diff --git a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb index 576b4ca..679a6ff 100644 --- a/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb +++ b/.ipynb_checkpoints/raremodel-nb-checkpoint.ipynb @@ -16,7 +16,7 @@ "name": "stderr", "output_type": "stream", "text": [ - "c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n", + "C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\util\\execution.py:57: UserWarning: Not running on Linux. Determining available cpus for thread can failand be overestimated. Workaround (only if too many cpus are used):`zfit.run.set_n_cpu(your_cpu_number)`\n", " warnings.warn(\"Not running on Linux. Determining available cpus for thread can fail\"\n" ] }, @@ -35,6 +35,10 @@ } ], "source": [ + "import os\n", + "\n", + "# os.environ[\"CUDA_VISIBLE_DEVICES\"] = \"-1\"\n", + "\n", "import numpy as np\n", "from pdg_const import pdg\n", "import matplotlib\n", @@ -342,8 +346,7 @@ "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", - " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width'#,\n", - " #'cusp_mass', 'sigma_L', 'sigma_R', 'cusp_scale'\n", + " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width'\n", " ] # the name of the parameters\n", "\n", " def _unnormalized_pdf(self, x):\n", @@ -414,7 +417,7 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", + "WARNING:tensorflow:From C:\\Users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\resource_variable_ops.py:435: colocate_with (from tensorflow.python.framework.ops) is deprecated and will be removed in a future version.\n", "Instructions for updating:\n", "Colocations handled automatically by placer.\n" ] @@ -428,7 +431,7 @@ "\n", "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n", "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n", - "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase))\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), floating = False)\n", "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale))\n", "\n", "#psi2s\n", @@ -437,7 +440,7 @@ "\n", "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n", "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n", - "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase))\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False)\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale))\n", "\n", "#cusp\n", @@ -509,7 +512,7 @@ "\n", "# calcs = zfit.run(total_test_tf(x_part))\n", "\n", - "test_q = np.linspace(x_min, x_max, 2000000)\n", + "test_q = np.linspace(x_min, x_max, 200000)\n", "\n", "probs = total_f.pdf(test_q)\n", "\n", @@ -527,7 +530,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -547,14 +550,39 @@ "# 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.yscale('log')\n", + "plt.ylim(0.0, 6e-6)\n", + "# plt.yscale('log')\n", "# plt.xlim(3080, 3110)\n", "plt.savefig('test.png')\n", "# print(jpsi_width)" ] }, { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [], + "source": [ + "\n", + "\n", + "# probs = mixture.prob(test_q)\n", + "# probs_np = zfit.run(probs)\n", + "# probs_np *= np.max(calcs_test) / np.max(probs_np)\n", + "# plt.figure()\n", + "# plt.semilogy(test_q, probs_np,label=\"importance sampling\")\n", + "# plt.semilogy(test_q, calcs_test, label = 'pdf')\n" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# 0.213/(0.00133+0.213+0.015)" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ @@ -563,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 11, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -576,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -624,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -653,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -670,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -700,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -709,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -731,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -768,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -790,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -818,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -851,25 +879,47 @@ "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),\n", "# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], \n", "# high=list_of_borders[-(splits-1):]))\n", - " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.1, dtype=dtype),\n", - " tf.constant(0.7, dtype=dtype),\n", - " tf.constant(0.2, dtype=dtype)]),\n", - " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - " tf.constant(2800, dtype=dtype),\n", - " tf.constant(3550, dtype=dtype)], \n", - " high=[tf.constant(x_max, dtype=dtype),\n", - " tf.constant(3300, dtype=dtype), \n", - " tf.constant(3900, dtype=dtype)]))\n", + "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),\n", + "# tf.constant(0.93, dtype=dtype),\n", + "# tf.constant(0.05, dtype=dtype),\n", + "# tf.constant(0.065, dtype=dtype),\n", + "# tf.constant(0.05, dtype=dtype)]),\n", + "# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + "# tf.constant(3090, dtype=dtype),\n", + "# tf.constant(3681, dtype=dtype), \n", + "# tf.constant(3070, dtype=dtype),\n", + "# tf.constant(3660, dtype=dtype)], \n", + "# high=[tf.constant(x_max, dtype=dtype),\n", + "# tf.constant(3102, dtype=dtype), \n", + "# tf.constant(3691, dtype=dtype),\n", + "# tf.constant(3110, dtype=dtype), \n", + "# tf.constant(3710, dtype=dtype)]))\n", + " dtype = tf.float64\n", + " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", + " tf.constant(0.90, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype),\n", + " tf.constant(0.07, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype)]),\n", + " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + " tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype)], \n", + " high=[tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype), \n", + " tf.constant(x_max, dtype=dtype)]))\n", "# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n", "# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n", " sample = mixture.sample((n_to_produce, 1))\n", "# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n", - " weights = mixture.prob(sample)\n", + " weights = mixture.prob(sample)[:,0]\n", "# weights = tf.broadcast_to(tf.constant(1., dtype=dtype), shape=(n_to_produce,))\n", " # sample = tf.expand_dims(sample, axis=-1)\n", "# print(sample, weights)\n", " \n", - " weights = tf.ones(shape=(n_to_produce,), dtype=dtype)\n", + "# weights = tf.ones(shape=(n_to_produce,), dtype=dtype)\n", " weights_max = None\n", " thresholds = tf.random_uniform(shape=(n_to_produce,), dtype=dtype)\n", " return sample, thresholds, weights, weights_max, n_to_produce" @@ -877,16 +927,45 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ - "# total_f._sample_and_weights = UniformSampleAndWeights" + "total_f._sample_and_weights = UniformSampleAndWeights" ] }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 25, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "0.001309082138940001" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" + ] + }, + { + "cell_type": "code", + "execution_count": 26, + "metadata": {}, + "outputs": [], + "source": [ + "# zfit.settings.set_verbosity(10)" + ] + }, + { + "cell_type": "code", + "execution_count": 27, "metadata": { "scrolled": false }, @@ -895,31 +974,9 @@ "name": "stdout", "output_type": "stream", "text": [ - "WARNING:tensorflow:From c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:163: to_int32 (from tensorflow.python.ops.math_ops) is deprecated and will be removed in a future version.\n", - "Instructions for updating:\n", - "Use tf.cast instead.\n" - ] - }, - { - "ename": "InvalidArgumentError", - "evalue": "assertion failed: [32254] [1.1806250218058597e-06 1.1806250218058597e-06 1.1806250218058597e-06...] [8.2086369510094e-06 2.2747180268017141e-06 1.6044321221245944e-06...]\n\t [[node create_sampler/while/assert_greater_equal/Assert/AssertGuard/Assert (defined at c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:294) ]]\n\nCaused by op 'create_sampler/while/assert_greater_equal/Assert/AssertGuard/Assert', defined at:\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in \n lambda f: self._run_callback(functools.partial(callback, future))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 357, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 267, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in execute_request\n user_expressions, allow_stdin,\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 13, in \n sampler = total_f.create_sampler(n=event_stack)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 814, in create_sampler\n limits=limits, n=n, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 835, in _create_sampler_tensor\n sample = self._single_hook_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 877, in _single_hook_sample\n return self._hook_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 491, in _hook_sample\n samples = super()._hook_sample(limits=limits, n=n, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 880, in _hook_sample\n return self._norm_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 884, in _norm_sample\n return self._limits_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 888, in _limits_sample\n return self._call_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 898, in _call_sample\n return self._fallback_sample(n=n, limits=limits)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 931, in _fallback_sample\n sample_and_weights_factory=self._sample_and_weights)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 346, in accept_reject_sample\n back_prop=False)[1] # backprop not needed here\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3556, in while_loop\n return_same_structure)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3087, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3022, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 294, in sample_body\n message=\"Not all weights are >= probs so the sampling \"\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\check_ops.py\", line 1023, in assert_greater_equal\n return control_flow_ops.Assert(condition, data, summarize=summarize)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py\", line 193, in wrapped\n return _add_should_use_warning(fn(*args, **kwargs))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 168, in Assert\n guarded_assert = cond(condition, no_op, true_assert, name=\"AssertGuard\")\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2108, in cond\n orig_res_f, res_f = context_f.BuildCondBranch(false_fn)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 1941, in BuildCondBranch\n original_result = fn()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 166, in true_assert\n condition, data, summarize, name=\"Assert\")\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_logging_ops.py\", line 72, in _assert\n name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): assertion failed: [32254] [1.1806250218058597e-06 1.1806250218058597e-06 1.1806250218058597e-06...] [8.2086369510094e-06 2.2747180268017141e-06 1.6044321221245944e-06...]\n\t [[node create_sampler/while/assert_greater_equal/Assert/AssertGuard/Assert (defined at c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:294) ]]\n", - "output_type": "error", - "traceback": [ - "\u001b[1;31m---------------------------------------------------------------------------\u001b[0m", - "\u001b[1;31mInvalidArgumentError\u001b[0m Traceback (most recent call last)", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1333\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1334\u001b[1;33m \u001b[1;32mreturn\u001b[0m \u001b[0mfn\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m*\u001b[0m\u001b[0margs\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1335\u001b[0m \u001b[1;32mexcept\u001b[0m \u001b[0merrors\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mOpError\u001b[0m \u001b[1;32mas\u001b[0m \u001b[0me\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run_fn\u001b[1;34m(feed_dict, fetch_list, target_list, options, run_metadata)\u001b[0m\n\u001b[0;32m 1318\u001b[0m return self._call_tf_sessionrun(\n\u001b[1;32m-> 1319\u001b[1;33m options, feed_dict, fetch_list, target_list, run_metadata)\n\u001b[0m\u001b[0;32m 1320\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_call_tf_sessionrun\u001b[1;34m(self, options, feed_dict, fetch_list, target_list, run_metadata)\u001b[0m\n\u001b[0;32m 1406\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_session\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0moptions\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeed_dict\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetch_list\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mtarget_list\u001b[0m\u001b[1;33m,\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1407\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1408\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;31mInvalidArgumentError\u001b[0m: assertion failed: [32254] [1.1806250218058597e-06 1.1806250218058597e-06 1.1806250218058597e-06...] [8.2086369510094e-06 2.2747180268017141e-06 1.6044321221245944e-06...]\n\t [[{{node create_sampler/while/assert_greater_equal/Assert/AssertGuard/Assert}}]]", - "\nDuring handling of the above exception, another exception occurred:\n", - "\u001b[1;31mInvalidArgumentError\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 23\u001b[0m \u001b[1;32mfor\u001b[0m \u001b[0mcall\u001b[0m \u001b[1;32min\u001b[0m \u001b[0mrange\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mcalls\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 24\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m---> 25\u001b[1;33m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mresample\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mevent_stack\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 26\u001b[0m \u001b[0ms\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0msampler\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0munstack_x\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 27\u001b[0m \u001b[0msam\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mzfit\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0ms\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\data.py\u001b[0m in \u001b[0;36mresample\u001b[1;34m(self, param_values, n)\u001b[0m\n\u001b[0;32m 637\u001b[0m \u001b[1;32mraise\u001b[0m \u001b[0mRuntimeError\u001b[0m\u001b[1;33m(\u001b[0m\u001b[1;34m\"Cannot set a new `n` if not a Tensor-like object was given\"\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 638\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mn_samples\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mload\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mvalue\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0msession\u001b[0m\u001b[1;33m=\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m--> 639\u001b[1;33m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msess\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mrun\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0msample_holder\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minitializer\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 640\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_initial_resampled\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;32mTrue\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 641\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36mrun\u001b[1;34m(self, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 927\u001b[0m \u001b[1;32mtry\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 928\u001b[0m result = self._run(None, fetches, feed_dict, options_ptr,\n\u001b[1;32m--> 929\u001b[1;33m run_metadata_ptr)\n\u001b[0m\u001b[0;32m 930\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mrun_metadata\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 931\u001b[0m \u001b[0mproto_data\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0mtf_session\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0mTF_GetBuffer\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mrun_metadata_ptr\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_run\u001b[1;34m(self, handle, fetches, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1150\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mfinal_fetches\u001b[0m \u001b[1;32mor\u001b[0m \u001b[0mfinal_targets\u001b[0m \u001b[1;32mor\u001b[0m \u001b[1;33m(\u001b[0m\u001b[0mhandle\u001b[0m \u001b[1;32mand\u001b[0m \u001b[0mfeed_dict_tensor\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1151\u001b[0m results = self._do_run(handle, final_targets, final_fetches,\n\u001b[1;32m-> 1152\u001b[1;33m feed_dict_tensor, options, run_metadata)\n\u001b[0m\u001b[0;32m 1153\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1154\u001b[0m \u001b[0mresults\u001b[0m \u001b[1;33m=\u001b[0m \u001b[1;33m[\u001b[0m\u001b[1;33m]\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_run\u001b[1;34m(self, handle, target_list, fetch_list, feed_dict, options, run_metadata)\u001b[0m\n\u001b[0;32m 1326\u001b[0m \u001b[1;32mif\u001b[0m \u001b[0mhandle\u001b[0m \u001b[1;32mis\u001b[0m \u001b[1;32mNone\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1327\u001b[0m return self._do_call(_run_fn, feeds, fetches, targets, options,\n\u001b[1;32m-> 1328\u001b[1;33m run_metadata)\n\u001b[0m\u001b[0;32m 1329\u001b[0m \u001b[1;32melse\u001b[0m\u001b[1;33m:\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1330\u001b[0m \u001b[1;32mreturn\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_do_call\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0m_prun_fn\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mhandle\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfeeds\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mfetches\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n", - "\u001b[1;32mc:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\client\\session.py\u001b[0m in \u001b[0;36m_do_call\u001b[1;34m(self, fn, *args)\u001b[0m\n\u001b[0;32m 1346\u001b[0m \u001b[1;32mpass\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1347\u001b[0m \u001b[0mmessage\u001b[0m \u001b[1;33m=\u001b[0m \u001b[0merror_interpolation\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0minterpolate\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mmessage\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mself\u001b[0m\u001b[1;33m.\u001b[0m\u001b[0m_graph\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[1;32m-> 1348\u001b[1;33m \u001b[1;32mraise\u001b[0m \u001b[0mtype\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0me\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mnode_def\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mop\u001b[0m\u001b[1;33m,\u001b[0m \u001b[0mmessage\u001b[0m\u001b[1;33m)\u001b[0m\u001b[1;33m\u001b[0m\u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0m\u001b[0;32m 1349\u001b[0m \u001b[1;33m\u001b[0m\u001b[0m\n\u001b[0;32m 1350\u001b[0m \u001b[1;32mdef\u001b[0m \u001b[0m_extend_graph\u001b[0m\u001b[1;33m(\u001b[0m\u001b[0mself\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;31mInvalidArgumentError\u001b[0m: assertion failed: [32254] [1.1806250218058597e-06 1.1806250218058597e-06 1.1806250218058597e-06...] [8.2086369510094e-06 2.2747180268017141e-06 1.6044321221245944e-06...]\n\t [[node create_sampler/while/assert_greater_equal/Assert/AssertGuard/Assert (defined at c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:294) ]]\n\nCaused by op 'create_sampler/while/assert_greater_equal/Assert/AssertGuard/Assert', defined at:\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 193, in _run_module_as_main\n \"__main__\", mod_spec)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\runpy.py\", line 85, in _run_code\n exec(code, run_globals)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel_launcher.py\", line 16, in \n app.launch_new_instance()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\traitlets\\config\\application.py\", line 658, in launch_instance\n app.start()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelapp.py\", line 505, in start\n self.io_loop.start()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\platform\\asyncio.py\", line 148, in start\n self.asyncio_loop.run_forever()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 539, in run_forever\n self._run_once()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\base_events.py\", line 1775, in _run_once\n handle._run()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\asyncio\\events.py\", line 88, in _run\n self._context.run(self._callback, *self._args)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 690, in \n lambda f: self._run_callback(functools.partial(callback, future))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\ioloop.py\", line 743, in _run_callback\n ret = callback()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 781, in inner\n self.run()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 742, in run\n yielded = self.gen.send(value)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 357, in process_one\n yield gen.maybe_future(dispatch(*args))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 267, in dispatch_shell\n yield gen.maybe_future(handler(stream, idents, msg))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\kernelbase.py\", line 534, in execute_request\n user_expressions, allow_stdin,\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tornado\\gen.py\", line 209, in wrapper\n yielded = next(result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\ipkernel.py\", line 294, in do_execute\n res = shell.run_cell(code, store_history=store_history, silent=silent)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\ipykernel\\zmqshell.py\", line 536, in run_cell\n return super(ZMQInteractiveShell, self).run_cell(*args, **kwargs)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2848, in run_cell\n raw_cell, store_history, silent, shell_futures)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 2874, in _run_cell\n return runner(coro)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\async_helpers.py\", line 67, in _pseudo_sync_runner\n coro.send(None)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3049, in run_cell_async\n interactivity=interactivity, compiler=compiler, result=result)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3214, in run_ast_nodes\n if (yield from self.run_code(code, result)):\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\IPython\\core\\interactiveshell.py\", line 3296, in run_code\n exec(code_obj, self.user_global_ns, self.user_ns)\n File \"\", line 13, in \n sampler = total_f.create_sampler(n=event_stack)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 814, in create_sampler\n limits=limits, n=n, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 835, in _create_sampler_tensor\n sample = self._single_hook_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 877, in _single_hook_sample\n return self._hook_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basepdf.py\", line 491, in _hook_sample\n samples = super()._hook_sample(limits=limits, n=n, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 880, in _hook_sample\n return self._norm_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 884, in _norm_sample\n return self._limits_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 888, in _limits_sample\n return self._call_sample(n=n, limits=limits, name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 898, in _call_sample\n return self._fallback_sample(n=n, limits=limits)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\basemodel.py\", line 931, in _fallback_sample\n sample_and_weights_factory=self._sample_and_weights)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 346, in accept_reject_sample\n back_prop=False)[1] # backprop not needed here\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3556, in while_loop\n return_same_structure)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3087, in BuildLoop\n pred, body, original_loop_vars, loop_vars, shape_invariants)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 3022, in _BuildLoop\n body_result = body(*packed_vars_for_body)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py\", line 294, in sample_body\n message=\"Not all weights are >= probs so the sampling \"\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\check_ops.py\", line 1023, in assert_greater_equal\n return control_flow_ops.Assert(condition, data, summarize=summarize)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\tf_should_use.py\", line 193, in wrapped\n return _add_should_use_warning(fn(*args, **kwargs))\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 168, in Assert\n guarded_assert = cond(condition, no_op, true_assert, name=\"AssertGuard\")\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 2108, in cond\n orig_res_f, res_f = context_f.BuildCondBranch(false_fn)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 1941, in BuildCondBranch\n original_result = fn()\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\control_flow_ops.py\", line 166, in true_assert\n condition, data, summarize, name=\"Assert\")\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\ops\\gen_logging_ops.py\", line 72, in _assert\n name=name)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\op_def_library.py\", line 788, in _apply_op_helper\n op_def=op_def)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\util\\deprecation.py\", line 507, in new_func\n return func(*args, **kwargs)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 3300, in create_op\n op_def=op_def)\n File \"c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\tensorflow\\python\\framework\\ops.py\", line 1801, in __init__\n self._traceback = tf_stack.extract_stack()\n\nInvalidArgumentError (see above for traceback): assertion failed: [32254] [1.1806250218058597e-06 1.1806250218058597e-06 1.1806250218058597e-06...] [8.2086369510094e-06 2.2747180268017141e-06 1.6044321221245944e-06...]\n\t [[node create_sampler/while/assert_greater_equal/Assert/AssertGuard/Assert (defined at c:\\users\\sa_li\\.conda\\envs\\rmd\\lib\\site-packages\\zfit\\core\\sample.py:294) ]]\n" + "6/6 of Toy 1/1\n", + "Time taken: 1 min, 6 s\n", + "Projected time left: \n" ] } ], @@ -928,6 +985,7 @@ "\n", "nr_of_toys = 1\n", "nevents = int(pdg[\"number_of_decays\"])\n", + "nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", "# zfit.settings.set_verbosity(10)\n", "calls = int(nevents/event_stack + 1)\n", @@ -953,10 +1011,11 @@ " sam = zfit.run(s)\n", " clear_output(wait=True)\n", "\n", - " c = call + 1 \n", - " print(\"{0}/{1}\".format(c, calls))\n", + " c = call + 1\n", + " \n", + " print(\"{0}/{1} of Toy {2}/{3}\".format(c, calls, toy+1, nr_of_toys))\n", " print(\"Time taken: {}\".format(display_time(int(time.time() - start))))\n", - " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/c*(calls-c)))))\n", + " print(\"Projected time left: {}\".format(display_time(int((time.time() - start)/(c+calls*(toy))*((nr_of_toys-toy)*calls-c)))))\n", "\n", " with open(\"data/zfit_toys/toy_{0}/{1}.pkl\".format(toy, call), \"wb\") as f:\n", " pkl.dump(sam, f, pkl.HIGHEST_PROTOCOL)" @@ -964,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -981,9 +1040,18 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 29, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Time to generate full toy: 66 s\n", + "(5404696,)\n" + ] + } + ], "source": [ "print(\"Time to generate full toy: {} s\".format(int(time.time()-start)))\n", "\n", @@ -1005,9 +1073,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 30, "metadata": {}, - "outputs": [], + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "(5404696,)\n" + ] + }, + { + "data": { + "image/png": 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FCN = -861523.9193443996TOTAL NCALL = 31NCALLS = 31
EDM = 1.78687712418138e-05GOAL EDM = 5e-06\n", + " UP = 0.5
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ValidValid ParamAccurate CovarPosDefMade PosDef
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Hesse FailHasCovAbove EDMReach calllim
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+NameValueHesse ErrorMinos Error-Minos Error+Limit-Limit+Fixed?
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1psi2s_s1239.483.62611No
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Error-28.78472940434378228.58795673661853
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At LimitFalseFalse
Max FCNFalseFalse
New MinFalseFalse
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Error-3.63829584578296843.6140145472909015
ValidTrueTrue
At LimitFalseFalse
Max FCNFalseFalse
New MinFalseFalse
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "jpsi_s: ^{+28.58795673661853}_{-28.784729404343782}\n", + "psi2s_s: ^{+3.6140145472909015}_{-3.6382958457829684}\n", + "Function minimum: -861523.9193443996\n" + ] + } + ], "source": [ "nll = zfit.loss.UnbinnedNLL(model=total_f, data=data2, fit_range = (x_min, x_max))\n", "\n", - "minimizer = zfit.minimize.MinuitMinimizer()\n", + "minimizer = zfit.minimize.MinuitMinimizer(verbosity = 5)\n", "# minimizer._use_tfgrad = False\n", "result = minimizer.minimize(nll)\n", "\n", @@ -1091,7 +1395,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1103,16 +1407,29 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 36, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "image/png": 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\n", + "text/plain": [ + "
" + ] + }, + "metadata": { + "needs_background": "light" + }, + "output_type": "display_data" + } + ], "source": [ "plt.clf()\n", "# plt.plot(x_part, calcs, '.')\n", "plt.plot(test_q, calcs_test, label = 'pdf')\n", "# plt.plot(test_q, res_y, label = 'res')\n", "plt.legend()\n", - "plt.ylim(0.0, 5e-4)\n", + "plt.ylim(0.0, 5e-6)\n", "# plt.yscale('log')\n", "# plt.xlim(3080, 3110)\n", "plt.savefig('test3.png')\n", @@ -1121,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1135,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1156,10 +1473,22 @@ }, { "cell_type": "code", - "execution_count": null, + "execution_count": 39, "metadata": {}, - "outputs": [], - "source": [] + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "1.522535170724314\n", + "1.522535170724314\n" + ] + } + ], + "source": [ + "print((zfit.run(jpsi_p)%(2*np.pi))/np.pi)\n", + "print((zfit.run(psi2s_p)%(2*np.pi))/np.pi)" + ] }, { "cell_type": "code", diff --git a/__pycache__/pdg_const.cpython-37.pyc b/__pycache__/pdg_const.cpython-37.pyc index 4f10b65..82e8b3a 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 c2768ce..aa45ff4 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 87d8510..344099a 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 89c4967..b67d41e 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 9d68762..f6fb010 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 f79f627..b21b688 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 8e018b7..d886d91 100644 --- a/data/zfit_toys/toy_0/5.pkl +++ b/data/zfit_toys/toy_0/5.pkl Binary files differ diff --git a/pdg_const.py b/pdg_const.py index 92a9684..62af75a 100644 --- a/pdg_const.py +++ b/pdg_const.py @@ -36,13 +36,13 @@ "C3" : -0.005, "C4" : -0.078, -"C7eff" : -0.306, -# "C7eff": 0.0, +# "C7eff" : -0.306, +"C7eff": 0.0, -"C9eff" : 4.211, -"C10eff" : -4.103, -# "C9eff": 0.0, -# "C10eff": 0.0, +# "C9eff" : 4.211, +# "C10eff" : -4.103, +"C9eff": 0.0, +"C10eff": 0.0, ###Other constants @@ -66,17 +66,48 @@ "NR_auc": 0.00133, #Resonances format(mass, width, phase, scale) -# "jpsi": (3096.0, 0.09, -1.66, 2e-2), #-------> pre scaling -"jpsi": (3096.0, 0.09, -1.66, 9897.0), #---> after scaling -# "jpsi": (3096.0, 0.09, -1.66, 0.0), +# "jpsi": (3096.0, 0.09, -1.5, 2e-2), #-------> pre scaling +# "jpsi": (3096.0, 0.09, -1.5, 9897.0), #---> after scaling +"jpsi": (3096.0, 0.09, -1.5, 0.0), "jpsi_BR": 6.02e-5, -"jpsi_auc": 0.2126825758464027, #----------------> pre scaling -# "jpsi_auc": 0.2126825758464027, #--------------> after scaling +# "jpsi_auc": 0.2126825758464027, #----------------> pre scaling +"jpsi_auc": 0.2126825758464027, #--------------> after scaling -# "psi2s": (3686.0, 0.3, -1.93, 3.14e-3), #-------> pre scaling -"psi2s": (3686.0, 0.3, -1.93, 1396.0), #--------> after scaling -# "psi2s": (3686.0, 0.3, -1.93, 0.0), +# "psi2s": (3686.0, 0.3, -1.5, 3.14e-3), #-------> pre scaling +# "psi2s": (3686.0, 0.3, -1.5, 1396.0), #--------> after scaling +"psi2s": (3686.0, 0.3, -1.5, 0.0), "psi2s_BR": 4.97e-6, -"psi2s_auc": 2.802257483178487e-10, #------------> pre scaling -"psi2s_auc": 0.0151332263 #--------------------> after scaling +# "psi2s_auc": 2.802257483178487e-10, #------------> pre scaling +"psi2s_auc": 0.0151332263, #--------------------> after scaling + +#------------------------------------------------------------------------------------- + +# "p3770": (3773.0, 27.2, -2.13, 3.14e-3), #-------> pre scaling +# "p3770": (3773.0, 27.2, -2.13, 1396.0), #--------> after scaling +"p3770": (3773.0, 27.2, -2.13, 0.0), +"p3770_BR": 1.38e-9, +# "p3770_auc": 2.802257483178487e-10, #------------> pre scaling +"p3770_auc": 0.0151332263, #--------------------> after scaling + +# "p4040": (4039.0, 80.0, -2.52, 3.14e-3), #-------> pre scaling +# "p4040": (4039.0, 80.0,, -2.52, 1396.0), #--------> after scaling +"p4040": (4039.0, 80.0,, -2.52, 0.0), +"p4040_BR": 4.2e-10, +# "p4040_auc": 2.802257483178487e-10, #------------> pre scaling +"p4040_auc": 0.0151332263, #--------------------> after scaling + +# "p4160": (4147.0, 22.0, -1.9, 3.14e-3), #-------> pre scaling +# "p4160": (4147.0, 22.0, -1.9, 1396.0), #--------> after scaling +"p4160": (4147.0, 22.0, -1.9, 0.0), +"p4160_BR": 2.6e-9, +# "p4160_auc": 2.802257483178487e-10, #------------> pre scaling +"p4160_auc": 0.0151332263, #--------------------> after scaling + +# "p4415": (4421.0, 62.0, -2.52, 3.14e-3), #-------> pre scaling +# "p4415": (4421.0, 62.0, -2.52, 1396.0), #--------> after scaling +"p4415": (4421.0, 62.0, -2.52, 0.0), +"p4415_BR": 6.1e-10, +# "p4415_auc": 2.802257483178487e-10, #------------> pre scaling +"p4415_auc": 0.0151332263, #--------------------> after scaling + } diff --git a/raremodel-nb.ipynb b/raremodel-nb.ipynb index 902dc0c..679a6ff 100644 --- a/raremodel-nb.ipynb +++ b/raremodel-nb.ipynb @@ -346,8 +346,7 @@ "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", - " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width'#,\n", - " #'cusp_mass', 'sigma_L', 'sigma_R', 'cusp_scale'\n", + " 'psi2s_mass', 'psi2s_scale', 'psi2s_phase', 'psi2s_width'\n", " ] # the name of the parameters\n", "\n", " def _unnormalized_pdf(self, x):\n", @@ -432,7 +431,7 @@ "\n", "jpsi_m = zfit.Parameter(\"jpsi_m\", ztf.constant(jpsi_mass), floating = False)\n", "jpsi_w = zfit.Parameter(\"jpsi_w\", ztf.constant(jpsi_width), floating = False)\n", - "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase))\n", + "jpsi_p = zfit.Parameter(\"jpsi_p\", ztf.constant(jpsi_phase), floating = False)\n", "jpsi_s = zfit.Parameter(\"jpsi_s\", ztf.constant(jpsi_scale))\n", "\n", "#psi2s\n", @@ -441,7 +440,7 @@ "\n", "psi2s_m = zfit.Parameter(\"psi2s_m\", ztf.constant(psi2s_mass), floating = False)\n", "psi2s_w = zfit.Parameter(\"psi2s_w\", ztf.constant(psi2s_width), floating = False)\n", - "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase))\n", + "psi2s_p = zfit.Parameter(\"psi2s_p\", ztf.constant(psi2s_phase), floating = False)\n", "psi2s_s = zfit.Parameter(\"psi2s_s\", ztf.constant(psi2s_scale))\n", "\n", "#cusp\n", @@ -531,7 +530,7 @@ "outputs": [ { "data": { - "image/png": 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\n", 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\n", 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" ] @@ -564,22 +563,7 @@ "metadata": {}, "outputs": [], "source": [ - "dtype = tf.float64\n", - "mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.007, dtype=dtype),\n", - " tf.constant(0.965, dtype=dtype),\n", - " tf.constant(0.04, dtype=dtype),\n", - " tf.constant(0.03, dtype=dtype),\n", - " tf.constant(0.006, dtype=dtype)]),\n", - " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - " tf.constant(3092, dtype=dtype),\n", - " tf.constant(3682, dtype=dtype), \n", - " tf.constant(3070, dtype=dtype),\n", - " tf.constant(3660, dtype=dtype)], \n", - " high=[tf.constant(x_max, dtype=dtype),\n", - " tf.constant(3100, dtype=dtype), \n", - " tf.constant(3690, dtype=dtype),\n", - " tf.constant(3110, dtype=dtype), \n", - " tf.constant(3710, dtype=dtype)]))\n", + "\n", "\n", "# probs = mixture.prob(test_q)\n", "# probs_np = zfit.run(probs)\n", @@ -590,6 +574,15 @@ ] }, { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [], + "source": [ + "# 0.213/(0.00133+0.213+0.015)" + ] + }, + { "cell_type": "markdown", "metadata": {}, "source": [ @@ -598,7 +591,7 @@ }, { "cell_type": "code", - "execution_count": 12, + "execution_count": 13, "metadata": {}, "outputs": [], "source": [ @@ -611,7 +604,7 @@ }, { "cell_type": "code", - "execution_count": 13, + "execution_count": 14, "metadata": {}, "outputs": [], "source": [ @@ -659,7 +652,7 @@ }, { "cell_type": "code", - "execution_count": 14, + "execution_count": 15, "metadata": {}, "outputs": [], "source": [ @@ -688,7 +681,7 @@ }, { "cell_type": "code", - "execution_count": 15, + "execution_count": 16, "metadata": {}, "outputs": [], "source": [ @@ -705,7 +698,7 @@ }, { "cell_type": "code", - "execution_count": 16, + "execution_count": 17, "metadata": {}, "outputs": [], "source": [ @@ -735,7 +728,7 @@ }, { "cell_type": "code", - "execution_count": 17, + "execution_count": 18, "metadata": {}, "outputs": [], "source": [ @@ -744,7 +737,7 @@ }, { "cell_type": "code", - "execution_count": 18, + "execution_count": 19, "metadata": {}, "outputs": [], "source": [ @@ -766,7 +759,7 @@ }, { "cell_type": "code", - "execution_count": 19, + "execution_count": 20, "metadata": {}, "outputs": [], "source": [ @@ -803,7 +796,7 @@ }, { "cell_type": "code", - "execution_count": 20, + "execution_count": 21, "metadata": {}, "outputs": [], "source": [ @@ -825,7 +818,7 @@ }, { "cell_type": "code", - "execution_count": 21, + "execution_count": 22, "metadata": {}, "outputs": [], "source": [ @@ -853,7 +846,7 @@ }, { "cell_type": "code", - "execution_count": 22, + "execution_count": 23, "metadata": {}, "outputs": [], "source": [ @@ -886,21 +879,37 @@ "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=_p[:(splits-1)]),\n", "# components_distribution=tfd.Uniform(low=list_of_borders[:(splits-1)], \n", "# high=list_of_borders[-(splits-1):]))\n", - " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.007, dtype=dtype),\n", - " tf.constant(0.95, dtype=dtype),\n", - " tf.constant(0.07, dtype=dtype),\n", - " tf.constant(0.025, dtype=dtype),\n", - " tf.constant(0.006, dtype=dtype)]),\n", - " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", - " tf.constant(3093, dtype=dtype),\n", - " tf.constant(3683, dtype=dtype), \n", - " tf.constant(3070, dtype=dtype),\n", - " tf.constant(3660, dtype=dtype)], \n", - " high=[tf.constant(x_max, dtype=dtype),\n", - " tf.constant(3099, dtype=dtype), \n", - " tf.constant(3690, dtype=dtype),\n", - " tf.constant(3110, dtype=dtype), \n", - " tf.constant(3710, dtype=dtype)]))\n", + "# mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.05, dtype=dtype),\n", + "# tf.constant(0.93, dtype=dtype),\n", + "# tf.constant(0.05, dtype=dtype),\n", + "# tf.constant(0.065, dtype=dtype),\n", + "# tf.constant(0.05, dtype=dtype)]),\n", + "# components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + "# tf.constant(3090, dtype=dtype),\n", + "# tf.constant(3681, dtype=dtype), \n", + "# tf.constant(3070, dtype=dtype),\n", + "# tf.constant(3660, dtype=dtype)], \n", + "# high=[tf.constant(x_max, dtype=dtype),\n", + "# tf.constant(3102, dtype=dtype), \n", + "# tf.constant(3691, dtype=dtype),\n", + "# tf.constant(3110, dtype=dtype), \n", + "# tf.constant(3710, dtype=dtype)]))\n", + " dtype = tf.float64\n", + " mixture = tfd.MixtureSameFamily(mixture_distribution=tfd.Categorical(probs=[tf.constant(0.04, dtype=dtype),\n", + " tf.constant(0.90, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype),\n", + " tf.constant(0.07, dtype=dtype),\n", + " tf.constant(0.02, dtype=dtype)]),\n", + " components_distribution=tfd.Uniform(low=[tf.constant(x_min, dtype=dtype), \n", + " tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype)], \n", + " high=[tf.constant(3089, dtype=dtype),\n", + " tf.constant(3103, dtype=dtype), \n", + " tf.constant(3681, dtype=dtype),\n", + " tf.constant(3691, dtype=dtype), \n", + " tf.constant(x_max, dtype=dtype)]))\n", "# mixture = tfd.Uniform(tf.constant(x_min, dtype=dtype), tf.constant(x_max, dtype=dtype))\n", "# sample = tf.random.uniform((n_to_produce, 1), dtype=dtype)\n", " sample = mixture.sample((n_to_produce, 1))\n", @@ -918,7 +927,7 @@ }, { "cell_type": "code", - "execution_count": 23, + "execution_count": 24, "metadata": {}, "outputs": [], "source": [ @@ -927,16 +936,27 @@ }, { "cell_type": "code", - "execution_count": 24, + "execution_count": 25, "metadata": {}, - "outputs": [], + "outputs": [ + { + "data": { + "text/plain": [ + "0.001309082138940001" + ] + }, + "execution_count": 25, + "metadata": {}, + "output_type": "execute_result" + } + ], "source": [ - "# psi2s_mass" + "0.00133/(0.00133+0.213+0.015)*(x_max-3750)/(x_max-x_min)" ] }, { "cell_type": "code", - "execution_count": 25, + "execution_count": 26, "metadata": {}, "outputs": [], "source": [ @@ -945,7 +965,7 @@ }, { "cell_type": "code", - "execution_count": 26, + "execution_count": 27, "metadata": { "scrolled": false }, @@ -954,8 +974,8 @@ "name": "stdout", "output_type": "stream", "text": [ - "6/6 of Toy 2/2\n", - "Time taken: 1 min, 27 s\n", + "6/6 of Toy 1/1\n", + "Time taken: 1 min, 6 s\n", "Projected time left: \n" ] } @@ -963,7 +983,7 @@ "source": [ "# zfit.run.numeric_checks = False \n", "\n", - "nr_of_toys = 2\n", + "nr_of_toys = 1\n", "nevents = int(pdg[\"number_of_decays\"])\n", "nevents = pdg[\"number_of_decays\"]\n", "event_stack = 1000000\n", @@ -1003,7 +1023,7 @@ }, { "cell_type": "code", - "execution_count": 27, + "execution_count": 28, "metadata": {}, "outputs": [], "source": [ @@ -1020,14 +1040,14 @@ }, { "cell_type": "code", - "execution_count": 28, + "execution_count": 29, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "Time to generate full toy: 87 s\n", + "Time to generate full toy: 66 s\n", "(5404696,)\n" ] } @@ -1053,7 +1073,7 @@ }, { "cell_type": "code", - "execution_count": 29, + "execution_count": 30, "metadata": {}, "outputs": [ { @@ -1065,7 +1085,7 @@ }, { "data": { - "image/png": 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\n", 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EDM = 1.78687712418138e-05GOAL EDM = 5e-06\n", + " UP = 0.5
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1psi2s_s1239.483.62611No
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" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { "name": "stdout", "output_type": "stream", "text": [ - "------------------------------------------------------------------\n", - "| FCN = -8.724E+05 | Ncalls=153 (153 total) |\n", - "| EDM = 8.38E-06 (Goal: 5E-06) | up = 0.5 |\n", - "------------------------------------------------------------------\n", - "| Valid Min. | Valid Param. | Above EDM | Reached call limit |\n", - "------------------------------------------------------------------\n", - "| True | True | False | False |\n", - "------------------------------------------------------------------\n", - "| Hesse failed | Has cov. | Accurate | Pos. def. | Forced |\n", - "------------------------------------------------------------------\n", - "| False | True | True | True | False |\n", - "------------------------------------------------------------------\n", - "jpsi_p: ^{+0.016484670160195978}_{-0.016715939969484755}\n", - "psi2s_s: ^{+3.5429799872792804}_{-3.5286564682010337}\n", - "psi2s_p: ^{+0.01844073930972661}_{-0.018643934945039613}\n", - "jpsi_s: ^{+28.041395536069153}_{-27.882719703773358}\n", - "Function minimum: -872362.5577928417\n" + "jpsi_s: ^{+28.58795673661853}_{-28.784729404343782}\n", + "psi2s_s: ^{+3.6140145472909015}_{-3.6382958457829684}\n", + "Function minimum: -861523.9193443996\n" ] } ], @@ -1192,7 +1395,7 @@ }, { "cell_type": "code", - "execution_count": 34, + "execution_count": 35, "metadata": {}, "outputs": [], "source": [ @@ -1204,12 +1407,12 @@ }, { "cell_type": "code", - "execution_count": 35, + "execution_count": 36, "metadata": {}, "outputs": [ { "data": { - "image/png": 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TOVbXhjGhS7futa7ziRmm5wNw4axUDte20trVG7L2qOBp8FFK2Tqvnk9mEi1dHs60h+6+Pr3WsFvMMD0fgOWz0zCGsFz8qoanwUcpZcvrM8NmktmZm5kIENKht57+4DN8G1fkTSXW5eC9Q3pLlWigwUcpZcvjMzhHOe42L8Of3ny8PnTpzX09n+HmfADiYpxcMi+d9w5r8IkGGnyUUra8Pt+orvMB/yoHsS5HSFcWGMmwG8CagkyO17dzrE6v94k0DT5KKVueUV7nA/5VBwqnp1BW3TzGrTqr1xN8wgHA1YunIQJbdg2+JZkKNw0+SilbvvOY8wFYNCOF/dUtIct4G8mcD8D0KfFcOi+DF0urQpqFp4anwUcpZet85nzAH3xaujxUhmiZnZHM+fT58kUzOdnQwYdHz4SkTSo4GnyUUrbOJ9sNYNGMKQAhG3ob6ZwPwLVLppORFMvj7x0NSZtUcDT4KKVsjXZh0T6F05JxOoS9VaEKPtaczwiCT1yMkztW5/H+kXr2VOqKB5GiwUcpFZCv/3YFo/+aiItxUjgtmZ0hurCzxzOyOZ8+t108m/REN//48n6d+4kQDT5KqYD6btQ2moVFB1o5ZyqlJ5v6A8VY6vX6iHEKMsLeWUpcDH9z9QJ2nGjkpV1VY94uNTwNPkqpgIK9UdtwVuZNpdvjC8nQW4/Hh2uUPbM/Kc7lwlmpPLClLOS3flDn0uCjlArI4/P3VM5nzgegOG8qADtONJx3mwbr9viIdztHdazTIfzrLRdiDNzzm1K6er1j3Do1FA0+SqmArNhz3j2fzORY5mYm8lEIUps7e73Ex4wu+ADMSk/gR19Zyu7KJv5qc2l/b0+FngYfpVRAfT2f853zAfhCQSYfHTtDR4/nvMsaqLPXS1zM+X2NrVs8nb+7rog3ymr43rO7+tO3VWhp8FFKBTRWcz4AVxZm0+Px8WH52PZ+unq8ox52G+iO1XO4d10hW3ZV8+1f76S9e2yDpDqXBh+lVEB92W7nO+cD/oy3RLeTbYdqz7usgTp7vcS5zj/4AHxnzTweunEx7xyq5cbHPuB4iG8BPtlp8FFKBdTX83GMQc/H7XJwWUEmb+2vGdN5lc7esen59Lnt4tn86o5V1Ld1c8NP/sDLe3QB0lDR4KOUCmg066YN5YYLZlDX2s0H5fVjUh5AZ4+XuPNIOAhkdX4GW+9ZzbzMJO75TSnffWYXLXrr7TGnwUcpFdBolq4ZyuWFWaTEuXipdOwu6uw6z2w3O7lTE3ju25fw12vz2bq7mmv+5X3+eEwXIh1LGnyUUgH1jvB2BcOJi3Fy3dLpvF52mtYx6kmcb6r1UFxOB3+9toDnvn0JbpeDW3/+R/7ptQN0e/R6oLGgwUcpFVB/8AnyRm3B2LhiFh09Xn5bUjkm5XWMUbbbUC6clcbLf7GajStm8bP3jnHTYx9yuKY1pHVOBhp8lFIB9Q+7ncfCooNdkJtK8ew0fvnh8fNOPPD5DO3dHpLjXGPUOnuJsS7+6ctLePIbxdS0dPGlf/8Dv/zgeP/iq2rkNPgopQLyjPGwW587V8+hoqGT1/adOq9y2ns8+Ix/kdBwWVuUzet/fRmfn5/BD3+3n9t/uZ36tu6w1T+RaPBRSgXUE4JhN4AvLppGQXYSP37zcH+AG42WLv+FoCnxoe/5DJSZHMuTtxfz8E2L2X68gRv+/Q/srQzN/YomMg0+SqmAQjHsBv4VE75/dSHH6tt5pqRi1OW0dPqTFsLZ8+kjInxt1Wye/87nANjw+Ie8WDo281iTRVCfKhFZJyKHRKRcRO4L8HqsiDxjvf6xiOQNeO1+a/shEbl6uDJFZI5VxhGrTPdQdYjIShHZZf3sFpGbRnsylFJnnU04GNthN4C1C7NYkZfGj944xJlRDlv1B5/48AefPotnTmHrX6xmWW4q331mNz99t1xvThekYYOPiDiBx4BrgCLgVhEpGrTbnUCjMWY+8CjwiHVsEbARWASsA34qIs5hynwEeNQYkw80WmXb1gHsA4qNMcusOn4mIuHthys1AZ1NtR77ARIR4eGbltDW7eHBl/ePqoz+YbcI9HwGykiK5T+/tYr1y2bwz68f4uFXDmgiQhCC+VStBMqNMceMMT3AZmD9oH3WA09bj58DrhT/rQXXA5uNMd3GmONAuVVewDKtY66wysAq88ah6jDGdBhj+lYBjAP0f12pMdA37DZWKxwMVpCdzD2X57NlVzW/2z3yZWwa2v09ptSEyAYf8AfoR/9kGX/6uTye/MNxfvDSXu0BDSOYT9VMYODAbKW1LeA+ViBoBtKHONZuezrQNCCYDKzLrg5EZJWIlAF7gW8POL6fiNwlIiUiUlJXVxfE21Zqcuvr+YzFLRXs/Pnl8yienca9z++hvHZk187UtPiDT1ZKbCiaNmIOh/D31xfx52vmsWl7Bf/48gENQEMIJvgE+uQNPqN2+4zV9iHbYYz52BizCFgB3C8icefsaMwTxphiY0xxZmZmgKKUUgOFctitT4zTwU++ehEJbid/9qudI0pbrmnpIi0hhtgxWtV6LIgI3796AX/6uTx+8cFxHn37SKSbFLWC+VRVArkDnucAg/vI/ftY8y1TgIYhjrXbXg+kDpizGViXXR39jDEHgHZgcRDvSyk1hLFe283OtClx/OzryznV3Mk3f7mDtiDvpVPb2k12yjl/Z0aciPDAl4rYsDyHf/v9EV74RLPgAgnmU7UDyLey0Nz4Ewi2DtpnK3C79XgDsM34+5tbgY1WptocIB/Yblemdcw7VhlYZW4Zqg6rDBeAiMwGFgAngj4DSqmAxnpV66Esnz2Vn37tIvafauGbv9we1CrSlY2dTJ8SfcEH/ENw//TlJVw8dyr3Pb+XnZ82DH/QJDPsp8qaP7kHeAM4ADxrjCkTkQdF5AZrt6eAdBEpB74H3GcdWwY8C+wHXgfuNsZ47cq0yroX+J5VVrpVtm0dwGpgt4jsAl4E/twYM3Zrtis1SfV6Qj/nM9AVhdn868ZllJ5s4qs//+OQKdhen+FoXRvzs5LC0rbRiHE6+I+vLWdGahx3/WonNS1dkW5SVJHJOCFWXFxsSkpKIt0MpaLaj988xL9tK+f4P12LjMHdTIP1zsFavv2fO5mRGs/Pv1EcMMCcqG9nzY/e5ZGbl3DLillha9tolNe2cv2/f8Cy3FT+81urxuS25JEiIjuNMcVjUZaucKCUCqjXZ3A7HWENPOC/78//+9YqWjp7uemnH/BOgFtvl3zaCMCSmalhbdtozM9K5ofrF/HRsTP8x7vlkW5O1NDgo5QKqNfjG/NFRYNVnDeVLfdcSm5aAnf+3x386I1D9HjOrgP3zsFapia6KZyWHJH2jdRXluewftkMfvzWYXZVNEW6OVFBg49SKqBerw9XGJIN7OSkJfDcdy7h5oty+Mk75ax/7APe3l/D+0fqeKPsNDddOBPHOBnCEhH+8cbFZCXHce9zez4TSCcrDT5KqYB6vCbkadbDSXC7+D9fuYCff6OY5o4evvWrEr7+1HayU+K4+/L5EW3bSKXExfDQjYs5VNPK4+8djXRzIk7XQFNKBdTrjdyw22BXFWWzZkEm7x+po7G9lysXZpGa4I50s0ZsbVE2N1wwg3/fdoRrl0yP6my9UNOej1IqoG6Pj7iY6Fk9IMbp4IrCbG5enjMuA0+fB64vIs7l5KFXRreg6kShwUcpFVB3r5fYMb6RnPKvgv2XV+bz7qG6gJl8k4V+spRSAXV5fMRGUc9nIrn9c3nkpSfw8CsH+leSmGw0+CilAtKeT+i4XQ5+cF0R5bVtPHsed3Mdz/STpZQKqCvK5nwmmrULs7hwViqPbSun2+ONdHPCToOPUiog7fmElojwvasKqG7u4tmSybfytX6ylFIBRVu220S0en4GK/LSeGxbOV29k6v3o8FHKRWQ9nxCT0T47toCTrd08fwku++PfrKUUgH553z0KyLULpmXztKcKTz1/nF8vslzlwH9ZCmlAvL3fHTYLdREhG99fi7H6tvZdnDyXPejwUcpFZD2fMLn2sXTmJkazxPvH4t0U8JGP1lKqXN4vD68PqM9nzBxOR1889I8th9vYG9lc6SbExYafJRS5+iylvzXnk/4/MmKXOJjnPxm+6eRbkpY6CdLKXWObivtV3s+4ZMSF8P1F0xny65qWrt6I92ckNPgo5Q6R1/PR1Otw+urq2bT0eNl6+7qSDcl5PSTpZQ6R2ePv+cT79aeTzhdkDOFhdNT+M3HJzFmYqdda/BRSp2jvdsDQFKs3m8ynESEr67Mpay6hX1VLZFuTkhp8FFKnaO9xx98EtwafMLthmUzcTsdvFA6sVc80OCjlDpHe7d/2E17PuE3JT6Gywsz+d3uU3gm8L1+NPgopc7R0dfzidU5n0i46cKZ1Ld18+HRM5FuSsho8FFKnUN7PpG1ZkEWyXEuXtpVFemmhIwGH6XUOfoSDhI02y0i4mKcXLt4Om/sO92feTjRaPBRSp1DEw4ib/2yGbT3eHnn0MRcbFSDj1LqHO3dHuJjnDgdEummTFor50wlLSGGN8pOR7opIaHBRyl1jvYeL4mabBBRLqeDq4qy2Xawlh7PxMt60+CjlDpHe7dHh9yiwNWLptHa5eGjYxMv602Dj1LqHC2dvUyJj4l0Mya9S+dnkOh2TsihNw0+SqlzNHX2kpqgwSfS4mKcrCnM4s2yGrwT7BbbGnyUUudo7tCeT7S4etE06tu62VXRGOmmjCkNPkqpc2jPJ3p8oSATp0N491BdpJsypoIKPiKyTkQOiUi5iNwX4PVYEXnGev1jEckb8Nr91vZDInL1cGWKyByrjCNWme6h6hCRq0Rkp4jstf69YrQnQykFPp+hqaOH1Hh3pJui8K/1dmFuKu8dnmTBR0ScwGPANUARcKuIFA3a7U6g0RgzH3gUeMQ6tgjYCCwC1gE/FRHnMGU+AjxqjMkHGq2ybesA6oHrjTFLgNuBX4/sFCilBmrr8eAzaM8niqxZkMmeymbq27oj3ZQxE0zPZyVQbow5ZozpATYD6wftsx542nr8HHCliIi1fbMxptsYcxwot8oLWKZ1zBVWGVhl3jhUHcaYUmNM323/yoA4EYkN9gQopT6rucN/C2ed84keXyjIAuD9IxOn9xNM8JkJVAx4XmltC7iPMcYDNAPpQxxrtz0daLLKGFyXXR0D3QyUGmPO+fNARO4SkRIRKamrmzj/gUqNtcaOHgBSE3TYLVosmpFCRpJ7Qs37BBN8Aq2vMTjnz26fsdo+bDtEZBH+obj/FmA/jDFPGGOKjTHFmZmZgXZRSgF1rf6/3bKSdQAhWjgcwmX5mfzX4boJk3IdTPCpBHIHPM8Bqu32EREXMAVoGOJYu+31QKpVxuC67OpARHKAF4FvGGOOBvGelFI2alqs4JOiwSeafGFBJo0dveytao50U8ZEMMFnB5BvZaG58ScQbB20z1b8k/0AG4Btxhhjbd9oZarNAfKB7XZlWse8Y5WBVeaWoeoQkVTgFeB+Y8wHI3nzSqlz1bR0IQIZSRp8osnn5mUA8NEEucHcsMHHml+5B3gDOAA8a4wpE5EHReQGa7engHQRKQe+B9xnHVsGPAvsB14H7jbGeO3KtMq6F/ieVVa6VbZtHVY584G/E5Fd1k/WKM+HUpNebWsX6YluYpx6GWA0yUyOJT8racKs8xbUyoHGmFeBVwdte2DA4y7gKzbHPgw8HEyZ1vZj+LPhBm8PWIcx5iHgoWHfhFIqKDUt3WQlx0W6GSqAS+al89zOSnq9vnH/x8H4br1SasxVN3UyfYoGn2h0ydx0Onq87KlsinRTzpsGH6VUP2MMn57pYHZ6YqSbogJYNdd/dclEmPfR4KOU6lfb2k1nr5e8jIRIN0UFMDXRTeG05Akx76PBRynV73h9OwB52vOJWpfMS6fkRCPdHm+km3JeNPgopfqV17YBMCdDg0+0umRuOt0eH7srxvf1Php8lFL99lU1k5oQQ05afKSbomwU500FYOen4/v+Php8lFL99lQ2s2TmFPxr/KpoNDXRzdyMRA0+SqmJobmzl0M1rSzLTY10U9QwLpqdxicnG/EvCjM+afBRSgHwYXk9Xp/hsgJdeDfaFc9OowlRWyIAABUISURBVKG9pz9BZDzS4KOUAuD1stOkxLm4UHs+UW/57DRgfM/7aPBRSnGmrZvX9p3mpgtn4hrny7ZMBvMyk0iJc/HJSQ0+Sqlx7F9/fwSP18fXL8mLdFNUEBwOYfnsNEpOaPBRSo1Tvy2p4Fcffco3LsljflZSpJujgrR8dhpHatv6b3s+3gS1qrVSamLw+QytXR6aO3s5eLqFF0ureG3faS6Zm8791xZGunlqBC6y5n1KKxpZs2D83UVGg49SE0Bnj5eTDR18eqadT890UN3cSVNHLw3tPTR19NDU2Uuz9TMwOzc51sVfXZnP3ZfPx+3SgZDxxH89lv/aLA0+SqmQ6/X62FPZzJ7KJvZWNrO3qpnyurbPBJVEt5OpSW7SEtykJrjJy0hkSnwMqfExpMTHkJrgJjctngtnpWnQGaeS42KYm5HInsrxucyOBh+lopzPZ9h/qoUPj9bz4dEzbD/eQEePf1HJrORYluZM4dol05mXlUReegKzpyYyJSEmwq1W4bA0J5UPj9ZHuhmjosFHqSjU1evlD0fqeWt/Db8/WEN9Ww8A87OS2LA8h0vmpnPR7DSyU/Smb5PZ0pwpvFhaRU1L17j7LGjwUSpKtHT18lZZDW+Uneb9I/V09npJinXxhQWZXFmYxaXzM8bdF4wKraU5UwD/vM9VRePrs6HBR6kI6ur1su1gLVt3VbPtUC09Hh/TUuLYsDyHq4qyuXhuus7JKFtF06fgdAh7K5u4qig70s0ZEQ0+SoWZx+vjg6Nn2LKrijfLamjr9pCRFMtXV87ihmUzuDA3VVeVVkGJdzvJz0pi9zhMOtDgo1SYHK9vZ/OOkzy/s5L6th6S41xcu2QaN1wwk0vmpeN0aMBRI3dBTipvHajBGDOu/mjR4KNUCHV7vLy+7zSbt1fw0bEzOB3CFYVZbFiew5oFmcS6nJFuohrnluRM4ZmSCiobO8mdmhDp5gRNg49SIVBe28qm7RW88EkljR295KTF8/2rF7BheY4mDagxdUGOfxXyvVXNGnyUmoy6er28uvcUm7afZMeJRlwO4YuLsrl15SwunZeBQ4fVVAjkZyfhcgj7q1u4dsn0SDcnaBp8lDpPB0+3sNnq5bR0echLT+D+awq5eXkOGUmxkW6emuDiYpzMy0ziwKmWSDdlRDT4KDUKHT0eXt59ik07TlJ6sgm308G6xdO4deUsLp47dVxN/Krxb+H0ZD4+3hDpZoyIBh+lRmBfVTObtp9ky65q2ro9zM9K4m+vW8iXL8phaqI70s1Tk1TRjBRe2lVNY3sPaePkc6jBR6lhtHb1snV3NZu3V7C3qplYl4Prlk7n1pWzKJ6dpr0cFXELp6cAcOBUC5+bnxHh1gRHg49SARhj2F3ZzObtJ9m6u5qOHi+F05L54Q2LuHHZTF24U0WVvuCzX4OPUuNTc2cvW3ZVsWl7BQdOtRAf4+T6C/y9nGW68oCKUhlJsWQlx7J/HCUdaPBRk54xhp2fNrJpewWv7K2mq9fH4pkpPHTjYtYvm0FynPZyVPRbOD2FA6daI92MoGnwUZNWY3sPL5RWsXn7SY7UtpEU6+LLF+Vw64pZLLFWC1ZqvCiakcKH7x+jx+MbF4vRavBRk4oxho+PN7Bp+0le23eaHo+PZbmpPHLzEr60dAaJsforocanhdNT6PUaymvbKJqREunmDCuo3zQRWQf8K+AEnjTG/O9Br8cCvwKWA2eAW4wxJ6zX7gfuBLzAXxpj3hiqTBGZA2wGpgKfAF83xvTY1SEi6cBzwArg/xpj7hnluVAT2Jm2bp7/pJLN2ys4Vt9OcpyLW1fksnHlrP7JWqXGs6IBGW8TIviIiBN4DLgKqAR2iMhWY8z+AbvdCTQaY+aLyEbgEeAWESkCNgKLgBnA2yJSYB1jV+YjwKPGmM0i8rhV9n/Y1QF0AX8HLLZ+lAL8t5/+8OgZNu04yZtlp+n1Gopnp3H35fO5dsl04t26qKeaOOZkJBIX42D/qRZujnRjghBMz2clUG6MOQYgIpuB9cDA4LMe+Afr8XPAT8SfFrQe2GyM6QaOi0i5VR6ByhSRA8AVwFetfZ62yv0PuzqMMe3AH0Rk/gjet5rAalq6+G1JBc+UVFDR0ElqQgzfuCSPjStyyc9OjnTzlAoJp0OYn5XE4ZrxkXQQTPCZCVQMeF4JrLLbxxjjEZFmIN3a/sdBx860HgcqMx1oMsZ4AuxvV0d9EO8BEbkLuAtg1qxZwRyixhGP18e7h+rYvKOCdw7V4vUZLpmbzt98cQFXL5pGXIz2ctTEV5CdzIflZyLdjKAEE3wCXdhggtzHbnugVIyh9g+2HbaMMU8ATwAUFxcHfZyKbhUNHTyzo4Lf7qygpqWbjKRY7rpsLrcU55KXkRjp5ikVVgXZybzwSRXNnb1MiY/uSwSCCT6VQO6A5zlAtc0+lSLiAqYADcMcG2h7PZAqIi6r9zNwf7s61CTT7fHy1v4antlRwftH6nEIfKEgkwfXz+KKwixinNGfZqpUKBRkJwFwpKaV4rypEW7N0IIJPjuAfCsLrQp/AsFXB+2zFbgd+AjYAGwzxhgR2Qr8RkR+jD/hIB/Yjr8Xc06Z1jHvWGVstsrcMlQdo3vbajwqr23137qgtIqG9h5mpsbz3bUFfKU4hxmp8ZFunlIRV2DNaR6uaRv/wceaX7kHeAN/WvQvjDFlIvIgUGKM2Qo8BfzaSihowB9MsPZ7Fn9ygge42xjjBQhUplXlvcBmEXkIKLXKxq4Oq6wTQArgFpEbgS8OysZT41Rnj5dX9p5i8/aTlHzqv0HbVUXZ3LIil8/nZ+LUG7Qp1W9majyJbue4SDqQydh5KC4uNiUlJZFuhrJhjKG0oonfllTy8u5qWrs9zMlIZOOKXL58UQ6ZyXqDNqXsrH/sAxLdTn7zZxePedkistMYUzwWZenl3Cpq1LR08cInVTy3s4Kjde3ExTi4dvF0/mRFLqvm6A3alArGguwkth2si3QzhqXBR0VUt8fL2/treW5nBe8drsNnoHh2Go/cPJdrl0zXRT2VGqGC7GSeLamkob0nqm9wqMFHhZ0xhrLqFn5bUsGW3dU0dfQyLSWO76yZx80X5TA3MynSTVRq3MrvTzpo5eK56RFujT0NPipszrR182JpFc/trOTg6VbcLgdfLMrmK8W5rJ6fockDSo2BBVbwOaLBR01mPR4f7xyq5fmdlWw7WIvHZ7ggZwr/eONiblg6Q+8IqtQYy06JJTnOxaEoz3jT4KPGnDGGT0428sInVbyy9xRNHb1kJMVyx+o5bFie038tglJq7IkIC7KTOVzTFummDEmDjxozx+vbebG0ipdKqzjZ0EFcjIMvFk3jpotm8vn5Gbh05QGlwiI/O5nX953CGBO1WaIafNR5aWjv4eU91bzwSRW7KpoQgUvnZfCXV+azbvE0kvTmbEqFXUF2Epu291LX1k1WclykmxOQfjOoEevq9fL2gRpeKq3i3UN1eHyGwmnJ/M9rC7nhgplMmxKdH3alJouC/qSDNg0+anzz+gwfHz/DS6VVvLb3NK3dHrJTYrlz9RxuvHCm3g1UqSiSby0werimlUvnZ0S4NYFp8FG2+pa5+d3ual7Zc4ra1m4S3U7WLZ7Oly+aycVz0zU9WqkolJkUS2pCDEdqozfpQIOP+gxjDAdOtbJ1dzUv76mmsrETt8vB5Qsyuf6CGVxZmK23n1YqyokIBVnJHInidGsNPgqAY3VtbN1dze92V3O0rh2nQ1g9P4Pvri3gqkXZpOgyN0qNK/nZSby8J3oz3jT4TGKVjR28vOcUv9tdTVl1CyKwas5U7lg9h2sWT4/qdaGUUkMryE6mufMkda3dZKVEX9KBBp9Jprqpk9f3neaVvafY+WkjAMtyU/m7LxVx3ZLpmqmm1ARxNumgTYOPioyKhg5e33eaV/edovRkEwCF05L5/tULuH7pDGalJ0S4hUqpsVYwYIHR1fnRl/GmwWeCOlHfzmv7TvPavlPsqWwGYPHMFL5/9QKuWTxNV45WaoLLSIplaqKbI7XRmXSgwWcCOVrXxmt7T/Hq3tPsP9UCwAW5qdx/TSHXLJ6uPRylJpn5WUlRu8abBp9xzBjDwdOtvFF2mtf2nu5fxXb57DT+9rqFrFs8jZw0DThKTVYF2Uls2VUdlRlvGnzGmR6Pj+3HG3j7QA1v7a+hqqkTEViRN5V/uL6IdYs1aUAp5VeQnUxrl4ealu6o+17Q4DMONHX08O6hOt4+UMN7h+po7fYQF+Ng9fwM/uKK+VyxMCtq129SSkVOftbZpAMNPioon55p5639Nbx9oIYdJxrx+gwZSbFct3Q6axdmc+n8DF1pQCk1pIIBa7xdVpAZ4dZ8lgafKOH1GXZVNPH2gRre3l/TvybTguxkvv2FuaxdmM0FOak4dC01pVSQ0pNiSU90cyQKkw40+ERQR4+H94/U8/sDNWw7WEt9Ww8uh7Bq7lRuXTmLtQuzNUNNKXVe8rOTOByF6dYafMKspqWL3x+o5e0DNfyhvJ4ej4/kOBeXL8hibVE2XyjIZEq8rqOmlBobBdnJvPhJVdRlvGnwCTFjDEdq23iz7DRv7a9ht3XBZ+7UeL62ahZXLcxmxZypxOgtppVSIZCfnUxrt4dTzV3MSI2PdHP6afAJAZ/P8MnJRt7cX8ObZac5caYD8K+h9v2rF7B2YTYF2UlR9VeIUmpiKsg6m3SgwWeCKq9t5flPqniptIpTzV3EOIXPzcvgzy7zJwxkR+Hifkqpia1vjbfy2jbWLMiKcGvO0uBznowxfFB+hsffO8ofyutxOoTL8jO475pCLi/M0vvgKKUiKi3RTUZSLIej7MZyGnzOQ2VjBw9sKWPbwVqykmO5d10hG5bnkJkcG+mmKaVUv4Ls6FvjTYPPKG0/3sB/+3UJPR4ff3vdQr5+yWxiXXrRp1Iq+hRkJ/PczsqoynjT4DMK+6tb+OYvt5M9JY6nbl/BnIzESDdJKaVs5Wcn0dbtobq5i5lRknSg+b0j5PH6+O+/3U1irIvffOtiDTxKqag3cI23aKHBZ4Re3XeaA6da+PvrF0XdQn1KKRXIgmn+4FNW1RzhlpylwWeEnt9ZSU5aPNcsnhbppiilVFCmxMdQkJ1EyaeNkW5Kv6CCj4isE5FDIlIuIvcFeD1WRJ6xXv9YRPIGvHa/tf2QiFw9XJkiMscq44hVpnu0dYw1j9dHyYkGrijM0gU+lVLjSnHeVHZ+6l8hPxoMG3xExAk8BlwDFAG3ikjRoN3uBBqNMfOBR4FHrGOLgI3AImAd8FMRcQ5T5iPAo8aYfKDRKnvEdYz0RASjprWb9h4vhdNSQlG8UkqFzIq8NFq7PJRVR8fQWzA9n5VAuTHmmDGmB9gMrB+0z3rgaevxc8CV4s/nWw9sNsZ0G2OOA+VWeQHLtI65wioDq8wbR1nHmGvq6AFgaqI7FMUrpVTIrCnIwu1y8JNt5ZFuChBcqvVMoGLA80pgld0+xhiPiDQD6db2Pw46dqb1OFCZ6UCTMcYTYP/R1NFPRO4C7rKetonIGaDe9l0P4ZpHRnNUVMtglOdiAtJz4afn4awJdS6OAD+/fVSHZgCzx6odwQSfQJMbgwcN7fax2x6oxzXU/qOp47MbjHkCeKLvuYiUGGOKAxw76ei5OEvPhZ+eh7P0XPhZ5yFvrMoLZtitEsgd8DwHqLbbR0RcwBSgYYhj7bbXA6lWGYPrGmkdSimlolQwwWcHkG9lobnxT+5vHbTPVqCvI7cB2GaMMdb2jVam2hwgH9huV6Z1zDtWGVhlbhllHUoppaLUsMNu1vzKPcAbgBP4hTGmTEQeBEqMMVuBp4Bfi0g5/t7IRuvYMhF5FtgPeIC7jTFegEBlWlXeC2wWkYeAUqtsRlPHMJ4YfpdJQ8/FWXou/PQ8nKXnwm9Mz4P4Ow9KKaVU+OgKB0oppcJOg49SSqmwm5TBZ7jlgiYCEfmFiNSKyL4B26aKyFvW0kVviUiatV1E5N+s87FHRC4acMzt1v5HRGR0VwdEkIjkisg7InJARMpE5K+s7ZPqXIhInIhsF5Hd1nn4obU9apezCjVrtZVSEXnZej4pz4WInBCRvSKyS0RKrG2h//0wxkyqH/wJDkeBuYAb2A0URbpdIXiflwEXAfsGbPtn4D7r8X3AI9bja4HX8F8zdTHwsbV9KnDM+jfNepwW6fc2wvMwHbjIepwMHMa/pNOkOhfW+0myHscAH1vv71lgo7X9ceA71uM/Bx63Hm8EnrEeF1m/M7HAHOt3yRnp9zfKc/I94DfAy9bzSXkugBNAxqBtIf/9mIw9n2CWCxr3jDH/hT8rcKCBSxQNXrroV8bvj/ivtZoOXA28ZYxpMMY0Am/hXz9v3DDGnDLGfGI9bgUO4F8BY1KdC+v99N1HOcb6MUTxclahJCI5wHXAk9bzqF7aKwJC/vsxGYNPoOWCzlmOZ4LKNsacAv+XMpBlbbc7JxPqXFnDJRfi/6t/0p0La5hpF1CL/8vhKEEuZwUMXM5qXJ8Hy78A/wPwWc+DXtqLiXcuDPCmiOwU/zJkEIbfj8l4G+2gluOZZM5r6aLxQESSgOeBvzbGtIj9fewn7Lkw/uvflolIKvAisDDQbta/E/Y8iMiXgFpjzE4RWdO3OcCuE/5cWC41xlSLSBbwlogcHGLfMTsXk7HnM5mX46mxushY/9Za20e6DNK4IiIx+APP/zPGvGBtnpTnAsAY0wS8i3/MfjIuZ3UpcIOInMA/7H4F/p7QZDwXGGOqrX9r8f9RspIw/H5MxuATzHJBE9XAJYoGL130DSuT5WKg2epqvwF8UUTSrGyXL1rbxg1rbP4p4IAx5scDXppU50JEMq0eDyISD6zFP/816ZazMsbcb4zJMf5FMjfif29fYxKeCxFJFJHkvsf4P9f7CMfvR6QzLSLxgz9j4zD+Me8fRLo9IXqPm4BTQC/+v0ruxD9O/Xv8q6r/Hphq7Sv4b+53FNgLFA8o5w78E6nlwDcj/b5GcR5W4+/+7wF2WT/XTrZzASzFv1zVHuvL5QFr+1z8X5jlwG+BWGt7nPW83Hp97oCyfmCdn0PANZF+b+d5XtZwNttt0p0L6z3vtn7K+r4Pw/H7ocvrKKWUCrvJOOymlFIqwjT4KKWUCjsNPkoppcJOg49SSqmw0+CjlFIq7DT4KKWUCjsNPkoppcLu/wNIdT85YgImXQAAAABJRU5ErkJggg==\n", + "image/png": 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\n", 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" ] @@ -1235,7 +1438,7 @@ }, { "cell_type": "code", - "execution_count": 36, + "execution_count": 37, "metadata": {}, "outputs": [], "source": [ @@ -1249,7 +1452,7 @@ }, { "cell_type": "code", - "execution_count": 37, + "execution_count": 38, "metadata": {}, "outputs": [], "source": [ @@ -1270,15 +1473,15 @@ }, { "cell_type": "code", - "execution_count": 42, + "execution_count": 39, "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ - "1.2311725935705207\n", - "1.4004132078629088\n" + "1.522535170724314\n", + "1.522535170724314\n" ] } ], diff --git a/test.png b/test.png index 3e3aa87..b9496fe 100644 --- a/test.png +++ b/test.png Binary files differ diff --git a/test2.png b/test2.png index 9e39f61..f7eb98e 100644 --- a/test2.png +++ b/test2.png Binary files differ diff --git a/test3.png b/test3.png index 25d099d..0174991 100644 --- a/test3.png +++ b/test3.png Binary files differ