317 -aF3095.9629398584366 -aF3719.6811064600943 -aF3065.1035227179527 -aF3096.1822260022163 -aF3096.273209321499 -aF3142.5583851575852 -aF2824.891760313511 -aF2267.9142168879507 -aF3105.9773702979087 -aF3096.104590535164 -aF3097.542344903946 -aF3688.4435909748076 -aF3175.639538681507 -aF3713.0003702163694 -aF3060.219843232632 -aF3710.1297647714614 -aF3687.75658519268 -aF447.8770878314972 -aF3043.4361448764803 -aF3095.701430916786 -aF3694.7421427965164 -aF3228.6408634305 -aF3115.435277020931 -aF3681.6833124279974 -aF3048.43014844656 -aF3162.627834403515 -aF3930.5924263834954 -aF3097.015785753727 -aF2028.4406724333762 -aF3086.9899142980576 -aF3095.286557877064 -aF3096.0245034217833 -aF3095.8182927250864 -aF3553.9078642964364 -aF3685.7604002714156 -aF3069.847185957432 -aF3082.833284151554 -aF3987.982742869854 -aF3095.620526587963 -aF2995.3040621399878 -aF3639.5174472332 -aF3098.5461578726768 -aF3095.8218339920045 -aF418.70331374406817 -aF3667.5057141184807 -aF3323.8401971817016 -aF3102.0509225010874 -aF3685.91131272316 -aF3894.483762049675 -aF3690.280963695049 -aF3073.6924570202827 -aF3693.5509150862695 -aF3091.1353758335113 -aF3682.5610018134116 -aF3019.7028463959696 -aF3690.405725252628 -aF3680.8584696412086 -aF3554.3954695105554 -aF3101.633597815037 -aF3096.3355901002883 -aF4444.729901874065 -aF3063.8918646216393 -aF3743.543524980545 -aF2204.279284799099 -aF3074.9891055226326 -aF3027.977697563171 -aF3084.9288969516756 -aF3362.653299820423 -aF3685.760672676563 -aF3096.17623308897 -aF3042.050147485733 -aF3682.504341542721 -aF3095.978466951847 -aF3845.464728152752 -aF1436.41357588768 -aF3095.6878106594086 -aF3096.113852310181 -aF3003.7461700677873 -aF3685.838035738468 -aF3686.078569483757 -aF2782.722626256943 -aF3579.273142015934 -aF1943.4815929889678 -aF3765.929507601261 -aF3685.8968752503392 -aF1280.8552543520927 -aF3701.744317114353 -aF3091.5170154452326 -aF3095.9021935105325 -aF3146.9727105736733 -aF3092.3276931643486 -aF3102.58592621088 -aF3095.9021935105325 -aF3092.6848163127897 -aF3264.716294336319 -aF3095.813661837578 -aF3687.7015593528745 -aF3121.698416173458 -aF2510.115626490116 -aF1264.919553220272 -aF3093.6902637124062 -aF1052.277098584175 -aF3655.571099793911 -aF3004.450609779358 -aF1296.0494686722757 -aF3798.6410073399543 -aF2068.909181153774 -aF3096.7926859378813 -aF3666.5313209056853 -aF3083.7455689907074 -aF3686.049966943264 -aF3669.1308832287787 -aF3080.9185483694077 -aF3096.6916236281395 -aF3096.0975080013277 -aF3105.2429660201074 -aF2018.5117772102356 -aF3090.3268773555756 -aF1303.1458951711654 -aF3094.688083767891 -aF345.598761510849 -aF3095.08552287817 -aF3069.925093829632 -aF3521.5172578215597 -aF3698.099536240101 -aF3096.4358351945875 -aF3049.309199857712 -aF1006.8510885834694 -aF2368.0854893922806 -aF1128.3996274471283 -aF4596.114976549148 -aF3676.432158398628 -aF3452.0280667066572 -aF3086.4145946264266 -aF3096.358199727535 -aF2499.6304799556733 -aF4089.58904569149 -aF2197.276020860672 -aF3689.7565837860107 -aF3686.1044479727743 -aF3061.234280002117 -aF3094.802493929863 -aF3108.426020169258 -aF3096.343762254715 -aF3685.7465076088906 -aF1675.5493379592897 -aF2850.774335408211 -aF3685.9186676621434 -aF2113.862840628624 -aF3865.487051308155 -aF3690.884341096878 -aF2736.898087525368 -aF3690.5236766815183 -aF3099.3603768587113 -aF3095.831095767021 -aF3097.0669979214667 -aF3102.2026521682737 -aF3099.7978595256805 -aF3096.104590535164 -aF2492.8132687330244 -aF3096.467434191704 -aF1882.9112188100814 -aF3153.379679644108 -aF3103.9139013051986 -aF3955.5213110566137 -aF2802.9606942892074 -aF3651.2354994654656 -aF3089.075720512867 -aF3687.951082468033 -aF3696.690384411812 -aF2265.2441016316416 -aF3095.273754835129 -aF3685.7805582523347 -aF3095.3753619551658 -aF3496.545877945423 -aF3137.7365416407583 -aF3631.636493909359 -aF3639.677621459961 -aF3097.7343905329703 -aF3369.041200530529 -aF2393.165558922291 -aF1569.2658387541771 -aF3769.4127522230146 -aF3077.358757901192 -aF3100.6428602933884 -aF3265.73563439846 -aF3096.470975458622 -aF3615.9533123493193 -aF3096.9223507881165 -aF2475.5806466937065 -aF3102.5597753167153 -aF3685.839397764206 -aF3089.2323534727097 -aF3070.711255085468 -aF4126.31770414114 -aF3095.7733458757402 -aF3674.8663736104963 -aF2897.976154565811 -aF3934.2573652386664 -aF3096.586747646332 -aF3106.3530169963838 -aF3076.06674028635 -aF3685.2381996035574 -aF3020.0071229457853 -aF3684.124607360363 -aF3623.135818874836 -aF2473.1409861922266 -aF2604.5179027795793 -aF3696.7350588560103 -aF4421.1930075049395 -aF3095.813661837578 -aF3641.5327005147933 -aF3162.4733806848526 -aF3686.157566976547 -aF3096.0694502711294 -aF3096.560324347019 -aF3091.0928806304933 -aF3095.9016487002373 -aF3686.057321882248 -aF3685.164105403423 -aF3659.5882585048676 -aF3331.8723353624346 -aF3714.182063746452 -aF3686.1256955742833 -aF3096.1999323368073 -aF3090.768446099758 -aF3686.178814578056 -aF2927.6568746328353 -aF3674.6435461997985 -aF970.7740232467651 -aF3667.796915221214 -aF3685.7816478729246 -aF3097.8084847331047 -aF3414.7654941678047 -aF2304.9218182086943 -aF3095.8109377861024 -aF2941.788436472416 -aF3683.830409801006 -aF3684.056506073475 -aF3095.9713844180105 -aF3685.6696893572807 -aF3690.96088694334 -aF3096.928343701363 -aF3686.208234333992 -aF1349.6740563988685 -aF3097.6564826607705 -aF3647.7748644709586 -aF3683.14994174242 -aF841.208873295784 -aF2079.6277788996695 -aF3685.768572425842 -aF3256.15841422081 -aF3687.446860539913 -aF3095.5886551856993 -aF3490.2764734745024 -aF2238.1160902023316 -aF219.08209756612777 -aF3207.290292775631 -aF3087.5883884072305 -aF3120.908986055851 -aF3489.848797392845 -aF3693.2433696746825 -aF3025.978788590431 -aF3096.411863541603 -aF3096.3037186980246 -aF2562.7859789848326 -aF3685.7426939368247 -aF3095.229352796078 -aF3705.066570293903 -aF3096.0610057115555 -aF3100.509654176235 -aF3684.056506073475 -aF3598.148639500141 -aF3685.7051020264626 -aF3433.99511834383 -aF3096.4663445711135 -aF3686.4757361888883 -aF3098.357925915718 -aF3829.2993894815445 -aF3686.1210646867753 -aF3096.089335846901 -aF3685.0134653568266 -aF3683.8148827075956 -aF2311.839002120495 -aF3421.0068409085275 -aF3077.431762480736 -aF3726.13138794899 -aF3687.0175500273704 -aF2943.5345534682274 -aF2766.2181431770323 -aF3360.2351593256 -aF3861.1198519825934 -aF1471.8235210180283 -aF485.3480503082276 -aF3686.362960457802 -aF3087.5434415578843 -aF1044.9131702303887 -aF3109.2108193993568 -aF3095.8161134839056 -aF1200.8027364253999 -aF3421.1207062602043 -aF3688.664239144325 -aF3687.695294034481 -aF3085.2282702088355 -aF3031.9030557394026 -aF3094.182772219181 -aF3319.777274405956 -aF1262.9329024791718 -aF3686.109078860283 -aF3914.735722744465 -aF3067.4660925626754 -aF3096.10840420723 -aF3058.338885688782 -aF3099.2511423945425 -aF3090.0912469029427 -aF3093.3680084228517 -aF514.3978800535202 -aF2981.335126173496 -aF3046.6047615528105 -aF3681.363508784771 -aF3095.3165224432946 -aF3328.0020030260084 -aF3095.0038013339044 -aF3324.859537243843 -aF3688.1537518978116 -aF2448.5951031565664 -aF3525.839510297775 -aF3072.6731169581412 -aF3681.286145722866 -aF3666.314214003086 -aF3096.011427974701 -aF3096.131831049919 -aF3094.340494799614 -aF3095.8041276574136 -aF986.1651864886284 -aF3686.5604541897774 -aF3019.086938357353 -aF4115.341138720512 -aF3196.2837627887725 -aF3102.447816801071 -aF3096.222541964054 -aF3476.334778022766 -aF4736.881970977783 -aF2696.192586326599 -aF2915.809157550335 -aF3079.8605267763137 -aF3062.872524559498 -aF1384.6969138145448 -aF3095.90464515686 -aF2956.967123699188 -aF4136.992989468574 -aF3713.444935417175 -aF1917.2724041223526 -aF3030.986140012741 -aF3096.1304690241814 -aF2361.047902405262 -aF3096.419218480587 -aF3686.161108243465 -aF3100.4268430113793 -aF3085.372100126743 -aF3680.5718994259832 -aF3114.2827308416367 -aF2055.7571882247926 -aF3846.1942291378973 -aF3094.906007885933 -aF3523.0075863838197 -aF3096.5418007969856 -aF3692.643533539772 -aF3096.4968539476395 -aF3102.776609814167 -aF3093.6810019373893 -aF3683.9118589401246 -aF3199.8547218680383 -aF3757.1384486794473 -aF3082.1969457268715 -aF3099.9781917333603 -aF3355.8597878456117 -aF3683.547653257847 -aF3685.9818656563757 -aF3099.1639727473257 -aF3095.304536616802 -aF2134.761763548851 -aF3096.0669986248017 -aF3686.377942740917 -aF1444.5881819605827 -aF3551.489723801613 -aF2065.7114171266553 -aF2827.229813694954 -aF3678.8715464949605 -aF3685.91349196434 -aF2179.6721106052396 -aF3689.1597441077233 -aF3866.38135740757 -aF3089.4456467032433 -aF3685.7530453324316 -aF3686.6291002869607 -aF3338.3920801639556 -aF3091.7975927472116 -aF4636.730856454372 -aF3095.7074238300324 -aF3669.728540122509 -aF3710.8271219491958 -aF3125.1522410392763 -aF2981.335398578644 -aF2384.5463876485824 -aF3414.7349847912787 -aF495.72014870643613 -aF3113.914711487293 -aF3724.661762177944 -aF2730.5055559277534 -aF2286.2007744431494 -aF3281.534315741062 -aF3095.8997418642043 -aF3742.1597068309784 -aF3686.7266213297844 -aF3097.1767771959303 -aF3056.1343108296396 -aF2927.903128886223 -aF2397.7092767834665 -aF3094.468797624111 -aF1318.9399456262588 -aF3160.574989211559 -aF3685.727439248562 -aF3153.0476177692412 -aF2994.020761489868 -aF3117.6847987294195 -aF3094.7706225275992 -aF3096.017420887947 -aF3096.038668489456 -aF2949.2381724476813 -aF3118.656740295887 -aF3099.2982684850695 -aF3107.925339508057 -aF3102.335858285427 -aF3095.8204719662667 -aF3096.2753885626794 -aF3685.824143075943 -aF3583.411248612404 -aF2185.5337245702744 -aF356.4364003062248 -aF3126.528431844711 -aF3684.9546258449554 -aF3091.7254053831102 -aF3686.1055375933647 -aF3682.6438129782678 -aF3091.878769481182 -aF3683.5179610967634 -aF1165.4426414370537 -aF3060.037059378624 -aF3095.6000962018966 -aF3753.246323931217 -aF3006.9352171301844 -aF3192.9566063165666 -aF2974.88157582283 -aF3688.056230854988 -aF3080.4037026405335 -aF3685.469199168682 -aF3096.0196001291274 -aF3096.4829612851145 -aF3052.3255420565606 -aF3077.6485969781875 -aF3092.262860739231 -aF3095.3018125653266 -aF3684.5454733133315 -aF3082.0800839185713 -aF3674.5868859291077 -aF707.5012582659721 -aF3132.2677358984947 -aF3096.5701309323313 -aF3013.9215919494627 -aF3667.3079479813573 -aF237.1809679746628 -aF3100.6235195279123 -aF2219.9733625650406 -aF3686.144763934612 -aF3091.3568412184713 -aF817.7820306062698 -aF678.4304533243179 -aF3689.350972521305 -aF3096.03158595562 -aF3073.992919898033 -aF3095.9738360643387 -aF3103.625151848793 -aF3174.6201986193655 -aF1097.0117443203926 -aF3672.9818747997283 -aF2837.8282807707787 -aF426.1157302141189 -aF3623.8707679629324 -aF3829.850465095043 -aF2904.358607172966 -aF3143.6008796572687 -aF3683.978598201275 -aF2222.946392345428 -aF4213.777735245228 -aF3096.5461592793463 -aF4717.331181132793 -aF3096.142454850674 -aF3114.4390913963316 -aF3161.6316487789154 -aF3095.4540870428086 -aF3686.1799041986465 -aF3098.742017173767 -aF3094.3298709988594 -aF2260.170828163624 -aF3400.1430582523344 -aF3041.432877421379 -aF2154.0875467419623 -aF3097.5314486980437 -aF3685.8206018090245 -aF2610.5195329904554 -aF3903.7199309825896 -aF3523.9353983163833 -aF2885.224869608879 -aF3091.8869416356088 -aF3111.83489818573 -aF3555.391655135155 -aF3069.1852414488794 -aF3096.2827435016634 -aF3715.0390503406525 -aF3695.7909026145935 -aF3095.457900714874 -aF4167.796291148662 -aF4632.474253618717 -aF3687.9006875157356 -aF3095.141910743713 -aF3663.0208357691763 -aF3448.4276878714563 -aF2440.291921854019 -aF3073.8387385845185 -aF3096.3791749238967 -aF3691.7514066815374 -aF3145.3042290449143 -aF3683.9282032489778 -aF3685.6094878196714 -aF3089.0108880877497 -aF3651.90725055933 -aF3092.936246263981 -aF2780.7297101974486 -aF3098.356836295128 -aF3093.532541131973 -aF3096.267216408253 -aF3707.4879796504974 -aF3096.4993055939676 -aF3110.1416277885437 -aF3474.2960978984834 -aF3679.924937200546 -aF3063.535831093788 -aF3228.1973878502845 -aF3095.316250038147 -aF3678.7917317867277 -aF3096.294456923008 -aF4131.580299186706 -aF3064.877154040337 -aF3694.3722166061402 -aF3284.8985193133353 -aF3703.929006397724 -aF3094.777432656288 -aF2384.267444777489 -aF3695.7511314630506 -aF2008.1636504650116 -aF3093.34812284708 -aF3095.774707901478 -aF3453.1383900880815 -aF2530.7544024944305 -aF3081.6398772001266 -aF3684.8260506153106 -aF3096.1860396742823 -aF3287.4130912303926 -aF3087.5167458534243 -aF3561.156020462513 -aF3054.1675456643106 -aF3448.068657886982 -aF3095.9716568231584 -aF3682.2616285562513 -aF3077.4434759020805 -aF3234.1663294434547 -aF3685.2150451660154 -aF3094.5031206727026 -aF3722.8592573165893 -aF1065.3293912291526 -aF3685.6081257939336 -aF3685.8440286517143 -aF3097.1239305973054 -aF3685.9211193084716 -aF1428.1945677757265 -aF3226.6253377437592 -aF3458.687010538578 -aF3095.7608152389525 -aF3099.077620315552 -aF3685.992761862278 -aF3096.128017377853 -aF3093.8063083052634 -aF3245.799663674831 -aF1437.8352583527565 -aF2980.292631673813 -aF3093.2832904219626 -aF3087.415138733387 -aF3692.0769308328627 -aF2573.3207032561304 -aF3686.447406053543 -aF2458.69288957119 -aF3686.0349846601484 -aF3683.2305736660956 -aF3071.6387946128843 -aF3100.0122423768044 -aF3259.0445467591285 -aF3034.1877177119254 -aF3685.932560324669 -aF3095.896200597286 -aF3691.0927310347556 -aF3685.859555745125 -aF3096.4322939276694 -aF3670.686044216156 -aF3104.8144727230074 -aF3741.504844856262 -aF3551.8691841721534 -aF2834.1565317869185 -aF3585.123587369919 -aF1938.1220217108726 -aF3095.817203104496 -aF3734.450641155243 -aF2945.956235229969 -aF3096.307259964943 -aF3578.022802388668 -aF3690.00038639307 -aF3685.920846903324 -aF3096.114941930771 -aF3093.403693497181 -aF2512.5106125473976 -aF3611.101504266262 -aF3095.817203104496 -aF3685.8475699186324 -aF3125.365806674957 -aF3188.0636650562287 -aF3686.3588743805885 -aF3077.276219141483 -aF3662.795829117298 -aF3219.030682229996 -aF3684.7056475400923 -aF3099.0765306949615 -aF3682.6871253967283 -aF3310.657150065899 -aF3095.903010725975 -aF3092.745017850399 -aF3138.4292679309847 -aF554.3430984854698 -aF3854.8872222065925 -aF3624.9750984311104 -aF3660.996593117714 -aF2901.9404666781425 -aF3467.7431196689604 -aF2980.34193700552 -aF1883.1424907803537 -aF3704.2417275071143 -aF3096.2718472957613 -aF3122.8871922373773 -aF2262.1754576444628 -aF3271.3188503026963 -aF3589.1540939331053 -aF3092.6905368208886 -aF2849.1456250309943 -aF3687.7544059515 -aF3686.111530506611 -aF3089.346491229534 -aF2023.376933145523 -aF3652.288072955608 -aF4549.854317176341 -aF3686.479277455807 -aF3681.2352059602736 -aF2415.928277862072 -aF3846.863528585434 -aF3098.3402195811273 -aF3489.273750126362 -aF3685.676771891117 -aF3667.069048666954 -aF3094.3195196032525 -aF3555.6120308995246 -aF2497.5917998313903 -aF2583.6424067020416 -aF3682.1003647089005 -aF3686.0407051682473 -aF3686.9998436927795 -aF3085.970574235916 -aF3095.6733731865884 -aF3096.807123410702 -aF3685.9941238880156 -aF2346.026665353775 -aF261.08397486209867 -aF3707.7080830097198 -aF2799.214306294918 -aF3094.414861404896 -aF3684.853291130066 -aF3680.9420980215073 -aF3100.876039099693 -aF3685.8960580348967 -aF3089.2312638521194 -aF1332.4847467780114 -aF3095.9700223922728 -aF3677.0600522637365 -aF3719.977755665779 -aF2911.9933062434197 -aF3602.2001212596892 -aF3097.0580085515976 -aF2855.195743358135 -aF3685.922208929062 -aF3686.2109583854676 -aF3687.7827360868455 -aF3105.268844509125 -aF3185.6654101371764 -aF3097.3794466257095 -aF3096.5194635748862 -aF3685.766120779514 -aF3097.505842614174 -aF3687.320736956596 -aF3096.6842686891555 -aF3473.28029910326 -aF3177.908128750324 -aF3148.7967354416846 -aF3079.1887756824494 -aF3075.838464772701 -aF2842.907002341747 -aF3682.6604296922683 -aF3614.3621938824654 -aF3092.402876985073 -aF3728.5598798394203 -aF3691.3141964197157 -aF3682.016736328602 -aF2725.9098086833956 -aF3096.3293247818947 -aF3098.888026332855 -aF2940.973945081234 -aF3021.8978870749474 -aF2969.9295226454733 -aF3080.831378722191 -aF3690.305480158329 -aF3102.20755546093 -aF3686.1142545580865 -aF2851.0935942411425 -aF3083.521651959419 -aF3103.966747903824 -aF1657.188413798809 -aF3681.704287624359 -aF3687.3370812654493 -aF3736.671287918091 -aF3636.6220529198645 -aF3685.767482805252 -aF3095.7308506727218 -aF4374.519926738739 -aF3096.1824984073637 -aF3094.7071521282196 -aF4176.466946995258 -aF3792.6107745885847 -aF3686.3261857628822 -aF3087.6504967808723 -aF2221.7518957734105 -aF3686.2128652215 -aF3096.086884200573 -aF3096.2094665169716 -aF3685.606218957901 -aF3097.8994680523874 -aF3096.842263674736 -aF3090.7845180034637 -aF1666.2020277261734 -aF3096.6878099560736 -aF2915.4307868003843 -aF3078.305093383789 -aF3367.250136685371 -aF4108.684374129772 -aF3066.0553063035013 -aF2944.434580075741 -asS'y_part' -p4 -(lp5 -sS'counter_tot' -p6 -I607381 -s. \ No newline at end of file diff --git a/scratch/3583790/helperfunctions.py b/scratch/3583790/helperfunctions.py deleted file mode 100644 index dabb34c..0000000 --- a/scratch/3583790/helperfunctions.py +++ /dev/null @@ -1,23 +0,0 @@ -# some helperfunctions - -#Dislpay time (e.g. while generating points) - -display_intervals = ( - ('w', 604800), # 60 * 60 * 24 * 7 - ('d', 86400), # 60 * 60 * 24 - ('h', 3600), # 60 * 60 - ('min', 60), - ('s', 1), - ) - -def display_time(seconds, granularity=2): - result = [] - - for name, count in display_intervals: - value = seconds // count - if value: - seconds -= value * count - if value == 1: - name = name.rstrip('s') - result.append("{} {}".format(value, name)) - return ', '.join(result[:granularity]) diff --git a/scratch/3583790/helperfunctions.pyc b/scratch/3583790/helperfunctions.pyc deleted file mode 100644 index 9ec715a..0000000 --- a/scratch/3583790/helperfunctions.pyc +++ /dev/null Binary files differ diff --git a/scratch/3583790/pdg_const.py b/scratch/3583790/pdg_const.py deleted file mode 100644 index 53ced55..0000000 --- a/scratch/3583790/pdg_const.py +++ /dev/null @@ -1,65 +0,0 @@ -pdg = { - -###Particle masses### - -"mbstar" : 5415.4, -"mbstar0" : 5711.0, -"B0_M" : 5279.5, -"Bs_M" : 5366.7, -"Bplus_M" : 5279.3, -"Lb_M" : 5619.4, -"D0_M" : 1864.8, -"Dst_M" : 2010, -"pi_M" : 139.6, -"Jpsi_M" : 3096.9, -"Psi2s_M" : 3685.6, -"kaon_M" : 493.7, -"Ks_M" : 497.6, -"phi_M" : 1019.5, -"rho_M" : 775.26, -"rho_width" : 149.1, -"omega_M" : 782.65, -"omega_width" : 8.49, - -"muon_M" : 105.7, - -"squark_M" : 95.0, -"bquark_M" : 4180.0, -"cquark_M" : 1275.0, - -"Bplus_tau" : 1.638e-12, - -###Wilson coefficients### - -"C1" : -0.257, -"C2" : 1.009, -"C3" : -0.005, -"C4" : -0.078, - -"C7eff" : -0.306, - -"C9eff" : 4.211, -"C10eff" : -4.103, - -###Other constants - -"GF" : 1.1663787e-5, -"alpha_ew" : 1.0/137.0, -"Vts" : 0.0394, -"Vtb" : 1.019, - -#Formfactor z coefficients - -#"b0" : [0.285, 0.19, -0.17], -#"bplus" : [0.437, -1.41, -2.5], -#"bT" : [0.440, -1.47, -2.7] - -"b0" : [0.292, 0.281, 0.150], -"bplus" : [0.466, -0.885, -0.213], -"bT" : [0.460, -1.089, -1.114], - -#Resonances format(mass, width, phase, scale) - -"jpsi": (3096, 0.09, -1.5, 2e-2), -"psi2s": (3686, 0.3, -1.5, 3.14e-3) -} diff --git a/scratch/3583790/pdg_const.pyc b/scratch/3583790/pdg_const.pyc deleted file mode 100644 index e2425de..0000000 --- a/scratch/3583790/pdg_const.pyc +++ /dev/null Binary files differ diff --git a/scratch/3583790/raremodel.py b/scratch/3583790/raremodel.py deleted file mode 100644 index 58aacbb..0000000 --- a/scratch/3583790/raremodel.py +++ /dev/null @@ -1,1068 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c - -mmu = pdg['muon_M'] -mb = pdg["bquark_M"] -ms = pdg["squark_M"] -mK = pdg["Ks_M"] -mB = pdg["Bplus_M"] - -class model: - - def __init__(self): - - - self.mmu = pdg['muon_M'] - self.mb = pdg["bquark_M"] - self.ms = pdg["squark_M"] - self.mK = pdg["Ks_M"] - self.mB = pdg["Bplus_M"] - - self.C7eff = pdg["C7eff"] - self.C9eff = pdg["C9eff"] - self.C10eff = pdg["C10eff"] - - #self.C1 = pdg["C1"] - #self.C2 = pdg["C2"] - #self.C3 = pdg["C3"] - #self.C4 = pdg["C4"] - - self.GF = pdg["GF"] #Fermi coupling const. - self.alpha_ew = pdg["alpha_ew"] - self.Vts = pdg["Vts"] - self.Vtb = pdg["Vtb"] - - self.x_min = 2*self.mmu - self.x_max = (self.mB - self.mK) - 0.1 - self.total_pdf_string = "self.total_nonres(q2)" - self.mode = "" - self.total_scale_amp = 1.0 - self._mean = 0.0 - - self.cusp_mean = 1 - self.cusp_sigma_L = 1 - self.cusp_sigma_R = 1 - self.cusp_amp = 0 - - - def formfactor(self, q2, subscript): - - #check if subscript is viable - - if subscript != "0" and subscript != "+" and subscript != "T": - raise ValueError('Wrong subscript entered, choose either 0, + or T') - - #get constants - - mh = self.mK - mbstar0 = pdg["mbstar0"] - mbstar = pdg["mbstar"] - b0 = pdg["b0"] - bplus = pdg["bplus"] - bT = pdg["bT"] - - N = 3 - - #some helperfunctions - - tpos = (self.mB - self.mK)**2 - tzero = (self.mB + self.mK)*(np.sqrt(self.mB)-np.sqrt(self.mK))**2 - - z_oben = np.sqrt(tpos - q2) - np.sqrt(tpos - tzero) - z_unten = np.sqrt(tpos - q2) + np.sqrt(tpos - tzero) - z = z_oben/z_unten - - #calculate f0 - - if subscript == "0": - prefactor = 1/(1 - q2/(mbstar0**2)) - _sum = 0 - - for i in range(N): - _sum += b0[i]*(z**i) - - return prefactor * _sum - - #calculate f+ or fT - - else: - prefactor = 1/(1 - q2/(mbstar**2)) - _sum = 0 - - if subscript == "T": - b = bT - else: - b = bplus - - for i in range(N): - _sum += b[i] * (z**i - ((-1)**(i-N)) * (i/N) * z**N) - - return prefactor * _sum - - def axiv_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #left term in bracket - - bracket_left = 2./3. * kabs**2 * beta**2 * abs(C10eff*self.formfactor(q2, "+"))**2 - - #middle term in bracket - - _top = 4. * mmu**2 * (mB**2 - mK**2) * (mB**2 - mK**2) - - _under = q2 * mB**2 - - bracket_middle = _top/_under * abs(C10eff * self.formfactor(q2, "0"))**2 - - return prefactor1 * (bracket_left + bracket_middle) * 2 * np.sqrt(q2) - - - def vec_nonres(self, q2): - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C7eff = self.C7eff - C9eff = self.C9eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #Some helperfunctions - - beta = np.sqrt(abs(1. - 4. * self.mmu**2. / q2)) - - kabs = np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #prefactor in front of whole bracket - - prefactor1 = GF**2. *alpha_ew**2. * (abs(Vtb*Vts))**2 * kabs * beta / (128. * np.pi**5.) - - #right term in bracket - - prefactor2 = kabs**2 * (1. - 1./3. * beta**2) - - abs_bracket = abs(C9eff * self.formfactor(q2, "+") + 2 * C7eff * (mb + ms)/(mB + mK) * self.formfactor(q2, "T"))**2 - - bracket_right = prefactor2 * abs_bracket - - return prefactor1 * bracket_right * 2 * np.sqrt(q2) - - def total_nonres(self, q2): - - #Get constants - - GF = self.GF - alpha_ew = self.alpha_ew - Vtb = self.Vtb - Vts = self.Vts - C10eff = self.C10eff - C9eff = self.C9eff - C7eff = self.C7eff - - mmu = self.mmu - mb = self.mb - ms = self.ms - mK = self.mK - mB = self.mB - - #vector nonresonant part - - vec_nonres_part = self.vec_nonres(q2) - - #axial verctor nonresonant part including C7 - - axiv_nonres_part = self.axiv_nonres(q2) - - #Complete term - - return self.total_scale_amp*complex(vec_nonres_part + axiv_nonres_part, 0.0) - - def generate_points(self, set_size = 10000, x_min = 2* mmu, x_max = (mB - mK) - 0.1, save = True, verbose = 1, mode = "slim_points", resolution = 7.0, min_bin_scaling = 100): - - #Setup contants and variables - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - - self.mode = mode - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - - x2 = 4000 - y2 = self.total_pdf(x2**2) - - #Translate to MeV**2 - - dgamma_tot = [] - - for i in dq: - dgamma_tot.append(self.total_pdf(i**2)) - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #Generate random points - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - _max = max(dgamma_tot) - - A1_x1 = (_max-y1)*x1 - A23_x1 = y1*x1 - - A1_x2 = (_max-y2)*x2 - A23_x2 = y2*x2 - - if mode == "slim_points": - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x = ROOT.TRandom1().Uniform(x_min, x_max) - y = ROOT.TRandom1().Uniform(0, _max) - - if y < self.total_pdf(x**2): - x_part.append(x) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - print(" {0} out of {1} particles chosen!".format(int(counter_x), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter_tot": counter, "counter_x": counter_x} - - pkl.dump( part_set, open("./data/set_{0}_range({1}-{2}).pkl".format(int(set_size),int(x_min), int(x_max)) , "wb" ) ) - - print(" Set saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - if mode == "full_points": - - x = [] - y = [] - - x_part = [] - y_part = [] - - print("Generating set of size {}...".format(int(set_size))) - - #print(len(y)) - - #ROOT.TRandom1().SetSeed(0) - - if verbose != 0: - verbose_calls = [] - j = 0 - o = 0 - while j < 100: - j += verbose - verbose_calls.append(int(set_size*j/100)) - - start = time.time() - - counter = 0 - counter_x = 0 - while len(x_part) < set_size: - counter += 1 - x.append(ROOT.TRandom1().Uniform(x_min, x_max)) - y.append(ROOT.TRandom1().Uniform(0, _max)) - - if y[-1] < self.total_pdf(x[-1]**2): - x_part.append(x) - y_part.append(y) - counter_x += 1 - - #progress calls - if verbose != 0: - end = time.time() - if o*verbose+verbose < 100 and counter%300 == 0: - print(" {0}{1} completed".format(o*verbose+verbose, "%")) - print(" Projected time left: {0}".format(display_time(int((end-start)*set_size/(len(x_part)+1)-(end-start))))) - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - if o*verbose + verbose >=100: - sys.stdout.write("\033[F") - sys.stdout.write("\x1b[2K") - print(" Time to generate set: {0}".format(display_time(int(end-start)))) - - if len(x_part) == verbose_calls[o]: - o += 1 - - - - print(" {0} out of {1} particles chosen!".format(len(x_part), counter)) - - print(" Set generated!") - - #Save the set - - if save: - - part_set = {"x_part" : x_part, "y_part": y_part, "counter": counter} - - raw_set = {"x" : x, "y": y, "counter": counter} - - pkl.dump( part_set, open("./data/set_{0}.pkl".format(int(set_size)) , "wb" ) ) - - pkl.dump( part_set, open("./data/set_raw_{0}.pkl".format(int(set_size)) , "wb" ) ) - - print(" Sets saved!") - - print - - #returns all the chosen points (x_part, y_part) and all the random points generated (x, y) - - return x_part, y_part, counter - - - if mode == "fast_binned": - - nbins = int((x_max-x_min)/resolution) - - print("Generating set with {} bins...".format(nbins)) - - dq = np.linspace(x_min, x_max ,nbins+1) - - bin_mean = [] - bin_true_height = [] - - for i in range(len(dq)-1): - a = dq[i] - b = dq[i+1] - c = (a+b)/2 - bin_mean.append(c) - - height = self.total_pdf(c**2) - bin_true_height.append(height) - - _min = min(bin_true_height) - - for i in range(len(bin_true_height)): - bin_true_height[i] = bin_true_height[i]/_min*min_bin_scaling - - start = time.time() - - bin_height = [] - - for i in range(len(bin_mean)): - bin_height.append(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - #print(int(ROOT.TRandom1().Gaus(bin_true_height[i], np.sqrt(bin_true_height[i])))) - - #progress calls - if verbose != 0: - end = time.time() - print(" Time to generate set: {0}s".format(end-start)) - - print(" {0} bins simulated".format(nbins)) - - print(" Set generated!") - - #Save the set - - if save: - - _ = 0 - - for i in bin_height: - _ += i - - part_set = {"bin_mean" : bin_mean, "bin_height": bin_height, "nbins": nbins} - - pkl.dump( part_set, open("./data/binned_set_{0}bins_{1}part.pkl".format(nbins, _) , "wb" ) ) - - print(" Set saved!") - - print - - return bin_mean, bin_height, nbins - - - - - def draw_plots(self, part_set, counter, mode,min_bin_scaling = 100, x_min = 2* mmu, x_max = (mB - mK) - 0.1, resolution = 7): - - if mode != "slim_points" and mode != "full_points" and mode != "fast_binned": - raise ValueError('Wrong mode entered, choose either slim_points, full_points or fast_binned') - if mode != self.mode: - raise ValueError('self.mode and mode are different, set them to the same value') - #Resolution based on experiment chosen to be ~7MeV - - #Setup contants and variables - - print("Generating plots") - - if mode == "fast_binned": - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/fast_binned/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/fast_binned/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/fast_binned/tot.png") - - print(" tot.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 1e-5) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/fast_binned/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - #Create histo with pdf - - #Translate to MeV**2 - - plt.clf() - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - dgamma_tot = [] - - for i in dq2: - dgamma_tot.append(self.total_pdf(i)) - - _min = min(dgamma_tot) - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/_min*min_bin_scaling - - bin_mean, bin_height = part_set - - nbins = counter - - plt.hist(bin_mean, bins=nbins, range=(self.x_min, self.x_max), weights = bin_height, label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - _sum = 0 - - for i in bin_height: - _sum += i - - #print(_max) - - plt.grid() - - plt.ylim(0, 2000) - - plt.legend() - - plt.title("{0} random points generated according to pdf ({1} particles)".format(len(bin_mean), _sum)) - - plt.savefig("./plots/fast_binned/histo.png") - - print(" histo.png created") - - print(" All plots drawn \n") - - return - - else: - - mB = self.mB - mK = self.mK - mmu = self.mmu - - #Range of function in MeV - - dq = np.linspace(x_min, x_max ,5000) - - #Translate to MeV**2 - - dq2 = [] - - for i in dq: - dq2.append(i**2) - - #calculate formfactors - - ff_plus = [] - ff_T = [] - ff_0 = [] - - for i in dq: - ff_0.append(self.formfactor(i**2, "0")) - ff_T.append(self.formfactor(i**2, "T")) - ff_plus.append(self.formfactor(i**2, "+")) - - #calculate nonresonant - - dgamma_axiv_nonres = [] - dgamma_vec_nonres = [] - dgamma_tot = [] - - for i in dq: - dgamma_axiv_nonres.append(self.axiv_nonres(i**2)) - dgamma_vec_nonres.append(self.vec_nonres(i**2)) - dgamma_tot.append(self.total_pdf(i**2)) - - - #Plot formfactors - - plt.plot(dq2, ff_0, label = "0") - plt.plot(dq2, ff_T, label = "T") - plt.plot(dq2, ff_plus, label = "+") - - plt.grid() - - plt.title("Formfactors") - - plt.legend() - - plt.savefig("./plots/points/ff.png") - - print(" ff.png created") - - plt.clf() - - - #Plot nonresonant part - - plt.plot(dq, dgamma_axiv_nonres, label = "axiv") - plt.plot(dq, dgamma_vec_nonres, label = "vec") - - plt.grid() - - plt.title("Nonresonant axial vector and vector parts") - - plt.legend() - - plt.savefig("./plots/points/vec_axiv.png") - - print(" vec_axiv.png created") - - plt.clf() - - plt.plot(dq, dgamma_tot, label = "total") - - plt.grid() - - plt.title("Total pdf") - - plt.legend() - - plt.savefig("./plots/points/tot.png") - - print(" tot.png created") - - - #Particle set - - x_part, y_part = part_set - - set_size = len(x_part) - - #Plot generated generate_points - - #plt.clf() - - #plt.plot(x_part, y_part, label = "Random points generated", marker = ".", linewidth = 0) - - #plt.plot(dq, dgamma_tot, label = "pdf") - - #plt.grid() - - #plt.title("Random points generated and pdf") - - #plt.legend() - - #plt.savefig("./plots/points/points_raw.png") - - #print(" points_raw.png created") - - #Histo unnormalized - - bins = int((x_max-x_min)/resolution) - - plt.clf() - - wheights = np.ones_like(x_part) - - _max = max(dgamma_tot) - - x1 = 2500 - y1 = self.total_pdf(x1**2) - x2 = 4000 - y2 = self.total_pdf(x2**2) - - for i in range(len(wheights)): - if x_part[i] < x1: - wheights[i] = x1*y1/(x1*_max) - elif x_part[i] > x2: - wheights[i] = x2*y2/(x2*_max) - - _y, _x, _ = plt.hist(x_part, bins=bins, weights = wheights, range=(x_min, x_max), label = "toy data binned ({0} points)".format(sum(wheights))) - - _mean_histo = float(np.mean(_y)) - - plt.legend() - - plt.title("Binned toy data") - - plt.savefig("./plots/points/histo_raw.png") - - print(" histo_raw.png created") - - - #Histo and pdf normailzed - - plt.clf() - - for i in range(len(dgamma_tot)): - dgamma_tot[i] = dgamma_tot[i]/(float(set_size)*_max * 2.0 * mmu / counter) - - _mean = np.mean(dgamma_tot) - - #Attempt for marked field of std-dev - - #dgamma_min = [] - #dgamma_plu = [] - #for i in range(len(dgamma_tot)): - #dgamma_min.append(dgamma_tot[i]-np.sqrt(dgamma_tot[i])) - #dgamma_plu.append(dgamma_tot[i]+np.sqrt(dgamma_tot[i])) - - #plt.plot(dq, dgamma_min, alpha = 0.5) - - #plt.plot(dq, dgamma_plu, alpha = 0.5) - - #plt.fill_between(dq, dgamma_min, dgamma_plu) - - #Plot histo - - plt.hist(x_part, bins=bins, range=(x_min, x_max), weights = wheights/(_mean_histo/_mean), label = "toy data binned") - - plt.plot(dq, dgamma_tot, label = "pdf") - - #print(_max) - - plt.grid() - - plt.legend() - - plt.ylim(0, 1e-5) - - plt.title("{0} random points generated according to pdf".format(sum(wheights))) - - plt.savefig("./plots/points/histo.png") - - print(" histo.png created") - - #All pdfs - - #print(test_x[1]**2 - self.x_min**2) - - tot_y = [] - jpsi_y = [] - psi2s_y = [] - total_nonres_y = [] - cusp_y = [] - - - jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] - - psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] - - for i in range(len(dq)): - #print(i**2 - 4*(mmu**2)) - tot_y.append(abs(self.total_pdf(dq[i]**2))) - jpsi_y.append(abs(self.resonance(dq[i]**2, jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale))) - psi2s_y.append(abs(self.resonance(dq[i]**2, psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale))) - total_nonres_y.append(abs(self.total_nonres(dq[i]**2))) - cusp_y.append(abs(self.bifur_gauss(dq[i]**2, self.cusp_mean, self.cusp_amp, self.cusp_sigma_L, self.cusp_sigma_R ))) - #resonance(self, q2, _mass, width, phase, scale): - #w[i] = np.sqrt(w[i]) - #print(test_y[i]) - - plt.clf() - - plt.title("All pdfs") - - #plt.yscale("log") - - plt.ylim(0, 2*self._mean) - - plt.grid() - - plt.plot(dq, tot_y, label = "total pdf") - plt.plot(dq, jpsi_y, label = "jpsi") - plt.plot(dq, psi2s_y, label = "psi2s") - plt.plot(dq, total_nonres_y, label = "nonres") - plt.plot(dq, cusp_y, label = "cusp") - - plt.legend() - - plt.savefig("./plots/points/pdf_and_parts.png") - - print(" pdf_and_parts.png created") - - print(" All plots drawn \n") - - return - - - def total_pdf(self, q2, cusp_amp = -1.0, scan = False): - - if scan: - self.normalize_pdf() - - if cusp_amp == -1.0: - cusp_amp = cusp_amp - else: - cusp_amp = self.cusp_amp - - #Calculate the pdf with the added resonances - - exec("_sum = abs({0})".format(self.total_pdf_string)) - - return _sum - - - def resonance(self, q2, _mass, width, phase, scale): #returns [real, imaginary] - - #calculate the resonance ---------------------------------------------> Formula correct? - - #if abs(np.sqrt(q2) - _mass) < 300: - #return 0., 0. - - np.sqrt(mB**2 + q2**2/mB**2 + mK**4/mB**2 - 2 * (mB**2 * mK**2 + mK**2 * q2 + mB**2 * q2) / mB**2) - - #print(q2) - - #Teiler erweitert mit kompl. konj. - - #p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - #p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - #gamma_j = p / p0 * _mass /q2 * width - - #_top_im = - _mass**2 * width * gamma_j - - #_top_re = _mass * width * (_mass**2 - q2) - - #_bottom = (_mass**2 - q2)**2 + _mass**2 * gamma_j**2 - - #real = _top_re/_bottom - - #imaginary = _top_im/_bottom - - #com = complex(real, imaginary) * scale - - #r = abs(com) - - #_phase = c.phase(com) - - #_phase += phase - - #x = c.cos(phase)*r - #y = c.sin(phase)*r - - #com = complex(x,y) - - - #Original formula - - p = 0.5 * np.sqrt(q2 - 4*(mmu**2)) - - p0 = 0.5 * np.sqrt(_mass**2 - 4*mmu**2) - - gamma_j = p / p0 * _mass /q2 * width - - _top = complex(_mass * width, 0.0) - - _bottom = complex((_mass**2 - q2), -_mass*gamma_j) - - com = _top/_bottom * scale - - r = abs(com) - - _phase = c.phase(com) - - _phase += phase - - x = c.cos(phase)*r - y = c.sin(phase)*r - - com = complex(x,y) - - return self.total_scale_amp*com - - - def add_resonance(self, _mass, width, phase, scale): - - #Adds the resonace to the pdf in form of a string (to be executed later) - - self.total_pdf_string += "+ self.resonance(q2,{0},{1},{2},{3})".format(_mass, width, phase, scale) - - def bifur_gauss(self, q2, mean, amp, sigma_L, sigma_R): - - q = np.sqrt(q2) - - if q < mean: - sigma = sigma_L - else: - sigma = sigma_R - - _exp = np.exp(- (q-mean)**2 / (2 * sigma**2)) - - dgamma = amp*_exp/(np.sqrt(2*np.pi))*2*(sigma_L*sigma_R)/(sigma_L+sigma_R) - - com = complex(dgamma, 0) - - return self.total_scale_amp*com - - def add_cusp(self, mean, amp, sigma_L, sigma_R): - - self.total_pdf_string += "+ self.bifur_gauss(q2,{0},{1},{2},{3})".format(mean, "cusp_amp", sigma_L, sigma_R) - - self.cusp_mean = mean - self.cusp_sigma_L = sigma_L - self.cusp_sigma_R = sigma_R - self.cusp_amp = amp - - - def normalize_pdf(self): - - self.total_scale_amp = 1.0 - - x_scan = np.linspace(self.x_min, self.x_max, 10000) - - y_scan = [] - - for i in x_scan: - y_scan.append(self.total_pdf(i**2)) - - _mean = np.mean(y_scan) - - self.total_scale_amp = 1.0/(self.x_max-self.x_min)*_mean - - self._mean = _mean * self.total_scale_amp - - def log_likelihood(self, x_part, cusp_amp): - - _sum = 0.0 - - for i in x_part: - - _sum += np.log(self.total_pdf(i**2, cusp_amp = cusp_amp, scan = True)) - - return _sum - diff --git a/scratch/3583790/raremodel.pyc b/scratch/3583790/raremodel.pyc deleted file mode 100644 index bf0fc4f..0000000 --- a/scratch/3583790/raremodel.pyc +++ /dev/null Binary files differ diff --git a/scratch/3583790/test.py b/scratch/3583790/test.py deleted file mode 100644 index 6ffae5b..0000000 --- a/scratch/3583790/test.py +++ /dev/null @@ -1,98 +0,0 @@ -import ROOT -#from ROOT import TTree, TFile, Double -import numpy as np -from pdg_const import pdg -import matplotlib -matplotlib.use("Qt5Agg") -import matplotlib.pyplot as plt -import pickle as pkl -import sys -import time -from helperfunctions import display_time -import cmath as c -import raremodel as rm - - -modl = rm.model() - -load_set = True - -draw = False - -mode = "slim_points" - -modl.mode = mode - -min_bin_scaling = 100 - -set_size = 1e3 - -x_min = 211.0 - -x_max= 4781.0 - -jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg["jpsi"] -modl.add_resonance(jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale) - -psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale = pdg["psi2s"] -modl.add_resonance(psi2s_mass, psi2s_width, psi2s_phase, psi2s_scale) - -modl.add_cusp(3525, 3e-7, 200, 7) - -modl.normalize_pdf() - -if load_set: - - with open(r"./data/set_{0}_range{1}-{2}.pkl".format(int(set_size), int(x_min), int(x_max)), "rb") as input_file: - set_dic = pkl.load(input_file) - - part_set = (set_dic["x_part"], set_dic["y_part"]) - counter_tot = set_dic["counter_tot"] - -else: - x_part, y_part, counter = modl.generate_points(set_size, mode = mode, min_bin_scaling = min_bin_scaling, verbose = 1) - part_set = (x_part, y_part) - -if draw: - modl.draw_plots(part_set = part_set, counter = counter_tot, mode = mode, min_bin_scaling = min_bin_scaling) - -cusp_amp_scan_y = [] -cusp_amp_scan_x = np.linspace(0, 2*modl.cusp_amp, 100) - -print("Likelihood scan starting") - -counter = 0 - -for i in cusp_amp_scan_x: - cusp_amp_scan_y.append(modl.log_likelihood(x_part = set_dic["x_part"], cusp_amp = i)) - counter +=1 - print("{0}{1}".format(counter, "%")) - -print("Scan finished") -print("Plotting...") - -plt.clf() - -plt.title("Cusp amp log_likelihood scan") - -#plt.yscale("log") - -#plt.ylim(0, 2*self._mean) - -plt.grid() - -plt.plot(cusp_amp_scan_x, cusp_amp_scan_y, label = "log_likelihood") - -plt.legend() - -plt.savefig("./cusp_amp_scan.png") - -print(" pdf_and_parts.png created") - -print(modl.log_likelihood(x_part = set_dic["x_part"], cusp_amp = modl.cusp_amp)) - -scan_set = (cusp_amp_scan_x, cusp_amp_scan_y) - -pkl.dump( scan_set, open("scan_set.pkl" , "wb" ) ) - -print("Run finished") diff --git a/test.png b/test.png index 30d59d7..9e4895d 100644 --- a/test.png +++ b/test.png Binary files differ