diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 4ebcc77..5441b6f 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -94,7 +94,7 @@ "metadata": {}, "outputs": [], "source": [ - "def formfactor( q2, subscript, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2): #returns real value\n", + "def formfactor(q2, subscript, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2): #returns real value\n", " #check if subscript is viable\n", "\n", " if subscript != \"0\" and subscript != \"+\" and subscript != \"T\":\n", @@ -205,9 +205,9 @@ "\n", " #Some helperfunctions\n", "\n", - " beta = ztf.sqrt(tf.abs(1. - 4. * mmu**2. / q2))\n", + " beta = 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", + " 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", @@ -215,7 +215,7 @@ "\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, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2))**2.\n", + " bracket_left = 2./3. * tf.pow(kabs,2) * tf.pow(beta,2) * tf.pow(tf.abs(ztf.to_complex(C10eff)*formfactor(q2, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)),2)\n", "\n", " #middle term in bracket\n", "\n", @@ -223,11 +223,11 @@ "\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\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)), 2)\n", - "\n", + " bracket_middle = _top/_under *tf.pow(tf.abs(ztf.to_complex(C10eff) * formfactor(q2, \"0\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)), 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", + " return prefactor1 * (bracket_left + bracket_middle) * 2 * q\n", "\n", "def vec(q, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2):\n", " \n", @@ -247,33 +247,31 @@ "\n", " #Some helperfunctions\n", "\n", - " beta = ztf.sqrt(tf.abs(1. - 4. * mmu**2. / q2))\n", + " beta = 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", + " \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", + " prefactor2 = tf.pow(kabs,2) * (1. - 1./3. * beta)\n", "\n", - " abs_bracket = tf.abs(c9eff(q, funcs) * formfactor(q2, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2) + tf.complex(2.0 * C7eff * (mb + ms)/(mB + mK), ztf.constant(0.0)) * formfactor(q2, \"T\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2))**2\n", + " abs_bracket = tf.pow(tf.abs(c9eff(q, funcs) * formfactor(q2, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2) + ztf.to_complex(2.0 * C7eff * (mb + ms)/(mB + mK)) * formfactor(q2, \"T\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)),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", + " return prefactor1 * bracket_right * 2 * q\n", "\n", "def c9eff(q, funcs):\n", "\n", - " C9eff_nr = tf.complex(ztf.constant(pdg['C9eff']), ztf.constant(0.0))\n", + " C9eff_nr = ztf.to_complex(ztf.constant(pdg['C9eff']))\n", "\n", - " c9 = C9eff_nr\n", - "\n", - " c9 = c9 + funcs\n", + " c9 = C9eff_nr + funcs\n", "\n", " return c9" ] @@ -379,28 +377,38 @@ " 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", - " phase = self.params['jpsi_phase'], width = self.params['jpsi_width'])\n", - "\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['jpsi_mass'], 2)) * resonance(q, _mass = self.params['jpsi_mass'], \n", + " scale = self.params['jpsi_scale'],\n", + " phase = self.params['jpsi_phase'], \n", + " width = self.params['jpsi_width'])\n", " def psi2s_res(q):\n", - " return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'],\n", - " phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n", - " \n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['psi2s_mass'], 2)) * resonance(q, _mass = self.params['psi2s_mass'], \n", + " scale = self.params['psi2s_scale'],\n", + " phase = self.params['psi2s_phase'], \n", + " width = self.params['psi2s_width'])\n", " def p3770_res(q):\n", - " return resonance(q, _mass = self.params['p3770_mass'], scale = self.params['p3770_scale'],\n", - " phase = self.params['p3770_phase'], width = self.params['p3770_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p3770_mass'], 2)) * resonance(q, _mass = self.params['p3770_mass'], \n", + " scale = self.params['p3770_scale'],\n", + " phase = self.params['p3770_phase'], \n", + " width = self.params['p3770_width'])\n", " \n", " def p4040_res(q):\n", - " return resonance(q, _mass = self.params['p4040_mass'], scale = self.params['p4040_scale'],\n", - " phase = self.params['p4040_phase'], width = self.params['p4040_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4040_mass'], 2)) * resonance(q, _mass = self.params['p4040_mass'], \n", + " scale = self.params['p4040_scale'],\n", + " phase = self.params['p4040_phase'], \n", + " width = self.params['p4040_width'])\n", " \n", " def p4160_res(q):\n", - " return resonance(q, _mass = self.params['p4160_mass'], scale = self.params['p4160_scale'],\n", - " phase = self.params['p4160_phase'], width = self.params['p4160_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4160_mass'], 2)) * resonance(q, _mass = self.params['p4160_mass'], \n", + " scale = self.params['p4160_scale'],\n", + " phase = self.params['p4160_phase'], \n", + " width = self.params['p4160_width'])\n", " \n", " def p4415_res(q):\n", - " return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'],\n", - " phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4415_mass'], 2)) * resonance(q, _mass = self.params['p4415_mass'], \n", + " scale = self.params['p4415_scale'],\n", + " phase = self.params['p4415_phase'], \n", + " width = self.params['p4415_width'])\n", " \n", " def P2_D(q):\n", " Dbar_contrib = ztf.to_complex(self.params['Dbar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['Dbar_phase']))*ztf.to_complex(h_S(self.params['Dbar_mass'], q))\n", @@ -664,9 +672,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bracket left [0.00000000e+00 7.85557385e-03 3.14212789e-02 ... 8.41573104e+04\n", + " 8.23198317e+04 8.04822841e+04]\n" + ] + } + ], "source": [ "# def total_test_tf(xq):\n", "\n", @@ -694,9 +711,9 @@ "\n", "# calcs = zfit.run(total_test_tf(x_part))\n", "\n", - "test_q = np.linspace(x_min, x_max, 200000)\n", + "test_q = np.linspace(x_min, x_max, int(2e6))\n", "\n", - "probs = total_f.pdf(test_q)\n", + "probs = total_f.pdf(test_q, norm_range=False)\n", "\n", "calcs_test = zfit.run(probs)\n", "res_y = zfit.run(jpsi_res(test_q))\n", @@ -710,12 +727,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 47, "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" + ] + }, + { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -735,7 +760,7 @@ "# plt.plot(test_q, fplus_y, label = '+')\n", "# plt.plot(test_q, res_y, label = 'res')\n", "plt.legend()\n", - "plt.ylim(0.0, 6e-6)\n", + "plt.ylim(0.0, 1.5e-6)\n", "# plt.yscale('log')\n", "# plt.xlim(770, 785)\n", "plt.savefig('test.png')\n", @@ -744,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -760,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -776,14 +801,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bracket left [27045942.55237162 18426966.5012879 47801965.39734218 ...\n", + " 26681129.22315679 21816963.88039205 467606.97925681]\n", + "4.442538182878187e-05\n" + ] + } + ], "source": [ - "total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (2000, x_max), norm_range=False)\n", - "# inte_fl = zfit.run(inte)\n", - "# print(inte_fl)\n", + "total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "inte = total_f.integrate(limits = (x_min, x_max), norm_range=False)\n", + "inte_fl = zfit.run(inte)\n", + "print(inte_fl/4500)\n", "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" ] }, @@ -1344,7 +1379,7 @@ " \n", " fcn = -w/d\n", " chisq = -2*fcn\n", - " return chisq\n", + " return chisq*10000.\n", "\n", "constraint4 = triGauss(bplus_0, bplus_1, bplus_2)\n", "\n", @@ -1378,7 +1413,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "scrolled": false }, @@ -1392,8 +1427,47 @@ "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" + "Use tf.random.categorical instead.\n" + ] + }, + { + "ename": "ShapeIncompatibleError", + "evalue": "Most probably, the number of obs the pdf was designed fordoes not coincide with the `n_obs` from the `space`/`obs`it received on initialization.Original Error: Operation 'create_sampler/while/unnormalized_pdf/mul_184' has been marked as not fetchable.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_call_unnormalized_pdf\u001b[1;34m(self, x, name)\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 290\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36m_unnormalized_pdf\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m \u001b[0maxiv_nr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxiv_nonres\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb0_0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb0_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb0_2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbplus_0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbplus_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbplus_2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbT_0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbT_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbT_2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36maxiv_nonres\u001b[1;34m(q, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\u001b[0m\n\u001b[0;32m 131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'bracket left'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbracket_left\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 133\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 928\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1136\u001b[0m fetch_handler = _FetchHandler(\n\u001b[1;32m-> 1137\u001b[1;33m self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)\n\u001b[0m\u001b[0;32m 1138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, graph, fetches, feeds, feed_handles)\u001b[0m\n\u001b[0;32m 483\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 484\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_assert_fetchable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 485\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_assert_fetchable\u001b[1;34m(self, graph, op)\u001b[0m\n\u001b[0;32m 496\u001b[0m raise ValueError(\n\u001b[1;32m--> 497\u001b[1;33m 'Operation %r has been marked as not fetchable.' % op.name)\n\u001b[0m\u001b[0;32m 498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: Operation 'create_sampler/while/unnormalized_pdf/mul_184' has been marked as not fetchable.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mShapeIncompatibleError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0msampler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtotal_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_sampler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtoy\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnr_of_toys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36mcreate_sampler\u001b[1;34m(self, n, limits, fixed_params, name)\u001b[0m\n\u001b[0;32m 819\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"limits are False/None, have to be specified\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 820\u001b[0m fixed_params, n, sample = self._create_sampler_tensor(fixed_params=fixed_params,\n\u001b[1;32m--> 821\u001b[1;33m limits=limits, n=n, name=name)\n\u001b[0m\u001b[0;32m 822\u001b[0m sample_data = Sampler.from_sample(sample=sample, n_holder=n, obs=limits, fixed_params=fixed_params,\n\u001b[0;32m 823\u001b[0m name=name)\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_create_sampler_tensor\u001b[1;34m(self, fixed_params, limits, n, name)\u001b[0m\n\u001b[0;32m 839\u001b[0m \u001b[1;31m# needed to be able to change the number of events in resampling\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_single_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 842\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfixed_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 843\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_single_hook_sample\u001b[1;34m(self, n, limits, name)\u001b[0m\n\u001b[0;32m 882\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 883\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 884\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 885\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 886\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'hook_sample'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_hook_sample\u001b[1;34m(self, limits, n, name)\u001b[0m\n\u001b[0;32m 489\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"`n` cannot be `None` if pdf is not extended.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 490\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m \u001b[0msamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 492\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msamples\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_hook_sample\u001b[1;34m(self, limits, n, name)\u001b[0m\n\u001b[0;32m 885\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 886\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'hook_sample'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 887\u001b[1;33m \u001b[1;32mreturn\u001b[0m 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name)\u001b[0m\n\u001b[0;32m 889\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_norm_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 890\u001b[0m \u001b[1;34m\"\"\"Dummy function\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 891\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_limits_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 893\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_limits_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_limits_sample\u001b[1;34m(self, n, limits, name)\u001b[0m\n\u001b[0;32m 893\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_limits_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 894\u001b[0m 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supported.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_call_sample\u001b[1;34m(self, n, limits, name)\u001b[0m\n\u001b[0;32m 903\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNotImplementedError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 904\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_analytic_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 905\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fallback_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 906\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 907\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_analytic_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mSpace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# TODO(Mayou36) implement multiple limits sampling\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_fallback_sample\u001b[1;34m(self, n, limits)\u001b[0m\n\u001b[0;32m 936\u001b[0m sample = zsample.accept_reject_sample(prob=self._func_to_sample_from, n=n, limits=limits,\n\u001b[0;32m 937\u001b[0m \u001b[0mprob_max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 938\u001b[1;33m sample_and_weights_factory=self._sample_and_weights)\n\u001b[0m\u001b[0;32m 939\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 940\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\u001b[0m in \u001b[0;36maccept_reject_sample\u001b[1;34m(prob, n, limits, sample_and_weights_factory, dtype, prob_max, efficiency_estimation)\u001b[0m\n\u001b[0;32m 353\u001b[0m \u001b[0mswap_memory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[0mparallel_iterations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m back_prop=False)[1] # backprop not needed here\n\u001b[0m\u001b[0;32m 356\u001b[0m \u001b[0mnew_sample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample_array\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmultiple_limits\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36mwhile_loop\u001b[1;34m(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations, return_same_structure)\u001b[0m\n\u001b[0;32m 3554\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_to_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWHILE_CONTEXT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloop_context\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3555\u001b[0m result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants,\n\u001b[1;32m-> 3556\u001b[1;33m return_same_structure)\n\u001b[0m\u001b[0;32m 3557\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmaximum_iterations\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3558\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36mBuildLoop\u001b[1;34m(self, pred, body, loop_vars, shape_invariants, return_same_structure)\u001b[0m\n\u001b[0;32m 3085\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mutation_lock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3086\u001b[0m original_body_result, exit_vars = self._BuildLoop(\n\u001b[1;32m-> 3087\u001b[1;33m pred, body, original_loop_vars, loop_vars, shape_invariants)\n\u001b[0m\u001b[0;32m 3088\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3089\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36m_BuildLoop\u001b[1;34m(self, pred, body, original_loop_vars, loop_vars, shape_invariants)\u001b[0m\n\u001b[0;32m 3020\u001b[0m flat_sequence=vars_for_body_with_tensor_arrays)\n\u001b[0;32m 3021\u001b[0m \u001b[0mpre_summaries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_SUMMARY_COLLECTION\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3022\u001b[1;33m \u001b[0mbody_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpacked_vars_for_body\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3023\u001b[0m \u001b[0mpost_summaries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_SUMMARY_COLLECTION\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3024\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_sequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\u001b[0m in \u001b[0;36msample_body\u001b[1;34m(n, sample, n_produced, n_total_drawn, eff, is_sampled, weights_scaling)\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[0mn_total_drawn\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mn_drawn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m \u001b[0mprobabilities\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprob\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrnd_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 248\u001b[0m \u001b[0mshape_rnd_sample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mshape\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrnd_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m0\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 249\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mnumeric_checks\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_func_to_sample_from\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 144\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 145\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_func_to_sample_from\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 146\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 148\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_integrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'hook_integrate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36munnormalized_pdf\u001b[1;34m(self, x, component_norm_range, name)\u001b[0m\n\u001b[0;32m 279\u001b[0m component_norm_range = self._check_input_norm_range(component_norm_range, caller_name=name,\n\u001b[0;32m 280\u001b[0m none_is_error=False)\n\u001b[1;32m--> 281\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_single_hook_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponent_norm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 282\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponent_norm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_single_hook_unnormalized_pdf\u001b[1;34m(self, x, component_norm_range, name)\u001b[0m\n\u001b[0;32m 282\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponent_norm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 286\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_call_unnormalized_pdf\u001b[1;34m(self, x, name)\u001b[0m\n\u001b[0;32m 292\u001b[0m \u001b[1;34m\"does not coincide with the `n_obs` from the `space`/`obs`\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 293\u001b[0m \u001b[1;34m\"it received on initialization.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 294\u001b[1;33m \"Original Error: {}\".format(error))\n\u001b[0m\u001b[0;32m 295\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 296\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0m_BasePDF_register_check_support\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mShapeIncompatibleError\u001b[0m: Most probably, the number of obs the pdf was designed fordoes not coincide with the `n_obs` from the `space`/`obs`it received on initialization.Original Error: Operation 'create_sampler/while/unnormalized_pdf/mul_184' has been marked as not fetchable." ] } ], diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index e91547c..5994d22 100644 --- a/__pycache__/pdg_const.cpython-37.pyc +++ b/__pycache__/pdg_const.cpython-37.pyc Binary files differ diff --git a/data/zfit_toys/toy_0/0.pkl b/data/zfit_toys/toy_0/0.pkl index b09e1b1..712943e 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 fa9cfc5..4cb083f 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 68e9674..71c85b1 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 f718ba1..aecc450 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 46dc2ba..a8441ac 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 0692106..3ed21b5 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 4ebcc77..5441b6f 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -94,7 +94,7 @@ "metadata": {}, "outputs": [], "source": [ - "def formfactor( q2, subscript, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2): #returns real value\n", + "def formfactor(q2, subscript, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2): #returns real value\n", " #check if subscript is viable\n", "\n", " if subscript != \"0\" and subscript != \"+\" and subscript != \"T\":\n", @@ -205,9 +205,9 @@ "\n", " #Some helperfunctions\n", "\n", - " beta = ztf.sqrt(tf.abs(1. - 4. * mmu**2. / q2))\n", + " beta = 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", + " 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", @@ -215,7 +215,7 @@ "\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, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2))**2.\n", + " bracket_left = 2./3. * tf.pow(kabs,2) * tf.pow(beta,2) * tf.pow(tf.abs(ztf.to_complex(C10eff)*formfactor(q2, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)),2)\n", "\n", " #middle term in bracket\n", "\n", @@ -223,11 +223,11 @@ "\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\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)), 2)\n", - "\n", + " bracket_middle = _top/_under *tf.pow(tf.abs(ztf.to_complex(C10eff) * formfactor(q2, \"0\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)), 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", + " return prefactor1 * (bracket_left + bracket_middle) * 2 * q\n", "\n", "def vec(q, funcs, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2):\n", " \n", @@ -247,33 +247,31 @@ "\n", " #Some helperfunctions\n", "\n", - " beta = ztf.sqrt(tf.abs(1. - 4. * mmu**2. / q2))\n", + " beta = 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", + " \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", + " prefactor2 = tf.pow(kabs,2) * (1. - 1./3. * beta)\n", "\n", - " abs_bracket = tf.abs(c9eff(q, funcs) * formfactor(q2, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2) + tf.complex(2.0 * C7eff * (mb + ms)/(mB + mK), ztf.constant(0.0)) * formfactor(q2, \"T\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2))**2\n", + " abs_bracket = tf.pow(tf.abs(c9eff(q, funcs) * formfactor(q2, \"+\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2) + ztf.to_complex(2.0 * C7eff * (mb + ms)/(mB + mK)) * formfactor(q2, \"T\", b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)),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", + " return prefactor1 * bracket_right * 2 * q\n", "\n", "def c9eff(q, funcs):\n", "\n", - " C9eff_nr = tf.complex(ztf.constant(pdg['C9eff']), ztf.constant(0.0))\n", + " C9eff_nr = ztf.to_complex(ztf.constant(pdg['C9eff']))\n", "\n", - " c9 = C9eff_nr\n", - "\n", - " c9 = c9 + funcs\n", + " c9 = C9eff_nr + funcs\n", "\n", " return c9" ] @@ -379,28 +377,38 @@ " 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", - " phase = self.params['jpsi_phase'], width = self.params['jpsi_width'])\n", - "\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['jpsi_mass'], 2)) * resonance(q, _mass = self.params['jpsi_mass'], \n", + " scale = self.params['jpsi_scale'],\n", + " phase = self.params['jpsi_phase'], \n", + " width = self.params['jpsi_width'])\n", " def psi2s_res(q):\n", - " return resonance(q, _mass = self.params['psi2s_mass'], scale = self.params['psi2s_scale'],\n", - " phase = self.params['psi2s_phase'], width = self.params['psi2s_width'])\n", - " \n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['psi2s_mass'], 2)) * resonance(q, _mass = self.params['psi2s_mass'], \n", + " scale = self.params['psi2s_scale'],\n", + " phase = self.params['psi2s_phase'], \n", + " width = self.params['psi2s_width'])\n", " def p3770_res(q):\n", - " return resonance(q, _mass = self.params['p3770_mass'], scale = self.params['p3770_scale'],\n", - " phase = self.params['p3770_phase'], width = self.params['p3770_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p3770_mass'], 2)) * resonance(q, _mass = self.params['p3770_mass'], \n", + " scale = self.params['p3770_scale'],\n", + " phase = self.params['p3770_phase'], \n", + " width = self.params['p3770_width'])\n", " \n", " def p4040_res(q):\n", - " return resonance(q, _mass = self.params['p4040_mass'], scale = self.params['p4040_scale'],\n", - " phase = self.params['p4040_phase'], width = self.params['p4040_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4040_mass'], 2)) * resonance(q, _mass = self.params['p4040_mass'], \n", + " scale = self.params['p4040_scale'],\n", + " phase = self.params['p4040_phase'], \n", + " width = self.params['p4040_width'])\n", " \n", " def p4160_res(q):\n", - " return resonance(q, _mass = self.params['p4160_mass'], scale = self.params['p4160_scale'],\n", - " phase = self.params['p4160_phase'], width = self.params['p4160_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4160_mass'], 2)) * resonance(q, _mass = self.params['p4160_mass'], \n", + " scale = self.params['p4160_scale'],\n", + " phase = self.params['p4160_phase'], \n", + " width = self.params['p4160_width'])\n", " \n", " def p4415_res(q):\n", - " return resonance(q, _mass = self.params['p4415_mass'], scale = self.params['p4415_scale'],\n", - " phase = self.params['p4415_phase'], width = self.params['p4415_width'])\n", + " return ztf.to_complex(tf.pow(q, 2) / tf.pow(self.params['p4415_mass'], 2)) * resonance(q, _mass = self.params['p4415_mass'], \n", + " scale = self.params['p4415_scale'],\n", + " phase = self.params['p4415_phase'], \n", + " width = self.params['p4415_width'])\n", " \n", " def P2_D(q):\n", " Dbar_contrib = ztf.to_complex(self.params['Dbar_scale'])*tf.exp(tf.complex(ztf.constant(0.0), self.params['Dbar_phase']))*ztf.to_complex(h_S(self.params['Dbar_mass'], q))\n", @@ -664,9 +672,18 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 40, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bracket left [0.00000000e+00 7.85557385e-03 3.14212789e-02 ... 8.41573104e+04\n", + " 8.23198317e+04 8.04822841e+04]\n" + ] + } + ], "source": [ "# def total_test_tf(xq):\n", "\n", @@ -694,9 +711,9 @@ "\n", "# calcs = zfit.run(total_test_tf(x_part))\n", "\n", - "test_q = np.linspace(x_min, x_max, 200000)\n", + "test_q = np.linspace(x_min, x_max, int(2e6))\n", "\n", - "probs = total_f.pdf(test_q)\n", + "probs = total_f.pdf(test_q, norm_range=False)\n", "\n", "calcs_test = zfit.run(probs)\n", "res_y = zfit.run(jpsi_res(test_q))\n", @@ -710,12 +727,20 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 47, "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" + ] + }, + { "data": { - "image/png": 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\n", 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\n", "text/plain": [ "
" ] @@ -735,7 +760,7 @@ "# plt.plot(test_q, fplus_y, label = '+')\n", "# plt.plot(test_q, res_y, label = 'res')\n", "plt.legend()\n", - "plt.ylim(0.0, 6e-6)\n", + "plt.ylim(0.0, 1.5e-6)\n", "# plt.yscale('log')\n", "# plt.xlim(770, 785)\n", "plt.savefig('test.png')\n", @@ -744,7 +769,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 43, "metadata": {}, "outputs": [], "source": [ @@ -760,7 +785,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 44, "metadata": {}, "outputs": [], "source": [ @@ -776,14 +801,24 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 46, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "bracket left [27045942.55237162 18426966.5012879 47801965.39734218 ...\n", + " 26681129.22315679 21816963.88039205 467606.97925681]\n", + "4.442538182878187e-05\n" + ] + } + ], "source": [ - "total_f.update_integration_options(draws_per_dim=200000, mc_sampler=None)\n", - "# inte = total_f.integrate(limits = (2000, x_max), norm_range=False)\n", - "# inte_fl = zfit.run(inte)\n", - "# print(inte_fl)\n", + "total_f.update_integration_options(draws_per_dim=2000000, mc_sampler=None)\n", + "inte = total_f.integrate(limits = (x_min, x_max), norm_range=False)\n", + "inte_fl = zfit.run(inte)\n", + "print(inte_fl/4500)\n", "# print(pdg[\"jpsi_BR\"]/pdg[\"NR_BR\"], inte_fl*pdg[\"psi2s_auc\"]/pdg[\"NR_auc\"])" ] }, @@ -1344,7 +1379,7 @@ " \n", " fcn = -w/d\n", " chisq = -2*fcn\n", - " return chisq\n", + " return chisq*10000.\n", "\n", "constraint4 = triGauss(bplus_0, bplus_1, bplus_2)\n", "\n", @@ -1378,7 +1413,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": { "scrolled": false }, @@ -1392,8 +1427,47 @@ "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" + "Use tf.random.categorical instead.\n" + ] + }, + { + "ename": "ShapeIncompatibleError", + "evalue": "Most probably, the number of obs the pdf was designed fordoes not coincide with the `n_obs` from the `space`/`obs`it received on initialization.Original Error: Operation 'create_sampler/while/unnormalized_pdf/mul_184' has been marked as not fetchable.", + "output_type": "error", + "traceback": [ + "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", + "\u001b[1;31mValueError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_call_unnormalized_pdf\u001b[1;34m(self, x, name)\u001b[0m\n\u001b[0;32m 288\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 289\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 290\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0mValueError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0merror\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36m_unnormalized_pdf\u001b[1;34m(self, x)\u001b[0m\n\u001b[0;32m 86\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 87\u001b[1;33m \u001b[0maxiv_nr\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0maxiv_nonres\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb0_0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb0_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mb0_2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbplus_0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbplus_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbplus_2\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbT_0\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbT_1\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mbT_2\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 88\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m\u001b[0m in \u001b[0;36maxiv_nonres\u001b[1;34m(q, b0_0, b0_1, b0_2, bplus_0, bplus_1, bplus_2, bT_0, bT_1, bT_2)\u001b[0m\n\u001b[0;32m 131\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 132\u001b[1;33m \u001b[0mprint\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m'bracket left'\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbracket_left\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 133\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py\u001b[0m in \u001b[0;36m__call__\u001b[1;34m(self, *args, **kwargs)\u001b[0m\n\u001b[0;32m 80\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m__call__\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 81\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m,\u001b[0m \u001b[1;33m**\u001b[0m\u001b[0mkwargs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 82\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 928\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1136\u001b[0m fetch_handler = _FetchHandler(\n\u001b[1;32m-> 1137\u001b[1;33m self._graph, fetches, feed_dict_tensor, feed_handles=feed_handles)\n\u001b[0m\u001b[0;32m 1138\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m__init__\u001b[1;34m(self, graph, fetches, feeds, feed_handles)\u001b[0m\n\u001b[0;32m 483\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 484\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_assert_fetchable\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mgraph\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mop\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 485\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fetches\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mappend\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mfetch\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_assert_fetchable\u001b[1;34m(self, graph, op)\u001b[0m\n\u001b[0;32m 496\u001b[0m raise ValueError(\n\u001b[1;32m--> 497\u001b[1;33m 'Operation %r has been marked as not fetchable.' % op.name)\n\u001b[0m\u001b[0;32m 498\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mValueError\u001b[0m: Operation 'create_sampler/while/unnormalized_pdf/mul_184' has been marked as not fetchable.", + "\nDuring handling of the above exception, another exception occurred:\n", + "\u001b[1;31mShapeIncompatibleError\u001b[0m Traceback (most recent call last)", + "\u001b[1;32m\u001b[0m in \u001b[0;36m\u001b[1;34m\u001b[0m\n\u001b[0;32m 15\u001b[0m \u001b[0mstart\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtime\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mtime\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 16\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 17\u001b[1;33m \u001b[0msampler\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtotal_f\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mcreate_sampler\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 18\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 19\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mtoy\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnr_of_toys\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36mcreate_sampler\u001b[1;34m(self, n, limits, fixed_params, name)\u001b[0m\n\u001b[0;32m 819\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"limits are False/None, have to be specified\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 820\u001b[0m fixed_params, n, sample = self._create_sampler_tensor(fixed_params=fixed_params,\n\u001b[1;32m--> 821\u001b[1;33m limits=limits, n=n, name=name)\n\u001b[0m\u001b[0;32m 822\u001b[0m sample_data = Sampler.from_sample(sample=sample, n_holder=n, obs=limits, fixed_params=fixed_params,\n\u001b[0;32m 823\u001b[0m name=name)\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_create_sampler_tensor\u001b[1;34m(self, fixed_params, limits, n, name)\u001b[0m\n\u001b[0;32m 839\u001b[0m \u001b[1;31m# needed to be able to change the number of events in resampling\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 840\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 841\u001b[1;33m \u001b[0msample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_single_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 842\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mfixed_params\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 843\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_single_hook_sample\u001b[1;34m(self, n, limits, name)\u001b[0m\n\u001b[0;32m 882\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 883\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 884\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 885\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 886\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'hook_sample'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_hook_sample\u001b[1;34m(self, limits, n, name)\u001b[0m\n\u001b[0;32m 489\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mtf\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mInvalidArgumentError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"`n` cannot be `None` if pdf is not extended.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 490\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 491\u001b[1;33m \u001b[0msamples\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msuper\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 492\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msamples\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 493\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_hook_sample\u001b[1;34m(self, limits, n, name)\u001b[0m\n\u001b[0;32m 885\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 886\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_hook_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'hook_sample'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 887\u001b[1;33m \u001b[1;32mreturn\u001b[0m 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name)\u001b[0m\n\u001b[0;32m 889\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_norm_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 890\u001b[0m \u001b[1;34m\"\"\"Dummy function\"\"\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 891\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_limits_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 892\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 893\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_limits_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_limits_sample\u001b[1;34m(self, n, limits, name)\u001b[0m\n\u001b[0;32m 893\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_limits_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 894\u001b[0m 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supported.\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_call_sample\u001b[1;34m(self, n, limits, name)\u001b[0m\n\u001b[0;32m 903\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0msuppress\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mNotImplementedError\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 904\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_analytic_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 905\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_fallback_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mlimits\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 906\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 907\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_analytic_sample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m:\u001b[0m \u001b[0mSpace\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# TODO(Mayou36) implement multiple limits sampling\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\u001b[0m in \u001b[0;36m_fallback_sample\u001b[1;34m(self, n, limits)\u001b[0m\n\u001b[0;32m 936\u001b[0m sample = zsample.accept_reject_sample(prob=self._func_to_sample_from, n=n, limits=limits,\n\u001b[0;32m 937\u001b[0m \u001b[0mprob_max\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mNone\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mdtype\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mdtype\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 938\u001b[1;33m sample_and_weights_factory=self._sample_and_weights)\n\u001b[0m\u001b[0;32m 939\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0msample\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 940\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\u001b[0m in \u001b[0;36maccept_reject_sample\u001b[1;34m(prob, n, limits, sample_and_weights_factory, dtype, prob_max, efficiency_estimation)\u001b[0m\n\u001b[0;32m 353\u001b[0m \u001b[0mswap_memory\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;32mTrue\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 354\u001b[0m \u001b[0mparallel_iterations\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 355\u001b[1;33m back_prop=False)[1] # backprop not needed here\n\u001b[0m\u001b[0;32m 356\u001b[0m \u001b[0mnew_sample\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msample_array\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mstack\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 357\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmultiple_limits\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36mwhile_loop\u001b[1;34m(cond, body, loop_vars, shape_invariants, parallel_iterations, back_prop, swap_memory, name, maximum_iterations, return_same_structure)\u001b[0m\n\u001b[0;32m 3554\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0madd_to_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mWHILE_CONTEXT\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mloop_context\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3555\u001b[0m result = loop_context.BuildLoop(cond, body, loop_vars, shape_invariants,\n\u001b[1;32m-> 3556\u001b[1;33m return_same_structure)\n\u001b[0m\u001b[0;32m 3557\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mmaximum_iterations\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3558\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mresult\u001b[0m\u001b[1;33m[\u001b[0m\u001b[1;36m1\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36mBuildLoop\u001b[1;34m(self, pred, body, loop_vars, shape_invariants, return_same_structure)\u001b[0m\n\u001b[0;32m 3085\u001b[0m \u001b[1;32mwith\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_default_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_mutation_lock\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3086\u001b[0m original_body_result, exit_vars = self._BuildLoop(\n\u001b[1;32m-> 3087\u001b[1;33m pred, body, original_loop_vars, loop_vars, shape_invariants)\n\u001b[0m\u001b[0;32m 3088\u001b[0m \u001b[1;32mfinally\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3089\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mExit\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\u001b[0m in \u001b[0;36m_BuildLoop\u001b[1;34m(self, pred, body, original_loop_vars, loop_vars, shape_invariants)\u001b[0m\n\u001b[0;32m 3020\u001b[0m flat_sequence=vars_for_body_with_tensor_arrays)\n\u001b[0;32m 3021\u001b[0m \u001b[0mpre_summaries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_SUMMARY_COLLECTION\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 3022\u001b[1;33m \u001b[0mbody_result\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mbody\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0mpacked_vars_for_body\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 3023\u001b[0m \u001b[0mpost_summaries\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mget_collection\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mops\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mGraphKeys\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_SUMMARY_COLLECTION\u001b[0m\u001b[1;33m)\u001b[0m \u001b[1;31m# pylint: disable=protected-access\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 3024\u001b[0m \u001b[1;32mif\u001b[0m \u001b[1;32mnot\u001b[0m \u001b[0mnest\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mis_sequence\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mbody_result\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\u001b[0m in \u001b[0;36msample_body\u001b[1;34m(n, sample, n_produced, n_total_drawn, eff, is_sampled, weights_scaling)\u001b[0m\n\u001b[0;32m 245\u001b[0m \u001b[0mn_total_drawn\u001b[0m \u001b[1;33m+=\u001b[0m \u001b[0mn_drawn\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 246\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 247\u001b[1;33m \u001b[0mprobabilities\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mprob\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrnd_sample\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 248\u001b[0m \u001b[0mshape_rnd_sample\u001b[0m \u001b[1;33m=\u001b[0m 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\u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 147\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 148\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_integrate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mlimits\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mnorm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[1;34m'hook_integrate'\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36munnormalized_pdf\u001b[1;34m(self, x, component_norm_range, name)\u001b[0m\n\u001b[0;32m 279\u001b[0m component_norm_range = self._check_input_norm_range(component_norm_range, caller_name=name,\n\u001b[0;32m 280\u001b[0m none_is_error=False)\n\u001b[1;32m--> 281\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_single_hook_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponent_norm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 282\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponent_norm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_single_hook_unnormalized_pdf\u001b[1;34m(self, x, component_norm_range, name)\u001b[0m\n\u001b[0;32m 282\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 283\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_single_hook_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mcomponent_norm_range\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 284\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_call_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 285\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 286\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_call_unnormalized_pdf\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mx\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mname\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;32m~\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\u001b[0m in \u001b[0;36m_call_unnormalized_pdf\u001b[1;34m(self, x, name)\u001b[0m\n\u001b[0;32m 292\u001b[0m \u001b[1;34m\"does not coincide with the `n_obs` from the `space`/`obs`\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 293\u001b[0m \u001b[1;34m\"it received on initialization.\"\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 294\u001b[1;33m \"Original Error: {}\".format(error))\n\u001b[0m\u001b[0;32m 295\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 296\u001b[0m \u001b[1;33m@\u001b[0m\u001b[0m_BasePDF_register_check_support\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;32mFalse\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", + "\u001b[1;31mShapeIncompatibleError\u001b[0m: Most probably, the number of obs the pdf was designed fordoes not coincide with the `n_obs` from the `space`/`obs`it received on initialization.Original Error: Operation 'create_sampler/while/unnormalized_pdf/mul_184' has been marked as not fetchable." ] } ], diff --git a/test.png b/test.png index e66e950..7eba9b1 100644 --- a/test.png +++ b/test.png Binary files differ