diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 96b984a..d3ce661 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -341,49 +341,24 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", - "omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n", - "phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n", "\n", "\n", - "r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n", - "o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n", - "p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "phi_s = np.linspace(-500, 5000, 100000)\n", + "# r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n", + "# o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n", + "# p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", + "# phi_s = np.linspace(-500, 5000, 100000)\n", "\n", - "p_y = r+o+p_\n", + "# p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "plt.plot(phi_s, p_y)\n", + "# p_y = r+o+p_\n", "\n", - "# print(r + o + p)" + "# plt.plot(phi_s, p_y)\n", + "\n", + "# # print(r + o + p)" ] }, { @@ -401,17 +376,31 @@ "source": [ "class total_pdf(zfit.pdf.ZPDF):\n", " _N_OBS = 1 # dimension, can be omitted\n", - " _PARAMS = ['jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n", + " _PARAMS = ['rho_mass', 'rho_scale', 'rho_phase', 'rho_width',\n", + " 'jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n", " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',\n", " 'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',\n", " 'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',\n", " 'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n", - " 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width'\n", - " ] # the name of the parameters\n", + " 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',\n", + " 'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',\n", + " 'phi_mass', 'phi_scale', 'phi_phase', 'phi_width'] # the name of the parameters\n", "\n", " def _unnormalized_pdf(self, x):\n", " \n", " x = x.unstack_x()\n", + " \n", + " def rho_res(q):\n", + " return resonance(q, _mass = self.params['rho_mass'], scale = self.params['rho_scale'],\n", + " phase = self.params['rho_phase'], width = self.params['rho_width'])\n", + " \n", + " def omega_res(q):\n", + " return resonance(q, _mass = self.params['omega_mass'], scale = self.params['omega_scale'],\n", + " phase = self.params['omega_phase'], width = self.params['omega_width'])\n", + " \n", + " def phi_res(q):\n", + " return resonance(q, _mass = self.params['phi_mass'], scale = self.params['phi_scale'],\n", + " phase = self.params['phi_phase'], width = self.params['phi_width'])\n", "\n", " def jpsi_res(q):\n", " return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'],\n", @@ -438,7 +427,7 @@ " phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n", " \n", "\n", - " funcs = jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x) + p4160_res(x) + p4415_res(x)\n", + " funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x)\n", "\n", " vec_f = vec(x, funcs)\n", "\n", @@ -500,6 +489,34 @@ } ], "source": [ + "#rho\n", + "\n", + "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", + "\n", + "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False)\n", + "rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n", + "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), floating = False)\n", + "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale))\n", + "\n", + "#omega\n", + "\n", + "omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n", + "\n", + "omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n", + "omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n", + "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), floating = False)\n", + "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale))\n", + "\n", + "\n", + "#phi\n", + "\n", + "phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n", + "\n", + "phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n", + "phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n", + "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), floating = False)\n", + "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale))\n", + "\n", "#jpsi\n", "\n", "jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n", @@ -574,7 +591,10 @@ " p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n", " p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n", " p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,\n", - " p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w) \n", + " p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n", + " rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n", + " omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n", + " phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w) \n", " \n", "# print(total_pdf.obs)\n", "\n", @@ -633,12 +653,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -660,7 +680,7 @@ "plt.legend()\n", "plt.ylim(0.0, 6e-6)\n", "# plt.yscale('log')\n", - "# plt.xlim(3080, 3110)\n", + "# plt.xlim(770, 785)\n", "plt.savefig('test.png')\n", "# print(jpsi_width)" ] @@ -704,7 +724,7 @@ "outputs": [], "source": [ "# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (4000, 4400), norm_range=False)\n", + "# inte = total_f.integrate(limits = (1000, 1040), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl)\n", "# # print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" @@ -712,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -724,7 +744,7 @@ "# # print(\"Increase am by factor:\", np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "# # print(\"New amp:\", pdg[\"psi2s\"][3]*np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", - "# name = \"p4160\"\n", + "# name = \"phi\"\n", "\n", "# print(name+\":\", inte_fl)\n", "# print(\"Increase am by factor:\", np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index d8a0f6b..62b0ac1 100644 --- a/__pycache__/pdg_const.cpython-37.pyc +++ b/__pycache__/pdg_const.cpython-37.pyc Binary files differ diff --git a/pdg_const.py b/pdg_const.py index a763428..d01dd30 100644 --- a/pdg_const.py +++ b/pdg_const.py @@ -70,11 +70,11 @@ # pre scaling - "rho": (775.26, 149.0, -1.5, 1.0), +# "rho": (775.26, 149.0, -0.35, 1.0), - "omega": (782.7, 8.5, -1.5, 50.0), +# "omega": (782.7, 8.5, 0.26, 1.0), - "phi": (1019.46, 4.25, -1.6, 300.89), +# "phi": (1019.46, 4.25, 0.5, 1.0), # "jpsi": (3096.0, 0.09, -1.5, 2e-2), # "jpsi_auc": 0.2126825758464027, @@ -93,6 +93,13 @@ # after scaling + + "rho": (775.26, 149.0, -0.35, 1.05), + + "omega": (782.7, 8.5, 0.26, 6.8), + + "phi": (1019.46, 4.25, 0.5, 19.2), + "jpsi": (3096.0, 0.09, -1.5, 9897.0), "jpsi_auc": 0.2126825758464027, @@ -110,6 +117,9 @@ # zeroing resonances +# "rho": (775.26, 149.0, -0.35, 0.0), +# "omega": (782.7, 8.5, 0.26, 0.0), +# "phi": (1019.46, 4.25, 0.5, 0.0), # "jpsi": (3096.0, 0.09, -1.5, 0.0), # "psi2s": (3686.0, 0.3, -1.5, 0.0), # "p3770": (3773.0, 27.2, -2.13, 0.0), @@ -118,6 +128,10 @@ # "p4415": (4421.0, 62.0, -2.52, 0.0), #general + + "rho_BR": 1.7e-10, + "omega_BR": 4.9e-10, + "phi_BR": 2.5e-9, "jpsi_BR": 6.02e-5, "psi2s_BR": 4.97e-6, "p3770_BR": 1.38e-9, diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 96b984a..d3ce661 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -341,49 +341,24 @@ "cell_type": "code", "execution_count": 5, "metadata": {}, - "outputs": [ - { - "data": { - "text/plain": [ - "[]" - ] - }, - "execution_count": 5, - "metadata": {}, - "output_type": "execute_result" - }, - { - "data": { - "image/png": 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\n", - "text/plain": [ - "
" - ] - }, - "metadata": { - "needs_background": "light" - }, - "output_type": "display_data" - } - ], + "outputs": [], "source": [ - "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", - "omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n", - "phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n", "\n", "\n", - "r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n", - "o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n", - "p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "phi_s = np.linspace(-500, 5000, 100000)\n", + "# r = rho_scale * rho_width/rho_mass * np.cos(rho_phase)*(1-np.tan(rho_phase)*rho_width/rho_mass)\n", + "# o = omega_scale*np.cos(omega_phase)*omega_width/omega_mass\n", + "# p = phi_scale*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", + "# phi_s = np.linspace(-500, 5000, 100000)\n", "\n", - "p_y = r+o+p_\n", + "# p_ = phi_s*np.cos(phi_phase)*phi_width/phi_mass\n", "\n", - "plt.plot(phi_s, p_y)\n", + "# p_y = r+o+p_\n", "\n", - "# print(r + o + p)" + "# plt.plot(phi_s, p_y)\n", + "\n", + "# # print(r + o + p)" ] }, { @@ -401,17 +376,31 @@ "source": [ "class total_pdf(zfit.pdf.ZPDF):\n", " _N_OBS = 1 # dimension, can be omitted\n", - " _PARAMS = ['jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n", + " _PARAMS = ['rho_mass', 'rho_scale', 'rho_phase', 'rho_width',\n", + " 'jpsi_mass', 'jpsi_scale', 'jpsi_phase', 'jpsi_width',\n", " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width',\n", " 'p3770_mass', 'p3770_scale', 'p3770_phase', 'p3770_width',\n", " 'p4040_mass', 'p4040_scale', 'p4040_phase', 'p4040_width',\n", " 'p4160_mass', 'p4160_scale', 'p4160_phase', 'p4160_width',\n", - " 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width'\n", - " ] # the name of the parameters\n", + " 'p4415_mass', 'p4415_scale', 'p4415_phase', 'p4415_width',\n", + " 'omega_mass', 'omega_scale', 'omega_phase', 'omega_width',\n", + " 'phi_mass', 'phi_scale', 'phi_phase', 'phi_width'] # the name of the parameters\n", "\n", " def _unnormalized_pdf(self, x):\n", " \n", " x = x.unstack_x()\n", + " \n", + " def rho_res(q):\n", + " return resonance(q, _mass = self.params['rho_mass'], scale = self.params['rho_scale'],\n", + " phase = self.params['rho_phase'], width = self.params['rho_width'])\n", + " \n", + " def omega_res(q):\n", + " return resonance(q, _mass = self.params['omega_mass'], scale = self.params['omega_scale'],\n", + " phase = self.params['omega_phase'], width = self.params['omega_width'])\n", + " \n", + " def phi_res(q):\n", + " return resonance(q, _mass = self.params['phi_mass'], scale = self.params['phi_scale'],\n", + " phase = self.params['phi_phase'], width = self.params['phi_width'])\n", "\n", " def jpsi_res(q):\n", " return resonance(q, _mass = self.params['jpsi_mass'], scale = self.params['jpsi_scale'],\n", @@ -438,7 +427,7 @@ " phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n", " \n", "\n", - " funcs = jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x) + p4160_res(x) + p4415_res(x)\n", + " funcs = rho_res(x) + omega_res(x) + phi_res(x) + jpsi_res(x) + psi2s_res(x) + p3770_res(x) + p4040_res(x)+ p4160_res(x) + p4415_res(x)\n", "\n", " vec_f = vec(x, funcs)\n", "\n", @@ -500,6 +489,34 @@ } ], "source": [ + "#rho\n", + "\n", + "rho_mass, rho_width, rho_phase, rho_scale = pdg[\"rho\"]\n", + "\n", + "rho_m = zfit.Parameter(\"rho_m\", ztf.constant(rho_mass), floating = False)\n", + "rho_w = zfit.Parameter(\"rho_w\", ztf.constant(rho_width), floating = False)\n", + "rho_p = zfit.Parameter(\"rho_p\", ztf.constant(rho_phase), floating = False)\n", + "rho_s = zfit.Parameter(\"rho_s\", ztf.constant(rho_scale))\n", + "\n", + "#omega\n", + "\n", + "omega_mass, omega_width, omega_phase, omega_scale = pdg[\"omega\"]\n", + "\n", + "omega_m = zfit.Parameter(\"omega_m\", ztf.constant(omega_mass), floating = False)\n", + "omega_w = zfit.Parameter(\"omega_w\", ztf.constant(omega_width), floating = False)\n", + "omega_p = zfit.Parameter(\"omega_p\", ztf.constant(omega_phase), floating = False)\n", + "omega_s = zfit.Parameter(\"omega_s\", ztf.constant(omega_scale))\n", + "\n", + "\n", + "#phi\n", + "\n", + "phi_mass, phi_width, phi_phase, phi_scale = pdg[\"phi\"]\n", + "\n", + "phi_m = zfit.Parameter(\"phi_m\", ztf.constant(phi_mass), floating = False)\n", + "phi_w = zfit.Parameter(\"phi_w\", ztf.constant(phi_width), floating = False)\n", + "phi_p = zfit.Parameter(\"phi_p\", ztf.constant(phi_phase), floating = False)\n", + "phi_s = zfit.Parameter(\"phi_s\", ztf.constant(phi_scale))\n", + "\n", "#jpsi\n", "\n", "jpsi_mass, jpsi_width, jpsi_phase, jpsi_scale = pdg[\"jpsi\"]\n", @@ -574,7 +591,10 @@ " p3770_mass = p3770_m, p3770_scale = p3770_s, p3770_phase = p3770_p, p3770_width = p3770_w,\n", " p4040_mass = p4040_m, p4040_scale = p4040_s, p4040_phase = p4040_p, p4040_width = p4040_w,\n", " p4160_mass = p4160_m, p4160_scale = p4160_s, p4160_phase = p4160_p, p4160_width = p4160_w,\n", - " p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w) \n", + " p4415_mass = p4415_m, p4415_scale = p4415_s, p4415_phase = p4415_p, p4415_width = p4415_w,\n", + " rho_mass = rho_m, rho_scale = rho_s, rho_phase = rho_p, rho_width = rho_w,\n", + " omega_mass = omega_m, omega_scale = omega_s, omega_phase = omega_p, omega_width = omega_w,\n", + " phi_mass = phi_m, phi_scale = phi_s, phi_phase = phi_p, phi_width = phi_w) \n", " \n", "# print(total_pdf.obs)\n", "\n", @@ -633,12 +653,12 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 23, "metadata": {}, "outputs": [ { "data": { - "image/png": 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1vE5upoOCLA0+sWjwUUqN4h8byYoj2y3TOsflTk7XW7hJpo6QbDdPAoNPnz/4ZDs5o8EnKg0+SqlR/N1T8axwkGlNRE1W8Bn2Rk448I/5+BMPEtbtZiUc9LjcupV3FBp8lFKj+FsI8Yz5+LddcLmTs4KzO8yYj78bzj/mE2j5JCj45GbaKcx2AtAzqK2fSDT4KKVG8QeSeIKPf9sF13CSWj4eM3aSqX1kzMcYE0iISESqdZ/LE0i1Bl3lIBoNPkqpUQaGfIEk3oQDSOKYjzfMfj4yMuYT3Os11anWXq+hb2ik2w00+ESjwUcpNcrAeBIOrDGfoWSN+VjbfQcLHvMJXn1hqhMO+oc9GEMg4QA0+ESjwUcpNYp/W4DcTEfMc0ey3ZIz5hNuu+/gzeSCB/ynutst+Llpyyc2DT5KqVF6rS/RvHiCjz153W4er8HtNYHWl5/NJoj45gAFb/cw1d1u/ufmn2QKGnyiiSv4iMgaEdkvIo0icl+Y9zNF5Cnr/U0iUh303v3W8f0iclOsMkWkxirjgFVmRqw6rPfni0iviPzteB+CUmrExFo+iQ8+/q6+zDDdg3YR3F4zapWDqc526x20nluGBp94xAw+ImIHHgJuBuqB20SkPuS0O4FOY0wt8CDwgHVtPbAOWAqsAX4oIvYYZT4APGiMqQM6rbIj1hHkQeCleD+4Uiq8QMsnI47g45/nk4BMslD+rr7QMR/wjft4gsZ88jMdUx58goN2ltNGht2mwSeKeFo+q4BGY8whY8wQsB5YG3LOWuAx6/UzwPUiItbx9cYYlzHmMNBolRe2TOua66wysMq8JUYdiMgtwCFgT/wfXSkVTp+1G2duZvzZbkNJWFw0WsvHYRM8QWM+eVkOhtzeKZ30GdztJiK6ykEM8QSfCuB40M9N1rGw5xhj3EA3UBLl2kjHS4Auq4zQusLWISK5wNeBf4r2IUTkLhFpEJGG1tbWGB9ZqfTVN+Qmy2kbM38mnJGWT+KDj7+rL1LLJ7jbLT/L14qbytZPb0h3ZWG2Q1s+UcQTfCTMsdB/PkQ6Z7KOR6vjn/B10/WGeX/kRGMeNsasNMasLCsri3aqUmmt1+WOK9kAglc4SF7wyQwzH8m32vZIS8f/eaYy3Xqk2813P7qydXTx/A1rAqqCfq4EmiOc0yQiDqAQ6IhxbbjjbUCRiDis1k3w+ZHquAS4VUS+CxQBXhEZNMb8II7PppQK4V8cMx6ZSVxex19npmPsv6EzHL7g4892y8/yJQBMZbp1r9VdmZ/pq6sw20lrr2vK6pvu4mn5bAHqrCy0DHwJBBtCztkA3GG9vhV4zfjWWN8ArLMy1WqAOmBzpDKtazZaZWCV+Wy0Oowxq40x1caYauDfgf9PA49SE+ffEC0eqZDtFjrPB0b2GQokHFjdblOZbt3rGsZuk8DkXG35RBfzb5gxxi0i9wKvAHbgUWPMHhH5FtBgjNkAPAI8LiKN+Foj66xr94jI08B7gBu4xxjjAQhXplXl14H1IvJtYJtVNpHqUEpNrnF1u/nn+SRxzCdcy8dpF4bc3oSO+fS5PORm2LHyoHzBp1+DTyRx/Q0zxrwIvBhy7JtBrweBj0e49jvAd+Ip0zp+CF82XOjxiHUEnfOP0d5XSsXW5/JQmpcR17kOuw2HTRjyJL7bbShq8PFtcpfIMZ+eQXegew8IbKvg9RpstnBD1ulNVzhQSo0ynjEf8H35J7flMzbhIDNkzCfPGocZGHaPOXey9LqGR6WnF2Q7McYXlNRYGnyUUqOMp9sNfNlmgymWcBCa7RbodhuauiDZ5/KMem66ykF0GnyUUqP4uo/iDz7ZTvuUfqlHEivhwDfm4zsnLwFjPj0uN3kh3W4AZ3RDubA0+CilAlxuDwPDHopy4hvzAcjJsNM/lPiuJVe04OOwMeQxgW63goQkHLjJC+l2A235RKLBRykV4P+i9H9xxiMnw07fFK8YHY4/eSDHObaVlmG3MewOTjiwxnymMEj2Do7urizK8dXZ2T80ZXVOZxp8lFIB/tTgonEFH8eUfqlH4m/FZGeMTTjIcMjohIMEjPn4xspGnltpXiYA7b0afMLR4KOUCvC3fArHEXxyM+2BxUgTqc/lxmmXyGM+Hi8e70g6dobdNmXdbv4ttIO73WblZGATaO3RVQ7C0eCjlAro8rd8cuIPPtkZU79dQTj9Qx6yw6zrBla2m3uk5eOwVh6YqhZa8BbafnabUJybSZsusROWBh+lVMCEWj4Z9sCimonUP+QmJ8IyQBlWwoF/zMduE3IyHFO2vE6k51aal6HBJwINPkqpgK4JBJ/sDPuUrhwQSf+Qh5wIew5lWPN8/MvrOGw2inKcgc832Tr7fOM6oVmCZfmZtOqYT1gafJRSAd0Dw4gwapmYWHIzHPQNufGtC5w4A0MecsIkG8DI2m6Blo9dfMFnijLP/C2fWSHdlWV5mbTpmE9YGnyUUgHd/UPkZzqwj2MtsuwMO16T+JWt+4bcYdOsYWSFg+Axn+LcDDr6pib4+NOpZ+WObvmU5vvGfBIdmKcDDT5KqYDugWEKx5FsAL4xH5ja7QrCGYjW7eaw4fYahq0VDuw2oSgnI5BQMdk6IyRqlOZl4HJ76UnCmFiq0+CjlApo7xuiODdzXNf4B/0TnXTQF7XbzffV5t88zmETinMy6Owfwuud/FZIl3/MJzuk5WPN9dGut7E0+CilAlp7XJTljTP4WK2PRKdb+8Z8ImS7WcHH3xrztXyceKdolemugWFyM+xj5hzNKcwC4GT34KTXOd1p8FFKBbT1DlGWH/+6bkBg19NEt3x6Xe5Al18op903ZuVv+ditMR+AjilIOmjvdVEcZg+kyqIcAE50Dkx6ndOdBh+lFAAer6GjzxXoKoqXf+maRO5b4/UazgwOR0wJz7Qmn/ZZk0rtNgkkA3T0TX4X2Kkzg8wpyBpzfE5hFiLQ1KXBJ5QGH6UU4MvY8hrGHXwKshK/dUCPy40xkRdA9a980GsFRIfNRnm+73OdPjMFwad7kNlhgk+Gw8bs/Cxt+YShwUcpBRCYiT/e4JOMTdPOxFh9O8sffKyuQJvAvMJsAJon2ArxeA3/+cdDvLbv9KjjxpiILR+AilnZE65zJtPgo5QCoK3HNxZSGmbsIppkBJ9YywD5V7ruGXTjsAkivoSDLKdtwoP/v91ziu+8uJfP/byBU0FldA8MMzjsDSQXhKooyuaEBp8xNPgopYCglk/++Fo+WU7fitHJaPlEDD5BLR//hFkRYV5h9qjAMR6/29sSeP3Y20cCr4+09wMwvzgn7HUVs7I52T0QWG1B+WjwUUoBI0v/j7fbTUQoyHZwZiBxCQf+8aWCCMsABQcfR9BqDXMKs2junlgr5GBrL1fWlrJm6RzWbz4WyKRrbOkFYFF5XtjrFhTnMOwx2vUWQoOPUgqA5u4BcjPsgS2nx6Mg2xlojSRCoNstwmoM2Rm+r7a+oJYPwLyi7AkP/jd1DlA5K5vPXL6Azv5hnt95EoADLT047RKx5eMPSo2tvROqd6bS4KOUAnwD8fOKshGJf103v8JsZ0qN+fgTDnoG3TjsI19zNaW5tPS46BlnZt7gsIe2XheVs7K5bGEJdeV5PPanIxhj2Hqkk/p5hYFVFULVlvmCz8EWDT7BNPgopQBo7hpkXlH2hK5NdPBp6x0iy2mLOMnUv/KBy+0d1fKptVohB1v7xlVfk9VaqpjlC86fubyaXSe6eX1/Kzuaurh0YXHEa2flZlCcm8FBbfmMosFHKQWMtHwmoiDLmdB5Pq09LsryMyO20oJ3OM0IapH4g0/jOFsh/my1ylm+rrU/v6CC/EwHn/35FoY9hltWVES9flFZLgdbxhfwZjoNPkopBoc9tPcNMS9CunAss3KcgQ3VEqG1J/pKDJlBa6wFr7e2oDgHp13GHXyaOn0ZbZWzfME5N9PBv3x8OYvKcvnqDedw7tyCqNfXlufR2NqrWysEiSv4iMgaEdkvIo0icl+Y9zNF5Cnr/U0iUh303v3W8f0iclOsMkWkxirjgFVmRrQ6RGSViGy3/uwQkT+b6MNQKl35575MtOVTmpfJmUE3LndiFheNtQCqzSZkOX1fb8GByGG3UVuez57m7nHV19Q5gNMulOePBOc1y+by+69ew5eur4t5/eLZ+XT0DU3J6grTVczgIyJ24CHgZqAeuE1E6kNOuxPoNMbUAg8CD1jX1gPrgKXAGuCHImKPUeYDwIPGmDqg0yo7Yh3AbmClMWaFVcdPRGT86TpKpTF/BthEg0+ZNTeoLUFbRrf2ugJ1RuLvegtdafqC+UVsP941rq0Vmjp9XZLj2WQv2HmVhQDsOjG+oDeTxdPyWQU0GmMOGWOGgPXA2pBz1gKPWa+fAa4XX2fsWmC9McZljDkMNFrlhS3TuuY6qwysMm+JVocxpt8Y459gkAVou1apcTrS7huPqC4Nny4ciz8QtCZg35phj5eOvqGY85ECwSckC+3C+bPoGXSPKwGgqbOfigkGZoD6uYXYBHY1dcU893BbX2AO0UwWT/CpAI4H/dxkHQt7jhUIuoGSKNdGOl4CdAUFk+C6ItWBiFwiInuAXcDdQdcHiMhdItIgIg2tra1xfGyl0sfhtj6ynXZm509szCeRwedkl6+LMFYwyLcmoIa2fC6cXwTAliOdcdd5wprjM1HZGXZqy/NitnyOtPVx/b+9zpef2j7huqaLeIJPuHZmaOsi0jmTdTzqfRhjNhljlgIXA/eLyJjfIGPMw8aYlcaYlWVlZWGKUip9HW7ro7o0F9sEu5USGXyOdfgG/6siTOr0809AzQwJPjWluVQUZfPavpZwl40xOOyhpccVyHSbqGUVhew6cSZq0sHWo514Dby0+1RgC/CZKp7g0wRUBf1cCTRHOscabykEOqJcG+l4G1AUNGYTXFekOgKMMXuBPmBZHJ9LKWU53NbHwtLcCV9fkpu44HPcyjybXxI9GBRlh2/5iAgfOLecNxtb4+reag6kWU+85QNwQVURbb2uwJyhcE6dGVl3brwZedNNPMFnC1BnZaFl4Esg2BByzgbgDuv1rcBrxhfeNwDrrEy1GqAO2BypTOuajVYZWGU+G60OqwwHgIgsABYDR+J+AkqluWGPl2Md/Swsm3jwyXDYmJXjpKVn6reLPtbRj9MuEbcw8POvfhA858fv+nNnMzjs5fX9sVs/gQmmZzHmA7CqpgSAdw61RzwneP2390/3nFV9qS5m8LHGT+4FXgH2Ak8bY/aIyLdE5KPWaY8AJSLSCHwFuM+6dg/wNPAe8DJwjzHGE6lMq6yvA1+xyiqxyo5YB3AlsENEtgO/Bv7aGNM2scehVPo52t6Px2uoLpl48AGYW5iYfWuOtfsG/2NlnhVZ3W7h9vy5fFEJswsyWb/l+Jj3xtTXEV9LK5a68jyKcpxsOtwR8ZxT3YPUlefhtAv7Ts3s4BNXSrIx5kXgxZBj3wx6PQh8PMK13wG+E0+Z1vFD+LLhQo+HrcMY8zjweMwPoZQKa+/JMwAsmZt/VuXML87hQMvUf2HuO3WGc2bHvld/yyc02w18830+ubKK/7OxkeMd/VHHj4539JNht004GcPPZhNWVRezOUrwae4eZH5xDiLw/gwPPrrCgVJp7r2TZ3Dahbrysww+JTk0dQ6Ma/7MeA0Oezjc1seSGCsKwEgShMsdfuD+tkvm47TZeGhjY9RyjnX0U1mcPeFkjGCXLCzhWEd/xBbiqe4B5hRmUTc7f8avgq3BR6k0917zGWrL88cMzI9XVXEOLreX1t6pSzo4cLoXr4Fz58QOlGuWzuWmpbP5q9U1Yd+fW5jNbauqeGZrE0faIq+7dryzn6qzzHTzu2yhb9znzcaxIwMDQx46+4eZV5RNbVkexzr6Z/R8Hw0+SqW5906eoT6OlkQsVVY2mH+MZCrssCZp1s+Lfb+FOU5+8umVLIgylnXPtbVkOe1849ndYVOgPV7DwZY+as4iEzDYuXPzmV2QGTbRwZ/pNqcgi7rZeRjDjF4JW4OPUmms5cwgrT2uuL7MY/F/yUdrRZytTYc7mF2QGXHjtvEqL8jia2sW88aBNn7Z0DTm/SPtfQwMe1g6Cc8HfGne1y4u543328bM4zlpdelKeoAAAB3SSURBVMXNLcoKdIHO5HRrDT5KpbGtR32z/P2z/s/G/OIcspy2KcvSMsaw+XA7l9SUTGjDu0huv2QBly0s4ZsbdgeSL/x2WysSTEZw9rtmcTk9Lnfg2fv5F3edW5hNdWkOdptw4LQGH6XUDLTlSCdZThtL5xWedVl2m7B4dv6YL/DJsvdkD6fPuLh8UcmklmuzCd+/7QIKs518/r8aONU9MlfpzQNtFGQ5WBxHdl28rqgtwWkXNoZ0vZ3stlo+hVlkOuwsKElM9mCyaPBRKo01HO1gRVXRWScb+J07t4C9J6MvITNRL+0+iU3ghvrZk152WX4m//mZlXT1D/Op/3yH4x399A+5eXXvaa5eXD5qK+6zlZ/l5OLqYn733ulRz6m5e5BZOc7AFuB15Xna7aaUmnn6XG72NJ/h4urIW0CPV/28Ajr7h2nuntyVDrxew4YdzVy6sISSGKtZT9TyyiJ+9tmLae1x8cHvv8EnfvI2Xf3D/OXlCya9rpuXzeFgax/vB3WrnewaYG7hyCoKdeX5HGnvZyhCqvh0p8FHqTT19sF2PF4TSP+dDBctmAXA5sORl5CZiI37Wzja3s9tq+ZParmhLq4u5oW/Wc3li0rod3n457VLuWjB5AVnv5uWzcEm8MKuk4FjJ7oGqAhaP65udh4erwlsdzHTaPBRKk29tr+F3Aw7Kyex5XPunAIKs528fXDygo8xhoc2NjKnIIs1y+ZMWrmRzC/J4SefXslrf3sNn76sekrqKM/PYlVNMS/sbMYYgzGGE50Do9aPqy3PA5ixSQcafJRKQ8YYXt/XwpV1pZM23gO+wftLaop5q7F90sZ9nt95knePdfHlG+pwTuLYS7J9aPk8Drb2sf90D90Dw/QNeUatnL2oLA8RZmzSwcz5P6mUitvekz00dw9yzeLySS/7hvrZnOgaYNvx2Lt2xnKqe5BvPrubZRUF3HpRVewLppGbl83BYRN+2dAUSCwInsya5bRTNSuHAzM06UCDj1Jp6LmdzdhtMiWZYzctm0OGw8az206cVTm9LjdfeGIrLreX/1h3QcxVrKeb0rxMbj5vLk83HGfzEd9io6Ep73XleTRqt5tSaibweg0btjezuq6U0inIHCvIcnLzsjk8s7WJ7v7hCZXR2TfEZ3+2md0nuvmPdRewqCxvku8yNfzl5dX0DLr57sv7qSrOZnbB6P8fdbPzOdTWi8s989Z40+CjVJppONrJia4B1q6YN2V13H31IvqGPPzoDwfHfe2O412sfegtdhzv5j/WrZiS1lmquGjBLP7iEl8G3+dXLxyzcsN5FYUMewz7Z+D2CnHt56OUmjn+e9NR8jMd3Fg/dZlj584t4NaLKvnPNw5xQ315XOnK7b0uHtp4kJ//6TDl+Vk8edelgdTtmezbtyzja2uWBPYfCra80tcNt6Opm+WVZ78EUirR4KNUGmk5M8gLO0/ymcuqyc2c2l//b3y4ni1HOvjsz7bww7+4iCvrSsOet+/UGZ7e0sT6LccYGPbwqVXz+frNSyjIGvtlPBOJSNjAA1A5K5tZOU52Hu+CSyd/smsyafBRKo08sekYHmP4zGVT/0VWmO3kiTsv4bM/38Ltj2zi8kUlrK4royQvg55BNwdbe3nnUDuHWvtw2IQPLZ/Ll66rpfYsN7WbSUSE5ZVF7LIWOJ1JNPgolSa6+4f52VuHueHc2VRP0v40sVQV5/DcvVfy6FuH+WXDcR54eV/gvfxMBxdVz+Izly7gI+fPm7Jlc6a75ZWFPLSxlf4hNzkZM+cre+Z8EqVUVD998xA9g26+fMM5Ca03O8POPdfWcs+1tXQPDHNmYJj8LAeF2c5J3RphplpeWYTX+HacnczVKJJNs92USgMtZwZ59M3DfOi8uZw7CbuWTlRhtpOq4hyKcjI08MTpfCvpYPskTNpNJRp8lEoD335hL8New9/etDjZt6LGqbwgi6ribBqOdMY+eRrR4KPUDPdWYxsbdjTzxasXjVq+RU0fKxcU03C0Y0r2SUoWDT5KzWDdA8N87ZmdVJfk8MVrFiX7dtQErayeRVvvEEfb+5N9K5NGg49SM5Qxhr//zW5Onxnk39ddENghU00//g3/tlhrwM0EGnyUmqH+e9MxntvRzJdvOIcVVTNrdny6qS3LoyDLwdajM2fcR4OPUjPQW41t/MOGPVy7uIy7r9butunOZhNWVhenX8tHRNaIyH4RaRSR+8K8nykiT1nvbxKR6qD37reO7xeRm2KVKSI1VhkHrDIzotUhIjeIyFYR2WX997qJPgylZoIDp3v44hNbWVSWy/dvm3lbEaSrldWzONjaR0ffULJvZVLEDD4iYgceAm4G6oHbRKQ+5LQ7gU5jTC3wIPCAdW09sA5YCqwBfigi9hhlPgA8aIypAzqtsiPWAbQBHzHGnAfcATw+vkeg1MxxuK2PT/10E5lOO4/ccTH5abI+WjpYaS3OOlO63uJp+awCGo0xh4wxQ8B6YG3IOWuBx6zXzwDXi28G2VpgvTHGZYw5DDRa5YUt07rmOqsMrDJviVaHMWabMabZOr4HyBIRXadDpZ3jHf186j/fweM1/OKvLqGqOCfZt6Qm0fLKQjLsthnT9RZP8KkAjgf93GQdC3uOMcYNdAMlUa6NdLwE6LLKCK0rUh3BPgZsM8a4Qj+EiNwlIg0i0tDa2hrjIys1vew9eYaP/ehP9A95eOLOS6ibrYtzzjRZTjvLKwvTKviE6zAOnekU6ZzJOh7zPkRkKb6uuC+EOQ9jzMPGmJXGmJVlZWXhTlFqWnr7YDuf+PHb2G3CL+++jPp5yVs+R02tldXF7D7RzcDQ9N/ZNJ7g0wRUBf1cCTRHOkdEHEAh0BHl2kjH24Aiq4zQuiLVgYhUAr8GPmOMGf/WiUpNU083HOeORzczuzCL//ni5ZyjLZ4ZbVXNLIY9Zkas8xZP8NkC1FlZaBn4Egg2hJyzAd9gP8CtwGvGtw7EBmCdlalWA9QBmyOVaV2z0SoDq8xno9UhIkXAC8D9xpi3xvPhlZquhtxevvGb3XztmZ1cXDOLZ+6+jHlF2cm+LTXFLppfjAg0zICut5hbKhhj3CJyL/AKYAceNcbsEZFvAQ3GmA3AI8DjItKIrzWyzrp2j4g8DbwHuIF7jDEegHBlWlV+HVgvIt8GtlllE6kO4F6gFviGiHzDOnajMaZlYo9EqdTW3DXA3zy5jYajnXzhqoX8PzctxmHXKXvpoDDHyeLZ+WyeAcFHZtJCdfFauXKlaWhoSPZtKDVuL+w8yf2/2onba3jgY8v5yPnzkn1LKsH+/je7+PW7J9jxDzcm/B8dIrLVGLNyMsrSfy4pNQ30utx89ekd3POLd6kpy+PFv1mtgSdNXVxdTN+Qh32nepJ9K2dFdzJVKsX98f1W/t9f7/J1t11Xy5eur8Op3Wxpy7/I6ObDHSyrKEzy3Uyc/g1WKkV19A3xlae285lHN5Nht/H0Fy7jKzcu1sCT5uYVZVNRlE3D0ek97qMtH6VSjDGGZ7c3863n3+PMwDBfuq6We66t1S0RVMDF1bN4s7EdY8y03Y5cg49SKWTvyTP803N7eOdQByuqivjfHzuPJXN00qga7eKaYn6zvZmj7f1UT9PdaTX4KJUCOvuG+N6r7/Pfm45SkO3kn29ZxqdWzdcVqVVYgXGfIx0afJRS4+f2ePnF5mP822/fp9fl5tOXLuDLN5xDUU5Gsm9NpbDasjyKcpw0HOngEyurYl+QgjT4KJUExhhe2XOaf/3tfhpberl8UQn/8JGlLJ6jy+Oo2Gw2YeWCYrYcmb7bK2jwUSrB3jnUzgMv72PbsS4WluXy49sv4qals6ftwLFKjourZ/G7vadp7XFRlj/9dpHR4KNUguw9eYbvvryPjftbmVOQxf/+8/O49aJKXRpHTcjFNb5xn4YjHdx83twk3834afBRaoodbO3l//z+AM/uaCY/08F9Ny/hLy+v1tRpdVaWzSsky2ljy5FODT5KqRGNLb384LUDbNjRTKbDzheuWsQXr15EYY5uba3OXobDxoqqomm7uZwGH6UmWWNLD9//fSPP7Wwmy2Hn86sX8vmrFlKaN/365VVqu7i6mIc2NtLrcpOXOb2+zqfX3SqVwg6c7uH7rzXy/M5msp2+ls7nV9dQokFHTZGLq4vxGth2rJPVddNrh2YNPkqdpXePdfLj1w/y6t7T5Djt3H31Ij6/eiHFuTpXR02tC+YXYRPYcrhDg49S6cAYw+v7W/nRHw6y+XAHhdlOvnRtLZ+9ooZZGnRUguRnOamfVzAt5/to8FFqHIY9Xp7f2cxP/nCIfad6mFeYxTc+XM+6i6vInWZ97mpmWLmgmPVbjjHs8U6rFc/1t0WpOPQPuVm/+TiPvHmYE10DnDM7j3/7+Pl8dMW8afULr2aeC+YX8fM/HeH90z0snTd99vfR4KNUFCe6BvivPx3hyc3HODPoZlV1Mf98y1KuOaccmy76qVLAiqoiALYf79Lgo9R0Zoxh69FOHn3rMK/sOQ3AmmVz+NwVNVy0YFaS706p0eYX5zArx8mO4138xSULkn07cdPgo5RlyO3lxV0nefStw+xs6qYw28lfra7hM5dVU1GUnezbUyosEeH8qiK2H+9K9q2MiwYflfbae138YtMxHn/nKC09LhaV5fLtW5bx5xdWkJOhvyIq9a2oKuIP77dOq8mm0+MulZpkxhjePdbJE+8c44VdJxlye7lmcRn/ckUNq2tLdTxHTSsrqoowBnY2dXH5otJk305cNPiotNLrcvObbSd44p2j7DvVQ36mg9suruLTl1VTW56X7NtTakLOrxxJOtDgo1QK2XfqDE+8c5Rfv3uCviEPS+cV8L/+/Dw+ev48nZ+jpr1ZuRlUl+SwYxqN++hvnZqxXG4PL+06xRPvHKXhaCcZDhsfXj6XT1+6gBVVRbp5m5pRzq8q4p1D7cm+jbhp8FEzzvune3hqy3F+ve0EHX1DVJfk8HcfPJdbL6rUpW/UjLWiqohntzdzqnuQOYVZyb6dmOKami0ia0Rkv4g0ish9Yd7PFJGnrPc3iUh10Hv3W8f3i8hNscoUkRqrjANWmRnR6hCREhHZKCK9IvKDiT4INb31utys33yMWx56ixsf/CP/9fYRLqkp5vE7V/HaV6/h81ct1MCjZrSRyabTY523mC0fEbEDDwE3AE3AFhHZYIx5L+i0O4FOY0ytiKwDHgA+KSL1wDpgKTAP+J2InGNdE6nMB4AHjTHrReTHVtk/ilQHMAh8A1hm/VFpwj8Z9Kktx3lh10n6hzzUlufxdx88lz+7sEL3z1Fp5dy5BTjtwrbjXaxZlvo7m8bT7bYKaDTGHAIQkfXAWiA4+KwF/tF6/QzwA/F1qK8F1htjXMBhEWm0yiNcmSKyF7gO+JR1zmNWuT+KVIcxpg94U0Rqx/G51TTW1uviV+828dSW4xxs7SMnw85Hls/jExdXceF8HctR6SnLaWfJnAJ2n+hO9q3EJZ7gUwEcD/q5Cbgk0jnGGLeIdAMl1vF3Qq6tsF6HK7ME6DLGuMOcH6mOtjg+g5rmhtxeNu5v4VfvNvH7vS24vYYL5xfx3Y8t50PL52rGmlLAsooCXtx1CmNMyv8jLJ7f2HCfwMR5TqTj4caaop0f731EJCJ3AXcBzJ8/P97LVBIZY9h+vItfvXuC53Y209U/TGleBp+9oppPrKyibnZ+sm9RqZSyrKKQJzcfp6lzgKrinGTfTlTxBJ8moCro50qgOcI5TSLiAAqBjhjXhjveBhSJiMNq/QSfH6mOuBhjHgYeBli5cmXcQUsl3vGOfn6z7QS/3naCQ219ZDps3FA/m49dWMnqulIcuoWBUmEts1a13n2ie0YEny1AnYjUACfwJRB8KuScDcAdwNvArcBrxhgjIhuAX4jI9/AlHNQBm/G1YsaUaV2z0SpjvVXms9HqmNjHVqmmZ3CYl3ad4n/ebWLTYd+/KS6pKeYLVy/k5vPmUpDlTPIdKpX6Fs/Jx2ETdp3o5ubzUjvpIGbwscZX7gVeAezAo8aYPSLyLaDBGLMBeAR43Eoo6MAXTLDOexpfcoIbuMcY4wEIV6ZV5deB9SLybWCbVTaR6rDKOgIUABkicgtwY0g2nkpBQ24vbxxo5Tfbm/ntnlO43F5qSnP56g3ncMsFFSn/LzelUk2W007d7Hx2N59J9q3EJOnYeFi5cqVpaGhI9m2kJY/XsOlQOxt2NPPS7lN0DwxTlOPkI8vn8ecXVujKA0qdpa89s4Pf7W1h699/YNJ/l0RkqzFm5WSUpSlCasr5VpDu4rkdzbyw6yStPS5yM+zcuHQOHzl/LlfWlpHh0HEcpSbDsopCnm5o4mT3IPNSeB8qDT5qShhjeO/kGZ7bcZLndjRzomuADIeN6xaX89EV87h2cTnZGfZk36ZSM86yCl/Swa4T3Rp8VPpobOnl+Z3NPLejmYOtfdhtwuq6Ur5ywzncuHQ2+Zo4oNSUOndOATaBPSe6uWnpnGTfTkQafNRZMcaw/3QPL+06xUu7T/L+6V5EYFV1MZ+7soabl82lWNdUUyphsjPs1JXnsyvFVzrQ4KPGzRjDnuYzvLT7JC/tOsWhtj5E4OLqYv7xI/WsWTZ3Wqyqq9RMtbSigDcOpPbiLxp8VFz8qw28vPsUL+4+yfGOAew24dKFvhbOjUtnU56vAUepVHBeRSG/evcEp88MMrsgNX8vNfioiLxew9Zjnby06xQv7z5Jc/cgTrtw+aJS7r22lhvq52iXmlIpyJ90sPtEtwYfNT0MDnt480Abr753mt/vO01b7xAZDhtX1ZXy1RsX84FzZ1OYo0kDSqWy+rkFiPgy3q4/d3aybycsDT6Kjr4hXtvXwm/3nOKNA20MDHvIy3RwzeIybqifzXVLyjVLTalpJDfTwcLS3JTeXkGDT5o62t7Hq++d5rfvnabhSAdeA3MKsrj1okpuqJ/NpQtLdOKnUtPYeRWFvHMo7rWXE06DT5rweg07T3Tz6nunePW907x/uheAJXPyA+M3yyoKdGkbpWaIZRWF/GZ7M609LsryU29XXw0+M9iZwWHePNDGxn0tvP5+K609Luw2YVV1Md/88HxuqJ+ti3cqNUMFJx1cu6Q8yXczlgafGcQYQ2NLL6/ta2Hj/hYajnTi9hoKshxcvbic65aUce3icopyNENNqZlu6bwCwJd0oMFHTbqBIQ9vH2pj475WNu5voalzAPB1p9111UKuXVLOBVVFugGbUmkmP8uZ0kkHGnymoaPtffzh/VZe29fC2wfbcbm9ZDvtXFFbyl9fU8u1S8qYW5i6CwoqpRJjWUUhDUdSM+lAg8800D0wzNsH2/jjgTbeONDK8Q5f66a6JIdPXTKfaxeXc8nCYjIdukq0UmrEsooCNuxopr3XRUleaiUdaPBJQcMeLzuOdwWCzY7jXXgN5GbYuWxRKZ9fvZDVdWXUlOYm+1aVUikseHuFaxan1riPBp8UYIzhSHs/bxxo5Y0Dbbx9sJ1elxubwPlVRdx7bS2rzyljRVURTh27UUrFyR989jSf0eCjfLr7h3nrYBtvWK0bf6JAVXE2H10xj9W1pVy+qFSXslFKTVhBlpPqkhx2NaVe0oEGnwQZcnt591gnbx5o443GNnY1+brS8jMdXF5bwheuXsRVdaUsKNGuNKXU5FlaUcj2Y13Jvo0xNPhMEWMMB1t7+eP7bbzZ2MY7h9rpH/Jgtwkrqor40nV1XHVOKedXahq0UmrqnFdRyAs7T9LZN8SsFFqFXoPPJHJ7vDQc7bTWTDsVyEqrKc3lYxdWsrqulEsXlVCgi3QqpRLkvKCkg6vOKUvy3YzQ4DMJjrb3sX7LcZ7Z2kRrj4sMh40rFpVw99WLuKquTJewUUolzbJ51jI7zRp8ZoyT3QP8yyv7+c22E4gI1y4u488uqOTqxWXkZeqjVUolX2GOk6ri7JRb6UC/ISfo1fdO89Wnt+Nye7nzyhr+avXClN0xUCmV3s6rKGSXBp/p75U9p/jr/36XpfMK+P66C6jWyZ5KqRR2XkURL+46lVIrHWia1TidPjPI3/5yB8vmFfDk5y/VwKOUSnmramYBsOlw6qzzpsFnnB558zD9Qx4e/OQKcnVcRyk1DSyvLCInw87bB9uTfSsBcQUfEVkjIvtFpFFE7gvzfqaIPGW9v0lEqoPeu986vl9EbopVpojUWGUcsMrMmGgdk80Yw/M7mrluSTkLy/KmqhqllJpUTruNi6uLefvQNAo+ImIHHgJuBuqB20SkPuS0O4FOY0wt8CDwgHVtPbAOWAqsAX4oIvYYZT4APGiMqQM6rbLHXcd4H0Q8WntcNHcPcvmikqkoXimlpsyVtaU0tvRyqLU32bcCxNfyWQU0GmMOGWOGgPXA2pBz1gKPWa+fAa4XEbGOrzfGuIwxh4FGq7ywZVrXXGeVgVXmLROsY9K19roAmFuoWW1Kqell7Yp5OGzCg787kOxbAeLLdqsAjgf93ARcEukcY4xbRLqBEuv4OyHXVlivw5VZAnQZY9xhzp9IHQEichdwl/Vjr4i0A20RP3UUNz8wkatSWikTfBYzkD4LH30OI2bUs/gB8INPTejSUmDBZN1HPMFHwhwzcZ4T6Xi4Fle08ydSx+gDxjwMPOz/WUQajDErw1ybdvRZjNBn4aPPYYQ+Cx/rOVRPVnnxdLs1AVVBP1cCzZHOEREHUAh0RLk20vE2oMgqI7Su8dahlFIqRcUTfLYAdVYWWga+wf0NIedsAO6wXt8KvGaMMdbxdVamWg1QB2yOVKZ1zUarDKwyn51gHUoppVJUzG43a3zlXuAVwA48aozZIyLfAhqMMRuAR4DHRaQRX2tknXXtHhF5GngPcAP3GGM8AOHKtKr8OrBeRL4NbLPKZiJ1xPBw7FPShj6LEfosfPQ5jNBn4TOpz0F8jQellFIqcXSFA6WUUgmnwUcppVTCpWXwibVc0EwgIo+KSIuI7A46Viwir1pLF70qIrOs4yIi37eex04RuTDomjus8w+IyB3h6kplIlIlIhtFZK+I7BGR/8s6nlbPQkSyRGSziOywnsM/WcdTdjmrqWattrJNRJ63fk7LZyEiR0Rkl4hsF5EG69jU/34YY9LqD74Eh4PAQiAD2AHUJ/u+puBzXgVcCOwOOvZd4D7r9X3AA9brDwIv4ZszdSmwyTpeDByy/jvLej0r2Z9tnM9hLnCh9TofeB/fkk5p9Sysz5NnvXYCm6zP9zSwzjr+Y+CL1uu/Bn5svV4HPGW9rrd+ZzKBGut3yZ7szzfBZ/IV4BfA89bPafksgCNAacixKf/9SMeWTzzLBU17xpg/4ssKDBa8RFHo0kX/ZXzewTfXai5wE/CqMabDGNMJvIpv/bxpwxhz0hjzrvW6B9iLbwWMtHoW1ufxL+rltP4YUng5q6kkIpXAh4CfWj+n9NJeSTDlvx/pGHzCLRc0ZjmeGWq2MeYk+L6UgXLreKRnMqOeldVdcgG+f/Wn3bOwupm2Ay34vhwOEudyVkDwclbT+jlY/h34GuC1fo57aS9m3rMwwG9FZKv4liGDBPx+pOOGNHEtx5NmzmrpoulARPKA/wH+b2PMGd8/XMOfGubYjHgWxjf/bYWIFAG/Bs4Nd5r13xn7HETkw0CLMWariFzjPxzm1Bn/LCxXGGOaRaQceFVE9kU5d9KeRTq2fNJ5OZ7TVhMZ678t1vHxLoM0rYiIE1/g+W9jzK+sw2n5LACMMV3A6/j67NNxOasrgI+KyBF83e7X4WsJpeOzwBjTbP23Bd8/SlaRgN+PdAw+8SwXNFMFL1EUunTRZ6xMlkuBbqup/Qpwo4jMsrJdbrSOTRtW3/wjwF5jzPeC3kqrZyEiZVaLBxHJBj6Ab/wr7ZazMsbcb4ypNL5FMtfh+2x/QRo+CxHJFZF8/2t8f693k4jfj2RnWiTjD76Mjffx9Xn/XbLvZ4o+45PASWAY379K7sTXT/174ID132LrXMG3ud9BYBewMqicz+EbSG0EPpvszzWB53Alvub/TmC79eeD6fYsgOX4lqvaaX25fNM6vhDfF2Yj8Esg0zqeZf3caL2/MKisv7Oez37g5mR/trN8Ltcwku2Wds/C+sw7rD97/N+Hifj90OV1lFJKJVw6drsppZRKMg0+SimlEk6Dj1JKqYTT4KOUUirhNPgopZRKOA0+SimlEk6Dj1JKqYT7/wHW5Ys9hK7QVQAAAABJRU5ErkJggg==\n", 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\n", "text/plain": [ "
" ] @@ -660,7 +680,7 @@ "plt.legend()\n", "plt.ylim(0.0, 6e-6)\n", "# plt.yscale('log')\n", - "# plt.xlim(3080, 3110)\n", + "# plt.xlim(770, 785)\n", "plt.savefig('test.png')\n", "# print(jpsi_width)" ] @@ -704,7 +724,7 @@ "outputs": [], "source": [ "# total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (4000, 4400), norm_range=False)\n", + "# inte = total_f.integrate(limits = (1000, 1040), norm_range=False)\n", "# inte_fl = zfit.run(inte)\n", "# print(inte_fl)\n", "# # print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" @@ -712,7 +732,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": null, "metadata": {}, "outputs": [], "source": [ @@ -724,7 +744,7 @@ "# # print(\"Increase am by factor:\", np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "# # print(\"New amp:\", pdg[\"psi2s\"][3]*np.sqrt(pdg[\"psi2s_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", "\n", - "# name = \"p4160\"\n", + "# name = \"phi\"\n", "\n", "# print(name+\":\", inte_fl)\n", "# print(\"Increase am by factor:\", np.sqrt(pdg[name+\"_BR\"]/pdg[\"NR_BR\"]*pdg[\"NR_auc\"]/inte_fl))\n", diff --git a/test.png b/test.png index 3b76a79..ab5aa70 100644 --- a/test.png +++ b/test.png Binary files differ