From 131e2366a4555c1a5c501499f6b51ec8918a4ac6 Mon Sep 17 00:00:00 2001 From: Zijian Zhang Date: Sun, 8 Dec 2024 16:19:25 -0500 Subject: [PATCH 1/4] dev: update prompts --- .../builtin/multi_qubit_gates/conditional_stark_ai.py | 3 +-- 1 file changed, 1 insertion(+), 2 deletions(-) diff --git a/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py b/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py index d916b4a..90cc819 100644 --- a/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py +++ b/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py @@ -2619,8 +2619,7 @@ def run(self, duts: List[TransmonElement], frequency: float): @text_inspection def next_parameter(self): prompt = f""" - Your objective is to find the optimal parameters for the conditional stark-shift gate that will allow you to entangle - two qubits. The parameters you need to find are + Your objective is to find the optimal parameters for the conditional stark-shift gate that will allow you to entangle two qubits. The parameters you need to find are 'amp_control': the amplitude of the control qubit (The first qubit), the required amplitude accuracy is 0.01. From 3e928dcd8fd1de8cc081baa68c7c52268215a064 Mon Sep 17 00:00:00 2001 From: Zijian Zhang Date: Sun, 8 Dec 2024 17:19:33 -0500 Subject: [PATCH 2/4] dev: update notebooks --- .../multi_qubit_gates/conditional_stark_ai.py | 33 +- .../procedures/two_qubit_calibration.md | 8 +- notebooks/SimulatedSystem/AITuneUpDemo.ipynb | 12566 +++++++++++++++- .../SimulatedSystem/TwoQubitTuneUp.ipynb | 34 +- 4 files changed, 12587 insertions(+), 54 deletions(-) diff --git a/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py b/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py index 90cc819..6918c1b 100644 --- a/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py +++ b/leeq/experiments/builtin/multi_qubit_gates/conditional_stark_ai.py @@ -2593,11 +2593,14 @@ def _run_next_experiment(self, run_class, params, filter_parameters=True): class ConditionalStarkTwoQubitGateAmplitudeAdvise(Experiment): + + n_points_to_try = 2 #5 _rewrite_json_requirement = True _experiment_result_analysis_instructions = """ Output a JSON dict with the following keys: - "success" (bool): true + "exp_continue" (bool): whether exp_continue is true + "success" (bool): whether exp_continue is true "best_amplitude" (float): The best amplitude found in a successful experiment. "advised_amplitude" (float): The next amplitude to try. """ @@ -2658,6 +2661,13 @@ def next_parameter(self): "You are a very smart and helpful assistant who only reply in JSON dict. Keep everything in a same line in the response.", dedent=True) res = chat.complete(parse="dict", expensive=True, cache=True) + tuning_env = TwoQubitTuningEnv() + results = tuning_env.amplitude_tuning_results.get(self.frequency, []) + n_points_tried = len(results) + if n_points_tried >= self.n_points_to_try: + res["exp_continue"] = False + else: + res["exp_continue"] = True return res @text_inspection @@ -2665,7 +2675,7 @@ def best_amplitude(self): tuning_env = TwoQubitTuningEnv() if self.frequency not in tuning_env.amplitude_tuning_results: return { - "best_amp": 'There is no successful experiment yet.' + "best_amp": 'There is no successful experiment yet.', } results = tuning_env.amplitude_tuning_results[self.frequency] amps = [] @@ -2675,11 +2685,11 @@ def best_amplitude(self): # the largest amp if len(amps) == 0: return { - "best_amp": 'There is no successful experiment yet.' + "best_amp": 'There is no successful experiment yet.', } best_amp = max(amps) return { - "best_amp": best_amp + "best_amp": best_amp, } def _experiment_history_to_prompt(self): @@ -2765,11 +2775,14 @@ def run( class ConditionalStarkTwoQubitGateFrequencyAdvise(Experiment): + n_points_to_try = 2 #15 + _rewrite_json_requirement = True _experiment_result_analysis_instructions = """ Output a JSON dict with the following keys: - "success" (bool): true + "success" (bool): if exp_continue is true + "exp_continue" (bool): if exp_continue is true "best_frequency" (float): The best frequency found in a successful experiment. "advised_frequency" (float): The next frequency to try. """ @@ -2826,7 +2839,7 @@ def next_frequency(self): "analysis" (str): An analysis of the current situation. "finished" (bool): whether the experiment is finished. "current_best" (float): The highest control frequency from a succeeded experiment. The value can be None if no experiment is successful. - "new_frequency_to_try" (float): The new frequency of the control qubit to try. If the experiment is finished, set this to the optimal amplitude. + "new_frequency_to_try" (float): The new frequency of the control qubit to try. If the experiment is finished, set this to the optimal amplitude, """ @@ -2835,6 +2848,12 @@ def next_frequency(self): "You are a very smart and helpful assistant who only reply in JSON dict. Keep everything in a same line in the response.", dedent=True) res = chat.complete(parse="dict", expensive=True, cache=True) + tuning_env = TwoQubitTuningEnv() + n_points_tried = len(tuning_env.frequency_to_good_amplitude.items()) + if n_points_tried >= self.n_points_to_try: + res["exp_continue"] = False + else: + res["exp_continue"] = True return res @text_inspection @@ -2851,7 +2870,7 @@ def best_frequency(self): if best_freq is not None: return { "best_freq": best_freq, - "best_amp": best_amp + "best_amp": best_amp, } else: return { diff --git a/leeq/experiments/procedures/two_qubit_calibration.md b/leeq/experiments/procedures/two_qubit_calibration.md index 2a07eb8..e26261e 100644 --- a/leeq/experiments/procedures/two_qubit_calibration.md +++ b/leeq/experiments/procedures/two_qubit_calibration.md @@ -2,8 +2,8 @@ ## Background This procedure should not be used when only calibrate a specific aspect of the qubit, such as frequency or amplitude. ## Steps -- Run ConditionalStarkTwoQubitGateFrequencyAdvise with `duts`. Proceed to the next stage whatever the result is. -- Do an Iterative two-qubit amplitude test at the advised frequency on `duts`. If failed, go back the Stage 1. If failed 5 times, proceed to the Fail stage. If succeeded, proceed to Complete. +- Run ConditionalStarkTwoQubitGateFrequencyAdvise with `duts`. Proceed to the next stage if exp_continue. If not exp_continue, go to Complete stage if there is a best frequency and go to Failed state if there is not. +- Do an Iterative two-qubit amplitude test at the advised frequency on `duts`. Go back the Stage 1 whether the result is. @@ -11,7 +11,7 @@ This procedure should not be used when only calibrate a specific aspect of the q ## Background This experiment searches for the optimal amplitude for the two-qubit gate at `frequency` on `duts`. This is not a single step experiment, but an iterative one. ## Steps -- Run ConditionalStarkTwoQubitGateAmplitudeAdvise with `frequency` and `duts`. Proceed to the next stage whatever the result is. -- Run ConditionalStarkTwoQubitGateAmplitudeAttempt with duts=`duts`, frequency=`frequency`, amplitude = new_amplitude_to_try. If failed, go back the Stage 1. If failed 5 times, proceed to the Fail stage. If succeeded, proceed to Complete. +- Run ConditionalStarkTwoQubitGateAmplitudeAdvise with `frequency` and `duts`. Proceed to the next stage if exp_continue. If not exp_continue, go to Complete stage if there is a best frequency and go to Failed state if there is not. +- Run ConditionalStarkTwoQubitGateAmplitudeAttempt with duts=`duts`, frequency=`frequency`, amplitude = new_amplitude_to_try. Go back the Stage 1 whether the result is. ## Results Whether there is a success experiment or not. If so, what is the amplitude. \ No newline at end of file diff --git a/notebooks/SimulatedSystem/AITuneUpDemo.ipynb b/notebooks/SimulatedSystem/AITuneUpDemo.ipynb index 3990b4a..52e67c8 100644 --- a/notebooks/SimulatedSystem/AITuneUpDemo.ipynb +++ b/notebooks/SimulatedSystem/AITuneUpDemo.ipynb @@ -6,7 +6,9 @@ "metadata": { "scrolled": true }, - "source": "# Automated tune-up for single qubit" + "source": [ + "# Automated tune-up for single qubit" + ] }, { "cell_type": "markdown", @@ -19,13 +21,18 @@ { "cell_type": "code", "id": "6bc05f9e-e8b3-4aa8-8382-9d6c202230a8", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-08T20:57:22.349734Z", + "start_time": "2024-12-08T20:57:19.073463Z" + } + }, "source": [ "from simulated_setup import * # Change to your customized setup file\n", "from leeq.experiments.builtin import *" ], "outputs": [], - "execution_count": null + "execution_count": 1 }, { "cell_type": "markdown", @@ -38,7 +45,12 @@ { "cell_type": "code", "id": "48ed15bd-87a4-4757-957e-9bdc393af3fa", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-08T20:57:23.217620Z", + "start_time": "2024-12-08T20:57:22.350915Z" + } + }, "source": [ "simulation_setup()\n", "\n", @@ -67,19 +79,253 @@ " dut.print_config_info()\n", " duts_dict[hrid] = dut" ], - "outputs": [], - "execution_count": null + "outputs": [ + { + "name": "stderr", + "output_type": "stream", + "text": [ + "[2024-12-08 15:57:22] [INFO] [labchronicle.chronicle] Log started at log/zijian/2024-12/2024-12-08/15.57.22\n", + "[2024-12-08 15:57:22] [INFO] [labchronicle.chronicle] Log started at log/zijian/2024-12/2024-12-08/15.57.22\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAUQAAADHCAYAAABhuSoEAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAACDaUlEQVR4nO2dd3gUVduH75lt2fRegECooYVQE3pXQEAQRUCKWLAA+ilW7B0r2EVRFAWkiBSlCIQOoRN6SwgJ6b1vts73x5ZkSUDABKLv3NfFFWbmzJlzzu48e8pzfo8gSZKEjIyMjAzirS6AjIyMTF1BNogyMjIyNmSDKCMjI2NDNogyMjIyNmSDKCMjI2NDNogyMjIyNmSDKCMjI2NDNogyMjIyNmSDKCMjI2NDNogyMjIyNpS3ugBfffUVH330ERkZGURGRvLFF18QFRVVbdqTJ0/y2muvcejQIZKSkpgzZw5PPfWUU5o33niDN9980+lceHg4Z86cueYyWSwW0tLS8PDwQBCE666TjIxM7SJJEsXFxdSrVw9RrLl+3S01iEuXLmXGjBnMnTuX6OhoPv30UwYNGsTZs2cJDAyskr6srIwmTZowevRonn766Svm26ZNGzZv3uw4Viqvr5ppaWmEhoZe1z0yMjI3n0uXLtGgQYMay++WGsTZs2czZcoUHnjgAQDmzp3L2rVrmT9/Pi+++GKV9F26dKFLly4A1V63o1QqCQ4OvuFyeXh4ANbG9vT0vOF8ZGRkaoeioiJCQ0Md72pNccsMosFg4NChQ8ycOdNxThRFBg4cSGxs7D/K+/z589SrVw8XFxe6devGrFmzaNiw4RXT6/V69Hq947i4uBgAT09P2SDKyNRhanpK65YtquTk5GA2mwkKCnI6HxQUREZGxg3nGx0dzU8//cSGDRv45ptvSExMpFevXg4jVx2zZs3Cy8vL8U8eLsvI/G9yyxdVapohQ4Y4/t+uXTuio6Np1KgRy5Yt46GHHqr2npkzZzJjxgzHsb07LiNTV8jPLCBm0U62LtmNJEn0G9uTAeN74hvsc6uL9p/ilhlEf39/FAoFmZmZTuczMzP/0fzf5Xh7e9OiRQvi4+OvmEaj0aDRaGrsmTIyNc3roz7izL7zYNNzjj+SyI7le/hi76xbXLL/FrdsyKxWq+nUqRMxMTGOcxaLhZiYGLp161ZjzykpKSEhIYGQkJAay1NG5majK9YhWSQkyWoTJYuErqT8VhfrP8ctdcyeMWMG8+bNY8GCBZw+fZrHH3+c0tJSx6rzpEmTnBZdDAYDcXFxxMXFYTAYSE1NJS4uzqn39+yzz7J9+3YuXrzInj17uOuuu1AoFIwbN+6m109GpqYYMW0IviHejmOfYG9GTBt86wr0H+WWziGOGTOG7OxsXnvtNTIyMmjfvj0bNmxwLLQkJyc7OV2mpaXRoUMHx/HHH3/Mxx9/TJ8+fdi2bRsAKSkpjBs3jtzcXAICAujZsyd79+4lICDgptZNRuafIEkSCUcvErvmIIEN/ek7pjt3TBlgHTYDLaOb16hDsowVQQ4yVZWioiK8vLwoLCyU3W5kbjoWi4WpnV8gIe4iokLEYragUCp4au4jDH6w/60uXp2gtt5R+SdGRqaOYTKYSIi7CIDFbLH+tVg4dzDhFpbqfwPZIMrI1DEUKgW+IVZ3GoVSRFSISBaJkCZBf3OnzD/lP+eHKCPzb0ehUPDLha84tPEoe1YfILChP/3G9qBBi3q3umj/eeQ5xGqQ5xBlZOo28hyijEwdQJIkjAbjTX+u2WTGYrHc9Of+ryEPmWVkroHCnCJWfbGemEU7yEzKoeNt7Rh0f1/6julRq889tuMUf367kT2rDqBx09B/bE9GTB8sD59rCXnIXA3ykFnmcn55azm/vLkc++siigIWi8TStO9qdT/xPYEPUZxf4lhtFkWBbnd25o3fn6+1Z/4bqK13VO4hyshcAxazBQTA1n2wWKSK87X6XOehsiSB2WxBr9Ozc8U+ti7ZRXpCJr3u7sqACb1p2LK+I218XCIxC3eyZ80BmkQ0pMPASEryi9nx215cPbT0G9uDvmN74OHjXqt1+Dch9xCrQe4hylxO+oVMfn5zGTtX7ENfpqd+8xAGP9CPMS+MrNUwE1t+3cXKz9ZyZn88CqVI50HtGTdzFHv/OMiSD1Y5HLdFhYgoCqwp/gWVWkV+ViFjQqYgKAQsJgsKpYjZZEEQrDZdEAQkSaLHyCjeWPFcrZW/tpB7iDIyt5CQJkG8sOAJnv72UXLT8wkOC7wp8XZ6jorGxVXD5oXb8fBx57ZJfWjVtTlbf92FIAoVjttmCxYzmE0WVGow6AxIkoRksvZ3zCZrOnv3x94P0hXrar0O/yZkgygjcx2oXdSENL55DtLTurzAxROXUChFQGDd9zEMe/Q2+tzbnSNbjpN8OhUAjauGgRN7o9GqAfCr50O/sT3YtXIfRr0JAN9gb3Sl5eiKrSo5/g18uf3+fjetLv8GZIMoI3MTyM8qZNfv+9AV6+g5Kpp6TZ01P80WCwcvpLA/IYXIhiF0bRZKyulU0uKt6vH2Hh5Adkou7fu15YeTn5J06hKZSTlE9m2NRluh6alUKXlp8VPoSnTEbT1Jo9YNqNc0GLPJzLEdp9C6uxDepZkcVfIy5DnEapDnEGVqkoVv/8bPbyxDQrLO3VkketxVMXeXmlfIfV8uIbekDFEQsFgsBC46jiKjpEpeCqWCB98dx73PjbjZ1ahTyI7ZMjK1jF6nJ/aPg5zae44b7SeUFevYtXIf8XGJjjxO7jlr/b9N2BXg+I7TjntS8grJLSkDwCJJYJaqNYbNOjRmWcY87nlmOCd2n2Hf2kMYyg0AJJ9JZfvyWIryqo8dVGYwsuVkAicuZdxw3f4XkIfMMjLA10/9yLp5m9HrrAbGr74v0z59gF53d72m+y0WCx9N/opty/ZgMljn7EKaBPLUt48R0sQaY9w6D2gd/trPAfh7uKFSiBjNFkRBQBIFzK4qFGVGJ1ef+COJTGo6HYDSQqsBVbuocHFzoSjXaghFhUjUkA68svRpNFoNkiTxxorN/HH4NAaT2Voubw9euas/fVo1+Qct9t9EHjJXgzxk/t/jDu04x+IDgCBAnzE9eHnxU9WmN+iNpMVnENqyHgqFgtKiMkZ63++URhQFRkwfwuNzJnPu0AW2L91NWXE5ve/pSmTfNhj0RrKSc2jYsj5FOj2bjp9nf8Il2jeqR98WjUjcfZ53xs5xGNjrYf7pTwkNr4/ZbCFy5mfO5RIERnZuw1ujb7vufOsKstuNjEwt4uqhpchYgiRJiKLVt8/NQ1slXV5GPj/MXMyOFXspLynHO9CTAeN7M+HVu1GqlTb3lwq/P1dPLYIgEN65KeGdmwJWn8b3J37O7tUHMJYb8W/gy6DJ/Zj0xr3cEx3heFa9kVH4BnuTnZLrGHI7YV8PkXA8zyoVZkGtVbPg9aVs+GkrwqhmSAoRBGvsc4sk4e6irqWW/Hcj9xCrQe4h/u+RnZLL1l93seO3WHyCvOk3tgfdR0bh4uocjXHd9zHMeWRulfs/2/Mu3gGebP11NztWxNKgeQh9x/ak67COqNQqp7SL3l3BgteXOuYT7fyc8KXDpcdsMqNQKshNz2f70j388PJi9DoDldeE1Vo19795L77BPhzefIxzhy7Q/c7O9BvXEzdPLePDpgJg8tRQ3tIfXbgfjev58+jY/vRr3RSN6t/bH5J7iDIytUhAAz/ufW7E367e2ucB7Ts9RFHEYrFK/CedSuHcoQSST6VQVqQjNLw+bbq3qLLXWaFUOC1sVN5tsuXXXWz6eRuHNx+nQYsQmt/bhZzGnlx6rCOUm3A9nI5bXAai3oyx3Mi5gxdw89Ry9mACKWfTOF/fl7A2oUT0ae3IW1mkx/1AKh4H0hj99lgGR4bXcOv9d5B7iNUg9xBlroRep2fdvBhiFu0k/sgFOvSPYMCE3nQf0ZkRXvc7jBuAIArc8/QwHvloklMepYWl/PntZmIW7+DS6VSi7ujIbZP60CQyjPubTXcIRwBkTu2C5KpyjJa9V5/B5Xyeo6doN8x27PcuOP8FF44lsenn7exfd5jQlvXpf18vhj16G+7ebrXdTLWO3EOUkakDaLQa7nryDu568g4kyepXGH8kkfkvLwGqij2YTRaKjeWsu3SKPy+doMRoYGhoa4Y90Y8xz49w5HFi9xkWvGbLo9JQWhJs2+xsDtSC/b+XbcGzU1l0oudd0fS8K9rxjMsp0xv469h51sWdIb9Ux+3tWjCsQ0vq+fzvdgLkHmI1yD1EmWsl42IWE5tMcyxq2FEoFfQcFcXE10bzSeE+NqSeRkSwOmcjEOLqydY7ngTgzP7zPNH1JUSFgMXs/DrqWvlT2qkexmB3BKCTwg33fSmc236mWn/CwEb+DJ7cn/teGYVCobhq2V9Z9herDp6yuvrYjKaXqws7X3/snzdMLSP3EGX+FaTGp7Nt6R4SjyfRZXAHeoyM+k8M0aojp7iU5QdPkj+8BerUYlzO5aIosfoxTnrzXu6bOQqA0p07AavTtVAmIuQpyTMZ2djgHM0a+rIs+zjZTzfE5UQJrrEFKEoqDKvL2VwEvRld6wAaNwnmqaeGwaMw68NlJJ5JRZNciOZiAYKtZ/j9iTnoJAu/7T/BvvhLRDQMpn/zMJL2nGfPmgP41/Ol75geNG0fRpne6CiXMrvU+qwiPVvDd9N1eCe0bi43sznrBHIPsRrkHuKNseXXXcwa/5k1gLpgHbZptGp+Sfwan0CvW128GuVMWhZjPl+MxSI59dT8fj1Bm2B/np0/jSbtGgHwR/JxPjwWQ/aFcpQpGrBt4TN7mDCFl1sluSwgFhupP+0MgkFyzBFmT4rEFOgGFsk6P2kXqBVsSjeigDK7lPpLTzFgQm/ueude7pr9C2aLxTpMliT8vzmAotSIqLAuCFnMFia9cS/Bd7Xnwz+2UxhzFq+tF5EE65wkFgl3HzeWps1DrXFeIa8r3NIeYocOHa55E/jhw4f/UYFk/r1kX8q1SlJVEjTV6wyU5Jf8pwyi0WBkV8xRzPa5vkrvxuPzH2Ps7VGkJWSw/OM1uPu40eeuKIYN/T/+b/EatqZeQJIEJAkktQQ2fUJEEMssiAbn/onZ0+b2IwoOYwi2LX6ibV7Rz43ZO94mvHNTDiemYrLYpb4kkCQUpbaeoH1+U4BDm47y5tRBbJz5EO9e/IYd25PAIjn0wUrySzHoDHXWINYW12QQR44cWcvFkPkv0LxjY1xcNehKyhFEq4hBgxYh+AZ73+qi1Rin9p7jpSHvUogZcVwEFne11YgIAj5uWjq1a8Irw2exb+1hRxt89vh3PDRrAv37NGPXiSQMZjPYhs8YJFBbe2VmLyXGIDWqTIPjeZrkQspb+DmeYbe9koTjnCIhl+lRL9K8UxNe+eslgr09yCgodqxAm0O9UFwqrKiEBCd3n2V08MNMfG00PQdEErtoNwadwVHm5h0bo3WXh8wyyEPm60GSJE7vPUdJQRnt+7cF4NDGo1w4mkTUHR1o1qHxdUtMSZJE4vFk0i9k0nFgBFr3qjtGbhXr5m1mzqPfAtaenbG+B/qGXjzyyBDunzgQhSgy3GMC5aV6p/va9mrFx1tep9xoYubzP3Bg/1lczuehKCyntLs3uk6euO3IQ3ukBEklYvbUIJabUJQaMXlqMLTwI7JrOLPefxBJkpj+zvecOZOM+84MVHnlmHxcMPq78vueDwnw9WTX0XjWbo+jV8cWDOoewcUzKUwd9CbGEj2KIr1jSN5xYAQfbHyN8jI9+9YeJi0+g+4jOtOodejNbdjrpLbe0RsyiAUFBfz2228kJCTw3HPP4evry+HDhwkKCqJ+/fp/n0EdRzaI18bWJbv59tkF5KblA+Di7sLI6YN56L3xN5zn0W0nmfPoXFLPW3UAVRolfUZ357mfplnnJm8xBzYc4aU73nMSXbDTulsLXlr8FC8PfY+kUymO3pYdVw8tSrWColyrko0xSE3Okw0xNHe1JjBJqOOUYHJxDIeVOWX4/JWAMr2Yh2aNx/2+Zrx/dBNZ5dY8hDwJ9Wk1kso6tFbqTYRtuUTZKauqjQQI97UnN9TD2jMFFIXleG+9iDo+jyEPD2DGd3V/Vfly6oz817Fjx2jRogUffPABH3/8MQUFBQD8/vvvzJw5s8YKJlP32bhgq8MYApSXlLPm678cxyajiaRTlzDbXsRrIXbNAdITMh3HRr2JzQt3ONRdbhY5aXnkZ1UMM0sKSklPzKTL4A4sSvqGIQ8OqHLPqdhznNh1mm8Of8g7f7xYZchZVqxzGEMAfUu3CmMIYBLBonUYQwCTn5ZmE7vy3bFPGPvCSFadj3MYQwBBr8aiqtiXLGSXUXoy3WmhJ72eq8MYApg9XfC7oy2fx77H098+SmFZOWn5RdfZQs7kZxWSk5r7j/KoC1y3282MGTOYPHkyH374IR4eHo7zd9xxB/fdd1+NFk6mbrL3z0Ms+WAlJ3efdToviAKunlqKcouZ//KvbFu6m9LCMjz9PRhwXy8eeGfs3w5/tR5aLPYV1UpOzg+3ncGgyX2Z9Ma9KGtxD+7OFXtZ9tFqzuyPRxAEmnYIQ6lScP5QImaTmUatG3Dn1MHcPrkv63+IqeJ/+PHD33Dwr6M88M5YQsPrc+5gAgASknXKD6wGzyKh0FuNlkIQMUsW2wKL1U8RsCUW2B2az+vrf0c7JZ1DXYzQy9vaOxUFEG3pBQkkAUFjW2hRSEhmwbrV0GhGUCmsHVqLdTEm9XgysxOXUtqjISdy87BIEs2D/ZnQswN3R7W95vY6uPEov876neM7TiNJEuFdmjH62TvpM7rbP/wkbg3XPWT28vLi8OHDNG3aFA8PD44ePUqTJk1ISkoiPDyc8vLy2irrTUMeMl+dRyKf4eKJZCp/c9QuKu5/cwz9xvXkxK4zvHffp1Xu+2Djq3Qc2K7aPO07LMwmE7FrDrLwnd9IPJZcJd13xz6hcduGNVKP6pjUbDrpiZlVlWUqIQiw0bycE7vP8Mc3fxGzeJeT6IIgwCMfTWLgxN5sW7qH7z9bQ1G5nvIWfkgqEZeEfNQlRh6YNhSvYY35I+8821LjEQuViNlKBJ2I5GJBcrVg8TMheZsJfv4c6uRyJI1IWbQXBfcGYfZXAQJCgQL/Ej0anUTbxkk0MiSRvV0gcYcHkb060WHkIJLcYN6SLZCnQ3smB5f4PAqGNLUu2AgVA0WtSsmBd5+45vZ6qtcrnNpztuK7IEBQwwAWJn59Xe1+vdQZx2yNRkNRUdXu9blz5wgICKiRQsnUbZQqBYIoItmDpytEQlvWdwgjVBVAsO6vVSir7pyIz8hh6d5jrI87i9ki0T8sFN+TOeSlFVT77I8mf8UdUwYy+MF+tdJTVKgUDvkvayVwMo6iQkQQBIxmM6cVBuI6+JIR3A1VRgna09m4Hs8Ek7WuaSodh7tbuBDUAJPZgpirQsxSYogMwdfdlXteGMnyvcdIO1SOOruy87qEMUJnbT/bwzPfaYZQZkZ72PruWTxsbWkGQSdSXuxCcamatBxfdB5qstt5kxAQjCnEA0VCKuV7L+L72xEA1E0EfGaINBt2CZQpZCX7kBYfiK7QDcV1ztMqlJd9F0QRherqO2TqMtfdQ3z44YfJzc1l2bJl+Pr6cuzYMRQKBSNHjqR37958+umntVTUm4fcQ7w6iceT+GPuJrYt3Y0kSfQb04Ohj95G08gwwCqeum7eZrYs2snZA/G07dWKAeN7M2hy3ypGcfyXSzh+KcPhY+e5Iwm3A6lX7KHZFyre/+sVOt0WWeN1O3/4Amu/3cS2ZXtQqBR0G94JhVLJwb/iKMgqosddXRj84AAMDb14eN4Kq4O0zd8PQSA8Lod7B0Yx7LHbGLP3F+KLsyv8ByVQlCm4R9uZe6IjSMkr5IVf11dZn2kS6EvHDsHkaIvYmHbGuYD2vGwr92KGCkWyumKYbV1GqdgkKEkEzY61bs+z9cIbzleibSMi2D4KyQK6Ei2BGfdwd1RbOjdpcM3tlXQ6hT/nbmTrkt0Y9Ub63tudOx65zaH9WFvUmR7iJ598wj333ENgYCA6nY4+ffqQkZFBt27dePfdd2usYDK1Q0m5no3Hz7PuyFlKyvXc3rY5IZnlHP7jEBeOJtH9zs6EtW3ImX3nObjxKBG9WhHRuxUZiVns/H0vwWGBRI2LxvuJtqhHKZCQ8G0UgU9ohSS+Sq2k3uC2+AYrUSY0xy88DEvDYL7YFMvWkwk0D/HnjvYt6d2yMSaLxXlP7mXiCJdjf6mXfrgai0Wi023tanT1uXnHJjz17aM8+c0UAIx6I7tX7icnNZec1DwatQ6lXtMgEvSVYqCAw0A98vH9RDYMYenhkyTl5mNR2icOASRciw20zS7nYkgmS+NO284608DXi47eoaTkFbLZfA6LaKnI43IPpirNJdjytP+1TS9WamNBcVk+AqiVAs2C/Kjv6+xAf+5QAlsW7WT/+iO06tqCvmN7OLV5o1YNmPbZgzw+ZzJAnfAE+CfcsB/irl27OHbsGCUlJXTs2JGBAwfWdNluGf/lHuIzC//kr2PnHRv6XQ+n4bnlomNxwN4Dq6zRZzE7n097vTH6cDcEQbD1biQifRuwrP8DAGw7dYHpP61GIQqYLZJ9DcHRm7Kff25Yb5oE+vL91gMcTkxFAtqo3Ag4mM6FHWcw6k0ENw7EJ8ibpJOXKKsUVN0uhDBz4ZP0v69XrbXXV0/OZ9WX653aQ+2iYkn2fD7/azdrj5yhoKwcDxcNd3QI58lBPRjxyc/klpRh9jJiDjEgeVhAktCcKcNrVRYW0Z2ifo0d7XI59h6jIIDJ14gl2IjkZgEkPNXWOfoivYvVUTtfgSJJg2gUQBJQuxhwcTOgK9JgNKoQ9CY8N13A9WyOw/K6dRfwm6xA20FEEKAwx43k08Hkpfni7ebCjtesbjhJpy7xcNsZju+G/W9tt/m1UGd6iHZ69uxJz549a6wgMrWL2WLh0IVUjialA9agSMqcMjTn8pCoiPsrVZKPqvy38nmLq8KxImp/n0uM1hf1Umk+azKOYWpajrlAgZivxGKxdkfsvSmrkRQo1Rvo1bIxvVo2Jqe4FItFItDLHQBdiY7c9ALqNwtGEARO7jnDUz1fddTHrgpTVlyO2WQmbusJdq7Yh9pFRd8x3WnVtcV1OYQb9EYObTzK7pX78QrwpN9YmwBCic66HbFSe5SX6tm3cj9PDe/K88P7cDE7n0b+Pihte4VL9Qbr3GmhAl+1iaCsJBRFBnTr9JQfBX198Fp3nvLmvugb+4CyolelUJrxb1CAX0ghJQVashJ9kP7QEyxeRONuwqevhDHYnTOnGlJaokUwWMe9rp6luLgZCAzNxzuwmOyznlz6yxt1cgGaS4VO3dCSWIncPDUlkxtg8dZgzNQiFCsQkCi1CT5YPwPrZ2r/btj/1lSb10VuyCDGxMQQExNDVlaW075VgPnz59dIwWRqlqk/rmL32SRE2xfWbV8qnruSrRv6K6UTRAEvPw8Ksq/sl1Y/zkBOQw9KsL48XmotY5p05M9LJ5ixbyUiAhZfCfxMmM16VEfd8NW4UqQrd+z/bRzoS8/wMEee/h7Oijhady0Nmle46IS1CaXL4PYc3HjUYZybd2pCuz6teabfG5zcfcaxmLPy83UMnNibFxZc+2rpIxEzSI3PcOSx7KPVjHl+BL1GdeXo1pNkJmVXtJEAs8Z/htbDhV8vfUvTID+nvMZ1j+TXPUdp3vUkfvWKkMyABLmFCsqPmtGkFiOlFeN6Khuzm4rcx7vg5aalxFRE12HHUagsSBYIbAjiBxfRn5MwiGAQICUpjLJO9UCQEKSKT66syJWyIlfy0n1wWL/GYAjzwvV0jlP5cp5qSFk3bzDbxBV9yxEKFXgmejK2W4UXQGh4vVpt87rIdRvEN998k7feeovOnTsTEhLyr/9F+F8hNdfqZGzvpSmK9Njd1yqzPGMeXv5enIo9y//1eMXpmiAI3DltENM+exALEgeyrW4xnf0bYjCZeW33OuszqDRvpoA3+kQxamA3ThxKYMW6/US2aMDwUd0pyC7i98/WonV3ocddUQhaNVtOxlNQVs6Ats1oUGk+y83LjffWvUxhThFHt52keacmjvgjGYlWR+7K/oAZFysM2LWQdSnHKQ9BgMzkHB4e1onooR2Z88hcNszfat39YWszXXE5ZUU6XD20nDt0gYN/xdE0Mownbu/K9Nu78frxGZRIOBYvTOmSdSuEpaLdFaVG5j08ii7NG3IuL5Evk45an2/rNBrTbAltVXOIPUiXv3dC1f8LFc+ojCnI5sitqLjHXSvy2/2jqNc4iG2nLnA2PZseLRrxzp8zObjxKJsWbKfzoEgGjO9FWmEJF86mOLUXXH+b10Wu2yDOnTuXn376iYkTJ9ZGeWRqiahmoSTlFDgmqEz13BFOZSNVmjds1bUFnn7W+ZhGbUKp1yyYtPgMh9uMWqsmemgnBEFAgUDXwDAA/jx8mtd+20S5qx6aAwocDsDKDD1z7/+CH4xfOOYoYy0S3ym+sc5N2lxL3vl2DcV9w7BgnWv86M8d9G7ZmK8fHOlUDy9/T3rf4+z0275/BDELd1jrYRM6jbTFFLlW2vVuzaFNxyrNlSpo3bUFYP0h6DqsM1sW70KvMzjaI7RlfQRRYHL4k6TFZzju1XpoeWvV87Rv2p5dOTGONV9tW5GiPyWrZJjtx8gQ4sGD81bQJMiPn6aOJEATRLY+075ojbazQOk22xY8QJ1WjL6pb8VEY8UEbfV/BTAEu6OuFPhee7oMY2NXaz/S5i0u7chg4ouvUjS6DSaFgCgIfPX7NoJ+PQEFVrGObUt388qmnejqe+AdqEWbU+KohyBef5vXRa57UcXPz4/9+/fTtGntLqvfSv6riypZhSVsOmFdULktojmuiOxZfYCclFy6jehCo1YNyE7J5cz+eNr1boWnnwfnDl3gwPojNI5oSJfB7VFfFr5SkiRmfryUzQdOUx7qiaQUUGdnoSwuRnukGPUFHZJWib6BJ+qMEhTFhmrLVtQ9FGMDDwwNvBxb1zQqJYeu0Uk4KzmbXb/vR61V03NUFN4B1y83lhqf7phDrE7YVq/Ts3/dEZJPpxI9rCNNI8NIOpXClIgZTukEQWDKhxMZ/cxwCg35xBXsRxAE2ntHodCpefG1nzlw5DzlzXwx+VVs3Vv/wgM08PXieN4pXtgzj2KLhhyTK2KqgeCX41GU2ubyXJSUdg/C7KbCfXcGykI9pk7eWJq6o4jNQ7xYiqKlK6rObphO6TDHlSIoJNT1BCK7t+Wlr59Bp5F46s0viT92Ede9hSjzTZS18qc83A9DQ28ktQJVejH+i4471S1vRDgmP1fMvlrEgnLcD6bRpEEAn897Et8g7+tu8xulzog7vPDCC7i7u/Pqq6/+feJr4KuvvuKjjz4iIyODyMhIvvjiC6KioqpNe/LkSV577TUOHTpEUlISc+bM4amnnvpHeVbHf9UgXo20hAzen/QFp2PPAdZf/E63RTJz0ZN4+npUe8/2ZXv49tmfyU6x7mG1KEUktQJFmU1/T6OgYGgL9GHeDiOnSivGd0M8Yp7O8ZzKAggWFyWl3RpQ0qkeYf4+/Pn85Fqqcc2Qm57PpKbTMJTbBFglaw/wlSVP0+fe7k5pd59L4t2VMSTnFlbJR6tWsfa5ycxas5WtJy84NA3d9yfhtj8dsdx6XNrNi/yJIZj9rT9MnpYy2nmk4hZk3ausNBuJNF0kJKAY0TZUj01qTJbG27Gtz18dyOjQyex4K44Vc/6sErpAUgiUtwyguGt9An6Mc6hxV8bsqkQwWhCN1nL5hnjz4Lv3MWhyv3/SnNdMnVllLi8v57vvvmPz5s20a9cOlcpZQHL27NnXnNfSpUuZMWMGc+fOJTo6mk8//ZRBgwZx9uxZAgMDq6QvKyujSZMmjB49mqeffrpG8rzZlJfpyUvPJ6RJ0C2dfy0sK0dvNDlWdeO2nnAYQ7CuKh/8K47E48lE9mkDQGpeIT5uWlw11pdxw8JtDmMIIJosUGlOyeTvir6JcwhOYz0P2j/WjyfGDSQrOYcPJn3uJHgglpvwP5XH+188RvcWjWq+4jWMX4gPS9PmsXvVfg5sOELTyMb0ubcb9ZoGk1FQjKtGhafWKvKwNu40l/KqLla9Pfo2botoTnZxKZuOxztdc9uXgaivaNPSnt6Y/SreOV8fHa6+FcINnmo99f2LHceSBNmePk4zjLmGbPbn7eHRjx+j39gezHlkLglHkxzXBbOE9mQW0z97gJ6vTWHP0j3Me36hU7kUZSan47z0Av76cesVDaLZbCYtPoN6zYL/NtbLreS6DeKxY8do3749ACdOnHC6dr0v+OzZs5kyZQoPPGD1X5s7dy5r165l/vz5vPjii1XSd+nShS5dugBUe/1G8rxZpJxLY8Eby9izaj+GciP1mwcz+MEBjHl+xE01jIcSU/guZj+x55OxSBKtXN0JOZjJ+c0nndLZfc607i4s3HWEZXuPcSErD5VCQeu2fugCdGQUJOKKbQHAgmOey7FwYLC9qPY5LRtbzEWojp1g2u3daBzRiGPbTzltkQut50ff1v+eKRl3bzcGTe7nMAarD55k4acxnE7LQiEKtG7ljynEwMmUHERJhb2igiChEC24+c3g3WO3sSXZ7jFtbUmFYEFSK8Bgts0XglB+WVQ/yepLaJ9zNNsdsm3HFpOAiAWLRUCyf88Eid8vnmTjxi/xXZHNhcv2jNu3J47sGoGLq4Y7Hh7I9y8uQrCFLXAIWtg9FGw+rVqPqoKypUVlLHhtKTGLd1KUU4ynvwf9x/Vk8ltjcPOqe7F2rtsgbt26tUYebDAYOHTokJNkmCiKDBw4kNjY2Juap16vR6+vEPSsbq/2P2XD/C1sW7rb8dKnns/gh5mLGPxgvxua77pWLg9BOW/LAfacS3K4pSVvOUPermQnHTgJHDF8wyIbMeqlLxzXjGYzB00XkQotCFPqo2vrjtvuAsQSE2XRXpi9lbhvzcflbBmq7DL8Fh2jrF0QuoigSnlYWBt3hkb+Pry2/Bm2Ld3D1iW7MBnN9Bvbg37jqvdvvVI4zavVHa7/h/p6ubxc763e6vDnM1skDhuTkArN0EBAdDUTWKJDbbLQvmkiHZvHc77EhRXJVp8/oa2IMldB4/IyWgbm0OPHHMqPlPLjkobotGrr8NWusg1cKvKm3KSktU86ftoy/JUllJg0qMqNlGYrSTnoim5vIUX+Hrjc70q5RUV6qSeZpR74LDhK1r5CJ08DQYBxM++i/329cHHVIEkS7t5ufHf0E2IW7WT3qv2EtQ2l8+2RlOSXsuO3WDSuGvqP61lligDg2PZTrPx8neO4KKeYVV+sp0P/CLqP6FIbH8c/4pZF3cvJycFsNhMUFOR0PigoiDNnzlzhrtrJc9asWbz55ps39My/I+tSDn/O3cgfczdWEQmw73yoDXadvcjKAyfZeiqBEG8PhnVoxb3d2qEQBafg5mXtgyntFIImPg+FzoQxxB1jkDsnGvkRrjLRxGLdBCYIFTE9lKe1oJCw+Jko7a6ktL8vAEKZGY/NuSjzKtw81OklKEsM6CKCnPfsSqBQiHj6eXDn1EHcOXVQtfUwGoxsmL+VLYt3cir2HG17trzivmg79ja390p63hXNkIcH0K53za2CmswWVh86xR+HT3MkMZV2DUMY2rEld3dpiyiIiIKA1rOUes2yCWycBwJcyvUhJTOQshItxXol6flebM0M56zRPpUj0b7eRbpGJBDhnYarAD6ihKkz/N/9BZSZ1Wy/FM6ek77os1zBLGDxNpGn1HBE54OhVEV4gxTOeBaTlunLhYxgVFmlKCjA5CZQluYFCFAuoMhSURIdTklnCddjWbgdTENRZkSSIOn0JVZ/tYELRy86tfnkt8bw0HvOEn92QY8rYf+M7N85+1+Fsm5u8btug1haWsr7779/RcfsCxcu1FjhbhYzZ85kxoyKlcKioiJCQ2tGQv2n15aw+ZcdTgsHANFDO3L7/X2vuGDxT9AZjDz2w0rHVrmknAK+3hxLqd7Ac8P60NDPm2X7jqE3mpFcrF8Bfbi/dVhrG2vFpWRyeNlGGvr78NUDI/n9wAk2n7DObwkWASwCigwVKoOSQf2bolYoif12J+LCjCrbbVuGhzIgKJRLXgr2xCfR0M+bYR1bMaZr9VJglTmwIY7Pp85zLL6c2HmaY9tPEdTI/4riDpe3+ZZfd7FzxV7+LF104416GQcvpPD6b5scbRyXnMaRpDT8Pdz4YvKd/LbvOFnBK3D10Dl8Ck3JrlguaSm3tVBcfiOMvgYE28+Er7qUh5vuwiIJSAiUSlBqtqZVK8yoFTry4v0xpro5xBwUeSokJEeep5MbYhd3QCFgCHbDGFVpTA0oE10QS0QklQAqEMuMiGUVP2K7Vx5AskjX1eZXosOAtjzx5cPELNrB6X3naRndnAH39aLjbX//2d8KrtsgPvzww2zfvp2JEyf+I8dsf39/FAoFmZmZTuczMzMJDg6+qXlqNBo0Gs0NPfPvsJgsVc4pVQreWvVCrTwPKrQFK0dpExAwWywEurjQPk9ie0Y5yb7Kiohx9jk+wXmb3ZKPVnPf6N7MmTiMHm98Q5GuUqwQCXwTTHR3VePqqSVzr5GKqfkK/B7oSoduLXHPyCW9sIjwkEAaBWs4WLSFuOR9uCi0RGg6U7oNdi3bT2F2Eb3v6Ua/cT2rbil06CZayErOZsviXexYsdcab3hsD3qM7FKlzS1mC+a/EY24Enqdnj2rD7J1yS5yUvPofXdXa7lsHQF7O9mb2myx0LlJAzo3acDbJ2PI0lfsv77cn8NymRCDwjZ2FSuNYS/TjrDOBTrlIl32A+ScJ4Jtoq/ye2qpSOd4SKXuu72tq2vz60WlVjlGAGaT+Yq9+rrCdRvE9evXs3btWnr06PGPHqxWq+nUqRMxMTGOqH4Wi4WYmBimT59eZ/L8p9w5bTCFucUc3nQMi9lCw1b1GTl9SK0+081FzdNDerJ833FS8gpRKkR6t2zMXV3aMnvKN2xfFosp2B1NtwbWvbRi5ZfFugAilhlxOZ3NkdgUDn+zhTdXPc/zw/vw887DnEvPAYuEOqUIaW8KX/9wELC60AAOZ12zuxpdmwBWXbjA74mJjt5UYnY+OfWX41pu2y2DwNbnjlC4xiYmYbFw7tAFfpv9Bz+c+pShUwaybdkeSgvLcPdxo/+4nrTpEc74Ro9TVqxDskgkxF0k9o+D1pevBtv8+xcXseqLCnGHhCOJLH7vdxZnfc+47pGsPXKGIp0edxc1QyLD6dqsQrx2eL0xbM78g6SyBCQJPEOLyS7wxJirQUBALFQgZSux+JpAAbl6N9ant6Gb3wW81ToMFgWpZd5oFUaCtUVYzNBSEU9yvifFPr5ICDT2KcBNY+Bsth9GsxKViwEXVwPlJRqMBhVKpQW3EiN6LwmdWQ0SWLxNiOUi2PaY6yKCUBTqcUnIq7JzCcDNy5UB43vRpkf4DbWhnbpuDOEGDKKPjw++vr418vAZM2Zw//3307lzZ6Kiovj0008pLS11rBBPmjSJ+vXrM2vWLMC6aHLq1CnH/1NTU4mLi8Pd3Z1mzZpdU543m9ZdWzBr3csU5RZTUlBKvaY31vu9Xh7q14UH+3bmYnY+AZ5uuKrVHEpMZZ+bicJ+YWjP5uKz8gzFPUIp7dqgQl+voBxlgQ6Xc3lokgooa+xNeWMf1sQnMrXrQBq0accbn35JWU4Jos6EKciNot6NsGgUaM/noU4upLyZLyXdQzH5adFqDHRpcpbmDVJJzgzkaGIYKi8TGq3BukPCIlF2zELZkQrxBJOPC7qW/hT6ubI3JZ2xr91Do7YNObDhMF2HdqbPvd1w83RFZzOG9vsEQaCsWFcjbS5JEmf2x3Nky3FH/mDtLelKynF30fDyyP68MLwvF7LyaBzog9kisfNMIttOXaC+rye3t2tBH9UjvHJ4BRm6YsrdBaQmJvC1IOoUiMUKtBki/u7FaOrrKDGo+SujLX+mtSPYpYhcvRtGkwKXEyU0XRaPOVdCl6fEjbN07luOb6iFOzpm0L5HMfvLApif1hJXz3JMRgVZyT7kpHjjE1REYL08DEcMnPvDDzNqDI39MPlX/Aia/F0pGNkS130peO5MrjLl8fGW16nfoh77/jzEvnWHadCiHn3HdKd+s5Drbte6znU7Zi9cuJDVq1ezYMECXF1d//6Gv+HLL790OFG3b9+ezz//nOjoaAD69u1LWFgYP/30EwAXL16kcePGVfLo06cP27Ztu6Y8r4X/omP24/NXsvPMRatHjK0X6HI2B9+TOZTf3YY8yeYiI0mV/Gew+W5ICAoB3wVHUWeVOuaWivqGUdq5XoWGlSigzColYN15jOPbg38hL4xZjkK0IEnWMdnmoraUS2rHlFbqTCPFmyr2+JZ2CKZoQBPrMwUBZUYxfouPIyJYVWcsFhQKBV/un8W672P4a/4WDOXW+a+AUD+mf/5QjaxefvLwN2yYv8WxTc+OykXFoMl9+b+vH3FKbzSZ6f/uPPJLdShEayB6+3Da2jO2YA4xYg41OIamvtpSOodccspHAnanNEYteFNqMuD33Gk0ieWOXrcDQUIUwWIWaNJaxzsbUllQGMnpHJH969sgWWxDZYtE4LeHUZQYQLSNjiUo7tkQqU9jREGkuNw6DeKWV069mCRKkqy+paJCpPPtkTwzfyoPtvo/SgvKbM7n1pHX0989xh0PVw22dTOoM47Zn3zyCQkJCQQFBREWFlbFMfvw4cPXld/06dOvOJytbOQAwsLCuBb7fbU86zpJpy6x98/D1GsWTNSQ9mi0V5/bNJvMHNlygnMHE+g4MILwLs2qzOtmJmVzKj4VsE0f2Ya22nbB/PDTDIJcPXnr+z9YlZRUMedUGdsLriy0uobYe2UmL43juoMgdz6Le5fwkHpsiP8dpWIpYPW5kyQol9S2Y2tyg90e2MULvFwcw3YJUBQbrAKnSEhm+1yWmdy0fJ788mEe+XAihzcdwzfEu9q63yjpF6zz0JWNobu3G4svzUXrVtXfTm8ykV9qnS80X7aAZjWMApLG4vRj46I0cXlxBWBGkx6MbzOIS2UpPP7Ai5ioKsKBJGCx/YalXnJD8lvO0yGN+XnVRvZZbB4VdgGJEtt2SbvOrACtvN2Y/fKDeKm1HLuUTkGpjq7NG+GiUpJyLo2EuIu0798WL39PctPzKS2wCeKaK8Qv9v15kH5ju2PUm9i1cj9GvZGeo6LxC3F2xr8WctPz2b1yP0qVgh53ReHlf2s6ItdtEO1zczI1i8lo4sluL3H+cKKjB6bRqnnhlyfpNar63u2Z/ed56Y73KM4rQRAFfnzlV4LCAvhs97uOL+Xrd33IntUH0N3WFCKDQJIcQik53jqGfvIBIXMuUe6pQbi3DZJGif2trYgAZz02hHrgEl/geL46vcQhNCDZ5gINnkZG7vkRpSDirizh+ZYueKnLHYsDPopS8s1uYBM4cG0PertHlACqjBJrjwprz8rk4wJaFeiMjnk8T38P6jezDoNdXDW14s/WtmdLq8O4rVySRaLzoMhqjSGAi0pFixB/zqXnOOZKndyMAKFEAf4mxwJGiUGN0SKiEq1yX4IIep2Sz9YcZWvmatx9i3HpYKJkh60MlxlFu0O8KlLi5T9eJW2qhM6gQJzQDou72vF0Q4g76vQSJFt6RDhar4jua+fwZOs+PN7K2e+zQYt6NGhRz3Hs4eNGaHg9Lp1Nc5yTJNiz5iAjvO+3HVu/K18++QNDHuzPjHmPX3Nbf/rYt6ydt9m6Ni5JfDZ1HmOeH8GD7978KJ43rJj9X+ZWDJmLcou5O+BBp3OiKHDPjOFM+bB6ZaFF76zgp9eWVDn/2e53aN3NOgE+3GMC5aXWIZHZQ032480xNnCxTuSrJTzW5OO6uxhNciFIUNw7iKI7G2DxNCG5W1DkW2iQWYxnAx1BbQsxXTKT9KAJi213mFmrJPeBFhgaumIONoKrBHoBoUxE8jQjKCx00CQxUnsIra8JVx8LeUZXVixtjSFBoux3PZJt51795iF8uPk13AI8+W3zQc4lZTBmSDStGgSyc91htm84RP9hXeg5uGOVCXqzycyJXWdQuahoFd38mnqKJQWlHNt+iqbtwwhqVDVAWn5mATtX7ENfpqfn3dEOubErIUkSxxPTWLZhP80aBjJ6YBeKyvU8Mm8Fidn51jQKCVPjciQfs21BSSKwpBi/gjJySvwoKPZGUFhoPeQ87mo9riojhksSp74JQC+5UN7cDwmJEP0FtKoyAm8349JQIGuzSMpyNS6nShDKLZRF+pN/b2MsXmYkPxOqi2V4rs/FGKymeJAfkqsCzfky2noGs3DqEygUCpJOp5BxIZPIfm1xcdVUqduJ3WeY0fu1v23XgFA/FifN/dt0diY0mUrmZdJh4V2a8eW+WVe8p84MmWVqB43NdaWsSOdw1raYLfg38KuS9sz+88x55FsuHKvq5KJQKfAKqPiCBDTw49LZNBRKEYXOhLKgAH1nPxRFSsQzavTB7ujvBsFoxvVEFpqzaZhDAhAsEgGfXUK7vwjMEkVAqR9IOrDYYsbrWvhR1DcMi6sGRQ6IeSpQSAhGm+Od3oj3xnPknC/mB0sLECT0zwWS2zkAYysltAJlJz3+C9LRHC6ibc+WZCRm8fmQd0k6ZdXbO+i3GvOEDmSqJSRPWLlzN9HpycwaO9ghKrvkg1Us+3AVxfmlAPjV9+XBd8Zx+/19q21rXYmODyZ9yd4/D2E2Wced4V2a8dxP02jUqiLAkk+Q9xUdxi9HkiTmPPotMQt3YCg3sg/Y2ao+T349hY6N65OYnW+NaCdJWAqUmH3NqLIM+H2RjPpsGaWARpWN8HxT9BGuHMm0lsNTXU7bwEzEQd6UpXuDnwlDAwMXNdbr50xmFBclDE2U8AJQbkF9VAWSC4o8ATFPieeCVFyPZjv2RHtsyEUwSShKzeSQwKjXDqNxUZOfaRWdULuo6H9fT2bMexxBEDCbzXw0+St2/Oa826uyKITd0dpsshDY8PqibwY1DCDzYrZTHkFh/teVR01x3QbRbDYzZ84cli1bRnJyMgaDs5xTXl5ejRXufwmNVsPi5LnErjnI3j8OUr95CH3HdCesmhjEe/88ROKJqjGLH3h3HHc8PMBpK+DcIx9xYEMcmxftwDvAkwETeqNrruXNFZu5WF6xRVFSKSjtEMLr9w2mdd+2/HnsCKtjnfeqm3OdDtG18sfiUSEHZnfYtqPK1qE9W6HsIiGQ0dl5ZdIUrMF7QjjvzhlOmx7hfDnjR5LPpDqul/q6UKCSHGNPiyQRez6ZUylZ9G5lXWBb9cV6hzEEyE3NY8P8LVc0iKnxGexetd/p3NmD8RzZfNzJIF4PpUVlrP8+xuncpbNpbF0Ry+ufPsSITq1Zs/8ExWV6hke3JTjEjR++WUnc2YodVGY/FfpI54XKIoMLHkSy4OGhpKUreOXYH6RQEfvcjMIpLpdgVIKgrdCIlcB9v7NfrrLQWZihrFBHWWGFv6Sh3Lo76NGP78fd242CrCJiFu2sUucWnZoy9bMH0Zfp2bE8FoPeSO97utFxYMS1NZqNDza9ypGY42xfHotKraTvmB607dXyuvKoKW5IMfv777/nmWee4ZVXXuHll1/m4sWLrFq1itde+/vutMyVcfN0ZeCE3gyc0Puq6bTuWqdAUAqliMUsMeyR2/D0c975suO3vaz8fB3nDiagUCrIFFPwmSxiUZUDQbbFDqsgqFIU6T2+B9+c2cnqtCNoAUkEwTqhZ533o2JiXjBe3VFX1FiHtaJSdDhLC3oLklKwrirbBE7Tjqfy1ZHfKc4PIj41Dk9LxaqzXV7KvtBiD1DlqqlYzHP11JKXUbE1DIlqhQbsuNjmAS8PnuTifuV7roTZZGbxu7+z4cctl1XeOu/4a1ocR779Gs+VmZzfdhazyYKxa3NG/d9Q7gxvTxwxFQG+7Ko2ZusuE/vfQ++fYaHWyIRXR9OxYX1SkvOtYRrs4U/FirSVfjkc7SUpRUSLZF0MqyzEcfkkp73othGKUm01D2oXFYIoIIoCZlNF4LHuI6JoFd0cgPb92l5329lRqpR0GdyBLoM73HAeNcV1zyE2bdqUzz//nKFDh+Lh4UFcXJzj3N69e1m8eHFtlfWmUdfdbowGI3tWH2Tbkl0knrhEj5FRDBjfiybtqspl3ek1kfKSikWNBp8pce8uYjaLZF/yJjPJD4POhQldejOsQysypEIm7fgFAPWFMtx2FuC6uwRjoBv6hl6I5Sa0Z3OwaJSUtQmgvKU/XCEw+fge7YmwaDi56hCbf9lhLXuwmtJePpR29UKZa8RtVz6uewvJnRZKWVdvMFpw3V+I1+9ZqFKsc5+Ghl7oWvpjaOjFwC6tGNO/E12aNHDME+ak5rJl8S62Lt2Ni11oYEz3q26LPLP/PFsW72Lvn4doGd2MvmN6ED2043VLU9kj0zl9PiFqjCEaSnt6UxblReAnSWjjiiuMj2BV/l6e8T0HNsTx+2drObTxKAD6xlpKe3qj6+yJOlGH254CtAeLUCAw9NHbeeDTCay7dIpvt2wis6AQ170FKArNlEe4Y2isRXuwCPVpI2YvD/SNfejaKozBrZpSuCeRn39ai8FPQVknTwSjhNfKLBQlZqeyC4LA2BdHMmB8Lxq1rti+mng8ic0LbeIObULpN7YH3Ud2QaV29jK5WdSZOcSMjAwiIqxdYnd3dwoLrUOiYcOG1ZhorIwzaQkZrP12EzG/7kJfpqffmB4MeXgAr6947or3HNp0lL9+2oqu2Dq8Mvq7UtYuiOxUf4RVEOiThfuJDDy2ZGLKkii9x40ClSeKtlYjIkigyDGizDSgKNYjqRWY3VSIBjOKIj2i2oQ6owR1ZgkuCfkIejO61v6URNXH4mntaV3KK8DSwp0zEzxIGhKBJsFqYL1WZeG9PBNBEDD6uFDSvREmMQjVERGzv4GyKF/KIz2Q1CIuBwpRnxcwe7pg9lCTUVLK5pSz/Jy5l+0Z8TRy96VXSSCKhAwyL2ajdlGRkZhFcV7JVQ1iy6jmtIxqztRPrQ77J3afYc6Ub9mxIhYvP08GjO/F0EdvI6CaOVyw9gw3L9zBunmbq1xT5BqRRAFllsEq7CoCApi1KsoigyhvFUCGp4YXf1lLg4sl5KZZp5kkwOyvwhSkxuSvQiy3YFFb3aAsZonNv2xHFQzedyuIbHEOPTqKCy0UrLKg35pv3X/s2LaciyQl8njMcAwNvdhfkEfWfR2Q1BbMfkYsASY0CTrc9hY4xXaWJIn0xCyyU/KcDGLjiEZM+aARUz6YAED8kUS+eeonti3bg9pFxYD7enHHIwP/9c7a191DDA8P5+effyY6OpqePXsybNgwXnzxRZYuXcoTTzxBVlZWbZX1plHXeogvDn6HIzHHHT5gokLEv4EfixK/rjZ9eZme4e4THEMbgOz7I61y9TafQc+YBFzjMh3+bfZh0prShfx+6Rgr9u+lcPw2xxALnDo41R6XdKpHcd9GFU6G7hYMrcuswzskh8tHs2WF9Pdogq6knFWaUvSBbo5ymRrosYQYK+bAckVUCa4OcQJBJaHvUOrIUwDqTT+NKtfk8FMUFSLt+7Xhg43XPoUz3HMiBp2hws9OFBg4oTfP/1S9P+vhmOO8cNtbVRy3KyMJYBwZQq+JPXH7K4e1KWkUhPs6fD1dz+Tg9ec5h5uVKUBF6letKoa/EjSYchKx2Oz4nOp/oMSjn4hdr00yg7lIwu+zdngHepJw5CIJcYm06tqCgRN6M+jB/nR5/Sugsn+khDJYYkizULw25rHrJ+sPrR3792ZN0c9o3bVUx8Sm08hKznH6Tl5vm/8T6kwP8a677iImJobo6GieeOIJJkyYwA8//EBycvIVVaxl/hlmk9lJVchitmAymK6Y/vKYygCSeJnDteWyQ1vaxW/9Rtv7mjA0VMFiWzo7lzuyVHFsuUzRyWIzmfa/9uteoyJp06A9usPJrDtzCn3ljC73QHaImgrV5ikBmHEYQ3tdzu5PYM3Xf9Hn3m5XdfJNS8iwBo8q1Vdx+jcZr9zG9tXpKxlDe1UaxpnpOdQPS2c/dhlLKBBwNJx0Wcxrye7gbo+GJ1jr5tQk4mUbiURQaERad2uB/7CmlJoak5zWmOAGzXAt92fxuyushrDSTaIgcmdoG9668za4E75Ru7Dy83VXjMldbf2NZqfrFosFk8F8xfT/Fv6xH2JsbCyxsbE0b96c4cOH11S5bil1rYd4cONRFr+7guO7ToMETduHcc+M4VddfPl11krWfrfJEU9Y19yX0ugGGIOtIQNaCy747k3lwu5zTtJk2jYijRYoEUwCWd8aKVhjwZxrjbNhDHBFNJhR5pUjAcYAV+v2uqxSBKw7V4p7NUTfwg9JFFFp9SjDSynTKDBKCuvLnadEk6rB75sjKAvK0Tf1pTiqPsZ61uGtNqgEGpVTJGmQBAFluhHVSRGLxs3qNF5uRHArxtRYwOyrQiEJdDnpgmJlKinHUyoawPbyN4loxLdxH1fbRrrScu7ymYwkSc4vvwAdB0Qw6Y0xtOlevaBBaVEZP7y4iK1LdlNSUIqrh5YG4fXIzywg+1LFcnzlmDGmEHeKuoU6Ysw0ULvQ6HAWqdvPodcZkEQoGBNMaV8fzD4qsID72gI8/8pBlWX1ddJ2FPCfosC1k3Uroz5BIm+xmcxL7mS+GIZCEDFLFlTFZkKmnEQUBIrbB1PWIQSztwsi0D08jOm3d6NtqNW5PfFEMj++8isH1h/BZDQT1CiAO6YMZNzMu67ozxmzaCfLP1lDQtxFECCiZyvGvTSKLoPaV5u+pqkzQab+F6hrBtFObno+Rr2R4LBA0hMz2b50D8lnU+k6tBOturbgwIY4ju04SZvuLel1dzRe/p4Mc5+AQVfhGmX2UBPUKIBXv5tKq+jm/PjKEn59/3eQwOyqwnKbP4qBnvgEFeFTr5Ccci3x2wIxa12gVA0uEoK5FEmpAKMWoUzEa0M8bieyHfmbRwRCRw8CG+bhE1hCRrIv8WfrIZSLCCYJzcUCfNacRTBLgETLjmV0f9OAur5AslLLiVIf4vd4UpCmxm1HAa5xxWAGk58WZa4ORAFdEx90HX3pHdmaGQ8OJf9kGm/d+wlFOcVObRYQ6s/ipG+qtGVZsY6YRTv5fOo8p/OCAHc+PojpXz5c5Z7L2zzqjo6o1EounrxEw1b1kSwS+9cf4e17Z2MxW/uwxvoe6Fr4IUjgcjYHVXoJkkaB6O3KW99NpUlkI5Z8sIo/v9noeI5FKZA+MwpBL1pdmQD/Hw6jyrf9EDVyQT/AE9FbRNxUgirZQNFtoZR18kXyNiOp9Lhvy8V3YYYjTwmw+GkZO20oU16/l6N5qaxPOY1ZsjCofis6+YeiKyojJzWPhq0aXPMWyIyLWag0qhvarvdPqFMG8fz582zdurVagdj/gutNXTWIdv76aSsfP/i1kyuEHbt8liiKzNnxFtuXx7L2203oKxlF+7xX79HduGfGcD6c/CUJZSXkjW5j084DSSlhjCyzxli+yuShmKHCL9ZIwJaL5HipKLgzHCTb5P7lm9ckCPzukDUUqe3Si19fpN/IQsxmQIQl6S14+0K0dc7QAigExEITDaaextPDjaJiHZmPdrIK29pETH2WnECTUlQlgp/Ww4Uxz41k/Ct3O7VfTmouk8P/zzpvdlkRQ5oEMmPe41XcSKprczdvVxYnzcXVQ4vRYOS+ho9TkFXoUIXOu6uldWujrUxuB1Lx3Gn1H62sWm5fCKnUTFjGtyevvrsj+l7IyVw0u5JIH+lD8fAA6zwjIBQqUcVrHZqGivxyAn6KA/PlOolQv0UIz/84jXniSdZeOonCplxrliz0DmrK971u/la5G6XOzCHOmzePxx9/HH9/f4KDg51+SQRB+E8YxLpMmcHIrthT1sA+FqmqkECl+Z/sS7k8PnsyQ18exvQ2z6HLsu6Rs897nYi7wKMtQ/ju6Me8++kKfstJr8hIIVmNIVx18jCieSCLH78fhWDgo19+YeHJEiShsohp5ZulirjM9kWCxtZju7dLcrkHIhIWQXA83+Kl5JV9b9G7XQu2rD3AUzv3WC/YBCCUBc6iEwCN2oTyzaEPqnULKcguqlhEqNR89z4/godnja+2d5R+IdNhcO1tXlpQRmlhGa4eWgzlRgqyrB4XdkNn8tE6ygmgKNQ7VGsq90Mu75JMeGUUE1+/l8Rzafy6chdh9QMY/UZPSgvLmLBmPsXoHPOMgl607jm3y7eVGm09b2cmvXkvE18dDcDFzVYXKLNU8UN6MjWFrEs5BIb+/Q6R1LxCtpxKwN/djT6tm+B6i1xvaoPrNojvvPMO7777Li+8UHuKzzLV8/uBE7yzcgtCVhY+IghmHN0Le6/PvkLo5uVKg/B6PBG7nL9Sz+DbVoXHFuvKJ9bOBJfcBYZM+Qj/tecp99IgjG5tnaeTJDAJKAwiZrXFIfNQMTNv/SsA/Ro2R2lYj1T4Et3DA1h2djB6k7Ii0l7lvwKYQjxQphc7sjm5343mETqrXZKgrXsegl0bzPY4oUzkie9XEbL0NJYCHYr722P21TryNoV6oTidjWhT5ZGQ6Dqs0xV95Pzr++Jf35ec1DxHe7m4aoga3OGKQ8VmHRqjVCkx6itEJuo1C8bD1zon6+KqIaxtKBdPXKrIM62YUl+ttaqSZJ2/PZ5pravN4b2KrBew6J3fWfLBasxGM4IgsEOSWPjwPCSLRMEIfxgb7PCOl7SWio/EImH21GDWKlHoKhaEtB5aOt/e3nHcyb8hpwoywG6YRQH9znTG3/c43Ud04c2Vz1/xO/jo97+z+1ySo1erVip4aUQ/7om+vt0pdZXrHjJ7enoSFxdHkyZNaqtMt5y6OGSWJImn31/EjuPxGOp5IpgsaBLyUObpWPDpNJq2DyN2zUG2Ld1Dj7ui6H1PVyxmCz3ee51SfwWmehoUWXq8f8kHo4LSziFYPF3wjLmA6xFrHBRJIaBv5I0xxJ3X35vMbd3bsDc1ntdivsLbT09wvVLKjEouZfnT2COU97s9hJ+LG+d3vURJ1kYioosxSEo+WhbJH3vDcD2YhTq5CENjH3StA5h4dy8eu28ASScvMf2utzAW6lEWmPANNtJjnA5RKbFnsZbMPBdy7w9D184LRaYKoVSBOr0E/0VWsVYJMAa7o2/iQ/8B7XnlpbEUpuazc8Ve1Fo1ve7uWu2cVk5qLvFHLhLRqyVaDy0nd5/l4F9xNO/UhKghHVC7qKu0+blDF9CX6WnTIxyDzkDsH4e4dCaVrsM60aJzUycDKkk25e41BwluEkj3EV0oMpv4vye/IeV0Ki7nclGUGNC1dsPsp6LoDn+MoRo8/8jBZ2nm5cW9ImYPBbn3N8UQ5oFYoEIoF1GlFKK5VITmQj6q9BJHv3z0M8OZ/M44UAgcvJBKkJc7TYP8yNAVMeOdr0k4noTr3kKUOVZNSY1WfdXYM+1e+NQ5NIUAwzq0YtbYwddc/pqgzswhPvTQQ3Tp0oXHHnusxgpR16hrBjFm0U7mv7SYrEs5AFi0Ssqi6lPcpT7+Hq6semwsH97/JYdskvkAfvV8KM4vdSyomL2VYLRu6AcwuyhBIaAoNVZ5ntpFxc/xX/Ltsz+za+U+jHprb0N3ZwNKWtXDZLYOLEJSy/DcnkRRmlXJRdlahe7ZeqS52/ZS62xCA7g4ho2+bZUUepSiM1ufqzonIWa6INm34qn0mOvrMAfaBi9mEHOVqOMkAufHWcNwXkbT9mE8NfcRWkY1r7b9Lp1N5ZOHvuFk7FmQrDFtug7vzPM/Tbuin93lbe7p78GY50b8bZS56pg95RvW/1BVbNbiIlLaz5fiAb6EPH8O0aqhZu21VfIPFYSKeCZ2lZ/CjsEU927kEKNVJhXgt+xklTlRBFCN70hWA3fKTdbPsXGADzNH9CPu6y2s/HwdCqVonbI1W6jXNIgF57+8Yl36vzOPrKISFDZfSrNF4qF+XXh6SPWhY2uLW2oQP//8c8f/S0tLmT17NkOHDiUiIqKKQOyTTz5ZY4W7VdQ1g/hs/zc4us05kLzSS8v/bXuZ3q2acHLLCV66470aedZTcx+h9+huFGYX8UDL/3O6ljE9yhGlD8Bn1Rlc4ivEPArvDKDgvmCH8ROKRVSnK8QKJCSMURUiDADKE1rEsortcqaGeixBxirzli/Xu4PeIfXZuySW+S87bw8VRIGxL4y8on7e6q828OUTP1Q5/+W+WYR3aVbtPdW1uU+wN8vS5lWb/mpYLBZO7DrDZ1PnkXwqpcr1h8+8SJQ6hCNrjrD3j0N0HBhB9NBOJB5LYsuvu6jXLJh+Y3tQUlBGzELr/F//8b3Qtgxiw9FzJGXmMaRLa1q5erDm0z9Z+23F7hkJyHjWOV6yIAjc1bkNb9w9gNN7z7Nt6W4Kc4roNaorUXd0uKoocZnByI7TF9h8Ip5AT3eGRIbTNjSo1mNfX84tXVSZM2eO07G7uzvbt29n+/btTucFQfhPGMS6htbdxfqFs/UeREEgMMCLwZFWH7nLxQqqYOs1OHxzKy1rXr4yu2TOH6xOuUR8eYl1gVkUrL0yUUAwmpE0igonaZXoJBIg6i1O84aCaNvFIFiwSDavbAvOxk4hVQjRShKYbdftc482firYTYl3e+57ZAA/vvIrosJZaMDlCsKt1vaxvuCVd+4AvHTHu9x+fz8mvHYPbp7OKjPVtbn2BsQfAERRpF3v1nS5PZLkUylOMbkVSpHh9Vrz67u/s2nhDnJT80g4epGDf8VRkF1E0skU1C4qEuIuYtQbObPfGgo2LT4dlYuas/vj0esM6Fo3wCfQy+oXeDkm2+ciWFfILZKEVq1EFEXadA+/oq9ldbiqVQyODHd89/5ryH6I1VDXeohFucVs+XUXW37dRXlpOf3H9qT/+F6OFUFJkjiy5QR/zt3IzhV7ne41eWkw+WrRh3kjGC1oz+fi6erCfVMGoXHVsOqL9aQlVPir6UM9yRtjdTlRpRejPZ2DJiEPY6AbZW0DMTT2RtKCpJKQtDq0Z3PxWpmJMt+EpICyzl4o7nbF1EJLO+8UQowlJF6oz6Hz1uGs5GLbS+trQjCIiNlKxHwlyrxyBIMZl/gcJDc9JUN8MTZwqdipYmPtbY8iJJYSU0looO/YHvQcFY1aU/0iitlsZu8fh/htzp+c2Hm6yvU3Vz1P9zudVbf/rs1vBF1pOTuWx7Jl8S6yknPofU9XBkzoTX5GAc/2f+OG8/07TL5adK380YX7ExrgzUOj+jAossW/enW4zrjdyNx8PP08GDl9yBVDaVosFnJT88jLyK9yTSw1olCKCCYLZZFB6LqF0imiOWMnDAVAX6Zn/suLK+L/UvHXGOKBMcQD+jdGEMDdu5SwJpcIbJyLSRJJveBOwQGzVcAAkJQiZj8VBncXyoxa8gxuNPfLYmjAfg6db44ogEklIaklUEuABWWRDvftubgdynByF3HbW4BgtFAa5UVpj3qYtR4IJSJvZMdwb5d2PPzhBB79eFK17ZGTlse67zYTs3gnBVmF9L67K4MfGsDYF0byys7TlQQQbOpZosip2LOs/2ELO36LxSvAkwH39WLoIwP/UcjYpFOXWPvdZrYt3Y0gCHQb0QW1RkV2Sg65aXlkpOTw2/7j7ElLJ/2prmiSCnDbn4o6tdjxORiC3NBFBKEL90MwmNGezsH1WCbKYn210l3VoczT4bH7Ep57Uhj31hju6tLmhuv0X+e6e4h33303UVFRVdxuPvzwQw4cOMDy5ctrtIC3grrWQ/w7jmw5zvMD36oyJKyMBJi7N+S254Yxvnt7GgVYV2ELc4pY9cV6YhbtIPNiNhH92uI2tDXnFEaOX8qgRYg/LesFkF+iw9JqFSq1CZs/L6kvmijeYnHsdy66w5/8+0OwaeNb/QkR+KTDWXIz+rHzpAc7fc5aE9uG2UGvxqM5X1ZtPGCA8mY+5I9s5Ti2xyuZN2UU3Zo3qvaejx78ik0/b3dMBYgKEZVGxe+5P/LnNxvZvGgH5w9doHnHxgwY35vhUwdxT+CD6MuuXdzhWnik/TMknUyp+EwuW/Aobx1A/h3NHU7aisJyAuc5B2nLfLwzFq2qYvrAIuF6sYA7Fd6oXNTsWXOAssKyvy2LV4Anwx69jbuevOOWBXCqSepMD3HHjh288cYbVc4PGTKETz75pCbKJHOdmIw2oYGrbMYXgNAMPd10aoLdK+bL3P3cafJ4Z04Pc6EoO51GYa3oEuBFB/0p2uZn0cLbl3ae7qilS3yRaMQoVQxhJZPkLB1lU2jBMV1oTZthGUhUmzb4N07C7lNtL5Rgkq7a05EuGzLbXT5WfLYW19F9aNe7dZUJfbPRWWTAYrag1+lZ/tEa+o3rwainhmIoNzi52VwuVoBkFXcw6I3s+/MQW5fuJiclj153d6Xf2O74169eFqwyJkPVPJ3KZT9t75NUs4IuKQTnqIYCuPu60zEqkt73dCWsbSg/vLjob6NR+twVSbPxXXG3+U3KVM919xC1Wi1xcXGEhztPqp45c4YOHTqg0+mucOe/h39bD7G0sJR5zy9ky9Ld6Ip0WDQCxhANiiIzyrwKtxpBISCZJQY/0I9nfpgKwC/xB3g7boNDFMBLU07X+hcREbFgQYHAYO/DKARI0vuRUB5IicUFySKRv9RMzncWR8ApoaOaokdDyPfxQEJELZqo51FIC98cBMG6cHKhwIdLRd7oTGqQwHVHMV6rslCnWleffZvoUbpYyD7rgmQW8GkSgOLeSM5joNxkQtCb0J7JwXt3ClKZgZd/fYq+Y3o4tceJ3WdY8PpSjm494bQLxOrCIvB77o+4eji726z6cj2rvlhH6vkMBEEgsm8b7n/zXnau2Mfvn621qZJbwLa4srrg57/9XLYv28OSD1YRfySx2ut+4SEI90Rw3lJOudGEh1pFq1OFFO2+QGF2EUq1ksBxnclq7s2lgiKQJNSpxXjuTUF1sYC2PVvy/E/TmffiQvb+cRCj3oRXgAcB9f1IS8yirLAMSSmia+5LWb8mGF2VvHBnHyb27Pi3Za/r1JkeYkREBEuXLq2yRW/JkiW0bt26xgomU4EkScQfSWT7sj3oywz0uqcrbXu2RBStXTE3Lzee+vZRBr97J/fM/xxDqAuCCvy1JQStvYSlyELxdokyyZ3yFn7sVui4PTmDwAALF8p208Y/g8xSd3J1bigEW2/T1n+RMKOwjWdDVbkYstX8/ktbytMElPvzUBbrcO0IygAB75EWxBbZZKVZKCh2JTigAB9FAUVrTZQekNC2FWjQOwf/5BzO/BmAxaJCH+ZH7rgQxrTfTHj9S/g0NJJT5MHBuCZkJ3sy+a4Z9GwZxtkD8Txx1/soc8oQbKEMALYt20PL6OYEhwU62qttj5Z8tPl1vnziB9Z8vcFhFO29tZhFOxkwvpeTURw5fQgjpg0m5Vwa7j7u+ARafSnXz9+CUHm/uCRRVnRtP/q97umKfwM/3hkzm5xU51hDbl6u/HrqMwqyi9iybA/74+Lp16stUdPacWz7abYs2UXLqObcNqkPAQ38mND1edJOpjj5jRbllhDSJIjHZ0+mRccmnIw9y4BxvYge3omcMh3Dn/0Sk6/WoWguCgLFOj0yV+a6e4h//PEHo0aN4r777qN///4AxMTE8Ouvv7J8+fL/RNzmutZDnPPYt6z7brMtKpmA2WQmolcrZm9/yyldqcnA9D3Lic1KoHfDeDRKM5IZQOL47mbkpvmAxbrNr37LDJq1T0FAsO6qEyR0JiX7UsLoHZqLqLAv0Ej08MzGT5HO7N+Gcyk70CrNahHw2JGE+0FbrF4F6Jr6WcUdsIo7iPl6An46Yh0WK7C61NgRbQsbEhT1akiHB83c1TOWPacasGJnTwTBgoiAWRLwdXdl1dSxvHfvbI5uO+VUZ7vb0DPfP87gB/s7XYvbeoKPH/zaIYFmvQGQQOOqYf7pT/921XjXyn18Mf178tILAGuckYETevPM938fd/jVEe+z949DVVybNFo1I6YPIWpIB54f+CYWSUKhcHaZUihFLLY4KC//+hTZl3JZ+PZvlBVbjbHWw4XxL99DSJNA3r53tpPohKunlu/PfMrr63aw6+xFxzRDqK8X744dRMew+n9b9rpOnekhDh8+nFWrVvHee+/x22+/odVqadeuHZs3b6ZPnz41VjCZClLPWUUXKr8wKefTq6RzU6r5sfd49h45xiLpAwAEBYBAWZHNh060BoHXutvirAiSw7NFqzTxS7P7aN8unHxDLueLT9HYrTkBLsEYLcW8UjQfqzSrVbHZLqpgLRyYvTSO5VtJArHEYDWGtutO2AVqBWit9eD9e17Ay1XDvmPLEYU0LJLouCWvpIyk4lI+3vIm548kMrVTxV5byaZ4Y3cdKikoZc/qAxj1RnrcFcUvF77i+xcXseyj1bYbrH/0ZXoKsgr/1iD2vCua7iO6cCr2HHnp+XQe1L7KcBusmoIHN8TRJLIR7fu1RaFUkGIL7F7ZGAY3DuT7E7PRaDVsXLDNsXPlcv9R+7EgCqRfyGLczLsY+eQQjsRYIyF2GNAWlVrF0g9XVxGdKCvSUV6o4+sHR3I87gLL/9xLu/BQRozoestioPxbuCG3m6FDhzJ06NCaLovMFWjVtTlHt59EEARrj85ioW2Pqo6xJQWlPNXrVZLOXKLxryo0Taxzhojg5VeCrkTrkMwqzndDIBckCcliNZzlZy08N+EVmkaGMWfn20T59XLkrRI96BDWkF1nL1aIFQS5oYnPc7iIKHPKrLJUSms3zOypweyqRFFmckTvc/SWKvWaTlp09H3nO54c1J0Ojdrx+74MLJKzgZj4zVKaBfnx3eSRhDQJIv1CZkXUQYVI0/aN+eXt5Sx6e4VVzVqAz6bOY9gjt9Hn3u6s/mo9+jKD4x7fYG/86/teU/uLokjbHtWHxXS0+clLjtViD1933vj9OVp3DyflXDqCbVFEskhEDanYCdKwVX1c3DSUl155GKtUKWnSzhqKVqVWETWkg9P1xhENq4hOBDTwQ+uh5fFOzxN/JBFBEIiVJBZ6fc/LS56+aSKu/0Zkx+xqqGtDZrAKE+z4ba9jDrFB8xAyLmZx6Wwa7Xq3QqPVcPHkJaZEVESAc2kl4N5bxJgqUbzNgkGhobyFH43bNuSTj6fg6SHy3cK57N93kOKtFvTnKr4Kc498RNPIsCrliE/P5vGHPqM0sxCXs7koypz3QltUIkW31UfX1gdTYwGLlwWX4yW4HClj4oh+PDJpGIknU3jkmW/QaZWUN/dF0lp7LW0aBPHr9HGU6PW8Mv8Pth+Nx+yuBmVFbILfn55I82A/Tu87z74/DxHasj7d7uyM1t2FR9s/y8UTl5zKExDqx+Kkueh1evavO8K5gwl0HtSetr1aXneEveq4vM3tjHlhJA/PGk96YiY7f9uLIIr0Gd21ShB3Q7mBpR+t5ufXl1XJY/jUQUx5f/wV91vb0ZXo2Pjzdo7vOM2Qh/rTYUAE2ZdymdB4qlM6QRSY+OpoJr4++gZqWreoM+IO/wvURYNYmbMHE/h86jzOHUwArHNSfcf2YOLro5nc4skrxlux9yBGTBvM9C8eAmDD/C188vA3Vea5FEoFXYd14pkfHsfDx+qqsfi931kx5w+KckuuqZxmNyV5Y1pi9PNAkKxyYUFeHhTpyikzVBWVAAjINxK4I5m881b1F4tKpLx1AEUDm6BUKlj/woMEeztH0/tt9h8s/XAVBVlFjnP2+rTu1oLPdr97TeW9EXLS8pjYZFq1bd40shHTv3iItj1bVXNnBYnHk3isw3MOCTRBsGpWfrDxVToObPe398557DtOx54DQOWioteoaB79eBITm0zDoDdaRxaCgMVs4elvH+WOKQNvtLp1Btkg3kRupkGUJIms5BwCQv0cq8aXU1hWjigIeNiGWj+9toTF7/3uZMAAFifPRaVRsnnhTr59ZkGVfBpHNLS9oBUr1JIkcfFEMvNf/pW9fx6qcs9HMa871KNHeE+65hVWAEN9D3LHXZ9OnsfWRNwPp1fx2Zuw4VmGdY/Az921yj1j6k0hL6PA6ZynvwfPfP84nQe1v+KWvpqiILuw2jYXRYER04c4Qp1ejeyUXHYsj+XIluO06d6SPvd2o17T4L+9b+mHq/lhZlU/xPmnP8XL35Ndv+9j+6p9NI8Mo//YnjSOaHjThRhqgzqzqCJTM+hKdCx6ZwWbF+0kNzUPv3o+DBjfm/Gv3O2YtN9++gI/7zzMgYQUBAF6tAjjgT6dcXHVIFUSg7WLOqhdVHj5ezL8sduY9/wvIIClkgBC9NBOtOvt7BolCAKNIxoxcGIf9v55CFEpYqk0wf/W6I9p1qEJBdmFzsawkuy9XdzBPofmKJfNYCsEAbMkVdJ9sO42sf+tjKRSOOnQ2qWpxvRsj4urswrLxgXbWPP1X1WMIUBRTjErP1uHKIp0Hdbphj6ja8U7wOuKba5xvbJyTGUCGvhx99PDuPvpYdf1bI2r2rnNHd8FNStOnGVlYSoX2rjirijg0rlzPBbqQ4hP3Rv11BVuuIeYk5ODWq2uk0PKf8rN6CHu/fMQr975fpXzlYUGhnwwn5TcwooQJgK0Cw3hxyl3s2vFXrYu2U3iiWR6joxiwITeNO9YIdqbeDyJzQt3snPFXuo3C6bfuJ70Ht2tilGxY7FY2PvnIVZ+vo64LSeuWnZjoBqzt5KyLp4IAmgPFKHVCYyfNASvAE/itp7g/OELdL+zC0G3t+JEeQm7zibRIaweHcLqkVlYwqbj5wn29qBPq8YYTGZ+3H6QIp0ejGa053JxOZ2DqqCccQ/fzoAJvWnSruo2vcHqsVjM5ioS/HYEQSCwkT8LL1Qfv7qmud42rwlMRhO7V+5n65LdjjbvP74X3uHB3Pbe905pBQGm3daNxwZ2rbXy3CzqRA+xoKCAl19+maVLl5Kfb/VTCwgI4IEHHuDVV1/F1bXqcOa/zsGNR9m4YCt7/zhEgxYh9L+vF0Me6o+bl9tV77OvPF6uzC+KImf2n2fDD1swLdyOv4sCXasAytoFgYcGURBQa1T0v68X/e+zrgKbzWa2LdnDj68u4ei2k7TpHk7zjk3IS8+nMKcIrbsL+ZmFGMoNjpezILuQ9d9vIWbxDrIv5dFzZBd8gr0pzrvy/KAmXMB7hIjbSBGTIJCWaKFgpQVFgQlJJ1GYU0S3EZ0Z+YSzIMI9lf5/ODGV02lZ5JeWoVKIZOiL0PvqKWlbhMFoQpECwgkDilIDYpmRnPR8Yhbt4NtnEzmx+yzt+rRmwH296DeuB4IoIEiiI76xU3va2lhUVD8NURs0jmjElA8aMeWDCTftmUqVkj73dqfPvVbNQ4tFYuPxc3yybGPVxLZevMyVueYeYl5eHt26dSM1NZXx48fTqpV1ovjUqVMsXryYli1bsmvXLo4dO8bevXv/1bqI1/rrU5RbzN0BDzqGK4LNsW7ia3+/kmfQG1nz1QZiFu0k/kgiTduHMWB8b0ZMG8Q9QQ85CQ1IAhjbBtH31RGM6x5Jy3qBTnldLu7gUHOxL5QI1ggoo58ZzpQPJwJYd3F885dDndk+3L18caUyzf5SofASEJSABKkzTRTHWJzUnT183fkts6oYK1hf1g4vfQZYlZYFwNCqDDwcoezxXpSO55psh9iDI1bMZTFjPtj4Kka9kbXzNnNg/RHcfdzoODASs8nMgQ1HMBvN9LgriuGPD7qiy8x/kePJGYz78tcq0xHeri7c27UdE3p2wLeaedh/G7e8h/jWW2+hVqtJSEggKCioyrXbb7+diRMnsnHjRieF7f8yRtvKosNwSSCIYLzCCmpl1BoV98wYzj0zhlNepncaVl0uCiAicFvrprxyz23V5mWqphxQySFYsqpV71q5jxZdmtFteKeKVVF7EttNVzKGAILK7uiNdQ7R6CzMYDFbHOEGTu87z7Yluzm+8zQdB0bQb1xPGkc0cooSKFkrZ3MQt2Vrcn6+3XHZ8ddWR6PBRPTQTkQP7YSh3IDSJngKYDaZsVgs/3NOyKf3nWfZ0u2gpMrc7Iw7ejEqqu0V7pSxc83jiVWrVvHxxx9XMYYAwcHBfPjhh6xYsYIZM2Zw//3312gh6yo+QV6MffEufIK9AaurSvQdHRkwvtfVb7yMy+eYpnwwgZCm1nYWBIGI3q0YMfXKQXxad2vBkIcGOJShr0TGxSzeGTObL6b/wJCHB9Chf9trHkKJChHPXaH4ilY/Oski4dpFRKzkAVO/RQgPvz+e/euP8GS3l1j91XrOH77A8k/+4LEOz3H+UAIz7uhJiM1tRhQEWlnq0cStYrdIeQtXa/yXK+DipmHwg/2dVJ7VLmqnFXqFUvE/Zwztbb5vbgyucRkI5dYfJrVC5I724fRqGXZrC/gv4ZqHzBqNhoSEBBo0aFDt9ZSUFMLCwjCZqveB+zdxvd1xi8XChWNJBIcF4u599bnDa0WSJJLPpOLh44ZvcNUIctURH5fI4x2vHELSTs9R0bz+27MA/DBzEUs+XFWtBJekFCkP88Lk70a/O7vw9ON3EujlziO3P8mluHRMORVpQ5oE8XP8l+Sm5/P9i4vY/Mv2Kvm9/9crRPZtw+GY46z7cx/1g3y5Y1wv6jcL4fnJH7F/QxyqLMNVy/71wfdp1KYhBzfEsW/tIRqE16fPvd3+dgteflYhO3/by/lDCXS6PZLoYZ3QXiXswL+NLb/uYtb4zxzHkihgCnDlvR+fpPvgf7+6zeXc8iGzv78/Fy9evKJBTExMJDAwsNpr/3VEUaRZ+8Y1mqcgCDRqVX1bX4n6zYLpMCCCuC0nHENgNy8t+jKDQzPR08+D7iMq5PK7Du/M9uV7SL+Q5ZSXxUVJ1sMdrUGlLBJ/5mXy57vz+Gj8HQy8bQAL9/2GCeteZq27C0MeHsDOFXt5+95PqBrZHlpGNyesbSiTw58k82K21Z3GAsteX85Ds8Yz6LauJKw7TRFXNojt+7XFr54f4xo8SlFusUMA4bvnfubZ+VMZNLlftfftX3+EV4bPsi5aKQQ2/LgVF3cX5p/6lIAGf69r+G8gvEtTGrdrSOKxZAAEi0REWD1a1vD38r/ONRvEQYMG8fLLL7Np0ybUauf4tXq9nldffZXBg29ubNb/BYpyi9mz+gAKlYLud3a+6uq11l3Lh5teIy8jn0ObjtGsfRhhbRtSXlrOwb+O4uHrTkTvVhh0BrYu2Y2uWEe3EV1YcP5LFr79Gz+/UbF9zOKirIiwJwqOhfDtpxN58+mhDHhkAAt/3IQETLh/IP4+Hiz9cLVV2OGyecgnv5nCsEduQ5IkMi9alWcqixeknk9n7Asj6Tu2O++N+5SdK/ZVcTR++48X6Dq0M7np+RTlFjvnIQjs/fMQfcd0rzZiXPqFTEeZzLY5yvKScvIzC/4zBrF+sxC+i/uElPPpnNpzlsi+bQhqFPD3N8o4cc1D5pSUFDp37oxGo2HatGm0bNkSSZI4ffo0X3/9NXq9ngMHDtCwYcPaLnOtU1e27n31f/NZ8/VfWCwWRzzhcS+NYtLr995wnr/N/oP5Ly+2Ln7YVp9vu78Pwx69necGvOEQQDApBXIf7IDJTV0lD6VolQyzT9wrRIFJvTrSU3DnjVEfVbuNLaRpEB9tfo3XR35IwtGkishzosDUzx5kxDTrj+mmn7fzycNfVxs9sHFEQ2ZteIWnerxCxsWsKpL8GlcN0794iMEPOPcU47ae4OWh72EorxBA8A705Nu4j695OkKmblEntu4lJiYydepUNm7c6PgFFwSB2267jS+//JJmzaqPcftv41YYRF1pOSd2naFpZCPHSzqhyVRHj8pOeJdmfLlvFgBZydlcOptGRK9WTnL4V2PmkHc5+Fec0zlPPw9WZM93CCCc2Xee9gMiaNe3NYeS0nnk+98rElskVOnFSGoFJn9XR1S8JoG+rHn2fsqKdXw/cxF/fP1XlWe/svRpet/TjTP7z7Pmq7+o1yyY4Y/fjneAl1O64vwSPp/6PduW7q6Sx3dHP6ZRm1BOxZ7j6V6vOl0TBIEBE3rxwoInqtynKy1n/9rDnDuYQKdB7Yns09oR9L2uUV6m58SuM4S1aXBNoQr+F7nlc4gAjRs3Zv369eTn53P+/HkAmjVrhq/vtckoXYmvvvqKjz76iIyMDCIjI/niiy+Iioq6Yvrly5fz6quvcvHiRZo3b84HH3zAHXfc4bg+efJkFixw3lc6aNAgNmzY8I/KWRuUl+mZ/fA37Fq5D6PehCAItO3VkhnfPYZ/PV/HfJtdGNa/gS/nD1/g86nzHDF6Na4a+o/rwf/NfeRvFVz8QryBii1xFrMFX9s5jVZDr7u70uvuip0MUU1D8XDRUFyux31/Km4HUhF11h6gyceFkj5h6Jr5EuhlFYBw9dDSa1Q0f3z9VxWfxnfHfcp3z/1CSX6pQ+h054q9PD5nMh36V+x59vBxp/uILmxbuhtRITryUChF3H3cHXJcPsHe5GcUOEIDmE3mK/b4tG4uTg7MdRGjwcicR75l+7I9GMqNVq3IbuE89c0UGkdUH1BLpma55eIOS5cuZdKkScydO5fo6Gg+/fRTli9fztmzZ6tdpNmzZw+9e/dm1qxZDBs2jMWLF/PBBx9w+PBh2ra1+llNnjyZzMxMfvzxR8d9Go0GH59rGx7dzB5itSvDAkz79EHumDKAAxvi2L58Dyq1ir5jutNhQAQL3/7tiuIOfzcnZjaZObrtJNuW7qGsuIze93Qj6o6OV91eVqQrZ8vJBL7p8y4WnbOPpVeHUKb/Mo3uLRqhVlb8vqacT2fph6vY8MOWq5ZHEAUGTOrDC/OnVbmWeCKZ7Uv3cP7IBboM7kDve7o6GbzSojJi1xxk1+/78KvnQ98xPWjTI/yKIhl1nZTz6TwQ7ryhQRAFHnh7HONm3nWLSlU3qRM9xNpg9uzZTJkyhQcesCqCzJ07l7Vr1zJ//nxefPHFKuk/++wzBg8ezHPPPQfA22+/zaZNm/jyyy+ZO3euI51GoyE4+O/VQm41Gq11qGvflF8hCqBG7aKmx8goeox07i1fTdzh71AoFXQc2O5vZaUqs2vRbjZ9u9HJGNp35bRv3oC+rZs6pS8tLGXdd5vYvWq/03l7j7HyDhqLReKP42dJW7CGB/t2IbJRiCN947YNadz2ynPSbp6uDJzQm4ETel9zXeoyV/suyNwcbqlBNBgMHDp0iJkzZzrOiaLIwIEDiY2Nrfae2NhYZsxwFuQcNGgQq1atcjq3bds2AgMD8fHxoX///rzzzjv4+VXfe9Lr9ej1FarFRUVF1aarDULD6zN7+1vELNrJ3j8P0jKqOf3G9qDnqOgr3jPqqaH41fdl65LdXDyRTI+RUQyc0LtW4u0a9EbmPDKXy/23vQK8uO+lUdU6oR/YEMfyT/6ocr5tz1b0GNmF3LQ8lv2wCaObmrJW/pS3CmDLqQR0RhPfPTyqxuvwbyGggR+f7X6HzQt3svePgzRtH0a/sT3odc+/X4zh38ItNYg5OTmYzeYqu1+CgoI4c+ZMtfdkZGRUmz4jI8NxPHjwYEaNGkXjxo1JSEjgpZdeYsiQIcTGxlY7xzZr1izefPPNGqhRVfIzC1j3fQxbFu+kIKuQXnd3Y/CD/WgZ1dyRJqJXKyJ6tQIeuaY81S5qbpvYh9sm1n4MG4chtG+Qxrq/uO+93el1dzSrv9xAzOIdlBSU0WNkFzx8Pdi9cl+1eU16fbRDW/FXj3LyS8ocK9UiAqIgUFJQyvoftrBl8U7SL2TSY2QUt9/fl8i+bWq9rnWB1t3Cad0tnCe/evhWF+V/kls+ZK4Nxo4d6/h/REQE7dq1o2nTpmzbto0BAwZUST9z5kynXmdRURGhoaE1UpbvZy5i08/bHfN96+fHsHnhDv4sWVgj+dc2KrWKV5fNYP33MRyOOY6nnzv9x/Vi1FND+WbGAnau2FtRt++3OAQiKtOodQOGPDSANpXiwLw/bjBLY4+x7dQF1EoFgyJbML5HB5Z+sMrqz4iEJMHmRTusuofFv/yndpbI1E1uqUH09/dHoVCQmZnpdD4zM/OK83/BwcHXlR6gSZMm+Pv7Ex8fX61B1Gg0aDS1o1l3uU+exWTBJNTe9sbU+HS2LdnDvnWHaNahCX3HdCeiV6vrkn1KPJHM1l93cTjmOG17tKTf2B7M2vAK5WV61C4qx6KFyWByWti5kkDE6yueJTTcOfRldLOGRDdriN5oQhQFVLae+xa9dXXVHmPKLlZrNl4etk9Gpua5pctxarWaTp06ERMT4zhnsViIiYmhW7du1d7TrVs3p/QAmzZtumJ6sDqV5+bmEhIScsU0tcXQR26zyk/Z7FFgI38eem98rTwr61IOD7T8P35+cxmn955n3bzNPNP3df76ces153H+8AUeafcMSz9czdn98az6Yh3To2eyb+0hXFw1Tiu4I6YNplW3Fo5jk4cao78WydZD9PB1Z9T/DSWkSVVBEDsaldJhDAEGTupDxwERjl6mb4g341+5Gzevf79klUzd55YPme3qOJ07dyYqKopPP/2U0tJSx6rzpEmTqF+/PrNmWZ2R/+///o8+ffrwySefMHToUJYsWcLBgwf57rvvACgpKeHNN9/k7rvvJjg4mISEBJ5//nmaNWvGoEGDbnr92vVuzeztb5GTlkdxbjFhbWsmpkVOWh47lseSfDqVrsM60en2dpQV6ZAsEhL2bWrWXtWulfvpODCiSsS39MRMti+LJftSDj1GRhHZtw0lBaVAhcyWfcdIcX5plTJ0HNgO/wZ+vP7Cj5xLSKUsIghjA08Egxllno6V3z6Pu6vzMNfu9rNn9QH86/vSZ0x3QhpXGMxm7Rvz/l+vUphTRFZyDk3bh/1r3Whk/n3ccoM4ZswYsrOzee2118jIyKB9+/Zs2LDBsXCSnJzs9EJ0796dxYsX88orr/DSSy/RvHlzVq1a5fBBVCgUHDt2jAULFlBQUEC9evW4/fbbefvtt2ttWHwt+Nfzxb/eP3Ngt7Ppl+18OPlLBAREhcDa7zbh5e/Bd8c+IbJva45uO+WUfv+6w4wPO8QTXz7MnVOtPwq/zf6Db5/92aoqLQqs+fovgsMCmbPrLcK7NOXsgQTH/Y1aN6BlVNVdSN+/uNARKF0LuJ7KwRDsTv7ESPre3hK3y/YVS5LEAy2fJP1ClkOY4YeXFjPlgwnc+9wIp7Re/p61smouI3M1brlBBJg+fTrTp0+v9tq2bduqnBs9ejSjR1evSK3Vavnrr6rbxv5LpJ5PtypcWySHWEFhTjFmk4WPt7xJ8tlUHmr1lCO9JFkVp1POpTnOpZxNc/gD2kVbMy5m4R3gxRd7Z7F//RE2/7KD7iO70Pfe7tX2ai+dteYn2dSvAdyLjax+7VG83arGEpYkyaGqU1ncwZ6PjMytRh6L/AtpHNEI0TbHZo8ZEhDqj7uPVQmnftNg6jcPqbguAIJAk0qB55u2t/1fqMgjrE0oeRn5PNjq/3hl2Cy2Ld3Ne+M+ZXzY4ySeSK5SjmYdGlsFIirFLmkd3bxaYwj2CH8NnctVuSwyMreYW751ry5SV9RurkZJQSkbftzC2f3xDHl4IO37tXGaWpAkidP7zhO7+gAhTYLoOSoaTz/nAO8F2VbR1OyUXHqMjKJF56bEbT3B8wPfqvK8F35+otodITmpuexYvpeSglJ63R39t3OkkiRx7mACu1ftx7++H73u6YpPoNcV08vIVEedULv5X6GuG8T4I4l8Mf17TsWeA8DF3YUB9/Xkia8e/ltxh7/jwrEkHuto3RaJJCGI1u1j7//1Cp1ui/ynRZeRqRH+s3uZZa6fXSv3cXrfecdxeUk5a7/bzPhX7nGIO+Rl5OMV4HndBrJJu0b8HP8l25fFciTmGG16tKTvmO5V/AhlZP6LyAbxX4hGW724g0IlsvzjNfy1YCtJJ1Pw9Peg/9ie3PfyKHyCvK85/+CwQMY8P4Ixz4/4+8QyMv8hZIP4L2TUU3fgV8+Hrb/u4uLJS/QYGcWACb0pzivlu+d/caQryilm1VfrCW4cyN1PD7uFJZaR+XcgG8R/IRqthtvv78vt9/d1Op98JhWoCDpvpyYcwWVk/heQ3W7+QzRoEcKTX0+hZbRVSSewoT/jXryLgRP/G3qBMjK1jbzKXA21sYJlNps5uu0U25fuJvlMKt3v7EKfMd3/Np7wjVJaVIarh1buHcr8J5Hdbm4itdHYSz9czfcvLkShVGA2mxEEAYVSwe8589G6V+/ILCMjUz21ZRDlIfNNojivGFG0BkJCsm53MxlMlJddOTC7jIzMzUU2iDeJDgMi8A6q2JEhCAKdB7fH09f9FpZKRkamMvKQuRpqqztusVg4vfc8KefSiBrS4bp8A/8JBdmF7P3jEO4+bkQN6XDNMZxlZOoq8k6V/wCiKNKmezhtuof/feIa4v1JX7Bl8U6HirXGVcNjn9zPsEdvu2llkJH5tyAPmf/j7F65z0nSX1+m59Cmo7ewRDIydRfZIP7H8bYpyYgKEYXSuq/ZO6DuCVbIyNQF5CHzf5y5Rz5iz+oD7FyxFy8/D/qM6UGH/m1vdbFkZOok8qJKNdR1+S8Zmf91ZD9EGRkZmVpGNogyMjIyNuQ5xGqwzyIUFRXd4pLIyMhUh/3drOkZP9kgVkNxcTEAoaGht7gkMjIyV6O4uBgvr5qLySMvqlSDxWIhLS0NDw+PWlGLKSoqIjQ0lEuXLv0nFm3+S/WR61J3qVwfDw8PiouLqVevnlNwtX+K3EOsBlEUadCgQa0/x9PT8z/xRbXzX6qPXJe6i70+NdkztCMvqsjIyMjYkA2ijIyMjA3ZIN4CNBoNr7/+OhqN5lYXpUb4L9VHrkvd5WbUR15UkZGRkbEh9xBlZGRkbMgGUUZGRsaGbBBlZGRkbMgGUUZGRsaGbBBriby8PMaPH4+npyfe3t489NBDlJSUXDX9E088QXh4OFqtloYNG/Lkk09SWFjolC45OZmhQ4fi6upKYGAgzz33HCaTqU7VBeC7776jb9++eHp6IggCBQUFVdKEhYUhCILTv/fff7+WamGltupyI/nWBDfy3PLycqZNm4afnx/u7u7cfffdZGZmOqW5/HMRBIElS5bUaNm/+uorwsLCcHFxITo6mv379181/fLly2nZsiUuLi5ERESwbt06p+uSJPHaa68REhKCVqtl4MCBnD9//voKJcnUCoMHD5YiIyOlvXv3Sjt37pSaNWsmjRs37orpjx8/Lo0aNUpas2aNFB8fL8XExEjNmzeX7r77bkcak8kktW3bVho4cKB05MgRad26dZK/v780c+bMOlUXSZKkOXPmSLNmzZJmzZolAVJ+fn6VNI0aNZLeeustKT093fGvpKSklmphpbbqciP51gQ38tzHHntMCg0NlWJiYqSDBw9KXbt2lbp37+6UBpB+/PFHp89Gp9PVWLmXLFkiqdVqaf78+dLJkyelKVOmSN7e3lJmZma16Xfv3i0pFArpww8/lE6dOiW98sorkkqlko4fP+5I8/7770teXl7SqlWrpKNHj0p33nmn1Lhx4+sqt2wQa4FTp05JgHTgwAHHufXr10uCIEipqanXnM+yZcsktVotGY1GSZIkad26dZIoilJGRoYjzTfffCN5enpKer2+5ipQiX9al61bt17VIM6ZM6cGS3t1aqsuNfV5Xy838tyCggJJpVJJy5cvd5w7ffq0BEixsbGOc4C0cuXKWit7VFSUNG3aNMex2WyW6tWrJ82aNava9Pfee680dOhQp3PR0dHSo48+KkmSJFksFik4OFj66KOPHNcLCgokjUYj/frrr9dcLnnIXAvExsbi7e1N586dHecGDhyIKIrs27fvmvOxqwErlUpHvhEREQQFBTnSDBo0iKKiIk6ePFlzFahETdXlSrz//vv4+fnRoUMHPvroo1od/tdWXWq7jWryuYcOHcJoNDJw4EDHuZYtW9KwYUNiY2Od0k6bNg1/f3+ioqKYP39+jUltGQwGDh065FQGURQZOHBglTLYiY2NdUoP1u++PX1iYiIZGRlOaby8vIiOjr5intUhizvUAhkZGQQGBjqdUyqV+Pr6kpGRcU155OTk8Pbbb/PII4845VvZGAKO42vN93qpibpciSeffJKOHTvi6+vLnj17mDlzJunp6cyePfsf5XslaqsutdlGNf3cjIwM1Go13t7eTueDgoKc7nnrrbfo378/rq6ubNy4kalTp1JSUsKTTz75j8udk5OD2Wyu9rt85syZK5a7uvT2Mtv/Xi3NtSD3EK+DF198sdrJ5sr/rvSBXg9FRUUMHTqU1q1b88Ybb/zzglfDzarL1ZgxYwZ9+/alXbt2PPbYY3zyySd88cUX6PX668qnLtSlJqkL9Xn11Vfp0aMHHTp04IUXXuD555/no48+qtVn1gXkHuJ18MwzzzB58uSrpmnSpAnBwcFkZWU5nTeZTOTl5REcHHzV+4uLixk8eDAeHh6sXLkSlUrluBYcHFxlJc6+Ovh3+V7OzajL9RIdHY3JZOLixYuEh4df8323ui41nW9t1ic4OBiDwUBBQYFTLzEzM/OqZY2Ojubtt99Gr9f/473E/v7+KBSKKivbVytDcHDwVdPb/2ZmZhISEuKUpn379tdeuOuYB5W5RuyT3QcPHnSc++uvv/52kr2wsFDq2rWr1KdPH6m0tLTKdfuiSuWVuG+//Vby9PSUysvLa7YSNm60LnautqhyOQsXLpREUZTy8vL+SZGvSG3V5Z/me6PcyHPtiyq//fab49yZM2eqLKpczjvvvCP5+PjUWNmjoqKk6dOnO47NZrNUv379qy6qDBs2zOlct27dqiyqfPzxx47rhYWF172oIhvEWmLw4MFShw4dpH379km7du2Smjdv7uQOkZKSIoWHh0v79u2TJMn64UVHR0sRERFSfHy8k7uDyWSSJKnC7eb222+X4uLipA0bNkgBAQE3xe3meuoiSZKUnp4uHTlyRJo3b54ESDt27JCOHDki5ebmSpIkSXv27JHmzJkjxcXFSQkJCdLChQulgIAAadKkSf+6ulxLvnWpPo899pjUsGFDacuWLdLBgwelbt26Sd26dXNcX7NmjTRv3jzp+PHj0vnz56Wvv/5acnV1lV577bUaK/eSJUskjUYj/fTTT9KpU6ekRx55RPL29nZ4UEycOFF68cUXHel3794tKZVK6eOPP5ZOnz4tvf7669W63Xh7e0urV6+Wjh07Jo0YMUJ2u6kr5ObmSuPGjZPc3d0lT09P6YEHHpCKi4sd1xMTEyVA2rp1qyRJFb2P6v4lJiY67rt48aI0ZMgQSavVSv7+/tIzzzzjcMupK3WRJEl6/fXXq63Ljz/+KEmSJB06dEiKjo6WvLy8JBcXF6lVq1bSe++9V2s93dqsy7XkW5fqo9PppKlTp0o+Pj6Sq6urdNddd0np6emO6+vXr5fat28vubu7S25ublJkZKQ0d+5cyWw212jZv/jiC6lhw4aSWq2WoqKipL179zqu9enTR7r//vud0i9btkxq0aKFpFarpTZt2khr1651um6xWKRXX31VCgoKkjQajTRgwADp7Nmz11UmWf5LRkZGxoa8yiwjIyNjQzaIMjIyMjZkgygjIyNjQzaIMjIyMjZkgygjIyNjQzaIMjIyMjZkgygjIyNjQzaIMv8q+vbty1NPPXWri3FN/PTTT1VUZWTqNrJBlLmpTJ48mZEjR97qYsjIVItsEGVkZGRsyAZRps6xe/du+vbti6urKz4+PgwaNIj8/HzHdYvFwvPPP4+vry/BwcFVNCNnz55NREQEbm5uhIaGOsRN7diHsn/99RetWrXC3d2dwYMHk56e7khj78l+/PHHhISE4Ofnx7Rp0zAajY40er2eZ599lvr16+Pm5kZ0dDTbtm2rtXaRqX1kgyhTp4iLi2PAgAG0bt2a2NhYdu3axfDhwzGbzY40CxYswM3NjX379vHhhx/y1ltvsWnTJsd1URT5/PPPOXnyJAsWLGDLli08//zzTs8pKyvj448/5pdffmHHjh0kJyfz7LPPOqXZunUrCQkJbN26lQULFvDTTz/x008/Oa5Pnz6d2NhYlixZwrFjxxg9ejSDBw++/khvMnWHGxSqkJG5Ie6//35pxIgRV7w+btw4qUePHle83qdPH6lnz55O57p06SK98MILV7xn+fLlkp+fn+P4xx9/lAApPj7ece6rr76SgoKCnMrZqFEjh/SaJEnS6NGjpTFjxkiSJElJSUmSQqGoojs4YMAAhxzbjz/+KHl5eV2xXDJ1D1kxW6ZOERcXx+jRo6+apl27dk7HISEhTsrRmzdvZtasWZw5c4aioiJMJhPl5eWUlZXh6uoKgKurK02bNr1iHgBt2rRBoVA4pTl+/DgAx48fx2w206JFC6d79Ho9fn5+11FjmbqEbBBl6hRarfZv01QOqwDWoOoWiwWAixcvMmzYMB5//HHeffddfH192bVrFw899BAGg8FhEKvLQ7pMCe9qzykpKUGhUHDo0CEnowng7u5+DTWVqYvIBlGmTtGuXTtiYmJ48803b+j+Q4cOYbFY+OSTTxBF6xT5smXLarKIAHTo0AGz2UxWVha9evWq8fxlbg3yoopMnWLmzJkcOHCAqVOncuzYMc6cOcM333xDTk7ONd3frFkzjEYjX3zxBRcuXOCXX35h7ty5NV7OFi1aMH78eCZNmsTvv/9OYmIi+/fvZ9asWaxdu7bGnydzc5ANokydokWLFmzcuJGjR48SFRVFt27dWL16NUrltQ1mIiMjmT17Nh988AFt27Zl0aJFzJo1q1bK+uOPPzJp0iSeeeYZwsPDGTlyJAcOHKBhw4a18jyZ2kcOISAjIyNjQ+4hysjIyNiQDaKMjIyMDdkgysjIyNiQDaKMjIyMDdkgysjIyNiQDaKMjIyMDdkgysjIyNiQDaKMjIyMDdkgysjIyNiQDaKMjIyMDdkgysjIyNiQDaKMjIyMjf8HMTadnUxfgpMAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "d8d517e9-7d7a-4322-b4d4-62dbe6d6e22b", + "record_entry_path": "/root/3-MeasurementCalibrationMultilevelGMM.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691442, + "print_time": "2024-12-08 15:57:22" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "MeasurementCalibrationMultilevelGMM" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "lpb_collections": { + "f01": { + "type": "SimpleDriveCollection", + "freq": 5040.4, + "channel": 2, + "shape": "blackman_drag", + "amp": 0.5487, + "phase": 0.0, + "width": 0.05, + "alpha": 500, + "trunc": 1.2, + "transition_name": "f01" + }, + "f12": { + "type": "SimpleDriveCollection", + "freq": 4842.4, + "channel": 2, + "shape": "blackman_drag", + "amp": 0.07071067811865475, + "phase": 0.0, + "width": 0.025, + "alpha": 425.1365229849309, + "trunc": 1.2, + "transition_name": "f12" + } + }, + "measurement_primitives": { + "0": { + "type": "SimpleDispersiveMeasurement", + "freq": 9645.5, + "channel": 1, + "shape": "square", + "amp": 0.15, + "phase": 0.0, + "width": 1, + "trunc": 1.2, + "distinguishable_states": [ + 0, + 1 + ] + } + } + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "Element QA parameters" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "9cb19c94-c085-496a-ac0f-c663a5bc9937", + "record_entry_path": "/root/4-MeasurementCalibrationMultilevelGMM.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691442, + "print_time": "2024-12-08 15:57:23" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "MeasurementCalibrationMultilevelGMM" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "lpb_collections": { + "f01": { + "type": "SimpleDriveCollection", + "freq": 4855.3, + "channel": 4, + "shape": "blackman_drag", + "amp": 0.5399696605966315, + "phase": 0.0, + "width": 0.05, + "alpha": 500, + "trunc": 1.2, + "transition_name": "f01" + }, + "f12": { + "type": "SimpleDriveCollection", + "freq": 4843.4, + "channel": 4, + "shape": "blackman_drag", + "amp": 0.07071067811865475, + "phase": 0.0, + "width": 0.025, + "alpha": 425.1365229849309, + "trunc": 1.2, + "transition_name": "f12" + } + }, + "measurement_primitives": { + "0": { + "type": "SimpleDispersiveMeasurement", + "freq": 9025.5, + "channel": 3, + "shape": "square", + "amp": 0.15, + "phase": 0.0, + "width": 1, + "trunc": 1.2, + "distinguishable_states": [ + 0, + 1 + ] + } + } + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "Element QB parameters" + } + }, + "output_type": "display_data" + } + ], + "execution_count": 2 }, { - "metadata": {}, "cell_type": "markdown", - "source": "## Run auto tuneup", - "id": "45f9aec7a2c7ecfc" + "id": "45f9aec7a2c7ecfc", + "metadata": {}, + "source": [ + "## Run auto tuneup" + ] }, { "cell_type": "code", "id": "b4d4f2c9-ad83-461c-ba24-a33aae217cde", - "metadata": {}, + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-08T21:01:13.833657Z", + "start_time": "2024-12-08T20:57:23.218577Z" + } + }, "source": [ "from k_agents.execution.agent import execute_procedure\n", "from leeq.utils.ai.translation_agent import init_leeq_translation_agents\n", @@ -92,8 +338,12306 @@ "init_leeq_translation_agents()\n", "execute_procedure(\"Full gate calibration on `dut`\", dut=duts_dict['Q1'])" ], + "outputs": [ + { + "data": { + "text/plain": [ + "Adding experiment to memory: 0%| | 0/17 [00:00" + ], + "text/html": [ + "\n", + "
\n", + " Generating state machine...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "extract_parameters: 0%| | 0/1 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('c3f19eae-e17e-4859-825a-e8904e264408');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c3f19eae-e17e-4859-825a-e8904e264408');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The planned experiments are:

\n", + "
\n", + "

Stage1

\n", + "

Description: Full gate calibration on `dut`

\n", + "

Next Steps: If Stage1 completes successfully, goto Complete. If Stage1 fails, goto Failed.

\n", + " \n", + "
\n", + "
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage1: Stage1...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage1

\n", + "

Description: Full gate calibration on `dut`

\n", + "

Next Steps: If Stage1 completes successfully, goto Complete. If Stage1 fails, goto Failed.

\n", + " \n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('78e6040a-a168-40ec-bb96-eec126971cbc');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('78e6040a-a168-40ec-bb96-eec126971cbc');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Full gate calibration on `dut`:
\n", + "

\n", + " 
experiment_instance = Experiment_FullCalibrationOfSingleQubitDut(instruction="Full calibration of Single Qubit dut", dut=dut)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Decomposing instructions...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('a1a72696-ac88-4f64-943b-c85d136b135d');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a1a72696-ac88-4f64-943b-c85d136b135d');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Generating state machine...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "extract_parameters: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('11612cd0-16e1-41bd-84e7-1a3574d1e351');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('11612cd0-16e1-41bd-84e7-1a3574d1e351');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The planned experiments are:

\n", + "
\n", + "

Stage1

\n", + "

Description: Full gate frequency calibration on `dut`

\n", + "

Next Steps: If Stage1 completes successfully, goto Stage2. If Stage1 fails, goto Failed.

\n", + " \n", + "
\n", + " \n", + "
\n", + "

Stage2

\n", + "

Description: Full gate amplitude calibration on `dut`

\n", + "

Next Steps: If Stage2 completes successfully, goto Stage3. If Stage2 fails, goto Failed.

\n", + " \n", + "
\n", + " \n", + "
\n", + "

Stage3

\n", + "

Description: DRAG Calibration on `dut`

\n", + "

Next Steps: If Stage3 completes successfully, goto Complete. If Stage3 fails, goto Failed.

\n", + " \n", + "
\n", + "
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage1: Stage1...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage1

\n", + "

Description: Full gate frequency calibration on `dut`

\n", + "

Next Steps: If Stage1 completes successfully, goto Stage2. If Stage1 fails, goto Failed.

\n", + " \n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('bb959677-4aa8-4e39-a42a-fe2c13519ca2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('bb959677-4aa8-4e39-a42a-fe2c13519ca2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Full gate frequency calibration on `dut`:
\n", + "

\n", + " 
experiment_FullGateFrequencyCalibrationOnDut = Experiment_FullGateFrequencyCalibrationOnDut(instruction="Full Gate frequency calibration on dut", dut=dut)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Decomposing instructions...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('a918f04d-15a8-4c27-aa49-05b218ed9c3b');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a918f04d-15a8-4c27-aa49-05b218ed9c3b');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Generating state machine...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "extract_parameters: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('97c711c4-335e-457d-a8ea-99901de83a21');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('97c711c4-335e-457d-a8ea-99901de83a21');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The planned experiments are:

\n", + "
\n", + "

Stage1

\n", + "

Description: Run simple Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop at `stop_in_us`, step `step_in_us`.

\n", + "

Next Steps: If Stage1 fails after 3 retries, goto Failed. If Stage1 completes, goto Stage2.

\n", + "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 10\n",
+       "VarName:`stop_in_us` Value: 0.3\n",
+       "VarName:`step_in_us` Value: 0.005

\n", + "
\n", + " \n", + "
\n", + "

Stage2

\n", + "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.

\n", + "

Next Steps: If Stage2 fails after 3 retries, goto Failed. If Stage2 completes, goto Stage3.

\n", + "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 1\n",
+       "VarName:`stop_at_in_us` Value: 3\n",
+       "VarName:`step_in_us` Value: 0.05

\n", + "
\n", + " \n", + "
\n", + "

Stage3

\n", + "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.

\n", + "

Next Steps: If Stage3 fails after 3 retries, goto Failed. If Stage3 completes, goto Complete.

\n", + "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 0.1\n",
+       "VarName:`stop_in_us` Value: 30\n",
+       "VarName:`step_in_us` Value: 0.5

\n", + "
\n", + "
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage1: Stage1...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage1

\n", + "

Description: Run simple Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop at `stop_in_us`, step `step_in_us`.

\n", + "

Next Steps: If Stage1 fails after 3 retries, goto Failed. If Stage1 completes, goto Stage2.

\n", + "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 10\n",
+       "VarName:`stop_in_us` Value: 0.3\n",
+       "VarName:`step_in_us` Value: 0.005

\n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('d37672b6-5a7d-44e0-a9d6-845ae57a215a');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d37672b6-5a7d-44e0-a9d6-845ae57a215a');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Run simple Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop at `stop_in_us`, step `step_in_us`.:
\n", + "

\n", + " 
experiment_simple_ramsey = SimpleRamseyMultilevel(dut=dut, collection_name='f01', mprim_index=0, initial_lpb=None, start=0.0, stop=stop_in_us, step=step_in_us, set_offset=frequency_offset_in_MHz, update=True)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling noise is enabled\n" + ] + }, + { + "data": { + "text/html": [ + " \n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "mode": "markers", + "name": "Data", + "x": [ + 0.0, + 0.005, + 0.01, + 0.015, + 0.02, + 0.025, + 0.03, + 0.035, + 0.04, + 0.045, + 0.05, + 0.055, + 0.06, + 0.065, + 0.07, + 0.075, + 0.08, + 0.085, + 0.09, + 0.095, + 0.1, + 0.105, + 0.11, + 0.115, + 0.12, + 0.125, + 0.13, + 0.135, + 0.14, + 0.145, + 0.15, + 0.155, + 0.16, + 0.165, + 0.17, + 0.17500000000000002, + 0.18, + 0.185, + 0.19, + 0.195, + 0.2, + 0.20500000000000002, + 0.21, + 0.215, + 0.22, + 0.225, + 0.23, + 0.23500000000000001, + 0.24, + 0.245, + 0.25, + 0.255, + 0.26, + 0.265, + 0.27, + 0.275, + 0.28, + 0.28500000000000003, + 0.29, + 0.295 + ], + "y": [ + 1.0, + 0.764, + 0.926, + 0.724, + 0.576, + 0.52, + 0.43, + 0.254, + 0.312, + 0.446, + 0.128, + 0.132, + 0.262, + 0.212, + 0.422, + 0.492, + 0.6, + 0.754, + 0.858, + 0.694, + 0.872, + 0.926, + 0.716, + 0.712, + 0.6, + 0.504, + 0.392, + 0.288, + 0.34, + 0.046, + 0.198, + 0.214, + 0.124, + 0.27, + 0.36, + 0.516, + 0.594, + 0.642, + 0.77, + 0.842, + 0.992, + 0.892, + 0.706, + 0.682, + 0.718, + 0.54, + 0.436, + 0.328, + 0.222, + 0.112, + 0.054, + 0.16, + 0.422, + 0.362, + 0.438, + 0.516, + 0.61, + 0.658, + 0.872, + 0.818 + ], + "type": "scatter", + "xaxis": "x", + "yaxis": "y" + }, + { + "mode": "lines", + "name": "Fit", + "visible": "legendonly", + "x": [ + 0.0, + 5.0E-4, + 0.001, + 0.0015, + 0.002, + 0.0025, + 0.003, + 0.0035, + 0.004, + 0.0045000000000000005, + 0.005, + 0.0055, + 0.006, + 0.006500000000000001, + 0.007, + 0.0075, + 0.008, + 0.0085, + 0.009000000000000001, + 0.0095, + 0.01, + 0.0105, + 0.011, + 0.0115, + 0.012, + 0.0125, + 0.013000000000000001, + 0.0135, + 0.014, + 0.0145, + 0.015, + 0.0155, + 0.016, + 0.0165, + 0.017, + 0.0175, + 0.018000000000000002, + 0.0185, + 0.019, + 0.0195, + 0.02, + 0.0205, + 0.021, + 0.021500000000000002, + 0.022, + 0.0225, + 0.023, + 0.0235, + 0.024, + 0.0245, + 0.025, + 0.025500000000000002, + 0.026000000000000002, + 0.0265, + 0.027, + 0.0275, + 0.028, + 0.0285, + 0.029, + 0.029500000000000002, + 0.03, + 0.0305, + 0.031, + 0.0315, + 0.032, + 0.0325, + 0.033, + 0.0335, + 0.034, + 0.0345, + 0.035, + 0.035500000000000004, + 0.036000000000000004, + 0.0365, + 0.037, + 0.0375, + 0.038, + 0.0385, + 0.039, + 0.0395, + 0.04, + 0.0405, + 0.041, + 0.0415, + 0.042, + 0.0425, + 0.043000000000000003, + 0.043500000000000004, + 0.044, + 0.0445, + 0.045, + 0.0455, + 0.046, + 0.0465, + 0.047, + 0.0475, + 0.048, + 0.0485, + 0.049, + 0.0495, + 0.05, + 0.0505, + 0.051000000000000004, + 0.051500000000000004, + 0.052000000000000005, + 0.0525, + 0.053, + 0.0535, + 0.054, + 0.0545, + 0.055, + 0.0555, + 0.056, + 0.0565, + 0.057, + 0.0575, + 0.058, + 0.0585, + 0.059000000000000004, + 0.059500000000000004, + 0.06, + 0.0605, + 0.061, + 0.0615, + 0.062, + 0.0625, + 0.063, + 0.0635, + 0.064, + 0.0645, + 0.065, + 0.0655, + 0.066, + 0.0665, + 0.067, + 0.0675, + 0.068, + 0.0685, + 0.069, + 0.0695, + 0.07, + 0.07050000000000001, + 0.07100000000000001, + 0.07150000000000001, + 0.07200000000000001, + 0.0725, + 0.073, + 0.0735, + 0.074, + 0.0745, + 0.075, + 0.0755, + 0.076, + 0.0765, + 0.077, + 0.0775, + 0.078, + 0.0785, + 0.079, + 0.0795, + 0.08, + 0.0805, + 0.081, + 0.0815, + 0.082, + 0.0825, + 0.083, + 0.0835, + 0.084, + 0.0845, + 0.085, + 0.0855, + 0.08600000000000001, + 0.08650000000000001, + 0.08700000000000001, + 0.08750000000000001, + 0.088, + 0.0885, + 0.089, + 0.0895, + 0.09, + 0.0905, + 0.091, + 0.0915, + 0.092, + 0.0925, + 0.093, + 0.0935, + 0.094, + 0.0945, + 0.095, + 0.0955, + 0.096, + 0.0965, + 0.097, + 0.0975, + 0.098, + 0.0985, + 0.099, + 0.0995, + 0.1, + 0.1005, + 0.101, + 0.1015, + 0.10200000000000001, + 0.10250000000000001, + 0.10300000000000001, + 0.10350000000000001, + 0.10400000000000001, + 0.1045, + 0.105, + 0.1055, + 0.106, + 0.1065, + 0.107, + 0.1075, + 0.108, + 0.1085, + 0.109, + 0.1095, + 0.11, + 0.1105, + 0.111, + 0.1115, + 0.112, + 0.1125, + 0.113, + 0.1135, + 0.114, + 0.1145, + 0.115, + 0.1155, + 0.116, + 0.1165, + 0.117, + 0.11750000000000001, + 0.11800000000000001, + 0.11850000000000001, + 0.11900000000000001, + 0.11950000000000001, + 0.12, + 0.1205, + 0.121, + 0.1215, + 0.122, + 0.1225, + 0.123, + 0.1235, + 0.124, + 0.1245, + 0.125, + 0.1255, + 0.126, + 0.1265, + 0.127, + 0.1275, + 0.128, + 0.1285, + 0.129, + 0.1295, + 0.13, + 0.1305, + 0.131, + 0.1315, + 0.132, + 0.1325, + 0.133, + 0.1335, + 0.134, + 0.1345, + 0.135, + 0.1355, + 0.136, + 0.1365, + 0.137, + 0.1375, + 0.138, + 0.1385, + 0.139, + 0.1395, + 0.14, + 0.1405, + 0.14100000000000001, + 0.14150000000000001, + 0.14200000000000002, + 0.14250000000000002, + 0.14300000000000002, + 0.14350000000000002, + 0.14400000000000002, + 0.1445, + 0.145, + 0.1455, + 0.146, + 0.1465, + 0.147, + 0.1475, + 0.148, + 0.1485, + 0.149, + 0.1495, + 0.15, + 0.1505, + 0.151, + 0.1515, + 0.152, + 0.1525, + 0.153, + 0.1535, + 0.154, + 0.1545, + 0.155, + 0.1555, + 0.156, + 0.1565, + 0.157, + 0.1575, + 0.158, + 0.1585, + 0.159, + 0.1595, + 0.16, + 0.1605, + 0.161, + 0.1615, + 0.162, + 0.1625, + 0.163, + 0.1635, + 0.164, + 0.1645, + 0.165, + 0.1655, + 0.166, + 0.1665, + 0.167, + 0.1675, + 0.168, + 0.1685, + 0.169, + 0.1695, + 0.17, + 0.1705, + 0.171, + 0.1715, + 0.17200000000000001, + 0.17250000000000001, + 0.17300000000000001, + 0.17350000000000002, + 0.17400000000000002, + 0.17450000000000002, + 0.17500000000000002, + 0.17550000000000002, + 0.176, + 0.1765, + 0.177, + 0.1775, + 0.178, + 0.1785, + 0.179, + 0.1795, + 0.18, + 0.1805, + 0.181, + 0.1815, + 0.182, + 0.1825, + 0.183, + 0.1835, + 0.184, + 0.1845, + 0.185, + 0.1855, + 0.186, + 0.1865, + 0.187, + 0.1875, + 0.188, + 0.1885, + 0.189, + 0.1895, + 0.19, + 0.1905, + 0.191, + 0.1915, + 0.192, + 0.1925, + 0.193, + 0.1935, + 0.194, + 0.1945, + 0.195, + 0.1955, + 0.196, + 0.1965, + 0.197, + 0.1975, + 0.198, + 0.1985, + 0.199, + 0.1995, + 0.2, + 0.2005, + 0.201, + 0.2015, + 0.202, + 0.2025, + 0.203, + 0.20350000000000001, + 0.20400000000000001, + 0.20450000000000002, + 0.20500000000000002, + 0.20550000000000002, + 0.20600000000000002, + 0.20650000000000002, + 0.20700000000000002, + 0.20750000000000002, + 0.20800000000000002, + 0.2085, + 0.209, + 0.2095, + 0.21, + 0.2105, + 0.211, + 0.2115, + 0.212, + 0.2125, + 0.213, + 0.2135, + 0.214, + 0.2145, + 0.215, + 0.2155, + 0.216, + 0.2165, + 0.217, + 0.2175, + 0.218, + 0.2185, + 0.219, + 0.2195, + 0.22, + 0.2205, + 0.221, + 0.2215, + 0.222, + 0.2225, + 0.223, + 0.2235, + 0.224, + 0.2245, + 0.225, + 0.2255, + 0.226, + 0.2265, + 0.227, + 0.2275, + 0.228, + 0.2285, + 0.229, + 0.2295, + 0.23, + 0.2305, + 0.231, + 0.2315, + 0.232, + 0.2325, + 0.233, + 0.2335, + 0.234, + 0.23450000000000001, + 0.23500000000000001, + 0.23550000000000001, + 0.23600000000000002, + 0.23650000000000002, + 0.23700000000000002, + 0.23750000000000002, + 0.23800000000000002, + 0.23850000000000002, + 0.23900000000000002, + 0.23950000000000002, + 0.24, + 0.2405, + 0.241, + 0.2415, + 0.242, + 0.2425, + 0.243, + 0.2435, + 0.244, + 0.2445, + 0.245, + 0.2455, + 0.246, + 0.2465, + 0.247, + 0.2475, + 0.248, + 0.2485, + 0.249, + 0.2495, + 0.25, + 0.2505, + 0.251, + 0.2515, + 0.252, + 0.2525, + 0.253, + 0.2535, + 0.254, + 0.2545, + 0.255, + 0.2555, + 0.256, + 0.2565, + 0.257, + 0.2575, + 0.258, + 0.2585, + 0.259, + 0.2595, + 0.26, + 0.2605, + 0.261, + 0.2615, + 0.262, + 0.2625, + 0.263, + 0.2635, + 0.264, + 0.2645, + 0.265, + 0.2655, + 0.266, + 0.2665, + 0.267, + 0.2675, + 0.268, + 0.2685, + 0.269, + 0.2695, + 0.27, + 0.2705, + 0.271, + 0.2715, + 0.272, + 0.2725, + 0.273, + 0.2735, + 0.274, + 0.2745, + 0.275, + 0.2755, + 0.276, + 0.2765, + 0.277, + 0.2775, + 0.278, + 0.2785, + 0.279, + 0.2795, + 0.28, + 0.2805, + 0.281, + 0.28150000000000003, + 0.28200000000000003, + 0.28250000000000003, + 0.28300000000000003, + 0.28350000000000003, + 0.28400000000000003, + 0.28450000000000003, + 0.28500000000000003, + 0.28550000000000003, + 0.28600000000000003, + 0.28650000000000003, + 0.28700000000000003, + 0.28750000000000003, + 0.28800000000000003, + 0.2885, + 0.289, + 0.2895, + 0.29, + 0.2905, + 0.291, + 0.2915, + 0.292, + 0.2925, + 0.293, + 0.2935, + 0.294, + 0.2945, + 0.295, + 0.2955, + 0.296, + 0.2965, + 0.297, + 0.2975, + 0.298, + 0.2985, + 0.299, + 0.2995 + ], + "y": [ + 0.862930707290807, + 0.8636043467881758, + 0.863929695780379, + 0.8639064298072531, + 0.8635345720749221, + 0.8628144934326512, + 0.8617469120030039, + 0.8603328924656699, + 0.8585738449956786, + 0.8564715238570592, + 0.8540280256533463, + 0.8512457872366789, + 0.8481275832775761, + 0.8446765234978137, + 0.8408960495691616, + 0.8367899316810751, + 0.8323622647807606, + 0.8276174644893703, + 0.822560262698393, + 0.8171957028506363, + 0.8115291349105057, + 0.8055662100285961, + 0.7993128749059157, + 0.7927753658633663, + 0.7859602026223881, + 0.7788741818029798, + 0.7715243701455698, + 0.7639180974635043, + 0.7560629493331776, + 0.7479667595290964, + 0.7396376022114197, + 0.7310837838737688, + 0.7223138350593363, + 0.7133365018535534, + 0.7041607371618044, + 0.6947956917808815, + 0.6852507052730888, + 0.6755352966520933, + 0.665659154889813, + 0.6556321292538108, + 0.645464219484827, + 0.6351655658242499, + 0.624746438901467, + 0.6142172294911843, + 0.6035884381509258, + 0.5928706647490501, + 0.5820745978937273, + 0.5712110042734153, + 0.5602907179194705, + 0.5493246294015984, + 0.5383236749669194, + 0.5272988256334817, + 0.5162610762490988, + 0.505221434526421, + 0.4941909100651778, + 0.48318050337253843, + 0.4722011948925407, + 0.4612639340555277, + 0.4503796283585142, + 0.4395591324873727, + 0.4288132374916869, + 0.4181526600230675, + 0.40758803164766555, + 0.39712988824353757, + 0.38678865949344043, + 0.3765746584835313, + 0.36649807141834756, + 0.35656894746232337, + 0.34679718871797294, + 0.3371925403507356, + 0.3277645808703322, + 0.3185227125783223, + 0.3094761521913908, + 0.3006339216497145, + 0.29200483911957426, + 0.28359751019918933, + 0.27542031933653804, + 0.2674814214677289, + 0.2597887338842588, + 0.2523499283372688, + 0.24517242338667278, + 0.23826337700278788, + 0.23162967942784568, + 0.22527794630450315, + 0.21921451207820492, + 0.21344542367997982, + 0.2079764344959671, + 0.20281299862969016, + 0.1979602654627981, + 0.19342307451970014, + 0.18920595064121404, + 0.1853130994720415, + 0.1817484032665716, + 0.1785154170171946, + 0.17561736490898627, + 0.17305713710430076, + 0.17083728686047606, + 0.16896002798352844, + 0.16742723262037396, + 0.16624042939177952, + 0.16540080186790573, + 0.16490918738796073, + 0.16476607622514305, + 0.16497161109770614, + 0.16552558702663178, + 0.16642745154005495, + 0.1676763052242356, + 0.16927090262052835, + 0.1712096534674551, + 0.17349062428664297, + 0.1761115403110436, + 0.17906978775351406, + 0.18236241641349443, + 0.18598614261918284, + 0.1899373525022756, + 0.19421210560200364, + 0.1988061387948737, + 0.20371487054619397, + 0.2089334054791443, + 0.21445653925683422, + 0.22027876377248046, + 0.22639427264252776, + 0.2327969669972345, + 0.23948046156294833, + 0.24643809103000747, + 0.25366291669991337, + 0.26114773340515096, + 0.26888507669475115, + 0.2768672302784318, + 0.28508623372189146, + 0.2935338903855842, + 0.3022017755990559, + 0.3110812450626911, + 0.32016344346849085, + 0.3294393133312856, + 0.338899604021574, + 0.3485348809909805, + 0.35833553518113215, + 0.3682917926065689, + 0.37839372410213357, + 0.388631255225118, + 0.3989941763022921, + 0.4094721526117948, + 0.42005473468973353, + 0.43073136875121315, + 0.4414914072154015, + 0.4523241193241385, + 0.46321870184349023, + 0.4741642898375869, + 0.4851499675039924, + 0.49616477905980233, + 0.5071977396676133, + 0.5182378463904678, + 0.5292740891648493, + 0.5402954617807841, + 0.5512909728581007, + 0.5622496568078977, + 0.5731605847682956, + 0.5840128755035526, + 0.5947957062556929, + 0.6054983235378105, + 0.6161100538582927, + 0.6266203143652652, + 0.6370186234006441, + 0.6472946109532693, + 0.657438029000695, + 0.6674387617293229, + 0.6772868356226867, + 0.6869724294078261, + 0.6964858838498322, + 0.7058177113847949, + 0.7149586055815467, + 0.7238994504227667, + 0.7326313293961891, + 0.7411455343868487, + 0.749433574361496, + 0.7574871838365241, + 0.765298331120954, + 0.7728592263262689, + 0.7801623291351014, + 0.7872003563210288, + 0.7939662890119774, + 0.8004533796899914, + 0.8066551589203868, + 0.8125654418035773, + 0.8181783341431407, + 0.8234882383239724, + 0.8284898588946659, + 0.8331782078485489, + 0.8375486095981144, + 0.8415967056378812, + 0.8453184588910356, + 0.8487101577355189, + 0.8517684197055457, + 0.854490194864862, + 0.8568727688483782, + 0.8589137655691443, + 0.8606111495879679, + 0.8619632281433105, + 0.862968652839439, + 0.8636264209911495, + 0.8639358766237195, + 0.8638967111270947, + 0.8635089635636555, + 0.8627730206292576, + 0.8616896162675853, + 0.8602598309382006, + 0.8584850905390216, + 0.8563671649843009, + 0.8539081664395243, + 0.85111054721499, + 0.8479770973201681, + 0.8445109416812804, + 0.8407155370248742, + 0.8365946684305001, + 0.8321524455559284, + 0.8273932985386725, + 0.8223219735779033, + 0.8169435282011627, + 0.8112633262205968, + 0.8052870323837359, + 0.7990206067241602, + 0.7924702986176819, + 0.7856426405499731, + 0.7785444416018537, + 0.7711827806587377, + 0.7635649993510084, + 0.7556986947323643, + 0.7475917117034366, + 0.7392521351882343, + 0.7306882820712188, + 0.7219086929030508, + 0.7129221233832774, + 0.7037375356284579, + 0.6943640892344336, + 0.6848111321416565, + 0.6750881913126826, + 0.6652049632311372, + 0.6551713042316141, + 0.6449972206701613, + 0.6346928589451543, + 0.6242684953785073, + 0.6137345259673147, + 0.6031014560161435, + 0.5923798896603155, + 0.5815805192906278, + 0.5707141148900586, + 0.5597915132930898, + 0.548823607378365, + 0.5378213352054507, + 0.5267956691065429, + 0.5157576047439923, + 0.5047181501445657, + 0.49368831472137326, + 0.48267909829441663, + 0.4717014801207018, + 0.46076640794486035, + 0.44988478708119695, + 0.4390674695380521, + 0.42832524319532544, + 0.41766882104595365, + 0.40710883051207125, + 0.3966558028465096, + 0.3863201626302034, + 0.3761122173759795, + 0.36604214724909434, + 0.3561199949147735, + 0.34635565552287667, + 0.3367588668396768, + 0.3273391995365947, + 0.31810604764557404, + 0.3090686191906132, + 0.30023592700480384, + 0.29161677974202427, + 0.2832197730922586, + 0.27505328120930056, + 0.26712544835939017, + 0.2594441807991129, + 0.25201713889066024, + 0.24485172946231643, + 0.23795509842178825, + 0.23133412362974654, + 0.22499540804068435, + 0.21894527311793405, + 0.21318975252940903, + 0.20773458613035795, + 0.20258521423913262, + 0.19774677221167675, + 0.19322408532014818, + 0.18902166394078063, + 0.18514369905578404, + 0.181594058073772, + 0.17837628097287828, + 0.17549357677041783, + 0.1729488203226034, + 0.17074454945751694, + 0.1688829624441907, + 0.1673659158003239, + 0.16619492244082024, + 0.16537115016899362, + 0.1648954205119465, + 0.16476820790128127, + 0.1649896391999635, + 0.16555949357580768, + 0.16647720272171185, + 0.16774185142242154, + 0.16935217846725809, + 0.17130657790790038, + 0.1736031006599668, + 0.17623945644679834, + 0.17921301608350698, + 0.1825208140990081, + 0.18615955169342668, + 0.19012560002792156, + 0.19441500384365373, + 0.19902348540628467, + 0.20394644877207296, + 0.20917898437131438, + 0.21471587390455416, + 0.22055159554668946, + 0.22668032945377048, + 0.23309596356701046, + 0.2397920997082142, + 0.24676205996054829, + 0.25399889332828796, + 0.2614953826688999, + 0.2692440518905475, + 0.27723717340784093, + 0.28546677584839564, + 0.2939246520025153, + 0.30260236700806853, + 0.31149126676240246, + 0.3205824865528935, + 0.3298669598975412, + 0.3393354275867789, + 0.3489784469174886, + 0.35878640111000903, + 0.368749508898748, + 0.3788578342868312, + 0.389101296455062, + 0.3994696798153097, + 0.40995264419829935, + 0.42053973516564347, + 0.43122039443583227, + 0.4419839704137841, + 0.45281972881345545, + 0.4637168633629165, + 0.47466450658121717, + 0.485651740616295, + 0.49666760813311717, + 0.5077011232411972, + 0.5187412824505925, + 0.5297770756454464, + 0.5407974970641479, + 0.5517915562751382, + 0.5627482891374372, + 0.573656768734947, + 0.5845061162736316, + 0.5952855119307073, + 0.6059842056450204, + 0.6165915278378535, + 0.6270969000534692, + 0.6374898455087771, + 0.6477599995416061, + 0.6578971199471603, + 0.6678910971923501, + 0.6777319644978137, + 0.6874099077775719, + 0.6969152754264064, + 0.7062385879451981, + 0.7153705473946282, + 0.724302046667813, + 0.7330241785726275, + 0.7415282447146543, + 0.749805764171906, + 0.7578484819526667, + 0.7656483772280166, + 0.7731976713308314, + 0.78048883551328, + 0.7875145984550806, + 0.7942679535150328, + 0.8007421657185874, + 0.8069307784744919, + 0.8128276200138076, + 0.8184268095448811, + 0.8237227631181301, + 0.8287101991947938, + 0.8333841439140968, + 0.8377399360535704, + 0.841773231677589, + 0.84548000846948, + 0.8488565697428929, + 0.8518995481284211, + 0.8546059089318057, + 0.8569729531603674, + 0.8589983202146514, + 0.8606799902426, + 0.862016286153904, + 0.8630058752925264, + 0.8636477707657277, + 0.8639413324282706, + 0.863886267520819, + 0.863482630961897, + 0.8627308252931178, + 0.861631600277732, + 0.860186052152903, + 0.8583956225364485, + 0.856262096989143, + 0.8537876032340117, + 0.8509746090343953, + 0.8478259197328983, + 0.8443446754536768, + 0.8405343479708567, + 0.8363987372462036, + 0.8319419676394992, + 0.8271684837954011, + 0.8220830462108905, + 0.8166907264877267, + 0.810996902274643, + 0.8050072519043303, + 0.7987277487305523, + 0.7921646551710453, + 0.7853245164621385, + 0.7782141541313268, + 0.7708406591943033, + 0.7632113850832385, + 0.7553339403133558, + 0.7472161808951184, + 0.7388662024995954, + 0.7302923323848186, + 0.7215031210911831, + 0.7125073339141735, + 0.7033139421629179, + 0.6939321142132919, + 0.6843712063644897, + 0.6746407535081841, + 0.6647504596195803, + 0.6547101880798467, + 0.6445299518395723, + 0.6342199034330639, + 0.6237903248534372, + 0.6132516172986036, + 0.6026142907983751, + 0.5918889537330344, + 0.581086302253822, + 0.5702171096158917, + 0.5592922154343711, + 0.5483225148742442, + 0.5373189477848345, + 0.526292487789725, + 0.5152541313429986, + 0.5042148867627045, + 0.4931857632525014, + 0.48217775992241013, + 0.47120185481963595, + 0.46026899398039633, + 0.44939008051367213, + 0.43857596372776947, + 0.4278374283105361, + 0.41718518357402234, + 0.40662985277431296, + 0.39618196251718085, + 0.38585193226012793, + 0.3756500639212832, + 0.36558653160552024, + 0.35567137145804023, + 0.34591447165553924, + 0.3363255625449415, + 0.32691420693953344, + 0.3176897905821748, + 0.30866151278509996, + 0.29983837725563767, + 0.29122918311701307, + 0.2828425161331676, + 0.2746867401463624, + 0.26676998873609864, + 0.25910015710767414, + 0.25168489421846557, + 0.2445315951497885, + 0.2376473937319426, + 0.23103915542979725, + 0.2247134704960123, + 0.218676647398722, + 0.21293470653023666, + 0.20749337420303676, + 0.202358076939046, + 0.19753393605787956, + 0.19302576256946424, + 0.18883805237612356, + 0.18497498178891308, + 0.18144040336267803, + 0.17823784205398502, + 0.1753704917057608, + 0.17284121186214518, + 0.17065252491673089, + 0.16880661359703775, + 0.16730531878772842, + 0.16615013769473658, + 0.1653422223521387, + 0.1648823784732587, + 0.1647710646471503, + 0.1650083918812596, + 0.165594123490723, + 0.16652767533441148, + 0.16780811639748577, + 0.1694341697198808, + 0.17140421366979425, + 0.17371628356090912, + 0.1763680736117368, + 0.17935693924512708, + 0.1826798997256523, + 0.18633364113223483, + 0.19031451966305268, + 0.1946185652694296, + 0.1992414856150827, + 0.2041786703567815, + 0.20942519574214846, + 0.21497582952001543, + 0.22082503615843907, + 0.22696698236517254, + 0.23339554290508657, + 0.2401043067087386, + 0.24708658326599925, + 0.2543354092983581, + 0.26184355570325574, + 0.26960353476351695, + 0.277607607614694, + 0.2858477919628737, + 0.29431587004525217, + 0.30300339682553645, + 0.3119017084160024, + 0.3210019307178071, + 0.33029498827094117, + 0.3397716133049938, + 0.3494223549817047, + 0.35923758882008594, + 0.36920752629471354, + 0.3793222245976174, + 0.3895715965540332, + 0.39994542068212885, + 0.41043335138667086, + 0.4210249292764721, + 0.4317095915953141, + 0.44247668275596475, + 0.4533154649667685, + 0.464215128940218, + 0.47516480467282735, + 0.4861535722855558, + 0.497170472913971, + 0.508204519637292, + 0.5192447084354114, + 0.5302800291629706, + 0.5412994765295436, + 0.5522920610749791, + 0.5632468201289543, + 0.5741528287438131, + 0.5849992105897834, + 0.5957751488017082, + 0.6064698967664751, + 0.617072788840384, + 0.6275732509857651, + 0.6379608113162417, + 0.6482251105401177, + 0.658355912291478, + 0.6683431133386972, + 0.678176753660176, + 0.687847026377257, + 0.6973442875344151, + 0.7066590657169671, + 0.7157820714967106, + 0.7247042066960703, + 0.7334165734615163, + 0.741910483137201, + 0.7501774649299687, + 0.7582092743570958, + 0.7659979014683349, + 0.7735355788340659, + 0.7808147892915843, + 0.7878282734418096, + 0.7945690368889206, + 0.801030357215719, + 0.8072057906877488, + 0.8130891786794947, + 0.8186746538162424, + 0.8239566458254852, + 0.8289298870920357, + 0.8335894179113025, + 0.8379305914354952, + 0.8419490783078237, + 0.8456408709800707, + 0.8490022877092304, + 0.8520299762292297, + 0.8547209170940685, + 0.8570724266890453, + 0.859082159907066, + 0.8607481124873659, + 0.8620686230143129, + 0.863042374574298, + 0.8636683960690628, + 0.8639460631841507 + ], + "type": "scatter", + "xaxis": "x", + "yaxis": "y" + } + ], + "layout": { + "template": { + "data": { + "histogram2dcontour": [ + { + "type": "histogram2dcontour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "choropleth": [ + { + "type": "choropleth", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "histogram2d": [ + { + "type": "histogram2d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmap": [ + { + "type": "heatmap", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmapgl": [ + { + "type": "heatmapgl", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "contourcarpet": [ + { + "type": "contourcarpet", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "contour": [ + { + "type": "contour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "surface": [ + { + "type": "surface", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "mesh3d": [ + { + "type": "mesh3d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "parcoords": [ + { + "type": "parcoords", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolargl": [ + { + "type": "scatterpolargl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "scattergeo": [ + { + "type": "scattergeo", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolar": [ + { + "type": "scatterpolar", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "scattergl": [ + { + "type": "scattergl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatter3d": [ + { + "type": "scatter3d", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattermapbox": [ + { + "type": "scattermapbox", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterternary": [ + { + "type": "scatterternary", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattercarpet": [ + { + "type": "scattercarpet", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ] + }, + "layout": { + "autotypenumbers": "strict", + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "hovermode": "closest", + "hoverlabel": { + "align": "left" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "bgcolor": "#E5ECF6", + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "ternary": { + "bgcolor": "#E5ECF6", + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "sequential": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ] + }, + "xaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "yaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "geo": { + "bgcolor": "white", + "landcolor": "#E5ECF6", + "subunitcolor": "white", + "showland": true, + "showlakes": true, + "lakecolor": "white" + }, + "title": { + "x": 0.05 + }, + "mapbox": { + "style": "light" + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "Time (us)
Frequency: 10.05+/-0.08 MHz" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "" + } + }, + "title": { + "text": "Ramsey decay QA transition f01:
(0.0+/-3.5)e+09 us" + }, + "plot_bgcolor": "white" + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "33f4e035-6b33-4172-9477-9ec231276e5b", + "record_entry_path": "/root/6-SimpleRamseyMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691493, + "print_time": "2024-12-08 15:58:13" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "SimpleRamseyMultilevel" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 10.000 MHz, and the measured oscillation is 10.053+/-0.080 MHz. Oscillation amplitude is 0.350+/-0.028. The number of oscillations is 3.016+/-0.024.'
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('6835cf21-84b2-47a0-bdeb-8cf8da18d493');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('6835cf21-84b2-47a0-bdeb-8cf8da18d493');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear and regular oscillatory pattern, indicating the presence of Ramsey oscillations.\\n2. Fit Quality: The fit line closely follows the data points throughout the plot, suggesting a good fit.\\n3. Data Spread: The data points are moderately clustered around the fit line, with some dispersion but not excessively wide.\\n4. Amplitude and Frequency: The amplitude of the oscillations is close to 1, which is ideal. The frequency is approximately 10.05 MHz, aligning well with the expected value.\\n5. Overall Pattern: The plot displays typical characteristics of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude and frequency.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('a9be2d86-b1d0-4230-b04c-7b2c6da53d97');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a9be2d86-b1d0-4230-b04c-7b2c6da53d97');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
analysis: 'The Ramsey experiment for qubit QA was successful. The oscillation frequency measured was 10.053 MHz, which is very close to the expected offset of 10.000 MHz. The amplitude of the oscillations was 0.350, which is above the threshold of 0.2, indicating a strong signal. The number of oscillations observed was 3.016, which is within the acceptable range of 3 to 10 oscillations. The data exhibited a clear and regular oscillatory pattern with a good fit, suggesting that the experiment was conducted properly.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('b457baf0-7b7f-4f9e-a867-1d41478b837f');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('b457baf0-7b7f-4f9e-a867-1d41478b837f');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Transitioning to the next stage Stage2 with the following description:
Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.
The Ramsey experiment for qubit QA was successful. The oscillation frequency measured was 10.053 MHz, which is very close to the expected offset of 10.000 MHz. The amplitude of the oscillations was 0.350, which is above the threshold of 0.2, indicating a strong signal. The number of oscillations observed was 3.016, which is within the acceptable range of 3 to 10 oscillations. The data exhibited a clear and regular oscillatory pattern with a good fit, suggesting that the experiment was conducted properly. According to the rule of transition, since Stage1 has been completed successfully, the experiment should proceed to Stage2.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage2: Stage2...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage2

\n", + "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.

\n", + "

Next Steps: If Stage2 fails after 3 retries, goto Failed. If Stage2 completes, goto Stage3.

\n", + "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 1\n",
+       "VarName:`stop_at_in_us` Value: 3\n",
+       "VarName:`step_in_us` Value: 0.05

\n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('1d341047-84f9-492d-892b-5bdf52e70a47');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('1d341047-84f9-492d-892b-5bdf52e70a47');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.:
\n", + "

\n", + " 
experiment_ramsey = SimpleRamseyMultilevel(dut=dut, collection_name='f01', mprim_index=0, initial_lpb=None, start=0.0, stop=stop_at_in_us, step=step_in_us, set_offset=frequency_offset_in_MHz, update=True)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling noise is enabled\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "mode": "markers", + "name": "Data", + "x": [ + 0.0, + 0.05, + 0.1, + 0.15000000000000002, + 0.2, + 0.25, + 0.30000000000000004, + 0.35000000000000003, + 0.4, + 0.45, + 0.5, + 0.55, + 0.6000000000000001, + 0.65, + 0.7000000000000001, + 0.75, + 0.8, + 0.8500000000000001, + 0.9, + 0.9500000000000001, + 1.0, + 1.05, + 1.1, + 1.1500000000000001, + 1.2000000000000002, + 1.25, + 1.3, + 1.35, + 1.4000000000000001, + 1.4500000000000002, + 1.5, + 1.55, + 1.6, + 1.6500000000000001, + 1.7000000000000002, + 1.75, + 1.8, + 1.85, + 1.9000000000000001, + 1.9500000000000002, + 2.0, + 2.0500000000000003, + 2.1, + 2.15, + 2.2, + 2.25, + 2.3000000000000003, + 2.35, + 2.4000000000000004, + 2.45, + 2.5, + 2.5500000000000003, + 2.6, + 2.6500000000000004, + 2.7, + 2.75, + 2.8000000000000003, + 2.85, + 2.9000000000000004, + 2.95 + ], + "y": [ + 1.0, + 0.728, + 0.926, + 0.736, + 0.586, + 0.522, + 0.306, + 0.356, + 0.196, + 0.086, + 0.0, + 0.184, + 0.088, + 0.362, + 0.364, + 0.5, + 0.688, + 0.682, + 0.708, + 0.858, + 0.766, + 0.922, + 0.534, + 0.838, + 0.558, + 0.492, + 0.434, + 0.264, + 0.182, + 0.0, + 0.15, + 0.206, + 0.222, + 0.398, + 0.29, + 0.472, + 0.644, + 0.758, + 0.862, + 0.83, + 0.77, + 0.94, + 1.0, + 0.618, + 0.682, + 0.534, + 0.444, + 0.23, + 0.0, + 0.022, + 0.09, + 0.306, + 0.116, + 0.166, + 0.328, + 0.498, + 0.6, + 0.826, + 0.906, + 0.97 + ], + "type": "scatter", + "xaxis": "x", + "yaxis": "y" + }, + { + "mode": "lines", + "name": "Fit", + "visible": "legendonly", + "x": [ + 0.0, + 0.005, + 0.01, + 0.015, + 0.02, + 0.025, + 0.03, + 0.035, + 0.04, + 0.045, + 0.05, + 0.055, + 0.06, + 0.065, + 0.07, + 0.075, + 0.08, + 0.085, + 0.09, + 0.095, + 0.1, + 0.105, + 0.11, + 0.115, + 0.12, + 0.125, + 0.13, + 0.135, + 0.14, + 0.145, + 0.15, + 0.155, + 0.16, + 0.165, + 0.17, + 0.17500000000000002, + 0.18, + 0.185, + 0.19, + 0.195, + 0.2, + 0.20500000000000002, + 0.21, + 0.215, + 0.22, + 0.225, + 0.23, + 0.23500000000000001, + 0.24, + 0.245, + 0.25, + 0.255, + 0.26, + 0.265, + 0.27, + 0.275, + 0.28, + 0.28500000000000003, + 0.29, + 0.295, + 0.3, + 0.305, + 0.31, + 0.315, + 0.32, + 0.325, + 0.33, + 0.335, + 0.34, + 0.34500000000000003, + 0.35000000000000003, + 0.355, + 0.36, + 0.365, + 0.37, + 0.375, + 0.38, + 0.385, + 0.39, + 0.395, + 0.4, + 0.405, + 0.41000000000000003, + 0.41500000000000004, + 0.42, + 0.425, + 0.43, + 0.435, + 0.44, + 0.445, + 0.45, + 0.455, + 0.46, + 0.465, + 0.47000000000000003, + 0.47500000000000003, + 0.48, + 0.485, + 0.49, + 0.495, + 0.5, + 0.505, + 0.51, + 0.515, + 0.52, + 0.525, + 0.53, + 0.535, + 0.54, + 0.545, + 0.55, + 0.555, + 0.56, + 0.5650000000000001, + 0.5700000000000001, + 0.5750000000000001, + 0.58, + 0.585, + 0.59, + 0.595, + 0.6, + 0.605, + 0.61, + 0.615, + 0.62, + 0.625, + 0.63, + 0.635, + 0.64, + 0.645, + 0.65, + 0.655, + 0.66, + 0.665, + 0.67, + 0.675, + 0.68, + 0.685, + 0.6900000000000001, + 0.6950000000000001, + 0.7000000000000001, + 0.705, + 0.71, + 0.715, + 0.72, + 0.725, + 0.73, + 0.735, + 0.74, + 0.745, + 0.75, + 0.755, + 0.76, + 0.765, + 0.77, + 0.775, + 0.78, + 0.785, + 0.79, + 0.795, + 0.8, + 0.805, + 0.81, + 0.8150000000000001, + 0.8200000000000001, + 0.8250000000000001, + 0.8300000000000001, + 0.835, + 0.84, + 0.845, + 0.85, + 0.855, + 0.86, + 0.865, + 0.87, + 0.875, + 0.88, + 0.885, + 0.89, + 0.895, + 0.9, + 0.905, + 0.91, + 0.915, + 0.92, + 0.925, + 0.93, + 0.935, + 0.9400000000000001, + 0.9450000000000001, + 0.9500000000000001, + 0.9550000000000001, + 0.96, + 0.965, + 0.97, + 0.975, + 0.98, + 0.985, + 0.99, + 0.995, + 1.0, + 1.0050000000000001, + 1.01, + 1.0150000000000001, + 1.02, + 1.025, + 1.03, + 1.035, + 1.04, + 1.045, + 1.05, + 1.055, + 1.06, + 1.065, + 1.07, + 1.075, + 1.08, + 1.085, + 1.09, + 1.095, + 1.1, + 1.105, + 1.11, + 1.115, + 1.12, + 1.125, + 1.1300000000000001, + 1.135, + 1.1400000000000001, + 1.145, + 1.1500000000000001, + 1.155, + 1.16, + 1.165, + 1.17, + 1.175, + 1.18, + 1.185, + 1.19, + 1.195, + 1.2, + 1.205, + 1.21, + 1.215, + 1.22, + 1.225, + 1.23, + 1.235, + 1.24, + 1.245, + 1.25, + 1.2550000000000001, + 1.26, + 1.2650000000000001, + 1.27, + 1.2750000000000001, + 1.28, + 1.285, + 1.29, + 1.295, + 1.3, + 1.305, + 1.31, + 1.315, + 1.32, + 1.325, + 1.33, + 1.335, + 1.34, + 1.345, + 1.35, + 1.355, + 1.36, + 1.365, + 1.37, + 1.375, + 1.3800000000000001, + 1.385, + 1.3900000000000001, + 1.395, + 1.4000000000000001, + 1.405, + 1.41, + 1.415, + 1.42, + 1.425, + 1.43, + 1.435, + 1.44, + 1.445, + 1.45, + 1.455, + 1.46, + 1.465, + 1.47, + 1.475, + 1.48, + 1.485, + 1.49, + 1.495, + 1.5, + 1.5050000000000001, + 1.51, + 1.5150000000000001, + 1.52, + 1.5250000000000001, + 1.53, + 1.5350000000000001, + 1.54, + 1.545, + 1.55, + 1.555, + 1.56, + 1.565, + 1.57, + 1.575, + 1.58, + 1.585, + 1.59, + 1.595, + 1.6, + 1.605, + 1.61, + 1.615, + 1.62, + 1.625, + 1.6300000000000001, + 1.635, + 1.6400000000000001, + 1.645, + 1.6500000000000001, + 1.655, + 1.6600000000000001, + 1.665, + 1.67, + 1.675, + 1.68, + 1.685, + 1.69, + 1.695, + 1.7, + 1.705, + 1.71, + 1.715, + 1.72, + 1.725, + 1.73, + 1.735, + 1.74, + 1.745, + 1.75, + 1.7550000000000001, + 1.76, + 1.7650000000000001, + 1.77, + 1.7750000000000001, + 1.78, + 1.7850000000000001, + 1.79, + 1.795, + 1.8, + 1.805, + 1.81, + 1.815, + 1.82, + 1.825, + 1.83, + 1.835, + 1.84, + 1.845, + 1.85, + 1.855, + 1.86, + 1.865, + 1.87, + 1.875, + 1.8800000000000001, + 1.885, + 1.8900000000000001, + 1.895, + 1.9000000000000001, + 1.905, + 1.9100000000000001, + 1.915, + 1.92, + 1.925, + 1.93, + 1.935, + 1.94, + 1.945, + 1.95, + 1.955, + 1.96, + 1.965, + 1.97, + 1.975, + 1.98, + 1.985, + 1.99, + 1.995, + 2.0, + 2.005, + 2.0100000000000002, + 2.015, + 2.02, + 2.025, + 2.0300000000000002, + 2.035, + 2.04, + 2.045, + 2.05, + 2.055, + 2.06, + 2.065, + 2.07, + 2.075, + 2.08, + 2.085, + 2.09, + 2.095, + 2.1, + 2.105, + 2.11, + 2.115, + 2.12, + 2.125, + 2.13, + 2.1350000000000002, + 2.14, + 2.145, + 2.15, + 2.1550000000000002, + 2.16, + 2.165, + 2.17, + 2.1750000000000003, + 2.18, + 2.185, + 2.19, + 2.195, + 2.2, + 2.205, + 2.21, + 2.215, + 2.22, + 2.225, + 2.23, + 2.235, + 2.24, + 2.245, + 2.25, + 2.255, + 2.2600000000000002, + 2.265, + 2.27, + 2.275, + 2.2800000000000002, + 2.285, + 2.29, + 2.295, + 2.3000000000000003, + 2.305, + 2.31, + 2.315, + 2.32, + 2.325, + 2.33, + 2.335, + 2.34, + 2.345, + 2.35, + 2.355, + 2.36, + 2.365, + 2.37, + 2.375, + 2.38, + 2.3850000000000002, + 2.39, + 2.395, + 2.4, + 2.4050000000000002, + 2.41, + 2.415, + 2.42, + 2.4250000000000003, + 2.43, + 2.435, + 2.44, + 2.445, + 2.45, + 2.455, + 2.46, + 2.465, + 2.47, + 2.475, + 2.48, + 2.485, + 2.49, + 2.495, + 2.5, + 2.505, + 2.5100000000000002, + 2.515, + 2.52, + 2.525, + 2.5300000000000002, + 2.535, + 2.54, + 2.545, + 2.5500000000000003, + 2.555, + 2.56, + 2.565, + 2.57, + 2.575, + 2.58, + 2.585, + 2.59, + 2.595, + 2.6, + 2.605, + 2.61, + 2.615, + 2.62, + 2.625, + 2.63, + 2.6350000000000002, + 2.64, + 2.645, + 2.65, + 2.6550000000000002, + 2.66, + 2.665, + 2.67, + 2.6750000000000003, + 2.68, + 2.685, + 2.69, + 2.695, + 2.7, + 2.705, + 2.71, + 2.715, + 2.72, + 2.725, + 2.73, + 2.735, + 2.74, + 2.745, + 2.75, + 2.755, + 2.7600000000000002, + 2.765, + 2.77, + 2.775, + 2.7800000000000002, + 2.785, + 2.79, + 2.795, + 2.8000000000000003, + 2.805, + 2.81, + 2.815, + 2.82, + 2.825, + 2.83, + 2.835, + 2.84, + 2.845, + 2.85, + 2.855, + 2.86, + 2.865, + 2.87, + 2.875, + 2.88, + 2.8850000000000002, + 2.89, + 2.895, + 2.9, + 2.9050000000000002, + 2.91, + 2.915, + 2.92, + 2.9250000000000003, + 2.93, + 2.935, + 2.94, + 2.945, + 2.95, + 2.955, + 2.96, + 2.965, + 2.97, + 2.975, + 2.98, + 2.985, + 2.99, + 2.995 + ], + "y": [ + 0.8927677675330036, + 0.892538426709964, + 0.8919158517828547, + 0.8909006588705457, + 0.8894938526383627, + 0.8876968253038401, + 0.8855113552589415, + 0.8829396053101094, + 0.8799841205378871, + 0.8766478257782312, + 0.8729340227280072, + 0.868846386677531, + 0.864388962873392, + 0.8595661625151567, + 0.8543827583899141, + 0.8488438801489825, + 0.8429550092314546, + 0.8367219734396012, + 0.8301509411715045, + 0.8232484153166271, + 0.8160212268203593, + 0.8084765279239118, + 0.8006217850862435, + 0.7924647715950327, + 0.7840135598739988, + 0.7752765134941912, + 0.7662622788971507, + 0.7569797768381328, + 0.747438193557862, + 0.7376469716915554, + 0.7276158009242074, + 0.7173546084013906, + 0.7068735489050562, + 0.6961829948040581, + 0.6852935257893484, + 0.6742159184040001, + 0.6629611353784184, + 0.6515403147812959, + 0.6399647589970487, + 0.628245923540639, + 0.6163954057208566, + 0.6044249331632765, + 0.592346352204251, + 0.5801716161674224, + 0.5679127735343578, + 0.5555819560210121, + 0.5431913665718213, + 0.530753267283305, + 0.5182799672691292, + 0.5057838104786412, + 0.493277163480931, + 0.48077240322650366, + 0.46828190479868176, + 0.4558180291668542, + 0.44339311095369205, + 0.4310194462284386, + 0.4187092803383533, + 0.40647479579035084, + 0.39432810019482967, + 0.3822812142836198, + 0.3703460600139086, + 0.3585344487699167, + 0.34685806967400057, + 0.3353284780187484, + 0.32395708383151867, + 0.3127551405827369, + 0.30173373404912623, + 0.290903771342893, + 0.2802759701177227, + 0.2698608479622713, + 0.2596687119916459, + 0.249709648647177, + 0.23999351371457517, + 0.23052992257035265, + 0.22132824066615991, + 0.21239757426045575, + 0.20374676140668285, + 0.19538436320686692, + 0.1873186553392951, + 0.17955761986865854, + 0.17210893734676286, + 0.16497997921162594, + 0.1581778004924833, + 0.1517091328279208, + 0.14558037780404515, + 0.13979760061928315, + 0.1343665240820811, + 0.12929252294744326, + 0.12458061859791408, + 0.12023547407426882, + 0.11626138946082937, + 0.11266229762997293, + 0.10944176035004494, + 0.106602964760526, + 0.10414872021794541, + 0.102081455515657, + 0.10040321648023398, + 0.09911566394685756, + 0.09822007211570571, + 0.09771732729096627, + 0.09760792700372467, + 0.097891979519592, + 0.09856920373156253, + 0.09963892943820596, + 0.10110009800691849, + 0.10295126342157768, + 0.10519059371356304, + 0.10781587277472704, + 0.11082450255052206, + 0.11421350561111232, + 0.11797952809792783, + 0.12211884304274256, + 0.12662735405599396, + 0.13150059938069086, + 0.13673375630790352, + 0.14232164594945795, + 0.1482587383631201, + 0.15453915802518997, + 0.161156689645096, + 0.16810478431623188, + 0.1753765659969494, + 0.1829648383152947, + 0.19086209169075252, + 0.19906051076594977, + 0.2075519821409691, + 0.21632810240260708, + 0.22538018644064695, + 0.2346992760428983, + 0.24427614876051523, + 0.2541013270348019, + 0.264165087576492, + 0.2744574709882006, + 0.28496829162054593, + 0.29568714765216686, + 0.30660343138367746, + 0.3177063397353641, + 0.32898488493823086, + 0.3404279054078282, + 0.3520240767900844, + 0.3637619231682293, + 0.3756298284196978, + 0.38761604771179586, + 0.3997087191247348, + 0.41189587539053885, + 0.4241654557362144, + 0.4365053178194459, + 0.44890324974502177, + 0.46134698215009157, + 0.47382420034628836, + 0.48632255650671674, + 0.49882968188572496, + 0.5113331990593892, + 0.5238207341745761, + 0.5362799291944815, + 0.5486984541285068, + 0.5610640192343901, + 0.5733643871804961, + 0.5855873851562494, + 0.597720916918706, + 0.609752974763357, + 0.621671651407314, + 0.6334651517731047, + 0.6451218046614398, + 0.6566300743013729, + 0.6679785717664493, + 0.6791560662455213, + 0.6901514961570985, + 0.7009539800962136, + 0.7115528276029887, + 0.7219375497422315, + 0.7320978694835968, + 0.7420237318720425, + 0.751705313978515, + 0.7611330346210079, + 0.7702975638463897, + 0.7791898321635989, + 0.7878010395190869, + 0.7961226640056086, + 0.8041464702957597, + 0.8118645177918986, + 0.8192691684844009, + 0.8263530945104587, + 0.8331092854059547, + 0.8395310550432237, + 0.845612048247844, + 0.8513462470879091, + 0.8567279768295497, + 0.8617519115528219, + 0.8664130794223933, + 0.8707068676078218, + 0.8746290268485475, + 0.8781756756590898, + 0.881343304170279, + 0.8841287776027282, + 0.8865293393691043, + 0.8885426138021281, + 0.8901666085056055, + 0.8913997163261624, + 0.8922407169437312, + 0.8926887780792154, + 0.8927434563181379, + 0.8924046975494571, + 0.8916728370191163, + 0.8905485989982744, + 0.8890330960665456, + 0.8871278280109565, + 0.8848346803417131, + 0.8821559224262427, + 0.8790942052433603, + 0.8756525587597818, + 0.8718343889315762, + 0.8676434743335326, + 0.8630839624197673, + 0.8581603654192819, + 0.8528775558705243, + 0.847240761799382, + 0.8412555615453694, + 0.8349278782411386, + 0.8282639739507685, + 0.8212704434726421, + 0.8139542078130351, + 0.8063225073368833, + 0.7983828946025, + 0.7901432268873416, + 0.7816116584122084, + 0.772796632271588, + 0.7637068720781135, + 0.754351373329421, + 0.7447393945059321, + 0.73488044790839, + 0.7247842902441995, + 0.7144609129719026, + 0.7039205324133282, + 0.6931735796432208, + 0.6822306901663349, + 0.6711026933922221, + 0.6598006019181278, + 0.6483356006305926, + 0.6367190356365595, + 0.6249624030349243, + 0.6130773375396577, + 0.6010756009657406, + 0.5889690705893278, + 0.5767697273936347, + 0.5644896442122058, + 0.5521409737812729, + 0.5397359367130511, + 0.5272868094018509, + 0.5148059118749982, + 0.5023055956005629, + 0.4897982312639834, + 0.4772961965256621, + 0.46481186377166955, + 0.452357587869657, + 0.4399456939421168, + 0.42758846516906845, + 0.41529813063226306, + 0.40308685321291526, + 0.3909667175549552, + 0.3789497181057073, + 0.3670477472458229, + 0.3552725835202315, + 0.3436358799817366, + 0.33214915265881195, + 0.3208237691589941, + 0.30967093741916085, + 0.29870169461382023, + 0.28792689623239864, + 0.27735720533631947, + 0.2670030820065258, + 0.2568747729918669, + 0.24698230156861573, + 0.23733545762113145, + 0.22794378795350273, + 0.21881658684174154, + 0.2099628868358953, + 0.2013914498211623, + 0.19311075834687275, + 0.18512900723190195, + 0.17745409545483676, + 0.17009361833690817, + 0.16305486002543806, + 0.15634478628522758, + 0.1499700376050302, + 0.143936922625927, + 0.1382514118981058, + 0.13291913197222882, + 0.12794535983122896, + 0.12333501766805194, + 0.1190926680145068, + 0.11522250922604882, + 0.11172837132696167, + 0.1086137122200524, + 0.10588161426460663, + 0.10353478122599558, + 0.10157553559994797, + 0.10000581631414085, + 0.09882717680937753, + 0.09804078350225753, + 0.09764741463085574, + 0.09764745948455522, + 0.09804091801879478, + 0.09882740085511305, + 0.10000612966648992, + 0.1015759379476015, + 0.10353527216922925, + 0.10588219331567861, + 0.1086143788036873, + 0.11172912478092228, + 0.11522334880179386, + 0.1190935928779352, + 0.12333602690033035, + 0.12794645242970576, + 0.13292030685142958, + 0.13825266789081192, + 0.14393825848433445, + 0.14997145200199097, + 0.15634627781556587, + 0.16305642720734875, + 0.17009525961342664, + 0.17745580919538712, + 0.18513079173391628, + 0.19311261183748313, + 0.2013933704589616, + 0.2099648727127667, + 0.21881863598475426, + 0.227945898326872, + 0.23733762712824108, + 0.24698452805410076, + 0.2568770542437552, + 0.26700541575843545, + 0.2773595892697104, + 0.2879293279788757, + 0.2987041717574868, + 0.3096734574990176, + 0.32082632967138686, + 0.33215175105991446, + 0.34363851369008214, + 0.35527524991927506, + 0.36705044368654166, + 0.3789524419092301, + 0.39096946601522825, + 0.40308962359938527, + 0.41530092019259546, + 0.4275912711318767, + 0.4399485135197189, + 0.4523604182608437, + 0.4648147021644878, + 0.4772990401002083, + 0.48980107719520494, + 0.502308441061065, + 0.5148087540378524, + 0.5272896454434041, + 0.5397387638157303, + 0.5521437891363846, + 0.5644924450227264, + 0.5767725108769892, + 0.5889718339801542, + 0.6010783415186373, + 0.6130800525319099, + 0.6249650897692125, + 0.6367216914436339, + 0.64833822287193, + 0.6598031879885491, + 0.6711052407224831, + 0.6822331962256761, + 0.6931760419418854, + 0.7039229485050316, + 0.7144632804562658, + 0.7247866067691326, + 0.7348827111724286, + 0.7447416022605247, + 0.7543535233811621, + 0.7637089622909232, + 0.7727986605688336, + 0.7816136227787684, + 0.790145125371607, + 0.7983847253183132, + 0.8063242684654124, + 0.8139558976045806, + 0.8212720602483756, + 0.8282655161043999, + 0.8349293442405126, + 0.8412569499339875, + 0.847242071197849, + 0.8528787849779202, + 0.8581615130144522, + 0.8630850273625388, + 0.8676444555658438, + 0.8718352854785301, + 0.8756533697306133, + 0.8790949298323226, + 0.8821565599134062, + 0.8848352300936824, + 0.8871282894814985, + 0.8890334687971326, + 0.8905488826185399, + 0.8916730312472227, + 0.8924048021923758, + 0.8927434712718414, + 0.8926887033287803, + 0.8922405525633534, + 0.8913994624790835, + 0.8901662654439506, + 0.8885421818666552, + 0.8865288189888663, + 0.8841281692946457, + 0.881342608538628, + 0.8781748933949012, + 0.8746281587289202, + 0.8707059144951503, + 0.866412042263513, + 0.8617507913780662, + 0.8567267747517278, + 0.8513449643011999, + 0.8456106860266104, + 0.8395296147407458, + 0.833107768453087, + 0.8263515024142065, + 0.8192675028264236, + 0.8118627802269409, + 0.8041446625500049, + 0.7961207878749651, + 0.7877990968674022, + 0.7791878249208122, + 0.77029549400661, + 0.761130904240536, + 0.7517031251737973, + 0.7420214868175741, + 0.7320955704097556, + 0.721935198933066, + 0.7115504273939455, + 0.7009515328718214, + 0.6901490043485966, + 0.6791535323284452, + 0.6679759982581686, + 0.6566274637585966, + 0.64511915967767, + 0.6334624749760632, + 0.6216689454563311, + 0.6097502423467298, + 0.5977181607510264, + 0.5855846079757108, + 0.573361591746169, + 0.5610612083234809, + 0.5486956305336046, + 0.5362770957207769, + 0.5238178936370785, + 0.5113303542801262, + 0.4988268356909454, + 0.4863197117240762, + 0.473821359802044, + 0.4613441486662903, + 0.44890042613670345, + 0.4365025068918396, + 0.42416266028195587, + 0.4118930981868926, + 0.39970596293083904, + 0.3876133152659191, + 0.37562712243651675, + 0.363759246336135, + 0.35202143176851086, + 0.34042529482460937, + 0.32898231138698897, + 0.3177038057729339, + 0.3066009395275674, + 0.2956847003780556, + 0.28496589135982614, + 0.2744551201255583, + 0.2641627884475407, + 0.25409908192376307, + 0.24427395989794726, + 0.23469714560347982, + 0.22537811654101692, + 0.2163260950992636, + 0.2075500394282246, + 0.19905863457394884, + 0.19086028388355525, + 0.18296310068902571, + 0.1753749002780126, + 0.16810319215959352, + 0.16115517263264284, + 0.15453771766415653, + 0.14825737608459222, + 0.14232036310694862, + 0.13673255417600988, + 0.13149947915382804, + 0.12662631684720782, + 0.12211788988260319, + 0.11797865993350609, + 0.11421272330503929, + 0.11082380688013149, + 0.10781526443128325, + 0.10519007330157315, + 0.102950831458192, + 0.10109975492141615, + 0.09963867557156841, + 0.09856903933613437, + 0.09789190475883058, + 0.09760794195203737, + 0.09771743193363636, + 0.09822026634890696, + 0.09911594757775732, + 0.1004035892271844, + 0.1020819170084748, + 0.10414926999827973, + 0.10660360228231347, + 0.10944248498005166, + 0.11266310864842383, + 0.11626228606212352, + 0.120236455367779, + 0.12458168360887167, + 0.12929367061790603, + 0.1343677532719857, + 0.13979891010757534, + 0.14558176628989578, + 0.15171059893201366, + 0.15817934275838963, + 0.16498159610724816, + 0.17211062726586013, + 0.1795593811324368, + 0.18732048619808067, + 0.19538626184183994, + 0.20374872593168925, + 0.2123996027238741, + 0.22133033105284688, + 0.23053207280364096, + 0.23999572165833777, + 0.2497119121079489, + 0.25967102872080255, + 0.2698632156582652, + 0.2802783864283669, + 0.29090623386769876, + 0.30173624034168667, + 0.31275768815316074, + 0.32395967014890065, + 0.3353311005136844, + 0.3468607257411391, + 0.3585371357705507, + 0.370348775278592, + 0.38228395511482616, + 0.39433086386962235, + 0.4064775795630977, + 0.41871208144344774, + 0.4310222618830537, + 0.44339593836053565, + 0.45582086551695766, + 0.46828474727418345, + 0.48077524900345536, + 0.493280009732094, + 0.5057866543763045, + 0.5182828059879132, + 0.5307560980029703, + 0.5431941864800698, + 0.5555847623162795, + 0.5679155634286022, + 0.580174386888889, + 0.5923491010002274, + 0.6044276573028263, + 0.6163981024975366, + 0.6282485902751834, + 0.6399673930400332, + 0.651542913515768, + 0.662963696222501, + 0.6742184388134522, + 0.6852960032600988, + 0.6961854268746839, + 0.7068759331592379, + 0.7173569424703059, + 0.7276180824888905, + 0.7376491984851936, + 0.7474403633680553, + 0.7569818875090859, + 0.7662643283318211, + 0.7752784996563649, + 0.7840154807903204, + 0.7924666253569626, + 0.8006235698519543, + 0.8084782419201185, + 0.8160228683440809, + 0.82324998273688, + 0.8301524329309216, + 0.8367233880559795, + 0.8429563452992272, + 0.8488451363406173, + 0.854383933457231, + 0.859567255290568, + 0.8643899722710624, + 0.8688473116944581, + 0.8729348624450158, + 0.8766485793608891, + 0.8799847872373314, + 0.8829401844637935, + 0.8855118462912916, + 0.8876972277268285, + 0.8894941660519913, + 0.8909008829632451, + 0.891915986331793, + 0.892538471581269, + 0.8927677226818953 + ], + "type": "scatter", + "xaxis": "x", + "yaxis": "y" + } + ], + "layout": { + "template": { + "data": { + "histogram2dcontour": [ + { + "type": "histogram2dcontour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "choropleth": [ + { + "type": "choropleth", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "histogram2d": [ + { + "type": "histogram2d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmap": [ + { + "type": "heatmap", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmapgl": [ + { + "type": "heatmapgl", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "contourcarpet": [ + { + "type": "contourcarpet", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "contour": [ + { + "type": "contour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "surface": [ + { + "type": "surface", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "mesh3d": [ + { + "type": "mesh3d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "parcoords": [ + { + "type": "parcoords", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolargl": [ + { + "type": "scatterpolargl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "scattergeo": [ + { + "type": "scattergeo", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolar": [ + { + "type": "scatterpolar", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "scattergl": [ + { + "type": "scattergl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatter3d": [ + { + "type": "scatter3d", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattermapbox": [ + { + "type": "scattermapbox", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterternary": [ + { + "type": "scatterternary", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattercarpet": [ + { + "type": "scattercarpet", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ] + }, + "layout": { + "autotypenumbers": "strict", + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "hovermode": "closest", + "hoverlabel": { + "align": "left" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "bgcolor": "#E5ECF6", + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "ternary": { + "bgcolor": "#E5ECF6", + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "sequential": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ] + }, + "xaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "yaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "geo": { + "bgcolor": "white", + "landcolor": "#E5ECF6", + "subunitcolor": "white", + "showland": true, + "showlakes": true, + "lakecolor": "white" + }, + "title": { + "x": 0.05 + }, + "mapbox": { + "style": "light" + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "Time (us)
Frequency: 1.001+/-0.008 MHz" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "" + } + }, + "title": { + "text": "Ramsey decay QA transition f01:
(0.0+/-1.5)e+13 us" + }, + "plot_bgcolor": "white" + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "a776bf93-85f9-4e89-98c3-b48e97da3e59", + "record_entry_path": "/root/7-SimpleRamseyMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691519, + "print_time": "2024-12-08 15:58:39" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "SimpleRamseyMultilevel" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 1.000 MHz, and the measured oscillation is 1.001+/-0.008 MHz. Oscillation amplitude is 0.398+/-0.032. The number of oscillations is 3.004+/-0.025.'
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('7e99194b-12a7-4e18-ab31-fe0aa4a9dd12');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('7e99194b-12a7-4e18-ab31-fe0aa4a9dd12');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear oscillatory pattern, indicating regular oscillations typical of Ramsey experiments.\\n\\n2. Fit Quality: The fit line closely follows the data points, suggesting a good fit and alignment with the expected oscillatory behavior.\\n\\n3. Data Spread: The data points are moderately clustered around the fit line, with some dispersion but not excessively wide, indicating a reasonable level of precision.\\n\\n4. Amplitude and Frequency: The amplitude of the oscillations appears to be around 1, which is ideal. The frequency is noted as 1.001 MHz, which is close to the expected value, indicating consistency in the experiment.\\n\\n5. Overall Pattern: The plot shows characteristics typical of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude and frequency.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('a7982297-dbeb-4232-a08b-35648fa1cdfc');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a7982297-dbeb-4232-a08b-35648fa1cdfc');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
analysis: \"The Ramsey experiment for qubit QA was successful. The measured oscillation frequency is 1.001 MHz, which is very close to the expected offset of 1.000 MHz, indicating consistency. The oscillation amplitude is 0.398, which is above the threshold of 0.2, confirming a clear oscillatory pattern. The number of oscillations observed is 3.004, which is within the acceptable range for the experiment's duration. The fit quality is good, with the fit line closely following the data points, and the data spread is reasonable, indicating precision. Overall, the experiment exhibits characteristics typical of a successful Ramsey oscillation experiment.\"
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('fdf9a598-850c-4d76-895c-0824db8fb190');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('fdf9a598-850c-4d76-895c-0824db8fb190');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Transitioning to the next stage Stage3 with the following description:
Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.
The Ramsey experiment for qubit QA was successful. The measured oscillation frequency is consistent with the expected offset, and the oscillation amplitude is above the threshold, confirming a clear oscillatory pattern. The number of oscillations and the fit quality are within acceptable ranges, indicating a successful experiment. According to the rule of transition, since Stage2 has completed successfully, the experiment should proceed to Stage3.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage3: Stage3...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage3

\n", + "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.

\n", + "

Next Steps: If Stage3 fails after 3 retries, goto Failed. If Stage3 completes, goto Complete.

\n", + "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 0.1\n",
+       "VarName:`stop_in_us` Value: 30\n",
+       "VarName:`step_in_us` Value: 0.5

\n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('eb4e527c-f55b-4316-82a4-ca70e9f9faf5');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('eb4e527c-f55b-4316-82a4-ca70e9f9faf5');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.:
\n", + "

\n", + " 
experiment_ramsey = SimpleRamseyMultilevel(dut=dut, collection_name='f01', mprim_index=0, initial_lpb=None, start=0.0, stop=stop_in_us, step=step_in_us, set_offset=frequency_offset_in_MHz, update=True)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Sampling noise is enabled\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "mode": "markers", + "name": "Data", + "x": [ + 0.0, + 0.5, + 1.0, + 1.5, + 2.0, + 2.5, + 3.0, + 3.5, + 4.0, + 4.5, + 5.0, + 5.5, + 6.0, + 6.5, + 7.0, + 7.5, + 8.0, + 8.5, + 9.0, + 9.5, + 10.0, + 10.5, + 11.0, + 11.5, + 12.0, + 12.5, + 13.0, + 13.5, + 14.0, + 14.5, + 15.0, + 15.5, + 16.0, + 16.5, + 17.0, + 17.5, + 18.0, + 18.5, + 19.0, + 19.5, + 20.0, + 20.5, + 21.0, + 21.5, + 22.0, + 22.5, + 23.0, + 23.5, + 24.0, + 24.5, + 25.0, + 25.5, + 26.0, + 26.5, + 27.0, + 27.5, + 28.0, + 28.5, + 29.0, + 29.5 + ], + "y": [ + 0.958, + 1.0, + 0.892, + 0.616, + 0.592, + 0.482, + 0.35, + 0.198, + 0.156, + 0.184, + 0.114, + 0.166, + 0.124, + 0.328, + 0.33, + 0.506, + 0.552, + 0.638, + 0.664, + 0.752, + 0.99, + 0.938, + 0.734, + 0.672, + 0.582, + 0.494, + 0.39, + 0.256, + 0.194, + 0.222, + 0.188, + 0.33, + 0.35, + 0.386, + 0.396, + 0.512, + 0.626, + 0.732, + 0.722, + 0.784, + 0.718, + 0.536, + 0.686, + 0.648, + 0.64, + 0.51, + 0.542, + 0.292, + 0.388, + 0.154, + 0.308, + 0.332, + 0.114, + 0.368, + 0.404, + 0.5, + 0.516, + 0.58, + 0.72, + 0.606 + ], + "type": "scatter", + "xaxis": "x", + "yaxis": "y" + }, + { + "mode": "lines", + "name": "Fit", + "visible": "legendonly", + "x": [ + 0.0, + 0.05, + 0.1, + 0.15000000000000002, + 0.2, + 0.25, + 0.30000000000000004, + 0.35000000000000003, + 0.4, + 0.45, + 0.5, + 0.55, + 0.6000000000000001, + 0.65, + 0.7000000000000001, + 0.75, + 0.8, + 0.8500000000000001, + 0.9, + 0.9500000000000001, + 1.0, + 1.05, + 1.1, + 1.1500000000000001, + 1.2000000000000002, + 1.25, + 1.3, + 1.35, + 1.4000000000000001, + 1.4500000000000002, + 1.5, + 1.55, + 1.6, + 1.6500000000000001, + 1.7000000000000002, + 1.75, + 1.8, + 1.85, + 1.9000000000000001, + 1.9500000000000002, + 2.0, + 2.0500000000000003, + 2.1, + 2.15, + 2.2, + 2.25, + 2.3000000000000003, + 2.35, + 2.4000000000000004, + 2.45, + 2.5, + 2.5500000000000003, + 2.6, + 2.6500000000000004, + 2.7, + 2.75, + 2.8000000000000003, + 2.85, + 2.9000000000000004, + 2.95, + 3.0, + 3.0500000000000003, + 3.1, + 3.1500000000000004, + 3.2, + 3.25, + 3.3000000000000003, + 3.35, + 3.4000000000000004, + 3.45, + 3.5, + 3.5500000000000003, + 3.6, + 3.6500000000000004, + 3.7, + 3.75, + 3.8000000000000003, + 3.85, + 3.9000000000000004, + 3.95, + 4.0, + 4.05, + 4.1000000000000005, + 4.15, + 4.2, + 4.25, + 4.3, + 4.3500000000000005, + 4.4, + 4.45, + 4.5, + 4.55, + 4.6000000000000005, + 4.65, + 4.7, + 4.75, + 4.800000000000001, + 4.8500000000000005, + 4.9, + 4.95, + 5.0, + 5.050000000000001, + 5.1000000000000005, + 5.15, + 5.2, + 5.25, + 5.300000000000001, + 5.3500000000000005, + 5.4, + 5.45, + 5.5, + 5.550000000000001, + 5.6000000000000005, + 5.65, + 5.7, + 5.75, + 5.800000000000001, + 5.8500000000000005, + 5.9, + 5.95, + 6.0, + 6.050000000000001, + 6.1000000000000005, + 6.15, + 6.2, + 6.25, + 6.300000000000001, + 6.3500000000000005, + 6.4, + 6.45, + 6.5, + 6.550000000000001, + 6.6000000000000005, + 6.65, + 6.7, + 6.75, + 6.800000000000001, + 6.8500000000000005, + 6.9, + 6.95, + 7.0, + 7.050000000000001, + 7.1000000000000005, + 7.15, + 7.2, + 7.25, + 7.300000000000001, + 7.3500000000000005, + 7.4, + 7.45, + 7.5, + 7.550000000000001, + 7.6000000000000005, + 7.65, + 7.7, + 7.75, + 7.800000000000001, + 7.8500000000000005, + 7.9, + 7.95, + 8.0, + 8.05, + 8.1, + 8.15, + 8.200000000000001, + 8.25, + 8.3, + 8.35, + 8.4, + 8.450000000000001, + 8.5, + 8.55, + 8.6, + 8.65, + 8.700000000000001, + 8.75, + 8.8, + 8.85, + 8.9, + 8.950000000000001, + 9.0, + 9.05, + 9.1, + 9.15, + 9.200000000000001, + 9.25, + 9.3, + 9.35, + 9.4, + 9.450000000000001, + 9.5, + 9.55, + 9.600000000000001, + 9.65, + 9.700000000000001, + 9.75, + 9.8, + 9.850000000000001, + 9.9, + 9.950000000000001, + 10.0, + 10.05, + 10.100000000000001, + 10.15, + 10.200000000000001, + 10.25, + 10.3, + 10.350000000000001, + 10.4, + 10.450000000000001, + 10.5, + 10.55, + 10.600000000000001, + 10.65, + 10.700000000000001, + 10.75, + 10.8, + 10.850000000000001, + 10.9, + 10.950000000000001, + 11.0, + 11.05, + 11.100000000000001, + 11.15, + 11.200000000000001, + 11.25, + 11.3, + 11.350000000000001, + 11.4, + 11.450000000000001, + 11.5, + 11.55, + 11.600000000000001, + 11.65, + 11.700000000000001, + 11.75, + 11.8, + 11.850000000000001, + 11.9, + 11.950000000000001, + 12.0, + 12.05, + 12.100000000000001, + 12.15, + 12.200000000000001, + 12.25, + 12.3, + 12.350000000000001, + 12.4, + 12.450000000000001, + 12.5, + 12.55, + 12.600000000000001, + 12.65, + 12.700000000000001, + 12.75, + 12.8, + 12.850000000000001, + 12.9, + 12.950000000000001, + 13.0, + 13.05, + 13.100000000000001, + 13.15, + 13.200000000000001, + 13.25, + 13.3, + 13.350000000000001, + 13.4, + 13.450000000000001, + 13.5, + 13.55, + 13.600000000000001, + 13.65, + 13.700000000000001, + 13.75, + 13.8, + 13.850000000000001, + 13.9, + 13.950000000000001, + 14.0, + 14.05, + 14.100000000000001, + 14.15, + 14.200000000000001, + 14.25, + 14.3, + 14.350000000000001, + 14.4, + 14.450000000000001, + 14.5, + 14.55, + 14.600000000000001, + 14.65, + 14.700000000000001, + 14.75, + 14.8, + 14.850000000000001, + 14.9, + 14.950000000000001, + 15.0, + 15.05, + 15.100000000000001, + 15.15, + 15.200000000000001, + 15.25, + 15.3, + 15.350000000000001, + 15.4, + 15.450000000000001, + 15.5, + 15.55, + 15.600000000000001, + 15.65, + 15.700000000000001, + 15.75, + 15.8, + 15.850000000000001, + 15.9, + 15.950000000000001, + 16.0, + 16.05, + 16.1, + 16.150000000000002, + 16.2, + 16.25, + 16.3, + 16.35, + 16.400000000000002, + 16.45, + 16.5, + 16.55, + 16.6, + 16.650000000000002, + 16.7, + 16.75, + 16.8, + 16.85, + 16.900000000000002, + 16.95, + 17.0, + 17.05, + 17.1, + 17.150000000000002, + 17.2, + 17.25, + 17.3, + 17.35, + 17.400000000000002, + 17.45, + 17.5, + 17.55, + 17.6, + 17.650000000000002, + 17.7, + 17.75, + 17.8, + 17.85, + 17.900000000000002, + 17.95, + 18.0, + 18.05, + 18.1, + 18.150000000000002, + 18.2, + 18.25, + 18.3, + 18.35, + 18.400000000000002, + 18.45, + 18.5, + 18.55, + 18.6, + 18.650000000000002, + 18.7, + 18.75, + 18.8, + 18.85, + 18.900000000000002, + 18.95, + 19.0, + 19.05, + 19.1, + 19.150000000000002, + 19.200000000000003, + 19.25, + 19.3, + 19.35, + 19.400000000000002, + 19.450000000000003, + 19.5, + 19.55, + 19.6, + 19.650000000000002, + 19.700000000000003, + 19.75, + 19.8, + 19.85, + 19.900000000000002, + 19.950000000000003, + 20.0, + 20.05, + 20.1, + 20.150000000000002, + 20.200000000000003, + 20.25, + 20.3, + 20.35, + 20.400000000000002, + 20.450000000000003, + 20.5, + 20.55, + 20.6, + 20.650000000000002, + 20.700000000000003, + 20.75, + 20.8, + 20.85, + 20.900000000000002, + 20.950000000000003, + 21.0, + 21.05, + 21.1, + 21.150000000000002, + 21.200000000000003, + 21.25, + 21.3, + 21.35, + 21.400000000000002, + 21.450000000000003, + 21.5, + 21.55, + 21.6, + 21.650000000000002, + 21.700000000000003, + 21.75, + 21.8, + 21.85, + 21.900000000000002, + 21.950000000000003, + 22.0, + 22.05, + 22.1, + 22.150000000000002, + 22.200000000000003, + 22.25, + 22.3, + 22.35, + 22.400000000000002, + 22.450000000000003, + 22.5, + 22.55, + 22.6, + 22.650000000000002, + 22.700000000000003, + 22.75, + 22.8, + 22.85, + 22.900000000000002, + 22.950000000000003, + 23.0, + 23.05, + 23.1, + 23.150000000000002, + 23.200000000000003, + 23.25, + 23.3, + 23.35, + 23.400000000000002, + 23.450000000000003, + 23.5, + 23.55, + 23.6, + 23.650000000000002, + 23.700000000000003, + 23.75, + 23.8, + 23.85, + 23.900000000000002, + 23.950000000000003, + 24.0, + 24.05, + 24.1, + 24.150000000000002, + 24.200000000000003, + 24.25, + 24.3, + 24.35, + 24.400000000000002, + 24.450000000000003, + 24.5, + 24.55, + 24.6, + 24.650000000000002, + 24.700000000000003, + 24.75, + 24.8, + 24.85, + 24.900000000000002, + 24.950000000000003, + 25.0, + 25.05, + 25.1, + 25.150000000000002, + 25.200000000000003, + 25.25, + 25.3, + 25.35, + 25.400000000000002, + 25.450000000000003, + 25.5, + 25.55, + 25.6, + 25.650000000000002, + 25.700000000000003, + 25.75, + 25.8, + 25.85, + 25.900000000000002, + 25.950000000000003, + 26.0, + 26.05, + 26.1, + 26.150000000000002, + 26.200000000000003, + 26.25, + 26.3, + 26.35, + 26.400000000000002, + 26.450000000000003, + 26.5, + 26.55, + 26.6, + 26.650000000000002, + 26.700000000000003, + 26.75, + 26.8, + 26.85, + 26.900000000000002, + 26.950000000000003, + 27.0, + 27.05, + 27.1, + 27.150000000000002, + 27.200000000000003, + 27.25, + 27.3, + 27.35, + 27.400000000000002, + 27.450000000000003, + 27.5, + 27.55, + 27.6, + 27.650000000000002, + 27.700000000000003, + 27.75, + 27.8, + 27.85, + 27.900000000000002, + 27.950000000000003, + 28.0, + 28.05, + 28.1, + 28.150000000000002, + 28.200000000000003, + 28.25, + 28.3, + 28.35, + 28.400000000000002, + 28.450000000000003, + 28.5, + 28.55, + 28.6, + 28.650000000000002, + 28.700000000000003, + 28.75, + 28.8, + 28.85, + 28.900000000000002, + 28.950000000000003, + 29.0, + 29.05, + 29.1, + 29.150000000000002, + 29.200000000000003, + 29.25, + 29.3, + 29.35, + 29.400000000000002, + 29.450000000000003, + 29.5, + 29.55, + 29.6, + 29.650000000000002, + 29.700000000000003, + 29.75, + 29.8, + 29.85, + 29.900000000000002, + 29.950000000000003 + ], + "y": [ + 0.9490671776019997, + 0.9475093621658602, + 0.9455119207563203, + 0.9430781133574035, + 0.9402116168223266, + 0.9369165200395031, + 0.9331973187101451, + 0.9290589097437195, + 0.9245065852778769, + 0.9195460263298132, + 0.9141832960863681, + 0.9084248328404929, + 0.9022774425820439, + 0.8957482912511735, + 0.8888448966628928, + 0.8815751201116808, + 0.8739471576652951, + 0.8659695311572218, + 0.8576510788874634, + 0.8490009460416266, + 0.8400285748385075, + 0.8307436944166179, + 0.8211563104703132, + 0.8112766946463978, + 0.8011153737122853, + 0.7906831185069813, + 0.7799909326863379, + 0.7690500412741887, + 0.7578718790311398, + 0.746468078652921, + 0.7348504588103493, + 0.7230310120430616, + 0.711021892519285, + 0.6988354036740132, + 0.6864839857380319, + 0.6739802031703064, + 0.6613367320063086, + 0.6485663471348992, + 0.6356819095164128, + 0.622696353354617, + 0.6096226732352258, + 0.5964739112436329, + 0.5832631440745251, + 0.5700034701459977, + 0.5567079967307562, + 0.5433898271169348, + 0.5300620478109885, + 0.5167377157950513, + 0.5034298458510502, + 0.4901513979637669, + 0.4769152648149292, + 0.46373425938028506, + 0.45062110264147875, + 0.4375884114243949, + 0.4246486863754901, + 0.4118143000874505, + 0.39909748538533885, + 0.38651032378420747, + 0.37406473412894664, + 0.3617724614269359, + 0.3496450658838352, + 0.33769391215263606, + 0.3259301588058451, + 0.3143647480404257, + 0.303008395624874, + 0.29187158109753164, + 0.2809645382249735, + 0.27029724572902136, + 0.2598794182906501, + 0.24972049783875916, + 0.2398296451314736, + 0.23021573163733827, + 0.22088733172344716, + 0.21185271515723286, + 0.20311983992831212, + 0.19469634539645375, + 0.1865895457713958, + 0.17880642392990054, + 0.1713536255750857, + 0.1642374537427279, + 0.15746386365887166, + 0.1510384579527279, + 0.1449664822284894, + 0.13925282099931702, + 0.13390199398639763, + 0.1289181527856102, + 0.12430507790395617, + 0.12006617616756021, + 0.11620447850266474, + 0.11272263809068017, + 0.10962292889798148, + 0.1069072445807745, + 0.10457709776498525, + 0.1026336197007639, + 0.10107756029082399, + 0.09990928849147912, + 0.09912879308487665, + 0.09873568382056902, + 0.09872919292420912, + 0.09910817697080193, + 0.09987111911959867, + 0.10101613170737223, + 0.10254095919647266, + 0.10444298147372744, + 0.10671921749591667, + 0.10936632927722972, + 0.11238062621378836, + 0.11575806974000619, + 0.1194942783112467, + 0.12358453270693781, + 0.12802378164800576, + 0.13280664772220446, + 0.1379274336106283, + 0.14338012860843125, + 0.14915841543249858, + 0.15525567730856582, + 0.1616650053300248, + 0.1683792060804145, + 0.17539080951136465, + 0.18269207706752705, + 0.19027501004982028, + 0.1981313582081029, + 0.2062526285541934, + 0.21463009438596864, + 0.22325480451308388, + 0.23211759267470983, + 0.2412090871394939, + 0.2505197204778322, + 0.2600397394963809, + 0.269759215324613, + 0.27966805364310965, + 0.28975600504316923, + 0.3000126755072073, + 0.3104275369993543, + 0.3209899381555663, + 0.33168911506249554, + 0.3425142021143357, + 0.35345424293677974, + 0.3644982013672259, + 0.3756349724803252, + 0.3868533936479529, + 0.3981422556227036, + 0.40949031363398825, + 0.42088629848586395, + 0.4323189276457333, + 0.4437769163131071, + 0.45524898845766076, + 0.4667238878158963, + 0.47819038883577925, + 0.48963730755879825, + 0.5010535124290135, + 0.5124279350187245, + 0.5237495806605367, + 0.5350075389757057, + 0.5461909942887706, + 0.5572892359186463, + 0.5682916683364679, + 0.5791878211806668, + 0.5899673591199025, + 0.6006200915546716, + 0.6111359821485751, + 0.621505158180454, + 0.6317179197087606, + 0.6417647485397885, + 0.6516363169915563, + 0.6613234964454033, + 0.6708173656775597, + 0.6801092189631995, + 0.6891905739457391, + 0.698053179264382, + 0.7066890219331683, + 0.715090334465064, + 0.7232496017348777, + 0.7311595675750752, + 0.7388132410988355, + 0.7462039027449829, + 0.7533251100397127, + 0.7601707030703124, + 0.7667348096663988, + 0.7730118502844607, + 0.778996542591825, + 0.7846839057464562, + 0.7900692643693069, + 0.7951482522062574, + 0.7999168154769787, + 0.8043712159083866, + 0.8085080334506514, + 0.812324168674055, + 0.8158168448453043, + 0.8189836096822188, + 0.8218223367860396, + 0.8243312267509137, + 0.8265088079504317, + 0.8283539370014094, + 0.829865798905421, + 0.8310439068689017, + 0.8318881018029538, + 0.8323985515042955, + 0.8325757495191044, + 0.8324205136918057, + 0.8319339844011637, + 0.831117622486329, + 0.8299732068657915, + 0.8285028318524803, + 0.8267089041685376, + 0.8245941396635801, + 0.822161559740535, + 0.8194144874934151, + 0.8163565435616684, + 0.8129916417059899, + 0.8093239841107586, + 0.8053580564184952, + 0.8010986225020001, + 0.7965507189800554, + 0.7917196494828161, + 0.7866109786732476, + 0.7812305260311656, + 0.7755843594066759, + 0.7696787883499908, + 0.7635203572248102, + 0.7571158381126479, + 0.7504722235156527, + 0.7435967188656648, + 0.7364967348474007, + 0.7291798795438272, + 0.7216539504119277, + 0.7139269260972161, + 0.7060069580954721, + 0.697902362270304, + 0.6896216102352684, + 0.681173320609361, + 0.6725662501548113, + 0.6638092848061955, + 0.6549114305999538, + 0.6458818045134778, + 0.6367296252229979, + 0.6274642037895347, + 0.6180949342822464, + 0.6086312843485134, + 0.5990827857401423, + 0.5894590248050824, + 0.5797696329540437, + 0.570024277111423, + 0.5602326501599062, + 0.5504044613881136, + 0.5405494269506195, + 0.5306772603496244, + 0.5207976629475345, + 0.5109203145196171, + 0.5010548638558542, + 0.49121091942103995, + 0.4813980400820672, + 0.4716257259112785, + 0.46190340907463656, + 0.4522404448133802, + 0.44264610252769104, + 0.43312955697079036, + 0.42369987956174715, + 0.41436602982513426, + 0.40513684696552066, + 0.3960210415846378, + 0.3870271875488968, + 0.3781637140147503, + 0.36943889761923165, + 0.3608608548428103, + 0.352437534551523, + 0.344176710725127, + 0.3360859753778434, + 0.32817273167802735, + 0.3204441872729037, + 0.31290734782428836, + 0.30556901076097476, + 0.29843575925326293, + 0.29151395641484945, + 0.284809739737073, + 0.2783290157602717, + 0.2720774549867435, + 0.2660604870395781, + 0.26028329607134904, + 0.25475081642641867, + 0.24946772856034155, + 0.24443845521958527, + 0.2396671578845369, + 0.2351577334784819, + 0.2309138113449864, + 0.226938750495836, + 0.22323563713142125, + 0.21980728243517805, + 0.21665622064342704, + 0.21378470739168076, + 0.21119471833821002, + 0.2088879480653919, + 0.2068658092590886, + 0.2051294321660298, + 0.20367966432890378, + 0.2025170705985912, + 0.20164193342270276, + 0.20105425340931998, + 0.20075375016456687, + 0.20073986340238187, + 0.20101175432459395, + 0.2015683072691501, + 0.202408131624087, + 0.20352956400458327, + 0.20493067069018445, + 0.20660925031904165, + 0.20856283683576854, + 0.21078870268927852, + 0.21328386227673418, + 0.21604507562951003, + 0.2190688523368453, + 0.22235145570264475, + 0.22588890713067084, + 0.22967699073316133, + 0.23371125815770188, + 0.2379870336269872, + 0.24249941918591172, + 0.24724330015024126, + 0.2522133507509424, + 0.2574040399680708, + 0.2628096375479472, + 0.26842422019720125, + 0.27424167794709486, + 0.2802557206814031, + 0.28645988482098084, + 0.2928475401580203, + 0.29941189683286284, + 0.3061460124461355, + 0.31304279929884515, + 0.3200950317529785, + 0.3272953537050559, + 0.3346362861649862, + 0.3421102349325179, + 0.3497094983634784, + 0.357426275217949, + 0.3652526725824595, + 0.37318071385823404, + 0.38120234680749104, + 0.38930945164974884, + 0.39749384920008246, + 0.4057473090412491, + 0.4140615577215867, + 0.4224282869706057, + 0.4308391619241822, + 0.43928582935128857, + 0.44775992587421026, + 0.45625308617423566, + 0.4647569511748423, + 0.47326317619444314, + 0.48176343906081526, + 0.4902494481793982, + 0.4987129505476969, + 0.5071457397081355, + 0.5155396636317555, + 0.5238866325252627, + 0.532178626554023, + 0.5404077034736953, + 0.5485660061633292, + 0.5566457700528448, + 0.5646393304379521, + 0.5725391296756961, + 0.5803377242539414, + 0.5880277917282726, + 0.5956021375199102, + 0.6030537015684204, + 0.6103755648331441, + 0.6175609556374351, + 0.6246032558499809, + 0.6314960068976448, + 0.6382329156044481, + 0.6448078598515103, + 0.65121489405294, + 0.6574482544428731, + 0.6635023641690538, + 0.6693718381885503, + 0.6750514879614139, + 0.6805363259382848, + 0.6858215698381818, + 0.6909026467129098, + 0.6957751967947496, + 0.7004350771243166, + 0.7048783649556847, + 0.7091013609361249, + 0.7131005920580072, + 0.7168728143806659, + 0.7204150155202457, + 0.7237244169057926, + 0.7267984758000678, + 0.7296348870838223, + 0.7322315848024794, + 0.7345867434744293, + 0.7366987791603584, + 0.7385663502932837, + 0.7401883582691898, + 0.7415639477984046, + 0.7426925070180806, + 0.7435736673663836, + 0.7442073032192187, + 0.7445935312905564, + 0.7447327097976484, + 0.7446254373926453, + 0.7442725518623599, + 0.7436751285981329, + 0.742834478837981, + 0.7417521476834263, + 0.74042991189361, + 0.7388697774595143, + 0.7370739769613155, + 0.7350449667120956, + 0.7327854236913423, + 0.7302982422718571, + 0.7275865307438891, + 0.7246536076404925, + 0.7215029978682915, + 0.7181384286480151, + 0.7145638252693339, + 0.710783306664702, + 0.7068011808070696, + 0.7026219399364869, + 0.6982502556207778, + 0.6936909736556004, + 0.6889491088093613, + 0.6840298394185826, + 0.6789385018394449, + 0.6736805847613621, + 0.6682617233885565, + 0.6626876934957123, + 0.6569644053638956, + 0.6510978976030217, + 0.6450943308672494, + 0.638959981469761, + 0.63270123490347, + 0.6263245792742737, + 0.619836598653522, + 0.6132439663564492, + 0.606553438153354, + 0.5997718454203651, + 0.5929060882366729, + 0.5859631284351242, + 0.57894998261312, + 0.5718737151107645, + 0.5647414309632219, + 0.5575602688342592, + 0.5503373939379237, + 0.5430799909553191, + 0.5357952569534191, + 0.5284903943128271, + 0.5211726036713806, + 0.5138490768904376, + 0.5065269900506651, + 0.4992134964840816, + 0.49191571984905735, + 0.48464074725492157, + 0.4773956224427388, + 0.47018733902876286, + 0.4630228338169805, + 0.45590898018708104, + 0.4488525815640896, + 0.44186036497580206, + 0.43493897470406256, + 0.4280949660358093, + 0.421334799119696, + 0.41466483293399176, + 0.40809131937131266, + 0.40162039744562256, + 0.3952580876268147, + 0.38901028630800794, + 0.3828827604106055, + 0.37688114213194224, + 0.3710109238402651, + 0.36527745312157245, + 0.3596859279827198, + 0.35424139221501205, + 0.34894873092232714, + 0.3438126662176698, + 0.3388377530918517, + 0.33402837545782027, + 0.32938874237399296, + 0.3249228844497487, + 0.320634650436045, + 0.3165277040039537, + 0.3126055207136956, + 0.30887138517657786, + 0.3053283884120258, + 0.30197942540172507, + 0.2988271928426647, + 0.29587418710069874, + 0.2931227023660218, + 0.29057482901176657, + 0.28823245215671833, + 0.2860972504329521, + 0.28417069495897745, + 0.2824540485187967, + 0.28094836494705194, + 0.2796544887202619, + 0.27857305475392646, + 0.2777044884050902, + 0.27704900567974866, + 0.27660661364428707, + 0.27637711103994045, + 0.2763600890990737, + 0.27655493256188046, + 0.2769608208919129, + 0.277576729688662, + 0.27840143229522196, + 0.27943350159888647, + 0.2806713120223435, + 0.2821130417029546, + 0.28375667485742995, + 0.2856000043290352, + 0.287640634314299, + 0.28987598326601904, + 0.29230328696920815, + 0.2949196017864558, + 0.2977218080690308, + 0.3007066137298984, + 0.3038705579746751, + 0.30721001518640545, + 0.31072119895990796, + 0.3144001662812984, + 0.3182428218481729, + 0.32224492252581505, + 0.3264020819346588, + 0.33070977516413513, + 0.33516334360792477, + 0.3397579999155155, + 0.34448883305489697, + 0.34935081348109015, + 0.354338798405169, + 0.359447537158303, + 0.3646716766453117, + 0.3700057668821212, + 0.37544426661146835, + 0.3809815489911234, + 0.38661190734886597, + 0.3923295609983736, + 0.3981286611101753, + 0.40400329663175094, + 0.4099475002508538, + 0.4159552543960913, + 0.42202049726879654, + 0.42813712890018985, + 0.4342990172278397, + 0.44050000418543145, + 0.44673391179983873, + 0.45299454828952346, + 0.459275714158303, + 0.4655712082785308, + 0.471874833957781, + 0.47818040498316133, + 0.4844817516374002, + 0.49077272668092153, + 0.49704721129415197, + 0.5032991209743827, + 0.5095224113815379, + 0.5157110841273079, + 0.5218591925021401, + 0.5279608471346823, + 0.534010221578343, + 0.5400015578197334, + 0.5459291717038197, + 0.5517874582707584, + 0.5575708969994246, + 0.5632740569528234, + 0.568891601820612, + 0.5744182948541371, + 0.5798490036894619, + 0.5851787050540118, + 0.5904024893525867, + 0.5955155651286053, + 0.6005132633965982, + 0.6053910418421002, + 0.6101444888852234, + 0.6147693276043454, + 0.6192614195165029, + 0.6236167682112057, + 0.6278315228345687, + 0.6319019814207893, + 0.6358245940681823, + 0.6395959659571111, + 0.6432128602073541, + 0.6466722005725761, + 0.649971073969764, + 0.6531067328416384, + 0.6560765973502382, + 0.6588782574000217, + 0.6615094744890306, + 0.6639681833867996, + 0.6662524936379065, + 0.6683606908901936, + 0.6702912380468976, + 0.6720427762420759, + 0.6736141256389048, + 0.6750042860505989, + 0.6762124373838654, + 0.6772379399049939, + 0.6780803343288466, + 0.6787393417311876, + 0.6792148632849635, + 0.6795069798213172 + ], + "type": "scatter", + "xaxis": "x", + "yaxis": "y" + } + ], + "layout": { + "template": { + "data": { + "histogram2dcontour": [ + { + "type": "histogram2dcontour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "choropleth": [ + { + "type": "choropleth", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "histogram2d": [ + { + "type": "histogram2d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmap": [ + { + "type": "heatmap", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmapgl": [ + { + "type": "heatmapgl", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "contourcarpet": [ + { + "type": "contourcarpet", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "contour": [ + { + "type": "contour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "surface": [ + { + "type": "surface", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "mesh3d": [ + { + "type": "mesh3d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "parcoords": [ + { + "type": "parcoords", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolargl": [ + { + "type": "scatterpolargl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "scattergeo": [ + { + "type": "scattergeo", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolar": [ + { + "type": "scatterpolar", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "scattergl": [ + { + "type": "scattergl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatter3d": [ + { + "type": "scatter3d", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattermapbox": [ + { + "type": "scattermapbox", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterternary": [ + { + "type": "scatterternary", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattercarpet": [ + { + "type": "scattercarpet", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ] + }, + "layout": { + "autotypenumbers": "strict", + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "hovermode": "closest", + "hoverlabel": { + "align": "left" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "bgcolor": "#E5ECF6", + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "ternary": { + "bgcolor": "#E5ECF6", + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "sequential": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ] + }, + "xaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "yaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "geo": { + "bgcolor": "white", + "landcolor": "#E5ECF6", + "subunitcolor": "white", + "showland": true, + "showlakes": true, + "lakecolor": "white" + }, + "title": { + "x": 0.05 + }, + "mapbox": { + "style": "light" + } + } + }, + "xaxis": { + "anchor": "y", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "Time (us)
Frequency: 0.0995+/-0.0009 MHz" + } + }, + "yaxis": { + "anchor": "x", + "domain": [ + 0.0, + 1.0 + ], + "title": { + "text": "" + } + }, + "title": { + "text": "Ramsey decay QA transition f01:
34+/-6 us" + }, + "plot_bgcolor": "white" + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "2281f300-c601-46a9-838b-8c53b478e420", + "record_entry_path": "/root/8-SimpleRamseyMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691544, + "print_time": "2024-12-08 15:59:04" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "SimpleRamseyMultilevel" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 0.100 MHz, and the measured oscillation is 0.099+/-0.001 MHz. Oscillation amplitude is 0.457+/-0.032. The number of oscillations is 2.985+/-0.027.'
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('1d9141a7-8b5a-481e-8712-ee4879ffe5ef');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('1d9141a7-8b5a-481e-8712-ee4879ffe5ef');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear and regular oscillatory pattern, indicating the presence of Ramsey oscillations.\\n2. Fit Quality: The fit line closely follows the data points throughout the plot, suggesting a good fit.\\n3. Data Spread: The data points are relatively tightly clustered around the fit line, with some minor deviations.\\n4. Amplitude and Frequency: The amplitude appears to be around 1, which is ideal. The frequency is approximately 0.0995 MHz, which is close to the expected value.\\n5. Overall Pattern: The plot displays typical characteristics of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude and frequency.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('36a4f499-589f-453d-9d7a-1e2db161e450');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('36a4f499-589f-453d-9d7a-1e2db161e450');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
analysis: 'The Ramsey experiment for qubit QA was successful. The expected offset was set to 0.100 MHz, and the measured frequency was 0.099 MHz, which is very close to the expected value. The oscillation amplitude was measured at 0.457, which is well above the threshold of 0.2, indicating a strong signal. The number of oscillations observed was 2.985, which is within the acceptable range for the experiment duration. The data showed a clear and regular oscillatory pattern with a good fit, suggesting the experiment was conducted correctly.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('2c1b98a1-ed6d-4dee-aaf8-3790091cc3ca');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('2c1b98a1-ed6d-4dee-aaf8-3790091cc3ca');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The experiment is complete.
The Ramsey experiment for qubit QA was successful, as indicated by the close match between the expected and measured frequency offsets, the strong oscillation amplitude, and the clear oscillatory pattern. The experiment was executed once with no failed attempts and one successful attempt. According to the rule of transition, since Stage3 has completed successfully, the next stage is 'Complete'.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 0.100 MHz, and the measured oscillation is 0.099+/-0.001 MHz. Oscillation amplitude is 0.457+/-0.032. The number of oscillations is 2.985+/-0.027.'
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('d8f84633-543e-4be8-b962-3988c09e1740');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d8f84633-543e-4be8-b962-3988c09e1740');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear oscillatory pattern, indicating the presence of Ramsey oscillations.\\n\\n2. Fit Quality: The fit line closely follows the data points, suggesting a good fit to the experimental data.\\n\\n3. Data Spread: The data points are moderately clustered around the fit line, with some dispersion but not excessively wide.\\n\\n4. Amplitude and Frequency: The amplitude of the oscillations appears to be around 1, which is ideal. The frequency is approximately 0.0995 MHz, which should be compared to the expected value to determine consistency.\\n\\n5. Overall Pattern: The plot shows characteristics typical of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('b2fcc883-b3b6-42f8-943f-e99dde5117f1');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('b2fcc883-b3b6-42f8-943f-e99dde5117f1');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
results: 'The experiment successfully calibrated the frequency of single qubit gates, with all measured frequencies closely matching expected values and strong oscillation signals observed.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('57c50dae-b034-488c-8955-33874487435c');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('57c50dae-b034-488c-8955-33874487435c');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Transitioning to the next stage Stage2 with the following description:
Full gate amplitude calibration on `dut`
The experiment in Stage1 was executed once and was successful, as indicated by the experiment report which states that the frequency calibration of single qubit gates was successful with expected values and strong oscillation signals. According to the rule of transition, if Stage1 completes successfully, the experiment should proceed to Stage2.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage2: Stage2...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage2

\n", + "

Description: Full gate amplitude calibration on `dut`

\n", + "

Next Steps: If Stage2 completes successfully, goto Stage3. If Stage2 fails, goto Failed.

\n", + " \n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('25a3e411-ac6d-45c7-b2d0-f99dda9bf3bd');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('25a3e411-ac6d-45c7-b2d0-f99dda9bf3bd');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Full gate amplitude calibration on `dut`:
\n", + "

\n", + " 
experiment_instance = Experiment_FullGateAmplitudeCalibrationOnDut(instruction="Full Gate Amplitude Calibration on dut", dut=dut)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Decomposing instructions...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('5c1904b0-77de-4d45-b515-3fabd379bff2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('5c1904b0-77de-4d45-b515-3fabd379bff2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Generating state machine...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "extract_parameters: 0%| | 0/2 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('d27882d2-fbe3-4950-885b-d8baa263f7e1');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d27882d2-fbe3-4950-885b-d8baa263f7e1');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The planned experiments are:

\n", + "
\n", + "

Stage1

\n", + "

Description: Conduct a Rabi experiment with amp=`amp` to determine the Rabi rate for rough amplitude calibration.

\n", + "

Next Steps: If Stage1 fails after 3 retries, goto Failed. Otherwise, goto Stage2.

\n", + "

Variables: 

VarName:`amp` Value: 1.0

\n", + "
\n", + " \n", + "
\n", + "

Stage2

\n", + "

Description: Run Pingpong experiment.

\n", + "

Next Steps: If Stage2 fails, goto Failed. Otherwise, goto Complete.

\n", + " \n", + "
\n", + "
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage1: Stage1...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage1

\n", + "

Description: Conduct a Rabi experiment with amp=`amp` to determine the Rabi rate for rough amplitude calibration.

\n", + "

Next Steps: If Stage1 fails after 3 retries, goto Failed. Otherwise, goto Stage2.

\n", + "

Variables: 

VarName:`amp` Value: 1.0

\n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('d662b9b8-39e9-43e2-8f87-9b5837ac32e4');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d662b9b8-39e9-43e2-8f87-9b5837ac32e4');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Conduct a Rabi experiment with amp=`amp` to determine the Rabi rate for rough amplitude calibration.:
\n", + "

\n", + " 
experiment_rabi = NormalisedRabi(dut_qubit=dut, amp=1.0, start=0.01, stop=0.3, step=0.002, fit=True, collection_name='f01', mprim_index=0, pulse_discretization=True, update=True)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Amplitude updated: 0.5489810209703299\n" + ] + }, + { + "data": { + "application/vnd.plotly.v1+json": { + "data": [ + { + "marker": { + "color": "Blue", + "line": { + "color": "Black", + "width": 2 + }, + "opacity": 0.5, + "size": 7 + }, + "mode": "markers", + "name": "data", + "x": [ + 0.01, + 0.012, + 0.014, + 0.016, + 0.018000000000000002, + 0.02, + 0.022, + 0.024, + 0.026000000000000002, + 0.028000000000000004, + 0.03, + 0.032, + 0.034, + 0.036000000000000004, + 0.038, + 0.04, + 0.042, + 0.044000000000000004, + 0.046000000000000006, + 0.048, + 0.05, + 0.052000000000000005, + 0.054, + 0.056, + 0.058, + 0.060000000000000005, + 0.062000000000000006, + 0.064, + 0.066, + 0.068, + 0.06999999999999999, + 0.072, + 0.074, + 0.076, + 0.078, + 0.08, + 0.082, + 0.08399999999999999, + 0.086, + 0.088, + 0.09, + 0.092, + 0.094, + 0.096, + 0.09799999999999999, + 0.09999999999999999, + 0.102, + 0.104, + 0.106, + 0.108, + 0.11, + 0.112, + 0.114, + 0.11599999999999999, + 0.118, + 0.12, + 0.122, + 0.124, + 0.126, + 0.128, + 0.13, + 0.132, + 0.134, + 0.136, + 0.138, + 0.14, + 0.14200000000000002, + 0.14400000000000002, + 0.14600000000000002, + 0.14800000000000002, + 0.15000000000000002, + 0.15200000000000002, + 0.15400000000000003, + 0.156, + 0.158, + 0.16, + 0.162, + 0.164, + 0.166, + 0.168, + 0.17, + 0.17200000000000001, + 0.17400000000000002, + 0.17600000000000002, + 0.17800000000000002, + 0.18000000000000002, + 0.18200000000000002, + 0.18400000000000002, + 0.186, + 0.188, + 0.19, + 0.192, + 0.194, + 0.196, + 0.198, + 0.2, + 0.202, + 0.20400000000000001, + 0.20600000000000002, + 0.20800000000000002, + 0.21000000000000002, + 0.21200000000000002, + 0.21400000000000002, + 0.21600000000000003, + 0.21800000000000003, + 0.22, + 0.222, + 0.224, + 0.226, + 0.228, + 0.23, + 0.232, + 0.234, + 0.23600000000000002, + 0.23800000000000002, + 0.24000000000000002, + 0.24200000000000002, + 0.24400000000000002, + 0.24600000000000002, + 0.24800000000000003, + 0.25, + 0.252, + 0.254, + 0.256, + 0.258, + 0.26, + 0.262, + 0.264, + 0.266, + 0.268, + 0.27, + 0.272, + 0.274, + 0.276, + 0.278, + 0.28, + 0.28200000000000003, + 0.28400000000000003, + 0.28600000000000003, + 0.28800000000000003, + 0.29000000000000004, + 0.29200000000000004, + 0.29400000000000004, + 0.29600000000000004, + 0.29800000000000004 + ], + "y": [ + -0.21390332137237916, + -0.33455651906357375, + -0.6758458060386849, + -0.4380615159887266, + -0.585699321706932, + -0.4051812641710309, + -0.19091074028326288, + 0.09469983808686908, + 0.31326604892669707, + 0.4363697205088402, + 0.7042614684404408, + 0.9597805182459105, + 0.723826399424825, + 0.4073726290119226, + 0.22308194276862423, + -0.1939667905379071, + -0.46849008891259103, + -0.3331844244911609, + -0.8091542925088802, + -0.47685052360030056, + -0.5751758089303948, + -0.22811622328833758, + -0.11491641840847107, + 0.14917906093844774, + 0.40093753678964394, + 0.5912310893067246, + 0.6604352996203146, + 0.29471137346739673, + 0.6780170560158136, + 0.32400030149206827, + 0.1998350892358563, + -0.17520324065164744, + -0.32543594340143894, + -0.4611838316335881, + -0.5427280895698837, + -0.5241222783307276, + -0.639862111192699, + -0.2497106802720657, + 0.09558239075916078, + 0.23451726818006435, + 0.41250824348895637, + 0.6090483203557797, + 0.6861800357937351, + 0.32648394328232916, + 0.2886697986156849, + 0.4064830584261609, + 0.05531596340639094, + -0.09850488180864758, + -0.32088384450808577, + -0.5025680058016019, + -0.5512803087945839, + -0.3589158880953657, + -0.371985352443221, + -0.13656947767310829, + 0.043126633362195514, + 0.171582906173518, + 0.44514061838048674, + 0.5252297055742938, + 0.733929081074823, + 0.42631404957123503, + 0.3717799212503733, + 0.08752290090544934, + -0.08149538741257853, + -0.14242786275251174, + -0.6366071793452346, + -0.8080911045790353, + -0.6994880548906053, + -0.40818715520343846, + -0.31232964734044366, + -0.18362078928407854, + -0.16708460479265883, + 0.24819887190062465, + 0.35023498794765606, + 0.5229461733946505, + 0.5917600168215101, + 0.577321029289585, + 0.2357760996523842, + 0.14317901071956846, + -0.2553982046114668, + -0.418752193931445, + -0.5852969759448694, + -0.28352503922929934, + -0.5387988296071975, + -0.3250696962272587, + -0.08468944244216137, + -0.3273354816523425, + 0.23326061795699565, + 0.31245131417273986, + 0.41411780603975173, + 0.5099328648135599, + 0.4374189336374904, + 0.5678999577782755, + 0.2889729095157373, + 0.02823791399924268, + -0.06449613542485022, + -0.20845268243999407, + -0.6838419096558713, + -0.6994499235263608, + -0.6874015259122793, + -0.4420659504527649, + -0.378046573259749, + -0.06722613105432752, + 0.24405301261248435, + 0.3397818530701297, + 0.5767018425967831, + 0.6806058482858154, + 0.695297838524893, + 0.2084639653402809, + 0.3252067371956664, + 0.20450152667556123, + 0.04068297659672648, + -0.37377765329831, + -0.716309438368932, + -0.8686731442928798, + -0.7044993991418196, + -0.37325918559090715, + -0.17280834927024025, + 0.008436128451603412, + 0.4924007727942826, + 0.20552265655526608, + 0.6463435169539405, + 0.7529498360293273, + 0.41526953799309874, + 0.18065322454676527, + 0.21406147589284477, + 0.04258120601637074, + -0.3025391755398708, + -0.505013155606579, + -0.4699027356380384, + -0.7471675252364958, + -0.4788480258389779, + -0.3145548575484932, + -0.15283076143546612, + 0.050553447030243295, + 0.3147976908869725, + 0.4688457162748748, + 0.3781476462856488, + 0.28391029637052745, + 0.8085522994761491, + 0.388051833741651, + 0.001919065664545222, + -0.1988721867729342, + -0.25496948223028687, + -0.6871001623949983, + -0.6848495282840344 + ], + "type": "scatter" + }, + { + "line": { + "color": "Red" + }, + "mode": "lines", + "name": "fit", + "visible": "legendonly", + "x": [ + 0.01, + 0.0104, + 0.010799999999999999, + 0.011199999999999998, + 0.011599999999999997, + 0.011999999999999997, + 0.012399999999999996, + 0.012799999999999995, + 0.013199999999999995, + 0.013599999999999994, + 0.013999999999999993, + 0.014399999999999993, + 0.014799999999999992, + 0.015199999999999991, + 0.01559999999999999, + 0.01599999999999999, + 0.01639999999999999, + 0.01679999999999999, + 0.017199999999999986, + 0.017599999999999987, + 0.017999999999999988, + 0.018399999999999986, + 0.018799999999999983, + 0.019199999999999984, + 0.019599999999999985, + 0.019999999999999983, + 0.02039999999999998, + 0.02079999999999998, + 0.021199999999999983, + 0.02159999999999998, + 0.021999999999999978, + 0.02239999999999998, + 0.02279999999999998, + 0.023199999999999978, + 0.023599999999999975, + 0.023999999999999976, + 0.024399999999999977, + 0.024799999999999975, + 0.025199999999999972, + 0.025599999999999973, + 0.025999999999999975, + 0.026399999999999972, + 0.02679999999999997, + 0.027199999999999974, + 0.027599999999999972, + 0.02799999999999997, + 0.028399999999999967, + 0.02879999999999997, + 0.02919999999999997, + 0.029599999999999967, + 0.029999999999999964, + 0.03039999999999997, + 0.030799999999999966, + 0.031199999999999964, + 0.03159999999999996, + 0.031999999999999966, + 0.032399999999999964, + 0.03279999999999996, + 0.03319999999999996, + 0.03359999999999996, + 0.03399999999999996, + 0.03439999999999996, + 0.034799999999999956, + 0.03519999999999996, + 0.03559999999999996, + 0.035999999999999956, + 0.03639999999999995, + 0.03679999999999996, + 0.037199999999999955, + 0.03759999999999995, + 0.03799999999999995, + 0.038399999999999955, + 0.03879999999999995, + 0.03919999999999995, + 0.03959999999999995, + 0.03999999999999995, + 0.04039999999999995, + 0.04079999999999995, + 0.041199999999999945, + 0.04159999999999995, + 0.04199999999999995, + 0.042399999999999945, + 0.04279999999999994, + 0.04319999999999995, + 0.043599999999999944, + 0.04399999999999994, + 0.044399999999999946, + 0.044799999999999944, + 0.04519999999999994, + 0.04559999999999994, + 0.04599999999999994, + 0.04639999999999994, + 0.04679999999999994, + 0.047199999999999936, + 0.04759999999999994, + 0.04799999999999994, + 0.048399999999999936, + 0.048799999999999934, + 0.04919999999999993, + 0.049599999999999936, + 0.04999999999999993, + 0.05039999999999993, + 0.050799999999999935, + 0.05119999999999993, + 0.05159999999999993, + 0.05199999999999993, + 0.052399999999999926, + 0.05279999999999993, + 0.05319999999999993, + 0.053599999999999925, + 0.05399999999999993, + 0.05439999999999993, + 0.054799999999999925, + 0.05519999999999992, + 0.05559999999999992, + 0.055999999999999925, + 0.05639999999999992, + 0.05679999999999992, + 0.057199999999999925, + 0.05759999999999992, + 0.05799999999999992, + 0.05839999999999992, + 0.058799999999999915, + 0.05919999999999992, + 0.05959999999999992, + 0.059999999999999915, + 0.06039999999999992, + 0.06079999999999992, + 0.061199999999999914, + 0.06159999999999991, + 0.06199999999999991, + 0.062399999999999914, + 0.06279999999999991, + 0.06319999999999991, + 0.0635999999999999, + 0.0639999999999999, + 0.0643999999999999, + 0.0647999999999999, + 0.0651999999999999, + 0.06559999999999991, + 0.0659999999999999, + 0.0663999999999999, + 0.0667999999999999, + 0.0671999999999999, + 0.0675999999999999, + 0.0679999999999999, + 0.06839999999999989, + 0.0687999999999999, + 0.0691999999999999, + 0.0695999999999999, + 0.0699999999999999, + 0.0703999999999999, + 0.07079999999999989, + 0.07119999999999989, + 0.07159999999999989, + 0.0719999999999999, + 0.0723999999999999, + 0.07279999999999989, + 0.07319999999999989, + 0.07359999999999989, + 0.07399999999999989, + 0.07439999999999988, + 0.07479999999999988, + 0.07519999999999988, + 0.07559999999999988, + 0.07599999999999989, + 0.07639999999999988, + 0.07679999999999988, + 0.07719999999999988, + 0.07759999999999988, + 0.07799999999999987, + 0.07839999999999987, + 0.07879999999999988, + 0.07919999999999988, + 0.07959999999999988, + 0.07999999999999988, + 0.08039999999999987, + 0.08079999999999987, + 0.08119999999999987, + 0.08159999999999987, + 0.08199999999999986, + 0.08239999999999988, + 0.08279999999999987, + 0.08319999999999987, + 0.08359999999999987, + 0.08399999999999987, + 0.08439999999999986, + 0.08479999999999986, + 0.08519999999999987, + 0.08559999999999987, + 0.08599999999999987, + 0.08639999999999987, + 0.08679999999999986, + 0.08719999999999986, + 0.08759999999999986, + 0.08799999999999986, + 0.08839999999999985, + 0.08879999999999987, + 0.08919999999999986, + 0.08959999999999986, + 0.08999999999999986, + 0.09039999999999986, + 0.09079999999999985, + 0.09119999999999985, + 0.09159999999999986, + 0.09199999999999986, + 0.09239999999999986, + 0.09279999999999985, + 0.09319999999999985, + 0.09359999999999985, + 0.09399999999999985, + 0.09439999999999985, + 0.09479999999999984, + 0.09519999999999985, + 0.09559999999999985, + 0.09599999999999985, + 0.09639999999999985, + 0.09679999999999984, + 0.09719999999999984, + 0.09759999999999984, + 0.09799999999999985, + 0.09839999999999985, + 0.09879999999999985, + 0.09919999999999984, + 0.09959999999999984, + 0.09999999999999984, + 0.10039999999999984, + 0.10079999999999983, + 0.10119999999999983, + 0.10159999999999984, + 0.10199999999999984, + 0.10239999999999984, + 0.10279999999999984, + 0.10319999999999983, + 0.10359999999999983, + 0.10399999999999983, + 0.10439999999999984, + 0.10479999999999984, + 0.10519999999999984, + 0.10559999999999983, + 0.10599999999999983, + 0.10639999999999983, + 0.10679999999999983, + 0.10719999999999982, + 0.10759999999999982, + 0.10799999999999983, + 0.10839999999999983, + 0.10879999999999983, + 0.10919999999999982, + 0.10959999999999982, + 0.10999999999999982, + 0.11039999999999982, + 0.11079999999999983, + 0.11119999999999983, + 0.11159999999999982, + 0.11199999999999982, + 0.11239999999999982, + 0.11279999999999982, + 0.11319999999999981, + 0.11359999999999981, + 0.11399999999999981, + 0.11439999999999982, + 0.11479999999999982, + 0.11519999999999982, + 0.11559999999999981, + 0.11599999999999981, + 0.11639999999999981, + 0.1167999999999998, + 0.11719999999999982, + 0.11759999999999982, + 0.11799999999999981, + 0.11839999999999981, + 0.11879999999999981, + 0.1191999999999998, + 0.1195999999999998, + 0.1199999999999998, + 0.1203999999999998, + 0.12079999999999981, + 0.12119999999999981, + 0.1215999999999998, + 0.1219999999999998, + 0.1223999999999998, + 0.1227999999999998, + 0.1231999999999998, + 0.12359999999999981, + 0.1239999999999998, + 0.1243999999999998, + 0.1247999999999998, + 0.1251999999999998, + 0.1255999999999998, + 0.1259999999999998, + 0.1263999999999998, + 0.1267999999999998, + 0.1271999999999998, + 0.1275999999999998, + 0.1279999999999998, + 0.1283999999999998, + 0.1287999999999998, + 0.1291999999999998, + 0.1295999999999998, + 0.1299999999999998, + 0.1303999999999998, + 0.1307999999999998, + 0.1311999999999998, + 0.1315999999999998, + 0.13199999999999978, + 0.1323999999999998, + 0.13279999999999978, + 0.1331999999999998, + 0.1335999999999998, + 0.13399999999999979, + 0.1343999999999998, + 0.13479999999999978, + 0.1351999999999998, + 0.1355999999999998, + 0.1359999999999998, + 0.1363999999999998, + 0.13679999999999978, + 0.1371999999999998, + 0.13759999999999978, + 0.1379999999999998, + 0.1383999999999998, + 0.13879999999999978, + 0.1391999999999998, + 0.13959999999999978, + 0.1399999999999998, + 0.14039999999999977, + 0.1407999999999998, + 0.14119999999999977, + 0.14159999999999978, + 0.1419999999999998, + 0.14239999999999978, + 0.1427999999999998, + 0.14319999999999977, + 0.14359999999999978, + 0.14399999999999977, + 0.14439999999999978, + 0.1447999999999998, + 0.14519999999999977, + 0.14559999999999979, + 0.14599999999999977, + 0.14639999999999978, + 0.14679999999999976, + 0.14719999999999978, + 0.1475999999999998, + 0.14799999999999977, + 0.14839999999999978, + 0.14879999999999977, + 0.14919999999999978, + 0.14959999999999976, + 0.14999999999999977, + 0.15039999999999976, + 0.15079999999999977, + 0.15119999999999978, + 0.15159999999999976, + 0.15199999999999977, + 0.15239999999999976, + 0.15279999999999977, + 0.15319999999999975, + 0.15359999999999976, + 0.15399999999999975, + 0.15439999999999976, + 0.15479999999999977, + 0.15519999999999975, + 0.15559999999999977, + 0.15599999999999975, + 0.15639999999999976, + 0.15679999999999975, + 0.15719999999999976, + 0.15759999999999977, + 0.15799999999999975, + 0.15839999999999976, + 0.15879999999999975, + 0.15919999999999976, + 0.15959999999999974, + 0.15999999999999975, + 0.16039999999999976, + 0.16079999999999975, + 0.16119999999999976, + 0.16159999999999974, + 0.16199999999999976, + 0.16239999999999974, + 0.16279999999999975, + 0.16319999999999973, + 0.16359999999999975, + 0.16399999999999976, + 0.16439999999999974, + 0.16479999999999975, + 0.16519999999999974, + 0.16559999999999975, + 0.16599999999999973, + 0.16639999999999974, + 0.16679999999999973, + 0.16719999999999974, + 0.16759999999999975, + 0.16799999999999973, + 0.16839999999999974, + 0.16879999999999973, + 0.16919999999999974, + 0.16959999999999972, + 0.16999999999999973, + 0.17039999999999975, + 0.17079999999999973, + 0.17119999999999974, + 0.17159999999999972, + 0.17199999999999974, + 0.17239999999999972, + 0.17279999999999973, + 0.17319999999999974, + 0.17359999999999973, + 0.17399999999999974, + 0.17439999999999972, + 0.17479999999999973, + 0.17519999999999972, + 0.17559999999999973, + 0.1759999999999997, + 0.17639999999999972, + 0.17679999999999974, + 0.17719999999999972, + 0.17759999999999973, + 0.17799999999999971, + 0.17839999999999973, + 0.1787999999999997, + 0.17919999999999972, + 0.1795999999999997, + 0.17999999999999972, + 0.18039999999999973, + 0.1807999999999997, + 0.18119999999999972, + 0.1815999999999997, + 0.18199999999999972, + 0.1823999999999997, + 0.1827999999999997, + 0.18319999999999972, + 0.1835999999999997, + 0.18399999999999972, + 0.1843999999999997, + 0.18479999999999971, + 0.1851999999999997, + 0.1855999999999997, + 0.18599999999999972, + 0.1863999999999997, + 0.18679999999999972, + 0.1871999999999997, + 0.1875999999999997, + 0.1879999999999997, + 0.1883999999999997, + 0.1887999999999997, + 0.1891999999999997, + 0.1895999999999997, + 0.1899999999999997, + 0.1903999999999997, + 0.1907999999999997, + 0.1911999999999997, + 0.1915999999999997, + 0.1919999999999997, + 0.19239999999999968, + 0.1927999999999997, + 0.1931999999999997, + 0.1935999999999997, + 0.1939999999999997, + 0.19439999999999968, + 0.1947999999999997, + 0.19519999999999968, + 0.1955999999999997, + 0.1959999999999997, + 0.19639999999999969, + 0.1967999999999997, + 0.19719999999999968, + 0.1975999999999997, + 0.19799999999999968, + 0.1983999999999997, + 0.1987999999999997, + 0.19919999999999968, + 0.1995999999999997, + 0.19999999999999968, + 0.2003999999999997, + 0.20079999999999967, + 0.20119999999999968, + 0.20159999999999967, + 0.20199999999999968, + 0.2023999999999997, + 0.20279999999999967, + 0.2031999999999997, + 0.20359999999999967, + 0.20399999999999968, + 0.20439999999999967, + 0.20479999999999968, + 0.20519999999999966, + 0.20559999999999967, + 0.20599999999999968, + 0.20639999999999967, + 0.20679999999999968, + 0.20719999999999966, + 0.20759999999999967, + 0.20799999999999966, + 0.20839999999999967, + 0.20879999999999968, + 0.20919999999999966, + 0.20959999999999968, + 0.20999999999999966, + 0.21039999999999967, + 0.21079999999999965, + 0.21119999999999967, + 0.21159999999999968, + 0.21199999999999966, + 0.21239999999999967, + 0.21279999999999966, + 0.21319999999999967, + 0.21359999999999965, + 0.21399999999999966, + 0.21439999999999965, + 0.21479999999999966, + 0.21519999999999967, + 0.21559999999999965, + 0.21599999999999966, + 0.21639999999999965, + 0.21679999999999966, + 0.21719999999999964, + 0.21759999999999965, + 0.21799999999999964, + 0.21839999999999965, + 0.21879999999999966, + 0.21919999999999965, + 0.21959999999999966, + 0.21999999999999964, + 0.22039999999999965, + 0.22079999999999964, + 0.22119999999999965, + 0.22159999999999966, + 0.22199999999999964, + 0.22239999999999965, + 0.22279999999999964, + 0.22319999999999965, + 0.22359999999999963, + 0.22399999999999964, + 0.22439999999999966, + 0.22479999999999964, + 0.22519999999999965, + 0.22559999999999963, + 0.22599999999999965, + 0.22639999999999963, + 0.22679999999999964, + 0.22719999999999962, + 0.22759999999999964, + 0.22799999999999965, + 0.22839999999999963, + 0.22879999999999964, + 0.22919999999999963, + 0.22959999999999964, + 0.22999999999999962, + 0.23039999999999963, + 0.23079999999999962, + 0.23119999999999963, + 0.23159999999999964, + 0.23199999999999962, + 0.23239999999999963, + 0.23279999999999962, + 0.23319999999999963, + 0.2335999999999996, + 0.23399999999999962, + 0.23439999999999964, + 0.23479999999999962, + 0.23519999999999963, + 0.23559999999999962, + 0.23599999999999963, + 0.2363999999999996, + 0.23679999999999962, + 0.23719999999999963, + 0.23759999999999962, + 0.23799999999999963, + 0.2383999999999996, + 0.23879999999999962, + 0.2391999999999996, + 0.23959999999999962, + 0.2399999999999996, + 0.24039999999999961, + 0.24079999999999963, + 0.2411999999999996, + 0.24159999999999962, + 0.2419999999999996, + 0.24239999999999962, + 0.2427999999999996, + 0.2431999999999996, + 0.2435999999999996, + 0.2439999999999996, + 0.24439999999999962, + 0.2447999999999996, + 0.2451999999999996, + 0.2455999999999996, + 0.2459999999999996, + 0.2463999999999996, + 0.2467999999999996, + 0.24719999999999961, + 0.2475999999999996, + 0.2479999999999996, + 0.2483999999999996, + 0.2487999999999996, + 0.2491999999999996, + 0.2495999999999996, + 0.2499999999999996, + 0.25039999999999957, + 0.2507999999999996, + 0.2511999999999996, + 0.2515999999999996, + 0.25199999999999956, + 0.25239999999999957, + 0.2527999999999996, + 0.2531999999999996, + 0.2535999999999996, + 0.25399999999999956, + 0.25439999999999957, + 0.2547999999999996, + 0.2551999999999996, + 0.25559999999999955, + 0.25599999999999956, + 0.2563999999999996, + 0.2567999999999996, + 0.2571999999999996, + 0.25759999999999955, + 0.25799999999999956, + 0.2583999999999996, + 0.2587999999999996, + 0.25919999999999954, + 0.25959999999999955, + 0.25999999999999956, + 0.2603999999999996, + 0.2607999999999996, + 0.2611999999999996, + 0.26159999999999956, + 0.26199999999999957, + 0.2623999999999996, + 0.2627999999999996, + 0.2631999999999996, + 0.26359999999999956, + 0.26399999999999957, + 0.2643999999999996, + 0.2647999999999996, + 0.26519999999999955, + 0.26559999999999956, + 0.26599999999999957, + 0.2663999999999996, + 0.2667999999999996, + 0.26719999999999955, + 0.26759999999999956, + 0.26799999999999957, + 0.2683999999999996, + 0.26879999999999954, + 0.26919999999999955, + 0.26959999999999956, + 0.2699999999999996, + 0.2703999999999996, + 0.27079999999999954, + 0.27119999999999955, + 0.27159999999999956, + 0.2719999999999996, + 0.27239999999999953, + 0.27279999999999954, + 0.27319999999999955, + 0.27359999999999957, + 0.2739999999999996, + 0.27439999999999953, + 0.27479999999999954, + 0.27519999999999956, + 0.27559999999999957, + 0.2759999999999996, + 0.27639999999999953, + 0.27679999999999955, + 0.27719999999999956, + 0.27759999999999957, + 0.2779999999999995, + 0.27839999999999954, + 0.27879999999999955, + 0.27919999999999956, + 0.27959999999999957, + 0.2799999999999995, + 0.28039999999999954, + 0.28079999999999955, + 0.28119999999999956, + 0.2815999999999995, + 0.28199999999999953, + 0.28239999999999954, + 0.28279999999999955, + 0.28319999999999956, + 0.2835999999999995, + 0.28399999999999953, + 0.28439999999999954, + 0.28479999999999955, + 0.28519999999999956, + 0.2855999999999995, + 0.28599999999999953, + 0.28639999999999954, + 0.28679999999999956, + 0.2871999999999995, + 0.2875999999999995, + 0.28799999999999953, + 0.28839999999999955, + 0.28879999999999956, + 0.2891999999999995, + 0.2895999999999995, + 0.28999999999999954, + 0.29039999999999955, + 0.2907999999999995, + 0.2911999999999995, + 0.2915999999999995, + 0.29199999999999954, + 0.29239999999999955, + 0.2927999999999995, + 0.2931999999999995, + 0.29359999999999953, + 0.29399999999999954, + 0.2943999999999995, + 0.2947999999999995, + 0.2951999999999995, + 0.29559999999999953, + 0.29599999999999954, + 0.2963999999999995, + 0.2967999999999995, + 0.2971999999999995, + 0.29759999999999953, + 0.2979999999999995, + 0.2983999999999995, + 0.2987999999999995, + 0.2991999999999995, + 0.29959999999999953 + ], + "y": [ + -0.26026707492328555, + -0.30254579128523407, + -0.34288810192546926, + -0.38103698757985094, + -0.4167494032079981, + -0.44979782642361865, + -0.4799717070306273, + -0.5070788084301087, + -0.5309464323520806, + -0.5514225191093358, + -0.5683766163636887, + -0.5817007102326598, + -0.5913099134416436, + -0.5971430061373757, + -0.5991628259171944, + -0.5973565045892371, + -0.5917355501551809, + -0.5823357734932199, + -0.5692170602083776, + -0.5524629891036876, + -0.5321802997029484, + -0.508498212217443, + -0.48156760428908585, + -0.45156004975494146, + -0.4186667255570985, + -0.3830971937619405, + -0.3450780664485151, + -0.3048515619719494, + -0.2626739617998988, + -0.21881397775346045, + -0.17355104005479816, + -0.12717351708825686, + -0.07997687821680395, + -0.032261811358412155, + 0.015667692684764454, + 0.06350627693975447, + 0.11094916367995847, + 0.1576940961519796, + 0.20344326424143605, + 0.24790520180941006, + 0.2907966436116803, + 0.3318443299703805, + 0.3707867477005921, + 0.40737579620048997, + 0.44137836809045783, + 0.47257783433097533, + 0.5007754243576545, + 0.5257914924406369, + 0.5474666622004252, + 0.5656628419884915, + 0.5802641046637008, + 0.5911774261595407, + 0.598333278136764, + 0.6016860709456885, + 0.6012144440760486, + 0.5969214022439615, + 0.5888342962489994, + 0.5770046487233284, + 0.5615078258830583, + 0.5424425573730627, + 0.5199303072643212, + 0.49411450021112974, + 0.4651596076983055, + 0.4332501001998694, + 0.3985892719249569, + 0.36139794563845995, + 0.32191306580793166, + 0.2803861890397635, + 0.23708188142200676, + 0.19227603298429363, + 0.14625410001338168, + 0.09930928642245163, + 0.051740675760583554, + 0.0038513257633009702, + -0.04405366241626216, + -0.09166908799473261, + -0.1386915949806033, + -0.18482160484325344, + -0.22976522511590197, + -0.27323612177288675, + -0.3149573434523968, + -0.354663085902648, + -0.3921003854102399, + -0.42703073042196404, + -0.45923158109246764, + -0.488497787076798, + -0.514642894535133, + -0.5375003340228066, + -0.5569244816976556, + -0.5727915870837474, + -0.5850005614807661, + -0.5934736219961205, + -0.5981567870966729, + -0.5990202205229508, + -0.5960584213747743, + -0.5892902591572736, + -0.578758853564019, + -0.5645312997631601, + -0.546698240936764, + -0.5253732907966929, + -0.5006923097561415, + -0.4728125393683233, + -0.441911600546759, + -0.4081863619494678, + -0.3718516857365413, + -0.33313905869182325, + -0.29229511742977876, + -0.24958007708337926, + -0.20526607348380166, + -0.15963542939380726, + -0.1129788558406054, + -0.06559360000740744, + -0.017781551483379416, + 0.030152681062968714, + 0.07790371053356167, + 0.1251673170098645, + 0.17164238592594538, + 0.2170328264574543, + 0.2610494579045446, + 0.3034118520506639, + 0.34385011975962076, + 0.3821066304285911, + 0.41793765334248495, + 0.4511149104726879, + 0.48142703082734445, + 0.5086808970875789, + 0.5327028759502928, + 0.5533399243390588, + 0.5704605644355107, + 0.5839557213193138, + 0.5937394178801666, + 0.5997493225745676, + 0.6019471465376103, + 0.6003188875198272, + 0.5948749190949663, + 0.5856499245703622, + 0.572702676020961, + 0.556115659854754, + 0.5359945512951386, + 0.5124675411282552, + 0.4856845190045621, + 0.45581611849780324, + 0.42305263000526566, + 0.38760278841519646, + 0.3496924432650835, + 0.3095631198631961, + 0.2674704805404132, + 0.2236826958356204, + 0.1784787359918374, + 0.13214659364782744, + 0.0849814490484116, + 0.037283789462799664, + -0.01064250520790121, + -0.0584920984375009, + -0.1059601423616607, + -0.15274421995156318, + -0.19854627170082734, + -0.2430744945508299, + -0.286045200956444, + -0.3271846262481556, + -0.3662306727759307, + -0.4029345797229794, + -0.4370625079510669, + -0.46839702978040204, + -0.4967385142127881, + -0.5219063987728041, + -0.5437403398641872, + -0.5621012343125393, + -0.5768721055861465, + -0.5879588490488298, + -0.5952908314968557, + -0.5988213411602848, + -0.5985278853018067, + -0.5944123335170787, + -0.5865009058236078, + -0.5748440056140556, + -0.5595158985382187, + -0.540614239359514, + -0.5182594498003487, + -0.4925939513400897, + -0.46378125785345803, + -0.4320049338701083, + -0.39746742509227506, + -0.36038876862121405, + -0.32100519110954295, + -0.27956760377059425, + -0.23634000383299686, + -0.19159779262478766, + -0.14562602100245675, + -0.0987175733032208, + -0.05117130139049983, + -0.0032901206805297147, + 0.04462091971975741, + 0.09225658046853076, + 0.13931337665681842, + 0.18549151129884267, + 0.2304967853267616, + 0.2740424719212779, + 0.31585114323685076, + 0.355656437883522, + 0.39320475790479137, + 0.4282568844401525, + 0.4605895017789876, + 0.48999662009611383, + 0.5162908878048156, + 0.539304785166377, + 0.5588916915517116, + 0.5749268195555866, + 0.5873080100122392, + 0.5959563828473798, + 0.6008168396200205, + 0.6018584145524642, + 0.599074471812047, + 0.5924827477877656, + 0.5821252380924459, + 0.5680679300103549, + 0.5504003820948247, + 0.529235153594257, + 0.5047070873416033, + 0.47697245067601507, + 0.44620793986981494, + 0.41260955440353836, + 0.3763913482609985, + 0.33778406619983087, + 0.2970336736857823, + 0.25439978985645495, + 0.21015403349806216, + 0.16457829257290013, + 0.1179629283222865, + 0.07060492538659184, + 0.022805999727856168, + -0.02512932359066581, + -0.07289565052163248, + -0.12018866368699757, + -0.1667070611719731, + -0.212154476107614, + -0.2562413648124019, + -0.29868685146357504, + -0.33922051754588045, + -0.37758412467723385, + -0.4135332598352254, + -0.44683889250280584, + -0.4772888338126544, + -0.5046890883940472, + -0.5288650903096824, + -0.5496628152083639, + -0.5669497616080377, + -0.5806157950574963, + -0.5905738497986018, + -0.5967604834587884, + -0.599136281239905, + -0.5976861070283338, + -0.5924191998265713, + -0.5833691148919102, + -0.5705935099572209, + -0.5541737778958215, + -0.5342145281707, + -0.5108429203717676, + -0.484207854087149, + -0.454479020269794, + -0.4218458201431379, + -0.3865161585333998, + -0.34871511931617816, + -0.30868353141596944, + -0.26667643449463657, + -0.22296145410381352, + -0.17781709665311768, + -0.13153097505682726, + -0.0843979763633931, + -0.03671838304174087, + 0.011204040106476848, + 0.05906398122039067, + 0.10655652650970052, + 0.15337910284835515, + 0.19923340545597368, + 0.24382729838578626, + 0.28687667571118125, + 0.3281072715533309, + 0.36725640741823334, + 0.4040746657110142, + 0.4383274787655367, + 0.46979662326572896, + 0.4982816105376868, + 0.5236009638550755, + 0.5455933746201782, + 0.5641187300545774, + 0.579059005852128, + 0.5903190181071329, + 0.5978270297272438, + 0.6015352074676327, + 0.601419926674704, + 0.5974819217978263, + 0.589746281710184, + 0.57826228986856, + 0.563103110330385, + 0.5443653216284193, + 0.5221683014727501, + 0.4966534662001186, + 0.4679833698160463, + 0.436340668369686, + 0.40192695625937935, + 0.3649614818827169, + 0.32567975081367173, + 0.2843320254059219, + 0.24118173038128782, + 0.19650377456127405, + 0.15058279943382613, + 0.10371136571370643, + 0.056188089449737216, + 0.00831573955379061, + -0.039600691127969906, + -0.08725592891244578, + -0.13434636416613716, + -0.18057198559012483, + -0.22563829158015242, + -0.2692581664847041, + -0.3111537098074584, + -0.3510580067003364, + -0.3887168284674477, + -0.42389025224603266, + -0.45635418954554335, + -0.4859018139065253, + -0.5123448785838156, + -0.5355149158591057, + -0.5552643103420979, + -0.5714672394223221, + -0.5840204748799989, + -0.592844040548949, + -0.597881721841575, + -0.5991014238897782, + -0.5964953760200912, + -0.5900801812603318, + -0.5798967105623686, + -0.5660098424149075, + -0.5485080495052019, + -0.527502835063074, + -0.5031280224782417, + -0.4755389027168075, + -0.44491124496865975, + -0.41144017682890976, + -0.3753389411477336, + -0.3368375374685816, + -0.29618125671022144, + -0.2536291184279833, + -0.20945222061043978, + -0.16393201252484918, + -0.11735850161499833, + -0.07002840587529505, + -0.0222432634721259, + 0.02569248834382584, + 0.0734734527952805, + 0.1207952192491198, + 0.16735630260450568, + 0.21286006404249797, + 0.25701660090023254, + 0.29954459362933405, + 0.3401730980715892, + 0.3786432716334782, + 0.41471002236207416, + 0.448143570416193, + 0.4787309119846435, + 0.5062771763250948, + 0.5306068672778509, + 0.5515649813449407, + 0.5690179952112714, + 0.5828547164163754, + 0.5929869917571838, + 0.5993502689085847, + 0.601904007683737, + 0.6006319383139936, + 0.5955421651029548, + 0.5866671147942688, + 0.5740633299821354, + 0.5578111088806696, + 0.5380139937471673, + 0.5147981112184739, + 0.48831136876318026, + 0.458722512368995, + 0.426220051468726, + 0.3910110579541623, + 0.353319846929238, + 0.3133865476073911, + 0.27146557345783523, + 0.22782400134744402, + 0.18273987000457448, + 0.13650040864536162, + 0.08940020704773713, + 0.04173933873161923, + -0.006178550797600081, + -0.05404817856256709, + -0.10156456905970072, + -0.14842499724608726, + -0.19433091718871587, + -0.23898986408881662, + -0.2821173175635124, + -0.3234385143139113, + -0.3626901986312224, + -0.39962229958843026, + -0.43399952423229915, + -0.4656028566254412, + -0.4942309531881968, + -0.5197014254506072, + -0.5418520020421345, + -0.5605415625161483, + -0.5756510364226909, + -0.5870841619016146, + -0.5947680989630718, + -0.5986538935482123, + -0.5987167894135532, + -0.5949563858520267, + -0.5873966402458671, + -0.5760857154350802, + -0.5610956728738911, + -0.5425220135300773, + -0.5204830694520804, + -0.4951192498802128, + -0.4665921467049361, + -0.4350835049713375, + -0.40079406498863623, + -0.3639422834216092, + -0.3247629415118463, + -0.28350564929574124, + -0.24043325534883792, + -0.19582017218794479, + -0.14995062799987996, + -0.10311685583491284, + -0.055617231801596737, + -0.007754374124380778, + 0.040166784825136234, + 0.08784094124022777, + 0.1349643649575663, + 0.18123683451001982, + 0.2263635498257724, + 0.2700570103878831, + 0.31203884688871153, + 0.35204159470977175, + 0.3898103979281894, + 0.4251046329937474, + 0.4576994417319582, + 0.4873871639066211, + 0.5139786602149461, + 0.5373045172865745, + 0.5572161270094622, + 0.5735866333062584, + 0.5863117403293767, + 0.595310376925694, + 0.6005252131376674, + 0.6019230254502186, + 0.5994949084564369, + 0.5932563315935727, + 0.5832470405878653, + 0.5695308042360944, + 0.5521950081371019, + 0.5313500979615946, + 0.507128875807159, + 0.4796856541213404, + 0.44919527258316166, + 0.4158519842064417, + 0.3798682177615354, + 0.34147322440005035, + 0.3009116171048208, + 0.25844181227030166, + 0.21433438334192007, + 0.168870337003391, + 0.12233932289424403, + 0.0750377882634396, + 0.02726708931568479, + -0.020668428717034632, + -0.06846337054684998, + -0.11581323649133356, + -0.16241636242889196, + -0.20797584168896066, + -0.25220141663264445, + -0.29481132787264164, + -0.3355341093511621, + -0.3741103178394093, + -0.4102941858401496, + -0.44385518736268426, + -0.47457950659482445, + -0.5022714001149614, + -0.5267544439656663, + -0.547872657643724, + -0.5654914978457544, + -0.5794987156382747, + -0.5898050715912024, + -0.5963449043187383, + -0.5990765488054544, + -0.5979826018524581, + -0.5930700329524835, + -0.5843701398875274, + -0.571938349331922, + -0.555853863731185, + -0.5362191567063745, + -0.5131593201987086, + -0.4868212675137505, + -0.4573727973425694, + -0.425001524722904, + -0.3899136857512005, + -0.35233282366059643, + -0.31249836463583364, + -0.27066409243851663, + -0.22709653156079845, + -0.18207324920841805, + -0.13588108693098894, + -0.08881433316586897, + -0.0411728483381595, + 0.00673984553818906, + 0.054618498587108995, + 0.10215807780530942, + 0.14905571041543575, + 0.19501261345657883, + 0.23973599731866213, + 0.28294093109347435, + 0.32435215785820226, + 0.36370584832638686, + 0.400751281693877, + 0.4352524429711246, + 0.46698952662536575, + 0.4957603369529479, + 0.5213815762601658, + 0.5436900126456075, + 0.5625435199441439, + 0.5778219832070899, + 0.5894280639497936, + 0.5972878202912719, + 0.6013511780350005, + 0.6015922496896415, + 0.598009499397227, + 0.590625752718046, + 0.5794880512099062, + 0.5646673527282321, + 0.5462580793563706, + 0.5243775158462444, + 0.49916506240183506, + 0.47078134656600523, + 0.4394071998687713, + 0.4052425057567989, + 0.36850492614387026, + 0.32942851469543666, + 0.28826222568200055, + 0.24526832790129993, + 0.20072073377418742, + 0.15490325425942358, + 0.10810779070532128, + 0.06063247515777817, + 0.01277977097284265, + -0.035145454165366866, + -0.08283787054462641, + -0.12999363166806982, + -0.17631231004825193, + -0.22149881121872816, + -0.26526525376925575, + -0.30733280342683117, + -0.3474334494978364, + -0.38531171235353545, + -0.42072627108067495, + -0.4534515009274986, + -0.4832789107500737, + -0.5100184713011432, + -0.5334998258989164, + -0.553573375762764, + -0.5701112331011131, + -0.5830080358795082, + -0.5921816190779714, + -0.5975735381610798, + -0.599149441425795, + -0.596899288854799, + -0.5908374160810493, + -0.5810024430560272, + -0.567457028003561, + -0.5502874682267522, + -0.5296031503112852, + -0.5055358532278075, + -0.47823890877333874, + -0.4478862247004449, + -0.4146711767578463, + -0.37880537670116854, + -0.3405173241228258, + -0.30005095069016696, + -0.25766406606646386, + -0.21362671541577788, + -0.1682194589558793, + -0.12173158452023296, + -0.07445926451664699, + -0.026703669024595, + 0.021230952947455705, + 0.06903921182040772, + 0.11641652306946595, + 0.16306104772010077, + 0.208675615352181, + 0.2529696173609741, + 0.29566085841305534, + 0.33647735430162123, + 0.37515906474708155, + 0.41145955010339996, + 0.44514754141533136, + 0.47600841382376247, + 0.503845553932206, + 0.5284816124229613, + 0.5497596339426063, + 0.5675440570583266, + 0.5817215779144468, + 0.5922018720868121, + 0.5989181700361108, + 0.6018276824939727, + 0.6009118730717019, + 0.5961765763548755, + 0.5876519607314292, + 0.5753923361900537, + 0.5594758083134048, + 0.5400037806705313, + 0.5171003087787429, + 0.4909113097508304, + 0.4616036326629096, + 0.4293639955655671, + 0.3943977959105392, + 0.35692780197169455, + 0.317192733597169, + 0.2754457413346648, + 0.23195279361934074, + 0.18699098229940841, + 0.14084675729498258, + 0.0938141016369485, + 0.0461926585127213, + -0.0017141777486566535, + -0.04960119458975078, + -0.09716330572187279, + -0.14409749481774486, + -0.19010474601652588, + -0.23489194894202037, + -0.27817376609729816, + -0.3196744507385686, + -0.35912960364678564, + -0.3962878576046024, + -0.430912478846956, + -0.46278287528253864, + -0.49169600187724155, + -0.5174676542460552, + -0.5399336422119064, + -0.5589508358547841, + -0.5743980773867826, + -0.5861769530435801, + -0.5942124200746337, + -0.598453284837572, + -0.598872528950866, + -0.5954674814268646, + -0.5882598356885547, + -0.5772955113616229, + -0.5626443617223297, + -0.5443997286650633, + -0.522677848024818, + -0.497617109043293, + -0.46937717269651347, + -0.4381379545010624, + -0.40409847827946194, + -0.36747560818726804, + -0.32850266708012976, + -0.2874279500230841, + -0.2445131424125626, + -0.20003165278907803, + -0.15426687096221925, + -0.10751036254541581, + -0.0600600114029194, + -0.012218121843546174, + 0.03571050734814107, + 0.08342052477281564, + 0.1306079717980606, + 0.17697221906680033, + 0.222217881795009, + 0.26605670165649364, + 0.3082093832652679, + 0.34840737355543006, + 0.3863945727220704, + 0.4219289658230234, + 0.45478416464646465, + 0.4847508500212605, + 0.5116381053811292, + 0.5352746330864987, + 0.5555098457550095, + 0.5722148256477595, + 0.5852831459991213, + 0.5946315490574436, + 0.6002004765168985, + 0.6019544489610872, + 0.5998822919009997, + 0.5939972069672389, + 0.5843366878029567, + 0.5709622811933359, + 0.5539591949534635, + 0.5334357550727098, + 0.5095227155741279, + 0.48237242548576953, + 0.4521578582310442, + 0.419071509621892, + 0.3833241714755735, + 0.3451435886683645, + 0.30477300818195213, + 0.262469629386547, + 0.21850296543388448, + 0.17315312619957213, + 0.1267090337142071, + 0.07946658145252557, + 0.0317267492077996, + -0.016206314438528102, + -0.06402722983539166, + -0.11143133182378737, + -0.1581166107464373, + -0.20378563653935677, + -0.2481474536470345, + -0.29091943468873466, + -0.3318290810663684, + -0.37061575904226435, + -0.4070323602264217, + -0.440846875894239, + -0.4718438751049436, + -0.4998258772035793, + -0.5246146099624407, + -0.5460521453463867, + -0.564001905666088, + -0.5783495337090888, + -0.5890036213050767, + -0.5958962916837214, + -0.5989836319148857, + -0.598245972676169, + -0.5936880135653809 + ], + "type": "scatter" + } + ], + "layout": { + "template": { + "data": { + "histogram2dcontour": [ + { + "type": "histogram2dcontour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "choropleth": [ + { + "type": "choropleth", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "histogram2d": [ + { + "type": "histogram2d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmap": [ + { + "type": "heatmap", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "heatmapgl": [ + { + "type": "heatmapgl", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "contourcarpet": [ + { + "type": "contourcarpet", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "contour": [ + { + "type": "contour", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "surface": [ + { + "type": "surface", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + }, + "colorscale": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ] + } + ], + "mesh3d": [ + { + "type": "mesh3d", + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + ], + "scatter": [ + { + "fillpattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + }, + "type": "scatter" + } + ], + "parcoords": [ + { + "type": "parcoords", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolargl": [ + { + "type": "scatterpolargl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "bar": [ + { + "error_x": { + "color": "#2a3f5f" + }, + "error_y": { + "color": "#2a3f5f" + }, + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "bar" + } + ], + "scattergeo": [ + { + "type": "scattergeo", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterpolar": [ + { + "type": "scatterpolar", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "histogram": [ + { + "marker": { + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "histogram" + } + ], + "scattergl": [ + { + "type": "scattergl", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatter3d": [ + { + "type": "scatter3d", + "line": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattermapbox": [ + { + "type": "scattermapbox", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scatterternary": [ + { + "type": "scatterternary", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "scattercarpet": [ + { + "type": "scattercarpet", + "marker": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + } + } + ], + "carpet": [ + { + "aaxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "baxis": { + "endlinecolor": "#2a3f5f", + "gridcolor": "white", + "linecolor": "white", + "minorgridcolor": "white", + "startlinecolor": "#2a3f5f" + }, + "type": "carpet" + } + ], + "table": [ + { + "cells": { + "fill": { + "color": "#EBF0F8" + }, + "line": { + "color": "white" + } + }, + "header": { + "fill": { + "color": "#C8D4E3" + }, + "line": { + "color": "white" + } + }, + "type": "table" + } + ], + "barpolar": [ + { + "marker": { + "line": { + "color": "#E5ECF6", + "width": 0.5 + }, + "pattern": { + "fillmode": "overlay", + "size": 10, + "solidity": 0.2 + } + }, + "type": "barpolar" + } + ], + "pie": [ + { + "automargin": true, + "type": "pie" + } + ] + }, + "layout": { + "autotypenumbers": "strict", + "colorway": [ + "#636efa", + "#EF553B", + "#00cc96", + "#ab63fa", + "#FFA15A", + "#19d3f3", + "#FF6692", + "#B6E880", + "#FF97FF", + "#FECB52" + ], + "font": { + "color": "#2a3f5f" + }, + "hovermode": "closest", + "hoverlabel": { + "align": "left" + }, + "paper_bgcolor": "white", + "plot_bgcolor": "#E5ECF6", + "polar": { + "bgcolor": "#E5ECF6", + "angularaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "radialaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "ternary": { + "bgcolor": "#E5ECF6", + "aaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "baxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + }, + "caxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "" + } + }, + "coloraxis": { + "colorbar": { + "outlinewidth": 0, + "ticks": "" + } + }, + "colorscale": { + "sequential": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "sequentialminus": [ + [ + 0.0, + "#0d0887" + ], + [ + 0.1111111111111111, + "#46039f" + ], + [ + 0.2222222222222222, + "#7201a8" + ], + [ + 0.3333333333333333, + "#9c179e" + ], + [ + 0.4444444444444444, + "#bd3786" + ], + [ + 0.5555555555555556, + "#d8576b" + ], + [ + 0.6666666666666666, + "#ed7953" + ], + [ + 0.7777777777777778, + "#fb9f3a" + ], + [ + 0.8888888888888888, + "#fdca26" + ], + [ + 1.0, + "#f0f921" + ] + ], + "diverging": [ + [ + 0, + "#8e0152" + ], + [ + 0.1, + "#c51b7d" + ], + [ + 0.2, + "#de77ae" + ], + [ + 0.3, + "#f1b6da" + ], + [ + 0.4, + "#fde0ef" + ], + [ + 0.5, + "#f7f7f7" + ], + [ + 0.6, + "#e6f5d0" + ], + [ + 0.7, + "#b8e186" + ], + [ + 0.8, + "#7fbc41" + ], + [ + 0.9, + "#4d9221" + ], + [ + 1, + "#276419" + ] + ] + }, + "xaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "yaxis": { + "gridcolor": "white", + "linecolor": "white", + "ticks": "", + "title": { + "standoff": 15 + }, + "zerolinecolor": "white", + "automargin": true, + "zerolinewidth": 2 + }, + "scene": { + "xaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "yaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + }, + "zaxis": { + "backgroundcolor": "#E5ECF6", + "gridcolor": "white", + "linecolor": "white", + "showbackground": true, + "ticks": "", + "zerolinecolor": "white", + "gridwidth": 2 + } + }, + "shapedefaults": { + "line": { + "color": "#2a3f5f" + } + }, + "annotationdefaults": { + "arrowcolor": "#2a3f5f", + "arrowhead": 0, + "arrowwidth": 1 + }, + "geo": { + "bgcolor": "white", + "landcolor": "#E5ECF6", + "subunitcolor": "white", + "showland": true, + "showlakes": true, + "lakecolor": "white" + }, + "title": { + "x": 0.05 + }, + "mapbox": { + "style": "light" + } + } + }, + "font": { + "family": "Courier New, monospace", + "size": 12, + "color": "Black" + }, + "title": { + "text": "Time Rabi" + }, + "xaxis": { + "title": { + "text": "Time (µs)" + } + }, + "yaxis": { + "title": { + "text": "" + } + }, + "legend": { + "title": { + "text": "Legend" + } + }, + "plot_bgcolor": "white" + }, + "config": { + "plotlyServerURL": "https://plot.ly" + } + }, + "text/html": [ + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "3da369a5-bb9c-4981-b122-dcd034ea56fc", + "record_entry_path": "/root/9-NormalisedRabi.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691592, + "print_time": "2024-12-08 15:59:52" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "NormalisedRabi" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
fitting: 'The fitting result of the Rabi oscillation suggest the amplitude of 0.6005610396184801, the frequency of 31.767127595333758, the phase of -2.6907780232415544. The offset of 0.0013967221773712873. The suggested new driving amplitude is 0.5489810209703299.From the fitting results, the plot should exhibit 9.21246700264679 oscillations.'
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('122a97eb-cf34-4e74-90df-eaebe0cdc1d2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('122a97eb-cf34-4e74-90df-eaebe0cdc1d2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points do not show a clear, regular oscillatory pattern. Instead, they appear scattered without a discernible periodic structure.\\n\\n2. Amplitude and Frequency: There are inconsistencies in both the amplitude and frequency of the data points. The amplitude does not show a consistent peak-to-peak variation, and the frequency of oscillations is not evident.\\n\\n3. Overall Pattern: The plot lacks the typical characteristics of successful Rabi oscillation experiments, which usually display a smooth sinusoidal pattern with consistent amplitude and frequency. Based on these observations, the experiment appears to be unsuccessful in demonstrating clear Rabi oscillations.'
success: False
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('4e3de177-77fa-4c3b-aada-4e0be72e346e');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('4e3de177-77fa-4c3b-aada-4e0be72e346e');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
analysis: 'The experiment aimed to observe Rabi oscillations but was unsuccessful. The fitting results suggested an amplitude of 0.6006 and a frequency of 31.7671, with a phase of -2.6908. However, the plot analysis indicated that the data points were scattered without a clear oscillatory pattern. The amplitude and frequency were inconsistent, and the plot did not exhibit the smooth sinusoidal pattern typical of successful Rabi oscillations. Furthermore, the experiment did not meet the criteria for success, as it did not show a clear oscillatory pattern with amplitude greater than 0.2, and the number of oscillations was not within the acceptable range of 3 to 10.'
success: False
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('9c43d116-f590-4a80-853e-535af5b5f820');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('9c43d116-f590-4a80-853e-535af5b5f820');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Transitioning to the next stage Stage2 with the following description:
Run Pingpong experiment.
The experiment in Stage1 was conducted once and was unsuccessful. The analysis of the experiment indicated that the data did not exhibit the expected Rabi oscillations, with inconsistent amplitude and frequency, and a lack of a clear oscillatory pattern. According to the rule of transition, Stage1 is allowed up to 3 retries before moving to the 'Failed' stage. Since this was the first attempt and there are 2 more retries available, the experiment should proceed to Stage2 for further attempts.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage2: Stage2...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage2

\n", + "

Description: Run Pingpong experiment.

\n", + "

Next Steps: If Stage2 fails, goto Failed. Otherwise, goto Complete.

\n", + " \n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('231ca6c1-babe-4184-9f87-9f155fce61b6');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('231ca6c1-babe-4184-9f87-9f155fce61b6');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for Run Pingpong experiment.:
\n", + "

\n", + " 
experiment_pingpong = AmpPingpongCalibrationSingleQubitMultilevel(dut=dut, iteration=9, points=10, mprim_index=0, collection_name='f01', repeated_gate='X', initial_lpb=None, flip_other=False)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "bab8d971-c290-44b5-a107-1c140abf7267", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/0-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.552+/-0.004\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "5b153bc4-b287-4f3c-84eb-abaf7f932615", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/1-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.5539+/-0.0015\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "d5b1a72f-a853-4231-bbcd-97c60bd55219", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/2-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.5548+/-0.0007\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "7c5b8657-6a6d-4133-9cb4-e182e2ef4afb", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/3-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.5563+/-0.0006\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "f2b04cc9-8470-4292-81b6-af263beac6b7", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/4-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.5566+/-0.0005\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "edd4bc87-9c94-47dc-947b-6456c4a9bbff", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/5-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.55685+/-0.00021\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "5377792c-a999-47d8-be0c-c6b0d5abf219", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/6-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.55694+/-0.00013\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "99b61bc8-3acd-4c1a-a2f1-ff0bd4110dce", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/7-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:14" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.55693+/-0.00006\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "d3ecd48a-35cc-44cb-bd33-8d0090c7f203", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/8-PingPongSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:15" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "PingPongSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Estimated best amplitude 0.556959+/-0.000033\n" + ] + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "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" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "931192ff-9a74-4059-ae72-eb38593b1c88", + "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691614, + "print_time": "2024-12-08 16:00:15" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "AmpPingpongCalibrationSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('f76c9811-1e53-48f2-ba7e-968e00d9bfdf');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('f76c9811-1e53-48f2-ba7e-968e00d9bfdf');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: 'The plot shows the amplitude values over several iterations. The amplitude appears to increase initially and then stabilize around a certain value, indicating convergence. The values become more consistent after the fourth iteration, suggesting that the amplitude has converged.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('ec2d80d7-d322-437b-a5ad-4543a8585c83');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('ec2d80d7-d322-437b-a5ad-4543a8585c83');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
analysis: 'The experiment results show that the amplitude values increase initially and then stabilize around a certain value, indicating convergence. The consistency of the values after the fourth iteration suggests that the amplitude has successfully converged, meeting the criteria for a successful experiment.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('a16d30f4-11fe-4ada-b149-22fb93600ed5');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a16d30f4-11fe-4ada-b149-22fb93600ed5');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The experiment is complete.
The experiment results indicate that the amplitude values initially increase and then stabilize, showing convergence. The consistency after the fourth iteration confirms the success of the experiment. According to the rule of transition, since Stage2 did not fail, we proceed to the next stage, which is Complete.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('c1859aff-d173-4d29-9b6c-e28310dda2e2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c1859aff-d173-4d29-9b6c-e28310dda2e2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: 'The plot shows the amplitude values over several iterations. The amplitude appears to increase initially and then stabilize around the 6th iteration, indicating convergence. The values level off towards the end, suggesting that convergence is achieved.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('b92152ee-b711-4a21-b693-12133242107c');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('b92152ee-b711-4a21-b693-12133242107c');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
results: 'The experiment succeeded in calibrating the qubit gate amplitude after initial challenges, with amplitude values stabilizing and meeting the success criteria.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('14dc3513-5b21-4588-aa25-57e5003a6379');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('14dc3513-5b21-4588-aa25-57e5003a6379');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Transitioning to the next stage Stage3 with the following description:
DRAG Calibration on `dut`
The experiment report indicates that the experiment succeeded in calibrating the qubit gate amplitude, with the amplitude values stabilizing and meeting the success criteria. There was 1 successful attempt and 0 failed attempts in Stage2. According to the rule of transition, if Stage2 completes successfully, the experiment should proceed to Stage3.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Executing Stage3: Stage3...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "
\n", + "
\n", + "

Stage3

\n", + "

Description: DRAG Calibration on `dut`

\n", + "

Next Steps: If Stage3 completes successfully, goto Complete. If Stage3 fails, goto Failed.

\n", + " \n", + "
\n", + "
" + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "Recalling: 0%| | 0/3 [00:00" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('c4c72e56-ff08-4f07-8d0e-dacc14880e07');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c4c72e56-ff08-4f07-8d0e-dacc14880e07');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution agent: Here is the generated code for DRAG Calibration on `dut`:
\n", + "

\n", + " 
experiment_drag_calibration = DragCalibrationSingleQubitMultilevel(dut, collection_name='f01', mprim_index=0, initial_lpb=None, N=1, inv_alpha_start=None, inv_alpha_stop=None, num=21)\n",
+       "
\n", + "\n", + "
\n", + "\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "
" + ], + "image/png": "iVBORw0KGgoAAAANSUhEUgAAAkIAAAGxCAYAAACOSdkqAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAABdeklEQVR4nO3deXhU1f0/8HcmIQtZgWwsCauQRBBIQkJwQwiLuBBKlVoU5EHUb0Wt0Vbyq7LY2ri1UtSKWje0KlIFNwRpABeILCGRJSGogNlIIASSECDbnN8fp7PczCRMkpm5c2fer+eZJ+TeM5MzF8j9zPmczzleQggBIiIiIg+kU7sDRERERGphIEREREQei4EQEREReSwGQkREROSxGAgRERGRx2IgRERERB6LgRARERF5LAZCRERE5LF81O6Aq9Pr9aioqEBwcDC8vLzU7g4RERHZQAiB+vp69OvXDzpd++M+DIQuoaKiAjExMWp3g4iIiLqgtLQUAwYMaPe85gKhl156Cc8++ywqKysxevRovPDCC0hJSbnk8z744APcdtttmDlzJjZs2GDzzwsODgYgL2RISEhXu01EREROVFdXh5iYGON9vD2aCoTWrl2LzMxMrF69GqmpqVi5ciWmTZuG4uJiREZGtvu848eP45FHHsHVV1/d6Z9pSIeFhIQwECIiItKYS01r0dRk6b///e9YtGgRFixYgISEBKxevRo9e/bEG2+80e5zWltbMXfuXKxYsQJDhgxxYm+JiIjI1WkmEGpqakJeXh7S09ONx3Q6HdLT05Gbm9vu85544glERkZi4cKFNv2cxsZG1NXVKR5ERETknjQTCFVXV6O1tRVRUVGK41FRUaisrLT6nO+++w6vv/46XnvtNZt/TnZ2NkJDQ40PTpQmIiJyX5oJhDqrvr4ed9xxB1577TWEh4fb/LysrCzU1tYaH6WlpQ7sJREREalJM5Olw8PD4e3tjaqqKsXxqqoqREdHW7T/+eefcfz4cdx0003GY3q9HgDg4+OD4uJiDB061OJ5fn5+8PPzs3PviYiIyBVpZkTI19cXSUlJyMnJMR7T6/XIyclBWlqaRfu4uDgcOHAABQUFxsfNN9+M6667DgUFBUx5ERERkXZGhAAgMzMT8+fPR3JyMlJSUrBy5Uo0NDRgwYIFAIB58+ahf//+yM7Ohr+/P0aOHKl4flhYGABYHCciIiLPpKlAaM6cOTh16hSWLl2KyspKjBkzBps2bTJOoC4pKelwGW0iIiIic15CCKF2J1xZXV0dQkNDUVtbywUViYiINMLW+7emRoSIiIjIufR6oKQEqK8HgoOB2FjAnZIvDISIiIjIqqIiYP164PBh4OJFwN8fiIsDZs0C4uPV7p19MBAiIiIiC0VFwKpVQHU1EBMDBAYCDQ1Afj5QWgo88IB7BENuNLhFRERE9qDXy5Gg6mogIQEICQG8veXXhAR5fMMG2U7rGAgRERGRQkmJTIfFxABtN2/38gIGDJAjRiUl6vTPnhgIERERkUJ9vZwTFBho/XxgoDxfX+/cfjkCAyEiIiJSCA6WE6MbGqyfb2iQ54ODndsvR2AgRERERAqxsbI6rLQUaLvaoBBAWZmcKB0bq07/7ImBEBERESnodLJEPjwcKCwEamuBlhb5tbBQHs/IcI/1hNzgLRAREZG9xcfLEvmxY4HTp4EjR+TXxET3KZ0HuI4QERERtSM+HhgxgitLExERkYfS6YBBg9TuheO4UUxHRERE1DkMhIiIiMhjMRAiIiIij8VAiIiIiDwWAyEiIiLyWAyEiIiIyGMxECIiIiKPxUCIiIiIPBYDISIiIvJYDISIiIjIYzEQIiIiIo/FQIiIiIg8FgMhIiIi8lgMhIiIiMhj+ajdASIiIuoevR4oKQHq64HgYCA2FtBxqMMmDISIiIg0rKgIWL8eOHwYuHgR8PcH4uKAWbOA+Hi1e+f6GAgRERFpVFERsGoVUF0NxMQAgYFAQwOQnw+UlgIPPMBg6FI4cEZERKRBer0cCaquBhISgJAQwNtbfk1IkMc3bJDtqH0MhIiIiDSopESmw2JiAC8v5TkvL2DAADliVFKiTv+0QnOB0EsvvYRBgwbB398fqamp2L17d7ttP/74YyQnJyMsLAyBgYEYM2YM3nnnHSf2loiIyDHq6+WcoMBA6+cDA+X5+nrn9ktrNBUIrV27FpmZmVi2bBn27duH0aNHY9q0aTh58qTV9r1798af/vQn5ObmYv/+/ViwYAEWLFiAzZs3O7nnRERE9hUcLCdGNzRYP9/QIM8HBzu3X1rjJYQQanfCVqmpqRg3bhxefPFFAIBer0dMTAzuv/9+LFmyxKbXSExMxA033IA///nPNrWvq6tDaGgoamtrERIS0uW+ExER2ZNeDzz1lJwYnZCgTI8JARQWAomJwKOPemYpva33b81cmqamJuTl5SE9Pd14TKfTIT09Hbm5uZd8vhACOTk5KC4uxjXXXNNuu8bGRtTV1SkeRERErkankyXy4eEy6KmtBVpa5NfCQnk8I8Mzg6DO0Mzlqa6uRmtrK6KiohTHo6KiUFlZ2e7zamtrERQUBF9fX9xwww144YUXMGXKlHbbZ2dnIzQ01PiIiYmx23sgIiKyp/h4WSI/dixw+jRw5Ij8mpjI0nlbuf06QsHBwSgoKMC5c+eQk5ODzMxMDBkyBBMnTrTaPisrC5mZmcbv6+rqGAwREZHLio8HRozgytJdpZlAKDw8HN7e3qiqqlIcr6qqQnR0dLvP0+l0GDZsGABgzJgxKCoqQnZ2druBkJ+fH/z8/OzWbyIiIkfT6YBBg9TuhTZpJl709fVFUlIScnJyjMf0ej1ycnKQlpZm8+vo9Xo0NjY6ootERESkMZoZEQKAzMxMzJ8/H8nJyUhJScHKlSvR0NCABQsWAADmzZuH/v37Izs7G4Cc75OcnIyhQ4eisbERGzduxDvvvIOXX35ZzbdBRERELkJTgdCcOXNw6tQpLF26FJWVlRgzZgw2bdpknEBdUlICnVlStKGhAb/73e9QVlaGgIAAxMXF4d1338WcOXPUegtERETkQjS1jpAauI4QERGR9rjdOkJERERE9sZAiIiIiDwWAyEiIiLyWAyEiIiIyGMxECIiIiKPxUCIiIiIPBYDISIiIvJYDISIiIjIYzEQIiIiIo/FQIiIiIg8lqb2GiPyBHo9UFIC1NcDwcFAbCyg40cWIiKHYCBE5EKKioD164HDh4GLFwF/fyAuDpg1C4iPV7t3RET2U1cH7N4N7NwJzJ0LDB2qTj8YCBG5iKIiYNUqoLoaiIkBAgOBhgYgPx8oLQUeeIDBEBFpkxDA0aMy6DE8DhyQxwEgOpqBEJFH0+vlSFB1NZCQAHh5yeMhIfL7wkJgwwZgxAimyYjI9V24AOTlAbm5psDn5EnLdoMHAxMmqBcEAQyEiFxCSYlMh8XEmIIgAy8vYMAAOWJUUgIMGqRKF4mI2lVRoRzt2bcPaG5WtvH1BZKSZOAzYQKQlgb07atOf80xECJyAfX1ck5QYKD184GBQHm5bEdEpKaWFuCHH5SjPb/8YtkuKgq48kpT4JOYCPj5Ob+/l8JAiMgFBAfLidENDTId1lZDgzwfHOz8vhGRZzt9Gvj+e1PQs3s3cP68so1OB1xxhSnomTBBjl63HeF2RQyEiFxAbKysDsvPV84RAuRkwrIy+WkqNla9PhKR+9PrZZp+507TiM/hw5btwsJkassQ9Iwbp90PagyEiFyATidL5EtL5cToAQNMVWNlZUB4OJCRwYnSROa45lb3nTtnKmE3BD9nz1q2GzFCOdoTF+c+15qBEJGLiI+XJfKGdYTKy2U6LDFRBkEsnScy4ZpbnScEcPy4clLz/v0yoDTXsyeQkmIKesaPB/r0UaXLTsFAiMiFxMfLT178lEvUPq65ZZuLF2X1lnmaq7LSst3AgcpKriuuAHr0cH5/1cJAiMjF6HQskSdqj9bX3HJkOu/ECWUlV14e0NSkbNOjhxxlNg98+ve3z8/XKgZCRESkGVpec8ue6byWFrkys/ncnmPHLNtFRirn9iQlyZ9LJgyEiIhIM7S65lZ303lnzihL2Hftks83p9MBI0fKtXsMFV1DhmijhF1NDISIiEgztLjmVmfTeXo9cOSIclJzUZHl64aEKEvYU1KsXxPqGAMhIiLSDC2uuXWpdF5UFLBtm1y48PBhmeaqqbF8neHDlWmu+HjXnAelNQyEiIhIM7S45pZ5Ok8IoLZW9r+0VPa5slIe37LF9JyAALlIoXkJe0SEeu/BnTEQIiIiTdHSmluNjcBPPwFHj8pRrKoq6/OXevYEJk0CpkyRgc/o0Z5Vwq4mBkJERBrBlZRNXHXNraoqZQn73r0yGDKn08ld1wcMkI8LF4CrrgIefVT9/nsiBkJERBrAlZQtqb3mVmsrcOiQclLzzz9btgsPB0aNkhuV+vvLP4eGmtJ5sbGul87zJAyEiIhcHFdSdg1nz8qydfMS9rZpLi8v4PLLlZOahw2Tx82D2aoq103neRrNBUIvvfQSnn32WVRWVmL06NF44YUXkJKSYrXta6+9hjVr1uDgwYMAgKSkJPz1r39ttz0RkavhSsrqEELO7TEf7Tl0SB43FxwsJzIbgp7UVDnaY42rpvM8naYCobVr1yIzMxOrV69GamoqVq5ciWnTpqG4uBiRkZEW7bdv347bbrsNEyZMgL+/P55++mlMnToVhw4dQn9PX1OciDSBKyk7x/nzcj6P+UrN1dWW7YYOVY72XH454O1t+89RO51HlryEaBvfuq7U1FSMGzcOL774IgBAr9cjJiYG999/P5YsWXLJ57e2tqJXr1548cUXMW/ePJt+Zl1dHUJDQ1FbW4sQrlRFRE524ADw5z/LkQRrN9yWFrn43uOPy7knrqK9dF5pqZwzo3Y6r7RUGfTk58trac7PT1nCnpYmt6wgbbD1/q2ZEaGmpibk5eUhKyvLeEyn0yE9PR25ubk2vcb58+fR3NyM3r17t9umsbERjWZT/Ovq6rreaSKibvKElZQdrbkZKChQprnKyizb9eunHO0ZM0YGQ+TeNBMIVVdXo7W1FVFRUYrjUVFROHz4sE2v8eijj6Jfv35IT09vt012djZWrFjRrb4SEdmLO66k7Oh03qlTyhL2PXtkas6ct7cMdMxHe2JjuS+XJ9JMINRdTz31FD744ANs374d/h1svZuVlYXMzEzj93V1dYiJiXFGF4mILGh9JWVr7LkxamurvC6GFNfOncCPP1q2693bFPBMmCBTXu31jzyLZgKh8PBweHt7o6qqSnG8qqoK0dHRHT73ueeew1NPPYX//ve/uOKKKzps6+fnBz+OhRKRC9HSSsqAY9N5dXXKEvbvv5fH2kpIUKa5hg/naA9Zp5lAyNfXF0lJScjJyUFGRgYAOVk6JycHixcvbvd5zzzzDJ588kls3rwZycnJTuotEZF9aan02l7pPCHkAoXmc3sOHrQsYQ8KkmXr5iXsvXrZ/32Re9JMIAQAmZmZmD9/PpKTk5GSkoKVK1eioaEBCxYsAADMmzcP/fv3R3Z2NgDg6aefxtKlS/Hee+9h0KBBqKysBAAEBQUhKChItfdB5G60ulaM1mil9Lqr6bwLF4C8PGXgc+qU5esPGWJKcU2YAIwcCfho6m5GrkRT/3TmzJmDU6dOYenSpaisrMSYMWOwadMm4wTqkpIS6Mz+Z7388stoamrCr3/9a8XrLFu2DMuXL3dm10klvEE7npbWiiHnsSWdV16uLGHft09WeJnz9QWSk5WTmi8xG4KoUzS1jpAauI6QdvEG7XiuvlYMqc/wYaSmRgY+x46ZJjWXlFi2j44GrrzSNOKTmMgSduoat1tHiKgzuDeT47naWjHkWk6fNgU8ubnA7t1y9WZzOh0werRyUvPAgZzUTM7FQIjcDm/QzqH2WjHkOvR6+W/BfG5PcbFlu7AwZYorJUVOdCZSEwMhcju8QTuHM9eKIddSXy9HeMxL2M+etWwXF6cMfOLi+OGDXA8DIXI7vEE7hxa3fqDOEwI4flw52rN/vxwFMtezp6mEPS1N7sjep48qXSbqFAZC5HZ4g3YOLW79QJd28aKs3jKv5vrfyiMKAwcq5/ZccQVL2Emb+M+W3A5v0M6hxa0fyNKJE8p9ufLygKYmZZsePYCkJGWaq18/dfpLZG8MhMjt8AbtPFrb+sHTtbQABw4o01zHj1u2i4xUjvYkJcm/VyJ3xHWELoHrCGmXtXWE4uN5g3YELlzpmmpq5ERmQ4pr1y75gcCcTgeMGmUa6bnySmDwYJawk/ZxHSHyeFram0nrtLL1gzvT62XJunmaq6jIsl1oqAx4DAsWpqRYn0tH5CkYCJFb4w2a3NW5c8CePcpJzWfOWLYbPlyZ5oqPt/5hgKN65KkYCBERuTghgF9+MQU8O3cCP/wAtLYq2wUEyBEeQ9AzfrycE3cp3I6GPBkDISIiF9PYKKsezSc1nzhh2S4mRjnaM3q0rPDqDG5HQ56OgRARkcoqK5X7cu3dK4Mhcz4+shrPMKl5wgRZEdkd3I6G1OQq6VgGQkRETtTaChw8qBztOXrUsl1EhDLoSU6WqS974nY0pBZXSscyECIicqCzZ00l7Dt3yhL2c+eUbby8gJEjlWmuoUMdX8LO7WhIDa6WjmUgRERkJ0IAR44oS9gPHbJsFxysLGFPTZVl7c7G7WjI2VwxHctAiIhclqvMIWjP+fOWJeynT1u2GzZMOdqTkAB4ezu/v21xOxpyNldMxzIQIiKX5EpzCAAZGJSWKoOeggK5bYU5f385n8ewSnNamtyywhVxOxpyNldMxzIQIiKX4wpzCJqaZKBjPqm5vNyyXb9+MuAxTGweOxbw9XVs3+yJ+8WRM7liOpaBEBG5FLXmEJw8qZzbs3ev/ORqztsbGDNGBj2G0R5rQ/xaw+1oyFlcMR3LQIioK1x98oqGOWMOQWurnMRsHvj89JNluz59TBOaDSXs7Q3pax23oyFncMV0LAMhos5ytckrbsYRcwhqa2XZuiHo+f57689PSFBOah4+XPujPUSuxtXSsQyEiDrDFSavuLnuziEQQo7umI/2HDwoj5sLCpJl64agJzUV6NXL/u+HiCy5UjqWgRCRrVxxAQw31Nk5BBcuyPk85pOaq6stX3fIEGWaa+RIuW0FEanDVdKx/DVAZCtXXADDDV1qDoG/P+DnBzz8sAx69u2zLGH39ZXzeQxBT1oaEB2tzvshItfGQIjIVq64AIabMswh+M9/5HyeigrgzBm5XUVtLfDuu8r20dHKEvbERBksERFdCgMhIlu54gIYbqa6Wrlg4Z49MvVlTqcDRo9WTmoeOJCTmomoaxgIEdnKFRfA0DC9XmYSzef2HDli2a5XL9PcnrQ0ICVFTnQmIrIHBkJEtnLFBTA0pK4O2L1bWcJeW2vZLi5OOdrDuedE5EgMhIg6w9UWwHBRQgDHjilHew4ckKNA5nr2VJawjx8P9O6tTp+JyDMxECLqLFdaAMNFXLwI5OUpA5+TJy3bDRqkLGG/4gqWsJPr4cLxnkVzv4JeeuklPPvss6isrMTo0aPxwgsvICUlxWrbQ4cOYenSpcjLy8Mvv/yC559/Hr///e+d22FyT66yAIZKKipME5p37pRBUHOzsk2PHkBSkrKEvV8/dfpLZCsuHO95NBUIrV27FpmZmVi9ejVSU1OxcuVKTJs2DcXFxYiMjLRof/78eQwZMgS33HILHnroIRV6TKR9LS3A/v3K0Z5ffrFsFxmpnNuTlCRvIkRawYXjPZOXEG0XnnddqampGDduHF588UUAgF6vR0xMDO6//34sWbKkw+cOGjQIv//97zs9IlRXV4fQ0FDU1tYixFrJNJGbqamRE5kNQc+uXcD588o2Oh0wapQy8Bk8mCXspF16PfDUU+0XhRYWyqmAjz7KNJlW2Hr/1syIUFNTE/Ly8pCVlWU8ptPpkJ6ejtzcXLv9nMbGRjQ2Nhq/r6urs9trE7kavR4oLlau3VNUZNkuNNSyhJ2fC8idcOF4z6WZQKi6uhqtra2IiopSHI+KisLhw4ft9nOys7OxYsUKu70ekStpaFCWsOfmyhWb2xo+XDnaEx/PT8Hk3rhwvOfSTCDkLFlZWcjMzDR+X1dXh5iYGBV7RNQ1Qsi5POZze/bvB1pble0CAuQIj3kJe3i4On0mUgsXjvdcmgmEwsPD4e3tjaqqKsXxqqoqRNtxN0U/Pz/4cZMi0qDGRjm/wTzwOXHCsl1MjHK0Z/RoWeFF5Mm4cLzn0kwg5Ovri6SkJOTk5CAjIwOAnCydk5ODxYsXq9s5IhVUVprK1w0l7GbT2wDINXoSE5Ul7AMGqNNfIlfGheM9l2YCIQDIzMzE/PnzkZycjJSUFKxcuRINDQ1YsGABAGDevHno378/srOzAcgJ1oWFhcY/l5eXo6CgAEFBQRg2bJhq74Oos1pb5crM5oHP0aOW7SIilEFPcrJMfRHRpXHheM+kqUBozpw5OHXqFJYuXYrKykqMGTMGmzZtMk6gLikpgc4sXK+oqMDYsWON3z/33HN47rnncO2112L79u3O7j6Rzc6etSxhP3dO2cbLCxg5UpnmGjqUJexE3cGF4z2PptYRUgPXESJHE0Luum4+t+d/A5kKISFyIrMh6ElJkWXtRERkye3WESJyF+fPA3v2KEvYT5+2bHfZZaYU14QJcgKnt7fz+0tE5M4YCBE5kBBy8qX5aE9BgWUJu78/MG6csoTdyq4xRERkZwyEiOyoqclUwm6Y2Fxebtmuf3/gyitNIz5jxgC+vk7vLhGRx2MgRNQNJ08qK7n27pWr05rz9gbGjlVOauYanUREroGBkAr0elYkaFFrK3DokDLN9fPPlu369FHO7UlObn/ZfiIiUhcDIScrKjKtUXHxopwbEhcnF/LiGhWupbZWlq0bgp7vv7e+z9DllytHey67zDVL2BmAExFZYiDkREVFwKpVQHW1TI0YVi3Nz5cTah94gMGQWoQAfvpJOdpz6JA8bi4oSE5kNoz2pKYCvXqp0+fOYABORGQdAyEn0evljai6WrmPTUiI/L6wENiwQS7kxU/pjnf+vJzPYz6pubrast2QIcrRnpEjtVfCzgCciKh9DIScpKREfhqPibFMm3h5yX1tiopku0GDVOmiWysrU4725OcDLS3KNn5+cj6PYX5PWhpgx/18VcEAnIioYwyEnKS+XqYk2ps0Gxgoy6ytzUGhzmlulmv1mI/2lJZatuvbVznaM3asDIbcCQNwIqKOMRBykuBgOS+joUF+Gm+roUGeDw52ft+0rrpaWcK+Zw9w4YKyjbc3cMUVprV7JkyQk4VdcVKzPTEAJyLqGAMhJ4mNlZNT8/OVKQpATsgtK5M7HMfGqtdHLdDrZTrHfHuKI0cs2/XqZZrQPGGCXLU5KMj5/VUbA/D2sYqOiAAGQk6j08kKndJSeSMfMMA0abWsDAgPBzIy+Iu4rbo6YPduZQl7ba1lu/h4ZZpr+HBeS4ABeHtYRUdEBgyEnCg+XlboGH4Bl5fLX8CJiTII8vRfwEIAR48qJzUfOGBZwh4YKMvWDUFPairQu7c6fXZ1DMAtsYqOiMwxEHKy+HhZocMheTmPJy9POb/n5EnLdoMHK9Nco0YBPvyXazMG4CasoiOitng7UYFO55kVOhUVytGefftkhZc5X18gKckU9KSlyeou6h4G4BKr6IioLQZC5BDNzcD+/cpJzb/8YtkuKkq5C3tSkvuVsLsKTw3AzbGKjojaYiBEdnH6tJzIbAh8du+Wqzeb0+lkCbv5pOZBg9y/hJ1cB6voiKgtBkLUaXq9TC+Yp7mKiy3bhYVZlrDzBkNqYhUdEbXFQIguqb5ejvAYJjXn5gJnz1q2GzFCOdoTF+d5c1DItbGKjojaYiBECkIAx48rR3v275ejQOZ69gRSUkxBz/jxQJ8+qnSZqFMcXUXHhRqJtIWBkIe7eFFWb5mXsFdWWraLjVVWco0eDfTo4fz+EtmDo6rouFAjkfYwEPIwJ04og568PKCpSdmmRw/56dg88OnfX53+EjmKvavouFAjkTYxEHJjLS3AwYPKNNexY5btIiJkCbthYnNSEhAQ4Pz+EmkVF2ok0i4GQm7kzBllCfuuXfITqTkvL7kys/mk5iFDWMJO1B1cqJFIuxgIaZQQsmTdUMW1c6f81NlWSIhppCctTe7LZW39FCLqOi7USKRdDIQ0oqEB2LNHuVJzTY1lu8sukwGPYbXm+HjA29v5/SXyJFyokUi7GAi5ICHkELp50FNQALS2Ktv5+1uWsEdEqNJlIo/GhRqJtIuBkAtoapK/QM0nNVdUWLaLiVGu1Dx6tNyklIjUxYUaibSLgZBKcnKAzZtl0LN3L9DYqDzv4wOMHassYY+JufTrcjE3InU4eqFGInIMBkIqeftt4J13TN+Hh5sCngkTgORkuXpzZ3AxNyJ1OWqhRiJyHM3993zppZcwaNAg+Pv7IzU1Fbt37+6w/bp16xAXFwd/f3+MGjUKGzdudFJPO3bzzcDddwNvvQUcOQKcPAl88gmwZAlwzTVdC4JWrZIptvBw+cs4PFx+v2qVPE9EjmdYqHHUKPmVQRCRa9PUf9G1a9ciMzMTy5Ytw759+zB69GhMmzYNJ0+etNp+586duO2227Bw4ULk5+cjIyMDGRkZOHjwoJN7bunXvwZeeQWYP19WenVnHZ+2i7mFhMhKMcNibtXVcjG3tvuFEREReTovIYRQuxO2Sk1Nxbhx4/Diiy8CAPR6PWJiYnD//fdjyZIlFu3nzJmDhoYGfP7558Zj48ePx5gxY7B69WqbfmZdXR1CQ0NRW1uLEHstwGPniTzHjwNLl8oRIGtdrK0FTp8GnniCi7kREZFnsPX+rZk5Qk1NTcjLy0NWVpbxmE6nQ3p6OnJzc60+Jzc3F5mZmYpj06ZNw4YNG9r9OY2NjWg0m7lcV1fXvY635YCJPFzMjYiIqGs0kxqrrq5Ga2sroqKiFMejoqJQaW27dACVlZWdag8A2dnZCA0NNT5ibCnVspWDJvKYL+ZmDRdzIyIisq7bgVBrayv279+PlpYWe/RHdVlZWaitrTU+SktL7fPCDpzIY1jMrbRULt5mzrCYW3x89xdz0+tlGu7AAfnVXnOOHPW6REREl9Lt1Nhnn32G2bNnY82aNZg7d649+mRVeHg4vL29UVVVpTheVVWF6Ohoq8+Jjo7uVHsA8PPzg5+fX/c73JYDd2V0xmJujirNZ8k/ERGpqdsjQm+//TYiIiLw1ltv2aE77fP19UVSUhJycnKMx/R6PXJycpCWlmb1OWlpaYr2ALBly5Z22zuULRN5Ll7s8kQew2JuY8fKidFHjsiviYnyeHeDFUeU5rPkn4iI1NatEaHq6mp8+eWX2LBhA26++WaUlZVhwIAB9uqbhczMTMyfPx/JyclISUnBypUr0dDQgAULFgAA5s2bh/79+yM7OxsA8OCDD+Laa6/F3/72N9xwww344IMPsHfvXrz66qsO62O7nLAroyMWc2ub0TMMZhkyeoWFMqM3YkTnfo6jXpeIiKgzunWLef/99zFy5EhMnz4dV199Nd4xXyrZAebMmYPnnnsOS5cuxZgxY1BQUIBNmzYZJ0SXlJTgxIkTxvYTJkzAe++9h1dffRWjR4/Gf/7zH2zYsAEjR450aD+tctJEHnsv5taZjJ4rvC4REVFndGtE6K233sL8+fMBALfffjueeeYZRXm7IyxevBiLFy+2em779u0Wx2655RbccsstDu2TTTS6K6OjSvNZ8k9ERK6gy3fdgwcP4uDBg/jtb38LQAYcJSUl2LVrl90653YcOZHHQRxVms+SfyIicgVdHhF6++23MXXqVISHhwMAgoKCkJGRgbfeegupqal266Db0diujIaMXn6+ci4PYMroJSZ2PqPnqNclIiLqjC7dfVtbW/Huu+9i3rx5iuO333471q5di6amJrt0zm1paFdGQ0YvPFxm9GprgZYW+bWwsOsZPUe9LhERUWd06TZz8uRJ/N///R9mzpypOD5t2jRkZmZ2uHIzaY+jMnoazBQSEZGb0dSmq2pwyKarGmXnvWJNr9uiR8n3FaivOo/gqJ6IHd8POh8OBRERUdc5fNPVNWvWIDk5GQkJCYrjFy9exIcffmiRNiPtM2T07KqoCLr16zHIfGnpb7i0NBEROUeXR4R0Oh0CAwPx1ltvYfbs2cbjVVVV6NevH1pbW+3WSTVxRMiBDEtLV1fLBYUMywmUlspJQsyPERFRF9l6/+5W/mHFihW44447sHz58u68DHkiB25CS2QT7vZLROjmgoq33347JkyYgFmzZuHgwYMOX1ma3IgDN6EluiTu9ktE/9PlESGv/928xo8fj127duGnn37ChAkTcPz4cXv1jdyZgzehJWoXd/slIjNdDoTMpxbFxsZi586dGDRoEKZMmWKXjpGb49LSpAamZImojS4HQsuWLUNQUJDx+549e2L9+vV46KGHcM0119ilc26toABYtw6oqbHv62pl3oOTNqElUuBuv0TURpfmCDU0NOBXv/oVevbsaXHu1ltvxSOPPNLtjrm9N94AXnhB/vIdNw6YOlU+xo8HevTo2mtqad6DRjehNXDUmkrkYNztl4ja6FIg1NzcjNTUVGzfvh0pKSnG44WFhRg7dixKSkoQzJRGx2JjgcsvBw4dAnbvlo+//AUICgImTQKmTJGB0WWXWX5ytaa9UvT8fBlsuGIpumFpaUPwVl4ug7fERBkEuVp//0dL8Sa1YZ6StVZOy5Qskcfp8jpCt956KyIjI/Hiiy8aj2VlZaGgoABffvml3TqoNoevI1ReDmzZAnz1FfDf/wKnTinPDxxoGi2aNAno3dvyNfR64Kmn2t/BtLBQBhePPuqawxYaGl7h0kcap/X/K0RkM4evIzR//nysXbsWLS0tAOTk6X//+99YsGBBV1/SM/XvD9x5J/Dee0BlJbBvn/xFPWkS4OsL/PIL8NprwC23ABERMnW2dCnw3XdAc7N8Da3Pe9DIJrScZ+sGuNsvEbXR5f/t06dPh4+PD7744gsAwPbt23Hu3DlkZGTYq2+eR6eTO5A++iiQkyMnUm/cCPz+9/JOq9cDu3YBf/4zcPXVQJ8+wMyZMlA6dQqwMmcLAEvR7UTr8Sb9D3f7JSIzXV5Q0dvbG3PnzsWaNWswc+ZMvPPOO5gzZw58fX3t2T/PFhgIXH+9fAByEvGWLaZHdTXw6afyAQA7dwLDhgFDhwKDBwMBAfI45z3YBefZupH4eLl+kEZSskTkON1aWXr+/PlISUlBeXk5PvroI2zevNle/SJrBgwAFiyQD71eluB/9ZV8fPMNUFcnU2v79skhin79gCFDZP5m8mSWoncT59m6GYfsIkxEWtPlydIGSUlJCA4ORmVlJQ4fPmyvfrkMzWy6mpcHPP448OOPMqXWdn2iwEAgPd008XroUNuq0ciI82yJiLTD1vt3t0aEAGDevHl46KGH8Je//KW7L0XdkZQE/O1vprpuQzDU0CCH/8+eBT75RD4AmTozlOhPmgT06qVq97VA40sfERGRFd0eEaqpqcELL7yAe+65B9HR0fbql8vQzIiQgbVSdEAOYxjSaDt2mCrOAHnnTkkxjRalpHR9UUcPYG0dofh4l176iIjI49h6/+52IOTuNBcI2eLcOeDrr03rF7XdZDIkRI4SmafRSEFDSx8REXkkBkJ24paBUFulpaagaMsWy/lFgwcrF3UMC1Olm0RERLZiIGQnHhEImWttVabRdu60TKOlpirTaD7dnmpGRERkVwyE7MTjAqG2DGm0r74CNm8GiouV55lGIyIiF8RAyE48PhBqq6REuTda2zTakCGmoOi665hGIyIiVTAQshMGQh1obZWLNxoCox075L5NBt7eltVoTKMReRZWFpBKGAjZCQOhTqivN6XRvvrKehpt8mRTYDRkiDr9JCLnsLbWRFycXJCLa02QgzEQshMGQt3QmTTapElAaKg6/SQi+ysqAlatknsixsSYVh8tLZWrj3KDW3IwBkJ2wkDITgxpNPNqtLZpNPNqtHHjmEYj0iruR0MugIGQnTAQcpD6emD7dtOIUds0WmioshqNaTQi7Th+HFi6VI78WPu9WVsLnD4NPPEEN74lh7H1/q2ZULympgZz585FSEgIwsLCsHDhQpw7d67D57z66quYOHEiQkJC4OXlhbNnzzqns3RpwcHATTfJofPDh+UvztdeA265Re57Vlsr5xb83//Jkvxhw4Df/Q7YsEGeIyLXVV8v5wQFBlo/Hxgoz9fXO7dfRFZoJhCaO3cuDh06hC1btuDzzz/HN998g7vvvrvD55w/fx7Tp0/H//t//89JvaQuGzgQuOsu4MMPgVOngF27gD//Gbj6apki+/ln4OWX5STLPn2AK68EVqwAcnOVKTYiUl9wsJwY3dBg/XxDgzwfHOzcfhFZoYnUWFFRERISErBnzx4kJycDADZt2oQZM2agrKwM/fr16/D527dvx3XXXYczZ84grJPr2jA15gIMaTTD/KIjR5TnQ0OV1WiDB6vSTSL6H84RIhdg6/1bE7NRc3NzERYWZgyCACA9PR06nQ67du3CrFmz7PazGhsb0djYaPy+rq7Obq9NXWRIo910k/z++HFlNdrZs8DHH8sHINNo5os6MoAlci6dTo7elpbKoGfAAFPVWFmZnDuUkcEgiFyCJv4VVlZWIjIyUnHMx8cHvXv3RmVlpV1/VnZ2NkJDQ42PmJgYu74+2cGgQcCiRcC6dbI0t20a7aefgH/+U/6i7d0buOoqOSnz+++ZRiNylvh4WSI/dqycGH3kiPyamMjSeXIpqo4ILVmyBE8//XSHbYqKipzUGykrKwuZmZnG7+vq6hgMuTLD6tUpKcBjjwF1dco02o8/yhWvd+wAli2TW34Y0mhTpjCNRuRI8fHAiBFcWZpcmqqB0MMPP4w777yzwzZDhgxBdHQ0Tp48qTje0tKCmpoaREdH27VPfn5+8PPzs+trkhOFhAA33ywfgCmNtnkzkJMj02gffSQfANNoRI6m07FEnlyaqoFQREQEIiIiLtkuLS0NZ8+eRV5eHpKSkgAAW7duhV6vR2pqqqO7SVpmSKMtWiTTYnv3muYX5ebKNJohlebtDaSlmQKj5GR5jIiI3JYmqsYA4Prrr0dVVRVWr16N5uZmLFiwAMnJyXjvvfcAAOXl5Zg8eTLWrFmDlJQUAHJuUWVlJfbu3YtFixbhm2++QXBwMGJjY9G7d2+bfi6rxtyYIY22ebMMjH76SXk+LAxIT5cptKlT+amWiEhD3G5l6ZqaGixevBifffYZdDodZs+ejVWrViEoKAgAcPz4cQwePBjbtm3DxIkTAQDLly/HihUrLF7rzTffvGRKzoCBkAc5dsw0WmRIo5m77DLTaNHEiUyjERG5MLcLhNTCQMhDmafRNm+WFWetrabzPj7KNFpSEtNoREQuhIGQnTAQIgByW49t20wjRm3TaL16KRd1HDhQnX4SEREABkJ2w0DIjF7PMliDY8dMJfo5OZb7nw0frkyjcSsBIiKnYiBkJwyE/qeoSG6Ceviw3CzR3x+Ii5Orx3r6wmgtLcCePabRImtptAkTTGsXMY1GRORwDITshIEQZBC0apVcxTkmxrRUfmmpXCqfq8QqGdJohhGjn39Wnu/VS1ajGUaMYmPV6ScRkRtjIGQnHh8IcfPE7vv5ZzlatGWL9TTaiBHKNNr/KiGJ6BKYrqcOMBCyE48PhI4fB5YulSM/1t5/ba3cP+iJJ7jOji0MaTTDaNGuXco0Wo8eMo1mWLsoMZFpNDLhjd+E6Xq6BAZCduLxgdCBA3JD0xEjrN+QW1rkZoqPPw6MGuX8/mnd2bPKNNrRo8rzvXub0mhTpjCN5sl44zdhup5sYOv9W9UtNkgDgoPlL9yGBusjQg0N8jyroromLEzeyGbNkt8b0miGarSaGuDDD+UDkDc+Qxrt2muZRvMU7d348/Plzd+Tbvx6vQwIq6uV6fqQEPl9YSGwYYP88Oapo2XUKfxXQh2LjZU339JSOSfInBBAWZn8BeyqIxV6vUzvHTggv+r1aveoY0OHAvfeC3z8sUw57tgBLFsmF2/U6eRowKpVwI03ytGiiROBv/5VLv7o6u+NuqbtjT8kRI7OGm781dXyxu8pf/8lJfL/QUyMcs4iIL8fMEAGjiUl6vSPNIcjQtQxnU6OVpSWyk9aAwaYPo2Wlclh6IwM1/zkpfVUgqHsfsIEYPlyZRpt82a5ltHXX8vHn/4E9OmjTKPFxKj9DsgeOnPj94R5evX18v9zYKD184GBQHm5bEdkAwZCdGnx8XLo3RBUlJfLoCIxUQZBrhhUuGMqwVoazTC3aOtWOYK0dq18APL9GSZdM42mXbzxKzFdT3bGQIhsEx8vc+5aqFjxlDkEQ4cC//d/8tHcDOzebQqMdu+WwaAhIOzRA7jyStP8orFjtf3ePQlv/EqGdH17S3qUlckPaa6arieXw9+EZDudTg69jxolv7rqjdQT5xAYAp0VK4DcXBkEfvQRcM898u+quRnYvh34f/8PSE4GIiOB224D3nxT3jjIdWl9np69GdL14eHyQ01traxera2V37tyup5cEkeEyP0wlSBXr/7Vr+RDCOtptA8+kA9A3kjNq9Hau3bkfFqep+coWkzXk8viOkKX4PHrCGkRF4HsmLU0mnnFUY8ewFVXmQKjMWM86ybrqqxN/o+P9+wbPxeYpA5wQUU7YSCkQdwWpHPOnJGjRIZqtF9+UZ4PD1dWow0YoE4/iTd+ok5gIGQnDIQ0yrxqzFoqQYtVY84gBPDTT8o02rlzyjYJCabRomuuYRqNiFwSAyE7YSCkYUwldF9zs9wPzRAY7dmjTKP5+so0mqFMn2k0InIRDITshIGQxjGVYF81NaY02ldfWU+jGYKiKVOA/v3V6ScReTwGQnbCQIioHUIAP/5o2hvNWhrt8stNQRHTaETkRAyE7ISBEJGNmpuB779XptHMf70Y0miG+UWjR196dI4jekTURQyE7ISBEFEX1dQAOTmmwKjtApYREbIabdo0OWLUr5/yvNb3iiMiVTEQshMGQkR2YEijGYKibdvaT6NNnSpXvn7tNcu94kpLWfVHRDZhIGQnDISIHKCpSZlG27tXmUbz9gb69JHbuQwdCkRHy/WguA4UEdmIgZCdMBAicoLTp03VaBs3AhUVyvM9e8qAaMgQmVJrbPTclcGJyCYMhOyEgRCRk+3fL0d7hACOHZOP5mZlm5AQuR7Ub38LXH21DJSIiMzYev/mpqtErsbTK6VCQuSoT3g4MH480Noq5wYdPSo3j62oAOrqgDVr5MPPT1mNdsUVnnW9iKhbOCJ0CRwRIqdipdSl94rLz5eTp3v2lGsYlZYqnx8ZqVzUsW9f5/afiFwCU2N2wkCInMZ8fzRPr5Syda84IYAjR5TVaA0NytcaNcoUGDGNRuQxGAjZCQMhcopLjYJ4YqVUV/aKa2oCcnNNgVFenrIazc9PBkOGNNqoUZ5zPYk8DAMhO2EgRE5x/DiwdKkc7bD276y2VlZWeVqlVHfnS50+bVrUcfNmOaJkLirKNFqUns40GpEbsfX+rZmPQjU1NZg7dy5CQkIQFhaGhQsX4lzbBdnatL///vsxYsQIBAQEIDY2Fg888ABqa2ud2GsiG9XXy1GP9vbiCgyU5+vrndsvtel0MvAbNUp+7ezoTZ8+wK23Av/6lwyoioqAf/wDuOEGmSKrqgLefReYN0+ubH3FFcAjj8jA6cIFR7wjIsfQ6+UHqgMH5Fe9Xu0eaYZmqsbmzp2LEydOYMuWLWhubsaCBQtw991347333rPavqKiAhUVFXjuueeQkJCAX375Bffeey8qKirwn//8x8m9J7qE4GCZ+mlosD4i1NAgzwcHO79v7sLLS048j4uTc4waG01ptC1bZBrtwAH5+NvfZBrtmmuUaTTzlCWRq2CRRbdoIjVWVFSEhIQE7NmzB8nJyQCATZs2YcaMGSgrK0O/tnsUtWPdunW4/fbb0dDQAB8f22JApsbIKThHSH3V1cq90TpKo02ZIle7JlIbiyza5VapsdzcXISFhRmDIABIT0+HTqfDrl27bH4dw8XoKAhqbGxEXV2d4kHkcDqd/PQWHi6DntpaoKVFfi0slMczMhgEOVJ4ODBnDvD66zKNVlgIrFwJzJhhmUbr2xcYPRr4wx/kaBLTaKQGvV6OBFVXyw9QISFye5qQEPl9dTWwYQPTZJegid+qlZWViIyMVBzz8fFB7969UVlZadNrVFdX489//jPuvvvuDttlZ2cjNDTU+IiJielyv4k6JT5efnobO1ZO8j1yRH5NTPToT3Wq8PKS1/vBB4EvvgBqamRpflYWkJQk2+zfDzz3nBwh6t0bmDZNptT271dWqhE5SkmJTIfFxFimbb285NITRUWyHbVL1TlCS5YswdNPP91hm6Kiom7/nLq6Otxwww1ISEjA8uXLO2yblZWFzMxMxXMZDJHTxMcDI0Z49srSrsjPD5g4UT7++lfg1CllGq283PRnQKbNzKvRmEYjR7ClyKK83POKLDpJ1UDo4Ycfxp133tlhmyFDhiA6OhonT55UHG9paUFNTQ2iL/ELpr6+HtOnT0dwcDDWr1+PHj16dNjez88Pfn5+NvWfyCEMlVLkuiIigN/8Rj6EkJ+6t2yRgdD27UBlJfDOO/IByDSaYW7RVVcBAQGqdp/cBIss7EJTk6X37t2LpP8NS3/11VeYPn16h5Ol6+rqMG3aNPj5+WHjxo3o2YUVZTlZmog6pbER2LnTNEK0b5/yvL+/shpt5EhWo1HXsMiiQ263oOL111+PqqoqrF692lg+n5ycbCyfLy8vx+TJk7FmzRqkpKSgrq4OU6dOxfnz57F+/XoEmg0dRkREwNvb26afy0CIiLrl1Cngv/81BUYVFcrzffsq02hRUer0k7TJ1u1oPJDbBUI1NTVYvHgxPvvsM+h0OsyePRurVq1CUFAQAOD48eMYPHgwtm3bhokTJ2L79u247rrrrL7WsWPHMMjG1AMDISKyG0MazRAUbd9uWXE2ZowpMLrqKjmCRNSRrmxH4wHcLhBSCwMhInKYxkZgxw5TYJSfrzzv7w9ce60pjXb55UyjkXXd3Y7GDTEQshMGQkTkNCdPKqvROkqjTZkCtFlWhIhMGAjZCQMhIlKFYbKrISj6+mvraTTDaNGVVzKNRmSGgZCdMBAiIpdw8aKsRtu8WZbqt02jBQQo02htq4iIPAwDITthIERELunkSWU12okTyvP9+imr0ZhGIw/DQMhOGAgRkcsTAjh0yLSoo7U02tixyjQaF44lN8dAyE4YCBGR5ly8qKxGKyhQnmcajTwAAyE7YSBERJpXVaWsRrOWRjMERenpcgsRIo1jIGQnDISIyK0Y0mjm1WgXLyrbJCaaSvSZRiONYiBkJwyEiMitmafRNm8GfvhBeb5nT2UaLT6eaTTSBAZCdsJAiIg8SlWVshqtslJ5vn9/U1A0eTLTaOSyGAjZCQMhIvJYQgAHD8qAaMuWjtNoU6cCEyYwjUYug4GQnTAQIiL6n4sXge++M40WWUujTZxoCozi4phGI9UwELITBkJERO2orFSm0aqqlOcHDFAu6hgerk4/ySMxELITBkJE5BG6u3u5EMCBA6Y02jffKNNoXl6WaTRfX/u/D6L/YSBkJwyEiMjtFRUB69cDhw/L4MXfX6a1Zs2SVWJdceGCKY22ebMMkswFBso02pQp8sFqNLIzBkJ2wkCIiNxaURGwahVQXQ3ExMgApaEBKC2VqawHHuh6MGTuxAlTGm3LFqbRyOEYCNkJAyEiclt6PfDUU3In+7bbbAgBFBbKdNajj3YuTWbLzz1wwLQ32jffAI2NpvOGNNq0aTI4YhqNuoCBkJ0wECIit3X8OLB0qRx9sfb7rbYWOH0aeOIJYNAgx/XjwgXg229Nk647SqOxGo1sZOv928eJfSIiIldSXy/nBAUGWj8fGAiUl8t2jhQQYJpEDSjTaF99BZw8CXzxhXwAMo1mvqgj02jUDQyEiIg8VXCwnBjd0GB9RKihQZ4PDnZuv/r2Be64Qz4MaTRDUPTtt0BZGfDGG/Lh5QUkJZkCo7Q0ptGoU5gauwSmxojIbak1R6g7bE2jGQKjESOYRvNQnCNkJwyEiKhTursej7OZV40NGGCqGisrs2/VmKNUVCir0U6eVJ6PiVGm0fr0Uaef5HQMhOyEgRAR2cwR6/E4g7V+x8cDGRmu3e+29Hpg/35TNdq331pWoyUny6BoyhSm0dwcAyE7YSBERDZx1no8jqK1kSxbnD+vTKMdPKg8HxgIXHedacRo+HCm0dwIAyE7YSBERJekxbk2nqiiQo4WGUaMTp1Sno+NVabRevdWp59kFwyE7ISBEBFdkqusx0O2M6TRzKvRmppM583TaFOnAuPHM42mMbbev/nRhIiou2xZj+fiRcevx0O20+mAMWOAP/5RTrY+cwb48kvgoYeAyy+XI3l79gBPPglce62cZH3zzcCLLwJHjsjz5Ba4jhARUXe56no8ZLuePYHp0+UDkAtJmlejnToFfPaZfADAwIGmSddMo2kaU2OXwNQYEV0S5wi5N70e+OEHUxrtu+8s02jjxinTaD16qNdfAsA5QnbDQIiIbKL19XjIdg0Nymq0Q4eU54OCgEmTTIHRsGGsRlMBAyE7YSBERDZzl/V4qHPKy02VaFu2yGDYnCGNZqhG69VLnX56GLcLhGpqanD//ffjs88+g06nw+zZs/GPf/wDQUFB7T7nnnvuwX//+19UVFQgKCgIEyZMwNNPP424uDibfy4DISLqFHdcj4dsp9cDBQWm0aIdO5RpNJ1OmUZLTWUazUHcLhC6/vrrceLECbzyyitobm7GggULMG7cOLz33nvtPufVV19FXFwcYmNjUVNTg+XLl6OgoADHjh2Dt7e3TT+XgRAREXVZQwPwzTemwKiwUHk+OFim0aZMYRrNztwqECoqKkJCQgL27NmD5ORkAMCmTZswY8YMlJWVoV+/fja9zv79+zF69Gj89NNPGDp0qE3PYSBERER2U1amTKOdPq08P2iQabRo0iSm0brBrdYRys3NRVhYmDEIAoD09HTodDrs2rXLptdoaGjAm2++icGDByMmJqbddo2Njairq1M8iIiI7GLAAGDBAuD99+UGsXv3AtnZcquPHj3k4pyvvgr8+tdykn1aGrBsmUyxNTer3Xu3pIlAqLKyEpGRkYpjPj4+6N27NyorKzt87j//+U8EBQUhKCgIX375JbZs2QLfDlYHzc7ORmhoqPHRUdBERETUZTodkJQELFkCbN0K1NQAX3wBPPignFyv1wPffy9XJL/qKhkYZWQA//wn8NNPXNTRTlQNhJYsWQIvL68OH4cPH+7Wz5g7dy7y8/Px9ddfY/jw4bj11ltx8eLFdttnZWWhtrbW+CgtLe3WzyciIrJJUBAwYwawcqWcS1RSArz+OvCb38iVrevqgE8+Ae67D7jsMmDoUODee4GPPwbOnlW795ql6hyhU6dO4XTb/GgbQ4YMwbvvvouHH34YZ86cMR5vaWmBv78/1q1bh1mzZtn085qamtCrVy/861//wm233WbTczhHiIiIVKfXywU7zavRzFNlOp2sQDPML0pJAXw8e/MIW+/fql6liIgIREREXLJdWloazp49i7y8PCQlJQEAtm7dCr1ej9TUVJt/nhACQgg0NjZ2uc9EREROZ0ijJSUBWVnAuXPA11+bAqPDh4HcXPlYsUJu9WK+qKONBUKeSBNVY4Asn6+qqsLq1auN5fPJycnG8vny8nJMnjwZa9asQUpKCo4ePYq1a9di6tSpiIiIQFlZGZ566ins2LEDRUVFFnOO2sMRISIishtHrTNVWiqr0DZvlnuk1dQozw8ZYtobbdIkICys+z/TxblV+TwgF1RcvHixYkHFVatWGRdUPH78OAYPHoxt27Zh4sSJqKiowF133YW8vDycOXMGUVFRuOaaa7B06VKMGDHC5p/LQIiIiOzC2srjcXHArFn2XXm8tVWZRtu50yPTaG4XCKmFgRC5Da54TKQe873oYmJMe9GVljp+LzpraTRzISFy6w/Doo5ukkZjIGQnDITILTjrkygRWdLrgaeekqM0CQnKlaOFkBViiYnAo48658NJSYlpUceO0mhTp8r1jTSaRmMgZCcMhEjz1PwkSkRykcSlS+X/N2v3kdpaucL0E0/IlaWdqbUV2LdPmUZraTGd9/aWaTTDaJGG0mhutbI0EXWRXi9Hgqqr5SfRkBD5iy0kRH5fXQ1s2CDbEZFj1NfLkdjAQOvnAwPl+fp65/YLkL8Pxo0D/vQnmT6rqQE++wy4/35gxAgZKO3cKSvRrrxSBnO/+hWwejVw9Kjz++sA2gjriKhrSkpkOiwmxnIjRy8vudx/UZFs5+xPokSeIjhYpqMbGqyPCDU0yPPBwc7vW1vBwcCNN8oHAPzyizKNduaM/HC1fr08P3SoMo0WGqpe37uII0JE7syVP4kSeYrYWDknr7TUclsMIeRGrPHxsp2rGTgQuOsu4MMPgVOngN27gb/8BbjmGpki+/ln4OWX5XzDPn3kqNGKFXI9I/MUmwtjIETkzsw/iVrjSp9EidyVTicDhfBwOTG6tlYGCbW18nvDHmKuXsVpLY326afA4sXA8OGmNNry5cCECfJ9zZ4NvPIKcOyY2r1vFydLXwInS5OmuVq1CpEns1a9GR8vgyB3KFiwlkYzN2yYMo3m4Hsqq8bshIEQaZ551diAAaaqsbIyVo0ROZunrOfV2grk5Zmq0dqmyry9gfHjTYFRcrLdq9EYCNkJAyFyC+7+SZSIXFt9PbB9uykwOnJEef7xx+XyAXakiU1XichJ4uNlKawnfBIlItcTHAzcdJN8AHJtJfM02nXXqdY1jghdAkeEiIiIHKi1VX719rbry3JEiIiIiFyfnQOgzuK4OBEREXksBkJERETksRgIERERkcdiIEREREQei4EQEREReSwGQkREROSxWD5PRERkzlO2wSAADISIiIhMrG1HExcnd4/ndjRuiYEQERERoNygOCbGtEFxfj5QWsoNit0Ux/qIiIj0ejkSVF0NJCQAISFyxeOQEPl9dTWwYYNsR26FgRAREVFJiUyHxcQAXl7Kc15ewIABcsSopESd/pHDMBAiIiKqr5dzggIDrZ8PDJTn6+ud2y9yOM4RIiIiCg6WE6MbGmQ6rK2GBnk+ONj5fXNXLlKdx0CIiIgoNlZWh+XnyzlB5ukxIYCyMiAxUbaj7nOh6jwGQkRERDqdvAmXlgKFhXJOkKFqrKwMCA8HMjK4npA9uFh1Hv9GiYiIAHnzfeABYOxY4PRp4MgR+TUxkaXz9uKC1XkcESIiIjKIjwdGjHCJuStuqTPVeYMGOaVLDISIiIjM6XROuwl7HFuq88rLnVqdxxCXiIiInMO8Os8aFarzNBMI1dTUYO7cuQgJCUFYWBgWLlyIc+fO2fRcIQSuv/56eHl5YcOGDY7tKBEREVlnqM4rLZXVeOYM1Xnx8U6tztNMIDR37lwcOnQIW7Zsweeff45vvvkGd999t03PXblyJbza5iKJiIjIuQzVeeHhsjqvthZoaZFfCwtVqc7zEqJtSOZ6ioqKkJCQgD179iA5ORkAsGnTJsyYMQNlZWXo169fu88tKCjAjTfeiL1796Jv375Yv349MjIybP7ZdXV1CA0NRW1tLUKsLbJFREREnWNtHaH4eBkE2ak6z9b7tyYmS+fm5iIsLMwYBAFAeno6dDoddu3ahVmzZll93vnz5/Hb3/4WL730EqKjo236WY2NjWhsbDR+X1dX173OExERkZILVedpIjVWWVmJyMhIxTEfHx/07t0blZWV7T7voYcewoQJEzBz5kybf1Z2djZCQ0ONj5iYmC73m4iIiNphqM4bNUp+VWmJAlUDoSVLlsDLy6vDx+HDh7v02p9++im2bt2KlStXdup5WVlZqK2tNT5KS0u79POJiIjI9amaGnv44Ydx5513dthmyJAhiI6OxsmTJxXHW1paUFNT027Ka+vWrfj5558RFhamOD579mxcffXV2L59u9Xn+fn5wc/Pz9a3QERERBqmaiAUERGBiIiIS7ZLS0vD2bNnkZeXh6SkJAAy0NHr9UhNTbX6nCVLluCuu+5SHBs1ahSef/553HTTTd3vPBEREWmeJiZLx8fHY/r06Vi0aBFWr16N5uZmLF68GL/5zW+MFWPl5eWYPHky1qxZg5SUFERHR1sdLYqNjcXgwYOd/RaIiIi0Sa93iUnNjqKJQAgA/v3vf2Px4sWYPHkydDodZs+ejVWrVhnPNzc3o7i4GOfPn1exl0RERG7EWpl7XJxcC8hNNqHVxDpCauI6QkQ2cPNPjEQeqagIWLVK7ggfEyP3AWtokKtCh4cDDzzg0sGQW60jREQuzAM+MRJ5HL1e/r+urgYSEkw7xYeEyO8LC4ENG+RaQBr/0KPt3hORugyfGPPz5SfEESPk1/x8ebyoSO0eElFXlJTIDzcxMaYgyMDLCxgwQP7/LilRp392xECIiLqm7SfGkBDA29v0ibG6Wn5i1OvV7ikRdVZ9vRzhDQy0fj4wUJ6vr3duvxyAgRARdY0HfWIk8jjBwTLN3dBg/XxDgzwfHOzcfjkAAyEi6hoP+sRI5HFiY+Vcv9JSoG1NlRBAWZmcAxgbq07/7IiBEBF1jQd9YiTyODqdLHgID5cTo2trgZYW+bWwUB7PyND8RGmAgRARdZUHfWIk8kjx8bJEfuxY4PRp4MgR+TUx0eVL5zuD5fNE1DWGT4ylpfIT4oABpnVGysrc6hMjkceKj5fVoG68ThgDISLqOsMnRsM6QuXlMh2WmCiDIDf5xEjk0XQ6YNAgtXvhMAyEiKh7POATIxG5LwZCRNR9bv6JkYjcFz+yERERkcdiIEREREQei4EQEREReSwGQkREROSxGAgRERGRx2IgRERERB6LgRARERF5LAZCRERE5LEYCBEREZHH4srSlyD+t6t2XV2dyj0hIiIiWxnu24b7eHsYCF1CfX09ACAmJkblnhAREVFn1dfXIzQ0tN3zXuJSoZKH0+v1qKioQHBwMLy8vOz2unV1dYiJiUFpaSlCQkLs9rruitfLdrxWtuO1sh2vle14rWznyGslhEB9fT369esHXQebQHNE6BJ0Oh0GDBjgsNcPCQnhf5RO4PWyHa+V7XitbMdrZTteK9s56lp1NBJkwMnSRERE5LEYCBEREZHHYiCkEj8/Pyxbtgx+fn5qd0UTeL1sx2tlO14r2/Fa2Y7XynaucK04WZqIiIg8FkeEiIiIyGMxECIiIiKPxUCIiIiIPBYDISIiIvJYDITspKamBnPnzkVISAjCwsKwcOFCnDt3rsPnXLx4Effddx/69OmDoKAgzJ49G1VVVYo2JSUluOGGG9CzZ09ERkbiD3/4A1paWqy+3o4dO+Dj44MxY8bY6205jFrX6+OPP8aUKVMQERGBkJAQpKWlYfPmzQ55j1310ksvYdCgQfD390dqaip2797dYft169YhLi4O/v7+GDVqFDZu3Kg4L4TA0qVL0bdvXwQEBCA9PR0//vijok1X/j5cgbOv1fHjx7Fw4UIMHjwYAQEBGDp0KJYtW4ampiaHvD97UuPflUFjYyPGjBkDLy8vFBQU2OstOYxa1+qLL75AamoqAgIC0KtXL2RkZNjzbTmEGtfqyJEjmDlzJsLDwxESEoKrrroK27Zt6/qbEGQX06dPF6NHjxbff/+9+Pbbb8WwYcPEbbfd1uFz7r33XhETEyNycnLE3r17xfjx48WECROM51taWsTIkSNFenq6yM/PFxs3bhTh4eEiKyvL4rXOnDkjhgwZIqZOnSpGjx5t77dnd2pdrwcffFA8/fTTYvfu3eLIkSMiKytL9OjRQ+zbt89h77UzPvjgA+Hr6yveeOMNcejQIbFo0SIRFhYmqqqqrLbfsWOH8Pb2Fs8884woLCwUjz32mOjRo4c4cOCAsc1TTz0lQkNDxYYNG8QPP/wgbr75ZjF48GBx4cIFY5uu/H2oTY1r9eWXX4o777xTbN68Wfz888/ik08+EZGRkeLhhx92ynvuKrX+XRk88MAD4vrrrxcARH5+vqPepl2oda3+85//iF69eomXX35ZFBcXi0OHDom1a9c6/P12h1rX6rLLLhMzZswQP/zwgzhy5Ij43e9+J3r27ClOnDjRpffBQMgOCgsLBQCxZ88e47Evv/xSeHl5ifLycqvPOXv2rOjRo4dYt26d8VhRUZEAIHJzc4UQQmzcuFHodDpRWVlpbPPyyy+LkJAQ0djYqHi9OXPmiMcee0wsW7bM5QMhV7he5hISEsSKFSu6+7bsIiUlRdx3333G71tbW0W/fv1Edna21fa33nqruOGGGxTHUlNTxT333COEEEKv14vo6Gjx7LPPGs+fPXtW+Pn5iffff18I0bW/D1egxrWy5plnnhGDBw/uzltxODWv1caNG0VcXJw4dOiQJgIhNa5Vc3Oz6N+/v/jXv/5l77fjUGpcq1OnTgkA4ptvvjG2qaurEwDEli1buvQ+mBqzg9zcXISFhSE5Odl4LD09HTqdDrt27bL6nLy8PDQ3NyM9Pd14LC4uDrGxscjNzTW+7qhRoxAVFWVsM23aNNTV1eHQoUPGY2+++SaOHj2KZcuW2futOYTa18ucXq9HfX09evfubY+31i1NTU3Iy8tTvEedTof09HTje2wrNzdX0R6Q79nQ/tixY6isrFS0CQ0NRWpqquK6dfbvQ21qXStramtrXeLfT3vUvFZVVVVYtGgR3nnnHfTs2dOeb8sh1LpW+/btQ3l5OXQ6HcaOHYu+ffvi+uuvx8GDB+39Fu1GrWvVp08fjBgxAmvWrEFDQwNaWlrwyiuvIDIyEklJSV16LwyE7KCyshKRkZGKYz4+PujduzcqKyvbfY6vry/CwsIUx6OioozPqaysVNzUDecN5wDgxx9/xJIlS/Duu+/Cx0cbe+iqeb3aeu6553Du3DnceuutXXkrdlVdXY3W1lar76Gj69JRe8PXS7Xp7N+H2tS6Vm399NNPeOGFF3DPPfd06X04g1rXSgiBO++8E/fee68iyHZlal2ro0ePAgCWL1+Oxx57DJ9//jl69eqFiRMnoqampvtvzAHUulZeXl7473//i/z8fAQHB8Pf3x9///vfsWnTJvTq1atL74WBUAeWLFkCLy+vDh+HDx9WrX+tra347W9/ixUrVmD48OGq9cPA1a9XW++99x5WrFiBDz/80CIQILqU8vJyTJ8+HbfccgsWLVqkdndczgsvvID6+npkZWWp3RWXp9frAQB/+tOfMHv2bCQlJeHNN9+El5cX1q1bp3LvXIsQAvfddx8iIyPx7bffYvfu3cjIyMBNN92EEydOdOk1tTGEoJKHH34Yd955Z4dthgwZgujoaJw8eVJxvKWlBTU1NYiOjrb6vOjoaDQ1NeHs2bOKUY6qqirjc6Kjoy1m4BuqpKKjo1FfX4+9e/ciPz8fixcvBiD/Qwkh4OPjg6+++gqTJk3qzFvuFle/XuY++OAD3HXXXVi3bp3FUK1awsPD4e3tbVEJZ/4e24qOju6wveFrVVUV+vbtq2hjqC7syt+H2tS6VgYVFRW47rrrMGHCBLz66qvdfTsOpda12rp1K3Jzcy32kEpOTsbcuXPx9ttvd+t9OYJa18pwPCEhwXjez88PQ4YMQUlJSffelIOo+e/q888/x5kzZxASEgIA+Oc//4ktW7bg7bffxpIlSzr9Xjgi1IGIiAjExcV1+PD19UVaWhrOnj2LvLw843O3bt0KvV6P1NRUq6+dlJSEHj16ICcnx3isuLgYJSUlSEtLAwCkpaXhwIEDipvUli1bEBISgoSEBISEhODAgQMoKCgwPu69916MGDECBQUF7f5sR3H162Xw/vvvY8GCBXj//fdxww032PsydJmvry+SkpIU71Gv1yMnJ8f4HttKS0tTtAfkeza0Hzx4MKKjoxVt6urqsGvXLsV16+zfh9rUulaAHAmaOHGi8VO7Tufav0bVularVq3CDz/8YPzdZCiTXrt2LZ588km7vkd7UetaJSUlwc/PD8XFxcY2zc3NOH78OAYOHGi392dPal2r8+fPA4DF/zudTmccWeu0Lk2xJgvTp08XY8eOFbt27RLfffeduOyyyxTlx2VlZWLEiBFi165dxmP33nuviI2NFVu3bhV79+4VaWlpIi0tzXjeUA4+depUUVBQIDZt2iQiIiKsls8baKFqTAj1rte///1v4ePjI1566SVx4sQJ4+Ps2bPOeeOX8MEHHwg/Pz/x1ltvicLCQnH33XeLsLAwYyXcHXfcIZYsWWJsv2PHDuHj4yOee+45UVRUJJYtW2a1HDUsLEx88sknYv/+/WLmzJlWy+c7+vtwRWpcq7KyMjFs2DAxefJkUVZWpvg35MrU+ndl7tixY5qoGlPrWj344IOif//+YvPmzeLw4cNi4cKFIjIyUtTU1DjvzXeSGtfq1KlTok+fPuJXv/qVKCgoEMXFxeKRRx4RPXr0EAUFBV16HwyE7OT06dPitttuE0FBQSIkJEQsWLBA1NfXG88bfgls27bNeOzChQvid7/7nejVq5fo2bOnmDVrlsUv1OPHj4vrr79eBAQEiPDwcPHwww+L5ubmdvuhlUBIret17bXXCgAWj/nz5zv6LdvshRdeELGxscLX11ekpKSI77//3nju2muvtejrhx9+KIYPHy58fX3F5ZdfLr744gvFeb1eLx5//HERFRUl/Pz8xOTJk0VxcbGizaX+PlyVs6/Vm2++afXfjxY+U6rx78qcVgIhIdS5Vk1NTeLhhx8WkZGRIjg4WKSnp4uDBw867D3aixrXas+ePWLq1Kmid+/eIjg4WIwfP15s3Lixy+/BSwghujaWRERERKRtrp3cJiIiInIgBkJERETksRgIERERkcdiIEREREQei4EQEREReSwGQkREROSxGAgRERGRx2IgRERERB6LgRARkY3Onz+P2bNnIyQkBF5eXjh79qzVY4MGDcLKlSttes233npLsZEwETkXAyEisurOO++El5cXvLy80KNHD0RFRWHKlCl44403LDY3HDRokLFtz549MWrUKPzrX/+y+rrvv/8+vL29cd9991k9X1dXh8cffxyXX345AgIC0KdPH4wbNw7PPPMMzpw5Y/f32Rlvv/02vv32W+zcuRMnTpxAaGio1WN79uzB3XffbdNrzpkzB0eOHLFrP7dv324MyoioYwyEiKhd06dPx4kTJ3D8+HF8+eWXuO666/Dggw/ixhtvREtLi6LtE088gRMnTuDgwYO4/fbbsWjRInz55ZcWr/n666/jj3/8I95//31cvHhRca6mpgbjx4/Hm2++iUceeQS7du3Cvn378OSTTyI/Px/vvfeeQ9/vpfz888+Ij4/HyJEjER0dDS8vL6vHIiIi0LNnT5teMyAgAJGRkQ7uORG1q8u7lBGRW5s/f76YOXOmxfGcnBwBQLz22mvGYwMHDhTPP/+8ol3v3r3FQw89pDh29OhRERAQIM6ePStSU1PFv//9b8X5e+65RwQGBory8nKrfdLr9R32+dNPPxXJycnCz89P9OnTR2RkZBjP1dTUiDvuuEOEhYWJgIAAMX36dHHkyBHF87/99ltx1VVXCX9/fzFgwABx//33i3PnzgkhLDfsvfbaa60es3Y9zpw5I+6++24RGRkp/Pz8xOWXXy4+++wzIYTcyDU0NFTRjw0bNoixY8cKPz8/MXjwYLF8+XLF5sGG65+RkSECAgLEsGHDxCeffCKEMG1uChfdVJjI1XBEiIg6ZdKkSRg9ejQ+/vhjq+f1ej0++ugjnDlzBr6+vopzb775Jm644QaEhobi9ttvx+uvv6543tq1a3H77bejX79+Vl/by8ur3X598cUXmDVrFmbMmIH8/Hzk5OQgJSXFeP7OO+/E3r178emnnyI3NxdCCMyYMQPNzc0A5GjP9OnTMXv2bOzfvx9r167Fd999h8WLFwMAPv74YyxatAhpaWk4ceIEPv74Y6vHrF2P66+/Hjt27MC7776LwsJCPPXUU/D29rb6Pr799lvMmzcPDz74IAoLC/HKK6/grbfewpNPPqlot2LFCtx6663Yv38/ZsyYgblz56KmpgYxMTH46KOPAADFxcU4ceIE/vGPf7R73Yg8ntqRGBG5pvZGhIQQYs6cOSI+Pt74/cCBA4Wvr68IDAwUPj4+AoDo3bu3+PHHH41tWltbRUxMjNiwYYMQQohTp04JX19fcfToUSGEEJWVlQKA+Pvf/674WYmJiSIwMFAEBgaK3/zmN+32Ny0tTcydO9fquSNHjggAYseOHcZj1dXVIiAgQHz44YdCCCEWLlwo7r77bsXzvv32W6HT6cSFCxeEEEI8+OCDxlEfA2vHzEeENm/eLHQ6nSguLrbat7YjQpMnTxZ//etfFW3eeecd0bdvX+P3AMRjjz1m/P7cuXMCgPjyyy+FEEJs27ZNABBnzpyx+jOJyIQjQkTUaUIIi9GZP/zhDygoKMDWrVuRmpqK559/HsOGDTOe37JlCxoaGjBjxgwAQHh4uHHydUfWr1+PgoICTJs2DRcuXGi3XUFBASZPnmz1XFFREXx8fJCammo81qdPH4wYMQJFRUUAgB9++AFvvfUWgoKCjI9p06ZBr9fj2LFjHV+QDhQUFGDAgAEYPny4Te1/+OEHPPHEE4p+LFq0CCdOnMD58+eN7a644grjnwMDAxESEoKTJ092uZ9EnspH7Q4QkfYUFRVh8ODBimPh4eEYNmwYhg0bhnXr1mHUqFFITk5GQkICADlJuqamBgEBAcbn6PV67N+/HytWrEBERATCwsJQXFyseN3Y2FgAQHBwcIdVUOav2xXnzp3DPffcgwceeMDinKEPXdHZfp07dw4rVqzAr371K4tz/v7+xj/36NFDcc7Ly8uimo+ILo0jQkTUKVu3bsWBAwcwe/bsdtvExMRgzpw5yMrKAgCcPn0an3zyCT744AMUFBQYH/n5+Thz5gy++uor6HQ63HrrrXj33XdRUVHR6X5dccUVyMnJsXouPj4eLS0t2LVrl/HY6dOnUVxcbAzUEhMTUVhYaAzmzB9t5zp1tl9lZWU2l8gnJiaiuLjYaj90Ott+ZRv629ra2uV+E3kKjggRUbsaGxtRWVmJ1tZWVFVVYdOmTcjOzsaNN96IefPmdfjcBx98ECNHjsTevXvx3XffoU+fPrj11lstUmozZszA66+/junTp+Ovf/0rtm/fjpSUFDzxxBNITk5GYGAg9u/fj9zcXIwcObLdn7ds2TJMnjwZQ4cOxW9+8xu0tLRg48aNePTRR3HZZZdh5syZWLRoEV555RUEBwdjyZIl6N+/P2bOnAkAePTRRzF+/HgsXrwYd911FwIDA1FYWIgtW7bgxRdf7PI1vPbaa3HNNddg9uzZ+Pvf/45hw4bh8OHD8PLywvTp0y3aL126FDfeeCNiY2Px61//GjqdDj/88AMOHjyIv/zlLzb9zIEDB8LLywuff/45ZsyYgYCAAAQFBXX5PRC5M44IEVG7Nm3ahL59+2LQoEGYPn06tm3bhlWrVuGTTz5pt+rJICEhAVOnTsXSpUvxxhtvYNasWVarvmbPno1PP/0U1dXV6NOnD3bv3o158+bh2WefRUpKCkaNGoXly5djzpw5eO2119r9eRMnTsS6devw6aefYsyYMZg0aRJ2795tPP/mm28iKSkJN954I9LS0iCEwMaNG40ppiuuuAJff/01jhw5gquvvhpjx47F0qVL261g64yPPvoI48aNw2233YaEhAT88Y9/bHe0Ztq0afj888/x1VdfYdy4cRg/fjyef/55DBw40Oaf179/f6xYsQJLlixBVFSUsfKNiCx5CSGE2p0gIiIiUgNHhIiIiMhjMRAiIiIij8VAiIiIiDwWAyEiIiLyWAyEiIiIyGMxECIiIiKPxUCIiIiIPBYDISIiIvJYDISIiIjIYzEQIiIiIo/FQIiIiIg81v8Hqgv3aQ1RFHIAAAAASUVORK5CYII=" + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/json": { + "record_id": "c4c70e40-628c-4a85-9971-229469b9cce0", + "record_entry_path": "/root/11-DragCalibrationSingleQubitMultilevel.run", + "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", + "record_time": 1733691646, + "print_time": "2024-12-08 16:00:46" + } + }, + "metadata": { + "application/json": { + "expanded": false, + "root": "DragCalibrationSingleQubitMultilevel" + } + }, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
fitting: 'Sweep start: -0.004\\nSweep stop: 0.008\\nThe fitting results are as follows: \\nThe estimated optimal DRAG coefficient is -0.005290111995956169\\nThe estimated optimal DRAG coefficient does not fall within the central half of the sweep.\\nResidual average for Xp: 0.013415791272201584\\nResidual average for Xm: 0.006936917638058088\\n'
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('7e29d7be-d38e-4cc8-9502-9ded90e173ff');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('7e29d7be-d38e-4cc8-9502-9ded90e173ff');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: '1. The blue trend line has a positive slope, while the red trend line has a negative slope, indicating distinct trends for each dataset. \\n2. The data points for both colors generally fit their respective trend lines well, though there are a few outliers, particularly in the blue dataset. \\n3. The distribution of data points along the DRAG coefficient axis is relatively even for both datasets, with no significant clustering. \\n4. The trend lines appear to accurately represent their datasets, capturing the overall direction of the data points. \\n5. The trends between the two datasets are clearly different, with the blue dataset showing an increasing trend and the red dataset showing a decreasing trend. \\n6. The fitting residuals seem to be within an acceptable range, with no large deviations from the trend lines. \\nThe trend lines intersect near the center of the plot, meeting the success criteria.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('c2f5c4d1-8abb-4742-bfac-f81508de2b20');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c2f5c4d1-8abb-4742-bfac-f81508de2b20');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
analysis: 'The experiment aimed to calibrate the DRAG coefficient using an AllXY DRAG experiment. The analysis of the results shows that the two trend lines (blue and red) exhibit distinct trends, with the blue line having a positive slope and the red line a negative slope. The fitting of the data points to these trend lines is appropriate, as indicated by the residuals being within an acceptable range. However, the predicted optimal DRAG coefficient (-0.005290111995956169) does not fall within the central half of the sweep range (-0.004 to 0.008). Despite meeting the first two success criteria, the third criterion is not met. Therefore, the experiment is considered unsuccessful. It is recommended to adjust the sweep range to center around the predicted optimal coefficient and repeat the experiment.'
success: False
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('ea169b56-efc1-48e2-ad6b-c990fcfb7643');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('ea169b56-efc1-48e2-ad6b-c990fcfb7643');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The experiment has failed.
The experiment aimed to calibrate the DRAG coefficient using an AllXY DRAG experiment. The analysis indicates that while the trend lines fit the data points well, the predicted optimal DRAG coefficient does not fall within the central half of the sweep range. This means the experiment did not meet all success criteria and is considered unsuccessful. According to the rule of transition, since Stage3 failed, the next stage is 'Failed'.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
fitting: 'Sweep start: -0.004\\nSweep stop: 0.008\\nThe fitting results are as follows: \\nThe estimated optimal DRAG coefficient is -0.005290111995956169\\nThe estimated optimal DRAG coefficient does not fall within the central half of the sweep.\\nResidual average for Xp: 0.013415791272201584\\nResidual average for Xm: 0.006936917638058088\\n'
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Inspection agent reading the plot...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('c72601e4-88a6-49a9-958d-a08f7b8be9ab');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c72601e4-88a6-49a9-958d-a08f7b8be9ab');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Inspection Agent:
analysis: '1. The slope of the blue trend line is positive, indicating an upward trend, while the slope of the red trend line is negative, indicating a downward trend. This shows distinct trends for each color.\\n2. The data points for both colors generally fit their respective trend lines well, although there are some outliers, particularly in the blue dataset where a few points deviate significantly from the line.\\n3. The distribution of data points along the DRAG coefficient axis is fairly even for both datasets, with points spread across the range from -0.004 to 0.008.\\n4. The trend lines appear to accurately represent their datasets, capturing the overall direction of the data points.\\n5. The trends between the two datasets are clearly different, with the blue dataset showing an increase and the red dataset showing a decrease.\\n6. The fitting residuals appear to be within an acceptable range, with most points close to their respective trend lines.'
success: True
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Analyzing experiment results...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('ea5d728c-411f-43d0-85b1-8da5705af3a0');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('ea5d728c-411f-43d0-85b1-8da5705af3a0');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: Experiment analysis results are as follows:
results: 'Frequency and amplitude calibrations succeeded, but DRAG coefficient calibration failed due to sweep range misalignment.'
success: False
\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "
\n", + " Considering the next stage...\n", + "
\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "application/javascript": "\n var spinnerElement = document.getElementById('e7c5d1a7-43d9-4187-a59c-08a6b6fc1625');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('e7c5d1a7-43d9-4187-a59c-08a6b6fc1625');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ], + "text/html": [ + "\n", + "

\n", + " Execution Agent: The experiment has failed.
The current stage, Stage1, was executed once and resulted in a failure. The experiment report indicates that while frequency and amplitude calibrations succeeded, the DRAG coefficient calibration failed due to sweep range misalignment. According to the rule of transition, if Stage1 fails, the next stage should be 'Failed'. Therefore, based on the experiment results and the rule of transition, the next stage is determined to be 'Failed'.\n", + "

\n", + " " + ] + }, + "metadata": {}, + "output_type": "display_data" + }, + { + "data": { + "text/plain": [ + "" + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "execution_count": 3 + }, + { + "cell_type": "code", + "id": "85cff806-1198-4265-b1b7-f9bd63d8100d", + "metadata": { + "ExecuteTime": { + "end_time": "2024-12-08T21:01:13.838633Z", + "start_time": "2024-12-08T21:01:13.835896Z" + } + }, + "source": [], "outputs": [], - "execution_count": null + "execution_count": 3 } ], "metadata": { diff --git a/notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb b/notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb index 2b6b6f7..fe5ff77 100644 --- a/notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb +++ b/notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb @@ -176,27 +176,21 @@ "source": [ "import os\n", "from k_agents.execution.agent import execute_procedure\n", - "from k_agents.translation.env import TranslationAgentEnv\n", "from leeq.utils.ai.translation_agent import init_leeq_translation_agents\n", "from leeq.experiments import experiments as exp\n", "from mllm import config, caching\n", "\n", - "#config.default_models.normal = \"chatgpt-4o-latest\"\n", - "#config.default_models.expensive = \"chatgpt-4o-latest\"\n", "config.default_models.normal = \"gpt-4o\"\n", "config.default_models.expensive = \"gpt-4o\"\n", "config.default_options.temperature = 0.2\n", "root = os.path.dirname(exp.__file__)\n", "init_leeq_translation_agents()\n", - "env = TranslationAgentEnv()\n", + "\n", "\n", "ExperimentManager().status().set_param(\"Plot_Result_In_Jupyter\", True)\n", "duts = [qubit_1, qubit_2]\n", "frequency1 = duts[0].get_c1('f01').get_parameters()[\"freq\"]\n", "\n", - "#AutoRun(\"\"\"\n", - "#- Run a Two-qubit calibration on `duts`\n", - "#\"\"\", frequency=frequency1, duts=duts)\n", "with caching.refresh_cache():\n", " execute_procedure(\n", " \"Two level Two-qubit calibration on `duts`\", duts=duts\n", @@ -204,30 +198,6 @@ ], "outputs": [], "execution_count": null - }, - { - "cell_type": "code", - "id": "6d4023c17026e7ba", - "metadata": {}, - "source": [], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "id": "53a496fcaf13871e", - "metadata": {}, - "source": [], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "id": "226c7e7d2663f966", - "metadata": {}, - "source": [], - "outputs": [], - "execution_count": null } ], "metadata": { @@ -246,7 +216,7 @@ "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", - "version": "3.10.14" + "version": "3.10.15" } }, "nbformat": 4, From f13ea77a37e1e742e0770753727061b6ee9d2bf3 Mon Sep 17 00:00:00 2001 From: Zijian Zhang Date: Mon, 9 Dec 2024 10:01:08 -0500 Subject: [PATCH 3/4] dev: update benchmark --- .../embedding_search_benchmarking.py | 31 +++++++++++-------- 1 file changed, 18 insertions(+), 13 deletions(-) diff --git a/benchmark/exp_recall/embedding_search_benchmarking.py b/benchmark/exp_recall/embedding_search_benchmarking.py index b8dc572..e4d7f5a 100644 --- a/benchmark/exp_recall/embedding_search_benchmarking.py +++ b/benchmark/exp_recall/embedding_search_benchmarking.py @@ -165,22 +165,24 @@ def check_code(codes, exp_class): from leeq.utils.ai.translation_agent import init_leeq_translation_agents -from k_agents.translation.agent import TranslationAgentGroup, get_codegen_wm +from k_agents.translation.agent import TranslationAgentGroup, get_codegen_wm, CodegenAgent from k_agents.variable_table import VariableTable class TransmonElementFake: def __repr__(self): return "TransmonElement" -def benchmark_single(key, exp_class, description, code_cog_model): + + +def benchmark_single(key, exp_class, description, code_gen_model): input_var_table = VariableTable() input_var_table.add_variable("dut", TransmonElementFake(), "device under test") print("Description:", description) codegen_wm = get_codegen_wm(description, input_var_table) - recall_res = code_cog_model.recall(codegen_wm) + recall_res = code_gen_model.recall(codegen_wm) additional_info = [] - codes = code_cog_model.codegen(codegen_wm, recall_res) + codes = code_gen_model.codegen(codegen_wm, recall_res) try: success = check_code(codes, exp_class) except Exception as e: @@ -198,12 +200,14 @@ def benchmark_all(rag, n_recall_items): env = TranslationAgentEnv() translation_agents = env.translation_agents if rag: - code_cog_model = TranslationAgentGroupRAG() + code_gen_model = TranslationAgentGroupRAG() else: - code_cog_model = TranslationAgentGroup() - code_cog_model.n_recall_items = n_recall_items - for idea in translation_agents.translation_agents: - code_cog_model.translation_agents.add_agent(idea) + code_gen_model = TranslationAgentGroup() + code_gen_model.codegen_agent = CodegenAgent() + code_gen_model.n_recall_items = n_recall_items + + for agent in translation_agents.translation_agents.agents: + code_gen_model.translation_agents.add_agent(agent) results_list = {} @@ -213,7 +217,7 @@ def benchmark_one_experiment(_exp_name): exp_prompts = experiment_prompt[_exp_name][1] def benchmark_one_prompt(_prompt): try: - success, additional_info = benchmark_single(_exp_name, exp_class, _prompt, code_cog_model) + success, additional_info = benchmark_single(_exp_name, exp_class, _prompt, code_gen_model) except Exception as e: success = False additional_info = str(e) @@ -224,7 +228,6 @@ def benchmark_one_prompt(_prompt): print(success, additional_info) results.append((prompt, success, additional_info)) return results - #t = ["RB1Q"] t = list(experiment_prompt.keys()) for exp_name, res in p_map(benchmark_one_experiment, t, n_workers=4): results_list[exp_name] = res @@ -267,6 +270,7 @@ def main(model, shots, rag, n_recall_items): } results[i] = result except Exception as e: + print(str(e)) result = { 'status': 'error', 'error': str(e) @@ -281,18 +285,19 @@ def entry(model, rag): from mllm.config import default_options default_parallel_map_config["n_workers"] = 3 default_options.timeout = 120 + default_options.temperature = 0.2 # You have to enable this option before using the `correct_json_by_model` rule parse_options.correct_json_by_model = True n_recall_items = 2 - shots = 3 + shots = 5 main(model, shots, rag, n_recall_items) if __name__ == '__main__': models = [ - "gpt-4o-2024-08-06", "gpt-4o-mini", + "gpt-4o-2024-08-06", "replicate/meta/meta-llama-3-70b-instruct", "claude-3-opus-20240229", "gemini/gemini-1.5-pro-latest", From 6d29a24500dfd70d0b62a19f22de7175446f5b40 Mon Sep 17 00:00:00 2001 From: Zijian Zhang Date: Mon, 9 Dec 2024 10:36:53 -0500 Subject: [PATCH 4/4] dev: update notebooks --- .../embedding_search_benchmarking.py | 2 +- notebooks/Agent/SingleQubitTuneUp.ipynb | 74 + notebooks/Agent/TwoQubitTuneUp.ipynb | 75 + notebooks/Agent/simulated_setup.py | 165 + notebooks/Agent/simulated_setup_2.py | 161 + notebooks/SimulatedSystem/AITuneUpDemo.ipynb | 12664 ---------------- .../SimulatedSystem/TwoQubitTuneUp.ipynb | 224 - 7 files changed, 476 insertions(+), 12889 deletions(-) create mode 100644 notebooks/Agent/SingleQubitTuneUp.ipynb create mode 100644 notebooks/Agent/TwoQubitTuneUp.ipynb create mode 100644 notebooks/Agent/simulated_setup.py create mode 100644 notebooks/Agent/simulated_setup_2.py delete mode 100644 notebooks/SimulatedSystem/AITuneUpDemo.ipynb delete mode 100644 notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb diff --git a/benchmark/exp_recall/embedding_search_benchmarking.py b/benchmark/exp_recall/embedding_search_benchmarking.py index e4d7f5a..56e23b6 100644 --- a/benchmark/exp_recall/embedding_search_benchmarking.py +++ b/benchmark/exp_recall/embedding_search_benchmarking.py @@ -290,7 +290,7 @@ def entry(model, rag): # You have to enable this option before using the `correct_json_by_model` rule parse_options.correct_json_by_model = True n_recall_items = 2 - shots = 5 + shots = 4 main(model, shots, rag, n_recall_items) diff --git a/notebooks/Agent/SingleQubitTuneUp.ipynb b/notebooks/Agent/SingleQubitTuneUp.ipynb new file mode 100644 index 0000000..f534957 --- /dev/null +++ b/notebooks/Agent/SingleQubitTuneUp.ipynb @@ -0,0 +1,74 @@ +{ + "cells": [ + { + "cell_type": "markdown", + "id": "46ed3b2a-adb8-4455-b96e-7f68696f672e", + "metadata": { + "scrolled": true + }, + "source": [ + "# Automated calibration for single qubit\n", + "## Load virtual qubit" + ] + }, + { + "cell_type": "code", + "id": "6bc05f9e-e8b3-4aa8-8382-9d6c202230a8", + "metadata": {}, + "source": [ + "from simulated_setup import *\n", + "from leeq.experiments.builtin import *\n", + "\n", + "qubit = simulation_setup()" + ], + "outputs": [], + "execution_count": null + }, + { + "cell_type": "markdown", + "id": "45f9aec7a2c7ecfc", + "metadata": {}, + "source": "## Run calibration by agents" + }, + { + "cell_type": "code", + "id": "b4d4f2c9-ad83-461c-ba24-a33aae217cde", + "metadata": {}, + "source": [ + "from k_agents.execution.agent import execute_procedure\n", + "from leeq.utils.ai.translation_agent import init_leeq_translation_agents\n", + "\n", + "from mllm.config import default_models\n", + "default_models.normal = \"gpt-4o\"\n", + "default_models.expensive = \"gpt-4o\"\n", + "setup().status().set_param(\"AIAutoInspectPlots\", True) \n", + "\n", + "init_leeq_translation_agents()\n", + "execute_procedure(\"Full gate calibration on `dut`\", dut=qubit)" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Agent/TwoQubitTuneUp.ipynb b/notebooks/Agent/TwoQubitTuneUp.ipynb new file mode 100644 index 0000000..76c406d --- /dev/null +++ b/notebooks/Agent/TwoQubitTuneUp.ipynb @@ -0,0 +1,75 @@ +{ + "cells": [ + { + "metadata": {}, + "cell_type": "markdown", + "source": [ + "# Automated calibration for two-qubit gate\n", + "## Load virtual qubits" + ], + "id": "66bd3e2100e94e47" + }, + { + "cell_type": "code", + "id": "initial_id", + "metadata": {}, + "source": [ + "from simulated_setup_2 import *\n", + "from leeq import ExperimentManager\n", + "from labchronicle import Chronicle\n", + "Chronicle().start_log()\n", + "qubit_1, qubit_2 = get_virtual_qubit_pair()\n", + "ExperimentManager().status().set_param(\"Plot_Result_In_Jupyter\", True)" + ], + "outputs": [], + "execution_count": null + }, + { + "metadata": {}, + "cell_type": "markdown", + "source": "## Run calibration by agents", + "id": "7440cf399d01c3e4" + }, + { + "cell_type": "code", + "id": "fed6c78a29e55398", + "metadata": {}, + "source": [ + "from k_agents.execution.agent import execute_procedure\n", + "from leeq.utils.ai.translation_agent import init_leeq_translation_agents\n", + "from mllm import config\n", + "\n", + "config.default_models.normal = \"gpt-4o\"\n", + "config.default_models.expensive = \"gpt-4o\"\n", + "config.default_options.temperature = 0.2\n", + "\n", + "init_leeq_translation_agents()\n", + "\n", + "execute_procedure(\"Two level Two-qubit calibration on `duts`\", duts=[qubit_1, qubit_2])" + ], + "outputs": [], + "execution_count": null + } + ], + "metadata": { + "kernelspec": { + "display_name": "Python 3 (ipykernel)", + "language": "python", + "name": "python3" + }, + "language_info": { + "codemirror_mode": { + "name": "ipython", + "version": 3 + }, + "file_extension": ".py", + "mimetype": "text/x-python", + "name": "python", + "nbconvert_exporter": "python", + "pygments_lexer": "ipython3", + "version": "3.10.15" + } + }, + "nbformat": 4, + "nbformat_minor": 5 +} diff --git a/notebooks/Agent/simulated_setup.py b/notebooks/Agent/simulated_setup.py new file mode 100644 index 0000000..1087352 --- /dev/null +++ b/notebooks/Agent/simulated_setup.py @@ -0,0 +1,165 @@ +# This file setup the high-level simulation and provides a 2Q virtual device. +import numpy as np + +from leeq.core.elements.built_in.qudit_transmon import TransmonElement +from leeq.experiments.builtin import MeasurementCalibrationMultilevelGMM +from leeq.setups.built_in.setup_simulation_high_level import HighLevelSimulationSetup +from leeq.experiments.experiments import ExperimentManager +from leeq.theory.simulation.numpy.rotated_frame_simulator import VirtualTransmon + +def simulation_setup(): + from labchronicle import Chronicle + Chronicle().start_log() + manager = ExperimentManager() + manager.clear_setups() + + virtual_transmon_a = VirtualTransmon( + name="VQubitA", + qubit_frequency=5040.4, + anharmonicity=-198, + t1=70, + t2=35, + readout_frequency=9645.4, + quiescent_state_distribution=np.asarray( + [ + 0.8, + 0.15, + 0.04, + 0.01])) + + virtual_transmon_b = VirtualTransmon( + name="VQubitB", + qubit_frequency=4855.3, + anharmonicity=-197, + t1=60, + t2=30, + readout_frequency=9025.1, + quiescent_state_distribution=np.asarray( + [ + 0.75, + 0.18, + 0.05, + 0.02])) + + setup = HighLevelSimulationSetup( + name='HighLevelSimulationSetup', + virtual_qubits={2: virtual_transmon_a, + 4: virtual_transmon_b}, + ) + + setup.set_coupling_strength_by_qubit( + virtual_transmon_a, virtual_transmon_b, coupling_strength=1.5) + + + manager.register_setup(setup) + + + configuration_a = { + 'hrid':'QA', + 'lpb_collections': { + 'f01': { + 'type': 'SimpleDriveCollection', + 'freq': 5040.4, + 'channel': 2, + 'shape': 'blackman_drag', + 'amp': 0.5487 , + 'phase': 0., + 'width': 0.05, + 'alpha': 500, + 'trunc': 1.2 + }, + 'f12': { + 'type': 'SimpleDriveCollection', + 'freq': 5040.4-198, + 'channel': 2, + 'shape': 'blackman_drag', + 'amp': 0.1 / np.sqrt(2), + 'phase': 0., + 'width': 0.025, + 'alpha': 425.1365229849309, + 'trunc': 1.2 + } + }, + 'measurement_primitives': { + '0': { + 'type': 'SimpleDispersiveMeasurement', + 'freq': 9645.5, + 'channel': 1, + 'shape': 'square', + 'amp': 0.15, + 'phase': 0., + 'width': 1, + 'trunc': 1.2, + 'distinguishable_states': [0, 1] + } + } + } + + configuration_b = { + 'hrid':'QB', + 'lpb_collections': { + 'f01': { + 'type': 'SimpleDriveCollection', + 'freq': 4855.3, + 'channel': 4, + 'shape': 'blackman_drag', + 'amp': 0.5399696605966315 , + 'phase': 0., + 'width': 0.05, + 'alpha': 500, + 'trunc': 1.2 + }, + 'f12': { + 'type': 'SimpleDriveCollection', + 'freq': 5040.4-197, + 'channel': 4, + 'shape': 'blackman_drag', + 'amp': 0.1 / np.sqrt(2), + 'phase': 0., + 'width': 0.025, + 'alpha': 425.1365229849309, + 'trunc': 1.2 + } + }, + 'measurement_primitives': { + '0': { + 'type': 'SimpleDispersiveMeasurement', + 'freq': 9025.5, + 'channel': 3, + 'shape': 'square', + 'amp': 0.15, + 'phase': 0., + 'width': 1, + 'trunc': 1.2, + 'distinguishable_states': [0, 1] + } + } + } + + # setup().start_live_monitor() # When needed you can setup the live monitor. + manager.status().set_param("Shot_Number", 500) + manager.status().set_param("Shot_Period", 500) + + dut_dict = { + 'Q1': {'Active': True, 'Tuneup': False, 'FromLog': False, 'Params': configuration_a}, + 'Q2': {'Active': True, 'Tuneup': False, 'FromLog': False, 'Params': configuration_b} + } + + duts_dict = {} + for hrid, dd in dut_dict.items(): + if (dd['Active']): + if (dd['FromLog']): + dut = TransmonElement.load_from_calibration_log(dd['Params']['hrid']) + else: + dut = TransmonElement(name=dd['Params']['hrid'], parameters=dd['Params']) + + if (dd['Tuneup']): + dut.save_calibration_log() + else: + lpb_scan = (dut.get_c1('f01')['I'], dut.get_c1('f01')['X']) + calib = MeasurementCalibrationMultilevelGMM(dut, mprim_index=0, + sweep_lpb_list=lpb_scan) + dut.print_config_info() + duts_dict[hrid] = dut + + return duts_dict['Q1'] \ No newline at end of file diff --git a/notebooks/Agent/simulated_setup_2.py b/notebooks/Agent/simulated_setup_2.py new file mode 100644 index 0000000..dd02e75 --- /dev/null +++ b/notebooks/Agent/simulated_setup_2.py @@ -0,0 +1,161 @@ +from leeq.core.elements.built_in.qudit_transmon import TransmonElement +from leeq.setups.built_in.setup_simulation_high_level import HighLevelSimulationSetup +import numpy as np +from leeq.theory.simulation.numpy.rotated_frame_simulator import VirtualTransmon +from leeq import ExperimentManager + +manager = ExperimentManager() +manager.clear_setups() + +def get_virtual_qubit_pair(): + virtual_transmon_1 = VirtualTransmon( + name="VQubit_1", + qubit_frequency=5040.4, + anharmonicity=-198, + t1=70, + t2=35, + readout_frequency=9645.5, + quiescent_state_distribution=np.asarray( + [ + 0.8, + 0.15, + 0.04, + 0.01])) + + virtual_transmon_2 = VirtualTransmon( + name="VQubit_2", + qubit_frequency=5040. - 123, + anharmonicity=-197, + t1=70, + t2=35, + readout_frequency=9645.5 + 100, + quiescent_state_distribution=np.asarray( + [ + 0.8, + 0.15, + 0.04, + 0.01])) + + setup = HighLevelSimulationSetup( + name='HighLevelSimulationSetup', + virtual_qubits={2: virtual_transmon_1, + 4: virtual_transmon_2 + } + ) + setup.set_coupling_strength_by_qubit( + virtual_transmon_1, virtual_transmon_2, coupling_strength=1.5) + + manager.register_setup(setup) + + configuration_q1 = { + 'lpb_collections': { + 'f01': { + 'type': 'SimpleDriveCollection', + 'freq': 5040.4, + 'channel': 2, + 'shape': 'blackman_drag', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 0.025, + 'alpha': 425.1365229849309, + 'trunc': 1.2 + }, + 'f12': { + 'type': 'SimpleDriveCollection', + 'freq': 5040.4 - 198, + 'channel': 2, + 'shape': 'blackman_drag', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 0.025, + 'alpha': 425.1365229849309, + 'trunc': 1.2 + } + }, + 'measurement_primitives': { + '0': { + 'type': 'SimpleDispersiveMeasurement', + 'freq': 9645.5, + 'channel': 1, + 'shape': 'square', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 1, + 'trunc': 1.2, + 'distinguishable_states': [0, 1] + }, + '1': { + 'type': 'SimpleDispersiveMeasurement', + 'freq': 9144.41, + 'channel': 1, + 'shape': 'square', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 1, + 'trunc': 1.2, + 'distinguishable_states': [0, 1, 2] + } + } + } + + configuration_q2 = { + 'lpb_collections': { + 'f01': { + 'type': 'SimpleDriveCollection', + 'freq': 5040. - 123, + 'channel': 4, + 'shape': 'blackman_drag', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 0.025, + 'alpha': 425.1365229849309, + 'trunc': 1.2 + }, + 'f12': { + 'type': 'SimpleDriveCollection', + 'freq': 5040.4 - 198 - 123, + 'channel': 4, + 'shape': 'blackman_drag', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 0.025, + 'alpha': 425.1365229849309, + 'trunc': 1.2 + } + }, + 'measurement_primitives': { + '0': { + 'type': 'SimpleDispersiveMeasurement', + 'freq': 9645.5 + 100, + 'channel': 3, + 'shape': 'square', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 1, + 'trunc': 1.2, + 'distinguishable_states': [0, 1] + }, + '1': { + 'type': 'SimpleDispersiveMeasurement', + 'freq': 9645.5 + 100, + 'channel': 3, + 'shape': 'square', + 'amp': 0.21323904814245054 / 5 * 4, + 'phase': 0., + 'width': 1, + 'trunc': 1.2, + 'distinguishable_states': [0, 1, 2] + } + } + } + + qubit_1 = TransmonElement( + name='test-qubit-1', + parameters=configuration_q1 + ) + + qubit_2 = TransmonElement( + name='test-qubit-2', + parameters=configuration_q2 + ) + return qubit_1, qubit_2 \ No newline at end of file diff --git a/notebooks/SimulatedSystem/AITuneUpDemo.ipynb b/notebooks/SimulatedSystem/AITuneUpDemo.ipynb deleted file mode 100644 index 52e67c8..0000000 --- a/notebooks/SimulatedSystem/AITuneUpDemo.ipynb +++ /dev/null @@ -1,12664 +0,0 @@ -{ - "cells": [ - { - "cell_type": "markdown", - "id": "46ed3b2a-adb8-4455-b96e-7f68696f672e", - "metadata": { - "scrolled": true - }, - "source": [ - "# Automated tune-up for single qubit" - ] - }, - { - "cell_type": "markdown", - "id": "dad87523-31cb-4c65-ada6-88ae4fc93273", - "metadata": {}, - "source": [ - "## Load the environment" - ] - }, - { - "cell_type": "code", - "id": "6bc05f9e-e8b3-4aa8-8382-9d6c202230a8", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-08T20:57:22.349734Z", - "start_time": "2024-12-08T20:57:19.073463Z" - } - }, - "source": [ - "from simulated_setup import * # Change to your customized setup file\n", - "from leeq.experiments.builtin import *" - ], - "outputs": [], - "execution_count": 1 - }, - { - "cell_type": "markdown", - "id": "b761dea8-5e93-493e-9d56-78b1249ea426", - "metadata": {}, - "source": [ - "## Initialize qubits configuration" - ] - }, - { - "cell_type": "code", - "id": "48ed15bd-87a4-4757-957e-9bdc393af3fa", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-08T20:57:23.217620Z", - "start_time": "2024-12-08T20:57:22.350915Z" - } - }, - "source": [ - "simulation_setup()\n", - "\n", - "# setup().start_live_monitor() # When needed you can setup the live monitor.\n", - "setup().status().set_param(\"Shot_Number\", 500)\n", - "setup().status().set_param(\"Shot_Period\", 500) \n", - " \n", - "dut_dict = {\n", - " 'Q1': {'Active': True, 'Tuneup': False,'FromLog':False, 'Params': configuration_a},\n", - " 'Q2': {'Active': True, 'Tuneup': False,'FromLog':False, 'Params': configuration_b}\n", - "} \n", - "\n", - "duts_dict = {}\n", - "for hrid, dd in dut_dict.items():\n", - " if (dd['Active']):\n", - " if (dd['FromLog']):\n", - " dut = TransmonElement.load_from_calibration_log(dd['Params']['hrid'])\n", - " else:\n", - " dut = TransmonElement(name=dd['Params']['hrid'],parameters=dd['Params'])\n", - " \n", - " if (dd['Tuneup']):\n", - " dut.save_calibration_log()\n", - " else:\n", - " lpb_scan = (dut.get_c1('f01')['I'], dut.get_c1('f01')['X'])\n", - " calib = MeasurementCalibrationMultilevelGMM(dut, mprim_index=0,sweep_lpb_list=lpb_scan)\n", - " dut.print_config_info()\n", - " duts_dict[hrid] = dut" - ], - "outputs": [ - { - "name": "stderr", - "output_type": "stream", - "text": [ - "[2024-12-08 15:57:22] [INFO] [labchronicle.chronicle] Log started at log/zijian/2024-12/2024-12-08/15.57.22\n", - "[2024-12-08 15:57:22] [INFO] [labchronicle.chronicle] Log started at log/zijian/2024-12/2024-12-08/15.57.22\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "iVBORw0KGgoAAAANSUhEUgAAArIAAAFUCAYAAADYjN+CAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjkuMiwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy8hTgPZAAAACXBIWXMAAA9hAAAPYQGoP6dpAADpnElEQVR4nOydd3zU9f3Hn7cvl0suew8gIWwEURCR6QAXaquoRdy2ilattVXbWkfrz1Fbq6g468KFti5UBGWobDBAgGBIgJC9c5fL7bvv74/LnbnscZl+no9HWu/7/d7n8/nekVfe3/fnPWSSJEkIBAKBQCAQCARDDPlAL0AgEAgEAoFAIOgJwpAVCAQCgUAgEAxJhCErEAgEAoFAIBiSCENWIBAIBAKBQDAkEYasQCAQCAQCgWBIIgxZgUAgEAgEAsGQRBiyAoFAIBAIBIIhiTBkBQKBQCAQCARDEmHICgQCgUAgEAiGJMKQFfQJDz74IDKZrF/mmjdvHvPmzfO/3rRpEzKZjA8//LBf5r/22msZMWJEv8wlEAh+3ghtFQgCEYasoFNef/11ZDKZ/0er1ZKUlMTChQt55plnaGhoCMo8paWlPPjgg+zduzco4wWTwby2V199lXHjxqHVahk9ejQrVqwY6CUJBIIuILR18K5t5cqVXHbZZaSlpSGTybj22msHekmCdhCGrKDLPPzww7z11lusXLmS3/72twDceeedTJo0if379wdc+5e//AWr1dqt8UtLS3nooYe6LWjr1q1j3bp13XpPd+lobS+//DI//vhjn87fHi+++CI33ngjEyZMYMWKFcycOZPbb7+dxx9/fEDWIxAIuo/Q1sGnrY8//jgbNmxgwoQJKJXKAVmDoGuIb0fQZc4991xOOeUU/+v77ruPDRs2cMEFF7B48WJyc3MJCQkBQKlU9vkvv8ViQafToVar+3SezlCpVAMyr9Vq5c9//jPnn3++f6vvpptuwuPx8Le//Y1f//rXREZGDsjaBAJB1xHa2jYDpa0Amzdv9ntj9Xr9gK1D0DnCIyvoFQsWLOD++++nsLCQVatW+Y+3Fce1fv16zjjjDCIiItDr9YwZM4Y//elPgDf26tRTTwXguuuu82+1vf7664A3VmvixIns2bOHOXPmoNPp/O9tGcflw+1286c//YmEhARCQ0NZvHgxRUVFAdeMGDGizS2j5mN2tra24rgaGxv5/e9/T2pqKhqNhjFjxvDkk08iSVLAdTKZjNtuu42PP/6YiRMnotFomDBhAmvXrm37A2/Gxo0bqampYfny5QHHb731VhobG/n88887HUMgEAxOhLYOnLYCpKen91sssqB3CENW0GuWLVsG0OEW1MGDB7nggguw2+08/PDD/POf/2Tx4sVs2bIFgHHjxvHwww8D8Otf/5q33nqLt956izlz5vjHqKmp4dxzz2XKlCn8+9//Zv78+R2u65FHHuHzzz/nnnvu4fbbb2f9+vWcddZZ3d6W68ramiNJEosXL+app55i0aJF/Otf/2LMmDH84Q9/4K677mp1/ffff8/y5cu54ooreOKJJ7DZbPzyl7+kpqamw3VlZ2cDBHhyAKZNm4ZcLvefFwgEQxOhrYH0l7YKhhiSQNAJr732mgRIu3btavcag8EgTZ061f/6gQcekJr/83rqqackQKqqqmp3jF27dkmA9Nprr7U6N3fuXAmQXnjhhTbPzZ071/9648aNEiAlJydLJpPJf3z16tUSID399NP+Y+np6dI111zT6Zgdre2aa66R0tPT/a8//vhjCZD+/ve/B1x36aWXSjKZTMrPz/cfAyS1Wh1wbN++fRIgrVixotVczbn11lslhULR5rnY2Fjpiiuu6PD9AoFgYBHaOji1tSWhoaFt3otgcCA8soKgoNfrO8ywjYiIAOCTTz7B4/H0aA6NRsN1113X5euvvvpqwsLC/K8vvfRSEhMT+eKLL3o0f1f54osvUCgU3H777QHHf//73yNJEl9++WXA8bPOOouMjAz/68mTJxMeHs7Ro0c7nMdqtbYbw6bVarvtHREIBIMPoa0/0V/aKhhaCENWEBTMZnOAsLXk8ssvZ9asWdx4443Ex8dzxRVXsHr16m4Jb3JycreSD0aPHh3wWiaTkZmZyfHjx7s8Rk8oLCwkKSmp1ecxbtw4//nmpKWltRojMjKSurq6DucJCQnB4XC0ec5ms/mTQwQCwdBFaOtP9Je2CoYWwpAV9Jri4mKMRiOZmZntXhMSEsK3337L119/zbJly9i/fz+XX345Z599Nm63u0vz9IVh1l4wf1fXFAwUCkWbx6UWyQstSUxMxO12U1lZGXDc4XBQU1NDUlJS0NYoEAj6H6GtvaOn2ioYWghDVtBr3nrrLQAWLlzY4XVyuZwzzzyTf/3rXxw6dIhHHnmEDRs2sHHjRqB94espR44cCXgtSRL5+fkBWbCRkZHU19e3em/LJ/vurC09PZ3S0tJW24GHDx/2nw8GU6ZMAWD37t0Bx3fv3o3H4/GfFwgEQxOhrYH0l7YKhhbCkBX0ig0bNvC3v/2NkSNHsnTp0navq62tbXXMZ2jZ7XYAQkNDAdoUv57w5ptvBgjehx9+SFlZGeeee67/WEZGBtu3bw/Yol+zZk2rUjLdWdt5552H2+3m2WefDTj+1FNPIZPJAubvDQsWLCAqKoqVK1cGHF+5ciU6nY7zzz8/KPMIBIL+R2hra/pLWwVDC9EQQdBlvvzySw4fPozL5aKiooINGzawfv160tPT+fTTT9Fqte2+9+GHH+bbb7/l/PPPJz09ncrKSp5//nlSUlI444wzAK/wRURE8MILLxAWFkZoaCgzZsxg5MiRPVpvVFQUZ5xxBtdddx0VFRX8+9//JjMzk5tuusl/zY033siHH37IokWLWLJkCQUFBaxatSogQaC7a7vwwguZP38+f/7znzl+/DgnnXQS69at45NPPuHOO+9sNXZPCQkJ4W9/+xu33norl112GQsXLuS7775j1apVPPLII0RFRQVlHoFA0LcIbR1c2grw2WefsW/fPgCcTif79+/n73//OwCLFy9m8uTJQZtL0EsGsmSCYGjgKxHj+1Gr1VJCQoJ09tlnS08//XRAGRYfLUvEfPPNN9JFF10kJSUlSWq1WkpKSpKuvPJKKS8vL+B9n3zyiTR+/HhJqVQGlGSZO3euNGHChDbX116JmHfffVe67777pLi4OCkkJEQ6//zzpcLCwlbv/+c//yklJydLGo1GmjVrlrR79+5WY3a0tpYlYiRJkhoaGqTf/e53UlJSkqRSqaTRo0dL//jHPySPxxNwHSDdeuutrdbUXumatnjppZekMWPGSGq1WsrIyJCeeuqpVvMIBILBh9DWjtc2kNp6zTXXBHw3zX/aKhUmGDhkkiSingUCgUAgEAgEQw8RIysQCAQCgUAgGJIIQ1YgEAgEAoFAMCQRhqxAIBAIBAKBYEgiDFmBQCAQCAQCwZBEGLICgUAgEAgEgiGJMGQFAoFAIBAIBEOSId0QwePxUFpaSlhYWNBb8AkEAkFnSJJEQ0MDSUlJyOXDxy8gtFUgEAwk3dHWIW3IlpaWkpqaOtDLEAgEP3OKiopISUkZ6GUEDaGtAoFgMNAVbR3ShmxYWBjgvdHw8PABXo1AIPi5YTKZSE1N9WvRcEFoq0AgGEi6o60Dasg++OCDPPTQQwHHxowZw+HDh7v0ft+WV3h4uBBbgUAwYAy37XehrQKBYDDQFW0dcI/shAkT+Prrr/2vlcoBX5JAIBAIBAKBYAgw4FajUqkkISFhoJchEAgEAoFAIBhiDHia7ZEjR0hKSmLUqFEsXbqUEydODPSSBAKBQCAQCARDgAH1yM6YMYPXX3+dMWPGUFZWxkMPPcTs2bM5cOBAmwG+drsdu93uf20ymbo0j9vtxul0Bm3dgv5HrVYPq/JGAsFwQGjr0Edoq2CoM6CG7Lnnnuv/78mTJzNjxgzS09NZvXo1N9xwQ6vrH3300VbJYR0hSRLl5eXU19cHY7mCAUQulzNy5EjUavVAL0Ug+NkjtHX4ILRVMNQZ8BjZ5kRERJCVlUV+fn6b5++77z7uuusu/2tfeYb28AltXFwcOp1u2GUW/1zwFWcvKysjLS1NfI8CwQAjtHV4ILRVMBwYVIas2WymoKCAZcuWtXleo9Gg0Wi6NJbb7fYLbXR0dDCXKRgAYmNjKS0txeVyoVKpBno5AsHPFqGtwwuhrYKhzoAGxtx9991s3ryZ48ePs3XrVi655BIUCgVXXnllr8f2xW3pdLpejyUYeHzbXm63e4BXIhD8vBHaOrwQ2ioY6gyoR7a4uJgrr7ySmpoaYmNjOeOMM9i+fTuxsbFBm0NslQwPxPcoEAwuxO/k8EB8j4KhzoAasu+9995ATi8QCAQCgUAgGMKImhsCgUAgEAgEgiGJMGQHId9++y0XXnghSUlJyGQyPv74407fc/vttzNt2jQ0Gg1Tpkxp85rVq1czZcoUdDod6enp/OMf/+hwzOPHj3PDDTcwcuRIQkJCyMjI4IEHHsDhcARct3//fmbPno1WqyU1NZUnnngi4Pz69evJysoiPDycZcuWBbzfaDSSlZVFYWFhp/coEAgEvaEn2rpr1y7OPPNMIiIiiIyMZOHChezbt6/Na/Pz8wkLCyMiIqLDMfft28eVV15JamoqISEhjBs3jqeffrrVdZs2beLkk09Go9GQmZnJ66+/HnD+7bffJjU1lcjIyICKPuDV76ysrC7XWxcIhirCkB2ENDY2ctJJJ/Hcc891633XX389l19+eZvnvvzyS5YuXcrNN9/MgQMHeP7553nqqad49tln2x3v8OHDeDweXnzxRQ4ePMhTTz3FCy+8wJ/+9Cf/NSaTiXPOOYf09HT27NnDP/7xDx588EFeeuklwFve5Ve/+hU333wz27ZtY/fu3f5zAPfeey8333wz6enp3bpXgUAg6C7d1Vaz2cyiRYtIS0tjx44dfP/994SFhbFw4cJWjSCcTidXXnkls2fP7nTcPXv2EBcXx6pVqzh48CB//vOfue+++wL0+NixY5x//vnMnz+fvXv3cuedd3LjjTfy1VdfAVBdXc2NN97Ik08+ybp161i1ahVr1qzxv3/58uU89thjhIeHd+leBYIhizSEMRqNEiAZjcZW56xWq3To0CHJarUOwMqCByB99NFHXb7+gQcekE466aRWx6+88krp0ksvDTj2zDPPSCkpKZLH4+ny+E888YQ0cuRI/+vnn39eioyMlOx2u//YPffcI40ZM0aSJEmqqKiQAP/38Mc//lFavny5JEmStGXLFmnatGmSy+XqdN7h8n0KhhcdadBQRmirl127dkmAdOLECf+x/fv3S4B05MiRgGv/+Mc/SldddZX02muvSQaDodvrWb58uTR//vyA8SZMmBBwzeWXXy4tXLhQkiRJ2rFjhxQfH+8/t2TJEumJJ56QJEmS3nnnHWnx4sVdmne4fJ+C4UV3tFV4ZIcgDz74ICNGjOjWe+x2O1qtNuBYSEgIxcXF3drWNxqNREVF+V9v27aNOXPmBHSFWbhwIT/++CN1dXXExsaSmJjIunXrsFgsfPfdd0yePBmn08ktt9zCiy++iEKh6Na9CAQCQV/QUlvHjBlDdHQ0r776Kg6HA6vVyquvvsq4ceMCrtuwYQMffPBBt3fRmtOWtp511lkB1yxcuJBt27YBMHr0aCwWC9nZ2dTW1rJr1y4mT55MXV0d999/f4e7bQLBcEIYsl3EaHFysNSI0TLwfcVjYmLIyMjo1nsWLlzI//73P7755hs8Hg95eXn885//BKCsrKxLY+Tn57NixQp+85vf+I+Vl5cTHx8fcJ3vdXl5OTKZjNWrV/O3v/2NCRMmMHXqVK6//noee+wx5s+fj1arZdasWYwZM0YIr0DwM2Qwa2tYWBibNm1i1apVhISEoNfrWbt2LV9++SVKpbfoT01NDddeey2vv/56j7fxt27dyvvvv8+vf/1r/7H2tNVkMmG1WomMjOSNN97g6quvZvr06Vx99dUsXLiQu+++m9tuu41jx44xdepUJk6cyIcfftijdQkEQ4FB1dlrsGK0OFm5OZ/CGgvp0TpumZuJQTdwHVBuu+02brvttm6956abbqKgoIALLrgAp9NJeHg4d9xxBw8++CByeefPMyUlJSxatIjLLruMm266qVtzn3HGGezatcv/Oi8vjzfffJPs7GzmzJnDHXfcwbnnnsvEiROZM2cOkydP7tb4AoFgaDLYtdVqtXLDDTcwa9Ys3n33XdxuN08++STnn38+u3btIiQkhJtuuolf/epXzJkzp0dzHjhwgIsuuogHHniAc845p1vvveSSS7jkkkv8rzdv3sz+/ftZsWIFmZmZvPvuuyQkJDB9+nTmzJlDXFxcj9YoEAxmhEe2CxTXWyissZAQrqWwxkJxvWWgl9RtZDIZjz/+OGazmcLCQsrLy5k+fToAo0aN6vC9paWlzJ8/n9NPPz0gUQsgISGBioqKgGO+1wkJCW2O95vf/IZ//vOfeDwesrOzueyyy4iLi2Pu3Lls3ry5p7coEAiGGINdW9955x2OHz/Oa6+9xqmnnsppp53GO++8w7Fjx/jkk08Ab1jBk08+iVKpRKlUcsMNN2A0GlEqlfznP//pcPxDhw5x5pln8utf/5q//OUvAefa09bw8HBCQkJajWW321m+fDkvvvgi+fn5uFwu5s6dy5gxY8jKymLHjh29/DQEgsGJMGS7QEqEjvRoHeUmG+nROlIihm5rRoVCQXJyMmq1mnfffZeZM2d22EmtpKSEefPmMW3aNF577bVW3tuZM2fy7bffBmTwrl+/njFjxhAZGdlqvFdffZWoqCgWL17sb4noe6/T6RRtEgWCnxGDXVstFgtyuTyg+5XvtcfjAbyxrHv37vX/PPzww4SFhbF3794Ab2lLDh48yPz587nmmmt45JFHWp2fOXMm33zzTcCx9evXM3PmzDbH+/vf/86iRYs4+eSTcbvduFwu/zmhrYJhTT8kn/UZ/ZlZW9/okA6U1Ev1jY6gjNcRDQ0NUnZ2tpSdnS0B0r/+9S8pOztbKiwslCRJklasWCEtWLAg4D1HjhyRsrOzpd/85jdSVlaW//2+agJVVVXSypUrpdzcXCk7O1u6/fbbJa1WK+3YscM/xo4dO6QxY8ZIxcXFkiRJUnFxsZSZmSmdeeaZUnFxsVRWVub/8VFfXy/Fx8dLy5Ytkw4cOCC99957kk6nk1588cVW91VRUSGNGDFCKikp8R8bN26c9OCDD0pbt26V9Hq9tHPnzjY/E5FZKxiMiKoFvWcwa2tubq6k0WikW265RTp06JB04MAB6aqrrpIMBoNUWlra5hxtVS343//+56/kIkmSlJOTI8XGxkpXXXVVgK5WVlb6rzl69Kik0+mkP/zhD1Jubq703HPPSQqFQlq7dm2rOQ8ePCiNHj1aMpvNkiRJksVikaKjo6VXXnlFWrNmjaTRaPy63hKhrYLBSHe0VRiyg5CNGzdKQKufa665RpIkb4mt9PT0gPfMnTu3zfccO3ZMkiSvIXvaaadJoaGhkk6nk84880xp+/btbc7re89rr73W5pgtn3/27dsnnXHGGZJGo5GSk5Olxx57rM37uuKKK6QVK1YEHNuxY4c0duxYKSoqSnrooYfa/UyG8vcpGL4IQ3Zo0RNtXbdunTRr1izJYDBIkZGR0oIFC6Rt27a1O0dbhqxPS3088MADba6j5dwbN26UpkyZIqnVamnUqFHSa6+91mo+j8cjzZo1S/rss88Cjn/22WdSWlqaFB8fL7388svtrncof5+C4Ut3tFUmSZLUlx7fvsRkMmEwGDAaja2yRW02G8eOHWPkyJGtyk4Jhh7i+xQMRjrSoKGM0NafD+L7FAxGuqOtIkZWIBAIBAKBQDAkEYasQCAQCAQCgWBIIgxZgUAgEAgEAsGQRBiyAoFAIBAIBIIhiTBkBQKBQCAQCARDEmHICgQCgUAgEAiGJMKQFQgEAoFAIBAMSYQhKxAIBAKBQCAYkghDViAQCAQCgUAwJBGGrEAgEAgEAoFgSCIM2UHKc889x4gRI9BqtcyYMYOdO3d2eP1XX33FaaedRlhYGLGxsfzyl7/k+PHjAdds2rSJk08+GY1GQ2ZmJq+//nqn6wjGuG+//TapqalERkZy1113BZw7fvw4WVlZmEymTtciEAgEwaA7+mqz2bj22muZNGkSSqWSiy++uNU1mzZtQiaTtfopLy/vcB1CXwWC3iMM2UHI+++/z1133cUDDzzADz/8wEknncTChQuprKxs8/pjx45x0UUXsWDBAvbu3ctXX31FdXU1v/jFLwKuOf/885k/fz579+7lzjvv5MYbb+Srr75qdx3BGLe6upobb7yRJ598knXr1rFq1SrWrFnjf//y5ct57LHHhlWfeoFAMHjprr663W5CQkK4/fbbOeusszoc+8cff6SsrMz/ExcX1+61Ql8FgiAhDWGMRqMESEajsdU5q9UqHTp0SLJarQOwst4xffp06dZbb/W/drvdUlJSkvToo4+2ef0HH3wgKZVKye12+499+umnkkwmkxwOhyRJkvTHP/5RmjBhQsD7Lr/8cmnhwoXtriMY4+7YsUOKj4/3n1uyZIn0xBNPSJIkSe+88460ePHi9j+IZgzl71MwfOlIg4Yyw1VbJan7+tqca665RrroootaHd+4caMESHV1dV1ex2DR16H+fQqGJ93RVuGRHWQ4HA727NkT8OQvl8s566yz2LZtGwDXXnst8+bN85+fNm0acrmc1157DbfbjdFo5K233uKss85CpVIBsG3btlbehIULF/rHbItgjDt69GgsFgvZ2dnU1taya9cuJk+eTF1dHffffz/PPvtszz8sgUAg6AY90dfuMGXKFBITEzn77LPZsmVLh9cKfRUIgoMwZLuI22jElpuL22js03mqq6txu93Ex8cHHI+Pj/fHWyUmJpKWluY/N3LkSNatW8ef/vQnNBoNERERFBcXs3r1av815eXlbY5pMpmwWq1triUY40ZGRvLGG29w9dVXM336dK6++moWLlzI3XffzW233caxY8eYOnUqEydO5MMPP+zZhyYQCNrlscceQyaTceeddw70Utqkv7QVeqavXSExMZEXXniB//73v/z3v/8lNTWVefPm8cMPP7T7HqGvAkFwUA70AoYCbqOR6ldewXniBKq0NGJuvBGFwTBg63n00UcDXpeXl3PTTTdxzTXXcOWVV9LQ0MBf//pXLr30UtavX49MJuvRPMEa95JLLuGSSy7xv968eTP79+9nxYoVZGZm8u6775KQkMD06dOZM2dOh3FlAoGg6+zatYsXX3yRyZMnD/RS2mSwaSu01teuMGbMGMaMGeN/ffrpp1NQUMBTTz3FW2+91eZ7hL4KBMFBGLJdwFlaivPECZRx8ThPnMBZWtpnYhsTE4NCoaCioiLgeEVFBQkJCW2+57nnnsNgMPDEE0/4j61atYrU1FR27NjBaaedRkJCQptjhoeHExIS0m/j2u12li9fzltvvUV+fj4ul4u5c+cCkJWVxY4dO7jwwgs7+IQEAkFXMJvNLF26lJdffpm///3vA72cNulPbYWe6WtPmT59Ot9//32754W+CgTBQYQWdAFVUhKqtDRclRWo0tJQJSX12VxqtZpp06bxzTff+I95PB6++eYbZs6c2eZ7LBYLcnngV6lQKPzvBZg5c2bAmADr169vd8y+Gvfvf/87ixYt4uSTT8btduNyufznnE4nbre73fUIBIKuc+utt3L++ed3mmk/kPSntkLP9LWn7N27l8TExHbPC30VCIJEPySf9Rn9mVnrqq+XrIcOSa76+qCM1xHvvfeepNFopNdff106dOiQ9Otf/1qKiIiQysvLJUmSpHvvvVdatmyZ//pvvvlGkslk0kMPPSTl5eVJe/bskRYuXCilp6dLFotFkiRJOnr0qKTT6aQ//OEPUm5urvTcc89JCoVCWrt2rX+cFStWSAsWLAj6uD4OHjwojR49WjKbzZIkSZLFYpGio6OlV155RVqzZo2k0Wik4uLiNj8TkVkrGIwM1qoF7777rjRx4kT/78vcuXOlO+64o93rbTabZDQa/T9FRUXDUlslqfv6Kkle7crOzpYuvPBCad68eVJ2draUnZ3tP//UU09JH3/8sXTkyBEpJydHuuOOOyS5XC59/fXX/msGq74KbQ0ObotFshcWSo179kgNmzdLxrVfSXUffSTVvv++VPfhh1LdRx9J9Z9+JpnWr5cad+6UbHl5kqOiQvK4XAO99EFJd7RVGLKDlBUrVkhpaWmSWq2Wpk+fLm3fvt1/7pprrpHmzp0bcP27774rTZ06VQoNDZViY2OlxYsXS7m5uQHXbNy4UZoyZYqkVqulUaNGSa+99lrA+QceeEBKT08P+riSJEkej0eaNWuW9NlnnwUc/+yzz6S0tDQpPj5eevnll9v9PIb69ykYngxGQ/bEiRNSXFyctG/fPv+xzgzZBx54QAJa/QxHbZWk7utrenp6m5+Pj8cff1zKyMiQtFqtFBUVJc2bN0/asGFDwBiDVV+Hw/fZX3jcbslWcFSq//QzqfLpp6WiO+6UChZfJB2edop0aMzYnv1MnCQdOeccqfD6G6TSBx+Uat99T7JkZ0vuxsaBvt0BpTvaKpMkSep3N3CQMJlMGAwGjEZjq4LPNpuNY8eOMXLkSLRa7QCtUBAsxPcpGIx0pEEDxccff8wll1zi36YGb1F/mUyGXC7HbrcHnANvbKXdbve/NplMpKamCm39GSC+z/aRXC6s+3No3LoVa3Y21pwcPB10SZOFhKCMiUERFoYsJAR5SAgytRo8HiS3G8nlRLJYcRuNuOvrcZtM0BRG0nowGZrRo9GdemrTzykoo6P76E4HH93RVpHsJRAIBMOIM888k5ycnIBj1113HWPHjuWee+5pZcQCaDQaNBpNfy1RIBi0uE0mGr7ZgHnDNzRu247HbA44L9No0I4bh2b0aNSjRqEZNRJVairKuDjkoaHdqhIkud24KipwFBXjLC7GcfwYtsM/Yjuci7uqGnteHva8POrefhsA7aRJhJ25AP38BWiyRve4ItFwQxiyAoFAMIwICwtj4sSJAcdCQ0OJjo5udVwgEIDHZqNh/XpMn3+BecsWcDr95xQGA7rTZxI6fTrayZPRZmUha2pY0VtkCoU34TEpCWZMDzjnqqrC8kM2lp07sezahT0vD1tODracHKr+/TTq9HQMF1+EYfFiVMnJQVnPUEUYsgKBQCAQCH522I8coe6DDzB+8imeZg05NKNHE7ZwIfq5c9COH4+sjV2MvkYZG0v4wnMIX3gO4DVsGzZuxPzNBhq3bcNRWEjV089Q9fQz6E47jchfXUnYmWcOyFoHmkFjyD722GPcd9993HHHHfz73/8e6OUIBALBsGHTpk0DvQSBYFAgSRKW7dupefkVGrdu9R9XJSVhuPhiws87F01m5gCusG2UsbFELllC5JIluM2NNKxfj/Hjj7Hs2IFl+3Ys27ejSk4mculSIi67FEVY2EAvud8YFIbsYO8+IxAMJ9xGI87SUlRJSQPeRUkgEAj6A0mSMG/YQPXKF7AdOOA9qFCgnz+PyMsvJ/T004eMN1OhDyXikouJuORiHMUl1H/4AfXvvY+zpITKJ56g+oUXiL7uWiKXLUOh1w/0cvucAW+I0Lz7TGRkZNDH97SXESgYUgzh4hqDCl9L0OoXXqD6lVf6pb+9YHgitHV48HPQVsvu3RRe+SuKb70N24EDyLRaIpcuJeOrr0h99ln0s2cPGSO2JeqUZOLuvJPMTRtJ/PvfUGdk4DGZqHr6GQrOPIvql1/G06wiyXBkwD2yzbvPdNZGsa0SMe2hVquRy+WUlpYSGxuLWq0WGX5DFEmSqKqqQiaToQpSkP3PDcnjwdPYiO3QIew//ogiIhJ7bi7WffvQTpiATKNFHqoTvyOCThHaOnwY7trqKC6m4tHHMDd1R5NptUQtW0bUddeijIoa4NUFF7lWS8Sll2K45BJMa9dS/exzOI4do+qf/6L+/dXE3fNHws46a1j+rg6oIfvee+/xww8/sGvXri5d/+ijj/LQQw916Vq5XM7IkSMpKyujtLS0N8sUDAJkMhkpKSltlg4SeHE3NGA/cgTH0aPYC47iOH4cV2UlrupqXDU10KxdpY/G77f4/1um0aCMifH+JCSgHjkCTUYG6pGj0IzORC7KMwkQ2jrcGI7aKjkc1PznNapfeAHJZgOFgohLLyVm+XJU8XEDvbw+RaZQYDj/fMIXLcL42WdU/espnMXFlPz2dnQzTyPxoYdQp6UN9DKDyoA1RCgqKuKUU05h/fr1/tjYefPmMWXKlHaTvbpTtNuHJEm4XC7RY3qIo1KphpXQBgNnaSmN27Zh3bsXS3Y2joKj0Nmvs0yGTK1GplSCXI7kcCB1ZdtJpUI7bhwhU05CN3UqoTNnooiICMp9DGUGY0OEYNCV+xLaOjwYbtpqzcmh9N77cBQUAKCbPp2Ev94/KBO4+gNPYyPVr7xC7av/QXI4kGm1xP3uTiKvumpQh1N0R1sHzJDtSfeZlgzXPyICQVtIHg/W7GzMGzdi3rwZ+5H8VtcoExPRjBqFOmMUmpEjUSYkIA8JQXK50IwZgzImptXWkiRJSFYrrto63NVVOKuqcJaU4Dh6rMm7W4C7ri5wIrmckJNOQj93LmFnLkAzenRf3vqgZbhq0HC9L8HwRXK5qH7xRaqfXwluN4roaOLv+SPhF144LLfTu4ujqIiy+/+KZft2AEKmTiXpH0+gTkkZ4JW1zZAwZBsaGigsLAw41rz7TFcKdwuxFfwcsBcUYPz0M0yffYaz+VZukzGpO2UaIVOmEDJlSqsWhr7kLueJE6jS0oi58cZuVyqQJAlnSQnW7L1e7+/OHa2MaM24cRguvJDw888f9lt3zRmuGjRc70swPHGWlFB8113Y9u0HIGzRIhIe+CvKPkggH8pIkkT96g+ofOIJPI2NyMPCSPzb3whftHCgl9aKIdGiVnSfEQjaR3K5aPj6G2rfegvrnj3+4/LQUPQLFqCfOxf9GbM63d53lpbiPHECZVw8zhMncJaWdtuQlclkqFNSUKekYLjwAv+45m+/xbxxE+atW7Hn5lKZm0vlk08SduaZRF29jJBTThGeEIFA0KeYv99C6d13466vRx4WRsJf7yf8gguE9rSBTCYj8vIl6M+YRcnv78a6dy8ld96J5VdXEn/vvcjU6oFeYo8Y8KoFAoHgJzxWK3XvvkftW2/hKivzHlQq0c+ejWHxhejnz0eu1XZ5PFVSEqq0NL9HVpWUFJR1qpKSiLziCiKvuAJXXR0Na9di/ORTrHv30rB+PQ3r16MZN47oG24g/NxFgzoWSyAQDD0kSaLmxZeoevppkCS0EyaQ/PTTqFN+3u1au4IqOZn0t96k6pkV1Lz8MnXvvIstL4+UZ54ZktUcBiy0IBiI7S/BcMFjt1P//vtUv/Qy7upqABSRkURccTmRV1yBKj6+x2P3ZwMEW14edW+twvjpp/4kMs3oTGJuvY2wc85GJh/w0tVBZbhq0HC9L8HwQHI4KHvgQYwffQRAxGWXEv+Xv4jKKj3AvHkzJb+/G4/ZjCo5mZSVz6PNyhroZQ2NGNlgIMRWMNSRJAnT519Q+eSTuMrLAe/TcvTNv8GwePGQFWZXXR31771HzWuv42mq96wdP574v/wZ3cknD/Dqgsdw1aDhel+CoY/bbKbk9ttp3LoN5HIS7v8LkVdeOdDLGtLYCwoouvkWnEVFyPV6Ul9Yie6UUwZ0TcKQFQiGALYff6Tib3/Hsns34K04EHPzzURccvGQjVVqidtkovaNN6l94w08ZjMA4YsvJO7uu1HFDf2ksOGqQcP1vgRDG1ddHSduuAH7oVxkOh3J//onYfPmDfSyhgWuujqKf/tbrLv3INNqSXnmafRz5gzYerqjQcNrn08gGAJIDgeV//43x37xSyy7dyPTaom943Yy1n5J5OVLho0RC6AIDyf2t7eRse4rIi67DGQyTJ9+xtFzz6P+v//9WbTHFAgEvcdVW8uJa6/DfigXRVQU6W++KYzYIKKMjCTtlVfQz52LZLNRtPxWTGu/GuhldQnhkRUI+hHb4cOU3nMv9h9/BCDsnHOIv/eeoCVhDXasOTmUP/w3bDk5AOjnzSPh4YeGrHd2uGrQcL2v/kKSJMx2F5UNdipNdoxWJzanG4vDjdXpxuX2oJDLUMplKBRyNAo5Bp2KiBAVkaFqInVqYvSi9a8PV00NJ669FvuRfBQxMaS//trPtsFBXyM5nZTe9ydMa9aAUknKM88QtmB+v69DhBYIBIMMSZKoe2sVFf/4BzidKCIjSXjwQcIXntNva+jPpK+OkNxual97jaqnn0FyOlFERJD0jyfQz549YGvqKcNVg4brfQUbu8vN4bIG8ioaOFrdSEGlmYIqM6X1NqzO3nU80yjlpESGkBqlIz1Kx5iEcMYnhTMmPowQ9c+nCojb3MiJa67BdvAgyrg40l5/Hc2okQO9rGGN5HZTeu99mD77DJlKRcoLK9HPmtWvaxCGrEAwSHAbjdgLCqh97XUa1q8HQH/WmSQ+9FCr5gV9vY7eNkbozlxdMZhteXmU3nsv9kO5IJMRc+utxCy/ZUhVNhiuGjRc76u3VJvtbMmvJvtEPdlF9eSWmnC4Pe1er9coiQvTEKFToVMrCVErCFEpUCpkeDwSLo+Eyy1hc7kxWp3UW5zUWxzUW53tdpuWy2BUrJ5T0iOZMSqKGSOjSYoI6aM7Hlg8DgdFv/kNlm3bUURGkv7228KI7Sckl4uS391Fw/r1yLRa0t98g5DJk/ttfmHICgSDALfRSMW//kXDms/xNDaCQkH8PX8kctmyft8ytOXmUv3CCyjj4nFVVhBz881ox40L+jzdNZg9DgcVj/wf9e+/D0DonNkk/+tfKPT6oK+tLxiuGjRc76u7uD0Su47XsunHKr47UsXBUlOrayJ1KsYnhZMRq2dUTCgZcXpSI3XEhWvQqXtWqt3h8lBmtFJUa6WozsLx6kYOlZk4VGqiptHR6vq0KB3zxsRy9vh4ZoyMRq0cOg+D7SFJEqW/vxvTF18g1+lIe+MNQiYNjmZJg2V3q6+RHA6Klt9K4/ffo4iJYeT776FK7p86vUOis5dAMNwxb96M6aOPkRwOZBoNCQ/8lYhf/GJA1tKVxgjdFee2ru9uJzG5Wk3iQw8SMmUK5Q8+SOO331F41TJSX3zxZ9XqVjB4cHskdh6r5fOcUtYeqKDabA84Pz4xnOkjo5iaFsGU1AjSonRBfzBVK+WkR4eSHh0acFySJKoa7OwvNrLzeC07jtZwoNTEiVoLb24r5M1thYRplcwbE8dFJyUxd0wsKsXQNGprXnwJ0xdfgEpF8opnBpUR21+7WwONTK0m+d//pnDpUuw//kjRzbeQ/s7bKMLCBnppAQhDViDoAxo2baLsrw8gORzIw8IIv/ACws48c8DWozAYiFyyBGtODiGTJrUS3u6Kc3vX97STWMQlF6PJzKTo5puxHz7M8SuuIO2lF9GMHt2r+xYIukpxnYXVu4p4f3cRFaafjFdDiIozx8YxJyuWWZkxxIYNXG1nmUxGXLiWs8ZrOWu8t0mK2e5ie0ENX+dW8HVuJdVmO5/tK+WzfaVEh6pZPCWJS6elMCFp6BhbDRs3ejt2AQn3/6Xf4zM7Ihhtv4cSCn0oqS+s5NiSJdiPHKH0nntJee7ZQZWIKAxZgSDImL5aR8nvfw8uF7oZM4i943Y0GRkDKnZuo5G61atxnjiBLTe3laHaXXFu73qFwUDMjTf2aNstZNJERrz3LkU3/RrH8eMULruatDdeRztmTK/uXSBoD49HYlNeJW9tK2RTXpU/LtUQomLhhHjOn5zE6RnRg9qrqdcoOWt8PGeNj8fjkdhbXM8X+8v4eG8p1WY7r205zmtbjnNKeiTXzRrJwgnxKAfx/TgKCyn9wx9Bkoj81ZVELlky0EsKoDdtv4dqSIIqMZHU55+n8FdLMW/YQO1/XiP6husHell+hCEr6BKSJGFxeMvH2JzeEjJOtwelXO4vIxOqURKhUw1q0e9rmhux4RdeSNL/PYJMpQrK2L0Rwc4M1e6Kc0fX+wzanqBOTSX93Xco+vVvsOXkcOKaa4UxKwg6TreHz/aV8sLmAvIqzP7jszKjuXJ6GmePj0ejHHqVAeRyGSenRXJyWiT3njuWb49U8d89Jaw7VM7uwjp2F9aRZNBy7awRLJ2RTqhmcJkAksNByd1/wGM2EzJtGvH33Teg62lLc3v6sD7UQxJCJk0i/k/3Uf7Qw1T+61+ETDkJ3bRpA70sQCR7CZphdbjJrzRztNrsLSNT3UiF0eathdhgw+ZsPzu3OXqNkshQFUkGb+mY1EgdI2J0jEsMZ1RM6KD2BvSGhq+/pvjO33mN2MUXkvToo8gUwflj2B0RbEt8u/L+YMTIBgu3ycSJG27ElpODIiKC9LfeHJRhBsNVg4brfTndHlbvLuL5jQWU1FsBr15dOT2VX81IZ2RMaCcjDE0qTTZWbS9k1Y4T1DYli0WHqvnN3FEsO23EoCnnVfnPf1Lz8ivIDQZGffwRqsTEAVtLsA3P/kq47UskSaL07j9g+vxzlImJjPrs0z5LzBVVCwRdoqjWwraCGrKL6tlXVM+PFQ24PZ3/cwhRKdCpFSjkMjzSTyVkGh2udkvG+FAr5YxNCOOklAh/6ZiBjDkLFpYfsjlx3XVIdnvQjVjougi2Jb7g9cjK9Xo8ZvOQ2dZqbswqExMZ8d57gy4BbLhq0HC7L0mS+PJAOU9+9SNHqxsBiNFruP4Mr2fSEBKcXZPBjs3p5tO9pTy3KZ/CGgsAMXo1t83PZOlp6QO6m9a4cycnrrkWJInkZ54m/Jz+q7HdFsE2PIe6R9aHp7GRoxdfgrOoiIjLLiXxb3/rk3mEIStoE5vTzfdHqtmc5y0lc7xJyJoTHaomI1ZPRlwoo2L0JEeGEBemIS5MS7RejU6taDfI2+2RMFmd1Fud1JjtlNRbKaq1UFRrJb/KzOEyE42O1kXCM2JDmT8mjrPGx3NKeuSQ89jajx6j8MorcRuN6OfPJ2XFM8iU3duy68y72VURbCm+kVddhfnbb/tEPPsj3stdX8/xK3+F49gxNGPHkr7qrUFVmmu4atBwuq99RfX89dOD7CuqByAqVM1vF2Ry5fQ0tKrB4Ynsb1xuD//LLmHFhiMU1Xo906Pj9Dxw4QTOGB3T7+vx2Gwcu+hiHIWFfWocdYe+MDyHaoxsSyy7dlG47GoAUl96Ef2cOUGfQxiyAj8Ol4dv86r4PKeM9YcqMNtd/nMKuYypqRFMS49kSmoEU9IiSDT0XWFtj0eiqM7CgRITu47XsuNYLYfLTQFe3EidirPGxfOLk1OYMTIKuXzwZEb6aC5GSBLHLluCs6gI7eTJpL/+GnKdrtvjdUUwuyKCLcfSz5lD3apVrbwKXR2rvWv607vgKC7m+BVX4q6uJvSMM0h98YWgert7w3DVoOFwX0ark398dZi3d5xAkkCnVnDj7FHcNHskYdqfhwe2M5xuD+/vKuKf636kzuIE4Ozx8Ty4eALJ/dhkofKpf1Pz4oso4+IY9fmaQVPeabgYnn1B+f/9H3VvvoUyIYGMLz7v9t+9zhCGrIBj1Y28t/MEH+wp9sdEASSEazlrfBxzRscyMyN6wAW93uJga0ENXx+qYMOPldQ3iSlAckQIvzw5mcunp/WrqHZEcwNOmZqK41AujVu3okpJYcT77/WoW1dfbGH5DW1oM9SgK/GyHV3T1ppVSUl9JvrWAwcpXLYMyWolZvktxN5+e1DH7ynDVYOG+n19tq+Uhz47SLXZq32XTE3mvvPGEhemHeCVDU6MFif//iaPN7cV4vZIhKoV3HfeOH41Pa3PnQn2I0c4eskvwOUi5dkVhJ11Vp/O11uCbdwO9vHaw2O1cvSCC3GWlBD9m98Q97s7gzq+aIjwM0WSJL49Us1L3xawJb/GfzwuTMP5kxO5YHIiU1MjB5WXM0Kn5rxJiZw3KRGX28PO47V8ureUz/eXUVJv5ZkN+Ty3qYBFExO4ftZITk6LGND6dc2z/xs3bcKRX4BMoyFlxTM9bjnbm3IubdGyakDLDFtbbm6npba6W+VArtf3qYc2ZOIEEh9+iNI//JHq51einTyZsHnzgja+YHhgtDj5yycH+GxfKeANW/rbxRM5PaP/t8uHEgadigcunMCvpqdx7/9y2FNYx18+PsCa/aU8/svJrRozBJOKx58Alwv9WWcOCSM2mDo32MfrCHlICPH33Uvxbb+l9j//IeKSi1GPGNEnc3WGMGSHAW6PxOc5ZbywqYBDZd4WijIZzMuK5Vcz0pk/JnZIxJ0qFXJOz4jh9IwYHlw8ga8OlvPeziK2Ha3h8/1lfL6/jJPTIrjzrCxmj44ZEIPWZ8BZ9+7FkV8AQMIDD3TqQe3oKbk3tVe7QkvDVq7XIwsNxVlSjDojo03DuTPj2rdmW14eAM7y8j4vEm648EKs2Xupe+cdSu+5l1GffIwqISGocwiGLt8fqebuD/ZRbrKhkMu4bX4mt87PHBbtWvuL0fFhrP7NTN7cdpwn1v7I9qO1nP/M9/zfLyax+KTePWC3hfm772j8/ntkKhXxf/xj0McPNsFuhtBX48kjIrAdOIgtL4/QU0/t8XidoT/zTEJnz6bxu++oePwJUlc+32dzdYQwZIcwkiTxdW4l//jqsL8Wok6t4IpT07j+jBGkRAY3ZqU/0aoUXDQlmYumJJNbZuK1Lcf4eG8pP5yo5+r/7OSU9EjuOjuL0zP719OiMBiIuvJKjn/8CQCGS39JxC8u6fA9XXlK7k3t1e7ga4zgqa9HYYggcsmSNuftqnHtSyRTxsWjiI/HVVERFK9ye8Tfew/WnBxsOTmU/eV+Ul9+aVB1mBH0P26PxNNf5/HMhnwARsWE8q/LpzAlNWJgF9YNjBYnxfUWUiJ0GHQDG+6lkMu4btZIzhwbz90f7GPn8VpufzebbQXV/PWCCUEr1SW53VQ8/jgAkcuWoU5LC8q4fUlvd89aOjSCvRunSkpCGReP+bvvQAam9evRZmX12d8WmUxG/H33cXTrVswbN2Ldu5eQKVP6ZK6OEIbsEOWHE3X83+e57C6sA7ydaK6fNZKrZ6YTGaoe4NUFl3GJ4Txx6UncvXAML2w6yqodhewurONXr+zgzLFx/OWC8f1a/7F65Qu4qqpQJScTf2/nBbsHQ0tDn4C6zWavaCan4KqswGM2d3h9R0Zs8/vyVUhQ6PV9GpslU6tJevwxjl3yCxq//5761R8Qefng6vwj6D/qGh3c8f5evs2rAmDpjDT+cv74QVMXtSsYLU5WbvaWw0qP1nHL3MwBN2YB0qJ1vHPTDJ755ggrNubz7s4ifiis5+WrTyEtuvdOEtMXX+LIL/A+NN/8myCsuG9oqYU93T3zOTQcBQXIIyKIveUW1KmpQd2NUxgMhJ1zNvb8I6hS03BXVPT53xvNqJEYLr4I43//R+W/nyb99df6bK72EIbsEKOu0cHjaw/z3q4iALQqOdfNGsnNczOGfS3EuDAtf71wPL+ZO4rnN+bz9o4TfHO4km+PVHHdrJHcfuZo9H3cqca8ZQv1H3wAQOKj/4dC37kBHeyn7u4SkKDW5Dl1Fhcjj4hArte3Euquxlm1vK++fPJvjmbUKGJ/dyeVjz1OxeOPo58ze0ALpwsGhtwyEze+sZuSeitalZxHfzGJS6amDPSyuk1xvYXCGgsJ4VoKaywU11sw6AZHhrxSIeeuc8YwY1Q0d76/lx8rGrjoue9ZedU0ThvVs5wA8Hpjq194AYCo665DMUgTCtvTwp7onLO0FEdBAc6KSlwHDyKTvDtMwd6N02ZloZ04sV//3sQuX47p08+wbN9O4/YdhJ42o8/nbI4wZIcIkiTxvx9KeOSLXH8VgkunpXD3OWNIMPy8MnHjw7U8dNFErj59BH9bc4hNP1bx0rdH+Xx/GY/9chKzR8f2ybySw0HF3x8BIHLpUkKnT+/S+7r6FN9X2as+L6zPc2q4+BIa1q3DXV9PzVtvIZPAVVnhF+quepD7Ora3I6KuvpqGdeux/vADFU88QcpTT/Xb3IKB59u8Kpa//QNmu4v0aB0vXDWNcYmD0xjqjJQIHenROr9HNiVi8IWEzcqMYc1vz+DXb+5mX7GRq17Zwd8vnsgV03sWDtCwbp3XMxkeTuRVS4O82uDRnd20zvRblZSEPCIC18GDKGNicRvre+UtbW++gdBlVXIyEZddRt0771Dz6qvCkBW0ptps577/5bD+UAUAY+LD+PslEzl1RNQAr2xgyYjV8/p109l4uJL7PzlAcZ2VZa/uZMkpKfzlgvGEB7m0WO1bb+E4dgxFdDSxd3Sv/FN7T90+MZLr9dStXt3qyd9tNPoTqrrj8WzuSVDEx3uFuKQYhSECZOCxNKJKTsF+5AggQ5OZ6Rfq7niQ+yu2tyUyuZyE+//CsV9eSsOXa2m8/Ip+F0/BwLB6dxF/+l8OLo/EaaOiePGqUwbFVnxPMehU3DI3c9DEyLZHfLiW938zk7s/2Mea/WXc+78cSuut/O7srG7FqUuSRM0rrwIQtWzZgDQ46UoDGp8ud0ULu5oHEXvLLcgkcBvr20207er6O5pvIHQ56rprqXvvPRq/+w5bXh7arKx+m1sYsoOcb3IruOe/+6k2O1ApZNx5Vha/njNqQFsJDjbmj43jq5Fz+MdXP/L61uOs3l3MtqM1PHvlyZwUpIQPV1UV1c95MzLjfv/7oGyFNRcjuS4Ut7EeVXKK36AEqHzuORq/+w4kCJ0xnfALLuiSQRsQv1pRgeGSSzA1eWEbt2z1J2apR48O8Mj2Ng6sP9GOG0fkFVdQ9847VDzyCCM//mjQNEoQ9A3Pb8rnibU/AnDRlCSeuHQyGuXQ/84NOtWgCSfoCK1KwYorp5IRq+fpb47wzIZ8TDYXf71gfJfLOtr278d28CAytZrIpb/q4xW3pjMjsOX5yCVLOm3t3VXPrTo1lfh77+m1tg6GvIuWqFNTCTvnHBrWrqX2tddJevT/+m1uYcgOUlxuD0+uy+OFzd4ST2Piw3jq8imMTxqa22d9TahGyYOLJ3DepETuWr2Xolorl76wlXsWjeWGM0b2OrO9+uWX8VgsaCdNwnDxRT0ao6UXIECMSrwxq80NSmdpKY4jR8DtQfJ4MH/3PY4TJ9BOnOgXX0dREdacHEImTUKdmuqfq1Wd19BQpMZGf5JX88QsoJWwdvRE35MQiL4q0h17+28xfv459iNHMH3+OYbFi4M2tmBw8fTXR3jqa+/uxPJ5Gfxh4RhRsaKLBLMqgkwm43dnZxGtV/PXTw7y+tbj3i5ql07uUpnH2rffBiD8vPNQRvX/rmJnRmDL8x6z2d8N0Zabi1yvb2XY9vcu1kDnXbRH9HXX0rB2LabPPyf+nj+iiIjol3mFITsIqTbb+e072Ww76m1qcN2sEdyzaOzPti94d5g+MorPb5/Nvf/dz5cHyvn757n8cKKOf142pceZzM7ycurfex+A2DvvQCbvvje8LS9AczFSZ2S0+eSvHj0aZ3k5uFzIlEqUCQnYDhzE8kM2yKBqxbO4qqtQxSeQ/M8nUaem+o3G5uMBHSZmdTdkwVFQgMIQQczyWwIM6K7ee7CMWUVEBNHXX0/VU09R9dxzhJ93HjKlkLXhhCRJPLX+p/Jaf1g4hlvnZw7wqnrGQJTZ6quqCFfPHEG4VsXvP9jHR9klSJLEP5dMQdGBZ9ZVV0fDl2sBb57BQNCZEdjW+ea656quQRkTjTojIyD5q7e7WN152B+su2bayZPRjB2L/fBhjGs+J6qf4p+F4g8y8ioauPY/Oyk12tCpFTxx6WQumDw4nraGCoYQFc8vPZlV2wt5eM0hvsgp50TtVl6++hQSDd1vdVvz0ktIDgchp0wj9PTTe7QmX8aqLETnzVwtLUU7blyHYqQwGIi79VZsZ5+Nx9xIw8aNWHbuBI+HqhUrQCbDceyY13tbUY41JwdFeHi7RmPkkiVYc3JQjxjhD13orgA2z7y1HjyIJIOEe+7pcJy+3gaLumopta+/jrPwBMZPPiHil78M2tiCgWfFhny/Efun88by6zkZA7yinjFQZbb6sirCxVOT0akVLH/7Bz7eW0qIWsH/XTKpXU+56csvkZxOtOPHEzJpYlDW0F06MwLbOu/rhijX6XBWHESVmtpKy3rjae3Jw/5A5Sd0hEwmI+KXv6TikUeo/+9/+82QFYGWg4itBdX8cuVWSo02RsWE8smts4QR20NkMhnLZo7gnZtOIypUzYESE4uf3cKBEmO3xnHV1lL/4X8BiL3ttz3eypTr9biqa7Ds3oWrugZ5U4KDwmBAO25cu4KkMBgIPfVUwubPw3DhBagSE9COHYurphplXCwypRJXbS2q+ARCJk1q02iEnxohmNasofzBh6hasYLqV17Bbeze5+EV9ghc1VUoY2Lx1Nf75/DNY8vNDRjX5+FoHjYRTOShoUTfeCMANa/+B8njCer4goHj7R2F/Gu9N5zgL+ePG7JGLLRtUPYHvqoI5SZbn1RFOGdCAk9dPgW5DN7dWcTDaw4hSVKb15o+/QyA8MUXBnUN3aUrutv8vE/DPBYLqvgEJKslqFrWnm4PRcIvOB+ZSoU9Nxfb4cP9MqfwyA4SPtlbwt0f7MPpljh1RCQvLTtl2DU2GAhOHRHFJ7fO4sY3dvNjRQNXvrSdV689lekjuxabVb96NZLDgXbCBHQzulZuqy08ZrN3OyotFY/F0m4jgo7w1Qd0FBSgik9AJpMRtmgRIZMnETpjhj+sQJWW5i+67TOYfUIpC/F6FNRpqQGC2a0treW3IMnAUx+YedtRzcW+3gaLuHwJ1c8/j+PoURq3bkN/xqygzyHoX9YeKOP+jw8AcPuCTG6cPWqAV9Q7elJmKxihCG1VRQh2iMOFJyVhc7r5w4f7eW3LcRIN2lYPHY6iIqx794JcTvh55/V6zv6kuYa1FSPbWwZrzGtPUEZGop83l4b1X2P66iu0Y8f2/Zx9PoOgUz7YXcQf/7sfSYLzJyfyz8tOEvGwQSQ1SseHt8zkhjd2s/NYLcte3cELV01j/ti4Dt8nOZ3UvfMuAFFXL+tVYokqKclb7io/H01mZo9bG/riXtsTU4XBQOSSJVQ/vxJ3fT11q1cHxOP6jGCPxYI6IwO5Xk/lc89hz81FGR9P3B13+GNe24vZUqemknBP68zbjkII+nobTKHXY/jFL6h76y1q33pTGLJDnD2Fddz+7l48Elw5PY3fnd1/pXz6iu6W2QpmKELzqgh9FeJw2SmpGK1O/v55Lo9+eZi0qFAWTUzwn29Ytw4A3YzpqOI61t5gEcwk077UsJYP+wC23NxBFf/aHcLOPpuG9V/TsH49cXfc0efzCUN2gHlv5wnu+ygHSfK2V/zbRRO7XMZE0HXCtCrevH46y9/+gQ2HK7npzd28dPU0FoyNb/c9Dd9swFVZiSImhrBzz+31GiQZgIQkA7fJ1GWB7Sh+yreV33wcj9nsrxPrKCjAtH496vT0No1gW14ejZs246qrg4OHkBxOopZdhSohIaCubctEtObGK9AnfcO7S9RVS6lbtYrGzd/iKC5GnTL0ujwJoMxo5Tdv7cHh9nDO+Hj+fvHEIV+doLkHdEJS1wyTvopt7cuY2RvOGElhjYW3thdy5/vZrI6YyeSUCADMmzYDELbgzB6N3V2jtDtxp31VVaU7+HQ1GMmxA30/+nnzQKnEkV+A/ehRNKP6djdFGLIDyOrdRdz7vxwArj19BA9cOH7IC/ZgRqtS8OKyadz5/l4+31/Gzat+4LVrT2VWZkyb1xs//RSAiEsuQa7uXZiHs7QUd0UFmszROIuLqVq50lsOqwsCa96yxetJbVZjtiPBa+59dVZUUv3iS8iUCkJnzybu1ltbzSW5XEguJzKFEuvevVSWl6FMSwOLxW8MVz+/0mscN80DtJobQD9njvez7qd2tc1Rp6cTOvM0Grduw/TZZ8Tccku/zj+YWLlyJStXruT48eMATJgwgb/+9a+cG4QHsr7E5nTzm7f2UG22MzbBW3Kwoyz4oUBPPaDdCUXoTqhAX3YSk8lkPHDheIrqLGz6sYqb3tzN57fPJtJjx/LDDwDo583t9rg9Me66mmTal1VVekJvk2MHw/0owsMJnTGDxi1bMH/7bZ8bsiLZa4D4+lAF9zUZsdfNEkZsf6FSyPn35VM4e3w8DpeHG9/YzZ7C2lbXuerqMH/7LQCGi1rXJm0rqanDeZslPCkMEXjq6zsN7PcJkmnNGlzVNThLigM8ne0lCPi2qcIvuACZWo3kdCI5XTiOHGk1lzYrC/3sM1BGRiHX6fDYrLhq67Dt349Mp8NVWYE8IgK3MXC9Lee25eVR/cor1K1a5f/c+pL2Pv/wC73flfGTT9tNOPk5kJKSwmOPPcaePXvYvXs3CxYs4KKLLuLgwYMDvbQO+fNHB9hfbCRSp+Llq08hVNN/vhajxcnBUiNGizOo4/Y0ycsXinDbgswOjV+fofzshnxWbs6nqMbS6j6a31tXx+0pSoW8qWlCKBUmO3e+t5eG77eA2406I6PTcn1t0ZVkqJaa0FmSqe96W17eoEq06m1y7GBJHPNV+LFs297ncwmP7ACw63gtt77zA26PxKXTUvjrBcKI7U9UCjnP/moqN76xm++OVHPDG7v5aPksRsaE+q9pWLsWXC4048ehyQysWdnTUinNkwWab9u3J1S+UldynQ65zkrI1KmEL1rUpSLcCoOBkEmTkBwO3EYjMrkc5SmntDlX+AUXoJ8/H0dRETVvvAkuJ6jUhM2fDzIZ6hEjMH70cav43uZzA/61+sqL+e4h2NtbHX3+YWefTflDD+E4fhzbgQOETJoUtHmHEhdeGJgV/sgjj7By5Uq2b9/OhAkTBmhVHfNRdjH//aEYuQye+9XJpEYFN7u+I7riNfV5PcM1Kkx2Z6fez+bXd8cD2tK72nLbv+X55oZyfqWZpzfk0Wh3++8DaPPe+rKTWJhWxcqrpnHRs1v4Pr+a7Ts3kg6EzpzZo/E6C1tqTxPaSzJtfr0yLt7f6bAvQqK622a8t8mxAx3i5UPX1DLcsmsXktOJTNV3peaEIdvPnKixcNObu7G7PJw5No7HftF+zT1B36FRKnhp2Slc8fJ29hXVc/3ru/jfLaf7K0U0fP0NAIY2smt7uvXTPLa0M6FyG404KypwVlTirKxAhgyZSoXbZGpVDcAnkv73NY3rMZtRRkXiNsXgrq/HY7W0mqO5+IfOno1crcbd2IgyIpKGDRtxFhWhSk0FpxPJ5WyK820ttm6Tyes1rjjoLU8jSW2GHgTDsO0wqUwfin7+PBq+XEvD19/8bA3Z5rjdbj744AMaGxuZ2Y4hYbfbsdvt/tcmk6m/lgdAYU0j93/s9RbfcWYWp7cT7tNXFNdbyK80E6pWkF9pbhU36jN08yvNVJvtxOg1ZMbp2/VotjSMl05P77Lx65snQqfijgVZpEbrOjzfPFQgQqei3uIkNVIX4P3tq5jYjsiKD+ORSyZy1+p9uPZlA6A7ZVqPxurMuGtPE9pL0Apo4V1ZgeHiS/BYLYRMmhT0h+7mbcb1s2cTe1vr8K6W7+mNTg6WZgm+8mVuoxHbwYOETJnSZ3MNaGjBypUrmTx5MuHh4YSHhzNz5ky+/PLLgVxSn9Jod3HTm7uptzg5KTWCZ391cpda+gl+IpjbfyFqBS9fPY3kiBCOVTfym1V7cLo9eBobvY0HAP38+a3eF4y6qB3VMfQZmLVvvIHbZEKTkdn0nvA2t4oa1q2n+vmVVPzzX1Q9+xzVL7xA9SuvINfrkRkMuGu8HeLsuYcDjF5/kwaVCvvhH6l9/XU8RiPy0FCQJMybNmHLy6Nh3Toad+zAY7HiKi4OCGHw3YOvvJjulFNRGMJpbIrrlRsisB04gCU7m+pXXvGvrbv1a5vT2ecfNm8eAObNm3s8x3AgJycHvV6PRqPh5ptv5qOPPmL8+PFtXvvoo49iMBj8P6k92P7tKU63h9vf24vZ7mL6iChuW9D/XbvCNSqqzXa2H6ul2mwnXBNobPq8njq1gjKjjVC1osMwgcPlJnKKjUTpVBTWWDDZnYRrVGw+UklRTfuhBT6Dutxk47sj1Ty9IS9A69o6D/hDBe5YkEVmnD6gZmx36sgGO7ziFyencNXEaEYYywCQJkzu8VgdaWZ3Nbn59cq4eMzbttLw1VfUrV7dK21qSfM243jc2PPzO9zq92l/b3Wyszq5/YFMLidk6lQArDkH+nSuAfXI+uK4Ro8ejSRJvPHGG1x00UVkZ2cP2u2vnuLxSPx+9T5+rGggNkzDS8um9bhl6s+VvigbExem5T/XnsovV25l57FaHv/yMHfoypGcTlQpKajbCFLv6yden7dAlZrmbU/rdqNKTPSXzGou0ra8PMzffQceN47jx1GlpKAdO9bfIzzigguw/ZANcjkSEo7CQrRZ3lJG9qNHsR8vxNPQgCIiwnsfMhmuqkoUjggktxuZXI7kcHjjZaur0GZltflHQq7XI4+IwFVahqumhsYtW3GbTNjzC0Aux7hmDVJT8lhvu3t19vmHzp4NMhn2w4dxVlSgim+/MsVwZsyYMezduxej0ciHH37INddcw+bNm9s0Zu+77z7uuusu/2uTydRvxuxL3x5lX1E94VolT13Rv8ldvm36BpuLGL2GtCgdFocbkz3QkPMZg/mVZhINWhodbjLj9G0ahUaLk7UHyig12ig1WpmXFYdMgt++9wNlRhuJBi0rrji5lafVF4YQoVORU2IkTq+hvum4z4OaEqFr8/yEJIP/mrZKfHWl7Fdn+trT2rN3pnuoQqJcF8mbu2p4NDX4W9090WRfYipA3apVfdJ5UJWU9FObcYlOSy/2dRfE/kY7YYLXIXJgGBuyQzGOq6e8+v0x1h4sR62Q88JV04gP1w70koYcfVU2ZkxCGE9edhI3r9rDK98f4xzrd4ThFbr2wj56246wI8FtHuMUOns24WefjSohod0i3JLHDQ4nklKBMj4uwCuhSkpCf9aZ2HNz8VisNHy1jsbt20GlwrpjB666epSxsci0GmQqJR6nA2VUNMr4eGQhIbhra5EZDGjS05HHRBO28JxWpcN8XcPcVVXe6gxmM5LbjTxUhzxMjyolBY/RhCI2JmjdvTr6/JVRUWgnT8K2bz+N33//s21Zq1aryWyK7542bRq7du3i6aef5sUXX2x1rUajQaPR9PcSOVpl5ulvjgDw4OIJJEd0v4V0T2m+Ta9TK4gLU1NncbVpoDavAdtZjGxxvYVyk52Zo6IorLGwcGIC+dVmyow2onVqyow2sovr/Ias0eLk31/ncajUSLxBy9Lp6QDUW5yt1mJoCido77zvmpa62JWY2I70tTdOBPkxb3vho4Zk3t15gkUTE5ibFdul93aHrmpyy5CqyCVL+iymtHmbccDvRGivRuxgiW8NFtomO852qG+TTAdNjGxX4riGKgdKjDzxlbdV218vHM+09MgBXtHQpC/LxiyamMCNZ4zkle+PUbNjF2GAbsaMoI3vo7OasD4DsaveBXloKHKlCldDA6roZKKvvx6ZTBbwvoiLL8YoSdgOH8ZVW4vz4EFkCjkovL/+rooKcDjwNFqQnC5c9fXIDQa0Y8bgOHYMTVYWhgsvoHHLVupXr8ZVXePtUpaR4V+n88QJ5OEGPI2NSG43zpJilHHxyBQKrD9ko0pMJPZ3d7ZaW3ufUW+93aHTZ2Dbtx9LdvbP1pBticfjCYiDHWg8Hon7/peDw+VhTlYsl0xN7tf5m2/TVzXYmTEyil9OSyGsnUoJXU2Qaq5Tk1IMjEsIJ9kQQqJB6/fITk356W/A4XIT3xyuoN7iJKfUu5X8h3PGtmssp0bruP/8CUHtzNVy3S31tTdOBPsR74OKYby3w9NfPs5h/e/mDljTn5ZeT4/Z3Kc7bL4249C+/ndX+we6TmxX0U7w7v7YC47icTh6XcayPQbckM3JyWHmzJnYbDb0en2HcVwDnZDQExrtLm5/NxunW2LhhHiWzkgb6CUNWbrbGae73HPuWA4WlJFa743n0p50UrfH6Exg2ts6astL0JW5aletwt3QgMIQgUytxtPYiDYryx+H5TaZKH/wIZxlZUhOJygUKKKjcdXWgsUEHg8ypRJ1Zia2Q4dQ6PUglwMStpwcZCol1v37UY9Ix1lS7G9xq0xMwHbgILa8PG+4QVPdWkVUFNTWIk9MwmOx4HY4UEZFoTCEI5PJ0I4b1+k9BaMGYsjUKQDelpg/Q+677z7OPfdc0tLSaGho4J133mHTpk189dVXA700Px/uKWbHsVpCVAoeGYCmBy236Y1Wb0hA84z/rmhM6yoDrXXKoFOx4oqTyS6uY2pKZEBYgQS4PBJOtweVXE6FyY7J7uywcUJfVB3oSF9740TwxeXPWTiDxINaimqtvLj5KHecNTqo6+8qbXk9+7rzoI/2SmO11LyOdHIw1IntKsq4OG9ZR4sFZ3EJmlEj+2aePhm1G3QnjuvRRx/loYceGoBV9pwn1h7maHUjCeFaHvvFZFGhoJf0ZdkYlULO/41VYEOiIiSCbfkWrk/o/H0+uiIw7W0dNRc4++HDVDz6qPf65BTCzjm7zbItztJS3FVVyEJDcVZUIDns1K9ZgxxwnChCk5mJZkwWzopylNHRuKqqUKWm4q6tRa5SgUKBJisL+/FjuOvqUMbF4bE0IpMrUKenY8vNRfJIeBpMNG7dBm43CoMTZUwsttzDyBRyTOvXo83K8nsRJEmidtUqnEVFOCsqUUdF4aqrRREb26VtsmDFiIU0PYQ48gtwG42DVuj7isrKSq6++mrKysowGAxMnjyZr776irObtjgHGrPdxT/W/QjAnWeN7tdSWz5abtO3lfHfmda0t+Xelk6lRusCDNiiGgvZxXVkxuiZlxXD5rxqlHIZmbF6Gmwuf83X/qQ9fe2NE8F5ogiA8KxM/jwinNveyeb5Tfn84uTkPv3e23MqDGRWf1v631zzHAUFmLdsQT9rVrvr6olGDpQHVyaToUpPx56bi6Pw+PA1ZLsTxzWQCQk9YU9hHW9uLwTgyctO8pd2EgxewouPYgN+jEzjX2sPM29MLKNi9a2ua0sYuiJI7YloQDeu8grc9fUooqOx5xdgzz+CduLEVoaxXK/HbTThMRqRKeSETJmK8+hRHEXF3gzZo0eJSk1BEW7AVVODKjmZyGVXYfzwvygT4rFm70Wy21AaIpDrdGhGjUQ/fz7y0FBUCQnUvPUW1j17kFwu1CPS8RiN6BcswGO1YvriS1Sxsf4qBs0zZBPuuQdLdjbGz9bgrqlBlZhI1FVXdUlAu1IvsiuCrIyORpWSgrO4GFvuYUJPC36YyGDm1VdfHegldMiLmwuoarAzIlrHdbP65o9bV2i+TR+uUfH2zsJueR17uuVeVGMJSP76v4snsXhKChabi+/yq3hj6/EuJVz1NAGrJ/TEieCxWnHX1QHe3+3zw8J4e9QJth2t4ZHPc3lhWc/KcXVGZ06FYHtgu6pL7ep/k/a7qmswrVmDLTe3XU9rd+NoB9qDq05LazJkC/tsjgE3ZFvSURzXQCUk9ASHy8O9/92PJMGl01I4Y3T/1kUU9Az70QIAZCNGYnd5+OsnB3nrhukBnvSutIbtSJDaElGfwJm3bKF+9QfgceOqKEceovNWL2i2DRVQJzYm2rvNn3sYd10tysgobLmHkWw2kCTq33kXVUIC6pQUYn57G+rkZGx792HPzyd0xnS0kyZh3rABVXKKNxErPt7vJYi4+GI8NbV4GsxYs/cSOmO6twvOsWM4i4txFhejSkxErg809N0mEw1frcNdW+M1oBPiMX35Jerk5F4VA++uIGsyMnAWF+M4dvRnZ8gOZkrrrbz07VEA7j13HGrlwJYgbG6gddfr2JMtd6PFyWc5JZTWW4kJ1VBmtLG/tJ5JyREAlJvsfsM4t9xEmFbpH7dlbdrmhndfdOnqLc6ycqAplj8sDJlMxkMXTWDRv79l7cFy9hXVc1JqRPDn7cfsf1+tWPOGDShjYkh78cVOjVnf+arnnwe3x6/9pjVrOq3s0l2P8kBXQvAZ2q6Kyj6bY0AV5L777uPbb7/l+PHj5OTkcN9997Fp0yaWLl06kMsKCq98f5QjlWaiQ9X8+byO4wIFXvqqRWR3cBR4/8CeuXAGaqWc7/OrWXugPOCarrSGVcZEBwhSV/B14/JYLLhMJhSRUehmTMdjrEeVloZcr6f6lVeoWrGC8scfx9UUG4vTSdjcucQsX47hkku8hqVCAWo1bqPXsytTq/zGuLepgQQaDaEzZqDOyPBXE/DNUf3CC9S9tQqP1YLutNNQJSagnTQJd0UFCoMBCQnNmDEoY6LxmM3+e3AbjVStXIl56xZclVW46+uRhxu63FbS9zn4YsSan+tu60V1RgYA9qPHuvT5C/qHZ745gt3lYfrIKBZOGFyl0Qw6VVMpq64ZhN1t9+oLRdhx1NsWu7rRTmyYhpwiE89uyOerA+UkhGsoN9lICNfw1YFyf+vZw+WmAO9vdnFdm61vB4OO+nBVeLVTmZjg15+s+DAubkrse7IpvCTYBKPWd1ex5eXR+N13eEwN2Pbuo/zvj7R7bXO9q3r+eaqfWQEKOQqDAf2sWQFa3Naafe8Hulwntj8/i7ZQRkcB4K6t6bs5+mzkLjDY47h6SlWDnec3ej17fzpvnAgp6AKddbTpL+zHvEZP8knjuDlSxjMb8vnbmkPMHROLTu39demsNax+1ixsubk9KqHi87KqUlORrBYifvELFHq930vqKCjAWVGJK+cAtpwDqBLiUcTGEnX1MtSpqbiNRvRz5tD4/ffeAdVqnKUlaCdN8o/hbmrF6DiSj7O8PODpPsBYLClGHhGBx1iPduJEQmfMwFFYiKOgAHViEnK1ClVyCm6z2R+H6iwtxVNfjzImFldlJYqICCRr6/q30HkFh+pXXvEmkBkiiFl+S7e31HzxWI6jR7v8+Qv6luI6Cx/uKQbgnkVjhkzOQEdb+N3ZcveFIoyK8e5izBgVxdi4cD74oZiEcC3lJhvXnj4CvVaJ2ebi5e+O+ruNzRsTF+D9nZoSyaFSU4A3uC9qbfcG30OoIiIi4Pjvzsris32lfHekmm0FNczMiA7qvP0eB+v2IEkSqFSYPvsMVWIicXf9LvCSZnrnqq7BumcPMbf/ltjly7u05p6GCPTFZ9GdmFtFlPe7ddXW9Xre9hhQQ3awx3H1lKe+zsNsdzE5xdDvJWWGKs1L4eSUeMXv/vMn9KsIeywWPE3Cq0pJ4ZZRIfz3hxJK6q28+t0xfnumN8u2M2HojXCokpJQZ2TgPHECdUZGqyQveUQEroMHUYSH466vR5OVhdTY6PeKKgwG4n9/F5YF86n/7/9o3L4dT02Nv36hKinJ28nmu+9Ahj9Zq3mWrKu6Bnt+PvoFC4hcsiSgfq3vvuR6Pc7yckzr11O3apVfWH3rB9CMySLqqqtaldzyiaDbbG63goN5yxbsh3/EVVuL9eBBJJk39rY7n6sqJQUAZ0V5h9cJ+o8XNhfg8kjMyoxmWnrUQC+nSwTTOEyJ0JEQriGvooGseD1Lp4/AZHUSqlFQVGchM07P2IRwDDoVRTUWqs12cpriaJMNIa1CH1q+PlhqHJB2tO3hbmgAQKEPCzieGqXj8lNTWbX9BE9/k8fMjOCX3OwoDjaYyU/arCy0kyfRuHUbquRksNupeeklZFqN30iFn3aUnOUV2PbtI+LKKwLOd0ZvQgSCGRPcXYNaEeUtNefrMNkXDLoY2aFOfmUD7+08AcBfzh+PvB+71Axl2utY058i7Gr6RZNptchDQwmRyfjjojHc8d5eXvruKFfPHOH/A9aZMPRUODoyghUGA7G33IJMAld1NfLQ0Da9nQqDAVV8PB5zAzK5HI/RSOPWbTiv8CZlhZ1zNvb8I6hS03BXVGDJzsZj8fYZN372GdY9e4i48gpibrzR253r2DEkSULWtCaf0esxm3FXVPjb0Nry8gg99dQuexUU8fH+Xuc+D2tzT6yzvBx3fb23mkJ9faukss5Qxnjj0l1V1d3+HgQ9pz3vZbnRxupdXm/sbxcMTOml9vBVEGhZGgt634il+edRUmfhaHUjZruTmkYHJXUWPt1f6q+a4GuGcLDUiLmp21hiuJbaRgelRmvT2nRNYQS6Vt7gvqy13RM8Dd4HbHl4WKtzy+dl8t7OIrYfrSWn2MiklP7R+q4aYm6j0V86rK2qMT4UBgNxd9xBlVqNp74edUYGMpncGzYAfmNVlZSEq7oG2759hEybRtydd3ZrXYOlWUJ3DWp5iPffoKcPa1gLQzbIrNiQj0eCc8bHM33k0PA4DAa60rGmr/EZPMqYGP+W54WTk1i5qYDD5Q28+G0Bf1w0ts/X0ZERrE5NJf7ee7Dl5eFpbEQeGtqmyAa0RpTJcBYVYfpyLdpx49BmZaHJHI09Px9lQjxVK57FVV2FDBmuigr/dpejqIiS39+Ns6wMJAlVagraSZOIXrYMZ3k5HnMjsogILNu2I7lc1P/vI4BWHl4fPk+ro6DAm1xWUUHkVVf5QycUBgO23FwcBQXIQnQoo6NRp6SAjDZDEzrDZ8h6jMY+LcYt+ImOvJdvbT+Ow+3h1BGRnDYquFvJvaFlBYGW7WPDNaoAj2lLXTJanBwuNyEB45q8qc3P+T6PSJ2KncdqOVFrQS6XUVJvpdHhRgakRuooN3lb2voSuBLCNcSHadl6tBqQsfZAGUmGkA4TvPq61nZbdBR24bF643Z9xkxzkiJCuGByIh/vLeXl747yzJVT+3yt0DVDzJfA1fjddyCBfvZsYm+7tc3rfA/tCffcE/AAL9NqAozZ2rff9jsJfEZs8w5fna2rPSdHf5fW6q5BLVN7/01IwpAdGhytMvPZPm8CykAVex7K9FXHmq7iC0ZXRv/0R1Yul/H7c8Zw05u7eW3LcW6cPYqoPox5bilKbb225eVhWr/eH+uqzcpqdV3L1ogNX39NzUsvARB9w/X+hC9XdTWuykqQvP+tO/10opYuxW00Ylq7FmdZGTKNBldpKR6XC1dZOY6iYpzHj4EE6pEj8DgcyORyzN98g7O4iJApU1p5E5p7Wl3V3s+5zdAJvR5XdQ3OioOo4hOI++MfutQNrC3kBgOoVOB0emvnJnSjKLCgR7TnvbQ53by701tP9IYzRg3wKgPJLq7zt48tqbfyWU4JS6eP8Je3entnIfUWJzq1glNHBDonfO1lN+VVAjLmZcVw/axR/q5czT+PH07UUdPoQKtS0GBzEatX43ZLxISpKTfZSI/WIYH/+nKTjbljYjhe00h6tI5yk73NBK+etKMNFp2GXXgk7/+3szN54+xRfLy3lM9zyvjjojGkRPa988IXXmXPz0eTmdmmIeYsLcVx5Ai4PYC3K2LLcopteVCbP8D7PLHVz6ygZuULSE6n30nQ1nu7YiC2dHIMRGmt7obOuaq9DiJncXGfrUkYskHkuY0FeCQ4a1xch11ZBO3TnyLcEo/N+8Qo0wX2ez9rXByTkg3klBhZtb2Q28/sm4eUtrp71a1e7X8dfu65Tc0GinFVVxFy0hRsBw5gyc7GsmdPKzHztUZ0G40YP1uDMj6empdewl6QD5KEJnM0juPHkFwuPHV1yHQ6ZAo5lc89h0wCx/FjXgO3ogI8HqSGBlxWK1LOfmTaEGRyGc7SMnC7kTweJJcLRWQUzhMnAsIV1Kmpfm+DKtkbtxp+wQVtFv32Jbup01LxWCxd6gbWHjKZDLlGg8fp9DaDEIZsn9Pe1vYXOWXUNjpIMmg5a1zcgK2vLe/h1JRIEg1aSuqtyICdR+swWV1+z2ZhjYX4MA1bj9ZQYbTxZXSZPxm1uN5CXkUDniaD7VCZiac35Pm7gy2dnu7/PCYkhePxSJSZbEToVKRFhTI2MYyl09P9hi8Q8PnNGhXL8WpLhwleffGZdJVOwy4k7+fSXlLfxGQDp2dEs7Wghvd2FnH3wjE9vo/u4HuQl9qJ/AvY0XJ78FgsrcopdsWzG7t8ud+IlalUfuO2rfdqx43rdm7FQJXW6lbonMvVt4tBGLJBo9Jk45O9JQDcNsjivwRdw7f1IVcH1iqWyWTcOHskd7y3lze3HefXc0b1SZ/wlqJkzckJaLBQ+Y9/YD3gTfSSJAnLzh2gVFH39jtITieK6GhvbGmzpClnaSn2o0cxfvEF2Gwgk2H+ZgOKqCgkpxN3QwOeujpva1ldCMrEJOy5uUh2B6rUFFApfxIitxtFTAwylQo8biSZEplMhkyjAZfLX2mBEB2V/34ad10tqvgEkv/5ZIC3QZ2R0W7nmpbJbr2JA3MbjUhuNwC1771HQlraz67DV3/T3tb2m9u8xdCXnpaOUjEwVR/b8x6mRutYccXJfJZTws6jdYyMCSW/0szmI5VMTYkkPVpHTrERtwca7C6+O+L1MN1//gRSInRkxYdRarQCMuLDtf7uYPmVZrKL6wIMVZPV6e/mJckI+Ix8BmXz69v6PIMZOtDbRLZgxOQunZHO1oIaPtxTzO/OzkLRx3klvsotmszRuCoq2jT+mu9oOQoL/bW2mxuLHXlQfdpr+nKt34iVnE6qnn+e2OXL231vd3MrBkvcbEcoIvs+xFIYst2kvafXd3aewOWROCU9kil9UOBZ0PdIjiaPbBtNN86blMjjXx6m1Gjj4+wSrpieFvT5W4pSyKRJWLP3Yjt8GEmSsB06BBaLt35sQgKKqCg8FgvWAwdAkpCpVP4GBY6iIiqefhp3RQUepwsaG/3eEeRy3LW1NH6/BSQJ9ciRqEdnYj14CNvBA0guN+4GE7bDh8Ht/ul9gEytJmzePEJnnY6rupqGr75ClpaKu66eyMsvp3HLFqyHDuEoPI4qKRlnRTnWnBwM553XpfiuYJaKcZaWIlmtANgPHer3QuA/V1ruquRXmtlbVI9SLuPyUwemE6PR4mTzkUryK81ttqBNjdY1VRBwcbisgVKjlf/uKSa7sI7ZmbGcOiKKT/aWsONYLbFhPyWjTkgycOdZWSyamIAEJDfFsOZXmqk22/l0bymHSk0BrWvbKivYkUHp/f/2E7x87++JcdvbRLbODGtZU1y6x+Fod4yzxscRqVNRbrLxbV4V88f2rce+q8afb0dLm5WFo7CwTaOzPU2rfuUVzBs34sgvIPrXvyburt/9VDcWr6c2GDrX72XGeoBM1VS2Mj34fzN9CEO2G7QnNg6Xh7d3eCsVXH36iIFdpKDHSG6P9z/krT1GKoWc62aN5JEvcnl7x4keGbKdBeW3FCUAt8OOs6TYG/bQ5FFFJkMWEoLbbPZ3/0Iu95a9cruwFxRgXLMG8zcbkMnlSIoW3mNP031KEigUyMPCsGbvBUlCcruRrFZUScm4K6tAowGLpWmBCtSZGf6atY6iIupXf4AzL89bN7au1hsP5fGAy42ztAT1iJHIQ3T+OrNAQCODtuK7glUqpvkfKHlY+KD0Vvwc8O1UzcmKJUbf/50Zm9eorjZ7H1bbStoyNFUNeGjNAeosDhRyGcerLeRVmMmK13P2OG/zBovDHfB+g07FjGbJa7fMzWTzkUo+3VsaYDT7jNFwjSrA4wodG5Qt/+609Nh25lXtyMgNhke1o3AweWgoAB5zY7vv1ygVXDw1mde2HOf9XUV9bsh2pV5rVx+u29IqZ2mp34hVZ2YQfu4iIDBm1vc6GDoXzNJafYHHZgNArtH22RzCkO0G7YnN17kVVDV4O7QsmiDi8LpDf/YK7wyZxus9kNrxHvxyWgpPfHWYnBIjh0pNjE8Kb/O6tgxWt9FI1bPP+RMM2sqAhZ9EyZfh7zx6FJlShUzt8RrYbjcoFGhGjECyWpEplbiM9SgNEd5gepeL2tffwFVXB04nkscDajXy6Gg89fWtPKy43XgaGlCNGIH98GE0MTE4i0vA7UKu13vjVPV6JJcLVVoaWG1Yc3JQhIcHtsg9eAjjxx/jsdnwmBpQjRyJTKFAnZ6G8eOPsPywp1XMr37OnD6N72o+VswN1w9qsR+uSJLEx02G7EVTBuZBwqfbqU2JRIunJDF3dFybemOyOwEZCWFaSoxWQlQKEg1aNuVVNxm0YfxqRkKr6gTNMehUzB0dFxDLGq5RBRjTMXoNmXF6v9HZlkFptDjJLTdRWN3o9yTnV5p5ekOev1zXHQuyMNmd5Fea0TU1TujICO5qlYNg6bKvfXXz7n9tcdm0VF7bcpwNP1ZitrvQa/rWNGnP+GsveaqrxqLbaKR21Sq/EaufPz/gAbotY3a447H4KleEdHJlzxGGbDdo7+n1o2yvUF86LWXA+4YPFXwla9YeKKPcZB8UXWjkTSEF7ZUJiQpVc/b4eL7IKWf17iIeXDyh1TXtCaEtL8/bhMDjxllWRtg5ZxN66qltzhOQ4V9vxN3YiEyhQBkXhzI5CVxuIpcswfLDHmSHD6MekY7utNOo+89ruM1mrHv3er2wnibjV6FAlZiAPDMTx9GjuCsrvZ7d0FDCzphFw9qvoKQEPB6se/ehm34qkUuWINPpqHvtdVz1ddgP/4jzaAHOwkIktwtbbi7h556LwhCBvSAfd30dkseDTC5HO2YMMrXK2xWsvh5lcgqOggJMa9f6S285T3h3MDrLHu4tcoMBj9GIKnVgtrR/7vxwop6iWiuhagXnjB+Yh/zmup0Zp2/XiPVdmxnnNb5GxoYSplFxvKYRkEgyhJBX0cDJqRFUNtjarDnro6WB6DOmdWoFZUYb6VGB4Q0trwd4fO0hNuVVI5MgRK3A6fYQG6ahqsFOTaODnBIjDpeHs8fFU26yUdVgJ9GgJVzz0711JXSgpUc1mA0gFGHez9LdYOrwunGJYYyMCeVYdSMbD1dy4UnB0YLulqbqLHmqo/HcRiNFy2/FumcP2iknEff737dZGrE7xmx/l9bqC3yNEBTRfVdyTxiy3aCtp9d6i4NNP1YCiC5eTXT2NO8TypxiI6VGGzNHRQ2KLjSypiQvj93W7jVLTknli5xyPt5bwp/PH4eqReJKh0Ioa/qfTnIZfGPIDQZcu3YjSRLK6Gh0J5+MZGlElZaG7uSpaDIzqHz6aVwVlVh37UYCJLcLmVwGkoQ8NhbJYkERGoomIxPrnt24KytRjRgBcjkhkyeTcN+9SE4n5m82II8w4LHacJaUYPxsDYYLLyDmt7dh/OgjbEePIpPJkKwWnCWlyLVaamtqcFdX4aqpRXI0lbiKjCRy6VJUCfHI9XrqVq/2l9yy7Nzlb1mpzshAlZDQafZwb/E9lMjU/b+lLYD1hyoAWDAunhB18BMku0JncZwt9aqlQZlbbuKzvaVsPVqN0yXx0OeHkMu8dVBb1pxtOa9Pz0xWby3aqgY7cXoN5SY74xPDA7bym1+//WgNm/OqqWt04PZIaFVyovVqLjs5lQ9+KOJweQPROjX7iowU1lposLo4OTUCp0dq8ip76awGblv0Nm62OcrYWABclVUdXieTyVg4IYEXNhew9mA5F56U1GsjrielqdqKn/Wtw6dn7Y1X+e9/e43Yk05CFR+PQq9vd76uGLMDUVqrL/CVW/TV9e4LhCHbTVo+vX6RU47TLTEuMZys+NbdS35udOVp3ieUI6J1lBqtFNZYmJRiGPAuNIqIpjCAuvp2r5k9OpboUDU1jQ52HqtlVmbgL2d7iQTarCxCZ8/GceQI6tGj/S1j28I3hmXHTiSXE2VMLB5LI7ppJxMyYYK3PWxpKc6KCqz79oPTgaO0lJBx47Dm5uIxm72lp1RKNDNnotDpMG/ahKe+Hs2ECYRMnIDjRBGKMO+/1/h778V24KC3zJZKhcdmx7xxI9Y9e1BERaEwhCNH1hTrJMNVW4syPh7J6QKlErdv29BuB4sFZXycv2RWzI03Ylq/HuMnn6JMSkSmUvpLb3WUPRwMT4THakVqis/yfbeC/mXjYe9D/kCW3IJA3W5uuAJt6lVzjfc1bzhe04gkSew5UUdyRAhlRhtbC6qZaP9Ju9oylpvXojWEqIjVaygz2oBmIT4tkAFKuQy5DOweCRkyjlY38sEPRVx/+kgcLg9Hq8xISGTFhrHbWovJ5grQ0ebz+rqGtedYaL7uYHYHUzaVvHNVViK53chaxus349yJXkN24+FKLDV1NLz+n14ZcT0pTdVWnoLPmJSFhuKpr29VvcCHPDSUkGnTUMZEd6mCgN949eVmBGH9gxFXtfchRhkjPLKDli8PlAEDF//VH3QnXspnpEbqVOQUG8ktN7Xq4tNcKOdlxbFwYmDMWcv5+iuO1t/StLr9lqYKuYxzJsTz7s4ivjxQ1sqQbS8xwFfOpSvGmW+MhrFjqfi/R3FVVqAIN6AdPx65Xk/VypW4q6rwOF3eWFiXC5lSgX7BfPRnLsD4yaeoUpJxlVcQvewqb+xqU+tEuS4Ex4kiNJmZuCor/PULU1Y+T/Htd+AqLsZVXIwsJATJ48FZXoZm1EivcV5UhEytxtPYiMdu88bkejz+H1lYGDKlEsfx44SMH++/H1teHq7qKlxVVehnzw4ovdWW0R8sT4Tve/S1HBb0LyX1Vn6saEAug7lZsQO9HKD1g/b8MXFd8j6OSwhnUoqB3DIThhAVZru3fezGHytZn1tBamQIIPM3Nrhlbibg1cMGm8sfw1ph9D5YxRu0FNVZW83n07okQwgLxsazv6SeSpMNi9NNbKiGolorZSYb0aEaSuqtqJ0eKs22NnW0eWxwuckW4Klt7/PwGfLBKvGljIkBhQLcblzVNaji23+gmZxi8DeC2L73KFm9NOJ6WpqqeTysLTf3J2OypBiFISKgpXZz4u++u9sP4O15Yn0e4MFeWqsrOEu8yb3K+L4LLRKGbC9otLvYcbQWgLOaMlqHG92Nl0qJ0JEQrmFTXjUg8dWBcsYleJOiulILsa0M3Y5aMgaTrrY0XTghgXd3FvHVwQoeXjwReYu6h+0lBnQnu1RhMKCKi0OdloZMo0GuUuFpbKTurVWYv/8OmQSS09kUBytDcnto3LGDuDvu8JeK0U6cQON332P87/9QREXhNpmQaTSETJrYSow9jY3IVErkYWF4Ghq8oQlaLXJ9DB6LBd2UKbjHj8ORexhXfR3q5BSsVdVoxo9HsllxVlTiaWxEERWFesQI/334vK66U6fjLDpB2DlnBxj3bRn9wfJE+FsOx8a2W5Bd0Hf4vLHT0iOJ0PVdN7yePGgnhGvJrzQzIkbnN54SwjWYbS6MFmercXyadbjcRLnJRp3ZwfZjNfxwoo5wrYqqBm9FkYmJ4RTWWNhzvJYvDpZRb3ESF6ahwmij0mwnWqcGGRw/VtsqnrW59iWEa5k1OoZFExNAguc353O0qhGry8Mne0swWV3YXR5MNicjFDqunzWyVYhDVzyr7YURBKsxjUyhQBUf7/2dLjrRoSErk8k4PTOa//1Qwi6zkgm9NOKCUZqqZf3ryCVL8JjNHVae6Y3XtK2mOB3NNxRwFHprSKtHjuizOYQh2wu25FfjcHtIi9KRETu4PD7B8mJ2N17KoFOxaGIieRVmRkR7PQGHy01s/LGyw+279ubrSkvGYCE3GJCp1UgOB67yctRpbZfYOj0jBr1GSVWDnUNlJiYm92497VU5aFi3HnddHcggdPbspuP1KEL1OEtKvNt2Vqu3XFZyEh6jCWtOToD4Vb/8Mppx43BVVyMPDUWm0XgzaePjW4uj5P0MJI8H9ahRxN66HE1Ghn8swN8e11VcjDIuDslmQzt2HDG//S11r7+O5HRh+vJL1MnJrYqGaydObBVS0Zbwd8WT0hXPh6uiHPgpTk/Qv+w45n3IPyOz/c+/tzrVkwft9Gidv3rA14cqSY0M4dJpKXx/pJrXtx5v5VFtvjafjoVqvG1mPRKcqG0kRK0kXKvi+/waxiWFs3pPEdlF9cTpNTTYnOg0Sk6LDaXc5A3PGR2np9HhxmR3+j8Ds81FYY2FKJ2KTXmV5FU0NIWrSdgcbpxuD2Pi9RitXs9qSZ2VCJ0Kp1tq09vaFc9qMMMI2kOdmeFtzJKfj+6UUzq89vSMGP73Qwlbi83cHaQ6q70xALtrDPc2JKrlQ7zHbO5xZ8PBgORw+FvTNndwBBthyPaCjT96Yz8WjI0bVB6fYGaddiR07f0RGtu0DeczQA+WGck+UUdGrL5TY7TlfH3RkrE9ZDIZ6vR07EeO4Dh2rF1DVq2UM2NkFN8crvTGyPXCkG1vG91ZWoqrsgLdjOk4jh5Dm5WFKiEBRUws7gMHQa1CcrtRRkbiVirxWK24ampatVHUz52Ldd8+5C4XrpISJIcd89atRC9b5vWWmkxeQzUhgZCpU2n89luQyXDX19O4ZSu6qVNRN8v49xUIt2RnU/f++7gqq/DY7f5//wpDOI6CAmx5eSj0elRJSd32iigMBiKXLMGak0PIpEltZgd3JfTAfvSo9/vqooAOhwzhwYIkSexqMmRPHRnZ5jXB0KmePGi3rPNabrJR3WAnr6KBEU3a09bDd/O5iuoshKjlyGUQH67F6vQwKlbHodIGCqvNWBweonVqKs12ZsRGER2qodxkY3xiOL4QhMw4vb8slzccS4lKISO/qhGQkR6tI6+iAYDUKB2HyxvILqonMSKEFEMITreHcqONUI0ywLPb8n678nn0ZeiWJnM0jd9+hz2/oNNrZ2V6w9Byiusxq3UYBoER153SW70NiRoKnbq6g6OwEDwe5DpdnzoUhCHbC3Ye82bjtYyTHGiCmXXaUZ3BjjrR+LbhPtlbzBtbCqm3Oig32lkwNrZDY7SvWzJ2hnrUKOxHjmA/egz93LntXjczI5pvDleyJb+GX8/J6PF87W2j+wTNUVCAZLdj3rABW14ebmM9uFyok5KRqVTIQ0PRTpyA7cBBZBo1qqZSV+YtWwiZNImGdetxVVUj2WzIw8PQTZ+Bq7iY6udX4jbW46qu8daCjY3FWVaG21d6TJKw5+e3u61vXLMGy46dyBQK3CYTodOn46yoxFlWhioujrr//Q93eTnq0aOJu/XWLnsV3EYjtrw877orKwKM8s4+s5Y4jh4DQDNqZJfmHQ4ZwoOFknor5SYbCrms3U6HwdCpnngUW9Z5TQjXsq+4jlKjjVKjlXlZcUjQam0ty3gtnpzEf7Yeo6rBTq3ZQV6FmUaHm8imHK6wEBXjk8O5Y0EW4SGqgASzlmW5InUqth2tJU6vJt6gJSNWR53F6ffI5lWYidCpGJ8YTq3FQVmDjQidCrdHIlStaDf+tSu6GawwgvbQZHq927UHcglpI2yjOYmGEP9nvL+4ntmjh85uSjBCooZCp67uYD1wEADNuHF96uwThmwPqW10UFDl7VZySnrbHoeBItjbRW0JXWd/hAw6FXqtksIab4vQMK2SiFBv2EFnxmjL+XortN3ZvtRkjKIBcBzt2Hvge3jZeawWp9vTqgxXV1ElJbVZS9UnaOYtWzCtWYMqOQX7kSNILjeqxERc1VWEnHoqytBQGnfsBI8HSaXCcfwYbqMJ05o1mL//Hk99vd+rq4iPQ7JakEdE4K6vB4USR3ExiuhozN99j8dqRa5W47FYkOx2VKmpAQlYPnF1lpbiLClFknnjdOVuF666WtwmE3K1CmdtLe5t25Gp1TjLy7Gd3X7N3Ob4jEnbgQM4y8rRzZje5h+Ernot/B7ZUZ0/aAyXDOHBwp7COgAmJoWjU7f9ZyZYXaV68qDb/H0NNhdvbD3uLwM4a7T3d9sXO+tbW1tz3X/+BIrrLZQZbbz8bQENVhc1FgczRkaxdEY6Y5slX5msKjYfqWRqSiQTknz/tryfQU6xEY9HIjJUjcXhZumMdPRar6e11Ghl1mgXW45U+726DTY3lU2JYxmxoa0+u44cDf3dhMY1yhtO5Dx0kJUbf+SW+WM6nHdSsqHJkDUOKUO2O97U5noKtOokNly0x5aTA0DIxIl9Ok+vDNnq6mrUajXh4W13OBrO+IR6dJyeyNC+S2ToCX3drQW69kcoJUJHVryeUqMNOXJOTo1kbEJw/q109V66u32paYrhtB482OH8Y+LDCNMoabC7OFJhbrfLV1dor5aqwmBAP2uWP3NWPXo0MgmcJcVos7KIXHYVtkOHsB89inrkKNzVVYRMnYo1O9tbIqakGJlOh/NEEdqxY4m6ehkesxm5Xk/NW29h3rQJyenEun8/co0aZXw87qqqJg9tnL9BREtvZfi553q7n3kkZEoFITNmoE5NRaZUgEyD3OFAAmRIbVYYam8L32dMqlLTcJaXe9c9cUKrPwhd8Vp47Hbs+fkAaLJGd/odDLYtvaGurYdKvQXwT2rHGwu929Zu+fvfkwdd3/uMFmczLQvh070lWBxuUiNDuPb0EQHGaHtjhGtUpEbpqGqw+72wzZOvimosLH97D6VGK0mGEJ5fOo3U6J+M492Ftfx7fZ43dMCgJckQQniIKkC7LpuWQn61mcwYvbcluoym36/Wnq7D5SZyio3+UAmfoyGYYWddpSI6EZdai9pho/FwHsVTUzv8viYlG1izv4wDJcY+XVew6ao3tbmeKuLjkUn4k2+7uhM0VMKgrAcOAKCdNKlP5+m2IVtfX8+f//xn3n//ferqvMZcbGws1113Hffffz863cDWAu0v9hZ5733aIPPG+ghGt5aOjMW2CocfLDUGXGvQqbjzrDEsnJiIDDr8g9AdunMv3d2+DJkyBQD7j3l4LBbk7fx7lstlTEgOZ/vRWg6UGHtkyPra0LqKiwNqqULgE3rLuobNi3N7Qw8cuKurUGdkEL5oEa7KKmyHD6OIjACVCpBwO+w4y8v9nWbCzz4be+5hZBotruoqZNoQVLGxqJMS8VisKKKjcZYU/7SeZt5Kx/HjqOLjUMbF4aqswHDOQnRTp/rr5CrT0pCDv8yXNiurS0XFmxuTobNnE3722W12xoHO49ZsBw+C04kiJgZVcueNSgbDlt5w0tbD5d7YzjEJHdfW7okRGixjzKdv4RoV88fE0WB38eneEnYcqyVO732I02uVnYZTNa/XGhumaWXEAmwpqKaguhGFDAqqG9lSUM0V0Wn+zyDBoCXeoGVkbCiWpiQwk90ZUGHhP1uP0Wh3exsc1FpRK+SArJWuGS1O1h4oCwiVCNeoOFhqpKEpoaw/kmd9pESHsXfEGOLy9jGhrrBTz/ukppyDnCFmyELX4ml/angTgW3vXmQaLdqxY7u8EzRUwqA8jY3YcnMBCDlpcp/O1S1Dtra2lpkzZ1JSUsLSpUsZ1xT3dujQIVasWMH69ev5/vvv2b9/P9u3b+f222/vk0UPBg6XeYW6N564/qS7Bl1RjcXf07t5T/DmNPdodBQv27KObEd0xdPanXvp7valKiEBZUICrvJyrDkHCJ0xvd1rJ6dEsP1oLTklRpac2r0WqAFtaJs6n6gzMpAkiYrHHsdt9NZ9bS5SPiNLO26c30urSk4B8DcZALxGa0kxjqIi5Go1ISefjHXnTlxNHs6YG2/0Jo+lpuA4UYg6KRllVCSGSy5GPWIE5Q8+hP3YUVTxCUiShKexEWVcvN9rEDJpEtbsvd6WuzIwb9uKJjOD8LPPhibjs/l64aei4nJdKG5j20XFg2lMWrP3AhAy5aQux2YN5JbecNPWH5sM2bGdGLI9IRixtT7Nyq80U26yEapWEB+uxWR1ERumobLBzpjEsAC9aFm6a+2BMtJjQpFBm/VajRYnueUmZIBOrUAll+Fwe1Ar5MSGaQLWYra5SI0ModxkD+jA5dOuCJ2KeouT1EgdRXUW4sM1VDbY8HggWq/2J3sZLU42H6mkqM7KzFFRFFSaSY3S8Z8tx5pKjGlJCNcEhEz0NQadioz5p9OQt48ZxuOdPnT4/qYW11lptLsI1QyvCEhVUhKK+Hgav/sOye1BEebBWVLs7XbYhZ2goRIGZdmzB5xOVCkpAQnDfUG3/oU8/PDDqNVqCgoKiI+Pb3XunHPOYdmyZaxbt45nnnkmqAsdbPg9DoO8m1dzr0NXDTqjxcnTG/L47ki13zPR0R+LYCWXddXT0h3jtCfblyFTptCwdi2WPbs7NGQnNAnuobKO+4i3hX8bvZkhGjJpElUrV2L+/jsUYeFITqffI9ryCbxlfcOQSZO8VQjMZlwnTiBTqrxxsy6XN95XAmVCPLYDB7Hl5RF66qnE3nILMgm/0ezrtqWMiUadlorbaKJ21SqkxkYU8fFEXnWV30Mads7Z2POPoExIwJ57mMqnn/aKVloaqoSEgNqHLYuKyyPaLyoeLGPS8sMPAOiaPOyDneGkrUaLs6nMFIzuA30MRmytT7OUchlHqxoxhCgpM9qZkmpAqZAxNiGMOxZkBehF89JdFUYbz2/KRy6XM3NUZKt4WqPFyb+//tFfT/v0UTGcPT6eI5VmJiSFc0p6FNC6duw1p4/wNzUwWpzMGxOHDEgyhPjraWfG6Vk6PZ0jFQ18vM8bBvH2zkJ/zW1faTGn24Pd7eGrg2VUNtg5fVQ05SYb154+Ar1W2aMQs56Gp0XNm03Diytx7NiO5HIhUyrbHS9CpyZSp6LO4vVIDxVnUVfx7Yg5juSjSkvFXVXld0R0RfsGWxhUezRu2QpA6MyZfT5XtwzZjz/+mBdffLGV0AIkJCTwxBNPcN555/HAAw9wzTXXBG2Rg40Gm5OSem8SU2dbZwNJW80FTHZnpyJUXG/xb5O15ZloSbCSy7pqEHfXOO3u9mXoaafRsHYtjd9932bnFR8ZsXoAjlc3dmnc5nFNzasSyCMiCJk0CY/ZjLuqyh8HK1MofmpH2+IJXDtunL9ElXrECP92vTIuHkVCgrfsiUKJbsZ0tOPHYzt0yOullIFp/XqvQRoejuEXlwAEbOGrMzK8Y8XE4G5qyeiqqAjoHa7NykKTORrzd98hOew4KyoInXkajoICqlauRGps9Bvdcr0euS7U73XoaZHvrsaFeRwOLNu2AaA7re9FNBgMJ209UWsBIDZMQ7g2+PGX3fn9b8/w8mlW9ok6lHIZHskbaXrRlGQSDNoOw6k2H6nk7e0nMNmceDwShTVWfnvmaMKaGYcHS43kVZjxeCRA4nhNI7efObqVAVlcb/F3/SqqsxDWFMrgM4TzKsxkxeu586wxre7ZZHfidEukRuoCam6nRnr19+T0CH4orCcuTENlg4PjNRay4vU02lw02F1U1NvQaZUBhnNHeRXQdjvfrhAyeTIKgwG30Yh13z5006Z16LhIjw6lzlLP8ZrGQWfIBiM+VZuVhXbiBL8joqtGLPy0c2XLy+vR3P2Fecv3AISePsgM2bKyMiZMmNDu+YkTJyKXy3nggQd6vbDBTGGNV6hj9Ooed6wJduZoW+O1NAxNdmezbNn2SYnQkRnnNdLa8ky0JFglstoziNu6t74sGaOfOwcA6759uOrqUEa2HQc9IsbbBKOm0YHJ5uzwj3ZbcU2RS5Z4y2DV11O3ejWRS5agiI2Fwz+iSkkN8Gz6nsCVcfG4zWYcRUV+4zWgB3hTgpcqOQVFZCRytRrr7t2gUqGMjfEmhVVUYMvLw/ztt/71+MIBmm/vt4xnbf7kH+CVjU/Aum8vzhNFqBIS/Mav88QJ/zxuYz3yiAgilyzp0TZTd+LCrLt347FYUMTGoB0/8HUou8Jw0lbfQ35yREifzdGV3/+OYlqL6y0snZ7OKSOi+Ne6H6kw2QjXa8iKC2sV39py3rmj48gurKeywdvcICteT7IhJKAEVvNEV4Cs+DB/omtxvQXwalm4RkW12U5pvZUInRpZU3LkrsJa1h2qQC6TUVxnZUxiGOdOSArQ745qbmfG6blwUjImq6upFXgMZ4yOZUNuJQ+tOUidxQlIROo0LBgby/WzRrXqoAiBhuu8LrbzbQuZQkHo7NneaiqbNqObNq1Dx8XImFD2FnkN2cFEb+NTfeUFgV537Wqu34MtTtZ+9CiO/AJQqQg9/fQ+n69bhmxMTAzHjx8nJSWlzfPHjh0jLq79FnTDhdJeCnWwM0fbG6+50CWEa2lop/1iS3pimPbGsGxuqLacdyCybFWJiWiysrDn5dH4/fcYLrywzev0GiUxeg3VZjuF1RYmpbR//215VQE8lka/0ecxm1tt9zdP+PJ11apbtaqV8errAS4L0eGqqEQ9aiTOEyew19Whio/DbTShSk3FY6xH1dToofl6mjcwaL6931HMqterMDEgOUuVkBBg/PrmUSWn4KqswGM29+g76U5cmHnzZu/3M3sOMnnPyqL1N8NJW3urj8GiLUMJdAF6Mn9MHEkRIWTG6f1JVm3R8mH6zrOymJ0ZQ2WjjclJEW220b5+1ijGJIYRF6pl2ghvKEFLLTPZnUSEqDHbXNRbHPxn6zHuWJDFp3tLqLM4UMhkyGQyPthVwu7jdQGJZG0l3M4f462B6/OyNj9fXG/heE0jDpcHu8sNgMPtJq/C3KqD4uFyExUNNvIrzX6Prwx6tfMWtmA+pjVrMK1dS+xdv+twJy81qqnebp21W3P0Nb2JT3UbjVQ9+5w/tyB09mzibr01KB3ABlucbMO6dQCEzjytX9bVLUN24cKF/PnPf2b9+vWoW/Sht9vt3H///SxatCioCxyM+IQ60dAzoQ5mw4KOxmvemGDtgTLeaNZ+savb8UaLs1U1gq7SFa9zyxixhRMT/CLc0b31Nfr587Hn5WFa+1W7hixASmQI1WY7JfXWDg3Z9uKaWh5TGAzE33tPm8ajq6oKV3FxK+NVnZFB+LnnYjt0iMY9e3BVV+GqqiJk6hTseUdo3LULVXwCsb+7E5lM1mpuZVw8pvXrcVe0LgHTUcxqyy0uX3hCyyoLwYjn6mpcmOTxYPrKK6L6ee03tBhsDCdt9eljUoR2QNfRlqHUUk8kIDNO7/ditmWctfUwXVJn4Z1dhTjdEruP1/kTsZobzG/vLOTrQxVEhqr5V3wY2cV1AYahTxtjwtTklpuI0WsoqrFw/ycHOFLRQEqkjgqTDa1SgcXp4rsj1QDcf77Xc+97/4QkQysdhZ+M2Z/0UkdWfBjFdRY0SgUgoVYoSI/WEapW+pPAEsK1rD1QRlGdlWqzt0FKZpyesQnhjE0Ib+Vo6KrDQz9vnrccYFERtv37MZx0UrsOk1i993eg1uzo+T+APqC78akt62/b8/PB4wZkOI4c6bEBOtjjZE1rvwIgfOHCfpmv28lep5xyCqNHj+bWW29l7NixSJJEbm4uzz//PHa7nTfffLOv1jpoKGvaLkrsoVAHu2FBR+P5GhOUm+ztGoPtiZFPHH1i/MrVp3bZmH1i7WF2HKshLkzbofHs+8PSvL/4pBRDm17l/sqyBTBccD41L76I+dtv/eEFbX1OMT7BbexYcNvLyG/rWEvjsb0KB76tKV8IgO3AQZzlZYScNAVXRTm6adPwGI2oR4xAslqQyWQBHbZ8c7vNZupWrWrlLfaFF/i2v3zHWhrYbW1xNT8fjEoEXa1oYNm1G1d5OfKwsA47sw02hpO21jT9LjTPzB8I2t5ZCtSTZENIKy9mS5rHseZXmtlVWMsTaw9TWNNIuFbF6Hg9SREhAclePl0L06rYdbyO37y9myRDSIBh6NOyRRMTabS5OVhmpKTehtHqxBCiQqGQc2p6JBaHmyNVZhLCtNRbnB22z22uo1nxYcwaHUOYRukvfXjnWVksmphAg92FXPLWsP7+SBUf7in2J5sBvLH1uD/WdvGUJOaOjmsW1vVTma/u7JbJdTrCFizAtGYNxjWfE3LSSe3u5EWFev/t1DTae/Td9xWd6VDLRgfNwxAilyxBk5mJs6wMZKAePbrHBuhgKBfYHrYff8R++DCoVOgXLOiXObtlyKakpLBt2zaWL1/OfffdhyR5A3pkMhlnn302zz77LGnt9KcfTtRZvEIdo++ZUAe77Wpn43VkDHYkRi3F+B/rDvP3izsvbPzMN0d4flMBo+P0TE7u2JPqW1tOsRFff/GW1zfP3G0eX9aXaEaPRjNuHPbcXBq++v/27ju8rfJ64PhXy7LlIe/txEkcZ5OEhATCSkIYYabsvdr+WGGlQAstUFpSaKEQ9izQsvfeEMIII5C9HWd6b0u2Ze37+0ORkB3Zlm1ZspzzeZ48EFvSfa8cH5373vOe91PUJ58W8H1K8wbc1p4DbqDZTe/XXCYT1s2bAyaMgToc+C8O8LXiGlaAo6oKZ001sRMnEj9zJvbdu30LCgJ1CABwlZSgycrCuXdGVp2Q0CFx9m5h625tw91u6dAWLJhbXKHqRBDM65jee9fzHh13rG9Dh2gwlGKrud1ze34gFnoFo7vNEvxjZZJe16EkYFwXm7V461irTFZyjLGUN1hottiJ12sxWx0YYjRcOmsE6ypMvuTdG9cAxrsS2VTZgl6jIT1B70sM4ddSA51WhdutYGp3EK/XcMjIVJra7Bj0WpxuSDHEkJoQQ1FmQsDtc5P0OnQaFavKmrDaXaTFx/Dllho+21SNVq1mcoGRy48YhaLq2M97Y6XJN8lRbbb6Fqz5b8Xrn8T668vdsqQTT/CUF3zwAZk3/KHL39G0vRMEDYNsRha6jkOd62cTjjiiQ2x0t7aSsfAqEo85GqDLHtn9HUekNb/+BgCJc+d2ub4k1HrdoG3EiBF8/PHHNDU1sW3bNgCKiopITU0N+eAGqxarE/Bsu9pXoV6s1N3rdZfodheMvAHN4XIzPjeRF37cQ2ZiLNcc1fVOSQ9+uY37Pi/hytmjcCtKjzOpncsfqs22Di1sfr1dpgdUvlmPcNTKGk86idrNm2l+8y0sR8wP+D6legNuFzOywaxw7Wmnl0AdDrrasjXh8MNJPObogLf5A80e+I6bnEzc9OloUlNpWbaM9jVr0aSm4KipRpud5dnC1tqOLufXRFtjNA6qW1yu1jZavLe0uikHGayGSmz1xsekuPAnssHMEnpj5cZKU1CJmNnmID1Bz/BUA212F/lpBnKT46hsbmd4Wjy/P2wUL/y0m69L6tCq1cwdm8l184o7JMyLXl/Dz7uaOKgwxZcYbqw0UVrbSnyMhrXlJsqbrYxMjycpTovTrVCQZqDZ4mBEejxajapDAvzr2gc9Nc1WXtyyi192N9HU5qmr/WlXIy6XG51GQ1O7jW+31bGluoVhqYYOfcEDTXIEs9kN9O7OovfiIu/AmWhzcnBWVdHy6acYTz454ONT9i6i9k4aDZRQ7pDV+aIeApeOBbNdd7RyW62Y3nsPgOTTTw/bcfuciaWkpDBjRtc9NoeyUCSy4dZVottTMDqoMJXKZs+2ivExWu773FMPGSiZ9Saxi44u5pqjRgddP2U06Jg5Mm2f+iv/D5qSGk/f3uKsxLDVyhpPOZm6++/Hun49mXu2MTwtfp/3KWFvs26L3bnP84Nd4eofAG3btgEqzy0ov0VYSfPn0/T8C74OB53rWLtKWLu7avftMJOcTOv3P8DX3+BqawNFAZUKjdGINj0DV1MzKq3WE6Bra9BlZqJOSOjx2PayMtp++gltejqGqVP7/EER7IeN6Z13cLe1ETNiBIbp0/t0rMEg2mOr2eqZkQ1HfOwcYwZisxRvFxfvDOVBw1N56OwDWV3eRFF6AusrTCzdUoup3YFOrWJTlZnyZgsTco2+dQY3HDOGN1aV8/ov5fz3h11cc9RoWtudlNS0UN9qw2J3c8LEbC6YVejrgJCk1/HM8p2+MgH/mdErjixic7WZTzdU85/lO9lR10Zzux2rw41eq8bhcqFVa2ixOXC5FdIT9TRb7IzL7hg/u0taO9feBtrsJpg7i51f4/zfnEbLow/T9NLLXSaysTrPIk270x38P4ZeCvUOWZ0v6mOLi4ktLh6Ut/8Hivmjj3GbzWhzc8LSdssrejKxQaTV5klaEvSRuXUWCt11CvB+f8kXW1m1p5kmi53Di9LRadScf/CwgMls5yQWej/r3Pnx/h80xVkJ+M/IhqNWVpuWRtIJJ2B65x3sr73MFXfevc/7pNd2HXCDXVnaYXOD0aN9M7LazCxM73+Ao6wMTVoqisWCLi8f+/bttC5f7isv6JzoBZv4eY9r3bARXE4UtxvF7pkB0RiT0Kamkn7lFajj4zF//jmOnTvB5UJxOjsk04GSZXtZGeXXXY9txw5UajWJRx9N9s1/6nUwD/bDRnG7aXr+eQBSzj8varoVDEXtDs+KeEOMZkCPEyjJCjY59W/B1VNv7UAJm9GgIylOx2Nfl7J6TzOtNhcqwOFWSImP8XWIgY6dCq6cPYr7Pi/x7MBVUkt5UztuxfNe1bfZWba1liuOLKIgzbD3+Xv7cfn+++uYEmO1VJutFKYZKK1r8cUgh8uNVq3liNHp7KxvJWFviYep3UGb3UVBiqFDB5tAOzR6F956dy3r6sIgmBjf+eLCNHc+6qeeoH3NGtrXrydu0r7lajHeuOoauEQ21Cv/u7qo3x8SWPDE4MZnnwEg5ZxzUGkG9vffnySyfeDeW7+midLPykAfAJ37y26uNrOspB6ny4253UFpbStTh6dwxZFFZCbGdkhmAyWxoRBotqCvK2b7KvXCCzC98w7mzz4j88YbmJCb0+H7pbWedlLvrKlkydlTO3wv2NvugVb6OyorcdTUUHP3P8HtwlFRgf6ASTgqynHWN2B6+x3avv2OlAvOp/mdd7Bv20bM6NGkXXBBh/ZX3c0y+HcdaP7gA9p/+BG3xeKZkdXFEDd5sm8mNba4mNblyzF/8EHA7WU7a1+/HntFBTidKC4XbcuX+3YU641gP2xav/4a++7dqBMSSF6woFfHEKGl+HKu4LYG7qtAs68Tco09zhL6b0+bbNB16JPdVUwJlLB5jz8sxcDmKjMqjYqMeD0JMVpfh5g5nXqvLpxbRKxO44ufXg6nm9oWK1uqWnzJ4uZqMyU1rQxPM1Bttu2TROYnG3x3qyblJbO+vBm3Amo1HFiQTJvdxUEj0nzJepJeR6WpvcsONoEXjCXss2tZb+1zcTGygLbjj8f07rvUP/EEBQ8/3OVzHS4Fm9O1t8tCaA1EWdRgrVsNh9ZvvsG2rRR1fDwpZ58d1mNLItsH3kCtCkGgHohkrKfXDOb2m+fMFNQqSDboOPXAfI6bmIPRoPMlq/d9XsLDS0uxu9whT2K99l2s0bcVs30VO348hhkzsKxYQf0TT5Dz1792+P7WvSUPgfRmZWnnAKgxGnG1tu79QahAoyb5xBNxWyyY3n4HZ2Mj1pKtOOvqsO+tx3JUV3tuZXXTH9afd+Y2triY7EWLsKxeTfNbb+Gsq0eXl0vqhRcA+BahJRx6qN/Csu4Df9ykSWhTU3GYzaDRoOrjwiv/+mCNMdlX0uBPURTqH3kUgOQzz0QdH9+nY4nQUPAuVBvY43Q1+9rTLKG3C0FlUzur9zRhd7r5+ymeWcHOMcX7+O4W0a4vN5FiiGF8TiK1LVa21bYwITfJ196r8xi9F/9O9973CdDr1FQ2t6PTqEnSey7SP91QTaWpnUqTldnF6V0kkZ7XyEyMYd64bHY1tFGYFs/ccZnE67X7dGIw2xxUm22kGHSsLzexudrMwSPT9jkf78LbarONi2YVdti1rLcCzWjHXvZ/mN57j9YvvsS6tYTYMcUdnuP/2dpuH5hEdjCs/A9ljW4kKYpCw1NPA5B81lloEsO746kksv3Rz0AdbDLWm2Q3mNcM5vbb2OwkZhdn+uqzvEms1zVHjfYlsTEada+S2FAk7+HsL5u+8Cr2XLiC5jffIu13vycmP8/3veGpBlbvae7yuf25Qo8tLib+8MN9s62GqZ4Z37Zvv8NastVTv9rWunfvcg0ooE1PD6o/bKBb9rqsLFQqFXETJ+KsrcFRXb3P7G6wgT+moIC8e++h7v4luFpbiZs40bd7WG9ojEZSzjyTusceC1gfDND61TKsGzagMhhI++2lvT6GCK1fL/RDr3Ps6Ev3l/xkAzqNmh31beg0ataUeRK6xFhth5iyudrMsk5trjrP0vovVC1rasfuVDBbnfywo5HZxemMy05iXKfaf28Sq8KThuo0KtQqyE2OI8cYi9nm2JtwWpk1Mo1dDZZ94i94YmC12UZxViLVZiunTcunvsXGuvJm3lhZ7uvE4P+eeWZx9SwrqQcUPt1Q7ddvNvDC267akvXm59T54kI/ciSJxxxDy6ef0vDE4+Tdd1+H52vUv/7r8ZYZDIRArQ7DlViGukY3ktq++472lStRxcSQetGFYT++JLJ9oN77S+Z0KT08snvBJGO9nXkM5jWD+QDw9hzs6jEPfrnNl8TaXW4e/HJbUMlsd+fTmwQ3nP1l42fMwHDIwVh++JH6xx4ld/Fi3/cm5hl5Z00lJ45IwGUyhTQQaYxGMq+6at/es1degaICd3Mz2vx8KLZh31OGvqgIw9SpGKZO7bI/rPc1At2y73yrDQj4GC//7RYDtZKJGz+evHvv6fcHg7u1FaWtLWBJg+J2U/fQQwCknnce2rS0Ph1DhI5671Ssy92/+OjljQud22V5Y0dfLmC97c1Q3KjwJN2dY0p39aH+scq7UPXrbbW8tbKCvJQ4miyODsmn93neMqzzDx5GtcnK7vo2ttW1odEoWO1u8pJjfbHMO5ZJ+UZyjXH8uKMBFb+2z+q4c6Oe5dvqKalpodLUzqyRab4durw9Z7OT9Bw3MYfDRmdQUtNKYZqBarO1w3l1tfC2tz+vYD6z0q+4nJbPPsP80cekXnIpcZMm+r7ndHtqY7VqFYaY8KQp4U4sB/vuXMFS3G5q77sfgJRzz0WXlRX2MUgi2wcJes9tjjbbvivVeyOYZKy3M4/BJnjBfAB09ZjONbHev0Pgbgb+Qb+r8+ltwh7qXrw9ybjmGnb/8COmt94m5dxziZvg2V2nvdXiecC2LdQ//UvIg1+gGd2YggKy//jHDomlN6H0f47LZOqQmKoTEnxlAoHqw3ralcvbX9YbfN02G5YVK7rdbjEUNWPd1bKZ3n4b2+bNqOPjSb30kn4dR4RG/N5OHm12V79fyz8uxOs1++yg1dsk1mRx8PW2WtodLkZmxFNjtjK5wOhLDs+bMZzV5U1MzU8hKU4XMJZ2Faum5qfwzHc7qaxqJ9kQQ6K+48erf9y86JBCHvu6lHa7i9pWG6Z2J263naY2J+Z2z4ysf23rM8t3sHRLHXaXiykFKdwyfxwFfpshtFqdPLe37rXSZGXX3gTY23M2xaBjWUk9m6payErSMzwtjkaLo8vPCO/mEX3p2x3sZ1bs2LEYTz4J07vvUfuvfzHsf/9FtfciyOrwJLL6AZyN7SzcieVgal3YH+aPP/bF4LTL/i8iY5BEtg+8LZda+5nIBpOMBZOY9vV2W+fnBTMjGmhhl3/NrP/fvcfwD/rnzRge8Hz6UioQ6l683TFMnUrS8cdj/ugjau5czPCXXkSlUtHWaAIgNi4Wx55NYbuq9k8QXSZTl7treRNT7+5f3ZUJBOp+kHDEEZ7z29tGxtcmrLQUd1tbSLZbDOZcA5U0uMxmav/tuSWZvnBh2Jpvi+55E7hWa//iI3SMC2VNFpINuj4vPPJf5GVqd5CRqGd8bpJvsZfJ4vDN+G6qNHPFkUUBY2mgWAUGVpc3YYjRkBSro8li55nvd3LrCRN85QT+Say3Y0LFmHb+8+12vtpaj83p5tvSOha+3I5Wo+KAvGTfXbFNVWYa22xY7C6+2lKLTqPi76dM8iWcW6rNe7eYtTG7OJ3jJuYwdu8GD966V6fLRW2LlZ31rRw4LIXZYzI5dFR6twvi+rIGoTd3yzKuvRbzJ59i+flnWpcuJfGoowCwOT0XQbG68K18H+jEsnN8HQw1uv3lbmuj9l/3AJD620sjFoMjmsjeddddvPXWW2zZsoW4uDhmzZrFP//5T8aMGRPJYfXIO+PQEoJA3VMy1lNi2lXA6SnBC5RgBrpt56+77gRdJbOdg77Z5gh4PpHairY3Mm+6kZavvqJ99WpM775L8oIFmLSebYqTLM0Ru6rubibBGzC9i7T8HxM7blyXNbMpZ57ZIfGNLS7uEOj1RUW4bTbafvgBXA60A3zugWZ26x56GFdjIzGjRpF6/nkDduxoFMnYmrC3f2x/71hBx7hQlJkQVLuszrwX6C1WJ7sbLGQl6mmxOpk/MYf5frf/u+qC0Hk77xars8NK/iS9zpcg17faMVsdZCX9up3s++sqeeHHPVw5exQzRqSy5IsS33PPmzEcjVqNVu1Z3tRqc7G23IRODeWN7RxelM60wlSykmJZW2ZCpQKtRkWNyepLoP1bZl00q3CfmtYrjizi592N3P3RZsqaLMTrtayrMFFltrK1uoXr5hXv8172Zw1CbyZTdLm5pF50EQ1PPknN4n8Qf8ghqA0GzO17W1yGsVf7QCaWXZUtRHuXg7pHH/XsCJmfT9qlkVufENFE9uuvv+aqq67ioIMOwul0csstt3DMMcewadMm4gfxyuO0eO/2eeHZB7pzYhrMrfqedH7e6vKmbl8nmBZbgZLZ3pQ6hKtUoK+LzXTZ2aRfcQV1991H7d3/JOHQQ6m3eWrthh1+MOlzxg5IUOppAUIwMwk9rf7vnAy3r18fMPFNOfPMDr0f3ZY2nDW1aGLCux2sZdVqml54AYDsP9+CShe9PZ0HQiRjq3cjBNPerWr7oz93mLxf80/0Ugw6vt/RAKjYWm1m/sRf2+l1rjlt9eu1uu9r6bl4VqGvlnR3g4WCFM8uiAWpnvraghQDd3+8mdVlJqYWeMqn7vpoM80WB4cVpfnirtOtMDI9nj2NFlwOF24FHG7PrlbvrK1gWmEqNx4zFrvTzZoyEzFaFeNzjeQnG9hcbWZ9uWlvhwHPFrOB1hzkGGPJS4kDoK7VhrndgVoFy0pqOW5iNjNHdqwt7+/EQleTKYF+RumXX4b5gw9wVFZS99DDZP3xJupaPJ+tGX3cBr6vAm0ZHoqYPlTqYf3ZSktp/O//AMj68y2oY2MjNpaIJrKffPJJh78/99xzZGZmsnLlSo7Ye0tzMMoxegJClcka9mMHe6u+J50D1dT8FDZVmgO+Tm/6xAZKZjv3gu1up5iBLhXob9uutIsv8tUEVd3+V+onnANAzujhA5bEdrUAwT/B7Zxgdg7CGqORpPnzqXv4YZz1dfus/u+cDMdNmrRPqy2XyeSbpbVu3uwpO3A4iB071tPlIEzB2W2xUHnzn0BRMC5YQPysWQN+zGgTydianeT5QKs2hyY+9uUOk/f32v+Cvdps5cjiDN9jOvdm9SbN3h2znuvUa7XzayX4ksZfY+m4nCTfrHGL1cm32+qYWpBMjFbFspJaVHju5G2pbmFERjxZCbEkG3Q4XG6GpRkorW1lR30bbsXTxcBid/lmhv952mR+3t1IQ4uNWaPSAXwtusqb2plcYCRJ3zHp9vbKvXTWCMblJKHTqMlLiWVHnWXvojxVp60W6PBehHJioaufkdpgIPHmP9N49VU0/ve/JJ1wAvWtnoutjMTwJrIwMIu+hko9rJfidFJ58y3gdJIwdy6Jc+ZEdDyDqkbWZPLUG3a1t7jNZsNm+3UW1Gw2h2VcneUYPYG6ytQe9mMHe6u+J4ECVee/e6+eLTZnr/rEeh/nXbXs/0EU7P7mA6W/bbtUMTHk3n03O08/ndalSymy5bI6Y8qABdyuruT9g602MwtFBa6aGtpXr/H9v38QdplMND3/AtaNG9GmZ6Davn2fEoTOt9X8k+NA5Qmw717i4VD77/tw7N6DNjubrFtuDssxo11PsTWUsvfGx+owXuh39Xvd+YL90FHp7Gpo6/LC33/HrJ5ey79vbeeLdbPNQZ4xjnnjs9hcZabV6kRxg1arJjFWR0KslroWG3d9shljnI6MRD2XzhrBE99sp7bFht3pxulyU5DScYzezgRbqls4dmI21WYr04el8v2OemrMVl5csds3ltLaVqrNVtZXeH72184t7rB4zLvZAtBh5tn/vQhlbO5uoe+zzhzGjj+YMZt+pPzGm2i86i4A0sM8IwsDM3s6FOph/dU/+STW9etRJyWRffttkR7O4Elk3W431113HYceeigTJ04M+Ji77rqLO+64I8wj25c3UFc0hz+RDRRMQxVw/F+n89XzRYcUBv06JouDo8ZlBpwdTtLriNdrKGvy1LyFuxY2FLW4sWOKybj6auruu49zfnyNn47MZkTawNyu7epKvvPCK1DQF43u8P/+QdhRWYnL1Iw2PQNnfR26EYW4Wls7tAzrvIDMf/Y1/Xe/GxR7iZs//YymF18EIGfxnWiSkgb8mNEumNgaykmCSFzoB5tk9meBrf9zk/S6Div6A23zOjzNwJziTJZuqaWxzY4KyInXk5dqwGJ3YYjRsKW6heGpBtpsLtZVmPhldxMOlxudRkVynI5jJ2b7xril2syyklrcboVKk6d+1ruQK0arYVRGgi9BzE82kGzQsb7CRGaCnmaLpxPChFzPGI+dmMPhRU6+La0LuMtXOH9G3gRXOeP/yPv3FhJ27qDgtach91jfZ204DdTsabTXw3q1b9xI/aOPAZB9618i0m6rs0GTyF511VVs2LCB7777rsvH3HzzzSxatMj3d7PZTEFBQTiG18HIDE99YY3ZhtnqICk2vPV5c8ZkokCvGlUH6lDQ3S127xW9IUZDaW1r0DOXPfWJfXHFbpotDpL3troBzyytd1xlDRaWb68nI1HP9OGpIQ+sobpllvbbS6lb9i1xq37mtl/+R5JyNhDT4TGhaK7d1ZV854VXigqcNTUd/t8/COtyc4kZNQqAmBGFqA3xNL3wQpe3zgLNSnSukQ33XuK2HTupuuUWAFIvvZSEQw8Ny3GjXTCxNZSTBN7Sq/Km8CWywfxem9t/jYGdt+QO9rW8pQT+tbLeDgHe8gP/uJkUp8XU7iAzQU99m42jJmRx0qQ8Xlyxm9LaVnKMsbTZXRRlJpCRqEetUqFWqXC5FUZmJnTY0KDF5uTXbSZUGGK1ATcw8I750lkjaLbYcbgU36RBT+3MvC23BmKdQlfv668JLqw7/xpmPf43Jqz4nJkz8xmRfmBIxxCMoTZ7Gkous5mK664Hp5PEY44h6cQTIz0kYJAksgsXLuSDDz7gm2++IT8/v8vH6fV69H3c6jKUjHE6spL01JhtbKtpZdrw8LSc6LzYwL85drDP8yaXPd1iT9LrqG+1UWWykmOM9dVe9XScr7fVUlrbGrDXo/+iiGqzlUpTe4dOCScfkMsf31zH9vo2dGoVJxyQwx+PGzcgAbW/M9gqjYbdV/6J+IWXkmeupeqWW8hbcj8qtafvYSjqrPwT4dhx4zp8L1DP10D/H6h0oLuNErwCzUp0nqVNOfNM3K2t4dkFp7WN8muuxt3WhuGgg8hcdP2AHm+oCDa2hnKSYESG5+5EbYsNU7sDY1x4LvQD/V6bLA6WfLGVTZVmLHYXWcZYijITgupT3VWM8MYxX2/WSjNZxlgWTM4j2xhLjckT23KNcUwrSOUzYw1VJiu5yXHMHp1Jpamd6YWpzBmTSaJeS2l9K0XpCVSarSTqtbTaHKTGx3L5EaMwtzt4YGmJL+E8ZGQK2+vayEqKJc8Yh/8GBpurzb4012Rx8N66SkBFRmIM580YjtGg61Da1bmdmbf7Ql/XDwQj0PvaMcE9AKt1D43PPccNK18mwXo8kBP4xUIk0ITDUJk9DSXF7abyTzfjKCtDl5dHzt/u8PX9jbSIJrKKonD11Vfz9ttvs2zZMkaMGBHJ4fRKcVbi3kS2JWyJrH8AXbqljlVlzRxYkBKwfUqg5/knrT3dYjfbHKQn6D23vewuzLbuVyD7Ly6o39vNoXPpQOdjept1e8e1bFstlaZ2NCqwu9xsrDT7to4Mx6YHvbWhTcMXMy7k3uWP0vLZZ9Tdfz+Zf/gD0P86q2AS4c7BNtD/B+pd2HmjhEC3znqqkbVv307dY495dtwa4F1wFIeDimuvxV66HW1GBnn3/RuVdlBcgw9avY2toZwkSIrVkWOMpcpkpbS2hWnDB74utyubq80sK6nHandhsjrIS4kLqjbeZHH4EsPOkwXeOLa+3ITL7aa53cH6SjMrdzcxPieR2hYbWo0as9UJKnjo7ANZXd5EUXoCr68s58stNTjdCrNGppEYq6Wsqd2zME5RMFsdHDwiDYdbwWxz8sz3O/l2Wz2Ze2tFzz1oGHZXFc177275J5vLttb6FncdPyGnw6SBN353186sr+sHuusCE2yHGP8E13n5Qja+t4wJjbvQ/fVPuF59FU3CwJRuDaVtYgdaw1NP07p0KSqdjrwHHkCTnBzpIflE9NPgqquu4qWXXuLdd98lMTGR6upqAIxGI3FxcZEcWo/GZCXy7bZ6NlSawnZMbxBavaeJ5nY7LsW9T/uUQIGjq7ra7m7F5ScbKMpM8AW8rmpJO/doLEjxPO7kKbkcOTpzn9tynRdF+I9r9uhMPl5Xzfb6NmI0akZnJvDphmrfwotjJ2b3ad/vgbK2vJktqcPZc+n1jHjqXhqeehptdjap553X7zqrYBPh7soXuutd2NOts55qZDXGZFzNzQG3jQ0lRVGouu122pYvRxUXR/6jj6LNyAj5cYaaSMfW0VmJVJmsbK1u7XMi29c2ef4880UKGjXo1CqaLA6mDkvutjbeO4u7rKQeUJhdnNlhssC/u8FLP+3mu9J6tGoVKLC6rJlWq4sYrZpYrQYFKEgzUJBmYGOliXVlTTS02nG53XxdUsfI9HhS42PYUddGol6L1eGisc3O1OEpqIBmi2fjhtoWGyMy4qltswbc3azz4i67092h1213NcO/6v36gZ5Kyfoyw1vSYGXxjAt55JsHSNm+nco//pH8Bx9ApQn95ghDsS3WQDB/8gl193u2oc36y1+ImzghwiPqKKKJ7GOPeQqGZ8+e3eHrzz77LBdffHH4B9QLU4elADtZtbs5bMf0BqGPN1by2Fc79n711/YpXW+OEDh4dXf7LJias849Gj07y1gpykzYJ4n1f13/Y3Y+xqPnTfPVyCbotTz3/S5SDTqWldRSUtPCpHzjgC9KCIaiKL7VwAVnnU6GwU7dAw9Ss/gfaFNTSZo/v191VsEkwj3NJgSzUUJXuqqR7WqnsIHoWKAoCnX3L8H09tugVpN3/30d9mMXXYt0bC3OTOCbkjq2VPdt0Vh/2+R5jc1OYnZxJpuqzKQYdJw5rYBphR1r7zsnzJurzaza04zd4cLucvPNtjoOL0pn7vhfF7UYDToOHplGnjEO2MLaMhNOt5s4nYY0g56aViuF6Ya93/dI0uswWZ20O1xo1SpiNGqyjLHUmK3oNCp0WhWxOh2nHpjPcXv72xZletZjFCQbsDvdfLy+2tef13+CofPiLovdxbkzswPezeoq7vdl/UB3s7h9neFdV95MU2wSn515PWe/uJjWL7+kZvFism69NeS3sodaW6yBYFm1msqb/ghAyvnnk3LWmREe0b4iXloQraYXesoJtlSbabU5fdvWDjSjQcf8CblsrWqhpKaV4izPggDoPnD0pS60p+d07qt48axCEnpZBuBdPOFdaFCQZuDstGGA5wPGewsPVL7ZgnC37ApkV4OFZouDGI2aMdmJ6C6/HEdVNc2vvUbFDTeCSkXScceFfJGXv55mE/oTpLt6rn8CPJALIhRFoe6BB2h48kkAsm+/ncROSZnoWqRj6wEFyQCs3tPcp+f3t02el9Gg49JDR3DPZ1uoMVv5trSOaYW/zhAH6sv96YZqGtts1LbacLgUmix27v1sK6OzEilI6zhLWZBm4O+nTGJztRmL1dMFYEd9Gy5FodXm5JnlO32zuWabg/wUAy5FodniYFphCjceM5YKUzvvr6lkV0MbxVmJHOe325h3MdeLP+3mp51NpBliiIvRMG98JvMn5HaYlLh2bjHgmcUt2rtQrLfJf3eb7/Smw0NP3+vOunLPBEHmwdPInfxPKhb9gaaXXkabmUn65ZcHfS7BLLaVhV3ds5WWUn7VVSh2Owlz5pB1858iPaSApNCsj7KSYslLjqOiuZ3Ve5o4fHT4bncaDTqumzcmrNu8BlOyEMzCs0Cv290GCVccWcTKXY28s7aC2hZbRFp2BfL99noApgxLJkbrWeCVffttKHY7pnfeoeIPNwCQdNxxfT5GT7OmnZNNdUJCh40Q+hOkIxngFUWhbskDNDzxBABZt9w8KGcBRNem7103sKnKjMXuxBDTu4+aUMUyk8XB8u31rCkzoQJqW+o5dmIOB+8txQq0w2G12crEXCONbfWAC71GTU2LldXlTfsksvDr7CzAtMJUPtlQxaPLSqlvsXUo/cpPNjA2JxGtRkXy3sTTW3YwLjuJLdXmfTYnMBo8PWctdhep8THs2rtGYmtVK/M73d0tSDNw6wkTQtZ1IJhZ8Z46PPSlQ8yasmYAJucnk1Q0H2d9AzWLF1O35AHUCYlBbUfdm9pXWdgVmH3XLnZfcgmupiZiJ04k79/3Dkh5RyhIItsPM0em8taqCr7dVh/WRBaCWf0ZusVRvS1ZCOb1erPF7ordjVjsLl/LrkiXFQB8t82TyB5elO77mkqjIWfxnQCeZHbRH3A1NZFyzjkDMgb/ZLPzrf7+7uXd02zGQC2SUJxOqu+8k+ZXXgU8SWzqhRf2+3VFeOUmx/kWfK0tM3HIqLSen+QnFLHMG7dW72nGZLGTGKtFrVbjf3O6qx0OS2tbGZ4WR3ljO05FIdcYx9T8nhf1Gg06hqfHo1arcbsV/Eu/ujsnc7uD11eW+WZTrziyCMDXs7YoM4EWq4MUg45ZI9OpNlsDtsvq7i5ab2uOA8XmQO25eipR681MepWpnYrmdlQqmJTveV7qBefjrK+n4YknqLnzTlAUUi84v9vXkdrX/rGXl7P74ktw1dWjLy6m4KknURsiP4HUFUlk+2HOmEzeWlXBsq213HL8uJ6fEAah3o0FQluyEOhWXnaSnpKaFoqz9p1t7dyyq6fuCeHgcit8v70BgENHp3f4njeZVel0NL/+OtV3/A1HZRUZ11/na83V7Wv3svesN1HtvOtWfwJ3MEnqQHxQuC0WKv5wA61ffQUqFVl/+TOp5/U8+yIGpwOHp/DhuipW7GzsdSIL/Y9l3thRlBFPtclKskHHgcNSGLu3FMub2Pmv3PdPNlUK/LizEZUK5o3NCjgbG8i47CRmF6fvU/rV1TmZLJ4WW99uq/ftELil2sxXfl0ILp01gtljMn2LX3vbLqsvNcedk/xwtOdaXuqJqwfkGTv0Z8+47lpwu2h46mlqFi8Gt4vUiy7q8nWk9rXvbKWl7Pnt73DW1BAzciTDnn0GbUp4OjP1lSSy/XD46HTUKiipaaWiuZ285MHdaaGvQlmy0DkprjS149/keyCPHSqr9jRhaneQGKvlgLx9P2hVGg3Zf7sDbU429Q8+RMNTT2EvKyN38Z2o47tuI9OfWc5QBu5gktRQf1A4Kispv+ZarBs2oNLryb3nXyQdc0y/XlNE1qGj0vlwXRVfl9Ry7bzgtrcOpfxkA9lJsZTUtHBYUTonTcn11Y32VNLkv+nB8DQDSb3ohdtV6VdXyps99faZCZ7uBGOzE1Fgny1mbz1hAuOyk3p1N8v/GL2tOe48gxyquuXuLC/13Ok6tKjTBIFKRcaiRaBS0/Dkk9TcdTfO+gYyFl0fcAGY1L72TfvatZT932W4TCZiikYx7D/PoE3r/UVouEki2w/JhhgOHJbCL7ub+HxjNRcfGlwf3FC0lQmnUJYsBOolW222UpyV6Ltd5h8ce3vscLy3H62vAuDo8VloNYFnWVUqFRlXXokuO4eq226j5ZNP2LltG/kPPYh+5MiAz+nPLGcoA3ewSWrCEUcAEFtc3K/jtX63nMobbsDV3IzGaCT/sUcxHBj+HX1EaM0e4ym3WlPWTFObnZT4mB6eEbzgf889N/ZjtKoOi596Ssq6+34wx+68iNWbPAd6Xn6ygYKUOFqsDmZmpHLt3GKS4nS+LgRp8TGUNVjYUm32zSZ7nxfoIj/YFozB6DiDPLCTCoqi8N3eRPawTne6YG9Mvf461HGx1D3wIA1PPYWztoacv/8dVcy+/7ak9rV3WpZ+RcUNN6BYLMROPoCCxx8f9DOxXpLI9tPxk3L4ZXcT762tDCqRDVVbmXALVclCT71kAwXHYI8djvfW7Vb4ZIOnJ+fxE3vecSb51N8QUziciuuux759O7tOP4PsO/5K0okn7jOT0N9ZzlAF7p6S4s4zx7HFxX06juJwUP/4E9Q/+igoCrETJpD3wAPE5Of1+xxE5OUmxzEmK5GtNS18s62OU6aE5uca7O95ebOFarMt4EVyT4ldd0liMMfuvAvj4UUZfFta59tGdt/nqdCq1aTF60mK0/m6ELRZXawpa6LN5uLdNeV8otX6SguuOLJon4t8/41p/BeUGfeuL1hd3sTU/JQ+x8W+bI8erPUVJupabMTpNBw4LHACpVKpSL/iCrSZWVTddhumd9/DUVFJ3pL70abvm/yKnimKQsOTT1G3ZAkoCvGHHkr+gw90e/dwsJFEtp9OPCCHOz/cxKo9zWysMIGKbq/Uw3F7JpqEcrY32MUJ/bFqTxNVJisJem3AWYNADAceyIi33qRi0R+wrFhB5Y030fL5F2T/9Xa0qb+2AhpMt8O6S4pDUR9rKy2l8o9/wrpxIwDJZ55J1p9vQT0ItqAWoTN7bAZba1r4YnNtyBLZYGNod8lqT3Gnq+8He+zO29iuKmvG3O5k2rBk1peb2Fxt7tA5IdBdqaQ4HTqtCpvLTXJ8DNvr2tCq1RRnJfqOPSHXuM9M8paqFsqbLJitnvUEt57gaW/g3Q58U6W51xf5nRN4/7rfUPlovWeCYO7YTGJ13a+OTz7tVLSZGVRcdz2WX35h52mnk//gA8RNnhzycQ1l7rY2qm69DfNHHwGQcu45ZN18Myrd4J9c89fz6pMhxmRxsLHShMkSmkVDmUmxvoUMd7y/kYeXlvLY16W+1+98PG9w7bzjyv7CGxD93yejQeerwerPz6Xze+tdnND5Z9Ifr/1SBsAxE7J6DLb+tOnpDHvmP6RfczVotbR89hk7TjwJ04cfduj5qTEaiR03blDfEvPOHDtra3o9c6zY7dQ/+RQ7Tz0N68aNqI1Gcu+9l5y/3SFJ7BA0f+9diy821WCxO0PymsHGUG8yunBuUZetozyJYNflAZ2/HyjGBPo88U+iQaE4IxGX280POxqoNLXz6YbqHj8TypstWOwu8oxxNLbZyUqK9SW7XZ13kl5HpamdsiYLbgXqWmyUN1u6uMgPXn+f3xNFUfh4g6dka/6k7KCek3D44RS+/hoxI0firKlh9/kX0Pi//6G43SEd21Bl3bSJnaed7klitVqy/3o72bfdFnVJLOxnM7IDdev5N1PzWV7awPoKM2dOz+8wGxjoeAPRIqsng6Uut6tZ01D8XAZ6cUKL1cH7az3B9pwZw3r9fJVWS8aVV5I4ezaVf/wTtm3bqPzDDTS/+hpZf/lzn2/Rh0Pnbgp9mTlu/fY7ahYvxr5rFwDxRxxOzt/vRJeVOYAjF5E0Od/oS+g+31TT46xssPWnwcbQUHdx8T92kl7nm+X0j1v+3RAqxrT7Og1MKUimxmzzdFHwm3nt6ny824QDjMlJ9NXOdnfeZpuDHGMsbreCyerZ3jbYEq7uDPSi201VZnY3WNBr1cwZE3w80I8cSeFrr1F18820fP45Nf+4i9ZvviVn8eIhF1d629GmK4rbTdMLL1B7z70oDgfarCzy/n0vhunTQzja8NqvEtmBuq1/4gE5/O39jZitTjZUmjmoMKXbRGogWmR1ZzDV5QYKiKH8uQzk4oT311bR7nAxKiPe1/C9L2LHj6fwzTdoePIpGp56CsuKFez8zamknHUmaZddhi4rq+cXCaOuuikEG0ytW7dS98CDtC5dCoAmLY3MG27AuOCUkG85KQYXlUrFyZNzeWhpKe+vrew2ke1NnOpLDA3Vxbz32BsrTd1elGcnxXLsxGzOmJZPaX0rWQmxvL6qjJoAG7v0pi94oPZd3sfkJxsYl5OETqP2te3yfq8/EygDPQHz5soKwFODG9/LXTI1CfHkPfgATS+/TO0//0Xbd9+x8+STyfrLX0g68YQhEWNC1bfbtmMnVbfdSvsvKwFImDuXnMV3Rs2irq7sV4nsQF1Vxuo0nDm9gKe/24lKhV8AHtir2GAD82Cqyw0cEAfmfQpl8HW7FZ77fifgmY3tb3BUx8SQsfAqjAsWUPvPf9Ly+ec0vfQyzW+8Sco5Z5P2u9+hzQjvJhudeWcAXK2tfaqJtZWWUvfwI7R88onnCxoNqeefT/rCq9AkJg7w6MVg4U1kl22to7bFSmZibMDHDWScCsXFfOd4291FeapBx5dbavh5ZwOoVaTH62lut2OM05GRqA96Y5eeEvZA53XFkUVsrjbTZnPy+sryDovDJuTu+1rBfo4M1ASMzenirdXlAJx1UEGfXkOlUpF67rnEz5xJ5Y03Yd20icobb8T09ltk33YbMYWFIRxx+FlLSrBu2ICuYFif1iUodjsNzz5H/SOPoNjtqAwGMm/4AynnnDMkEv39KpEdyKvK8w4eztPf7WTl7iZqW6z0Z+erYPQmMIejF2tvZjs6B8SBfJ9CFXyXldRSUtNKgl7LmX0MtoHE5OeR/9CDtP20groHH6R95Uoa//s/ml5+haQTTyT1wguIHTs2ZMcLlv8MgDYzC01WFs6anmtiFUWhbfn3ND3/PK3ffAN763+Tjp9P+sKFXbYeE0PX6KxEDhyWzKo9zby6ooyrjwrcU3Yg41R/k+Su4q03afSmAt5z+HlHAw2tdlqsTmwOF4Y8NXsaLcwoTKXN5grZxi6Bzis/2cCnG6pYtaeZZoudw4rSuzznwXC37rONNTRbHGQnxXJEcf8u3vWjRlH4yss0PPMM9Y89Ttv3P7Dj5FNIveQS0n7/OzQJCSEadfi4TCZaPvscR1U1jupq4g8/POh1CYqi0Lp0KTX/+heO3XsAiD/sMHLu+Cu6vKHTHWa/SmRh4K4qR6THc+yELD7dWMOjy7Zz/1lTBvR4vQnMA31bKBTBMNzlFr31+Nc7ADh35rAOO86ESvzMGRheeJ6277+n/sGHaF+7FtNbb2F66y0M06djPP00EucdjSYhPC1R/DsTOGtrSDn/fDQJCV3WZzlqazF/9BHNr7+Bfft239cTj55H+sKriR0zeOt/xcC78JBCVu1Zw0sr9nDF7FEB+y+Halvarnq19idJLm+2UFrbiiFGQ2lta4d4u2xrbYfYd96M4awvN2FzurA6XKjVKnbWW3C5FTZXtTB3bEbIkvRA57W52syXW+qwOVxYHC5K69qYOiw54DEHw926l1d4Eqwzp+ejUfd/dlAVE0P65ZeTdPzxVN/xN9qWL6fhiSdofu010q+8kpSzzgzYd3awclRW4qytwTBzBo49ZSQdfXRQs7Hta9ZQu+QBLD/+CIAmI53MP/wB4ylDr6Rrv0tkB9LCOaP5dGMN766p4Lp5oxmeNnBJR28D80AmioMhGA6kn3c1smJnI1q1iksOLRyw46hUKhIOPZT4WbNoX72Gxuf/R8tnn2P55Rcsv/xCdewdJB51FEnzjyP+kEMGtM9f5562gTY9cDY00Pr1N5g//JC2H36AvauF1QYDxlNPJfX886L+lp4IjfmTsvnbBzFUmax8sbmW4yYGXpnenzhlsjhY8kXJ3u2uE7luXrFfXWn/kuQkvY76VhtVJis5xliS9J7nb642s77c5IvF3tX8TpebWJ0Gh8tNvF5LpjGW8VlJVJraOW5iTsgmEwKdV5vNiclix+5yo1OrOG5iNqdOze+wEK278ohw2lhp4vvtDWjUqpDe6QKIGTaMgqefouWLL6j7933Yd+2iZvFiGp99ltTfXkryaaehjg1c5jKY+Mfi2IkTelwUbFm9mvpHHqXtu+8AT2LvmZH+fdgmQsJNEtkQmpRvZPaYDJZtreOhpaXce8bA9bSLVPeDQCIdDDsLZYcGRVH458dbADhjej45xoHfhlilUmE4cCqGA6fiqK6m+c03Mb/3PvbduzF/+CHmDz9EpdNhOOgg4o84HMO06cSOHRPStimBOhO4LRbaN2zAsuJnWr/5Buv69b7SAYC4yZNJOvkkjCefLDWwogO9VsNZBxXw2LLtPPnNdo6dkBXyWaEt1WaWldTidit7E8ZsZo78dXvN/iTJZpuD9AQ9w1MNtNk9pQEmi4NPN1RTaWqn0mRldnG6L/aNzzVSZbbidCvMGplKYmwM1WYrk/KNHXbn6kp/SrUS9FqSDTHYnS5itBrG53S/LW8kP0ee/MZzp+v4STnkp4T+c0OlUpF09NEkzp5N85tvUvfwIzgqK6n5+53UP/oYqRdcQPIZpw/qbViD6RKjOJ20fLmUpuefx/LLL3ufqMG44BTSr7hyyG8yI4lsiF171GiWba3jzVXlXHroCMbnhr5xtNdguR0f6WDoL9Q1X19treWX3U3otWquPSr8t8d12dlkXHUV6VdeiXXDBkzvv0/r0q9wlJfT9v33tH3/PQCq2FjiJk4kdsIEYkaNRD9qFDEjRqBJSel1wqC43Thra7Ft3459x05s21/Fum491q1bweXq8Fj9+HEkzj0K40knEjN8eMjOWww9l8wq5D/f7WTVnmZ+2NHArFGh3YnJc0nl/beuQun6ob3mbYW1u8Hi6ziwpdpMSU0L04el7jPTet28Yo6bmO3bBQvo1Tbb/YlhY7OTmDs20zczPTY7CZPFwdfbaimtbaUgxdDhzllXnyMD3bKxvMnCB+s87QwvO2Jga+dVOh0pZ5+NccECmt96i8an/4OjspK6JUuoe+QRko4+muSzz8Jw0EGD8rZ7V11iHJWVmN57j6ZXX8NZ5Xkv0Wo9CexllxFTENpZ7sFKEtkQmzoshRMPyOGDdVXc9fFmnv/tzEgPKSy6S6rD2cM2lGUOTpebf32yFYCLZxWSbYzcbSiVSkXcpEnETZqEcvPN2HfuovXrr2n7/nva167FbTb7ShA6PC8mBm16OtqMDE9SGxuLWh+DKkYPKLhtNhSrDbe1HVdDI876epwNDeAM3Lxem5VF3NSpJBx2KPGHHzHkejWKgZOZFMvZBxXwvx9288hXpSFPZMdlJzG7OJ2SmlaKsxJCuvtUoK21P9lQRaXJSqWpndnFmR1mWo0GXYfZYM/XgotDXcWw3nQXuG5ecYexeretrW+1AezT/quzgejy0NmT3+zA5VY4rCidiXnhmZBRx8aSeu65pJxxBuaPPqLxxZewrluH+aOPMH/0EbrcXJKOn0/S8cejHzduUCa1zqYmWpcuxfTue1hWrPB9XZOaSvKZZ5By9tnosoPbVGKokEQ2xEwWB7+ZmsenG6v5dls9X22pZc7Y/ffDPtyrYkNZ5vD8j7vZUt2CMU7H5UeOCuEo+0elUqEfOQL9yBGkXXIxituNfedO2teswVZSgm3HTuzbt+OorESx2z0Ltyore3cQjYaYYcM8s7sjRhI7fhxxU6agy8kZmJMS+4XLjhzFSz/tYXlpAyt3NzJteGrPTwqSJ4EbE5aLZs+2sjYOGZnK9rpWxuSEbjV8oBjW2zjqP7Hg7XdbsPfW/clTcjlydOaAtmzsabxljRbfIq8r54Q/tqp0OoynnILxlFNo37iR5ldfw/zBBzgqK2l4+j80PP0fdAUFJBx+GPGHHYZhxsyI1Zcqbje2khJav/2W1q+W0b5mjW89AoBhxgyMv/kNScfP3293R5RENoT8f3nH5ySxttzEre9u4LORR2CI2T/f6nAvBAtVmUOt2cp9n5UAcNNxY0iJH7yrXFVqNfpRo9CP8nwgePu/atLSUOwOXPV1npnWpiYUux3FZkexWUGlRqXXo9LHoNbHoklLRZuegTYjHW1qalRuVSgGt7zkOE6fls8rP5dx10dbeP3yQ0I66xWKcqtAM4mdE7PzZgxneJqB0tpWbE43X2yqZVe9JSQX6oFiWKDNF4I9T//EuCgzoccktvNz+trlobvx3v95CQ6XwuGj00M+M99bcRMmEPe3O8i65WbP4tWPPqJ12TIcZWU0vfQyTS+9DFotsePGETd5MnFTphA3cQK6ggJUmuC3KQ+Ws6EB65YtWDdtov2XlVhWr8ZtNnd4jH7sWJLmz8d40om92iJ8qNo/s6sB4v/L63S5yUjUU97UzgNfbOPm48dFengR0dXswkDOmoTiw+zODzfTYnMyOd/I2Qf1fjvaSAm0A8xQL/QX0eW6ecW8s6aCX3Y38enGao6b+Ossf6S30u5qJrFzYma2ObjiyCK+3lbLe2sqfXWn3p6y3tpY/04BSXodZpujTwu4+pNY9uXivr8TAt2Nd2t1C2+v8ezkdeOxY3r1ugNJHRtL0rHHkHTsMbjb2mj7aQVt331L67ff4Sgrw7p+Pdb162l64QXAU7YVU1hIzKiRxOTloc3MQpuVhTYjHXV8Aup4A2qDAZVGg+J2g9uN4nThbmvFZTLhbmnB2dCIo7ICR0UljooKbNu346qv33dsBgNx06eRMHs2ibNnS/LaiSSyIeT/yzsqM4GTJ+dx9Surefq7nZx4QC6T8iO/MCvcAtWWRboBd08+2VDFe2srUavgb6dMDElvw3Dx7//alx1ghBho2cZY/u/wkTy4tJS7P97C3LFZxGjVvb59PhBJb1cziYESM6NBx5GjM9lUad77HD3vr6nk+x31gIpDRqZw1LhsvttWT1mThfpWG+kJeooyE3p9bv1NLL2JscniYGOlqU/JdG+PF2i8iqJwx/sbURQ4flI2B+Qn9+n1B5o6Pp7EuXNInDsHAHt5Oe1r1tK+Zo2nhGvbNhSbzVPKVVIS2oOrVMQMG4Z+7Fjipk7xdKUZNxaVVtK1rsg7E0KBfnk/2VTNh+uquO7V1Xxw9eHExYT+VsRgF6hea7D2nK1rsXHL2xsAuPzIUUwuSI7sgHqpc/9XuXIXg9H/HTmKl1bsYVeDhWeW7+TyI0f1qgxpoGrvu5pJ7Cox8/96q9XJg19uw+1WcCsKy0rq2VjZgtnqYGxWIlUmK8NTDX0+t/7eaQr3eoVA4/1gXRXfb29Ar1Vz8/zouUsZk59PTH4+xhNPAEBxuXwzqPYdO3HUVOOsqcVZU4OzoQG3xYLbYkFpb+/4QioV6oQENElJqI1JaJNT0ObmEJOXhy43l5jCQvSjR6M2RLaFZbSRRDbEOv/y/v2Uify8s5HtdW3c+eEmFv9mUgRHF3mdPyiS9LqgZwgGmqIo3PzWehrb7IzNTuTaeYG30hzMguk5KESkJei1/PG4sdz4xjqWfFHC8RNzenX7fKBq77ub+ewqkfSf7SzOSqTS1I7T6UarVjE6M4GVe5ppbLOTY4ylze7qsWPAQJ1bpDeuabU5ufPDTQBcObuIgtToTdZU3sWww4bBnDldPk5xuTwLs9RqUKsHZReEoUAS2QGWGh/DfWdO4fz//MSLP+3h8NEZXe5qsz/w/6BI0ut4ccXuQVNm8NS3O/hicw0xGjX3nzUFvTY6Z8+76jkoxGBy+rR83lpVwQ87GvjzO+v536Uzgr59PpCbsPR15tPb9uq4idm02Jws31ZPtdmzUcJxE3PINcYFVSM7UOcW6Y1r/v3ZVmrMNoanGbjsyIHtGztYqDQaGIAFYaIjSWTD4LDR6fzfESN58psd3PD6WooyEyjK7Fu7lkgvhggF7wfFYCoz+GlHA//c2zP21pPGMy5n4DayEEJ42sj949RJHLvkG77dVs/bqys49cD8oGLAYNqExZ9//9iDhqf2aXxGg47zZgxndXkTU/NTQnZukXzPvt9ez7PLdwGeu5SxOknuROioIz2A/cWNx45hxohUWm1O/u/5XzBbHb1+DW+N08NLS3ns61JMlt6/xmDinSGoNlsjurVttcnKwpdX43IrLJiSy/kzo6dLgRDRbER6PNce5Snhuf3djZQ1WoJ+rtGgY0KucdAksZ31dXwmi4MXV+zm4/XVvLhid5dx3rtwqzefA5F4z1qsDm58fR0A58wYxhHFGWE7ttg/SCIbJjqNmkfPO5AcYyw76tq47pU1OF3unp/oJ1CNUzTzzhAsnFsUsbKCVpuTS577mboWG8VZCfzj1ElSxyREGF12xEgOHJZMi83J9a/2Pi4OBn1JKrt6jc3V5h7jfDRNavz9g01UNLdTkBrHn0+IngVeInpIIhtG6Ql6nrhgGnqtmqVbavnz2xtQlOB3BB/oGcxQBOPeiuSsitPl5qoXV7G5ykx6Qgz/ueig/XbjCiH6q6/xQ6tR88DZU0nQa/lldxOPLts+QCMcGKFIKv1f49MN1WQn6buN89EyqfHWqnJe+6UclQruPX0yCXqJryL05F9VmB2Qn8yD50zlihdW8uovZWQl6Vl0THBNoQPVOIWqZjbcrVkize32dCj4uqSOWJ2a/1x0UFSvohUikvobPwpSDfx9wQSuf3UtS74oYeqwZA4fHR23oEPRDcD/NarNVi6eVUhCrLbLuN7XhVvhXGOxpdrMLW+vB+DquaN9tcNChJrMyEbAsROy+fuCiQA8uLSUp77ZEfRz/WcwQ3l7KVqu8ENBURRue28Dr68sR62CB8+eGnX9YoUYTEIRPxZMyePM6fm4FVj40mr2NERHDOrrnTL/GezOrzE2O6nbO1V9KcsKZzlCi9XBFS+swupwc/jodF8dtBADQWZkI+S8mcOpb7Fz/xclLP5oM25F4bIjR/XqNULZFzDSrVnCRVEU7vxwMy/8uAeVCu47cwrHTNh/26EJEQqhiB8qlYq/nTKRrTWtrC1r5v+e/4W3rpw16Mt9etsNwGRxsLnazKcbqn2J6xVHFvVpG9nexPtw9ZF1utxc/fJqdta3kWuM5YGzp0bV7ogi+gzuCDHEXTtvNG5F4YEvt3HXx1twKQpXzi4K+vmhTD4HazubUHK5FW59dwMv/bQHgLtPncSCqXkRHpUQ0S9U8SNWp+GJ86dx4kPfsaW6hWteXs3j509DqxncNw+DTSq9s6Lry01UmtqZPiyV9eUmtlSbmTkybUDbD4ZjskJRFG5/byPLtnpKth47fxqp8TEhP44Q/lRKb1YbDTJmsxmj0YjJZCIpKXr7fi75ooQlX2wD4PyZwzhrRgHDUuKDvl00lJPPULE73Vz/2ho+XFeFSgWLF0ziXGmzJfppqMSgziJ9Xit3N3HuUz9ic7o5+6AC7hoi3UQ2Vpp4eGkpKQYdy0sbcLrdxGg1zC5O57p5YwY8hg/058WT32znHx9tQaWCx8+fxrFyt0v0UW9i0OC+zN1PXDevmJvnjwXghZ/28Pv/ruShr0qCqmEa7L0UBwOTxcGlz/3Mh+uq0GlUPHzOgZLECjGITRuewkPnTEWtgld+LuP+vRf60c47K9pkcTC5wEh+ShyHjEyl2mwLy7qEgfy8eP2XMv7x0RYA/nLCeEliRdhIIjtIXHbkKG48thiVCqrNVt5YWcHa8qZIDyvqlda2suDR5XxXWo8hRsMzFx/ECQfkRHpYQogeHDMhmzsXTALgwS+38fjX+7blCnXLwIFuQei/SOvGY8YydVgKTRZH0Lf6I9EiMRjvrqngpjc9mx5ceugILj20MLIDEvuViCay33zzDSeddBK5ubmoVCreeeedSA4n4s6fWciJk3LQqlU0Wxwsem0tv+xqjPSwotbSLTX85pHl7KxvIy85jjcunxU1LX2EEHDuzGH84ehiAO7+eAuP+fWYDfUq/HCt6jcadOQnGzDbHJw3Y3jQnQcG6yYIH6+vYtFra1EUz8/r1hPHDYkyEBE9IprItrW1MXnyZB555JFIDmPQMBp03LlgEo+efyCjMuKpb7Vz9pM/8vS3O3C7o7aUOezsTjd3frCJS5/7hRabkxmFqby78FDG5w6dGkYh9hdXHzWa6+d5ktl/frKFR74qBULfMjBcLQj9E9IXV+wOul51MLZIfHt1uW9779On5XPnKRMliRVhF9GuBfPnz2f+/PmRHMKgYzToOGZ8NoeOSuemN9fx4boq7vxwM8u21nHvGZPJNsZGeoiD2q76Nq55ZTXryk0AXDyrkFuOH0eMVqpohIhW184bjVoF//68hHs+3Uqzxc6Vs4tCugo/XC0I+9oGa7C1SHx2+U7ueH8TAKdOzeOfpx2AWtpsiQiQ9luDVLxey8PnTOXgkWks/nAT35XWc+ySb/jbKRM4eXKuXPV24nIrPPPdTv79+VasDjfJBh33nD6Zo8dnRXpoQogQuPqo0ei0au7+eAtPfbuTarON204YT22rNSSr8HvTQqw/q//7mpAOlhaJiqJw3+clPLTUMzN+8axCbjtxvCSxImKiKpG12WzYbDbf381mcwRHM/BUKhUXHDycWaPSuO6VNayvMHHtK2t4Y2U5fz9lIoXp8ZEe4qCwucrMn95cx9q9s7CzRqXx7zMnk2OMi/DIhBChdPmRo8hM1HPTG+t4f20ldS1WHjtvWsiSumD6wfZ3O97+JKS93QQh1NrtLm54fS0frq8CYNHRxVw9t0gmVkRERdX91rvuuguj0ej7U1BQEOkhhcWojATeunIWfzi6mBitmm+31XPMkm9Y8kUJbTZnpIcXMQ2tNv7yznpOePBb1pabSIzV8s/TJvHi72ZKEivEEHXqgfk8e8lBJOi1/LijkZMe/o4NFaawHT8UtarR2Daxsrmd0x//ng/XV6FVq7jr1Elcc9RoSWJFxEVVInvzzTdjMpl8f8rKyiI9pLDRadRcfdRoPrvuCA4rSsfudLPki23MvncZL/60G6fLHekhho3V4eKpb3Yw+95lvPDjHtwKzJ+YzReLjuSsg4ZJYBX7vaHeEebw0Rm8fvkhDEs1UN7UzmmPfc/bq8vDcmxvaYB3e9lI16qGw/LSek5+eDkbK82kxsfw4u9mcs4M6cUtBoeoKi3Q6/Xo9fpIDyOiCtPjef63M/hwfRX/+mQrexot/PntDfzn251cPnsUC6bkDdmFTe12Fy/+tJsnvtlBXYunxGRCbhK3njieg0emRXh0Qgwe3o4wl156KaeeemqkhzMgxuUk8f7Cw7j21dUs21rH9a+u5acdjdx64nji9doB28VqsNSqhoPD5eb+z0t47OvtKAqMzU7kqQunU5A69JN3ET0iukVta2srpaWegvGpU6dy3333MWfOHFJTUxk2rOervUhvoxhpdqebl37azYNLS2lsswOQa4zl90eM5IzpBSToo+o6pUt1LTZeXrGH//2wm/pWTwKbnxLHNUeN5rQD89HIIgMRIdEQg1QqFW+//TYLFiwI+jnRcF5eLrfCki9KePirUhQFCtMM/P2UiSzfXt/nOlYBexosXPfqalbtaQbgnBnDuO3E8cTFaCI7MLFf6E0Mimgiu2zZMubMmbPP1y+66CKee+65Hp8fTcF2ILXanLz0026e+nanb6YyPkbDgql5nDdzeFT2T1UUhdVlzbzw424+WFuFfW/pRH5KHFfPLeLUA/PRaYbmzLOIHtEQg4Z6Iuv1/fZ6/vDaWqpMVtQqGJEez6xRadS32lk4t4gJuZFbJBVNXG6F577fxb2fbqXd4SIxVsvdpx4gOyKKsOpNDIrolN3s2bOJYB49ZCTotfzfEaO48JBC3lhZzjPf7WRHfRsv/rSHF3/awwH5Rk46IJcTDsghN3lwL4La02Dh7dUVvLOmgp31bb6vTylI5uJZhZxwQI4ksEKE2FDoCDNrVDqfXHsEt767gffWVrK9ro0qk5W5YzP3izrWUCipaeGmN9axpqwZgINHpnLP6ZOllEAMakPj3rMAIFan4fyDh3PezGH8sL2BF3/aw6cbq1lXbmJduYnFH21m+vAU5o7L5MjiDMbnJEV8YZTbrbC2vJkvN9fyxeYatlS3+L4Xp9Mwf2I2F84qZEpBcuQGKcQQd9ddd3HHHXdEehj9ZjToePCcqRw3MZvb391IXauND9Z5Vtn/af442VCmC80WO0u+2MYLP+7G6VZI1Gu55YRxnH1QQcQ/I4ToSURLC/orGm9/hVt9q42P11fx/toqVuxq7PC9jEQ9s0alceCwFKYOS2ZcTtKAz3ZaHS42V5n5eVcjK3Y28fOuRkztv+4ZrlZ5ZlZOPTCPYydkEz9E6nzF0BQNMSiY0oJAM7IFBQWD+rx6YrY6+PenW/nfj7tRFIjVqfndYSO57MiRJMZKvSx4FnO9+ONu7v9imy8OHz0+i7+fMlGSfhFRUVMj21/R8CEymFSZ2vl8Uw1fb63j++0NtDtcHb6v16oZnZVAUUYCo7MSGZkeT7YxlqykWDIS9UEnuQ6Xm7oWGxXN7VQ0tVPWaKGktpUtVWZ21Lfhcnf8J5eo13LEmAyOGpvJ7DGZpMbHhOychRhI0RCD9pca2a6sLWvm7x9s4pfdTQCkxsdw1Zwizp0xbL9duORwuXlzZTkPf1VKeVM7AGOyErn1xPEcNjo9wqMTQhJZEQSb08XKXU38vKuJ1WVNrN7T3GFmtDOVCoxxOuJjtBhiNBhiNKBSoSgKLreC06VgtjowtTuw2F1dvg5AikHHtOGpzBiRwowRaUzIHfiZYCEGwmCNQdIRpiNFUfh8Uw13f7KFHXWe2vvU+BguPbSQCw4pxBi3f8zQWh0u3l5dwSN+CWx6QgzXH13MWdML0EocFoOEJLKi1xRFYVeDhW01LWyrbWV7bSs7G9qoMVmpbbHhdPfun4lWrSI3OY685DjyUuIYlZHA2JxExuckkZmol7orMSQM1hgUqY4wA9W7NVScLjevryznsWXb2dPo2ZErQa/lzOkFnDtzGEWZCREe4cCoMrXzwo+7eXlFma9VY3qCnsuPHMl5M4fvtzPTYvCSRFaElNut0NBmx9Rup83mwmJ3YbE7URTQqFWoVKBVq0mK02KM02GM05EYq5P+rmLIG6oxqC/nZbI4eOzr0qjo3ep0uflwfRWPfrWdrTW/LjA9eGQq580cztHjs4jVRXdy53C5+XprHW+uKuezTTW+kq685DguPWzEfl1aIQa/qGm/JaKDWq0iI1FPRuL+vauaEKJr5c0WdjdYyE6KZXeDhfJmC0bD4OzdqtWoOWVKHicdkMvX2+p48cc9LN1Sw487GvlxRyOJei1HT8jipMm5HFaUHjWlTy63wpqyZt5fW8l7ayt9s68AM0ekcsmhhcwblyUlBGJIkURWCCFEv+UnGxieZvDNyEZD71a1WsWcMZnMGZNJZXM7r/xcxhu/lFFpsvLWqgreWlVBskHHEaMzOKI4gyOK08lMHFyr+VusDpaX1vPF5lq+2lJLg1/ymp6g55QpuZw+LZ9xOUPnjoEQ/qS0QAgh+mioxqChWiMbDLdbYdWeJj5YV8UH66p822J7jctJYvpwT8vCA4elMDzNELaaf0VRqG2xsXpPEyt2NrFiVwObKs34L2FIjNUyd2wmv5max2FF6TL7KqKS1MgKIUQYDNUYNFTPq7eJtmtvUvtNSR1fl9Sxrty0z2OSDTqKMxMp2tu6cFRmAnnJnraFCXptn5Jcq8NFZXM75U2eP6W1rWypNrOluqVDuYBXYZqBo8ZlcdTYTA4akRo1pRBCdEVqZIUQQgg/fVmMplGrOKgwlYMKU/nDMWNoaLXx445GVu9pYnVZM+srTDRbHKzY1bjPhjMAhhgNWUmxJMVqMcRoiddriIvRogLcioJ7b/tCi92Fqd2Bud3TwrDJ0nUrRLUKRmcmctDe9oUzClNl8wKxX5NEVgghxJAXisVoaQl6TjgghxMOyAE8/bi31bRSWtvKttoWSmtb2VHXRrXZSovVicXuYmd9W5/Ga4jRkJ8SR36Kp/Z4XHYS43KSGJ2VEPUdFYQIJUlkhRBCDHkDsRhNr9UwMc/IxLx9E2KL3UmN2Uat2Uqb3bm3daHnvwqgUXkWm6lUKuJjNBjjdCTtbV+YkaAn2aCTfttCBEESWSGEEEOe0aDjiiOLwrYYzRCjZUS6lhHp8QN6HCH2d5LICiGE2C8YDbpB29tWCNE3srRRCCGEEEJEJUlkhRBCCCFEVJJEVgghhBBCRCVJZIUQQgghRFSSRFYIIYQQQkQlSWSFEEIIIURUkkRWCCGEEEJEJUlkhRBCCCFEVJJEVgghhBBCRCVJZIUQQgghRFSSRFYIIYQQQkQlSWSFEEIIIURUkkRWCCGEEEJEJUlkhRBCCCFEVJJEVgghhBBCRCVJZIUQQgghRFSSRFYIIYQQQkQlSWSFEEIIIURUkkRWCCGEEEJEJUlkhRBCCCFEVJJEVgghhBBCRKVBkcg+8sgjFBYWEhsby8yZM1mxYkWkhySEEEIIIQa5iCeyr776KosWLeL2229n1apVTJ48mWOPPZba2tpID00IIYQQQgxiEU9k77vvPn7/+99zySWXMH78eB5//HEMBgPPPPNMpIcmhBBCCCEGsYgmsna7nZUrVzJv3jzf19RqNfPmzeOHH37Y5/E2mw2z2dzhjxBCCCGE2D9FNJGtr6/H5XKRlZXV4etZWVlUV1fv8/i77roLo9Ho+1NQUBCuoQohhBBCiEEm4qUFvXHzzTdjMpl8f8rKyiI9JCGEEEIIESHaSB48PT0djUZDTU1Nh6/X1NSQnZ29z+P1ej16vT5cwxNCCCGEEINYRGdkY2JimDZtGl9++aXva263my+//JJDDjkkgiMTQgghhBCDXURnZAEWLVrERRddxPTp05kxYwZLliyhra2NSy65JNJDE0IIIYQQg1jEE9mzzjqLuro6brvtNqqrq5kyZQqffPLJPgvAhBBCCCGE8BfxRBZg4cKFLFy4MNLDEEIIIYQQUSSquhYIIYQInmz/LYQY6iSRFUKIIUi2/xZC7A8kkRVCiCFItv8WQuwPJJEVQoghRrb/FkLsLySRFUKIIUa2/xZC7C8kkRVCiP2cbP8thIhWg6L9lhBCiNCR7b+FEPsLmZEVQoghRrb/FkLsL6J6RlZRFABZmCCEiAhv7PHGosGkP9t/S2wVQkRSb2JrVCeyLS0tALIwQQgRUS0tLRiNxkgPo4P+bP8tsVUIMRgEE1tVymCcSgiS2+2msrKSxMREVCqV7+tms5mCggLKyspISkqK4Aj3H/Keh5+85+HX+T1XFIWWlhZyc3NRq4dOpVZXsXWwGGr/9ofS+Qylc4GhdT7RdC69ia1RPSOrVqvJz8/v8vtJSUmD/oc11Mh7Hn7ynoef/3s+2GZiQ6Gn2DpYDLV/+0PpfIbSucDQOp9oOZdgY+vQmUIQQgghhBD7FUlkhRBCCCFEVBqSiaxer+f222+XvohhJO95+Ml7Hn7yng8OQ+3nMJTOZyidCwyt8xlK5+Ivqhd7CSGEEEKI/deQnJEVQgghhBBDnySyQgghhBAiKkkiK4QQQgghotJ+kch++OGHzJw5k7i4OFJSUliwYEGkh7RfsNlsTJkyBZVKxZo1ayI9nCFr165d/Pa3v2XEiBHExcUxatQobr/9dux2e6SHNuQ88sgjFBYWEhsby8yZM1mxYkWkh7TfKywsRKVSdfhz9913R3pY/TYU4ufJJ5/MsGHDiI2NJScnhwsuuIDKyspID6tPhmKcXbx4MbNmzcJgMJCcnBzp4fTZkE9k33zzTS644AIuueQS1q5dy/Llyzn33HMjPaz9wk033URubm6khzHkbdmyBbfbzRNPPMHGjRu5//77efzxx7nlllsiPbQh5dVXX2XRokXcfvvtrFq1ismTJ3PsscdSW1sb6aHt9/72t79RVVXl+3P11VdHekj9NhTi55w5c3jttdfYunUrb775Jtu3b+f000+P9LD6ZCjGWbvdzhlnnMEVV1wR6aH0jzKEORwOJS8vT3n66acjPZT9zkcffaSMHTtW2bhxowIoq1evjvSQ9iv/+te/lBEjRkR6GEPKjBkzlKuuusr3d5fLpeTm5ip33XVXBEclhg8frtx///2RHkZIDdX4+e677yoqlUqx2+2RHkpIDJU4++yzzypGozHSw+izIT0ju2rVKioqKlCr1UydOpWcnBzmz5/Phg0bIj20Ia2mpobf//73PP/88xgMhkgPZ79kMplITU2N9DCGDLvdzsqVK5k3b57va2q1mnnz5vHDDz9EcGQC4O677yYtLY2pU6dyzz334HQ6Iz2kPhuq8bOxsZEXX3yRWbNmodPpIj2ckJA4OzgM6UR2x44dAPz1r3/lL3/5Cx988AEpKSnMnj2bxsbGCI9uaFIUhYsvvpjLL7+c6dOnR3o4+6XS0lIeeughLrvsskgPZcior6/H5XKRlZXV4etZWVlUV1dHaFQC4JprruGVV17hq6++4rLLLuMf//gHN910U6SH1SdDMX7+8Y9/JD4+nrS0NPbs2cO7774b6SGFhMTZwSMqE9k//elP+xT3d/7jrWcB+POf/8xpp53GtGnTePbZZ1GpVLz++usRPovoEux7/tBDD9HS0sLNN98c6SFHvWDfc38VFRUcd9xxnHHGGfz+97+P0MiF6J/e/NtftGgRs2fP5oADDuDyyy/n3//+Nw899BA2my3CZ/GroRQ/exuXbrzxRlavXs1nn32GRqPhwgsvRBlE+zANtTjbl/OJdlG5s1ddXR0NDQ3dPmbkyJEsX76cuXPn8u2333LYYYf5vjdz5kzmzZvH4sWLB3qoQ0aw7/mZZ57J+++/j0ql8n3d5XKh0Wg477zz+O9//zvQQx0ygn3PY2JiAKisrGT27NkcfPDBPPfcc6jVUXmdOijZ7XYMBgNvvPFGh64nF110Ec3NzUNmlmmw6O2/fX8bN25k4sSJbNmyhTFjxgzUEHtlKMXP/vxsysvLKSgo4Pvvv+eQQw4ZqCH2ylCLs335+Tz33HNcd911NDc3D/DoBoY20gPoi4yMDDIyMnp83LRp09Dr9WzdutWXyDocDnbt2sXw4cMHephDSrDv+YMPPsidd97p+3tlZSXHHnssr776KjNnzhzIIQ45wb7n4JkhmDNnju+uw2ALrtEuJiaGadOm8eWXX/oSWbfbzZdffsnChQsjO7ghqDf/9jtbs2YNarWazMzMEI+q74ZS/OzPz8Z7l3QwzZYPtTjbn59PtIrKRDZYSUlJXH755dx+++0UFBQwfPhw7rnnHgDOOOOMCI9uaBo2bFiHvyckJAAwatQo8vPzIzGkIa+iooLZs2czfPhw7r33Xurq6nzfy87OjuDIhpZFixZx0UUXMX36dGbMmMGSJUtoa2vjkksuifTQ9ls//PADP/30E3PmzCExMZEffviB66+/nvPPP5+UlJRID6/XhlL8/Omnn/j555857LDDSElJYfv27dx6662MGjVq0MzG9sZQjLN79uyhsbGRPXv24HK5fP2Ki4qKfP/2osGQTmQB7rnnHrRaLRdccAHt7e3MnDmTpUuXRmWQEyKQzz//nNLSUkpLS/f5sIvCyqFB66yzzqKuro7bbruN6upqpkyZwieffLLPAjARPnq9nldeeYW//vWv2Gw2RowYwfXXX8+iRYsiPbT9nsFg4K233uL222+nra2NnJwcjjvuOP7yl7+g1+sjPbxeG4px9rbbbutQrjJ16lQAvvrqK2bPnh2hUfVeVNbICiGEEEIIMfgKPIQQQgghhAiCJLJCCCGEECIqSSIrhBBCCCGikiSyQgghhBAiKkkiK4QQQgghopIkskIIIYQQIipJIiuEEEIIIaKSJLJCCCGEECIqSSIrhBBCCCGikiSyYr928cUXs2DBgkgPQwghhhSJrSJcJJEVQgghhBBRSRJZIYQQQggRlSSRFUIIIYQQUUkSWSGEEEIIEZUkkRVCCCGEEFFJElkhhBBCCBGVJJEVQgghhBBRSRJZIYQQQggRlSSRFUIIIYQQUUmlKIoS6UEIIYQQQgjRWzIjK4QQQgghopIkskIIIYQQIipJIiuEEEIIIaKSJLJCCCGEECIqSSIrhBBCCCGikiSyQgghhBAiKkkiK4QQQgghopIkskIIIYQQIipJIiuEEEIIIaKSJLJCCCGEECIqSSIrhBBCCCGikiSyQgghhBAiKv0/z5Efrb7kZ9YAAAAASUVORK5CYII=" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "d8d517e9-7d7a-4322-b4d4-62dbe6d6e22b", - "record_entry_path": "/root/3-MeasurementCalibrationMultilevelGMM.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691442, - "print_time": "2024-12-08 15:57:22" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "MeasurementCalibrationMultilevelGMM" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "lpb_collections": { - "f01": { - "type": "SimpleDriveCollection", - "freq": 5040.4, - "channel": 2, - "shape": "blackman_drag", - "amp": 0.5487, - "phase": 0.0, - "width": 0.05, - "alpha": 500, - "trunc": 1.2, - "transition_name": "f01" - }, - "f12": { - "type": "SimpleDriveCollection", - "freq": 4842.4, - "channel": 2, - "shape": "blackman_drag", - "amp": 0.07071067811865475, - "phase": 0.0, - "width": 0.025, - "alpha": 425.1365229849309, - "trunc": 1.2, - "transition_name": "f12" - } - }, - "measurement_primitives": { - "0": { - "type": "SimpleDispersiveMeasurement", - "freq": 9645.5, - "channel": 1, - "shape": "square", - "amp": 0.15, - "phase": 0.0, - "width": 1, - "trunc": 1.2, - "distinguishable_states": [ - 0, - 1 - ] - } - } - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "Element QA parameters" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "9cb19c94-c085-496a-ac0f-c663a5bc9937", - "record_entry_path": "/root/4-MeasurementCalibrationMultilevelGMM.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691442, - "print_time": "2024-12-08 15:57:23" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "MeasurementCalibrationMultilevelGMM" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "lpb_collections": { - "f01": { - "type": "SimpleDriveCollection", - "freq": 4855.3, - "channel": 4, - "shape": "blackman_drag", - "amp": 0.5399696605966315, - "phase": 0.0, - "width": 0.05, - "alpha": 500, - "trunc": 1.2, - "transition_name": "f01" - }, - "f12": { - "type": "SimpleDriveCollection", - "freq": 4843.4, - "channel": 4, - "shape": "blackman_drag", - "amp": 0.07071067811865475, - "phase": 0.0, - "width": 0.025, - "alpha": 425.1365229849309, - "trunc": 1.2, - "transition_name": "f12" - } - }, - "measurement_primitives": { - "0": { - "type": "SimpleDispersiveMeasurement", - "freq": 9025.5, - "channel": 3, - "shape": "square", - "amp": 0.15, - "phase": 0.0, - "width": 1, - "trunc": 1.2, - "distinguishable_states": [ - 0, - 1 - ] - } - } - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "Element QB parameters" - } - }, - "output_type": "display_data" - } - ], - "execution_count": 2 - }, - { - "cell_type": "markdown", - "id": "45f9aec7a2c7ecfc", - "metadata": {}, - "source": [ - "## Run auto tuneup" - ] - }, - { - "cell_type": "code", - "id": "b4d4f2c9-ad83-461c-ba24-a33aae217cde", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-08T21:01:13.833657Z", - "start_time": "2024-12-08T20:57:23.218577Z" - } - }, - "source": [ - "from k_agents.execution.agent import execute_procedure\n", - "from leeq.utils.ai.translation_agent import init_leeq_translation_agents\n", - "\n", - "from mllm.config import default_models\n", - "default_models.normal = \"gpt-4o\"\n", - "default_models.expensive = \"gpt-4o\"\n", - "setup().status().set_param(\"AIAutoInspectPlots\", True) \n", - "\n", - "init_leeq_translation_agents()\n", - "execute_procedure(\"Full gate calibration on `dut`\", dut=duts_dict['Q1'])" - ], - "outputs": [ - { - "data": { - "text/plain": [ - "Adding experiment to memory: 0%| | 0/17 [00:00" - ], - "text/html": [ - "\n", - "
\n", - " Generating state machine...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "extract_parameters: 0%| | 0/1 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('c3f19eae-e17e-4859-825a-e8904e264408');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c3f19eae-e17e-4859-825a-e8904e264408');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The planned experiments are:

\n", - "
\n", - "

Stage1

\n", - "

Description: Full gate calibration on `dut`

\n", - "

Next Steps: If Stage1 completes successfully, goto Complete. If Stage1 fails, goto Failed.

\n", - " \n", - "
\n", - "
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage1: Stage1...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage1

\n", - "

Description: Full gate calibration on `dut`

\n", - "

Next Steps: If Stage1 completes successfully, goto Complete. If Stage1 fails, goto Failed.

\n", - " \n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('78e6040a-a168-40ec-bb96-eec126971cbc');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('78e6040a-a168-40ec-bb96-eec126971cbc');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Full gate calibration on `dut`:
\n", - "

\n", - " 
experiment_instance = Experiment_FullCalibrationOfSingleQubitDut(instruction="Full calibration of Single Qubit dut", dut=dut)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Decomposing instructions...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('a1a72696-ac88-4f64-943b-c85d136b135d');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a1a72696-ac88-4f64-943b-c85d136b135d');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Generating state machine...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "extract_parameters: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('11612cd0-16e1-41bd-84e7-1a3574d1e351');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('11612cd0-16e1-41bd-84e7-1a3574d1e351');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The planned experiments are:

\n", - "
\n", - "

Stage1

\n", - "

Description: Full gate frequency calibration on `dut`

\n", - "

Next Steps: If Stage1 completes successfully, goto Stage2. If Stage1 fails, goto Failed.

\n", - " \n", - "
\n", - " \n", - "
\n", - "

Stage2

\n", - "

Description: Full gate amplitude calibration on `dut`

\n", - "

Next Steps: If Stage2 completes successfully, goto Stage3. If Stage2 fails, goto Failed.

\n", - " \n", - "
\n", - " \n", - "
\n", - "

Stage3

\n", - "

Description: DRAG Calibration on `dut`

\n", - "

Next Steps: If Stage3 completes successfully, goto Complete. If Stage3 fails, goto Failed.

\n", - " \n", - "
\n", - "
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage1: Stage1...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage1

\n", - "

Description: Full gate frequency calibration on `dut`

\n", - "

Next Steps: If Stage1 completes successfully, goto Stage2. If Stage1 fails, goto Failed.

\n", - " \n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('bb959677-4aa8-4e39-a42a-fe2c13519ca2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('bb959677-4aa8-4e39-a42a-fe2c13519ca2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Full gate frequency calibration on `dut`:
\n", - "

\n", - " 
experiment_FullGateFrequencyCalibrationOnDut = Experiment_FullGateFrequencyCalibrationOnDut(instruction="Full Gate frequency calibration on dut", dut=dut)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Decomposing instructions...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('a918f04d-15a8-4c27-aa49-05b218ed9c3b');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a918f04d-15a8-4c27-aa49-05b218ed9c3b');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Generating state machine...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "extract_parameters: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('97c711c4-335e-457d-a8ea-99901de83a21');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('97c711c4-335e-457d-a8ea-99901de83a21');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The planned experiments are:

\n", - "
\n", - "

Stage1

\n", - "

Description: Run simple Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop at `stop_in_us`, step `step_in_us`.

\n", - "

Next Steps: If Stage1 fails after 3 retries, goto Failed. If Stage1 completes, goto Stage2.

\n", - "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 10\n",
-       "VarName:`stop_in_us` Value: 0.3\n",
-       "VarName:`step_in_us` Value: 0.005

\n", - "
\n", - " \n", - "
\n", - "

Stage2

\n", - "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.

\n", - "

Next Steps: If Stage2 fails after 3 retries, goto Failed. If Stage2 completes, goto Stage3.

\n", - "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 1\n",
-       "VarName:`stop_at_in_us` Value: 3\n",
-       "VarName:`step_in_us` Value: 0.05

\n", - "
\n", - " \n", - "
\n", - "

Stage3

\n", - "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.

\n", - "

Next Steps: If Stage3 fails after 3 retries, goto Failed. If Stage3 completes, goto Complete.

\n", - "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 0.1\n",
-       "VarName:`stop_in_us` Value: 30\n",
-       "VarName:`step_in_us` Value: 0.5

\n", - "
\n", - "
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage1: Stage1...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage1

\n", - "

Description: Run simple Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop at `stop_in_us`, step `step_in_us`.

\n", - "

Next Steps: If Stage1 fails after 3 retries, goto Failed. If Stage1 completes, goto Stage2.

\n", - "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 10\n",
-       "VarName:`stop_in_us` Value: 0.3\n",
-       "VarName:`step_in_us` Value: 0.005

\n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('d37672b6-5a7d-44e0-a9d6-845ae57a215a');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d37672b6-5a7d-44e0-a9d6-845ae57a215a');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Run simple Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop at `stop_in_us`, step `step_in_us`.:
\n", - "

\n", - " 
experiment_simple_ramsey = SimpleRamseyMultilevel(dut=dut, collection_name='f01', mprim_index=0, initial_lpb=None, start=0.0, stop=stop_in_us, step=step_in_us, set_offset=frequency_offset_in_MHz, update=True)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling noise is enabled\n" - ] - }, - { - "data": { - "text/html": [ - " \n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "mode": "markers", - "name": "Data", - "x": [ - 0.0, - 0.005, - 0.01, - 0.015, - 0.02, - 0.025, - 0.03, - 0.035, - 0.04, - 0.045, - 0.05, - 0.055, - 0.06, - 0.065, - 0.07, - 0.075, - 0.08, - 0.085, - 0.09, - 0.095, - 0.1, - 0.105, - 0.11, - 0.115, - 0.12, - 0.125, - 0.13, - 0.135, - 0.14, - 0.145, - 0.15, - 0.155, - 0.16, - 0.165, - 0.17, - 0.17500000000000002, - 0.18, - 0.185, - 0.19, - 0.195, - 0.2, - 0.20500000000000002, - 0.21, - 0.215, - 0.22, - 0.225, - 0.23, - 0.23500000000000001, - 0.24, - 0.245, - 0.25, - 0.255, - 0.26, - 0.265, - 0.27, - 0.275, - 0.28, - 0.28500000000000003, - 0.29, - 0.295 - ], - "y": [ - 1.0, - 0.764, - 0.926, - 0.724, - 0.576, - 0.52, - 0.43, - 0.254, - 0.312, - 0.446, - 0.128, - 0.132, - 0.262, - 0.212, - 0.422, - 0.492, - 0.6, - 0.754, - 0.858, - 0.694, - 0.872, - 0.926, - 0.716, - 0.712, - 0.6, - 0.504, - 0.392, - 0.288, - 0.34, - 0.046, - 0.198, - 0.214, - 0.124, - 0.27, - 0.36, - 0.516, - 0.594, - 0.642, - 0.77, - 0.842, - 0.992, - 0.892, - 0.706, - 0.682, - 0.718, - 0.54, - 0.436, - 0.328, - 0.222, - 0.112, - 0.054, - 0.16, - 0.422, - 0.362, - 0.438, - 0.516, - 0.61, - 0.658, - 0.872, - 0.818 - ], - "type": "scatter", - "xaxis": "x", - "yaxis": "y" - }, - { - "mode": "lines", - "name": "Fit", - "visible": "legendonly", - "x": [ - 0.0, - 5.0E-4, - 0.001, - 0.0015, - 0.002, - 0.0025, - 0.003, - 0.0035, - 0.004, - 0.0045000000000000005, - 0.005, - 0.0055, - 0.006, - 0.006500000000000001, - 0.007, - 0.0075, - 0.008, - 0.0085, - 0.009000000000000001, - 0.0095, - 0.01, - 0.0105, - 0.011, - 0.0115, - 0.012, - 0.0125, - 0.013000000000000001, - 0.0135, - 0.014, - 0.0145, - 0.015, - 0.0155, - 0.016, - 0.0165, - 0.017, - 0.0175, - 0.018000000000000002, - 0.0185, - 0.019, - 0.0195, - 0.02, - 0.0205, - 0.021, - 0.021500000000000002, - 0.022, - 0.0225, - 0.023, - 0.0235, - 0.024, - 0.0245, - 0.025, - 0.025500000000000002, - 0.026000000000000002, - 0.0265, - 0.027, - 0.0275, - 0.028, - 0.0285, - 0.029, - 0.029500000000000002, - 0.03, - 0.0305, - 0.031, - 0.0315, - 0.032, - 0.0325, - 0.033, - 0.0335, - 0.034, - 0.0345, - 0.035, - 0.035500000000000004, - 0.036000000000000004, - 0.0365, - 0.037, - 0.0375, - 0.038, - 0.0385, - 0.039, - 0.0395, - 0.04, - 0.0405, - 0.041, - 0.0415, - 0.042, - 0.0425, - 0.043000000000000003, - 0.043500000000000004, - 0.044, - 0.0445, - 0.045, - 0.0455, - 0.046, - 0.0465, - 0.047, - 0.0475, - 0.048, - 0.0485, - 0.049, - 0.0495, - 0.05, - 0.0505, - 0.051000000000000004, - 0.051500000000000004, - 0.052000000000000005, - 0.0525, - 0.053, - 0.0535, - 0.054, - 0.0545, - 0.055, - 0.0555, - 0.056, - 0.0565, - 0.057, - 0.0575, - 0.058, - 0.0585, - 0.059000000000000004, - 0.059500000000000004, - 0.06, - 0.0605, - 0.061, - 0.0615, - 0.062, - 0.0625, - 0.063, - 0.0635, - 0.064, - 0.0645, - 0.065, - 0.0655, - 0.066, - 0.0665, - 0.067, - 0.0675, - 0.068, - 0.0685, - 0.069, - 0.0695, - 0.07, - 0.07050000000000001, - 0.07100000000000001, - 0.07150000000000001, - 0.07200000000000001, - 0.0725, - 0.073, - 0.0735, - 0.074, - 0.0745, - 0.075, - 0.0755, - 0.076, - 0.0765, - 0.077, - 0.0775, - 0.078, - 0.0785, - 0.079, - 0.0795, - 0.08, - 0.0805, - 0.081, - 0.0815, - 0.082, - 0.0825, - 0.083, - 0.0835, - 0.084, - 0.0845, - 0.085, - 0.0855, - 0.08600000000000001, - 0.08650000000000001, - 0.08700000000000001, - 0.08750000000000001, - 0.088, - 0.0885, - 0.089, - 0.0895, - 0.09, - 0.0905, - 0.091, - 0.0915, - 0.092, - 0.0925, - 0.093, - 0.0935, - 0.094, - 0.0945, - 0.095, - 0.0955, - 0.096, - 0.0965, - 0.097, - 0.0975, - 0.098, - 0.0985, - 0.099, - 0.0995, - 0.1, - 0.1005, - 0.101, - 0.1015, - 0.10200000000000001, - 0.10250000000000001, - 0.10300000000000001, - 0.10350000000000001, - 0.10400000000000001, - 0.1045, - 0.105, - 0.1055, - 0.106, - 0.1065, - 0.107, - 0.1075, - 0.108, - 0.1085, - 0.109, - 0.1095, - 0.11, - 0.1105, - 0.111, - 0.1115, - 0.112, - 0.1125, - 0.113, - 0.1135, - 0.114, - 0.1145, - 0.115, - 0.1155, - 0.116, - 0.1165, - 0.117, - 0.11750000000000001, - 0.11800000000000001, - 0.11850000000000001, - 0.11900000000000001, - 0.11950000000000001, - 0.12, - 0.1205, - 0.121, - 0.1215, - 0.122, - 0.1225, - 0.123, - 0.1235, - 0.124, - 0.1245, - 0.125, - 0.1255, - 0.126, - 0.1265, - 0.127, - 0.1275, - 0.128, - 0.1285, - 0.129, - 0.1295, - 0.13, - 0.1305, - 0.131, - 0.1315, - 0.132, - 0.1325, - 0.133, - 0.1335, - 0.134, - 0.1345, - 0.135, - 0.1355, - 0.136, - 0.1365, - 0.137, - 0.1375, - 0.138, - 0.1385, - 0.139, - 0.1395, - 0.14, - 0.1405, - 0.14100000000000001, - 0.14150000000000001, - 0.14200000000000002, - 0.14250000000000002, - 0.14300000000000002, - 0.14350000000000002, - 0.14400000000000002, - 0.1445, - 0.145, - 0.1455, - 0.146, - 0.1465, - 0.147, - 0.1475, - 0.148, - 0.1485, - 0.149, - 0.1495, - 0.15, - 0.1505, - 0.151, - 0.1515, - 0.152, - 0.1525, - 0.153, - 0.1535, - 0.154, - 0.1545, - 0.155, - 0.1555, - 0.156, - 0.1565, - 0.157, - 0.1575, - 0.158, - 0.1585, - 0.159, - 0.1595, - 0.16, - 0.1605, - 0.161, - 0.1615, - 0.162, - 0.1625, - 0.163, - 0.1635, - 0.164, - 0.1645, - 0.165, - 0.1655, - 0.166, - 0.1665, - 0.167, - 0.1675, - 0.168, - 0.1685, - 0.169, - 0.1695, - 0.17, - 0.1705, - 0.171, - 0.1715, - 0.17200000000000001, - 0.17250000000000001, - 0.17300000000000001, - 0.17350000000000002, - 0.17400000000000002, - 0.17450000000000002, - 0.17500000000000002, - 0.17550000000000002, - 0.176, - 0.1765, - 0.177, - 0.1775, - 0.178, - 0.1785, - 0.179, - 0.1795, - 0.18, - 0.1805, - 0.181, - 0.1815, - 0.182, - 0.1825, - 0.183, - 0.1835, - 0.184, - 0.1845, - 0.185, - 0.1855, - 0.186, - 0.1865, - 0.187, - 0.1875, - 0.188, - 0.1885, - 0.189, - 0.1895, - 0.19, - 0.1905, - 0.191, - 0.1915, - 0.192, - 0.1925, - 0.193, - 0.1935, - 0.194, - 0.1945, - 0.195, - 0.1955, - 0.196, - 0.1965, - 0.197, - 0.1975, - 0.198, - 0.1985, - 0.199, - 0.1995, - 0.2, - 0.2005, - 0.201, - 0.2015, - 0.202, - 0.2025, - 0.203, - 0.20350000000000001, - 0.20400000000000001, - 0.20450000000000002, - 0.20500000000000002, - 0.20550000000000002, - 0.20600000000000002, - 0.20650000000000002, - 0.20700000000000002, - 0.20750000000000002, - 0.20800000000000002, - 0.2085, - 0.209, - 0.2095, - 0.21, - 0.2105, - 0.211, - 0.2115, - 0.212, - 0.2125, - 0.213, - 0.2135, - 0.214, - 0.2145, - 0.215, - 0.2155, - 0.216, - 0.2165, - 0.217, - 0.2175, - 0.218, - 0.2185, - 0.219, - 0.2195, - 0.22, - 0.2205, - 0.221, - 0.2215, - 0.222, - 0.2225, - 0.223, - 0.2235, - 0.224, - 0.2245, - 0.225, - 0.2255, - 0.226, - 0.2265, - 0.227, - 0.2275, - 0.228, - 0.2285, - 0.229, - 0.2295, - 0.23, - 0.2305, - 0.231, - 0.2315, - 0.232, - 0.2325, - 0.233, - 0.2335, - 0.234, - 0.23450000000000001, - 0.23500000000000001, - 0.23550000000000001, - 0.23600000000000002, - 0.23650000000000002, - 0.23700000000000002, - 0.23750000000000002, - 0.23800000000000002, - 0.23850000000000002, - 0.23900000000000002, - 0.23950000000000002, - 0.24, - 0.2405, - 0.241, - 0.2415, - 0.242, - 0.2425, - 0.243, - 0.2435, - 0.244, - 0.2445, - 0.245, - 0.2455, - 0.246, - 0.2465, - 0.247, - 0.2475, - 0.248, - 0.2485, - 0.249, - 0.2495, - 0.25, - 0.2505, - 0.251, - 0.2515, - 0.252, - 0.2525, - 0.253, - 0.2535, - 0.254, - 0.2545, - 0.255, - 0.2555, - 0.256, - 0.2565, - 0.257, - 0.2575, - 0.258, - 0.2585, - 0.259, - 0.2595, - 0.26, - 0.2605, - 0.261, - 0.2615, - 0.262, - 0.2625, - 0.263, - 0.2635, - 0.264, - 0.2645, - 0.265, - 0.2655, - 0.266, - 0.2665, - 0.267, - 0.2675, - 0.268, - 0.2685, - 0.269, - 0.2695, - 0.27, - 0.2705, - 0.271, - 0.2715, - 0.272, - 0.2725, - 0.273, - 0.2735, - 0.274, - 0.2745, - 0.275, - 0.2755, - 0.276, - 0.2765, - 0.277, - 0.2775, - 0.278, - 0.2785, - 0.279, - 0.2795, - 0.28, - 0.2805, - 0.281, - 0.28150000000000003, - 0.28200000000000003, - 0.28250000000000003, - 0.28300000000000003, - 0.28350000000000003, - 0.28400000000000003, - 0.28450000000000003, - 0.28500000000000003, - 0.28550000000000003, - 0.28600000000000003, - 0.28650000000000003, - 0.28700000000000003, - 0.28750000000000003, - 0.28800000000000003, - 0.2885, - 0.289, - 0.2895, - 0.29, - 0.2905, - 0.291, - 0.2915, - 0.292, - 0.2925, - 0.293, - 0.2935, - 0.294, - 0.2945, - 0.295, - 0.2955, - 0.296, - 0.2965, - 0.297, - 0.2975, - 0.298, - 0.2985, - 0.299, - 0.2995 - ], - "y": [ - 0.862930707290807, - 0.8636043467881758, - 0.863929695780379, - 0.8639064298072531, - 0.8635345720749221, - 0.8628144934326512, - 0.8617469120030039, - 0.8603328924656699, - 0.8585738449956786, - 0.8564715238570592, - 0.8540280256533463, - 0.8512457872366789, - 0.8481275832775761, - 0.8446765234978137, - 0.8408960495691616, - 0.8367899316810751, - 0.8323622647807606, - 0.8276174644893703, - 0.822560262698393, - 0.8171957028506363, - 0.8115291349105057, - 0.8055662100285961, - 0.7993128749059157, - 0.7927753658633663, - 0.7859602026223881, - 0.7788741818029798, - 0.7715243701455698, - 0.7639180974635043, - 0.7560629493331776, - 0.7479667595290964, - 0.7396376022114197, - 0.7310837838737688, - 0.7223138350593363, - 0.7133365018535534, - 0.7041607371618044, - 0.6947956917808815, - 0.6852507052730888, - 0.6755352966520933, - 0.665659154889813, - 0.6556321292538108, - 0.645464219484827, - 0.6351655658242499, - 0.624746438901467, - 0.6142172294911843, - 0.6035884381509258, - 0.5928706647490501, - 0.5820745978937273, - 0.5712110042734153, - 0.5602907179194705, - 0.5493246294015984, - 0.5383236749669194, - 0.5272988256334817, - 0.5162610762490988, - 0.505221434526421, - 0.4941909100651778, - 0.48318050337253843, - 0.4722011948925407, - 0.4612639340555277, - 0.4503796283585142, - 0.4395591324873727, - 0.4288132374916869, - 0.4181526600230675, - 0.40758803164766555, - 0.39712988824353757, - 0.38678865949344043, - 0.3765746584835313, - 0.36649807141834756, - 0.35656894746232337, - 0.34679718871797294, - 0.3371925403507356, - 0.3277645808703322, - 0.3185227125783223, - 0.3094761521913908, - 0.3006339216497145, - 0.29200483911957426, - 0.28359751019918933, - 0.27542031933653804, - 0.2674814214677289, - 0.2597887338842588, - 0.2523499283372688, - 0.24517242338667278, - 0.23826337700278788, - 0.23162967942784568, - 0.22527794630450315, - 0.21921451207820492, - 0.21344542367997982, - 0.2079764344959671, - 0.20281299862969016, - 0.1979602654627981, - 0.19342307451970014, - 0.18920595064121404, - 0.1853130994720415, - 0.1817484032665716, - 0.1785154170171946, - 0.17561736490898627, - 0.17305713710430076, - 0.17083728686047606, - 0.16896002798352844, - 0.16742723262037396, - 0.16624042939177952, - 0.16540080186790573, - 0.16490918738796073, - 0.16476607622514305, - 0.16497161109770614, - 0.16552558702663178, - 0.16642745154005495, - 0.1676763052242356, - 0.16927090262052835, - 0.1712096534674551, - 0.17349062428664297, - 0.1761115403110436, - 0.17906978775351406, - 0.18236241641349443, - 0.18598614261918284, - 0.1899373525022756, - 0.19421210560200364, - 0.1988061387948737, - 0.20371487054619397, - 0.2089334054791443, - 0.21445653925683422, - 0.22027876377248046, - 0.22639427264252776, - 0.2327969669972345, - 0.23948046156294833, - 0.24643809103000747, - 0.25366291669991337, - 0.26114773340515096, - 0.26888507669475115, - 0.2768672302784318, - 0.28508623372189146, - 0.2935338903855842, - 0.3022017755990559, - 0.3110812450626911, - 0.32016344346849085, - 0.3294393133312856, - 0.338899604021574, - 0.3485348809909805, - 0.35833553518113215, - 0.3682917926065689, - 0.37839372410213357, - 0.388631255225118, - 0.3989941763022921, - 0.4094721526117948, - 0.42005473468973353, - 0.43073136875121315, - 0.4414914072154015, - 0.4523241193241385, - 0.46321870184349023, - 0.4741642898375869, - 0.4851499675039924, - 0.49616477905980233, - 0.5071977396676133, - 0.5182378463904678, - 0.5292740891648493, - 0.5402954617807841, - 0.5512909728581007, - 0.5622496568078977, - 0.5731605847682956, - 0.5840128755035526, - 0.5947957062556929, - 0.6054983235378105, - 0.6161100538582927, - 0.6266203143652652, - 0.6370186234006441, - 0.6472946109532693, - 0.657438029000695, - 0.6674387617293229, - 0.6772868356226867, - 0.6869724294078261, - 0.6964858838498322, - 0.7058177113847949, - 0.7149586055815467, - 0.7238994504227667, - 0.7326313293961891, - 0.7411455343868487, - 0.749433574361496, - 0.7574871838365241, - 0.765298331120954, - 0.7728592263262689, - 0.7801623291351014, - 0.7872003563210288, - 0.7939662890119774, - 0.8004533796899914, - 0.8066551589203868, - 0.8125654418035773, - 0.8181783341431407, - 0.8234882383239724, - 0.8284898588946659, - 0.8331782078485489, - 0.8375486095981144, - 0.8415967056378812, - 0.8453184588910356, - 0.8487101577355189, - 0.8517684197055457, - 0.854490194864862, - 0.8568727688483782, - 0.8589137655691443, - 0.8606111495879679, - 0.8619632281433105, - 0.862968652839439, - 0.8636264209911495, - 0.8639358766237195, - 0.8638967111270947, - 0.8635089635636555, - 0.8627730206292576, - 0.8616896162675853, - 0.8602598309382006, - 0.8584850905390216, - 0.8563671649843009, - 0.8539081664395243, - 0.85111054721499, - 0.8479770973201681, - 0.8445109416812804, - 0.8407155370248742, - 0.8365946684305001, - 0.8321524455559284, - 0.8273932985386725, - 0.8223219735779033, - 0.8169435282011627, - 0.8112633262205968, - 0.8052870323837359, - 0.7990206067241602, - 0.7924702986176819, - 0.7856426405499731, - 0.7785444416018537, - 0.7711827806587377, - 0.7635649993510084, - 0.7556986947323643, - 0.7475917117034366, - 0.7392521351882343, - 0.7306882820712188, - 0.7219086929030508, - 0.7129221233832774, - 0.7037375356284579, - 0.6943640892344336, - 0.6848111321416565, - 0.6750881913126826, - 0.6652049632311372, - 0.6551713042316141, - 0.6449972206701613, - 0.6346928589451543, - 0.6242684953785073, - 0.6137345259673147, - 0.6031014560161435, - 0.5923798896603155, - 0.5815805192906278, - 0.5707141148900586, - 0.5597915132930898, - 0.548823607378365, - 0.5378213352054507, - 0.5267956691065429, - 0.5157576047439923, - 0.5047181501445657, - 0.49368831472137326, - 0.48267909829441663, - 0.4717014801207018, - 0.46076640794486035, - 0.44988478708119695, - 0.4390674695380521, - 0.42832524319532544, - 0.41766882104595365, - 0.40710883051207125, - 0.3966558028465096, - 0.3863201626302034, - 0.3761122173759795, - 0.36604214724909434, - 0.3561199949147735, - 0.34635565552287667, - 0.3367588668396768, - 0.3273391995365947, - 0.31810604764557404, - 0.3090686191906132, - 0.30023592700480384, - 0.29161677974202427, - 0.2832197730922586, - 0.27505328120930056, - 0.26712544835939017, - 0.2594441807991129, - 0.25201713889066024, - 0.24485172946231643, - 0.23795509842178825, - 0.23133412362974654, - 0.22499540804068435, - 0.21894527311793405, - 0.21318975252940903, - 0.20773458613035795, - 0.20258521423913262, - 0.19774677221167675, - 0.19322408532014818, - 0.18902166394078063, - 0.18514369905578404, - 0.181594058073772, - 0.17837628097287828, - 0.17549357677041783, - 0.1729488203226034, - 0.17074454945751694, - 0.1688829624441907, - 0.1673659158003239, - 0.16619492244082024, - 0.16537115016899362, - 0.1648954205119465, - 0.16476820790128127, - 0.1649896391999635, - 0.16555949357580768, - 0.16647720272171185, - 0.16774185142242154, - 0.16935217846725809, - 0.17130657790790038, - 0.1736031006599668, - 0.17623945644679834, - 0.17921301608350698, - 0.1825208140990081, - 0.18615955169342668, - 0.19012560002792156, - 0.19441500384365373, - 0.19902348540628467, - 0.20394644877207296, - 0.20917898437131438, - 0.21471587390455416, - 0.22055159554668946, - 0.22668032945377048, - 0.23309596356701046, - 0.2397920997082142, - 0.24676205996054829, - 0.25399889332828796, - 0.2614953826688999, - 0.2692440518905475, - 0.27723717340784093, - 0.28546677584839564, - 0.2939246520025153, - 0.30260236700806853, - 0.31149126676240246, - 0.3205824865528935, - 0.3298669598975412, - 0.3393354275867789, - 0.3489784469174886, - 0.35878640111000903, - 0.368749508898748, - 0.3788578342868312, - 0.389101296455062, - 0.3994696798153097, - 0.40995264419829935, - 0.42053973516564347, - 0.43122039443583227, - 0.4419839704137841, - 0.45281972881345545, - 0.4637168633629165, - 0.47466450658121717, - 0.485651740616295, - 0.49666760813311717, - 0.5077011232411972, - 0.5187412824505925, - 0.5297770756454464, - 0.5407974970641479, - 0.5517915562751382, - 0.5627482891374372, - 0.573656768734947, - 0.5845061162736316, - 0.5952855119307073, - 0.6059842056450204, - 0.6165915278378535, - 0.6270969000534692, - 0.6374898455087771, - 0.6477599995416061, - 0.6578971199471603, - 0.6678910971923501, - 0.6777319644978137, - 0.6874099077775719, - 0.6969152754264064, - 0.7062385879451981, - 0.7153705473946282, - 0.724302046667813, - 0.7330241785726275, - 0.7415282447146543, - 0.749805764171906, - 0.7578484819526667, - 0.7656483772280166, - 0.7731976713308314, - 0.78048883551328, - 0.7875145984550806, - 0.7942679535150328, - 0.8007421657185874, - 0.8069307784744919, - 0.8128276200138076, - 0.8184268095448811, - 0.8237227631181301, - 0.8287101991947938, - 0.8333841439140968, - 0.8377399360535704, - 0.841773231677589, - 0.84548000846948, - 0.8488565697428929, - 0.8518995481284211, - 0.8546059089318057, - 0.8569729531603674, - 0.8589983202146514, - 0.8606799902426, - 0.862016286153904, - 0.8630058752925264, - 0.8636477707657277, - 0.8639413324282706, - 0.863886267520819, - 0.863482630961897, - 0.8627308252931178, - 0.861631600277732, - 0.860186052152903, - 0.8583956225364485, - 0.856262096989143, - 0.8537876032340117, - 0.8509746090343953, - 0.8478259197328983, - 0.8443446754536768, - 0.8405343479708567, - 0.8363987372462036, - 0.8319419676394992, - 0.8271684837954011, - 0.8220830462108905, - 0.8166907264877267, - 0.810996902274643, - 0.8050072519043303, - 0.7987277487305523, - 0.7921646551710453, - 0.7853245164621385, - 0.7782141541313268, - 0.7708406591943033, - 0.7632113850832385, - 0.7553339403133558, - 0.7472161808951184, - 0.7388662024995954, - 0.7302923323848186, - 0.7215031210911831, - 0.7125073339141735, - 0.7033139421629179, - 0.6939321142132919, - 0.6843712063644897, - 0.6746407535081841, - 0.6647504596195803, - 0.6547101880798467, - 0.6445299518395723, - 0.6342199034330639, - 0.6237903248534372, - 0.6132516172986036, - 0.6026142907983751, - 0.5918889537330344, - 0.581086302253822, - 0.5702171096158917, - 0.5592922154343711, - 0.5483225148742442, - 0.5373189477848345, - 0.526292487789725, - 0.5152541313429986, - 0.5042148867627045, - 0.4931857632525014, - 0.48217775992241013, - 0.47120185481963595, - 0.46026899398039633, - 0.44939008051367213, - 0.43857596372776947, - 0.4278374283105361, - 0.41718518357402234, - 0.40662985277431296, - 0.39618196251718085, - 0.38585193226012793, - 0.3756500639212832, - 0.36558653160552024, - 0.35567137145804023, - 0.34591447165553924, - 0.3363255625449415, - 0.32691420693953344, - 0.3176897905821748, - 0.30866151278509996, - 0.29983837725563767, - 0.29122918311701307, - 0.2828425161331676, - 0.2746867401463624, - 0.26676998873609864, - 0.25910015710767414, - 0.25168489421846557, - 0.2445315951497885, - 0.2376473937319426, - 0.23103915542979725, - 0.2247134704960123, - 0.218676647398722, - 0.21293470653023666, - 0.20749337420303676, - 0.202358076939046, - 0.19753393605787956, - 0.19302576256946424, - 0.18883805237612356, - 0.18497498178891308, - 0.18144040336267803, - 0.17823784205398502, - 0.1753704917057608, - 0.17284121186214518, - 0.17065252491673089, - 0.16880661359703775, - 0.16730531878772842, - 0.16615013769473658, - 0.1653422223521387, - 0.1648823784732587, - 0.1647710646471503, - 0.1650083918812596, - 0.165594123490723, - 0.16652767533441148, - 0.16780811639748577, - 0.1694341697198808, - 0.17140421366979425, - 0.17371628356090912, - 0.1763680736117368, - 0.17935693924512708, - 0.1826798997256523, - 0.18633364113223483, - 0.19031451966305268, - 0.1946185652694296, - 0.1992414856150827, - 0.2041786703567815, - 0.20942519574214846, - 0.21497582952001543, - 0.22082503615843907, - 0.22696698236517254, - 0.23339554290508657, - 0.2401043067087386, - 0.24708658326599925, - 0.2543354092983581, - 0.26184355570325574, - 0.26960353476351695, - 0.277607607614694, - 0.2858477919628737, - 0.29431587004525217, - 0.30300339682553645, - 0.3119017084160024, - 0.3210019307178071, - 0.33029498827094117, - 0.3397716133049938, - 0.3494223549817047, - 0.35923758882008594, - 0.36920752629471354, - 0.3793222245976174, - 0.3895715965540332, - 0.39994542068212885, - 0.41043335138667086, - 0.4210249292764721, - 0.4317095915953141, - 0.44247668275596475, - 0.4533154649667685, - 0.464215128940218, - 0.47516480467282735, - 0.4861535722855558, - 0.497170472913971, - 0.508204519637292, - 0.5192447084354114, - 0.5302800291629706, - 0.5412994765295436, - 0.5522920610749791, - 0.5632468201289543, - 0.5741528287438131, - 0.5849992105897834, - 0.5957751488017082, - 0.6064698967664751, - 0.617072788840384, - 0.6275732509857651, - 0.6379608113162417, - 0.6482251105401177, - 0.658355912291478, - 0.6683431133386972, - 0.678176753660176, - 0.687847026377257, - 0.6973442875344151, - 0.7066590657169671, - 0.7157820714967106, - 0.7247042066960703, - 0.7334165734615163, - 0.741910483137201, - 0.7501774649299687, - 0.7582092743570958, - 0.7659979014683349, - 0.7735355788340659, - 0.7808147892915843, - 0.7878282734418096, - 0.7945690368889206, - 0.801030357215719, - 0.8072057906877488, - 0.8130891786794947, - 0.8186746538162424, - 0.8239566458254852, - 0.8289298870920357, - 0.8335894179113025, - 0.8379305914354952, - 0.8419490783078237, - 0.8456408709800707, - 0.8490022877092304, - 0.8520299762292297, - 0.8547209170940685, - 0.8570724266890453, - 0.859082159907066, - 0.8607481124873659, - 0.8620686230143129, - 0.863042374574298, - 0.8636683960690628, - 0.8639460631841507 - ], - "type": "scatter", - "xaxis": "x", - "yaxis": "y" - } - ], - "layout": { - "template": { - "data": { - "histogram2dcontour": [ - { - "type": "histogram2dcontour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "choropleth": [ - { - "type": "choropleth", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "histogram2d": [ - { - "type": "histogram2d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmap": [ - { - "type": "heatmap", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmapgl": [ - { - "type": "heatmapgl", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "contourcarpet": [ - { - "type": "contourcarpet", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "contour": [ - { - "type": "contour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "surface": [ - { - "type": "surface", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "mesh3d": [ - { - "type": "mesh3d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "parcoords": [ - { - "type": "parcoords", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolargl": [ - { - "type": "scatterpolargl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "scattergeo": [ - { - "type": "scattergeo", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolar": [ - { - "type": "scatterpolar", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "scattergl": [ - { - "type": "scattergl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatter3d": [ - { - "type": "scatter3d", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattermapbox": [ - { - "type": "scattermapbox", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterternary": [ - { - "type": "scatterternary", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattercarpet": [ - { - "type": "scattercarpet", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ] - }, - "layout": { - "autotypenumbers": "strict", - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "hovermode": "closest", - "hoverlabel": { - "align": "left" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "bgcolor": "#E5ECF6", - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "ternary": { - "bgcolor": "#E5ECF6", - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "sequential": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ] - }, - "xaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "yaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "geo": { - "bgcolor": "white", - "landcolor": "#E5ECF6", - "subunitcolor": "white", - "showland": true, - "showlakes": true, - "lakecolor": "white" - }, - "title": { - "x": 0.05 - }, - "mapbox": { - "style": "light" - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0.0, - 1.0 - ], - "title": { - "text": "Time (us)
Frequency: 10.05+/-0.08 MHz" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0.0, - 1.0 - ], - "title": { - "text": "" - } - }, - "title": { - "text": "Ramsey decay QA transition f01:
(0.0+/-3.5)e+09 us" - }, - "plot_bgcolor": "white" - }, - "config": { - "plotlyServerURL": "https://plot.ly" - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "33f4e035-6b33-4172-9477-9ec231276e5b", - "record_entry_path": "/root/6-SimpleRamseyMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691493, - "print_time": "2024-12-08 15:58:13" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "SimpleRamseyMultilevel" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 10.000 MHz, and the measured oscillation is 10.053+/-0.080 MHz. Oscillation amplitude is 0.350+/-0.028. The number of oscillations is 3.016+/-0.024.'
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('6835cf21-84b2-47a0-bdeb-8cf8da18d493');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('6835cf21-84b2-47a0-bdeb-8cf8da18d493');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear and regular oscillatory pattern, indicating the presence of Ramsey oscillations.\\n2. Fit Quality: The fit line closely follows the data points throughout the plot, suggesting a good fit.\\n3. Data Spread: The data points are moderately clustered around the fit line, with some dispersion but not excessively wide.\\n4. Amplitude and Frequency: The amplitude of the oscillations is close to 1, which is ideal. The frequency is approximately 10.05 MHz, aligning well with the expected value.\\n5. Overall Pattern: The plot displays typical characteristics of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude and frequency.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('a9be2d86-b1d0-4230-b04c-7b2c6da53d97');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a9be2d86-b1d0-4230-b04c-7b2c6da53d97');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
analysis: 'The Ramsey experiment for qubit QA was successful. The oscillation frequency measured was 10.053 MHz, which is very close to the expected offset of 10.000 MHz. The amplitude of the oscillations was 0.350, which is above the threshold of 0.2, indicating a strong signal. The number of oscillations observed was 3.016, which is within the acceptable range of 3 to 10 oscillations. The data exhibited a clear and regular oscillatory pattern with a good fit, suggesting that the experiment was conducted properly.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('b457baf0-7b7f-4f9e-a867-1d41478b837f');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('b457baf0-7b7f-4f9e-a867-1d41478b837f');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Transitioning to the next stage Stage2 with the following description:
Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.
The Ramsey experiment for qubit QA was successful. The oscillation frequency measured was 10.053 MHz, which is very close to the expected offset of 10.000 MHz. The amplitude of the oscillations was 0.350, which is above the threshold of 0.2, indicating a strong signal. The number of oscillations observed was 3.016, which is within the acceptable range of 3 to 10 oscillations. The data exhibited a clear and regular oscillatory pattern with a good fit, suggesting that the experiment was conducted properly. According to the rule of transition, since Stage1 has been completed successfully, the experiment should proceed to Stage2.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage2: Stage2...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage2

\n", - "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.

\n", - "

Next Steps: If Stage2 fails after 3 retries, goto Failed. If Stage2 completes, goto Stage3.

\n", - "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 1\n",
-       "VarName:`stop_at_in_us` Value: 3\n",
-       "VarName:`step_in_us` Value: 0.05

\n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('1d341047-84f9-492d-892b-5bdf52e70a47');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('1d341047-84f9-492d-892b-5bdf52e70a47');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset_in_MHz`, stop_at=`stop_at_in_us`, step=`step_in_us`.:
\n", - "

\n", - " 
experiment_ramsey = SimpleRamseyMultilevel(dut=dut, collection_name='f01', mprim_index=0, initial_lpb=None, start=0.0, stop=stop_at_in_us, step=step_in_us, set_offset=frequency_offset_in_MHz, update=True)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling noise is enabled\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "mode": "markers", - "name": "Data", - "x": [ - 0.0, - 0.05, - 0.1, - 0.15000000000000002, - 0.2, - 0.25, - 0.30000000000000004, - 0.35000000000000003, - 0.4, - 0.45, - 0.5, - 0.55, - 0.6000000000000001, - 0.65, - 0.7000000000000001, - 0.75, - 0.8, - 0.8500000000000001, - 0.9, - 0.9500000000000001, - 1.0, - 1.05, - 1.1, - 1.1500000000000001, - 1.2000000000000002, - 1.25, - 1.3, - 1.35, - 1.4000000000000001, - 1.4500000000000002, - 1.5, - 1.55, - 1.6, - 1.6500000000000001, - 1.7000000000000002, - 1.75, - 1.8, - 1.85, - 1.9000000000000001, - 1.9500000000000002, - 2.0, - 2.0500000000000003, - 2.1, - 2.15, - 2.2, - 2.25, - 2.3000000000000003, - 2.35, - 2.4000000000000004, - 2.45, - 2.5, - 2.5500000000000003, - 2.6, - 2.6500000000000004, - 2.7, - 2.75, - 2.8000000000000003, - 2.85, - 2.9000000000000004, - 2.95 - ], - "y": [ - 1.0, - 0.728, - 0.926, - 0.736, - 0.586, - 0.522, - 0.306, - 0.356, - 0.196, - 0.086, - 0.0, - 0.184, - 0.088, - 0.362, - 0.364, - 0.5, - 0.688, - 0.682, - 0.708, - 0.858, - 0.766, - 0.922, - 0.534, - 0.838, - 0.558, - 0.492, - 0.434, - 0.264, - 0.182, - 0.0, - 0.15, - 0.206, - 0.222, - 0.398, - 0.29, - 0.472, - 0.644, - 0.758, - 0.862, - 0.83, - 0.77, - 0.94, - 1.0, - 0.618, - 0.682, - 0.534, - 0.444, - 0.23, - 0.0, - 0.022, - 0.09, - 0.306, - 0.116, - 0.166, - 0.328, - 0.498, - 0.6, - 0.826, - 0.906, - 0.97 - ], - "type": "scatter", - "xaxis": "x", - "yaxis": "y" - }, - { - "mode": "lines", - "name": "Fit", - "visible": "legendonly", - "x": [ - 0.0, - 0.005, - 0.01, - 0.015, - 0.02, - 0.025, - 0.03, - 0.035, - 0.04, - 0.045, - 0.05, - 0.055, - 0.06, - 0.065, - 0.07, - 0.075, - 0.08, - 0.085, - 0.09, - 0.095, - 0.1, - 0.105, - 0.11, - 0.115, - 0.12, - 0.125, - 0.13, - 0.135, - 0.14, - 0.145, - 0.15, - 0.155, - 0.16, - 0.165, - 0.17, - 0.17500000000000002, - 0.18, - 0.185, - 0.19, - 0.195, - 0.2, - 0.20500000000000002, - 0.21, - 0.215, - 0.22, - 0.225, - 0.23, - 0.23500000000000001, - 0.24, - 0.245, - 0.25, - 0.255, - 0.26, - 0.265, - 0.27, - 0.275, - 0.28, - 0.28500000000000003, - 0.29, - 0.295, - 0.3, - 0.305, - 0.31, - 0.315, - 0.32, - 0.325, - 0.33, - 0.335, - 0.34, - 0.34500000000000003, - 0.35000000000000003, - 0.355, - 0.36, - 0.365, - 0.37, - 0.375, - 0.38, - 0.385, - 0.39, - 0.395, - 0.4, - 0.405, - 0.41000000000000003, - 0.41500000000000004, - 0.42, - 0.425, - 0.43, - 0.435, - 0.44, - 0.445, - 0.45, - 0.455, - 0.46, - 0.465, - 0.47000000000000003, - 0.47500000000000003, - 0.48, - 0.485, - 0.49, - 0.495, - 0.5, - 0.505, - 0.51, - 0.515, - 0.52, - 0.525, - 0.53, - 0.535, - 0.54, - 0.545, - 0.55, - 0.555, - 0.56, - 0.5650000000000001, - 0.5700000000000001, - 0.5750000000000001, - 0.58, - 0.585, - 0.59, - 0.595, - 0.6, - 0.605, - 0.61, - 0.615, - 0.62, - 0.625, - 0.63, - 0.635, - 0.64, - 0.645, - 0.65, - 0.655, - 0.66, - 0.665, - 0.67, - 0.675, - 0.68, - 0.685, - 0.6900000000000001, - 0.6950000000000001, - 0.7000000000000001, - 0.705, - 0.71, - 0.715, - 0.72, - 0.725, - 0.73, - 0.735, - 0.74, - 0.745, - 0.75, - 0.755, - 0.76, - 0.765, - 0.77, - 0.775, - 0.78, - 0.785, - 0.79, - 0.795, - 0.8, - 0.805, - 0.81, - 0.8150000000000001, - 0.8200000000000001, - 0.8250000000000001, - 0.8300000000000001, - 0.835, - 0.84, - 0.845, - 0.85, - 0.855, - 0.86, - 0.865, - 0.87, - 0.875, - 0.88, - 0.885, - 0.89, - 0.895, - 0.9, - 0.905, - 0.91, - 0.915, - 0.92, - 0.925, - 0.93, - 0.935, - 0.9400000000000001, - 0.9450000000000001, - 0.9500000000000001, - 0.9550000000000001, - 0.96, - 0.965, - 0.97, - 0.975, - 0.98, - 0.985, - 0.99, - 0.995, - 1.0, - 1.0050000000000001, - 1.01, - 1.0150000000000001, - 1.02, - 1.025, - 1.03, - 1.035, - 1.04, - 1.045, - 1.05, - 1.055, - 1.06, - 1.065, - 1.07, - 1.075, - 1.08, - 1.085, - 1.09, - 1.095, - 1.1, - 1.105, - 1.11, - 1.115, - 1.12, - 1.125, - 1.1300000000000001, - 1.135, - 1.1400000000000001, - 1.145, - 1.1500000000000001, - 1.155, - 1.16, - 1.165, - 1.17, - 1.175, - 1.18, - 1.185, - 1.19, - 1.195, - 1.2, - 1.205, - 1.21, - 1.215, - 1.22, - 1.225, - 1.23, - 1.235, - 1.24, - 1.245, - 1.25, - 1.2550000000000001, - 1.26, - 1.2650000000000001, - 1.27, - 1.2750000000000001, - 1.28, - 1.285, - 1.29, - 1.295, - 1.3, - 1.305, - 1.31, - 1.315, - 1.32, - 1.325, - 1.33, - 1.335, - 1.34, - 1.345, - 1.35, - 1.355, - 1.36, - 1.365, - 1.37, - 1.375, - 1.3800000000000001, - 1.385, - 1.3900000000000001, - 1.395, - 1.4000000000000001, - 1.405, - 1.41, - 1.415, - 1.42, - 1.425, - 1.43, - 1.435, - 1.44, - 1.445, - 1.45, - 1.455, - 1.46, - 1.465, - 1.47, - 1.475, - 1.48, - 1.485, - 1.49, - 1.495, - 1.5, - 1.5050000000000001, - 1.51, - 1.5150000000000001, - 1.52, - 1.5250000000000001, - 1.53, - 1.5350000000000001, - 1.54, - 1.545, - 1.55, - 1.555, - 1.56, - 1.565, - 1.57, - 1.575, - 1.58, - 1.585, - 1.59, - 1.595, - 1.6, - 1.605, - 1.61, - 1.615, - 1.62, - 1.625, - 1.6300000000000001, - 1.635, - 1.6400000000000001, - 1.645, - 1.6500000000000001, - 1.655, - 1.6600000000000001, - 1.665, - 1.67, - 1.675, - 1.68, - 1.685, - 1.69, - 1.695, - 1.7, - 1.705, - 1.71, - 1.715, - 1.72, - 1.725, - 1.73, - 1.735, - 1.74, - 1.745, - 1.75, - 1.7550000000000001, - 1.76, - 1.7650000000000001, - 1.77, - 1.7750000000000001, - 1.78, - 1.7850000000000001, - 1.79, - 1.795, - 1.8, - 1.805, - 1.81, - 1.815, - 1.82, - 1.825, - 1.83, - 1.835, - 1.84, - 1.845, - 1.85, - 1.855, - 1.86, - 1.865, - 1.87, - 1.875, - 1.8800000000000001, - 1.885, - 1.8900000000000001, - 1.895, - 1.9000000000000001, - 1.905, - 1.9100000000000001, - 1.915, - 1.92, - 1.925, - 1.93, - 1.935, - 1.94, - 1.945, - 1.95, - 1.955, - 1.96, - 1.965, - 1.97, - 1.975, - 1.98, - 1.985, - 1.99, - 1.995, - 2.0, - 2.005, - 2.0100000000000002, - 2.015, - 2.02, - 2.025, - 2.0300000000000002, - 2.035, - 2.04, - 2.045, - 2.05, - 2.055, - 2.06, - 2.065, - 2.07, - 2.075, - 2.08, - 2.085, - 2.09, - 2.095, - 2.1, - 2.105, - 2.11, - 2.115, - 2.12, - 2.125, - 2.13, - 2.1350000000000002, - 2.14, - 2.145, - 2.15, - 2.1550000000000002, - 2.16, - 2.165, - 2.17, - 2.1750000000000003, - 2.18, - 2.185, - 2.19, - 2.195, - 2.2, - 2.205, - 2.21, - 2.215, - 2.22, - 2.225, - 2.23, - 2.235, - 2.24, - 2.245, - 2.25, - 2.255, - 2.2600000000000002, - 2.265, - 2.27, - 2.275, - 2.2800000000000002, - 2.285, - 2.29, - 2.295, - 2.3000000000000003, - 2.305, - 2.31, - 2.315, - 2.32, - 2.325, - 2.33, - 2.335, - 2.34, - 2.345, - 2.35, - 2.355, - 2.36, - 2.365, - 2.37, - 2.375, - 2.38, - 2.3850000000000002, - 2.39, - 2.395, - 2.4, - 2.4050000000000002, - 2.41, - 2.415, - 2.42, - 2.4250000000000003, - 2.43, - 2.435, - 2.44, - 2.445, - 2.45, - 2.455, - 2.46, - 2.465, - 2.47, - 2.475, - 2.48, - 2.485, - 2.49, - 2.495, - 2.5, - 2.505, - 2.5100000000000002, - 2.515, - 2.52, - 2.525, - 2.5300000000000002, - 2.535, - 2.54, - 2.545, - 2.5500000000000003, - 2.555, - 2.56, - 2.565, - 2.57, - 2.575, - 2.58, - 2.585, - 2.59, - 2.595, - 2.6, - 2.605, - 2.61, - 2.615, - 2.62, - 2.625, - 2.63, - 2.6350000000000002, - 2.64, - 2.645, - 2.65, - 2.6550000000000002, - 2.66, - 2.665, - 2.67, - 2.6750000000000003, - 2.68, - 2.685, - 2.69, - 2.695, - 2.7, - 2.705, - 2.71, - 2.715, - 2.72, - 2.725, - 2.73, - 2.735, - 2.74, - 2.745, - 2.75, - 2.755, - 2.7600000000000002, - 2.765, - 2.77, - 2.775, - 2.7800000000000002, - 2.785, - 2.79, - 2.795, - 2.8000000000000003, - 2.805, - 2.81, - 2.815, - 2.82, - 2.825, - 2.83, - 2.835, - 2.84, - 2.845, - 2.85, - 2.855, - 2.86, - 2.865, - 2.87, - 2.875, - 2.88, - 2.8850000000000002, - 2.89, - 2.895, - 2.9, - 2.9050000000000002, - 2.91, - 2.915, - 2.92, - 2.9250000000000003, - 2.93, - 2.935, - 2.94, - 2.945, - 2.95, - 2.955, - 2.96, - 2.965, - 2.97, - 2.975, - 2.98, - 2.985, - 2.99, - 2.995 - ], - "y": [ - 0.8927677675330036, - 0.892538426709964, - 0.8919158517828547, - 0.8909006588705457, - 0.8894938526383627, - 0.8876968253038401, - 0.8855113552589415, - 0.8829396053101094, - 0.8799841205378871, - 0.8766478257782312, - 0.8729340227280072, - 0.868846386677531, - 0.864388962873392, - 0.8595661625151567, - 0.8543827583899141, - 0.8488438801489825, - 0.8429550092314546, - 0.8367219734396012, - 0.8301509411715045, - 0.8232484153166271, - 0.8160212268203593, - 0.8084765279239118, - 0.8006217850862435, - 0.7924647715950327, - 0.7840135598739988, - 0.7752765134941912, - 0.7662622788971507, - 0.7569797768381328, - 0.747438193557862, - 0.7376469716915554, - 0.7276158009242074, - 0.7173546084013906, - 0.7068735489050562, - 0.6961829948040581, - 0.6852935257893484, - 0.6742159184040001, - 0.6629611353784184, - 0.6515403147812959, - 0.6399647589970487, - 0.628245923540639, - 0.6163954057208566, - 0.6044249331632765, - 0.592346352204251, - 0.5801716161674224, - 0.5679127735343578, - 0.5555819560210121, - 0.5431913665718213, - 0.530753267283305, - 0.5182799672691292, - 0.5057838104786412, - 0.493277163480931, - 0.48077240322650366, - 0.46828190479868176, - 0.4558180291668542, - 0.44339311095369205, - 0.4310194462284386, - 0.4187092803383533, - 0.40647479579035084, - 0.39432810019482967, - 0.3822812142836198, - 0.3703460600139086, - 0.3585344487699167, - 0.34685806967400057, - 0.3353284780187484, - 0.32395708383151867, - 0.3127551405827369, - 0.30173373404912623, - 0.290903771342893, - 0.2802759701177227, - 0.2698608479622713, - 0.2596687119916459, - 0.249709648647177, - 0.23999351371457517, - 0.23052992257035265, - 0.22132824066615991, - 0.21239757426045575, - 0.20374676140668285, - 0.19538436320686692, - 0.1873186553392951, - 0.17955761986865854, - 0.17210893734676286, - 0.16497997921162594, - 0.1581778004924833, - 0.1517091328279208, - 0.14558037780404515, - 0.13979760061928315, - 0.1343665240820811, - 0.12929252294744326, - 0.12458061859791408, - 0.12023547407426882, - 0.11626138946082937, - 0.11266229762997293, - 0.10944176035004494, - 0.106602964760526, - 0.10414872021794541, - 0.102081455515657, - 0.10040321648023398, - 0.09911566394685756, - 0.09822007211570571, - 0.09771732729096627, - 0.09760792700372467, - 0.097891979519592, - 0.09856920373156253, - 0.09963892943820596, - 0.10110009800691849, - 0.10295126342157768, - 0.10519059371356304, - 0.10781587277472704, - 0.11082450255052206, - 0.11421350561111232, - 0.11797952809792783, - 0.12211884304274256, - 0.12662735405599396, - 0.13150059938069086, - 0.13673375630790352, - 0.14232164594945795, - 0.1482587383631201, - 0.15453915802518997, - 0.161156689645096, - 0.16810478431623188, - 0.1753765659969494, - 0.1829648383152947, - 0.19086209169075252, - 0.19906051076594977, - 0.2075519821409691, - 0.21632810240260708, - 0.22538018644064695, - 0.2346992760428983, - 0.24427614876051523, - 0.2541013270348019, - 0.264165087576492, - 0.2744574709882006, - 0.28496829162054593, - 0.29568714765216686, - 0.30660343138367746, - 0.3177063397353641, - 0.32898488493823086, - 0.3404279054078282, - 0.3520240767900844, - 0.3637619231682293, - 0.3756298284196978, - 0.38761604771179586, - 0.3997087191247348, - 0.41189587539053885, - 0.4241654557362144, - 0.4365053178194459, - 0.44890324974502177, - 0.46134698215009157, - 0.47382420034628836, - 0.48632255650671674, - 0.49882968188572496, - 0.5113331990593892, - 0.5238207341745761, - 0.5362799291944815, - 0.5486984541285068, - 0.5610640192343901, - 0.5733643871804961, - 0.5855873851562494, - 0.597720916918706, - 0.609752974763357, - 0.621671651407314, - 0.6334651517731047, - 0.6451218046614398, - 0.6566300743013729, - 0.6679785717664493, - 0.6791560662455213, - 0.6901514961570985, - 0.7009539800962136, - 0.7115528276029887, - 0.7219375497422315, - 0.7320978694835968, - 0.7420237318720425, - 0.751705313978515, - 0.7611330346210079, - 0.7702975638463897, - 0.7791898321635989, - 0.7878010395190869, - 0.7961226640056086, - 0.8041464702957597, - 0.8118645177918986, - 0.8192691684844009, - 0.8263530945104587, - 0.8331092854059547, - 0.8395310550432237, - 0.845612048247844, - 0.8513462470879091, - 0.8567279768295497, - 0.8617519115528219, - 0.8664130794223933, - 0.8707068676078218, - 0.8746290268485475, - 0.8781756756590898, - 0.881343304170279, - 0.8841287776027282, - 0.8865293393691043, - 0.8885426138021281, - 0.8901666085056055, - 0.8913997163261624, - 0.8922407169437312, - 0.8926887780792154, - 0.8927434563181379, - 0.8924046975494571, - 0.8916728370191163, - 0.8905485989982744, - 0.8890330960665456, - 0.8871278280109565, - 0.8848346803417131, - 0.8821559224262427, - 0.8790942052433603, - 0.8756525587597818, - 0.8718343889315762, - 0.8676434743335326, - 0.8630839624197673, - 0.8581603654192819, - 0.8528775558705243, - 0.847240761799382, - 0.8412555615453694, - 0.8349278782411386, - 0.8282639739507685, - 0.8212704434726421, - 0.8139542078130351, - 0.8063225073368833, - 0.7983828946025, - 0.7901432268873416, - 0.7816116584122084, - 0.772796632271588, - 0.7637068720781135, - 0.754351373329421, - 0.7447393945059321, - 0.73488044790839, - 0.7247842902441995, - 0.7144609129719026, - 0.7039205324133282, - 0.6931735796432208, - 0.6822306901663349, - 0.6711026933922221, - 0.6598006019181278, - 0.6483356006305926, - 0.6367190356365595, - 0.6249624030349243, - 0.6130773375396577, - 0.6010756009657406, - 0.5889690705893278, - 0.5767697273936347, - 0.5644896442122058, - 0.5521409737812729, - 0.5397359367130511, - 0.5272868094018509, - 0.5148059118749982, - 0.5023055956005629, - 0.4897982312639834, - 0.4772961965256621, - 0.46481186377166955, - 0.452357587869657, - 0.4399456939421168, - 0.42758846516906845, - 0.41529813063226306, - 0.40308685321291526, - 0.3909667175549552, - 0.3789497181057073, - 0.3670477472458229, - 0.3552725835202315, - 0.3436358799817366, - 0.33214915265881195, - 0.3208237691589941, - 0.30967093741916085, - 0.29870169461382023, - 0.28792689623239864, - 0.27735720533631947, - 0.2670030820065258, - 0.2568747729918669, - 0.24698230156861573, - 0.23733545762113145, - 0.22794378795350273, - 0.21881658684174154, - 0.2099628868358953, - 0.2013914498211623, - 0.19311075834687275, - 0.18512900723190195, - 0.17745409545483676, - 0.17009361833690817, - 0.16305486002543806, - 0.15634478628522758, - 0.1499700376050302, - 0.143936922625927, - 0.1382514118981058, - 0.13291913197222882, - 0.12794535983122896, - 0.12333501766805194, - 0.1190926680145068, - 0.11522250922604882, - 0.11172837132696167, - 0.1086137122200524, - 0.10588161426460663, - 0.10353478122599558, - 0.10157553559994797, - 0.10000581631414085, - 0.09882717680937753, - 0.09804078350225753, - 0.09764741463085574, - 0.09764745948455522, - 0.09804091801879478, - 0.09882740085511305, - 0.10000612966648992, - 0.1015759379476015, - 0.10353527216922925, - 0.10588219331567861, - 0.1086143788036873, - 0.11172912478092228, - 0.11522334880179386, - 0.1190935928779352, - 0.12333602690033035, - 0.12794645242970576, - 0.13292030685142958, - 0.13825266789081192, - 0.14393825848433445, - 0.14997145200199097, - 0.15634627781556587, - 0.16305642720734875, - 0.17009525961342664, - 0.17745580919538712, - 0.18513079173391628, - 0.19311261183748313, - 0.2013933704589616, - 0.2099648727127667, - 0.21881863598475426, - 0.227945898326872, - 0.23733762712824108, - 0.24698452805410076, - 0.2568770542437552, - 0.26700541575843545, - 0.2773595892697104, - 0.2879293279788757, - 0.2987041717574868, - 0.3096734574990176, - 0.32082632967138686, - 0.33215175105991446, - 0.34363851369008214, - 0.35527524991927506, - 0.36705044368654166, - 0.3789524419092301, - 0.39096946601522825, - 0.40308962359938527, - 0.41530092019259546, - 0.4275912711318767, - 0.4399485135197189, - 0.4523604182608437, - 0.4648147021644878, - 0.4772990401002083, - 0.48980107719520494, - 0.502308441061065, - 0.5148087540378524, - 0.5272896454434041, - 0.5397387638157303, - 0.5521437891363846, - 0.5644924450227264, - 0.5767725108769892, - 0.5889718339801542, - 0.6010783415186373, - 0.6130800525319099, - 0.6249650897692125, - 0.6367216914436339, - 0.64833822287193, - 0.6598031879885491, - 0.6711052407224831, - 0.6822331962256761, - 0.6931760419418854, - 0.7039229485050316, - 0.7144632804562658, - 0.7247866067691326, - 0.7348827111724286, - 0.7447416022605247, - 0.7543535233811621, - 0.7637089622909232, - 0.7727986605688336, - 0.7816136227787684, - 0.790145125371607, - 0.7983847253183132, - 0.8063242684654124, - 0.8139558976045806, - 0.8212720602483756, - 0.8282655161043999, - 0.8349293442405126, - 0.8412569499339875, - 0.847242071197849, - 0.8528787849779202, - 0.8581615130144522, - 0.8630850273625388, - 0.8676444555658438, - 0.8718352854785301, - 0.8756533697306133, - 0.8790949298323226, - 0.8821565599134062, - 0.8848352300936824, - 0.8871282894814985, - 0.8890334687971326, - 0.8905488826185399, - 0.8916730312472227, - 0.8924048021923758, - 0.8927434712718414, - 0.8926887033287803, - 0.8922405525633534, - 0.8913994624790835, - 0.8901662654439506, - 0.8885421818666552, - 0.8865288189888663, - 0.8841281692946457, - 0.881342608538628, - 0.8781748933949012, - 0.8746281587289202, - 0.8707059144951503, - 0.866412042263513, - 0.8617507913780662, - 0.8567267747517278, - 0.8513449643011999, - 0.8456106860266104, - 0.8395296147407458, - 0.833107768453087, - 0.8263515024142065, - 0.8192675028264236, - 0.8118627802269409, - 0.8041446625500049, - 0.7961207878749651, - 0.7877990968674022, - 0.7791878249208122, - 0.77029549400661, - 0.761130904240536, - 0.7517031251737973, - 0.7420214868175741, - 0.7320955704097556, - 0.721935198933066, - 0.7115504273939455, - 0.7009515328718214, - 0.6901490043485966, - 0.6791535323284452, - 0.6679759982581686, - 0.6566274637585966, - 0.64511915967767, - 0.6334624749760632, - 0.6216689454563311, - 0.6097502423467298, - 0.5977181607510264, - 0.5855846079757108, - 0.573361591746169, - 0.5610612083234809, - 0.5486956305336046, - 0.5362770957207769, - 0.5238178936370785, - 0.5113303542801262, - 0.4988268356909454, - 0.4863197117240762, - 0.473821359802044, - 0.4613441486662903, - 0.44890042613670345, - 0.4365025068918396, - 0.42416266028195587, - 0.4118930981868926, - 0.39970596293083904, - 0.3876133152659191, - 0.37562712243651675, - 0.363759246336135, - 0.35202143176851086, - 0.34042529482460937, - 0.32898231138698897, - 0.3177038057729339, - 0.3066009395275674, - 0.2956847003780556, - 0.28496589135982614, - 0.2744551201255583, - 0.2641627884475407, - 0.25409908192376307, - 0.24427395989794726, - 0.23469714560347982, - 0.22537811654101692, - 0.2163260950992636, - 0.2075500394282246, - 0.19905863457394884, - 0.19086028388355525, - 0.18296310068902571, - 0.1753749002780126, - 0.16810319215959352, - 0.16115517263264284, - 0.15453771766415653, - 0.14825737608459222, - 0.14232036310694862, - 0.13673255417600988, - 0.13149947915382804, - 0.12662631684720782, - 0.12211788988260319, - 0.11797865993350609, - 0.11421272330503929, - 0.11082380688013149, - 0.10781526443128325, - 0.10519007330157315, - 0.102950831458192, - 0.10109975492141615, - 0.09963867557156841, - 0.09856903933613437, - 0.09789190475883058, - 0.09760794195203737, - 0.09771743193363636, - 0.09822026634890696, - 0.09911594757775732, - 0.1004035892271844, - 0.1020819170084748, - 0.10414926999827973, - 0.10660360228231347, - 0.10944248498005166, - 0.11266310864842383, - 0.11626228606212352, - 0.120236455367779, - 0.12458168360887167, - 0.12929367061790603, - 0.1343677532719857, - 0.13979891010757534, - 0.14558176628989578, - 0.15171059893201366, - 0.15817934275838963, - 0.16498159610724816, - 0.17211062726586013, - 0.1795593811324368, - 0.18732048619808067, - 0.19538626184183994, - 0.20374872593168925, - 0.2123996027238741, - 0.22133033105284688, - 0.23053207280364096, - 0.23999572165833777, - 0.2497119121079489, - 0.25967102872080255, - 0.2698632156582652, - 0.2802783864283669, - 0.29090623386769876, - 0.30173624034168667, - 0.31275768815316074, - 0.32395967014890065, - 0.3353311005136844, - 0.3468607257411391, - 0.3585371357705507, - 0.370348775278592, - 0.38228395511482616, - 0.39433086386962235, - 0.4064775795630977, - 0.41871208144344774, - 0.4310222618830537, - 0.44339593836053565, - 0.45582086551695766, - 0.46828474727418345, - 0.48077524900345536, - 0.493280009732094, - 0.5057866543763045, - 0.5182828059879132, - 0.5307560980029703, - 0.5431941864800698, - 0.5555847623162795, - 0.5679155634286022, - 0.580174386888889, - 0.5923491010002274, - 0.6044276573028263, - 0.6163981024975366, - 0.6282485902751834, - 0.6399673930400332, - 0.651542913515768, - 0.662963696222501, - 0.6742184388134522, - 0.6852960032600988, - 0.6961854268746839, - 0.7068759331592379, - 0.7173569424703059, - 0.7276180824888905, - 0.7376491984851936, - 0.7474403633680553, - 0.7569818875090859, - 0.7662643283318211, - 0.7752784996563649, - 0.7840154807903204, - 0.7924666253569626, - 0.8006235698519543, - 0.8084782419201185, - 0.8160228683440809, - 0.82324998273688, - 0.8301524329309216, - 0.8367233880559795, - 0.8429563452992272, - 0.8488451363406173, - 0.854383933457231, - 0.859567255290568, - 0.8643899722710624, - 0.8688473116944581, - 0.8729348624450158, - 0.8766485793608891, - 0.8799847872373314, - 0.8829401844637935, - 0.8855118462912916, - 0.8876972277268285, - 0.8894941660519913, - 0.8909008829632451, - 0.891915986331793, - 0.892538471581269, - 0.8927677226818953 - ], - "type": "scatter", - "xaxis": "x", - "yaxis": "y" - } - ], - "layout": { - "template": { - "data": { - "histogram2dcontour": [ - { - "type": "histogram2dcontour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "choropleth": [ - { - "type": "choropleth", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "histogram2d": [ - { - "type": "histogram2d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmap": [ - { - "type": "heatmap", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmapgl": [ - { - "type": "heatmapgl", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "contourcarpet": [ - { - "type": "contourcarpet", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "contour": [ - { - "type": "contour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "surface": [ - { - "type": "surface", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "mesh3d": [ - { - "type": "mesh3d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "parcoords": [ - { - "type": "parcoords", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolargl": [ - { - "type": "scatterpolargl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "scattergeo": [ - { - "type": "scattergeo", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolar": [ - { - "type": "scatterpolar", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "scattergl": [ - { - "type": "scattergl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatter3d": [ - { - "type": "scatter3d", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattermapbox": [ - { - "type": "scattermapbox", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterternary": [ - { - "type": "scatterternary", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattercarpet": [ - { - "type": "scattercarpet", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ] - }, - "layout": { - "autotypenumbers": "strict", - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "hovermode": "closest", - "hoverlabel": { - "align": "left" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "bgcolor": "#E5ECF6", - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "ternary": { - "bgcolor": "#E5ECF6", - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "sequential": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ] - }, - "xaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "yaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "geo": { - "bgcolor": "white", - "landcolor": "#E5ECF6", - "subunitcolor": "white", - "showland": true, - "showlakes": true, - "lakecolor": "white" - }, - "title": { - "x": 0.05 - }, - "mapbox": { - "style": "light" - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0.0, - 1.0 - ], - "title": { - "text": "Time (us)
Frequency: 1.001+/-0.008 MHz" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0.0, - 1.0 - ], - "title": { - "text": "" - } - }, - "title": { - "text": "Ramsey decay QA transition f01:
(0.0+/-1.5)e+13 us" - }, - "plot_bgcolor": "white" - }, - "config": { - "plotlyServerURL": "https://plot.ly" - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "a776bf93-85f9-4e89-98c3-b48e97da3e59", - "record_entry_path": "/root/7-SimpleRamseyMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691519, - "print_time": "2024-12-08 15:58:39" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "SimpleRamseyMultilevel" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 1.000 MHz, and the measured oscillation is 1.001+/-0.008 MHz. Oscillation amplitude is 0.398+/-0.032. The number of oscillations is 3.004+/-0.025.'
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('7e99194b-12a7-4e18-ab31-fe0aa4a9dd12');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('7e99194b-12a7-4e18-ab31-fe0aa4a9dd12');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear oscillatory pattern, indicating regular oscillations typical of Ramsey experiments.\\n\\n2. Fit Quality: The fit line closely follows the data points, suggesting a good fit and alignment with the expected oscillatory behavior.\\n\\n3. Data Spread: The data points are moderately clustered around the fit line, with some dispersion but not excessively wide, indicating a reasonable level of precision.\\n\\n4. Amplitude and Frequency: The amplitude of the oscillations appears to be around 1, which is ideal. The frequency is noted as 1.001 MHz, which is close to the expected value, indicating consistency in the experiment.\\n\\n5. Overall Pattern: The plot shows characteristics typical of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude and frequency.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('a7982297-dbeb-4232-a08b-35648fa1cdfc');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a7982297-dbeb-4232-a08b-35648fa1cdfc');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
analysis: \"The Ramsey experiment for qubit QA was successful. The measured oscillation frequency is 1.001 MHz, which is very close to the expected offset of 1.000 MHz, indicating consistency. The oscillation amplitude is 0.398, which is above the threshold of 0.2, confirming a clear oscillatory pattern. The number of oscillations observed is 3.004, which is within the acceptable range for the experiment's duration. The fit quality is good, with the fit line closely following the data points, and the data spread is reasonable, indicating precision. Overall, the experiment exhibits characteristics typical of a successful Ramsey oscillation experiment.\"
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('fdf9a598-850c-4d76-895c-0824db8fb190');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('fdf9a598-850c-4d76-895c-0824db8fb190');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Transitioning to the next stage Stage3 with the following description:
Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.
The Ramsey experiment for qubit QA was successful. The measured oscillation frequency is consistent with the expected offset, and the oscillation amplitude is above the threshold, confirming a clear oscillatory pattern. The number of oscillations and the fit quality are within acceptable ranges, indicating a successful experiment. According to the rule of transition, since Stage2 has completed successfully, the experiment should proceed to Stage3.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage3: Stage3...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage3

\n", - "

Description: Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.

\n", - "

Next Steps: If Stage3 fails after 3 retries, goto Failed. If Stage3 completes, goto Complete.

\n", - "

Variables: 

VarName:`frequency_offset_in_MHz` Value: 0.1\n",
-       "VarName:`stop_in_us` Value: 30\n",
-       "VarName:`step_in_us` Value: 0.5

\n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('eb4e527c-f55b-4316-82a4-ca70e9f9faf5');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('eb4e527c-f55b-4316-82a4-ca70e9f9faf5');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Run Ramsey experiment on `dut`, with frequency_offset=`frequency_offset` and stop=`stop` and step=`step`.:
\n", - "

\n", - " 
experiment_ramsey = SimpleRamseyMultilevel(dut=dut, collection_name='f01', mprim_index=0, initial_lpb=None, start=0.0, stop=stop_in_us, step=step_in_us, set_offset=frequency_offset_in_MHz, update=True)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Sampling noise is enabled\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "mode": "markers", - "name": "Data", - "x": [ - 0.0, - 0.5, - 1.0, - 1.5, - 2.0, - 2.5, - 3.0, - 3.5, - 4.0, - 4.5, - 5.0, - 5.5, - 6.0, - 6.5, - 7.0, - 7.5, - 8.0, - 8.5, - 9.0, - 9.5, - 10.0, - 10.5, - 11.0, - 11.5, - 12.0, - 12.5, - 13.0, - 13.5, - 14.0, - 14.5, - 15.0, - 15.5, - 16.0, - 16.5, - 17.0, - 17.5, - 18.0, - 18.5, - 19.0, - 19.5, - 20.0, - 20.5, - 21.0, - 21.5, - 22.0, - 22.5, - 23.0, - 23.5, - 24.0, - 24.5, - 25.0, - 25.5, - 26.0, - 26.5, - 27.0, - 27.5, - 28.0, - 28.5, - 29.0, - 29.5 - ], - "y": [ - 0.958, - 1.0, - 0.892, - 0.616, - 0.592, - 0.482, - 0.35, - 0.198, - 0.156, - 0.184, - 0.114, - 0.166, - 0.124, - 0.328, - 0.33, - 0.506, - 0.552, - 0.638, - 0.664, - 0.752, - 0.99, - 0.938, - 0.734, - 0.672, - 0.582, - 0.494, - 0.39, - 0.256, - 0.194, - 0.222, - 0.188, - 0.33, - 0.35, - 0.386, - 0.396, - 0.512, - 0.626, - 0.732, - 0.722, - 0.784, - 0.718, - 0.536, - 0.686, - 0.648, - 0.64, - 0.51, - 0.542, - 0.292, - 0.388, - 0.154, - 0.308, - 0.332, - 0.114, - 0.368, - 0.404, - 0.5, - 0.516, - 0.58, - 0.72, - 0.606 - ], - "type": "scatter", - "xaxis": "x", - "yaxis": "y" - }, - { - "mode": "lines", - "name": "Fit", - "visible": "legendonly", - "x": [ - 0.0, - 0.05, - 0.1, - 0.15000000000000002, - 0.2, - 0.25, - 0.30000000000000004, - 0.35000000000000003, - 0.4, - 0.45, - 0.5, - 0.55, - 0.6000000000000001, - 0.65, - 0.7000000000000001, - 0.75, - 0.8, - 0.8500000000000001, - 0.9, - 0.9500000000000001, - 1.0, - 1.05, - 1.1, - 1.1500000000000001, - 1.2000000000000002, - 1.25, - 1.3, - 1.35, - 1.4000000000000001, - 1.4500000000000002, - 1.5, - 1.55, - 1.6, - 1.6500000000000001, - 1.7000000000000002, - 1.75, - 1.8, - 1.85, - 1.9000000000000001, - 1.9500000000000002, - 2.0, - 2.0500000000000003, - 2.1, - 2.15, - 2.2, - 2.25, - 2.3000000000000003, - 2.35, - 2.4000000000000004, - 2.45, - 2.5, - 2.5500000000000003, - 2.6, - 2.6500000000000004, - 2.7, - 2.75, - 2.8000000000000003, - 2.85, - 2.9000000000000004, - 2.95, - 3.0, - 3.0500000000000003, - 3.1, - 3.1500000000000004, - 3.2, - 3.25, - 3.3000000000000003, - 3.35, - 3.4000000000000004, - 3.45, - 3.5, - 3.5500000000000003, - 3.6, - 3.6500000000000004, - 3.7, - 3.75, - 3.8000000000000003, - 3.85, - 3.9000000000000004, - 3.95, - 4.0, - 4.05, - 4.1000000000000005, - 4.15, - 4.2, - 4.25, - 4.3, - 4.3500000000000005, - 4.4, - 4.45, - 4.5, - 4.55, - 4.6000000000000005, - 4.65, - 4.7, - 4.75, - 4.800000000000001, - 4.8500000000000005, - 4.9, - 4.95, - 5.0, - 5.050000000000001, - 5.1000000000000005, - 5.15, - 5.2, - 5.25, - 5.300000000000001, - 5.3500000000000005, - 5.4, - 5.45, - 5.5, - 5.550000000000001, - 5.6000000000000005, - 5.65, - 5.7, - 5.75, - 5.800000000000001, - 5.8500000000000005, - 5.9, - 5.95, - 6.0, - 6.050000000000001, - 6.1000000000000005, - 6.15, - 6.2, - 6.25, - 6.300000000000001, - 6.3500000000000005, - 6.4, - 6.45, - 6.5, - 6.550000000000001, - 6.6000000000000005, - 6.65, - 6.7, - 6.75, - 6.800000000000001, - 6.8500000000000005, - 6.9, - 6.95, - 7.0, - 7.050000000000001, - 7.1000000000000005, - 7.15, - 7.2, - 7.25, - 7.300000000000001, - 7.3500000000000005, - 7.4, - 7.45, - 7.5, - 7.550000000000001, - 7.6000000000000005, - 7.65, - 7.7, - 7.75, - 7.800000000000001, - 7.8500000000000005, - 7.9, - 7.95, - 8.0, - 8.05, - 8.1, - 8.15, - 8.200000000000001, - 8.25, - 8.3, - 8.35, - 8.4, - 8.450000000000001, - 8.5, - 8.55, - 8.6, - 8.65, - 8.700000000000001, - 8.75, - 8.8, - 8.85, - 8.9, - 8.950000000000001, - 9.0, - 9.05, - 9.1, - 9.15, - 9.200000000000001, - 9.25, - 9.3, - 9.35, - 9.4, - 9.450000000000001, - 9.5, - 9.55, - 9.600000000000001, - 9.65, - 9.700000000000001, - 9.75, - 9.8, - 9.850000000000001, - 9.9, - 9.950000000000001, - 10.0, - 10.05, - 10.100000000000001, - 10.15, - 10.200000000000001, - 10.25, - 10.3, - 10.350000000000001, - 10.4, - 10.450000000000001, - 10.5, - 10.55, - 10.600000000000001, - 10.65, - 10.700000000000001, - 10.75, - 10.8, - 10.850000000000001, - 10.9, - 10.950000000000001, - 11.0, - 11.05, - 11.100000000000001, - 11.15, - 11.200000000000001, - 11.25, - 11.3, - 11.350000000000001, - 11.4, - 11.450000000000001, - 11.5, - 11.55, - 11.600000000000001, - 11.65, - 11.700000000000001, - 11.75, - 11.8, - 11.850000000000001, - 11.9, - 11.950000000000001, - 12.0, - 12.05, - 12.100000000000001, - 12.15, - 12.200000000000001, - 12.25, - 12.3, - 12.350000000000001, - 12.4, - 12.450000000000001, - 12.5, - 12.55, - 12.600000000000001, - 12.65, - 12.700000000000001, - 12.75, - 12.8, - 12.850000000000001, - 12.9, - 12.950000000000001, - 13.0, - 13.05, - 13.100000000000001, - 13.15, - 13.200000000000001, - 13.25, - 13.3, - 13.350000000000001, - 13.4, - 13.450000000000001, - 13.5, - 13.55, - 13.600000000000001, - 13.65, - 13.700000000000001, - 13.75, - 13.8, - 13.850000000000001, - 13.9, - 13.950000000000001, - 14.0, - 14.05, - 14.100000000000001, - 14.15, - 14.200000000000001, - 14.25, - 14.3, - 14.350000000000001, - 14.4, - 14.450000000000001, - 14.5, - 14.55, - 14.600000000000001, - 14.65, - 14.700000000000001, - 14.75, - 14.8, - 14.850000000000001, - 14.9, - 14.950000000000001, - 15.0, - 15.05, - 15.100000000000001, - 15.15, - 15.200000000000001, - 15.25, - 15.3, - 15.350000000000001, - 15.4, - 15.450000000000001, - 15.5, - 15.55, - 15.600000000000001, - 15.65, - 15.700000000000001, - 15.75, - 15.8, - 15.850000000000001, - 15.9, - 15.950000000000001, - 16.0, - 16.05, - 16.1, - 16.150000000000002, - 16.2, - 16.25, - 16.3, - 16.35, - 16.400000000000002, - 16.45, - 16.5, - 16.55, - 16.6, - 16.650000000000002, - 16.7, - 16.75, - 16.8, - 16.85, - 16.900000000000002, - 16.95, - 17.0, - 17.05, - 17.1, - 17.150000000000002, - 17.2, - 17.25, - 17.3, - 17.35, - 17.400000000000002, - 17.45, - 17.5, - 17.55, - 17.6, - 17.650000000000002, - 17.7, - 17.75, - 17.8, - 17.85, - 17.900000000000002, - 17.95, - 18.0, - 18.05, - 18.1, - 18.150000000000002, - 18.2, - 18.25, - 18.3, - 18.35, - 18.400000000000002, - 18.45, - 18.5, - 18.55, - 18.6, - 18.650000000000002, - 18.7, - 18.75, - 18.8, - 18.85, - 18.900000000000002, - 18.95, - 19.0, - 19.05, - 19.1, - 19.150000000000002, - 19.200000000000003, - 19.25, - 19.3, - 19.35, - 19.400000000000002, - 19.450000000000003, - 19.5, - 19.55, - 19.6, - 19.650000000000002, - 19.700000000000003, - 19.75, - 19.8, - 19.85, - 19.900000000000002, - 19.950000000000003, - 20.0, - 20.05, - 20.1, - 20.150000000000002, - 20.200000000000003, - 20.25, - 20.3, - 20.35, - 20.400000000000002, - 20.450000000000003, - 20.5, - 20.55, - 20.6, - 20.650000000000002, - 20.700000000000003, - 20.75, - 20.8, - 20.85, - 20.900000000000002, - 20.950000000000003, - 21.0, - 21.05, - 21.1, - 21.150000000000002, - 21.200000000000003, - 21.25, - 21.3, - 21.35, - 21.400000000000002, - 21.450000000000003, - 21.5, - 21.55, - 21.6, - 21.650000000000002, - 21.700000000000003, - 21.75, - 21.8, - 21.85, - 21.900000000000002, - 21.950000000000003, - 22.0, - 22.05, - 22.1, - 22.150000000000002, - 22.200000000000003, - 22.25, - 22.3, - 22.35, - 22.400000000000002, - 22.450000000000003, - 22.5, - 22.55, - 22.6, - 22.650000000000002, - 22.700000000000003, - 22.75, - 22.8, - 22.85, - 22.900000000000002, - 22.950000000000003, - 23.0, - 23.05, - 23.1, - 23.150000000000002, - 23.200000000000003, - 23.25, - 23.3, - 23.35, - 23.400000000000002, - 23.450000000000003, - 23.5, - 23.55, - 23.6, - 23.650000000000002, - 23.700000000000003, - 23.75, - 23.8, - 23.85, - 23.900000000000002, - 23.950000000000003, - 24.0, - 24.05, - 24.1, - 24.150000000000002, - 24.200000000000003, - 24.25, - 24.3, - 24.35, - 24.400000000000002, - 24.450000000000003, - 24.5, - 24.55, - 24.6, - 24.650000000000002, - 24.700000000000003, - 24.75, - 24.8, - 24.85, - 24.900000000000002, - 24.950000000000003, - 25.0, - 25.05, - 25.1, - 25.150000000000002, - 25.200000000000003, - 25.25, - 25.3, - 25.35, - 25.400000000000002, - 25.450000000000003, - 25.5, - 25.55, - 25.6, - 25.650000000000002, - 25.700000000000003, - 25.75, - 25.8, - 25.85, - 25.900000000000002, - 25.950000000000003, - 26.0, - 26.05, - 26.1, - 26.150000000000002, - 26.200000000000003, - 26.25, - 26.3, - 26.35, - 26.400000000000002, - 26.450000000000003, - 26.5, - 26.55, - 26.6, - 26.650000000000002, - 26.700000000000003, - 26.75, - 26.8, - 26.85, - 26.900000000000002, - 26.950000000000003, - 27.0, - 27.05, - 27.1, - 27.150000000000002, - 27.200000000000003, - 27.25, - 27.3, - 27.35, - 27.400000000000002, - 27.450000000000003, - 27.5, - 27.55, - 27.6, - 27.650000000000002, - 27.700000000000003, - 27.75, - 27.8, - 27.85, - 27.900000000000002, - 27.950000000000003, - 28.0, - 28.05, - 28.1, - 28.150000000000002, - 28.200000000000003, - 28.25, - 28.3, - 28.35, - 28.400000000000002, - 28.450000000000003, - 28.5, - 28.55, - 28.6, - 28.650000000000002, - 28.700000000000003, - 28.75, - 28.8, - 28.85, - 28.900000000000002, - 28.950000000000003, - 29.0, - 29.05, - 29.1, - 29.150000000000002, - 29.200000000000003, - 29.25, - 29.3, - 29.35, - 29.400000000000002, - 29.450000000000003, - 29.5, - 29.55, - 29.6, - 29.650000000000002, - 29.700000000000003, - 29.75, - 29.8, - 29.85, - 29.900000000000002, - 29.950000000000003 - ], - "y": [ - 0.9490671776019997, - 0.9475093621658602, - 0.9455119207563203, - 0.9430781133574035, - 0.9402116168223266, - 0.9369165200395031, - 0.9331973187101451, - 0.9290589097437195, - 0.9245065852778769, - 0.9195460263298132, - 0.9141832960863681, - 0.9084248328404929, - 0.9022774425820439, - 0.8957482912511735, - 0.8888448966628928, - 0.8815751201116808, - 0.8739471576652951, - 0.8659695311572218, - 0.8576510788874634, - 0.8490009460416266, - 0.8400285748385075, - 0.8307436944166179, - 0.8211563104703132, - 0.8112766946463978, - 0.8011153737122853, - 0.7906831185069813, - 0.7799909326863379, - 0.7690500412741887, - 0.7578718790311398, - 0.746468078652921, - 0.7348504588103493, - 0.7230310120430616, - 0.711021892519285, - 0.6988354036740132, - 0.6864839857380319, - 0.6739802031703064, - 0.6613367320063086, - 0.6485663471348992, - 0.6356819095164128, - 0.622696353354617, - 0.6096226732352258, - 0.5964739112436329, - 0.5832631440745251, - 0.5700034701459977, - 0.5567079967307562, - 0.5433898271169348, - 0.5300620478109885, - 0.5167377157950513, - 0.5034298458510502, - 0.4901513979637669, - 0.4769152648149292, - 0.46373425938028506, - 0.45062110264147875, - 0.4375884114243949, - 0.4246486863754901, - 0.4118143000874505, - 0.39909748538533885, - 0.38651032378420747, - 0.37406473412894664, - 0.3617724614269359, - 0.3496450658838352, - 0.33769391215263606, - 0.3259301588058451, - 0.3143647480404257, - 0.303008395624874, - 0.29187158109753164, - 0.2809645382249735, - 0.27029724572902136, - 0.2598794182906501, - 0.24972049783875916, - 0.2398296451314736, - 0.23021573163733827, - 0.22088733172344716, - 0.21185271515723286, - 0.20311983992831212, - 0.19469634539645375, - 0.1865895457713958, - 0.17880642392990054, - 0.1713536255750857, - 0.1642374537427279, - 0.15746386365887166, - 0.1510384579527279, - 0.1449664822284894, - 0.13925282099931702, - 0.13390199398639763, - 0.1289181527856102, - 0.12430507790395617, - 0.12006617616756021, - 0.11620447850266474, - 0.11272263809068017, - 0.10962292889798148, - 0.1069072445807745, - 0.10457709776498525, - 0.1026336197007639, - 0.10107756029082399, - 0.09990928849147912, - 0.09912879308487665, - 0.09873568382056902, - 0.09872919292420912, - 0.09910817697080193, - 0.09987111911959867, - 0.10101613170737223, - 0.10254095919647266, - 0.10444298147372744, - 0.10671921749591667, - 0.10936632927722972, - 0.11238062621378836, - 0.11575806974000619, - 0.1194942783112467, - 0.12358453270693781, - 0.12802378164800576, - 0.13280664772220446, - 0.1379274336106283, - 0.14338012860843125, - 0.14915841543249858, - 0.15525567730856582, - 0.1616650053300248, - 0.1683792060804145, - 0.17539080951136465, - 0.18269207706752705, - 0.19027501004982028, - 0.1981313582081029, - 0.2062526285541934, - 0.21463009438596864, - 0.22325480451308388, - 0.23211759267470983, - 0.2412090871394939, - 0.2505197204778322, - 0.2600397394963809, - 0.269759215324613, - 0.27966805364310965, - 0.28975600504316923, - 0.3000126755072073, - 0.3104275369993543, - 0.3209899381555663, - 0.33168911506249554, - 0.3425142021143357, - 0.35345424293677974, - 0.3644982013672259, - 0.3756349724803252, - 0.3868533936479529, - 0.3981422556227036, - 0.40949031363398825, - 0.42088629848586395, - 0.4323189276457333, - 0.4437769163131071, - 0.45524898845766076, - 0.4667238878158963, - 0.47819038883577925, - 0.48963730755879825, - 0.5010535124290135, - 0.5124279350187245, - 0.5237495806605367, - 0.5350075389757057, - 0.5461909942887706, - 0.5572892359186463, - 0.5682916683364679, - 0.5791878211806668, - 0.5899673591199025, - 0.6006200915546716, - 0.6111359821485751, - 0.621505158180454, - 0.6317179197087606, - 0.6417647485397885, - 0.6516363169915563, - 0.6613234964454033, - 0.6708173656775597, - 0.6801092189631995, - 0.6891905739457391, - 0.698053179264382, - 0.7066890219331683, - 0.715090334465064, - 0.7232496017348777, - 0.7311595675750752, - 0.7388132410988355, - 0.7462039027449829, - 0.7533251100397127, - 0.7601707030703124, - 0.7667348096663988, - 0.7730118502844607, - 0.778996542591825, - 0.7846839057464562, - 0.7900692643693069, - 0.7951482522062574, - 0.7999168154769787, - 0.8043712159083866, - 0.8085080334506514, - 0.812324168674055, - 0.8158168448453043, - 0.8189836096822188, - 0.8218223367860396, - 0.8243312267509137, - 0.8265088079504317, - 0.8283539370014094, - 0.829865798905421, - 0.8310439068689017, - 0.8318881018029538, - 0.8323985515042955, - 0.8325757495191044, - 0.8324205136918057, - 0.8319339844011637, - 0.831117622486329, - 0.8299732068657915, - 0.8285028318524803, - 0.8267089041685376, - 0.8245941396635801, - 0.822161559740535, - 0.8194144874934151, - 0.8163565435616684, - 0.8129916417059899, - 0.8093239841107586, - 0.8053580564184952, - 0.8010986225020001, - 0.7965507189800554, - 0.7917196494828161, - 0.7866109786732476, - 0.7812305260311656, - 0.7755843594066759, - 0.7696787883499908, - 0.7635203572248102, - 0.7571158381126479, - 0.7504722235156527, - 0.7435967188656648, - 0.7364967348474007, - 0.7291798795438272, - 0.7216539504119277, - 0.7139269260972161, - 0.7060069580954721, - 0.697902362270304, - 0.6896216102352684, - 0.681173320609361, - 0.6725662501548113, - 0.6638092848061955, - 0.6549114305999538, - 0.6458818045134778, - 0.6367296252229979, - 0.6274642037895347, - 0.6180949342822464, - 0.6086312843485134, - 0.5990827857401423, - 0.5894590248050824, - 0.5797696329540437, - 0.570024277111423, - 0.5602326501599062, - 0.5504044613881136, - 0.5405494269506195, - 0.5306772603496244, - 0.5207976629475345, - 0.5109203145196171, - 0.5010548638558542, - 0.49121091942103995, - 0.4813980400820672, - 0.4716257259112785, - 0.46190340907463656, - 0.4522404448133802, - 0.44264610252769104, - 0.43312955697079036, - 0.42369987956174715, - 0.41436602982513426, - 0.40513684696552066, - 0.3960210415846378, - 0.3870271875488968, - 0.3781637140147503, - 0.36943889761923165, - 0.3608608548428103, - 0.352437534551523, - 0.344176710725127, - 0.3360859753778434, - 0.32817273167802735, - 0.3204441872729037, - 0.31290734782428836, - 0.30556901076097476, - 0.29843575925326293, - 0.29151395641484945, - 0.284809739737073, - 0.2783290157602717, - 0.2720774549867435, - 0.2660604870395781, - 0.26028329607134904, - 0.25475081642641867, - 0.24946772856034155, - 0.24443845521958527, - 0.2396671578845369, - 0.2351577334784819, - 0.2309138113449864, - 0.226938750495836, - 0.22323563713142125, - 0.21980728243517805, - 0.21665622064342704, - 0.21378470739168076, - 0.21119471833821002, - 0.2088879480653919, - 0.2068658092590886, - 0.2051294321660298, - 0.20367966432890378, - 0.2025170705985912, - 0.20164193342270276, - 0.20105425340931998, - 0.20075375016456687, - 0.20073986340238187, - 0.20101175432459395, - 0.2015683072691501, - 0.202408131624087, - 0.20352956400458327, - 0.20493067069018445, - 0.20660925031904165, - 0.20856283683576854, - 0.21078870268927852, - 0.21328386227673418, - 0.21604507562951003, - 0.2190688523368453, - 0.22235145570264475, - 0.22588890713067084, - 0.22967699073316133, - 0.23371125815770188, - 0.2379870336269872, - 0.24249941918591172, - 0.24724330015024126, - 0.2522133507509424, - 0.2574040399680708, - 0.2628096375479472, - 0.26842422019720125, - 0.27424167794709486, - 0.2802557206814031, - 0.28645988482098084, - 0.2928475401580203, - 0.29941189683286284, - 0.3061460124461355, - 0.31304279929884515, - 0.3200950317529785, - 0.3272953537050559, - 0.3346362861649862, - 0.3421102349325179, - 0.3497094983634784, - 0.357426275217949, - 0.3652526725824595, - 0.37318071385823404, - 0.38120234680749104, - 0.38930945164974884, - 0.39749384920008246, - 0.4057473090412491, - 0.4140615577215867, - 0.4224282869706057, - 0.4308391619241822, - 0.43928582935128857, - 0.44775992587421026, - 0.45625308617423566, - 0.4647569511748423, - 0.47326317619444314, - 0.48176343906081526, - 0.4902494481793982, - 0.4987129505476969, - 0.5071457397081355, - 0.5155396636317555, - 0.5238866325252627, - 0.532178626554023, - 0.5404077034736953, - 0.5485660061633292, - 0.5566457700528448, - 0.5646393304379521, - 0.5725391296756961, - 0.5803377242539414, - 0.5880277917282726, - 0.5956021375199102, - 0.6030537015684204, - 0.6103755648331441, - 0.6175609556374351, - 0.6246032558499809, - 0.6314960068976448, - 0.6382329156044481, - 0.6448078598515103, - 0.65121489405294, - 0.6574482544428731, - 0.6635023641690538, - 0.6693718381885503, - 0.6750514879614139, - 0.6805363259382848, - 0.6858215698381818, - 0.6909026467129098, - 0.6957751967947496, - 0.7004350771243166, - 0.7048783649556847, - 0.7091013609361249, - 0.7131005920580072, - 0.7168728143806659, - 0.7204150155202457, - 0.7237244169057926, - 0.7267984758000678, - 0.7296348870838223, - 0.7322315848024794, - 0.7345867434744293, - 0.7366987791603584, - 0.7385663502932837, - 0.7401883582691898, - 0.7415639477984046, - 0.7426925070180806, - 0.7435736673663836, - 0.7442073032192187, - 0.7445935312905564, - 0.7447327097976484, - 0.7446254373926453, - 0.7442725518623599, - 0.7436751285981329, - 0.742834478837981, - 0.7417521476834263, - 0.74042991189361, - 0.7388697774595143, - 0.7370739769613155, - 0.7350449667120956, - 0.7327854236913423, - 0.7302982422718571, - 0.7275865307438891, - 0.7246536076404925, - 0.7215029978682915, - 0.7181384286480151, - 0.7145638252693339, - 0.710783306664702, - 0.7068011808070696, - 0.7026219399364869, - 0.6982502556207778, - 0.6936909736556004, - 0.6889491088093613, - 0.6840298394185826, - 0.6789385018394449, - 0.6736805847613621, - 0.6682617233885565, - 0.6626876934957123, - 0.6569644053638956, - 0.6510978976030217, - 0.6450943308672494, - 0.638959981469761, - 0.63270123490347, - 0.6263245792742737, - 0.619836598653522, - 0.6132439663564492, - 0.606553438153354, - 0.5997718454203651, - 0.5929060882366729, - 0.5859631284351242, - 0.57894998261312, - 0.5718737151107645, - 0.5647414309632219, - 0.5575602688342592, - 0.5503373939379237, - 0.5430799909553191, - 0.5357952569534191, - 0.5284903943128271, - 0.5211726036713806, - 0.5138490768904376, - 0.5065269900506651, - 0.4992134964840816, - 0.49191571984905735, - 0.48464074725492157, - 0.4773956224427388, - 0.47018733902876286, - 0.4630228338169805, - 0.45590898018708104, - 0.4488525815640896, - 0.44186036497580206, - 0.43493897470406256, - 0.4280949660358093, - 0.421334799119696, - 0.41466483293399176, - 0.40809131937131266, - 0.40162039744562256, - 0.3952580876268147, - 0.38901028630800794, - 0.3828827604106055, - 0.37688114213194224, - 0.3710109238402651, - 0.36527745312157245, - 0.3596859279827198, - 0.35424139221501205, - 0.34894873092232714, - 0.3438126662176698, - 0.3388377530918517, - 0.33402837545782027, - 0.32938874237399296, - 0.3249228844497487, - 0.320634650436045, - 0.3165277040039537, - 0.3126055207136956, - 0.30887138517657786, - 0.3053283884120258, - 0.30197942540172507, - 0.2988271928426647, - 0.29587418710069874, - 0.2931227023660218, - 0.29057482901176657, - 0.28823245215671833, - 0.2860972504329521, - 0.28417069495897745, - 0.2824540485187967, - 0.28094836494705194, - 0.2796544887202619, - 0.27857305475392646, - 0.2777044884050902, - 0.27704900567974866, - 0.27660661364428707, - 0.27637711103994045, - 0.2763600890990737, - 0.27655493256188046, - 0.2769608208919129, - 0.277576729688662, - 0.27840143229522196, - 0.27943350159888647, - 0.2806713120223435, - 0.2821130417029546, - 0.28375667485742995, - 0.2856000043290352, - 0.287640634314299, - 0.28987598326601904, - 0.29230328696920815, - 0.2949196017864558, - 0.2977218080690308, - 0.3007066137298984, - 0.3038705579746751, - 0.30721001518640545, - 0.31072119895990796, - 0.3144001662812984, - 0.3182428218481729, - 0.32224492252581505, - 0.3264020819346588, - 0.33070977516413513, - 0.33516334360792477, - 0.3397579999155155, - 0.34448883305489697, - 0.34935081348109015, - 0.354338798405169, - 0.359447537158303, - 0.3646716766453117, - 0.3700057668821212, - 0.37544426661146835, - 0.3809815489911234, - 0.38661190734886597, - 0.3923295609983736, - 0.3981286611101753, - 0.40400329663175094, - 0.4099475002508538, - 0.4159552543960913, - 0.42202049726879654, - 0.42813712890018985, - 0.4342990172278397, - 0.44050000418543145, - 0.44673391179983873, - 0.45299454828952346, - 0.459275714158303, - 0.4655712082785308, - 0.471874833957781, - 0.47818040498316133, - 0.4844817516374002, - 0.49077272668092153, - 0.49704721129415197, - 0.5032991209743827, - 0.5095224113815379, - 0.5157110841273079, - 0.5218591925021401, - 0.5279608471346823, - 0.534010221578343, - 0.5400015578197334, - 0.5459291717038197, - 0.5517874582707584, - 0.5575708969994246, - 0.5632740569528234, - 0.568891601820612, - 0.5744182948541371, - 0.5798490036894619, - 0.5851787050540118, - 0.5904024893525867, - 0.5955155651286053, - 0.6005132633965982, - 0.6053910418421002, - 0.6101444888852234, - 0.6147693276043454, - 0.6192614195165029, - 0.6236167682112057, - 0.6278315228345687, - 0.6319019814207893, - 0.6358245940681823, - 0.6395959659571111, - 0.6432128602073541, - 0.6466722005725761, - 0.649971073969764, - 0.6531067328416384, - 0.6560765973502382, - 0.6588782574000217, - 0.6615094744890306, - 0.6639681833867996, - 0.6662524936379065, - 0.6683606908901936, - 0.6702912380468976, - 0.6720427762420759, - 0.6736141256389048, - 0.6750042860505989, - 0.6762124373838654, - 0.6772379399049939, - 0.6780803343288466, - 0.6787393417311876, - 0.6792148632849635, - 0.6795069798213172 - ], - "type": "scatter", - "xaxis": "x", - "yaxis": "y" - } - ], - "layout": { - "template": { - "data": { - "histogram2dcontour": [ - { - "type": "histogram2dcontour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "choropleth": [ - { - "type": "choropleth", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "histogram2d": [ - { - "type": "histogram2d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmap": [ - { - "type": "heatmap", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmapgl": [ - { - "type": "heatmapgl", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "contourcarpet": [ - { - "type": "contourcarpet", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "contour": [ - { - "type": "contour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "surface": [ - { - "type": "surface", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "mesh3d": [ - { - "type": "mesh3d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "parcoords": [ - { - "type": "parcoords", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolargl": [ - { - "type": "scatterpolargl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "scattergeo": [ - { - "type": "scattergeo", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolar": [ - { - "type": "scatterpolar", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "scattergl": [ - { - "type": "scattergl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatter3d": [ - { - "type": "scatter3d", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattermapbox": [ - { - "type": "scattermapbox", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterternary": [ - { - "type": "scatterternary", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattercarpet": [ - { - "type": "scattercarpet", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ] - }, - "layout": { - "autotypenumbers": "strict", - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "hovermode": "closest", - "hoverlabel": { - "align": "left" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "bgcolor": "#E5ECF6", - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "ternary": { - "bgcolor": "#E5ECF6", - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "sequential": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ] - }, - "xaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "yaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "geo": { - "bgcolor": "white", - "landcolor": "#E5ECF6", - "subunitcolor": "white", - "showland": true, - "showlakes": true, - "lakecolor": "white" - }, - "title": { - "x": 0.05 - }, - "mapbox": { - "style": "light" - } - } - }, - "xaxis": { - "anchor": "y", - "domain": [ - 0.0, - 1.0 - ], - "title": { - "text": "Time (us)
Frequency: 0.0995+/-0.0009 MHz" - } - }, - "yaxis": { - "anchor": "x", - "domain": [ - 0.0, - 1.0 - ], - "title": { - "text": "" - } - }, - "title": { - "text": "Ramsey decay QA transition f01:
34+/-6 us" - }, - "plot_bgcolor": "white" - }, - "config": { - "plotlyServerURL": "https://plot.ly" - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "2281f300-c601-46a9-838b-8c53b478e420", - "record_entry_path": "/root/8-SimpleRamseyMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691544, - "print_time": "2024-12-08 15:59:04" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "SimpleRamseyMultilevel" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 0.100 MHz, and the measured oscillation is 0.099+/-0.001 MHz. Oscillation amplitude is 0.457+/-0.032. The number of oscillations is 2.985+/-0.027.'
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('1d9141a7-8b5a-481e-8712-ee4879ffe5ef');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('1d9141a7-8b5a-481e-8712-ee4879ffe5ef');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear and regular oscillatory pattern, indicating the presence of Ramsey oscillations.\\n2. Fit Quality: The fit line closely follows the data points throughout the plot, suggesting a good fit.\\n3. Data Spread: The data points are relatively tightly clustered around the fit line, with some minor deviations.\\n4. Amplitude and Frequency: The amplitude appears to be around 1, which is ideal. The frequency is approximately 0.0995 MHz, which is close to the expected value.\\n5. Overall Pattern: The plot displays typical characteristics of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude and frequency.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('36a4f499-589f-453d-9d7a-1e2db161e450');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('36a4f499-589f-453d-9d7a-1e2db161e450');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
analysis: 'The Ramsey experiment for qubit QA was successful. The expected offset was set to 0.100 MHz, and the measured frequency was 0.099 MHz, which is very close to the expected value. The oscillation amplitude was measured at 0.457, which is well above the threshold of 0.2, indicating a strong signal. The number of oscillations observed was 2.985, which is within the acceptable range for the experiment duration. The data showed a clear and regular oscillatory pattern with a good fit, suggesting the experiment was conducted correctly.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('2c1b98a1-ed6d-4dee-aaf8-3790091cc3ca');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('2c1b98a1-ed6d-4dee-aaf8-3790091cc3ca');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The experiment is complete.
The Ramsey experiment for qubit QA was successful, as indicated by the close match between the expected and measured frequency offsets, the strong oscillation amplitude, and the clear oscillatory pattern. The experiment was executed once with no failed attempts and one successful attempt. According to the rule of transition, since Stage3 has completed successfully, the next stage is 'Complete'.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
fitting: 'The Ramsey experiment for qubit QA has been analyzed. The expected offset was set to 0.100 MHz, and the measured oscillation is 0.099+/-0.001 MHz. Oscillation amplitude is 0.457+/-0.032. The number of oscillations is 2.985+/-0.027.'
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('d8f84633-543e-4be8-b962-3988c09e1740');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d8f84633-543e-4be8-b962-3988c09e1740');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points exhibit a clear oscillatory pattern, indicating the presence of Ramsey oscillations.\\n\\n2. Fit Quality: The fit line closely follows the data points, suggesting a good fit to the experimental data.\\n\\n3. Data Spread: The data points are moderately clustered around the fit line, with some dispersion but not excessively wide.\\n\\n4. Amplitude and Frequency: The amplitude of the oscillations appears to be around 1, which is ideal. The frequency is approximately 0.0995 MHz, which should be compared to the expected value to determine consistency.\\n\\n5. Overall Pattern: The plot shows characteristics typical of a successful Ramsey oscillation experiment, with clear oscillations, a good fit, and appropriate amplitude.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('b2fcc883-b3b6-42f8-943f-e99dde5117f1');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('b2fcc883-b3b6-42f8-943f-e99dde5117f1');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
results: 'The experiment successfully calibrated the frequency of single qubit gates, with all measured frequencies closely matching expected values and strong oscillation signals observed.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('57c50dae-b034-488c-8955-33874487435c');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('57c50dae-b034-488c-8955-33874487435c');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Transitioning to the next stage Stage2 with the following description:
Full gate amplitude calibration on `dut`
The experiment in Stage1 was executed once and was successful, as indicated by the experiment report which states that the frequency calibration of single qubit gates was successful with expected values and strong oscillation signals. According to the rule of transition, if Stage1 completes successfully, the experiment should proceed to Stage2.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage2: Stage2...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage2

\n", - "

Description: Full gate amplitude calibration on `dut`

\n", - "

Next Steps: If Stage2 completes successfully, goto Stage3. If Stage2 fails, goto Failed.

\n", - " \n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('25a3e411-ac6d-45c7-b2d0-f99dda9bf3bd');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('25a3e411-ac6d-45c7-b2d0-f99dda9bf3bd');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Full gate amplitude calibration on `dut`:
\n", - "

\n", - " 
experiment_instance = Experiment_FullGateAmplitudeCalibrationOnDut(instruction="Full Gate Amplitude Calibration on dut", dut=dut)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Decomposing instructions...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('5c1904b0-77de-4d45-b515-3fabd379bff2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('5c1904b0-77de-4d45-b515-3fabd379bff2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Generating state machine...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "extract_parameters: 0%| | 0/2 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('d27882d2-fbe3-4950-885b-d8baa263f7e1');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d27882d2-fbe3-4950-885b-d8baa263f7e1');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The planned experiments are:

\n", - "
\n", - "

Stage1

\n", - "

Description: Conduct a Rabi experiment with amp=`amp` to determine the Rabi rate for rough amplitude calibration.

\n", - "

Next Steps: If Stage1 fails after 3 retries, goto Failed. Otherwise, goto Stage2.

\n", - "

Variables: 

VarName:`amp` Value: 1.0

\n", - "
\n", - " \n", - "
\n", - "

Stage2

\n", - "

Description: Run Pingpong experiment.

\n", - "

Next Steps: If Stage2 fails, goto Failed. Otherwise, goto Complete.

\n", - " \n", - "
\n", - "
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage1: Stage1...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage1

\n", - "

Description: Conduct a Rabi experiment with amp=`amp` to determine the Rabi rate for rough amplitude calibration.

\n", - "

Next Steps: If Stage1 fails after 3 retries, goto Failed. Otherwise, goto Stage2.

\n", - "

Variables: 

VarName:`amp` Value: 1.0

\n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('d662b9b8-39e9-43e2-8f87-9b5837ac32e4');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('d662b9b8-39e9-43e2-8f87-9b5837ac32e4');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Conduct a Rabi experiment with amp=`amp` to determine the Rabi rate for rough amplitude calibration.:
\n", - "

\n", - " 
experiment_rabi = NormalisedRabi(dut_qubit=dut, amp=1.0, start=0.01, stop=0.3, step=0.002, fit=True, collection_name='f01', mprim_index=0, pulse_discretization=True, update=True)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Amplitude updated: 0.5489810209703299\n" - ] - }, - { - "data": { - "application/vnd.plotly.v1+json": { - "data": [ - { - "marker": { - "color": "Blue", - "line": { - "color": "Black", - "width": 2 - }, - "opacity": 0.5, - "size": 7 - }, - "mode": "markers", - "name": "data", - "x": [ - 0.01, - 0.012, - 0.014, - 0.016, - 0.018000000000000002, - 0.02, - 0.022, - 0.024, - 0.026000000000000002, - 0.028000000000000004, - 0.03, - 0.032, - 0.034, - 0.036000000000000004, - 0.038, - 0.04, - 0.042, - 0.044000000000000004, - 0.046000000000000006, - 0.048, - 0.05, - 0.052000000000000005, - 0.054, - 0.056, - 0.058, - 0.060000000000000005, - 0.062000000000000006, - 0.064, - 0.066, - 0.068, - 0.06999999999999999, - 0.072, - 0.074, - 0.076, - 0.078, - 0.08, - 0.082, - 0.08399999999999999, - 0.086, - 0.088, - 0.09, - 0.092, - 0.094, - 0.096, - 0.09799999999999999, - 0.09999999999999999, - 0.102, - 0.104, - 0.106, - 0.108, - 0.11, - 0.112, - 0.114, - 0.11599999999999999, - 0.118, - 0.12, - 0.122, - 0.124, - 0.126, - 0.128, - 0.13, - 0.132, - 0.134, - 0.136, - 0.138, - 0.14, - 0.14200000000000002, - 0.14400000000000002, - 0.14600000000000002, - 0.14800000000000002, - 0.15000000000000002, - 0.15200000000000002, - 0.15400000000000003, - 0.156, - 0.158, - 0.16, - 0.162, - 0.164, - 0.166, - 0.168, - 0.17, - 0.17200000000000001, - 0.17400000000000002, - 0.17600000000000002, - 0.17800000000000002, - 0.18000000000000002, - 0.18200000000000002, - 0.18400000000000002, - 0.186, - 0.188, - 0.19, - 0.192, - 0.194, - 0.196, - 0.198, - 0.2, - 0.202, - 0.20400000000000001, - 0.20600000000000002, - 0.20800000000000002, - 0.21000000000000002, - 0.21200000000000002, - 0.21400000000000002, - 0.21600000000000003, - 0.21800000000000003, - 0.22, - 0.222, - 0.224, - 0.226, - 0.228, - 0.23, - 0.232, - 0.234, - 0.23600000000000002, - 0.23800000000000002, - 0.24000000000000002, - 0.24200000000000002, - 0.24400000000000002, - 0.24600000000000002, - 0.24800000000000003, - 0.25, - 0.252, - 0.254, - 0.256, - 0.258, - 0.26, - 0.262, - 0.264, - 0.266, - 0.268, - 0.27, - 0.272, - 0.274, - 0.276, - 0.278, - 0.28, - 0.28200000000000003, - 0.28400000000000003, - 0.28600000000000003, - 0.28800000000000003, - 0.29000000000000004, - 0.29200000000000004, - 0.29400000000000004, - 0.29600000000000004, - 0.29800000000000004 - ], - "y": [ - -0.21390332137237916, - -0.33455651906357375, - -0.6758458060386849, - -0.4380615159887266, - -0.585699321706932, - -0.4051812641710309, - -0.19091074028326288, - 0.09469983808686908, - 0.31326604892669707, - 0.4363697205088402, - 0.7042614684404408, - 0.9597805182459105, - 0.723826399424825, - 0.4073726290119226, - 0.22308194276862423, - -0.1939667905379071, - -0.46849008891259103, - -0.3331844244911609, - -0.8091542925088802, - -0.47685052360030056, - -0.5751758089303948, - -0.22811622328833758, - -0.11491641840847107, - 0.14917906093844774, - 0.40093753678964394, - 0.5912310893067246, - 0.6604352996203146, - 0.29471137346739673, - 0.6780170560158136, - 0.32400030149206827, - 0.1998350892358563, - -0.17520324065164744, - -0.32543594340143894, - -0.4611838316335881, - -0.5427280895698837, - -0.5241222783307276, - -0.639862111192699, - -0.2497106802720657, - 0.09558239075916078, - 0.23451726818006435, - 0.41250824348895637, - 0.6090483203557797, - 0.6861800357937351, - 0.32648394328232916, - 0.2886697986156849, - 0.4064830584261609, - 0.05531596340639094, - -0.09850488180864758, - -0.32088384450808577, - -0.5025680058016019, - -0.5512803087945839, - -0.3589158880953657, - -0.371985352443221, - -0.13656947767310829, - 0.043126633362195514, - 0.171582906173518, - 0.44514061838048674, - 0.5252297055742938, - 0.733929081074823, - 0.42631404957123503, - 0.3717799212503733, - 0.08752290090544934, - -0.08149538741257853, - -0.14242786275251174, - -0.6366071793452346, - -0.8080911045790353, - -0.6994880548906053, - -0.40818715520343846, - -0.31232964734044366, - -0.18362078928407854, - -0.16708460479265883, - 0.24819887190062465, - 0.35023498794765606, - 0.5229461733946505, - 0.5917600168215101, - 0.577321029289585, - 0.2357760996523842, - 0.14317901071956846, - -0.2553982046114668, - -0.418752193931445, - -0.5852969759448694, - -0.28352503922929934, - -0.5387988296071975, - -0.3250696962272587, - -0.08468944244216137, - -0.3273354816523425, - 0.23326061795699565, - 0.31245131417273986, - 0.41411780603975173, - 0.5099328648135599, - 0.4374189336374904, - 0.5678999577782755, - 0.2889729095157373, - 0.02823791399924268, - -0.06449613542485022, - -0.20845268243999407, - -0.6838419096558713, - -0.6994499235263608, - -0.6874015259122793, - -0.4420659504527649, - -0.378046573259749, - -0.06722613105432752, - 0.24405301261248435, - 0.3397818530701297, - 0.5767018425967831, - 0.6806058482858154, - 0.695297838524893, - 0.2084639653402809, - 0.3252067371956664, - 0.20450152667556123, - 0.04068297659672648, - -0.37377765329831, - -0.716309438368932, - -0.8686731442928798, - -0.7044993991418196, - -0.37325918559090715, - -0.17280834927024025, - 0.008436128451603412, - 0.4924007727942826, - 0.20552265655526608, - 0.6463435169539405, - 0.7529498360293273, - 0.41526953799309874, - 0.18065322454676527, - 0.21406147589284477, - 0.04258120601637074, - -0.3025391755398708, - -0.505013155606579, - -0.4699027356380384, - -0.7471675252364958, - -0.4788480258389779, - -0.3145548575484932, - -0.15283076143546612, - 0.050553447030243295, - 0.3147976908869725, - 0.4688457162748748, - 0.3781476462856488, - 0.28391029637052745, - 0.8085522994761491, - 0.388051833741651, - 0.001919065664545222, - -0.1988721867729342, - -0.25496948223028687, - -0.6871001623949983, - -0.6848495282840344 - ], - "type": "scatter" - }, - { - "line": { - "color": "Red" - }, - "mode": "lines", - "name": "fit", - "visible": "legendonly", - "x": [ - 0.01, - 0.0104, - 0.010799999999999999, - 0.011199999999999998, - 0.011599999999999997, - 0.011999999999999997, - 0.012399999999999996, - 0.012799999999999995, - 0.013199999999999995, - 0.013599999999999994, - 0.013999999999999993, - 0.014399999999999993, - 0.014799999999999992, - 0.015199999999999991, - 0.01559999999999999, - 0.01599999999999999, - 0.01639999999999999, - 0.01679999999999999, - 0.017199999999999986, - 0.017599999999999987, - 0.017999999999999988, - 0.018399999999999986, - 0.018799999999999983, - 0.019199999999999984, - 0.019599999999999985, - 0.019999999999999983, - 0.02039999999999998, - 0.02079999999999998, - 0.021199999999999983, - 0.02159999999999998, - 0.021999999999999978, - 0.02239999999999998, - 0.02279999999999998, - 0.023199999999999978, - 0.023599999999999975, - 0.023999999999999976, - 0.024399999999999977, - 0.024799999999999975, - 0.025199999999999972, - 0.025599999999999973, - 0.025999999999999975, - 0.026399999999999972, - 0.02679999999999997, - 0.027199999999999974, - 0.027599999999999972, - 0.02799999999999997, - 0.028399999999999967, - 0.02879999999999997, - 0.02919999999999997, - 0.029599999999999967, - 0.029999999999999964, - 0.03039999999999997, - 0.030799999999999966, - 0.031199999999999964, - 0.03159999999999996, - 0.031999999999999966, - 0.032399999999999964, - 0.03279999999999996, - 0.03319999999999996, - 0.03359999999999996, - 0.03399999999999996, - 0.03439999999999996, - 0.034799999999999956, - 0.03519999999999996, - 0.03559999999999996, - 0.035999999999999956, - 0.03639999999999995, - 0.03679999999999996, - 0.037199999999999955, - 0.03759999999999995, - 0.03799999999999995, - 0.038399999999999955, - 0.03879999999999995, - 0.03919999999999995, - 0.03959999999999995, - 0.03999999999999995, - 0.04039999999999995, - 0.04079999999999995, - 0.041199999999999945, - 0.04159999999999995, - 0.04199999999999995, - 0.042399999999999945, - 0.04279999999999994, - 0.04319999999999995, - 0.043599999999999944, - 0.04399999999999994, - 0.044399999999999946, - 0.044799999999999944, - 0.04519999999999994, - 0.04559999999999994, - 0.04599999999999994, - 0.04639999999999994, - 0.04679999999999994, - 0.047199999999999936, - 0.04759999999999994, - 0.04799999999999994, - 0.048399999999999936, - 0.048799999999999934, - 0.04919999999999993, - 0.049599999999999936, - 0.04999999999999993, - 0.05039999999999993, - 0.050799999999999935, - 0.05119999999999993, - 0.05159999999999993, - 0.05199999999999993, - 0.052399999999999926, - 0.05279999999999993, - 0.05319999999999993, - 0.053599999999999925, - 0.05399999999999993, - 0.05439999999999993, - 0.054799999999999925, - 0.05519999999999992, - 0.05559999999999992, - 0.055999999999999925, - 0.05639999999999992, - 0.05679999999999992, - 0.057199999999999925, - 0.05759999999999992, - 0.05799999999999992, - 0.05839999999999992, - 0.058799999999999915, - 0.05919999999999992, - 0.05959999999999992, - 0.059999999999999915, - 0.06039999999999992, - 0.06079999999999992, - 0.061199999999999914, - 0.06159999999999991, - 0.06199999999999991, - 0.062399999999999914, - 0.06279999999999991, - 0.06319999999999991, - 0.0635999999999999, - 0.0639999999999999, - 0.0643999999999999, - 0.0647999999999999, - 0.0651999999999999, - 0.06559999999999991, - 0.0659999999999999, - 0.0663999999999999, - 0.0667999999999999, - 0.0671999999999999, - 0.0675999999999999, - 0.0679999999999999, - 0.06839999999999989, - 0.0687999999999999, - 0.0691999999999999, - 0.0695999999999999, - 0.0699999999999999, - 0.0703999999999999, - 0.07079999999999989, - 0.07119999999999989, - 0.07159999999999989, - 0.0719999999999999, - 0.0723999999999999, - 0.07279999999999989, - 0.07319999999999989, - 0.07359999999999989, - 0.07399999999999989, - 0.07439999999999988, - 0.07479999999999988, - 0.07519999999999988, - 0.07559999999999988, - 0.07599999999999989, - 0.07639999999999988, - 0.07679999999999988, - 0.07719999999999988, - 0.07759999999999988, - 0.07799999999999987, - 0.07839999999999987, - 0.07879999999999988, - 0.07919999999999988, - 0.07959999999999988, - 0.07999999999999988, - 0.08039999999999987, - 0.08079999999999987, - 0.08119999999999987, - 0.08159999999999987, - 0.08199999999999986, - 0.08239999999999988, - 0.08279999999999987, - 0.08319999999999987, - 0.08359999999999987, - 0.08399999999999987, - 0.08439999999999986, - 0.08479999999999986, - 0.08519999999999987, - 0.08559999999999987, - 0.08599999999999987, - 0.08639999999999987, - 0.08679999999999986, - 0.08719999999999986, - 0.08759999999999986, - 0.08799999999999986, - 0.08839999999999985, - 0.08879999999999987, - 0.08919999999999986, - 0.08959999999999986, - 0.08999999999999986, - 0.09039999999999986, - 0.09079999999999985, - 0.09119999999999985, - 0.09159999999999986, - 0.09199999999999986, - 0.09239999999999986, - 0.09279999999999985, - 0.09319999999999985, - 0.09359999999999985, - 0.09399999999999985, - 0.09439999999999985, - 0.09479999999999984, - 0.09519999999999985, - 0.09559999999999985, - 0.09599999999999985, - 0.09639999999999985, - 0.09679999999999984, - 0.09719999999999984, - 0.09759999999999984, - 0.09799999999999985, - 0.09839999999999985, - 0.09879999999999985, - 0.09919999999999984, - 0.09959999999999984, - 0.09999999999999984, - 0.10039999999999984, - 0.10079999999999983, - 0.10119999999999983, - 0.10159999999999984, - 0.10199999999999984, - 0.10239999999999984, - 0.10279999999999984, - 0.10319999999999983, - 0.10359999999999983, - 0.10399999999999983, - 0.10439999999999984, - 0.10479999999999984, - 0.10519999999999984, - 0.10559999999999983, - 0.10599999999999983, - 0.10639999999999983, - 0.10679999999999983, - 0.10719999999999982, - 0.10759999999999982, - 0.10799999999999983, - 0.10839999999999983, - 0.10879999999999983, - 0.10919999999999982, - 0.10959999999999982, - 0.10999999999999982, - 0.11039999999999982, - 0.11079999999999983, - 0.11119999999999983, - 0.11159999999999982, - 0.11199999999999982, - 0.11239999999999982, - 0.11279999999999982, - 0.11319999999999981, - 0.11359999999999981, - 0.11399999999999981, - 0.11439999999999982, - 0.11479999999999982, - 0.11519999999999982, - 0.11559999999999981, - 0.11599999999999981, - 0.11639999999999981, - 0.1167999999999998, - 0.11719999999999982, - 0.11759999999999982, - 0.11799999999999981, - 0.11839999999999981, - 0.11879999999999981, - 0.1191999999999998, - 0.1195999999999998, - 0.1199999999999998, - 0.1203999999999998, - 0.12079999999999981, - 0.12119999999999981, - 0.1215999999999998, - 0.1219999999999998, - 0.1223999999999998, - 0.1227999999999998, - 0.1231999999999998, - 0.12359999999999981, - 0.1239999999999998, - 0.1243999999999998, - 0.1247999999999998, - 0.1251999999999998, - 0.1255999999999998, - 0.1259999999999998, - 0.1263999999999998, - 0.1267999999999998, - 0.1271999999999998, - 0.1275999999999998, - 0.1279999999999998, - 0.1283999999999998, - 0.1287999999999998, - 0.1291999999999998, - 0.1295999999999998, - 0.1299999999999998, - 0.1303999999999998, - 0.1307999999999998, - 0.1311999999999998, - 0.1315999999999998, - 0.13199999999999978, - 0.1323999999999998, - 0.13279999999999978, - 0.1331999999999998, - 0.1335999999999998, - 0.13399999999999979, - 0.1343999999999998, - 0.13479999999999978, - 0.1351999999999998, - 0.1355999999999998, - 0.1359999999999998, - 0.1363999999999998, - 0.13679999999999978, - 0.1371999999999998, - 0.13759999999999978, - 0.1379999999999998, - 0.1383999999999998, - 0.13879999999999978, - 0.1391999999999998, - 0.13959999999999978, - 0.1399999999999998, - 0.14039999999999977, - 0.1407999999999998, - 0.14119999999999977, - 0.14159999999999978, - 0.1419999999999998, - 0.14239999999999978, - 0.1427999999999998, - 0.14319999999999977, - 0.14359999999999978, - 0.14399999999999977, - 0.14439999999999978, - 0.1447999999999998, - 0.14519999999999977, - 0.14559999999999979, - 0.14599999999999977, - 0.14639999999999978, - 0.14679999999999976, - 0.14719999999999978, - 0.1475999999999998, - 0.14799999999999977, - 0.14839999999999978, - 0.14879999999999977, - 0.14919999999999978, - 0.14959999999999976, - 0.14999999999999977, - 0.15039999999999976, - 0.15079999999999977, - 0.15119999999999978, - 0.15159999999999976, - 0.15199999999999977, - 0.15239999999999976, - 0.15279999999999977, - 0.15319999999999975, - 0.15359999999999976, - 0.15399999999999975, - 0.15439999999999976, - 0.15479999999999977, - 0.15519999999999975, - 0.15559999999999977, - 0.15599999999999975, - 0.15639999999999976, - 0.15679999999999975, - 0.15719999999999976, - 0.15759999999999977, - 0.15799999999999975, - 0.15839999999999976, - 0.15879999999999975, - 0.15919999999999976, - 0.15959999999999974, - 0.15999999999999975, - 0.16039999999999976, - 0.16079999999999975, - 0.16119999999999976, - 0.16159999999999974, - 0.16199999999999976, - 0.16239999999999974, - 0.16279999999999975, - 0.16319999999999973, - 0.16359999999999975, - 0.16399999999999976, - 0.16439999999999974, - 0.16479999999999975, - 0.16519999999999974, - 0.16559999999999975, - 0.16599999999999973, - 0.16639999999999974, - 0.16679999999999973, - 0.16719999999999974, - 0.16759999999999975, - 0.16799999999999973, - 0.16839999999999974, - 0.16879999999999973, - 0.16919999999999974, - 0.16959999999999972, - 0.16999999999999973, - 0.17039999999999975, - 0.17079999999999973, - 0.17119999999999974, - 0.17159999999999972, - 0.17199999999999974, - 0.17239999999999972, - 0.17279999999999973, - 0.17319999999999974, - 0.17359999999999973, - 0.17399999999999974, - 0.17439999999999972, - 0.17479999999999973, - 0.17519999999999972, - 0.17559999999999973, - 0.1759999999999997, - 0.17639999999999972, - 0.17679999999999974, - 0.17719999999999972, - 0.17759999999999973, - 0.17799999999999971, - 0.17839999999999973, - 0.1787999999999997, - 0.17919999999999972, - 0.1795999999999997, - 0.17999999999999972, - 0.18039999999999973, - 0.1807999999999997, - 0.18119999999999972, - 0.1815999999999997, - 0.18199999999999972, - 0.1823999999999997, - 0.1827999999999997, - 0.18319999999999972, - 0.1835999999999997, - 0.18399999999999972, - 0.1843999999999997, - 0.18479999999999971, - 0.1851999999999997, - 0.1855999999999997, - 0.18599999999999972, - 0.1863999999999997, - 0.18679999999999972, - 0.1871999999999997, - 0.1875999999999997, - 0.1879999999999997, - 0.1883999999999997, - 0.1887999999999997, - 0.1891999999999997, - 0.1895999999999997, - 0.1899999999999997, - 0.1903999999999997, - 0.1907999999999997, - 0.1911999999999997, - 0.1915999999999997, - 0.1919999999999997, - 0.19239999999999968, - 0.1927999999999997, - 0.1931999999999997, - 0.1935999999999997, - 0.1939999999999997, - 0.19439999999999968, - 0.1947999999999997, - 0.19519999999999968, - 0.1955999999999997, - 0.1959999999999997, - 0.19639999999999969, - 0.1967999999999997, - 0.19719999999999968, - 0.1975999999999997, - 0.19799999999999968, - 0.1983999999999997, - 0.1987999999999997, - 0.19919999999999968, - 0.1995999999999997, - 0.19999999999999968, - 0.2003999999999997, - 0.20079999999999967, - 0.20119999999999968, - 0.20159999999999967, - 0.20199999999999968, - 0.2023999999999997, - 0.20279999999999967, - 0.2031999999999997, - 0.20359999999999967, - 0.20399999999999968, - 0.20439999999999967, - 0.20479999999999968, - 0.20519999999999966, - 0.20559999999999967, - 0.20599999999999968, - 0.20639999999999967, - 0.20679999999999968, - 0.20719999999999966, - 0.20759999999999967, - 0.20799999999999966, - 0.20839999999999967, - 0.20879999999999968, - 0.20919999999999966, - 0.20959999999999968, - 0.20999999999999966, - 0.21039999999999967, - 0.21079999999999965, - 0.21119999999999967, - 0.21159999999999968, - 0.21199999999999966, - 0.21239999999999967, - 0.21279999999999966, - 0.21319999999999967, - 0.21359999999999965, - 0.21399999999999966, - 0.21439999999999965, - 0.21479999999999966, - 0.21519999999999967, - 0.21559999999999965, - 0.21599999999999966, - 0.21639999999999965, - 0.21679999999999966, - 0.21719999999999964, - 0.21759999999999965, - 0.21799999999999964, - 0.21839999999999965, - 0.21879999999999966, - 0.21919999999999965, - 0.21959999999999966, - 0.21999999999999964, - 0.22039999999999965, - 0.22079999999999964, - 0.22119999999999965, - 0.22159999999999966, - 0.22199999999999964, - 0.22239999999999965, - 0.22279999999999964, - 0.22319999999999965, - 0.22359999999999963, - 0.22399999999999964, - 0.22439999999999966, - 0.22479999999999964, - 0.22519999999999965, - 0.22559999999999963, - 0.22599999999999965, - 0.22639999999999963, - 0.22679999999999964, - 0.22719999999999962, - 0.22759999999999964, - 0.22799999999999965, - 0.22839999999999963, - 0.22879999999999964, - 0.22919999999999963, - 0.22959999999999964, - 0.22999999999999962, - 0.23039999999999963, - 0.23079999999999962, - 0.23119999999999963, - 0.23159999999999964, - 0.23199999999999962, - 0.23239999999999963, - 0.23279999999999962, - 0.23319999999999963, - 0.2335999999999996, - 0.23399999999999962, - 0.23439999999999964, - 0.23479999999999962, - 0.23519999999999963, - 0.23559999999999962, - 0.23599999999999963, - 0.2363999999999996, - 0.23679999999999962, - 0.23719999999999963, - 0.23759999999999962, - 0.23799999999999963, - 0.2383999999999996, - 0.23879999999999962, - 0.2391999999999996, - 0.23959999999999962, - 0.2399999999999996, - 0.24039999999999961, - 0.24079999999999963, - 0.2411999999999996, - 0.24159999999999962, - 0.2419999999999996, - 0.24239999999999962, - 0.2427999999999996, - 0.2431999999999996, - 0.2435999999999996, - 0.2439999999999996, - 0.24439999999999962, - 0.2447999999999996, - 0.2451999999999996, - 0.2455999999999996, - 0.2459999999999996, - 0.2463999999999996, - 0.2467999999999996, - 0.24719999999999961, - 0.2475999999999996, - 0.2479999999999996, - 0.2483999999999996, - 0.2487999999999996, - 0.2491999999999996, - 0.2495999999999996, - 0.2499999999999996, - 0.25039999999999957, - 0.2507999999999996, - 0.2511999999999996, - 0.2515999999999996, - 0.25199999999999956, - 0.25239999999999957, - 0.2527999999999996, - 0.2531999999999996, - 0.2535999999999996, - 0.25399999999999956, - 0.25439999999999957, - 0.2547999999999996, - 0.2551999999999996, - 0.25559999999999955, - 0.25599999999999956, - 0.2563999999999996, - 0.2567999999999996, - 0.2571999999999996, - 0.25759999999999955, - 0.25799999999999956, - 0.2583999999999996, - 0.2587999999999996, - 0.25919999999999954, - 0.25959999999999955, - 0.25999999999999956, - 0.2603999999999996, - 0.2607999999999996, - 0.2611999999999996, - 0.26159999999999956, - 0.26199999999999957, - 0.2623999999999996, - 0.2627999999999996, - 0.2631999999999996, - 0.26359999999999956, - 0.26399999999999957, - 0.2643999999999996, - 0.2647999999999996, - 0.26519999999999955, - 0.26559999999999956, - 0.26599999999999957, - 0.2663999999999996, - 0.2667999999999996, - 0.26719999999999955, - 0.26759999999999956, - 0.26799999999999957, - 0.2683999999999996, - 0.26879999999999954, - 0.26919999999999955, - 0.26959999999999956, - 0.2699999999999996, - 0.2703999999999996, - 0.27079999999999954, - 0.27119999999999955, - 0.27159999999999956, - 0.2719999999999996, - 0.27239999999999953, - 0.27279999999999954, - 0.27319999999999955, - 0.27359999999999957, - 0.2739999999999996, - 0.27439999999999953, - 0.27479999999999954, - 0.27519999999999956, - 0.27559999999999957, - 0.2759999999999996, - 0.27639999999999953, - 0.27679999999999955, - 0.27719999999999956, - 0.27759999999999957, - 0.2779999999999995, - 0.27839999999999954, - 0.27879999999999955, - 0.27919999999999956, - 0.27959999999999957, - 0.2799999999999995, - 0.28039999999999954, - 0.28079999999999955, - 0.28119999999999956, - 0.2815999999999995, - 0.28199999999999953, - 0.28239999999999954, - 0.28279999999999955, - 0.28319999999999956, - 0.2835999999999995, - 0.28399999999999953, - 0.28439999999999954, - 0.28479999999999955, - 0.28519999999999956, - 0.2855999999999995, - 0.28599999999999953, - 0.28639999999999954, - 0.28679999999999956, - 0.2871999999999995, - 0.2875999999999995, - 0.28799999999999953, - 0.28839999999999955, - 0.28879999999999956, - 0.2891999999999995, - 0.2895999999999995, - 0.28999999999999954, - 0.29039999999999955, - 0.2907999999999995, - 0.2911999999999995, - 0.2915999999999995, - 0.29199999999999954, - 0.29239999999999955, - 0.2927999999999995, - 0.2931999999999995, - 0.29359999999999953, - 0.29399999999999954, - 0.2943999999999995, - 0.2947999999999995, - 0.2951999999999995, - 0.29559999999999953, - 0.29599999999999954, - 0.2963999999999995, - 0.2967999999999995, - 0.2971999999999995, - 0.29759999999999953, - 0.2979999999999995, - 0.2983999999999995, - 0.2987999999999995, - 0.2991999999999995, - 0.29959999999999953 - ], - "y": [ - -0.26026707492328555, - -0.30254579128523407, - -0.34288810192546926, - -0.38103698757985094, - -0.4167494032079981, - -0.44979782642361865, - -0.4799717070306273, - -0.5070788084301087, - -0.5309464323520806, - -0.5514225191093358, - -0.5683766163636887, - -0.5817007102326598, - -0.5913099134416436, - -0.5971430061373757, - -0.5991628259171944, - -0.5973565045892371, - -0.5917355501551809, - -0.5823357734932199, - -0.5692170602083776, - -0.5524629891036876, - -0.5321802997029484, - -0.508498212217443, - -0.48156760428908585, - -0.45156004975494146, - -0.4186667255570985, - -0.3830971937619405, - -0.3450780664485151, - -0.3048515619719494, - -0.2626739617998988, - -0.21881397775346045, - -0.17355104005479816, - -0.12717351708825686, - -0.07997687821680395, - -0.032261811358412155, - 0.015667692684764454, - 0.06350627693975447, - 0.11094916367995847, - 0.1576940961519796, - 0.20344326424143605, - 0.24790520180941006, - 0.2907966436116803, - 0.3318443299703805, - 0.3707867477005921, - 0.40737579620048997, - 0.44137836809045783, - 0.47257783433097533, - 0.5007754243576545, - 0.5257914924406369, - 0.5474666622004252, - 0.5656628419884915, - 0.5802641046637008, - 0.5911774261595407, - 0.598333278136764, - 0.6016860709456885, - 0.6012144440760486, - 0.5969214022439615, - 0.5888342962489994, - 0.5770046487233284, - 0.5615078258830583, - 0.5424425573730627, - 0.5199303072643212, - 0.49411450021112974, - 0.4651596076983055, - 0.4332501001998694, - 0.3985892719249569, - 0.36139794563845995, - 0.32191306580793166, - 0.2803861890397635, - 0.23708188142200676, - 0.19227603298429363, - 0.14625410001338168, - 0.09930928642245163, - 0.051740675760583554, - 0.0038513257633009702, - -0.04405366241626216, - -0.09166908799473261, - -0.1386915949806033, - -0.18482160484325344, - -0.22976522511590197, - -0.27323612177288675, - -0.3149573434523968, - -0.354663085902648, - -0.3921003854102399, - -0.42703073042196404, - -0.45923158109246764, - -0.488497787076798, - -0.514642894535133, - -0.5375003340228066, - -0.5569244816976556, - -0.5727915870837474, - -0.5850005614807661, - -0.5934736219961205, - -0.5981567870966729, - -0.5990202205229508, - -0.5960584213747743, - -0.5892902591572736, - -0.578758853564019, - -0.5645312997631601, - -0.546698240936764, - -0.5253732907966929, - -0.5006923097561415, - -0.4728125393683233, - -0.441911600546759, - -0.4081863619494678, - -0.3718516857365413, - -0.33313905869182325, - -0.29229511742977876, - -0.24958007708337926, - -0.20526607348380166, - -0.15963542939380726, - -0.1129788558406054, - -0.06559360000740744, - -0.017781551483379416, - 0.030152681062968714, - 0.07790371053356167, - 0.1251673170098645, - 0.17164238592594538, - 0.2170328264574543, - 0.2610494579045446, - 0.3034118520506639, - 0.34385011975962076, - 0.3821066304285911, - 0.41793765334248495, - 0.4511149104726879, - 0.48142703082734445, - 0.5086808970875789, - 0.5327028759502928, - 0.5533399243390588, - 0.5704605644355107, - 0.5839557213193138, - 0.5937394178801666, - 0.5997493225745676, - 0.6019471465376103, - 0.6003188875198272, - 0.5948749190949663, - 0.5856499245703622, - 0.572702676020961, - 0.556115659854754, - 0.5359945512951386, - 0.5124675411282552, - 0.4856845190045621, - 0.45581611849780324, - 0.42305263000526566, - 0.38760278841519646, - 0.3496924432650835, - 0.3095631198631961, - 0.2674704805404132, - 0.2236826958356204, - 0.1784787359918374, - 0.13214659364782744, - 0.0849814490484116, - 0.037283789462799664, - -0.01064250520790121, - -0.0584920984375009, - -0.1059601423616607, - -0.15274421995156318, - -0.19854627170082734, - -0.2430744945508299, - -0.286045200956444, - -0.3271846262481556, - -0.3662306727759307, - -0.4029345797229794, - -0.4370625079510669, - -0.46839702978040204, - -0.4967385142127881, - -0.5219063987728041, - -0.5437403398641872, - -0.5621012343125393, - -0.5768721055861465, - -0.5879588490488298, - -0.5952908314968557, - -0.5988213411602848, - -0.5985278853018067, - -0.5944123335170787, - -0.5865009058236078, - -0.5748440056140556, - -0.5595158985382187, - -0.540614239359514, - -0.5182594498003487, - -0.4925939513400897, - -0.46378125785345803, - -0.4320049338701083, - -0.39746742509227506, - -0.36038876862121405, - -0.32100519110954295, - -0.27956760377059425, - -0.23634000383299686, - -0.19159779262478766, - -0.14562602100245675, - -0.0987175733032208, - -0.05117130139049983, - -0.0032901206805297147, - 0.04462091971975741, - 0.09225658046853076, - 0.13931337665681842, - 0.18549151129884267, - 0.2304967853267616, - 0.2740424719212779, - 0.31585114323685076, - 0.355656437883522, - 0.39320475790479137, - 0.4282568844401525, - 0.4605895017789876, - 0.48999662009611383, - 0.5162908878048156, - 0.539304785166377, - 0.5588916915517116, - 0.5749268195555866, - 0.5873080100122392, - 0.5959563828473798, - 0.6008168396200205, - 0.6018584145524642, - 0.599074471812047, - 0.5924827477877656, - 0.5821252380924459, - 0.5680679300103549, - 0.5504003820948247, - 0.529235153594257, - 0.5047070873416033, - 0.47697245067601507, - 0.44620793986981494, - 0.41260955440353836, - 0.3763913482609985, - 0.33778406619983087, - 0.2970336736857823, - 0.25439978985645495, - 0.21015403349806216, - 0.16457829257290013, - 0.1179629283222865, - 0.07060492538659184, - 0.022805999727856168, - -0.02512932359066581, - -0.07289565052163248, - -0.12018866368699757, - -0.1667070611719731, - -0.212154476107614, - -0.2562413648124019, - -0.29868685146357504, - -0.33922051754588045, - -0.37758412467723385, - -0.4135332598352254, - -0.44683889250280584, - -0.4772888338126544, - -0.5046890883940472, - -0.5288650903096824, - -0.5496628152083639, - -0.5669497616080377, - -0.5806157950574963, - -0.5905738497986018, - -0.5967604834587884, - -0.599136281239905, - -0.5976861070283338, - -0.5924191998265713, - -0.5833691148919102, - -0.5705935099572209, - -0.5541737778958215, - -0.5342145281707, - -0.5108429203717676, - -0.484207854087149, - -0.454479020269794, - -0.4218458201431379, - -0.3865161585333998, - -0.34871511931617816, - -0.30868353141596944, - -0.26667643449463657, - -0.22296145410381352, - -0.17781709665311768, - -0.13153097505682726, - -0.0843979763633931, - -0.03671838304174087, - 0.011204040106476848, - 0.05906398122039067, - 0.10655652650970052, - 0.15337910284835515, - 0.19923340545597368, - 0.24382729838578626, - 0.28687667571118125, - 0.3281072715533309, - 0.36725640741823334, - 0.4040746657110142, - 0.4383274787655367, - 0.46979662326572896, - 0.4982816105376868, - 0.5236009638550755, - 0.5455933746201782, - 0.5641187300545774, - 0.579059005852128, - 0.5903190181071329, - 0.5978270297272438, - 0.6015352074676327, - 0.601419926674704, - 0.5974819217978263, - 0.589746281710184, - 0.57826228986856, - 0.563103110330385, - 0.5443653216284193, - 0.5221683014727501, - 0.4966534662001186, - 0.4679833698160463, - 0.436340668369686, - 0.40192695625937935, - 0.3649614818827169, - 0.32567975081367173, - 0.2843320254059219, - 0.24118173038128782, - 0.19650377456127405, - 0.15058279943382613, - 0.10371136571370643, - 0.056188089449737216, - 0.00831573955379061, - -0.039600691127969906, - -0.08725592891244578, - -0.13434636416613716, - -0.18057198559012483, - -0.22563829158015242, - -0.2692581664847041, - -0.3111537098074584, - -0.3510580067003364, - -0.3887168284674477, - -0.42389025224603266, - -0.45635418954554335, - -0.4859018139065253, - -0.5123448785838156, - -0.5355149158591057, - -0.5552643103420979, - -0.5714672394223221, - -0.5840204748799989, - -0.592844040548949, - -0.597881721841575, - -0.5991014238897782, - -0.5964953760200912, - -0.5900801812603318, - -0.5798967105623686, - -0.5660098424149075, - -0.5485080495052019, - -0.527502835063074, - -0.5031280224782417, - -0.4755389027168075, - -0.44491124496865975, - -0.41144017682890976, - -0.3753389411477336, - -0.3368375374685816, - -0.29618125671022144, - -0.2536291184279833, - -0.20945222061043978, - -0.16393201252484918, - -0.11735850161499833, - -0.07002840587529505, - -0.0222432634721259, - 0.02569248834382584, - 0.0734734527952805, - 0.1207952192491198, - 0.16735630260450568, - 0.21286006404249797, - 0.25701660090023254, - 0.29954459362933405, - 0.3401730980715892, - 0.3786432716334782, - 0.41471002236207416, - 0.448143570416193, - 0.4787309119846435, - 0.5062771763250948, - 0.5306068672778509, - 0.5515649813449407, - 0.5690179952112714, - 0.5828547164163754, - 0.5929869917571838, - 0.5993502689085847, - 0.601904007683737, - 0.6006319383139936, - 0.5955421651029548, - 0.5866671147942688, - 0.5740633299821354, - 0.5578111088806696, - 0.5380139937471673, - 0.5147981112184739, - 0.48831136876318026, - 0.458722512368995, - 0.426220051468726, - 0.3910110579541623, - 0.353319846929238, - 0.3133865476073911, - 0.27146557345783523, - 0.22782400134744402, - 0.18273987000457448, - 0.13650040864536162, - 0.08940020704773713, - 0.04173933873161923, - -0.006178550797600081, - -0.05404817856256709, - -0.10156456905970072, - -0.14842499724608726, - -0.19433091718871587, - -0.23898986408881662, - -0.2821173175635124, - -0.3234385143139113, - -0.3626901986312224, - -0.39962229958843026, - -0.43399952423229915, - -0.4656028566254412, - -0.4942309531881968, - -0.5197014254506072, - -0.5418520020421345, - -0.5605415625161483, - -0.5756510364226909, - -0.5870841619016146, - -0.5947680989630718, - -0.5986538935482123, - -0.5987167894135532, - -0.5949563858520267, - -0.5873966402458671, - -0.5760857154350802, - -0.5610956728738911, - -0.5425220135300773, - -0.5204830694520804, - -0.4951192498802128, - -0.4665921467049361, - -0.4350835049713375, - -0.40079406498863623, - -0.3639422834216092, - -0.3247629415118463, - -0.28350564929574124, - -0.24043325534883792, - -0.19582017218794479, - -0.14995062799987996, - -0.10311685583491284, - -0.055617231801596737, - -0.007754374124380778, - 0.040166784825136234, - 0.08784094124022777, - 0.1349643649575663, - 0.18123683451001982, - 0.2263635498257724, - 0.2700570103878831, - 0.31203884688871153, - 0.35204159470977175, - 0.3898103979281894, - 0.4251046329937474, - 0.4576994417319582, - 0.4873871639066211, - 0.5139786602149461, - 0.5373045172865745, - 0.5572161270094622, - 0.5735866333062584, - 0.5863117403293767, - 0.595310376925694, - 0.6005252131376674, - 0.6019230254502186, - 0.5994949084564369, - 0.5932563315935727, - 0.5832470405878653, - 0.5695308042360944, - 0.5521950081371019, - 0.5313500979615946, - 0.507128875807159, - 0.4796856541213404, - 0.44919527258316166, - 0.4158519842064417, - 0.3798682177615354, - 0.34147322440005035, - 0.3009116171048208, - 0.25844181227030166, - 0.21433438334192007, - 0.168870337003391, - 0.12233932289424403, - 0.0750377882634396, - 0.02726708931568479, - -0.020668428717034632, - -0.06846337054684998, - -0.11581323649133356, - -0.16241636242889196, - -0.20797584168896066, - -0.25220141663264445, - -0.29481132787264164, - -0.3355341093511621, - -0.3741103178394093, - -0.4102941858401496, - -0.44385518736268426, - -0.47457950659482445, - -0.5022714001149614, - -0.5267544439656663, - -0.547872657643724, - -0.5654914978457544, - -0.5794987156382747, - -0.5898050715912024, - -0.5963449043187383, - -0.5990765488054544, - -0.5979826018524581, - -0.5930700329524835, - -0.5843701398875274, - -0.571938349331922, - -0.555853863731185, - -0.5362191567063745, - -0.5131593201987086, - -0.4868212675137505, - -0.4573727973425694, - -0.425001524722904, - -0.3899136857512005, - -0.35233282366059643, - -0.31249836463583364, - -0.27066409243851663, - -0.22709653156079845, - -0.18207324920841805, - -0.13588108693098894, - -0.08881433316586897, - -0.0411728483381595, - 0.00673984553818906, - 0.054618498587108995, - 0.10215807780530942, - 0.14905571041543575, - 0.19501261345657883, - 0.23973599731866213, - 0.28294093109347435, - 0.32435215785820226, - 0.36370584832638686, - 0.400751281693877, - 0.4352524429711246, - 0.46698952662536575, - 0.4957603369529479, - 0.5213815762601658, - 0.5436900126456075, - 0.5625435199441439, - 0.5778219832070899, - 0.5894280639497936, - 0.5972878202912719, - 0.6013511780350005, - 0.6015922496896415, - 0.598009499397227, - 0.590625752718046, - 0.5794880512099062, - 0.5646673527282321, - 0.5462580793563706, - 0.5243775158462444, - 0.49916506240183506, - 0.47078134656600523, - 0.4394071998687713, - 0.4052425057567989, - 0.36850492614387026, - 0.32942851469543666, - 0.28826222568200055, - 0.24526832790129993, - 0.20072073377418742, - 0.15490325425942358, - 0.10810779070532128, - 0.06063247515777817, - 0.01277977097284265, - -0.035145454165366866, - -0.08283787054462641, - -0.12999363166806982, - -0.17631231004825193, - -0.22149881121872816, - -0.26526525376925575, - -0.30733280342683117, - -0.3474334494978364, - -0.38531171235353545, - -0.42072627108067495, - -0.4534515009274986, - -0.4832789107500737, - -0.5100184713011432, - -0.5334998258989164, - -0.553573375762764, - -0.5701112331011131, - -0.5830080358795082, - -0.5921816190779714, - -0.5975735381610798, - -0.599149441425795, - -0.596899288854799, - -0.5908374160810493, - -0.5810024430560272, - -0.567457028003561, - -0.5502874682267522, - -0.5296031503112852, - -0.5055358532278075, - -0.47823890877333874, - -0.4478862247004449, - -0.4146711767578463, - -0.37880537670116854, - -0.3405173241228258, - -0.30005095069016696, - -0.25766406606646386, - -0.21362671541577788, - -0.1682194589558793, - -0.12173158452023296, - -0.07445926451664699, - -0.026703669024595, - 0.021230952947455705, - 0.06903921182040772, - 0.11641652306946595, - 0.16306104772010077, - 0.208675615352181, - 0.2529696173609741, - 0.29566085841305534, - 0.33647735430162123, - 0.37515906474708155, - 0.41145955010339996, - 0.44514754141533136, - 0.47600841382376247, - 0.503845553932206, - 0.5284816124229613, - 0.5497596339426063, - 0.5675440570583266, - 0.5817215779144468, - 0.5922018720868121, - 0.5989181700361108, - 0.6018276824939727, - 0.6009118730717019, - 0.5961765763548755, - 0.5876519607314292, - 0.5753923361900537, - 0.5594758083134048, - 0.5400037806705313, - 0.5171003087787429, - 0.4909113097508304, - 0.4616036326629096, - 0.4293639955655671, - 0.3943977959105392, - 0.35692780197169455, - 0.317192733597169, - 0.2754457413346648, - 0.23195279361934074, - 0.18699098229940841, - 0.14084675729498258, - 0.0938141016369485, - 0.0461926585127213, - -0.0017141777486566535, - -0.04960119458975078, - -0.09716330572187279, - -0.14409749481774486, - -0.19010474601652588, - -0.23489194894202037, - -0.27817376609729816, - -0.3196744507385686, - -0.35912960364678564, - -0.3962878576046024, - -0.430912478846956, - -0.46278287528253864, - -0.49169600187724155, - -0.5174676542460552, - -0.5399336422119064, - -0.5589508358547841, - -0.5743980773867826, - -0.5861769530435801, - -0.5942124200746337, - -0.598453284837572, - -0.598872528950866, - -0.5954674814268646, - -0.5882598356885547, - -0.5772955113616229, - -0.5626443617223297, - -0.5443997286650633, - -0.522677848024818, - -0.497617109043293, - -0.46937717269651347, - -0.4381379545010624, - -0.40409847827946194, - -0.36747560818726804, - -0.32850266708012976, - -0.2874279500230841, - -0.2445131424125626, - -0.20003165278907803, - -0.15426687096221925, - -0.10751036254541581, - -0.0600600114029194, - -0.012218121843546174, - 0.03571050734814107, - 0.08342052477281564, - 0.1306079717980606, - 0.17697221906680033, - 0.222217881795009, - 0.26605670165649364, - 0.3082093832652679, - 0.34840737355543006, - 0.3863945727220704, - 0.4219289658230234, - 0.45478416464646465, - 0.4847508500212605, - 0.5116381053811292, - 0.5352746330864987, - 0.5555098457550095, - 0.5722148256477595, - 0.5852831459991213, - 0.5946315490574436, - 0.6002004765168985, - 0.6019544489610872, - 0.5998822919009997, - 0.5939972069672389, - 0.5843366878029567, - 0.5709622811933359, - 0.5539591949534635, - 0.5334357550727098, - 0.5095227155741279, - 0.48237242548576953, - 0.4521578582310442, - 0.419071509621892, - 0.3833241714755735, - 0.3451435886683645, - 0.30477300818195213, - 0.262469629386547, - 0.21850296543388448, - 0.17315312619957213, - 0.1267090337142071, - 0.07946658145252557, - 0.0317267492077996, - -0.016206314438528102, - -0.06402722983539166, - -0.11143133182378737, - -0.1581166107464373, - -0.20378563653935677, - -0.2481474536470345, - -0.29091943468873466, - -0.3318290810663684, - -0.37061575904226435, - -0.4070323602264217, - -0.440846875894239, - -0.4718438751049436, - -0.4998258772035793, - -0.5246146099624407, - -0.5460521453463867, - -0.564001905666088, - -0.5783495337090888, - -0.5890036213050767, - -0.5958962916837214, - -0.5989836319148857, - -0.598245972676169, - -0.5936880135653809 - ], - "type": "scatter" - } - ], - "layout": { - "template": { - "data": { - "histogram2dcontour": [ - { - "type": "histogram2dcontour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "choropleth": [ - { - "type": "choropleth", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "histogram2d": [ - { - "type": "histogram2d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmap": [ - { - "type": "heatmap", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "heatmapgl": [ - { - "type": "heatmapgl", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "contourcarpet": [ - { - "type": "contourcarpet", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "contour": [ - { - "type": "contour", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "surface": [ - { - "type": "surface", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - }, - "colorscale": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ] - } - ], - "mesh3d": [ - { - "type": "mesh3d", - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - ], - "scatter": [ - { - "fillpattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - }, - "type": "scatter" - } - ], - "parcoords": [ - { - "type": "parcoords", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolargl": [ - { - "type": "scatterpolargl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "bar": [ - { - "error_x": { - "color": "#2a3f5f" - }, - "error_y": { - "color": "#2a3f5f" - }, - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "bar" - } - ], - "scattergeo": [ - { - "type": "scattergeo", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterpolar": [ - { - "type": "scatterpolar", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "histogram": [ - { - "marker": { - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "histogram" - } - ], - "scattergl": [ - { - "type": "scattergl", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatter3d": [ - { - "type": "scatter3d", - "line": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattermapbox": [ - { - "type": "scattermapbox", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scatterternary": [ - { - "type": "scatterternary", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "scattercarpet": [ - { - "type": "scattercarpet", - "marker": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - } - } - ], - "carpet": [ - { - "aaxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "baxis": { - "endlinecolor": "#2a3f5f", - "gridcolor": "white", - "linecolor": "white", - "minorgridcolor": "white", - "startlinecolor": "#2a3f5f" - }, - "type": "carpet" - } - ], - "table": [ - { - "cells": { - "fill": { - "color": "#EBF0F8" - }, - "line": { - "color": "white" - } - }, - "header": { - "fill": { - "color": "#C8D4E3" - }, - "line": { - "color": "white" - } - }, - "type": "table" - } - ], - "barpolar": [ - { - "marker": { - "line": { - "color": "#E5ECF6", - "width": 0.5 - }, - "pattern": { - "fillmode": "overlay", - "size": 10, - "solidity": 0.2 - } - }, - "type": "barpolar" - } - ], - "pie": [ - { - "automargin": true, - "type": "pie" - } - ] - }, - "layout": { - "autotypenumbers": "strict", - "colorway": [ - "#636efa", - "#EF553B", - "#00cc96", - "#ab63fa", - "#FFA15A", - "#19d3f3", - "#FF6692", - "#B6E880", - "#FF97FF", - "#FECB52" - ], - "font": { - "color": "#2a3f5f" - }, - "hovermode": "closest", - "hoverlabel": { - "align": "left" - }, - "paper_bgcolor": "white", - "plot_bgcolor": "#E5ECF6", - "polar": { - "bgcolor": "#E5ECF6", - "angularaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "radialaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "ternary": { - "bgcolor": "#E5ECF6", - "aaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "baxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - }, - "caxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "" - } - }, - "coloraxis": { - "colorbar": { - "outlinewidth": 0, - "ticks": "" - } - }, - "colorscale": { - "sequential": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "sequentialminus": [ - [ - 0.0, - "#0d0887" - ], - [ - 0.1111111111111111, - "#46039f" - ], - [ - 0.2222222222222222, - "#7201a8" - ], - [ - 0.3333333333333333, - "#9c179e" - ], - [ - 0.4444444444444444, - "#bd3786" - ], - [ - 0.5555555555555556, - "#d8576b" - ], - [ - 0.6666666666666666, - "#ed7953" - ], - [ - 0.7777777777777778, - "#fb9f3a" - ], - [ - 0.8888888888888888, - "#fdca26" - ], - [ - 1.0, - "#f0f921" - ] - ], - "diverging": [ - [ - 0, - "#8e0152" - ], - [ - 0.1, - "#c51b7d" - ], - [ - 0.2, - "#de77ae" - ], - [ - 0.3, - "#f1b6da" - ], - [ - 0.4, - "#fde0ef" - ], - [ - 0.5, - "#f7f7f7" - ], - [ - 0.6, - "#e6f5d0" - ], - [ - 0.7, - "#b8e186" - ], - [ - 0.8, - "#7fbc41" - ], - [ - 0.9, - "#4d9221" - ], - [ - 1, - "#276419" - ] - ] - }, - "xaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "yaxis": { - "gridcolor": "white", - "linecolor": "white", - "ticks": "", - "title": { - "standoff": 15 - }, - "zerolinecolor": "white", - "automargin": true, - "zerolinewidth": 2 - }, - "scene": { - "xaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "yaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - }, - "zaxis": { - "backgroundcolor": "#E5ECF6", - "gridcolor": "white", - "linecolor": "white", - "showbackground": true, - "ticks": "", - "zerolinecolor": "white", - "gridwidth": 2 - } - }, - "shapedefaults": { - "line": { - "color": "#2a3f5f" - } - }, - "annotationdefaults": { - "arrowcolor": "#2a3f5f", - "arrowhead": 0, - "arrowwidth": 1 - }, - "geo": { - "bgcolor": "white", - "landcolor": "#E5ECF6", - "subunitcolor": "white", - "showland": true, - "showlakes": true, - "lakecolor": "white" - }, - "title": { - "x": 0.05 - }, - "mapbox": { - "style": "light" - } - } - }, - "font": { - "family": "Courier New, monospace", - "size": 12, - "color": "Black" - }, - "title": { - "text": "Time Rabi" - }, - "xaxis": { - "title": { - "text": "Time (µs)" - } - }, - "yaxis": { - "title": { - "text": "" - } - }, - "legend": { - "title": { - "text": "Legend" - } - }, - "plot_bgcolor": "white" - }, - "config": { - "plotlyServerURL": "https://plot.ly" - } - }, - "text/html": [ - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "3da369a5-bb9c-4981-b122-dcd034ea56fc", - "record_entry_path": "/root/9-NormalisedRabi.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691592, - "print_time": "2024-12-08 15:59:52" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "NormalisedRabi" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
fitting: 'The fitting result of the Rabi oscillation suggest the amplitude of 0.6005610396184801, the frequency of 31.767127595333758, the phase of -2.6907780232415544. The offset of 0.0013967221773712873. The suggested new driving amplitude is 0.5489810209703299.From the fitting results, the plot should exhibit 9.21246700264679 oscillations.'
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('122a97eb-cf34-4e74-90df-eaebe0cdc1d2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('122a97eb-cf34-4e74-90df-eaebe0cdc1d2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: '1. Clarity of Oscillation: The data points do not show a clear, regular oscillatory pattern. Instead, they appear scattered without a discernible periodic structure.\\n\\n2. Amplitude and Frequency: There are inconsistencies in both the amplitude and frequency of the data points. The amplitude does not show a consistent peak-to-peak variation, and the frequency of oscillations is not evident.\\n\\n3. Overall Pattern: The plot lacks the typical characteristics of successful Rabi oscillation experiments, which usually display a smooth sinusoidal pattern with consistent amplitude and frequency. Based on these observations, the experiment appears to be unsuccessful in demonstrating clear Rabi oscillations.'
success: False
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('4e3de177-77fa-4c3b-aada-4e0be72e346e');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('4e3de177-77fa-4c3b-aada-4e0be72e346e');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
analysis: 'The experiment aimed to observe Rabi oscillations but was unsuccessful. The fitting results suggested an amplitude of 0.6006 and a frequency of 31.7671, with a phase of -2.6908. However, the plot analysis indicated that the data points were scattered without a clear oscillatory pattern. The amplitude and frequency were inconsistent, and the plot did not exhibit the smooth sinusoidal pattern typical of successful Rabi oscillations. Furthermore, the experiment did not meet the criteria for success, as it did not show a clear oscillatory pattern with amplitude greater than 0.2, and the number of oscillations was not within the acceptable range of 3 to 10.'
success: False
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('9c43d116-f590-4a80-853e-535af5b5f820');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('9c43d116-f590-4a80-853e-535af5b5f820');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Transitioning to the next stage Stage2 with the following description:
Run Pingpong experiment.
The experiment in Stage1 was conducted once and was unsuccessful. The analysis of the experiment indicated that the data did not exhibit the expected Rabi oscillations, with inconsistent amplitude and frequency, and a lack of a clear oscillatory pattern. According to the rule of transition, Stage1 is allowed up to 3 retries before moving to the 'Failed' stage. Since this was the first attempt and there are 2 more retries available, the experiment should proceed to Stage2 for further attempts.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage2: Stage2...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage2

\n", - "

Description: Run Pingpong experiment.

\n", - "

Next Steps: If Stage2 fails, goto Failed. Otherwise, goto Complete.

\n", - " \n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('231ca6c1-babe-4184-9f87-9f155fce61b6');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('231ca6c1-babe-4184-9f87-9f155fce61b6');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for Run Pingpong experiment.:
\n", - "

\n", - " 
experiment_pingpong = AmpPingpongCalibrationSingleQubitMultilevel(dut=dut, iteration=9, points=10, mprim_index=0, collection_name='f01', repeated_gate='X', initial_lpb=None, flip_other=False)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "bab8d971-c290-44b5-a107-1c140abf7267", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/0-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.552+/-0.004\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "5b153bc4-b287-4f3c-84eb-abaf7f932615", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/1-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.5539+/-0.0015\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "d5b1a72f-a853-4231-bbcd-97c60bd55219", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/2-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.5548+/-0.0007\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "7c5b8657-6a6d-4133-9cb4-e182e2ef4afb", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/3-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.5563+/-0.0006\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "f2b04cc9-8470-4292-81b6-af263beac6b7", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/4-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.5566+/-0.0005\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "edd4bc87-9c94-47dc-947b-6456c4a9bbff", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/5-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.55685+/-0.00021\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "5377792c-a999-47d8-be0c-c6b0d5abf219", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/6-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.55694+/-0.00013\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "99b61bc8-3acd-4c1a-a2f1-ff0bd4110dce", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/7-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:14" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.55693+/-0.00006\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "d3ecd48a-35cc-44cb-bd33-8d0090c7f203", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run/8-PingPongSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:15" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "PingPongSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "name": "stdout", - "output_type": "stream", - "text": [ - "Estimated best amplitude 0.556959+/-0.000033\n" - ] - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "931192ff-9a74-4059-ae72-eb38593b1c88", - "record_entry_path": "/root/10-AmpPingpongCalibrationSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691614, - "print_time": "2024-12-08 16:00:15" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "AmpPingpongCalibrationSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('f76c9811-1e53-48f2-ba7e-968e00d9bfdf');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('f76c9811-1e53-48f2-ba7e-968e00d9bfdf');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: 'The plot shows the amplitude values over several iterations. The amplitude appears to increase initially and then stabilize around a certain value, indicating convergence. The values become more consistent after the fourth iteration, suggesting that the amplitude has converged.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('ec2d80d7-d322-437b-a5ad-4543a8585c83');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('ec2d80d7-d322-437b-a5ad-4543a8585c83');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
analysis: 'The experiment results show that the amplitude values increase initially and then stabilize around a certain value, indicating convergence. The consistency of the values after the fourth iteration suggests that the amplitude has successfully converged, meeting the criteria for a successful experiment.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('a16d30f4-11fe-4ada-b149-22fb93600ed5');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('a16d30f4-11fe-4ada-b149-22fb93600ed5');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The experiment is complete.
The experiment results indicate that the amplitude values initially increase and then stabilize, showing convergence. The consistency after the fourth iteration confirms the success of the experiment. According to the rule of transition, since Stage2 did not fail, we proceed to the next stage, which is Complete.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('c1859aff-d173-4d29-9b6c-e28310dda2e2');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c1859aff-d173-4d29-9b6c-e28310dda2e2');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: 'The plot shows the amplitude values over several iterations. The amplitude appears to increase initially and then stabilize around the 6th iteration, indicating convergence. The values level off towards the end, suggesting that convergence is achieved.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('b92152ee-b711-4a21-b693-12133242107c');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('b92152ee-b711-4a21-b693-12133242107c');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
results: 'The experiment succeeded in calibrating the qubit gate amplitude after initial challenges, with amplitude values stabilizing and meeting the success criteria.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('14dc3513-5b21-4588-aa25-57e5003a6379');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('14dc3513-5b21-4588-aa25-57e5003a6379');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Transitioning to the next stage Stage3 with the following description:
DRAG Calibration on `dut`
The experiment report indicates that the experiment succeeded in calibrating the qubit gate amplitude, with the amplitude values stabilizing and meeting the success criteria. There was 1 successful attempt and 0 failed attempts in Stage2. According to the rule of transition, if Stage2 completes successfully, the experiment should proceed to Stage3.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Executing Stage3: Stage3...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "
\n", - "
\n", - "

Stage3

\n", - "

Description: DRAG Calibration on `dut`

\n", - "

Next Steps: If Stage3 completes successfully, goto Complete. If Stage3 fails, goto Failed.

\n", - " \n", - "
\n", - "
" - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "Recalling: 0%| | 0/3 [00:00" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('c4c72e56-ff08-4f07-8d0e-dacc14880e07');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c4c72e56-ff08-4f07-8d0e-dacc14880e07');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution agent: Here is the generated code for DRAG Calibration on `dut`:
\n", - "

\n", - " 
experiment_drag_calibration = DragCalibrationSingleQubitMultilevel(dut, collection_name='f01', mprim_index=0, initial_lpb=None, N=1, inv_alpha_start=None, inv_alpha_stop=None, num=21)\n",
-       "
\n", - "\n", - "
\n", - "\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "
" - ], - "image/png": "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" - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/json": { - "record_id": "c4c70e40-628c-4a85-9971-229469b9cce0", - "record_entry_path": "/root/11-DragCalibrationSingleQubitMultilevel.run", - "record_book_path": "log/zijian/2024-12/2024-12-08/15.57.22", - "record_time": 1733691646, - "print_time": "2024-12-08 16:00:46" - } - }, - "metadata": { - "application/json": { - "expanded": false, - "root": "DragCalibrationSingleQubitMultilevel" - } - }, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
fitting: 'Sweep start: -0.004\\nSweep stop: 0.008\\nThe fitting results are as follows: \\nThe estimated optimal DRAG coefficient is -0.005290111995956169\\nThe estimated optimal DRAG coefficient does not fall within the central half of the sweep.\\nResidual average for Xp: 0.013415791272201584\\nResidual average for Xm: 0.006936917638058088\\n'
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('7e29d7be-d38e-4cc8-9502-9ded90e173ff');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('7e29d7be-d38e-4cc8-9502-9ded90e173ff');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: '1. The blue trend line has a positive slope, while the red trend line has a negative slope, indicating distinct trends for each dataset. \\n2. The data points for both colors generally fit their respective trend lines well, though there are a few outliers, particularly in the blue dataset. \\n3. The distribution of data points along the DRAG coefficient axis is relatively even for both datasets, with no significant clustering. \\n4. The trend lines appear to accurately represent their datasets, capturing the overall direction of the data points. \\n5. The trends between the two datasets are clearly different, with the blue dataset showing an increasing trend and the red dataset showing a decreasing trend. \\n6. The fitting residuals seem to be within an acceptable range, with no large deviations from the trend lines. \\nThe trend lines intersect near the center of the plot, meeting the success criteria.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('c2f5c4d1-8abb-4742-bfac-f81508de2b20');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c2f5c4d1-8abb-4742-bfac-f81508de2b20');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
analysis: 'The experiment aimed to calibrate the DRAG coefficient using an AllXY DRAG experiment. The analysis of the results shows that the two trend lines (blue and red) exhibit distinct trends, with the blue line having a positive slope and the red line a negative slope. The fitting of the data points to these trend lines is appropriate, as indicated by the residuals being within an acceptable range. However, the predicted optimal DRAG coefficient (-0.005290111995956169) does not fall within the central half of the sweep range (-0.004 to 0.008). Despite meeting the first two success criteria, the third criterion is not met. Therefore, the experiment is considered unsuccessful. It is recommended to adjust the sweep range to center around the predicted optimal coefficient and repeat the experiment.'
success: False
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('ea169b56-efc1-48e2-ad6b-c990fcfb7643');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('ea169b56-efc1-48e2-ad6b-c990fcfb7643');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The experiment has failed.
The experiment aimed to calibrate the DRAG coefficient using an AllXY DRAG experiment. The analysis indicates that while the trend lines fit the data points well, the predicted optimal DRAG coefficient does not fall within the central half of the sweep range. This means the experiment did not meet all success criteria and is considered unsuccessful. According to the rule of transition, since Stage3 failed, the next stage is 'Failed'.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
fitting: 'Sweep start: -0.004\\nSweep stop: 0.008\\nThe fitting results are as follows: \\nThe estimated optimal DRAG coefficient is -0.005290111995956169\\nThe estimated optimal DRAG coefficient does not fall within the central half of the sweep.\\nResidual average for Xp: 0.013415791272201584\\nResidual average for Xm: 0.006936917638058088\\n'
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Inspection agent reading the plot...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('c72601e4-88a6-49a9-958d-a08f7b8be9ab');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('c72601e4-88a6-49a9-958d-a08f7b8be9ab');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Inspection Agent:
analysis: '1. The slope of the blue trend line is positive, indicating an upward trend, while the slope of the red trend line is negative, indicating a downward trend. This shows distinct trends for each color.\\n2. The data points for both colors generally fit their respective trend lines well, although there are some outliers, particularly in the blue dataset where a few points deviate significantly from the line.\\n3. The distribution of data points along the DRAG coefficient axis is fairly even for both datasets, with points spread across the range from -0.004 to 0.008.\\n4. The trend lines appear to accurately represent their datasets, capturing the overall direction of the data points.\\n5. The trends between the two datasets are clearly different, with the blue dataset showing an increase and the red dataset showing a decrease.\\n6. The fitting residuals appear to be within an acceptable range, with most points close to their respective trend lines.'
success: True
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Analyzing experiment results...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('ea5d728c-411f-43d0-85b1-8da5705af3a0');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('ea5d728c-411f-43d0-85b1-8da5705af3a0');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: Experiment analysis results are as follows:
results: 'Frequency and amplitude calibrations succeeded, but DRAG coefficient calibration failed due to sweep range misalignment.'
success: False
\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "
\n", - " Considering the next stage...\n", - "
\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "application/javascript": "\n var spinnerElement = document.getElementById('e7c5d1a7-43d9-4187-a59c-08a6b6fc1625');\n if (spinnerElement) {\n spinnerElement.remove(); // Remove the spinner element from the DOM\n } else {\n // Retry removing the spinner after a short delay if not immediately found\n window.setTimeout(() => {\n var spinnerElement = document.getElementById('e7c5d1a7-43d9-4187-a59c-08a6b6fc1625');\n if (spinnerElement) {\n spinnerElement.remove();\n }\n }, 100);\n }\n " - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ], - "text/html": [ - "\n", - "

\n", - " Execution Agent: The experiment has failed.
The current stage, Stage1, was executed once and resulted in a failure. The experiment report indicates that while frequency and amplitude calibrations succeeded, the DRAG coefficient calibration failed due to sweep range misalignment. According to the rule of transition, if Stage1 fails, the next stage should be 'Failed'. Therefore, based on the experiment results and the rule of transition, the next stage is determined to be 'Failed'.\n", - "

\n", - " " - ] - }, - "metadata": {}, - "output_type": "display_data" - }, - { - "data": { - "text/plain": [ - "" - ] - }, - "execution_count": 3, - "metadata": {}, - "output_type": "execute_result" - } - ], - "execution_count": 3 - }, - { - "cell_type": "code", - "id": "85cff806-1198-4265-b1b7-f9bd63d8100d", - "metadata": { - "ExecuteTime": { - "end_time": "2024-12-08T21:01:13.838633Z", - "start_time": "2024-12-08T21:01:13.835896Z" - } - }, - "source": [], - "outputs": [], - "execution_count": 3 - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -} diff --git a/notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb b/notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb deleted file mode 100644 index fe5ff77..0000000 --- a/notebooks/SimulatedSystem/TwoQubitTuneUp.ipynb +++ /dev/null @@ -1,224 +0,0 @@ -{ - "cells": [ - { - "cell_type": "code", - "id": "initial_id", - "metadata": {}, - "source": [ - "from leeq.core.elements.built_in.qudit_transmon import TransmonElement\n", - "from leeq.setups.built_in.setup_simulation_high_level import HighLevelSimulationSetup\n", - "import numpy as np\n", - "from leeq.theory.simulation.numpy.rotated_frame_simulator import VirtualTransmon\n", - "from leeq import ExperimentManager\n", - "from labchronicle import Chronicle\n", - "Chronicle().start_log()\n", - "manager = ExperimentManager()\n", - "manager.clear_setups()\n", - "\n", - "virtual_transmon_1 = VirtualTransmon(\n", - " name=\"VQubit_1\",\n", - " qubit_frequency=5040.4,\n", - " anharmonicity=-198,\n", - " t1=70,\n", - " t2=35,\n", - " readout_frequency=9645.5,\n", - " quiescent_state_distribution=np.asarray(\n", - " [\n", - " 0.8,\n", - " 0.15,\n", - " 0.04,\n", - " 0.01]))\n", - "\n", - "virtual_transmon_2 = VirtualTransmon(\n", - " name=\"VQubit_2\",\n", - " qubit_frequency=5040. - 123,\n", - " anharmonicity=-197,\n", - " t1=70,\n", - " t2=35,\n", - " readout_frequency=9645.5 + 100,\n", - " quiescent_state_distribution=np.asarray(\n", - " [\n", - " 0.8,\n", - " 0.15,\n", - " 0.04,\n", - " 0.01]))\n", - "\n", - "setup = HighLevelSimulationSetup(\n", - " name='HighLevelSimulationSetup',\n", - " virtual_qubits={2: virtual_transmon_1,\n", - " 4: virtual_transmon_2\n", - " }\n", - ")\n", - "setup.set_coupling_strength_by_qubit(\n", - " virtual_transmon_1, virtual_transmon_2, coupling_strength=1.5)\n", - "\n", - "manager.register_setup(setup)\n", - "\n", - "configuration_q1 = {\n", - " 'lpb_collections': {\n", - " 'f01': {\n", - " 'type': 'SimpleDriveCollection',\n", - " 'freq': 5040.4,\n", - " 'channel': 2,\n", - " 'shape': 'blackman_drag',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 0.025,\n", - " 'alpha': 425.1365229849309,\n", - " 'trunc': 1.2\n", - " },\n", - " 'f12': {\n", - " 'type': 'SimpleDriveCollection',\n", - " 'freq': 5040.4 - 198,\n", - " 'channel': 2,\n", - " 'shape': 'blackman_drag',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 0.025,\n", - " 'alpha': 425.1365229849309,\n", - " 'trunc': 1.2\n", - " }\n", - " },\n", - " 'measurement_primitives': {\n", - " '0': {\n", - " 'type': 'SimpleDispersiveMeasurement',\n", - " 'freq': 9645.5,\n", - " 'channel': 1,\n", - " 'shape': 'square',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 1,\n", - " 'trunc': 1.2,\n", - " 'distinguishable_states': [0, 1]\n", - " },\n", - " '1': {\n", - " 'type': 'SimpleDispersiveMeasurement',\n", - " 'freq': 9144.41,\n", - " 'channel': 1,\n", - " 'shape': 'square',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 1,\n", - " 'trunc': 1.2,\n", - " 'distinguishable_states': [0, 1, 2]\n", - " }\n", - " }\n", - "}\n", - "\n", - "configuration_q2 = {\n", - " 'lpb_collections': {\n", - " 'f01': {\n", - " 'type': 'SimpleDriveCollection',\n", - " 'freq': 5040. - 123,\n", - " 'channel': 4,\n", - " 'shape': 'blackman_drag',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 0.025,\n", - " 'alpha': 425.1365229849309,\n", - " 'trunc': 1.2\n", - " },\n", - " 'f12': {\n", - " 'type': 'SimpleDriveCollection',\n", - " 'freq': 5040.4 - 198 - 123,\n", - " 'channel': 4,\n", - " 'shape': 'blackman_drag',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 0.025,\n", - " 'alpha': 425.1365229849309,\n", - " 'trunc': 1.2\n", - " }\n", - " },\n", - " 'measurement_primitives': {\n", - " '0': {\n", - " 'type': 'SimpleDispersiveMeasurement',\n", - " 'freq': 9645.5 + 100,\n", - " 'channel': 3,\n", - " 'shape': 'square',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 1,\n", - " 'trunc': 1.2,\n", - " 'distinguishable_states': [0, 1]\n", - " },\n", - " '1': {\n", - " 'type': 'SimpleDispersiveMeasurement',\n", - " 'freq': 9645.5 + 100,\n", - " 'channel': 3,\n", - " 'shape': 'square',\n", - " 'amp': 0.21323904814245054 / 5 * 4,\n", - " 'phase': 0.,\n", - " 'width': 1,\n", - " 'trunc': 1.2,\n", - " 'distinguishable_states': [0, 1, 2]\n", - " }\n", - " }\n", - "}\n", - "\n", - "qubit_1 = TransmonElement(\n", - " name='test-qubit-1',\n", - " parameters=configuration_q1\n", - " )\n", - "\n", - "qubit_2 = TransmonElement(\n", - " name='test-qubit-2',\n", - " parameters=configuration_q2\n", - " )" - ], - "outputs": [], - "execution_count": null - }, - { - "cell_type": "code", - "id": "fed6c78a29e55398", - "metadata": {}, - "source": [ - "import os\n", - "from k_agents.execution.agent import execute_procedure\n", - "from leeq.utils.ai.translation_agent import init_leeq_translation_agents\n", - "from leeq.experiments import experiments as exp\n", - "from mllm import config, caching\n", - "\n", - "config.default_models.normal = \"gpt-4o\"\n", - "config.default_models.expensive = \"gpt-4o\"\n", - "config.default_options.temperature = 0.2\n", - "root = os.path.dirname(exp.__file__)\n", - "init_leeq_translation_agents()\n", - "\n", - "\n", - "ExperimentManager().status().set_param(\"Plot_Result_In_Jupyter\", True)\n", - "duts = [qubit_1, qubit_2]\n", - "frequency1 = duts[0].get_c1('f01').get_parameters()[\"freq\"]\n", - "\n", - "with caching.refresh_cache():\n", - " execute_procedure(\n", - " \"Two level Two-qubit calibration on `duts`\", duts=duts\n", - " )" - ], - "outputs": [], - "execution_count": null - } - ], - "metadata": { - "kernelspec": { - "display_name": "Python 3 (ipykernel)", - "language": "python", - "name": "python3" - }, - "language_info": { - "codemirror_mode": { - "name": "ipython", - "version": 3 - }, - "file_extension": ".py", - "mimetype": "text/x-python", - "name": "python", - "nbconvert_exporter": "python", - "pygments_lexer": "ipython3", - "version": "3.10.15" - } - }, - "nbformat": 4, - "nbformat_minor": 5 -}