week-05-broken.ipynb
{"cells":[{"cell_type":"code","execution_count":1,"source":["# INFO: IPython extension for auto reloading modified custom packages\"\"\"\n","%load_ext autoreload\n","%autoreload 2\n","\n","\n","# INFO: IPython extension for package/system spec output\n","%load_ext watermark\n","\n","\n","# INFO: Core imports for practicaly any data science activity\n","import numpy as np\n","import pandas as pd\n","\n","\n","# INFO: Customize settings for Pandas\n","pd.options.display.max_columns = 500\n","pd.options.display.max_rows = 500\n","pd.options.display.max_colwidth = 500\n","\n","# INFO: to display dataframes as tables on call\n","from IPython.display import display\n","\n","\n","# INFO: Plotting setup (matplotlib is only for compatibility with legacy code)\n","# import matplotlib.pyplot as plt\n","%matplotlib inline\n","import plotly.io as pio\n","import plotly.express as px\n","import plotly.graph_objects as go\n","\n","\n","# INFO: Customize plotting backend for Pandas (matplotlib for compat)\n","pd.options.plotting.backend = \"plotly\"\n","# pd.options.plotting.backend = \"matplotlib\"\n","\n","\n","# INFO: Customize Plotly theme\n","pio.templates.default = \"plotly_dark\"\n","# pio.templates.default = \"plotly\"\n","\n","\n","# INFO: Logging setup (replaces 'print' in development & seamlessly transitions to production code)\n","import logging\n","import sys\n","\n","root = logging.getLogger()\n","root.setLevel(logging.INFO)\n","\n","handler = logging.StreamHandler(sys.stdout)\n","handler.setLevel(logging.INFO)\n","formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n","handler.setFormatter(formatter)\n","root.addHandler(handler)\n","\n","# INFO: Use logging like this:\n","logging.info(\"Logging is set!\")\n","\n","\n","# INFO: Call for package/system spec output\n","%watermark --iversions"],"outputs":[{"output_type":"stream","name":"stdout","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n","2022-04-25 23:58:24,713 - root - INFO - Logging is set!\n","matplotlib: 3.5.1\n","logging : 0.5.1.2\n","numpy : 1.22.3\n","plotly : 5.6.0\n","pandas : 1.4.1\n","sys : 3.10.4 (main, Mar 25 2022, 00:00:00) [GCC 11.2.1 20220127 (Red Hat 11.2.1-9)]\n","\n"]}],"metadata":{}},{"cell_type":"code","execution_count":2,"source":["# INFO: PPL specific imports\n","\n","import jax.numpy as jnp\n","from jax import random\n","\n","import numpyro\n","import numpyro.distributions as dist\n","import numpyro.optim as optim\n","from numpyro.infer import SVI, Trace_ELBO, Predictive\n","from numpyro.infer import MCMC, NUTS\n","from numpyro.infer.autoguide import AutoLaplaceApproximation, AutoNormal\n","\n","from jax import lax, random\n","from jax.scipy.special import expit\n","\n","import arviz as az"],"outputs":[],"metadata":{}},{"cell_type":"code","execution_count":3,"source":["data_uri = \"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/NWOGrants.csv\"\n","df_dev = pd.read_csv(data_uri, sep=\";\")\n","df_dev.head()\n","df_dev[\"gender\"] = df_dev[\"gender\"] == \"m\"\n","df_dev[\"gender\"] = df_dev[\"gender\"].astype(int)"],"outputs":[],"metadata":{}},{"cell_type":"code","execution_count":4,"source":["# INFO: total effect for starters; DAG synced with answers (coz my personal DAG is different)\n","\n","\n","def model(data: pd.DataFrame, observed=True):\n"," applications = data[\"applications\"].values\n"," awards = data[\"awards\"].values\n","\n"," discipline = data[\"discipline\"].values\n"," discipline_card = np.unique(discipline).shape[0]\n"," gender = data[\"gender\"].values\n"," gender_card = np.unique(gender).shape[0]\n","\n"," alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1).expand([gender_card]))\n"," logit_p = numpyro.deterministic(\"logit_p\", alpha_gender[gender_card])\n","\n"," # alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1).expand([gender_card, discipline_card]))\n"," # logit_p = numpyro.deterministic(\"logit_p\", alpha_gender[gender_card, discipline_card])\n","\n"," numpyro.sample(\"awards\", dist.Binomial(total_count=applications, logits=logit_p), obs=awards if observed else None)\n"," # numpyro.sample(\"awards\", dist.Binomial(total_count=applications, probs=logit_p), obs=awards if observed else None)\n"," # numpyro.sample(\n"," # \"awards\", dist.Binomial(total_count=applications, logits=expit(logit_p)), obs=awards if observed else None\n"," # )\n","\n"," # dist.Binomial(applications, logits=logits)\n","\n"," # with numpyro.plate(\"applications\", applications):\n"," # alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1).expand([gender_card]))\n"," # logit_p = numpyro.deterministic(\"logit_p\", alpha_gender[gender_card])\n","\n"," # numpyro.sample(\"obs\", dist.Bernoulli(logit_p), obs=data)\n","\n","\n","kernel = NUTS(model)\n","mcmc = MCMC(\n"," kernel,\n"," # num_warmup=500,\n"," num_warmup=1000,\n"," # num_warmup=2000,\n"," # num_samples=2000,\n"," num_samples=5000,\n"," # num_samples=10_000,\n"," num_chains=1,\n"," # num_chains=4,\n"," progress_bar=True,\n",")\n","mcmc.run(random.PRNGKey(0), df_dev)\n","samples = mcmc.get_samples()\n","\n","numpyro.diagnostics.print_summary(samples, prob=0.89, group_by_chain=False)"],"outputs":[{"output_type":"stream","name":"stdout","text":["2022-04-25 23:58:57,453 - absl - INFO - Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker: \n","2022-04-25 23:58:57,454 - absl - INFO - Unable to initialize backend 'gpu': NOT_FOUND: Could not find registered platform with name: \"cuda\". Available platform names are: Host Interpreter\n","2022-04-25 23:58:57,455 - absl - INFO - Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.\n"]},{"output_type":"stream","name":"stderr","text":["sample: 100%|██████████| 6000/6000 [00:10<00:00, 584.37it/s, 1023 steps of size 1.63e-03. acc. prob=0.84]\n"]},{"output_type":"stream","name":"stdout","text":["\n"," mean std median 5.5% 94.5% n_eff r_hat\n","alpha_gender[0] -0.98 0.99 -0.99 -2.50 0.62 952.90 1.00\n","alpha_gender[1] -1.62 0.05 -1.62 -1.71 -1.54 586.28 1.00\n"," logit_p -1.62 0.05 -1.62 -1.71 -1.54 586.28 1.00\n","\n"]}],"metadata":{}},{"cell_type":"code","execution_count":5,"source":["az.plot_trace(az.from_numpyro(mcmc))"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[<AxesSubplot:title={'center':'alpha_gender'}>,\n"," <AxesSubplot:title={'center':'alpha_gender'}>],\n"," [<AxesSubplot:title={'center':'logit_p'}>,\n"," <AxesSubplot:title={'center':'logit_p'}>]], dtype=object)"]},"metadata":{},"execution_count":5},{"output_type":"display_data","data":{"image/png":"iVBORw0KGgoAAAANSUhEUgAAAq8AAAEGCAYAAABLmnwmAAAAOXRFWHRTb2Z0d2FyZQBNYXRwbG90bGliIHZlcnNpb24zLjUuMSwgaHR0cHM6Ly9tYXRwbG90bGliLm9yZy/YYfK9AAAACXBIWXMAAAsTAAALEwEAmpwYAACIMklEQVR4nO2dd5jc5LX/v5qZ3Z3ZnZ3t3m6ve6+4gQGbYsD8ckNIuSEJKZCEcNN7L9wkJCEhIb0QSAIJKYQkF5JAjLENNgYb977u3t77zs5OfX9/SJqRNJJGmr675/M888yovTp6pZGOznsKxxgDQRAEQRAEQUwELJkWgCAIgiAIgiCMQsorQRAEQRAEMWEg5ZUgCIIgCIKYMJDyShAEQRAEQUwYSHklCIIgCIIgJgykvBIEQRAEQRATBlJeCYIgCIIgiAkDKa+EEgag3MB6vwPw6dSKknQaAIxmWgiCIIgkQvdsYspByitBxMaWaQEIgiAIw9A9e5JDyuvU5TEA+wEcA/A8gOkq61wG8G0ABwCcB/A9yK+ZuQC2AmgEsA1AiTB/EYBdAA4BOAXgfgPyzAfwCoCTAJ4G8E9ErAQ2AN8A8BqAwwCeAzBDWHYfgCcB/APACQD7wL+ti3xFkP0ggLsV+1wF4AXh+I4C+JBkGQPwNWGfvzQgP0EQRCqhezbdswkBUl6nLp8GsAbAMgDPAHhAY72ZANYK610H4O2SZWsAvAnAQgBjAO4R5rcAuAn8jWYFgE0Abo4hzx8APA5gMYBPCNtIZbUCWAdgJYA/A/iVZPlVAD4AYAn4G+HnhPlbALwTwGoAVwCokmxTBH4Y7W5h+VUA7gWwXrKOTTj298WQnSAIItXQPZvu2YQAmdanLm8Hf5PIA5ALIKSx3iPCsjHwN6vNwjcA/B0Rf6RXwL/VA4ADwM/A37RCAGrA3xC3auzDBf6muUGYvghgu2T57eAtBLcK01bIr93/AOgWfu8B8H7h92bwb/iDwvQvELmRXwX+bf8ZSTtO8DfivcL0oxryEgRBpBu6Z9M9mxAg5XVqcjX4N+X1ADoBXA/gN3G0My75HUTkevoWgCHwNzc/+Ldzh8m2meS3BcDHATxrUg6mWE/ZZiP4t3QtKFCAIIhsgO7ZdM8mJJDbwNSkBPyfvBf8TeN/dNa9G/x1kg/+DXibwfbbwN8EZwJ4XYz1h8H7Rb1HmG4AcINk+d8BfApAoTCdC/4mG4ttAN4MfrgJ4IepRPYAqFfINgdAmYF2CYIg0gnds+meTUgg5XVq8h/wjvJnwA8dndJZ9zJ4B/hjAF4C8EcD7X8TwJ0AjgP4CXgH+1i8E8Bd4J3/fwreD2pQWPZdYd+vCHIcgfxGqcV/ADwB3vH/IIAeybJB8P5VHwPv+H8K/JBTgYF2CYIg0gnds+meTUjgGFNa6QkizGXwb8EH0rCvAgAe8P5WteCjam8Cf8MmCIIgYnMZdM8mpgDk80pkC+sB/ED4nQPgf0E3QYIgiGyF7tlExiDLK5FOvgrgjSrzPwJgd5plIQiCIPShezaRlZDyShAEQRAEQUwYTLkN9PT0sKamplTJQhAEkTJWr17dC6Ai03KkE7pnEwQxUdG7Z5tSXpuamrBmzZqkCEUQBJFOGGNTToujezZBEBMVvXs2pcoiCIIgCIIgJgykvBIEQRAEQRATBlJeiTCrVs3Ge997U6bFIAiCIIi00bB8KTgLqUMTCTpbRJgHv/9e/PqRj2RaDIIgCIJIGyU1VcgryM+0GIQJSHklwoyMeAAAFRVFMdYkCIIgiIkPx3H8D0obOqEg5ZUIk5vLJ59YvHh6hiUhCIIgiNTDWUkNmojQWSPCOJ12BINBzJ1bk2lRCIIgCCLlcByvBlHBpokFKa9EGKfTgfPnOzBtGrkNEARBEJMfMVAr7D5ATAhIeSXCOJ12XLjQicrK4kyLQhBENPUAdgI4BeAkgI9lVhxiouNwuTItQkaw5uSEf1tE5TXObAPWnBzYC51JkYswDimvRBin046W5h6UlBZmWhSCIKIJAPgUgEUA1gP4kPCbIExjdxZgwYZ1mRYDhWWlabd6LrtxE6rmzgYAOFyi4hmfDA0rlmLh1VcmSTLCKKS8EmEKC/PR0dEPp9OeaVEIgoimA8Ah4fcIgNMAajMnzuQl1+FA9bzZSW/XYrVi5ZbNWTFEnS15TeesvQJl9em/jPPyHQCAgNcHALBY4jsnVpstaTIRxsmOq5fIOBaLBfn5eejsHITT6ci0OARB6NMAYCWAfSrL7gFwAMCB8vLydMo0aSifXoeq2bOS3q7FagWQHYpjNgUoZbQ/xBeJLHihiIeC4iI4S0syLYYhch3J0y0y/w8isoKCgjwAQGfnAFleCSK7cQL4G4CPAxhWWf4wgNUAVvf29qZRrMlDyiyjQrvZpDhOJqw5OVi86eq4tuUMWF7z8rOvkMGs1Ssxd93qTIthiMWbrk6aAkvKKwGAzzQwPu5Df/8ICgvJ8koQWUoOeMX1CQB/z7AshEmy0biXDS4MycJR6IxbOeJi+LyW1ddh0cYNcbWdSmyS4LOJgCVJeXVJeSUA8MFao6PjGB0dJ7cBgshOOACPgvd1/UEmBCiqrMDs1Sszsev0Mnn0OW2yyPqbSQWaM+g2YLVZ0yANYRRSXgkAQGGhA6OjHoyOesjyShDZyQYA7wRwPYAjwufWdApQVlcLV4XcjzYvPx8OF2UoUaLmx5mVVs5slCkDxOt3m8pzarXZMpaGKxtdJKSQ8koA4N0GRMur6P9KEERW8TJ4m+AyACuEz7PpFEDNV3P+1euxYMP6dIqREGV1tVhy/UbddRJVSEpqqrDi5hsSaiPlTEKlNS5fYqEbLDH6IxN+ytNmNWQsDdeijRtQXFWZkX0bgZRXAoDoNuDB2JgXubk5sNEQCUEQSlQe4FlpTdRh+tJFyMnLTek+7M4CjSV8X5VPr8uaCPGJdv5SRhYqr7mOzAZPZ3MaMFJeCQC88joy4oHbPQ4AyM8n6ytBTCVmr16JgmL90tBZ5CY54ZAO/9YtnI/pSxcnpd1Yw93WCRbQkyliKvFJvPgLy0oxbeaMpLWXKhhS+4dPRDkm5ZUAEHEbCAZD8Hr9KCigdFkEMZVwVZSjpKZKfyWdB3h+UfaUGuUsFlQ0TFedb7CF8C+7swC23MQstQ5XIRZefaVMQUqWwXPFzTdoKqjFVZVYduMm/QYmk+E1DgVTzDJgzUmPldGWm4s5a69A7YJ5adlfQjAW0/eVs1hgEUZqS2urUTmrQb9N4cKfuWo5lm2+Lm7RSHklAIgBW7zVdWzMS5ZXgiCi0LPE1C6cn0ZJ9HFVlKFORZ54/FAXXnMVZl2xIiF5xOIEUpKR71JUhrXSD+XYzd/H84tcpoJ1LDYrnKUlYQVGSVl9Zl0kGpYvhbMs9v7rFy/UXZ4sw2s2D8UrySvIj5kebPbqlVi++XoAQP2SRaiZP9dQ28WV0wCo/zeMQMorAYB3G3CPegAAbjcFbRHEVMBqs0VlD9BD1+8vwad7Ng1vK4eQzSsc/PaVs2fK28siK6fWMPn8q9Zh9ppVhttZvvl6zF23GrOvUE+hNn3JQplimF/kSpkyq3Z9ltRUoWJ6fUr2B8C0CX0iFajguNgqokPiDpPOYyPllQAQcRsAeMsruQ0QxOSnau4sWd7WihnRQ+0yYjycLDaraV8+0WK37MZNqJqT/JKsycThKjSlyNbMm5NCaVJHPNYwW54xg8f8q9alrCKU1uWp6y5iVPlMkWJm1CVl5ZbNaXfNYSwU13ZaVng1XBXlcaUDI+WVABDJNgDwlldyGyCIKUCcz2OtB2lJVVXYl69m/tyoodiiygrULYoM59ctWhAecgQyH10dRqHQiNkDFmxYH3N4Wb09sdkkm15NtpfrsEdZuNUqSyVbTCOBPxzHJS1fsLKfjfT7QEdXQvtcuWVzXNstvWEjymcYsww7CtObT5mF+PNmNgfushs2GV535splcRU+IeWVAAAUOB0YGRGVV7K8EsRkpHJWA5xlJSgoKQbHcaaH+aSr5xXkC/OY7Fu6r/LpdbJ5VbNnyay7DoXFpayuVmaV07JKTV+6yLT1TvSxSxQzVqVMIJ4XNRZvugZzwi4BOgqdhrIXd+Cageuscs6s5OULViqvBpQvn8eTnH3HQa5d/XlbWlMtm+asFrimVaRDJAD6bgDha0Ha1yw+ZTfXbkfDimXhac5iifkyQMorAYACtghiKlAzfy5qF8zHvPVrUDa9TvXhpKaghOdJ11d5rk1fukh3/8phSLX9i/6QOfY8LL1ho6o8xdVV5v0mTZgTdVc10I6mpS/JJk21/Sy6Vj+5vE3IcVs0zbivMwCUVFdh6Q36xR3iheM4VKfQZURUppbddH1UDl6j1nCp9TgvPz8jQWgVM6ZjdoLBg1KKqyqxaOPVMddT9lFRZYXqtSB1qeE4Dg3Ll6q0ZVENoCupjlyzFgPKLymvBADRbYBXXvmALbK8EsRkQrTYiM8hq4Zfo8Vqlfkv5jocWHrDRpTV1aKoMmL1iSc4Q7mJXhsLr7lKkCdJj6kk+SzGo36G0zHFGVltFlGJkPZvWb3CCi4oiy4VJVZNoYuVuUC6zdIbNqKwrDQ8rdb1qYm6Z1GySKetVivyY+QyNsLsNatULf8cx2HelWuj5pXPqMfKLZvjjqyXtmWE6nmzDY0QVM6eibx8edYLi80aSX0lWn4Vu7UXaBXhiJBjt6um3iufXoe5axP3eSbllQAQ7fNK2QYIYvJQWFaK4iph2DzGA7C4ahqWXn9teFp8kPGVqbTvC0aUWRZSBIDobKOn3Bh9iGtXukqABKyndYsWaC5btPHqKDcLgLfyaQ6haoiSo+I7HFVVTOh7NetYothyc2X+q/muwqj0Wwuv1U/BpMfKLZtRUFKsvYKiXziLBdXzZitWCTsiC1/yjQrLy+T+zZJrVetat9hsKCgukl27+UUu1AvnXSuXbLJ8oa05OVi5ZTOqZs+Cs7Q05vpqPuZLrrs2rICL/tFKv2hbAhXqOEtyjpWUVwKAPNuAh9wGCGLS4HAVYs7aK8IPSOVDW4n0QWrLzdX2FTVoyZy5clm4DfGhL1qgjLSweNM1KKmOWHCWXH9tzId9YVlZ9EwzCoLOuvEoGqJSqnzor9yyGbkOB6w2G/LyHarD/fEEsakOwcsKJHCm/RITRWm5lSrTUmXQ6AuHmm+vllKZk5cLZwk/VC36XBeWqyt3s9esgsNViGkN01VfJoQdGZJRKVM8gWRaFBQXyf4XAFBeXytpO3YbPU0tAOQvilabDQ6n8eh/sxb0stoa3eW5Drt2v0sg5ZUAECkPC1DAFkFMRozkbATkD9vahfNQPXe2+npgWLllc8yh8OKqSsxctRy2vLyw5XX5TXyGAfH5Gush3rBiKZylJeAsFuTk5cVcv0KI3k5m3snS2urYKwko5dPzQc3Nt4crDZlVKM0UEwiLxBgaViRubc112JNSaAEAiiQvSFVzItebxWbVDUDTQmkpzMnLC/uo5gsWYe/YGL+u4lJylZehqHIagoFgeF79kkVwVURcZrQU7DmS/LjWnBzUL1mkqDgl35lT4lqxcstmUymjGlYslZ1HW26urECAwxU7rZb4f9SqdBV+wTDwMpesTCFVc2YZKnRAyisBQO42QHleCWLyUVAi+PrFUPwMB7Ao9UIDimJIohAAEWXNaiSKPR4jVQyRtJRFtT4Q0xQlPd2VREa9pqfNnBFlvZQGDa3csjmGMhtpXJnmrKJhOpylJVhwzZXaW0uEK6muwuJN12DxptjBPiKMMU0/TKfEBUDq4zx9yWIsUrgXiMfMgUNpbbV6EJmBczTS16/YBjLlUSpHeX2tLKBIa1fhfuU4FJaVory+VvfFRYnS/1SJ1Gc26u+mOGZDAXAGX+6MFO1YvOkaQ9smi4lTp4xIKYWF+bKArfLy7KlTThBE/IjWRzEdj03wu4sVEW/LyzNluZQGeakFpiTlGRZDHo7jYDeYC3PWFStQNK0Ch5/bBoBXDrsvNWm3bcIqGk/S9VjULpiHnLw8DPf2wTfmgXdsLOr85Di03b30lIi6hfPhGRkJDxfHOu+5KkqWWvNKq+ncdWt021U2pOYjWiAJuHJVVMiyURgdXVCjtKYa0xr4Aht6ip+0+ljVnFmauVdnrlymOj8RZS7Kb1mC1E892RSWlWKgozM8XVbHuyekSjE1AlleCVgsFuTn58mLFFDAFkFMDpSKiOSBUyWUL5Uv5pcvvf5aFBQXG243XxKgs+zGTVGruyoqZNHHlbMaZBHphlB5WFbNmRUexi2tq8WCDeui1imprkKZxB8QAIqE7AsrbrkRhWWlqF0wL2xRUkvcL7oiyHxHhXyUUsV25splcJWr+NxqIFcUVZQB6TFz/ND0zFXLxI3lq2qYpx0uV3j4WksvTdQFQC2YT2k1NeLPGksdCveX4lpYuWVzWGEUr2E9lwPG+JeMWatWADDuuyk9t1ouNVpWWiWpVv5iW+P1GR0YBICkuJkkG1JeiXBmASoPSxCTH1HRytFIjC5Fz9KjZ51Ts1JK02wBMOTXJsVqs0VZdCtnz0T13NmYJihmWrpAw4qlmgolx3GYs/YKAIjKPylamKQ4S4pR0cAH/YjySPNSKoeJpW1qJaNXw+EqxPyr1sl8ipUR5GIFpLC8knRY869aB7ugvEkV+rx8h2pHmQm8UVO6lOc86hpgLDrbhHrjBmWI3ld42F1YqFSelSy8WttNQqRKQ0HVQ6tUrhFlVYzwr05SaWFHoRPOshLTZZuBiF+wFNkLp5FzJaxjJAjLDKS8EnA6HRgf9yEg+KO53ZRtgCCylFsAnAFwHsDnjWygLGcqPkCLKzUq9aSo1vuo0sfQJLNWrYiy6NYID3gxglmpzJn1k3UUFvIKoNAHWkUXlIrAss3XoaC4SNWSXFRRoavoz1uvPpReNK0C+UUu2RB9vqJ8qtcjVy6kLwj5RS5ZNTOzVM2dLTvO0hhR4kqUSnxheZmhPKdqLxlz162OyquqpSACsX1H1dByC1Erg5wQ0mtSmX0AHJbduAkrt2yGq8JcEQk96hYtCJdtNoNS2bZYreEXvdjI3ZWKklThLixLUlsjJiTSAgUAFSkgiCzFCuBnALYAWATgbcK3LgWKpOzhB5JmqiyticRIZuS/FmX1cgXL7LBsWX0tpi9ZGHO9XLs9KnjK7nSq9peZ4y4oLgrnXRWVKS2lo2H5UtQvlp9+I5WJjFI9Z1Z43yU1VaZz5irzeVbPnW36fIj7dJaWRFW00vNLnbd+jaFoeyMUxaFEKsseh1Ecfrhalkq3aLYB/tqIR0F3uFxYuWUz6pfw143y2py9eqVsukhRila5vpHzuWjjBtViBYlCyishlIaN1HWm8rAEkZWsBW9xvQjAB+DPAG7T20CttGo436vmg4eLWleN1FRISgxdH10DBLw+AMaqei25zliATEl1pSmlraSmCjn22OnASmqqwm4BZjHjvjB/w/q4Chlo+d+aQU0ZV7um1VDzfU4XdhN5UgHI0nCJ6J1/pf+2HoUSS7aztBiAPB+sSN2i+VEjB7JyrxaLrhuRHkUqx6eJwf8KKa+ErEABQBW2CCJLqQXQIpluFeYpuQfAAQAHyqbpPDQMPCT01pi5annM7eNFs6KUDqrDu0JpTrNt6FZv0kHNbSCeQKgl112bsA9qQkgsbEpXBWGHqptJ3QJSUd2sfHqdIm+qKE7mot7VCPh8qvMrZzWoymr2JcQm+MUaoXx6nWrRgZLqKplVv2LGdIR0RglmrVqOOcqyrrqp3Roi+xJeyGIxc9Vyw+eSlFdCluMVILcBgpjgPAxgNYDVIyMjhippSZFZuyaQ28D8q6Itbbl2e7g0pxmMKge23Mh602bOUFWs4kWvjKYysj+RFFFqGLVuKpmxbHFS9u9wuVR9UJUvAvZCQUGOKwdw6q5Hp8bLT1ldrWoQoFm0shzEQiqXagYBnT6xOwtMlYWtULw0GqnaVVw5zbDFnpRXIsrnldwGCCIraQMgfSLUCfN0Me1nKPG1S5dFK5m+mlKUPnzJRhrNngpLoxZK5XLRRv2o+nRhSZIryYIN61CpksYtpMhWYKaMqZJ4FcBEcemNhqSYWAUT9Kz9uQ5HVDU9iyV2AJ4WmpknyPJKGMXpdIRLwwJUHpYgspT9AOYCmAkgF8AdAJ6JtzGpJdRZWoKA3w9APdo71Zip1ERMHvRejuwFBVGGQK0grUSUqKxCoz9qF5rPFJAOEgnE8oyMqs7XG3GQQsorIQRsyS2veXk5sBoIWiAIIm0EAHwYwFYApwE8CeBkvI35PZH/vDINEZF5Eg0+SxWltdVJa0vPEsgHuhlrJ5nuGqlGT2HX8gsVK39lG6kYmSGfV8IwTqcdboXPKwByHSCI7ONZAPMAzAZwfyINKYe5sy3oZaqTTjcEM9QtXqipVJq9hmJWbjKqyMTprtDf3hHXdqmibuH8TIuQcYyWYSbllYjKNhAIBOHz+cl1gCCIpBFP4BSRfSj9HqWYLvcbA6OBc2qpn4jUk4oXXqP+76S8ElHZBgAqEUsQUw6yvBJTjGQr20aYTH+zeErOimhlHzGa6YKUVwIFioAtgErEEsRUQ8+iRhBGmUjuJ8qUY4Q54k2ppodRdxlSXomogC2AChUQBEEQ6uilVFp+8w1plGQCMoGU+1SiLFttFlJeiag8rwC5DRAEQRDqFJZpp1ObSJZXYuJCyiuh6vPqdo+T2wBBEAQRjUJBHe7ty5AgEw9S7pMDKa9EVLYBgCyvBEEQhDr2gnzZNClkxplMfrZD3T0Z2zcprwScTjsFbBEEQRAEYZwMvrSQ8koIAVvRbgNkeSUIgiBiQZbXqUkmzzspr4Sq24BnzEvZBgiCmPJkWxUmgkgHRvyYjRYUSAWkvE5xLBYL8vPzKGCLIIgpjdbDeri7N82SxI97cDAj+w34fBnZL5E6QsFgzHU4C1leiQwhWlfdbq9svttNAVsEQSTGaP9AyvfReeFSUtppOnpCdX5JTVVS2k8Hw739Gdmvb3w89krEhIKF1CtgiQT8fnBcbBXSO+ZBb3NrssQKQ8rrFMfpdMDr9cPvD8jmU7YBgpj4cByHYpcjPG2VWEoc9hw47JHa8TaruceB1N2tqsKJYpfK/UJYyWazIC/XCpvVgs3XzoU9z4a8XCssFg4up3yEZ/WyOtx07VzN/VosHEqKIsck+t25nHm4eeM82fGIx+QsyMWM2mJN+QGAhUKyaXHbvtY2TVnEdnJzsqQ6mUbJTYDvNynOfO3qSDk2q+4xOQtyYbPx/VtWki/zfXTYc1TjeAqdebhhw5xw+/Y8eaGDlpOnMaO2GC5nXkrjgGqrXCh22VHgyJX1yeHntgEA6qoiyfOLXY6Y5zbfkSObHmxtjTo2NWxWS9Kvm8XzKuHMz0V5aaRKFcfJ//dSOA64eeM8lJXw2SMsFi7SJ5JrSXpvkLXFceFjra1yhdsBgAIHf31xFg6tpxo15cmxWbFicQ1sNnP3n9g9TExq1DINALzbgIPcBghiQlNYkId1K+sBACfPdmHxvEqMur3IybEiL5e//V9s7ses6XyN9x17LiAnx4KlC6rCSq9n3I/G8z1w2G1oahvEtDInZk4vkSnFIp09I2hpH8Sa5fV4+bXLYN5cmULp8wVg4ThsXD9Ltl1vvxtFLjtePdgcfgDevHEeOlq6UF1fGV4vxBgsgmZz4FgrnPm5KGAVsJfko76aVzoa6krQ2jkUVpSk+AMhLF0QsaQ2tQ1gRm0JAGBBqR8XR6zw+YNw2HNw7bqZGHV70XzwIBz2HHjG/ZheW4yFc6bh6Kl29A96UFaSj2ULq8PtHTzWipLifMyaXorhUS/6B8bQ1jWEYpcDi+fxx9HVO4pzF3lXhKvXNuDY6Q5UTSvEicYuMMZw3YbZ6OkdRXP7INxjPnh9/PCtw56DGbXF4CwcxsZ8yMmxoqbShZb2IVxq6Ud5aQGWX78I3lwXvL4g5s0qBwBs230O9jwbrlk7EwDw6sEmVE0rxMz6Uuw91Iwxjx8F+TkIhRiuvGIGxr2BsEJy7HQHli2sxojbi5NnulBRVoC8XBvqquXVkc7tO4CbN84Lry9y9HQHXM48zKwvhWfcD5vNglVLalFRxitX218+jyuvmIF8Rw4O+HuwYM60qHMWCIRw4kwncnKsmD2jLCzbqwebEGIMG1Y34MyFHpSXFqCsJB+jbi/2HGiCzWpBIBgKX399A2My5QoAhke9cDnzEGIMh5/bhmKXHYvnV8IfCKKrdzT83wGA5vZBDA55MH92BXz+IHJsVry092K4X7ftPoe8XBuWrZ2B6rmz0NPnxqlzXaiaVoiBQQ/8gSAK8nMRCIQwrcyJhvqSSD+dase4N4B1K6ej8Xx3uB/OXOhBZ88INq6fhX2HW+AqzENDXQkc9hycPNOFxfP5a+picz8KC/JQUVYQPjdnL/aivqZI9oJ67lIv5s4sR1vnMC4294VlX72sDrv2XcK16/jpju5hHB0bgrMgF+UlBZg/uwKdPSPo6h3FcuH87nz5DDZfvRi5djtaO4airgmxzwcHR7Cs6lrMWD5PtqylfRD1NcXh6cryOdhz4DJm1JZgeHQco259VxSO6bypKTlw4ABbs2aN4fWJ7GfVqtl46m9fwKyZ75PNf9/7bsItW67Am9/07QxJRhDJhTF2EMDqTMuRTlo7mtnPHv9u0trzjIzAUVhoeP1xtxv2AmO1ytUYGx5Gvsulu05/ewdKa6p11zFC0/GTmLF0sUxBBoDWU42oW7TAVFveMQ/y8qOV+1TQ396BUTgwvaYYl48cR8OKpXG1M9rfD2dpaVzbJrJfaRsFJcWomFEfe+UUkIxjAICOcxdQPXe26e2CgYBu2d10M9LXj8Iy7eshGAggFAzGzFsbDATQerIRM5YviVo2NjiEfJ0ysd/+3E8179nkNjDFUcs0AJDbAEFMBkbdXlxoir/60YkznbJppe9ae9dwZFlLG8aGR1TbudQS8cV8+bXLeH7XWfj82gEhyv3q4RkZ1Vx2+EQ7/AF+P01tEf9btT4Rh74tijFrxhjcA4Ph6f7BsZgyed1uKA1DQ8P6fqGDnd1R86RtDPeqB46N9vVjusSCpUdPn1s27ZGcr9H+QUNtAMCufZfQ2aN+rqXXhEiXsK54PE2tAzI/2f2HmwAAg328DLtfu4TT57txuSW2z/SYxy/5nfzAsQtNffD6ArFX1KHlxGnV+V2SPmShkKxPjRxL4/noa0aNfYebNZcdPC53ixHblF57aucUDDh2qj082T84hqOSaRGrzYbu7kHVfff388d79qL5oMjsUfOJjKCW4xWgIgUEkWV8D8B/AfABuADgLgCDsTYKMYbzl/vQ2+/GoKA8DbY2Y8mapejpGw0PSQPCMH33MMY8flxs7oczPxfDo150do/AVWiHZ9wPf3sbTvXK/fQutfTDarHg4I5XUDWjFhs2rUBL+yAsFgvcg4Ow5BXA7fFFPaB2vnIBNpsFgUAI9jwbxr1yBaGrZxQONoarrlmGVw82wecPhtfJy7GgtKQAXb2jWDSNVwIutQxgaGQcxS4HFs6pwOnTLRj0MOzYMwqLhUMoxHDmQk/Yle/8ZbkCe+Q/L+Ldn6xHaZEDR093oH/Qg6ULqsAYwwsvnUJhRSUYYwgEed/Y3Bxr2F93WrkTPf1u+P1BDA6Po6epGQ6XCw2za1HgyMH82RXYKygQDnsOptcU48zFHtisFqxeVocTZzrhxBiOXBpB17lzuPkNG3HkZDtm1JVgZj1v/QoFQ9ix5wICwSByc6zhcxdob8L0pYvh9QXw17++jNc5KzG3oRzb95yHzWrB1WsbsGd/U1iJXzR3Glo6hpCXY0VNiRUOF29J373nNCr7OCyeVwmrlcPZi73w+gIIBkPYdCVvSTx8oh39Q2MIBEI4eqoDR9GBuuoi/PPPu7ByKPK8ON7YGT5WzzivXFaUDWNkyI2NG+bh7KVeXLzgw3UbF+PgsVZ0945g+/ZdyCvIx3JPPvyBIJrbBgEAr7x0CEuuWIQRtxcsBLg9PlgsHEqLHPD6gxgZ9aKy3AmbzYK2zmE47DmwcBzKSvNRXlKAs5d6EAiEsH7VdJy92CtTxHJsVjAw1FcXIxfA7x9+BtPm81b21cvq0N41jPauYZy/3Bf2Ba2rLoJ7zIe+gTHkO3JQWpSPoQuNCJZPx5jHj5ajR1HfHoQzPxcb1jTg6Kl2WAZGcOqlswB4X2OfP4hQSLyWOlBV4URrSzds9nw4VubA7fGF+3DuzHJcau4HYwzBEMOM2mJYLBY44UZTmxdNbYPIy7UiN9eGkVF54LXNakFtlQuDw+PY+tJZWC0cGIBQiKG8tAC9/W5wHHD6fDfGxnzoHeBfzDgLB5t3FPsb+ZeHQJD/j54+143RMR84DvCN+wAw7HzlAqxWS/g841Q7hke9GPP4sXH9LLR3DWPHjmPoz424hJQUOTAwxAdyVXWMIxDgFfeaykJcaOLvPaNjPnz7c9CElNcpTmGhA8PD0corWV4JIqvYBuALAAIAHhB+69za5QxKrH6e8QBaO4ai1tkqPFxFhoUHYTDEMDDE3yOOHb6IZTdOl60n9U3z+oM4eiqSF9U96EZBsTygRUogwCuCSsUV4B+Yl1r6kFfcFpZFZNwbQEc3b7XZtvM0ahfzD2T+WD149VAz3IODKCguBhBZpuclxxjDv57aiZKqSjhLeX/E442dOLZtN2oXzYc9ILcU+/zB8MNe/A4v84zDlpuH3n43egE0CYoYwPsQn7nYEz5GUak9d64J1XNn49KpC/hz3whmrlqOsxd7cfZiL+bNKsfhV0+hoLIGAGQvHSdPNqPiSAsGhjwIBkNobhsMK36BYAgvvnpRJtupc7xlbQTAmeNtqGoYQyAYRGfHAFwNfhw4Fh0Zrrw2pKhdS9JjFbnQ2IS2xrMYF0714JBHtV2/op89o2Phcy0SCjFZn3f1Rqzv4j7dbb5wPwCI6gfpvi619OPwc7tQUFIMUcVS9oN4DUnbdI/5MObx4/CuE1i5hXdd8QgW09ExX/j4Dj+3Hyu3bA7PV9LZMwqfLwibHeHrQeTcJflLn3gttZw8jfrFCwHw14P0mhAJBEOyay8oySDQ289b4RmTHxMAXG4ZQMf5i6ieE/FN33800h+MATl5fECWzx8EJKMonT2Rc/HS3ovC+vI/nng/ASL3AM+4Hxea+BEatT5SQm4DU5zCQodmwBZZXgkia3gevOIKAHsB1MXTSMf5i4lFcmtof36vT3f43gznXjsgmxYfsjIkBzHu8YUVi3gZ7OIVut6mFowNy4dIGRj6W9tN1XHvvtQkmz66bQeObtthePtgQK7Mn73Yi+ER/gUkKk6FyZUBJZ5R7fPiGRmFPxAEY9B0+UgW46Nu+McjLyHS4+htic7oIMoTTxWnkT79lGEDHV2m21RDLRfq2GBEme+6eNl4Y4yhcc/eJEiVHJiBPK8AMDak4lJgkP629ujr2SCkvE5xXK58TeWVKmwRRFZyN4Dn4tmw89yFhHbMoP6gObHjJQS8XnScPa+6/PxrB421HwphtG9AMs0/QN2DQzj76mtR64+PujVl0nsm9jS1yKaLK6Oj3CMyMIz2D+DiwSM6kusTCgQRCugrA9JUXeIxqeVPVRYEiPXw9+gopT2XI1Y+ZaqwUYmfb7z4vXKLudq5CoVCmtdNytDoM++Y+ktAwO+Pmnf4uW3gVCpMSc+H1x2xDsdS8hgLwTM8giNbt+uuF1nf0GpxwxgLv9TpkUiOX/fgEC4fPhbXtqS8TnEKCx0YGY4OQCC3AYJIOy8AOKHyuU2yzpfAW2Cf0GnnHgAHABwolGQGaDomJuFPwPQa44EptawlvC/wfp6Hn9uGs6++BvfgELovRyyavc2t6Lp4SfMhrlTGRBr37JXlnYxJnFqCaDD0jMS2aJ5++VV1JVQyT+t4UoXUgmgE6bkRUbMSG0V8cTFimVMqybFQU0YBIOD1hvO9SrHl8K4vjXv2yeabubrPvLJPd7l4mEbPs19DaTz10h60GLi+Y40klNbV4PKR47EFEQQXq7ulq8obKa9THD3LK7kNEERauRHAEpXP08Ly9wB4HYB3QF8NeBh8epnVIxLFqb+tQ7ZS4559aD19xpSAaorEyRd3x1zHMDGGiMUhZMYYWk6ejjomKaP9Azi37wCObdspm69niVRDeTxNx0+a2r7x5dhDweNKlwuVLhzs7FKVJ9kmuLGhYXjHPBjo6DSmvAj0t8XOECGVPZY7gHvAuPJsRs5ECAXl7hxqlldAUtjCjMuDynns0Bgp6b7cBO+YetYL79hY2J1hoF37nEjPhZoyz4EzpEgrK3HFctlQYrHFV6iBAramOIWFDnR2Rqcjcbu9sNtzYbFYEErzGz9BEFHcAuCzADYCiJ2ryQCe4WFYzVb4MaAoHXthJ5wlJZi9eqWppsdH3Wg+cUp3HbvTCUBhxdKQqbe5NWqIXQsz1iK/JzWlUKVWa1Fu6aGJSoLSH1akWSMdk6F9eyP91Hr6TDg1WK7DTK5aA0q0CUW78/xFTJs5w9C6wYDoXhIJ0lPdfSikqXAawqD48bxPqL30qVntT774MvxeL3IdCY6MSvbnHx8PW5fDiw0ebIjJ9QOL1dw9RQyONAtZXqc4hRqW17Ex/kZKfq8EkRX8FEAh+KwDRwD8MhU7UQYaKREfsHq+cKFAECFhyHdcCBYyojAM9/TK8qlK9yciJk2PslSqIFVcpcqZGtLdxLIIGn2om2WgoxPHt78EgFfktbiw/5BcHkF4LZ9KaR+qWcVO7NyFxpdfNS2v3n60LHZM3tH8l1Z7Qj+rtaU0qNhyecUrltVPVPy1XgASIaERBxPb+zwevk8kqyuDHMNtCiudemmPbpucJVrhVFpUNRHkHuri3RA4LvJfD/h8MY1fFmHfZl0/SHmd4mhlG/D7A/D7A+Q6QBDZwRwA9QBWCJ97U7EToxH1Po3AFiUtJ07j6PM74Ch0qi43+8BSY7jHSILzFEe3qBFHlLwRS3G0X7Gc868dxMVDRyIzJEpR8/Foy7Z/3CvbbzIUu/B1ZEAh69Nx/Ti7dz/6VDIRhBQy2p0FRneHxj170Xn+oqbCBwDjbvODG8O9krzB8ZleTa0uu1YUm7oHBhGUZAvQcjEQuXjwcNQ8o/9NUcntungZx3fswkhfpB9O7XoFJ3fu1tqUl81j7F6ihJTXKQ6f51X9wuYzDlDQFkFMVAI+v2oaIhFOYfca7R/A4ee2yaxdJ198OWo7uZVIRUljkfVCwWDCVimzKH340r1/gPdRjUcJkmJObn7dkb7+sBVMax0tTuzcJbdqm9m/ZN3OC5f5HyoKvDLzRIuWqwjjlTC1PlDOEv2BlQv62tox2i93i/MMj0RltVByepe+pdIoQYNuKwDgV1lX+f+UtR0I4PiOXQCirxOvewzHnpenZtO7lrwq12l7o3ZuX3m7kXtFwOuVWWyDfn/MF7KBjs6Y8qlByusUx+VSt7wClHGAICY6Po8HI73ySlJG8mZKh/l9CsuI3+vVLFWaLM7t461iSgtvf7u6lU5vmF0L6XCmvEsiE2p5N6WWTzEyXW24eqC9U1UJUvanHhZrch/RUgVBLao+llVXyuWjx+XpvWTKR/jtJWq52FexLkNpe9IofxYKybbtvHApLHdU2ibGMNKvraSmCql7jahgSuk4H10wQZouTOs6V9mRbDJoMDerEYxa4JVKp+gyZBQtt5Cze/frbkfK6xSnsDBf0/I6OjoOp5OUV4KYyIz2D8h8VGXR9nEMbZ/YsUvXagVED7sasapIFY/R/gEc3bYjErUt0HT0BE6pKISi9UayQ/V9SBRHqUwtJ9WzLijlPrpth6qVKhgIoPNCtEKiRiz/Wym59uTef80aoJX+vdI8qAPtnfKl0uAyxY7O7z9kuH+0JBH3eVGRF1Qcpj6+/SX0t7YDUC8ekAoa9+zF6Rj+wgGV4XdVhU2SB7jpqJDWTvh/agUUhvtZ+DbyYsoYQ19rGwY7+XvC6d2vqK+n4vPacf4ihrp7ZEUeDPvGauB1j4VfVqUyKv3flVC2gSmOnuW1p2cIFRVFaZaIIIhkEvD5cOnQ0fD0QEdntLKX7H1q5MvU4vj2l6KGF7WS+qspj0aHHC8eiig+4oP+1Et7tH0ClVYlnUIDsYoQSBoN/xrp648Meccg4PNF+SEq846q7o3x6y3YsM5w1SSjxOp3cbHS+g/AiOk1aj99rW2aQVni9dO4Zy98nnEsu3GTfvsx6LxwCVWzZwLg/Vld5WXhIXLRv1X6Iig/Gv1+EYPrDj+3DeUz6lG/aIFmXx7dtgOhQBDWnBxMX7IwvoORicbQfPxUOMpffGn0e33hkq+AuoItLXJSUr1ZaE4rn7L82jy//xDmrFmluq7StcMIZHmd4uj5vHZ1DaKysji9AhEEkTbiKb2pxMgweCioH31uNKWVFj1NzbrLAz71pPRAdDBL3H1icDvGeIWkt6UNnecvore5NeY2HecvIhQM4oRiCNozrF21Sezb3uZWeIaHcWzbzpRE2Sv3J0zE1YbeeTKKZ3gEQY0iBEqUln0p0mH80b5+eEZGTLlVqCEqvaN9/eGXuyFhVETNWsxxXPilKOj345KyGpWin5VXoN7LhXKZss+MvhA6S9RTXSmvTekLzNlXXzP04qUHKa9TGIcjD7m5OZrKazcprwQxuTGocCWSQxSA7CErtch2X2pKSp35WFZPsbpYnsncmFoPcOn8vta2mMqziN/rw2BnF0KBIFpOnDJscRrQicgHeMuV0iI97ub9gEUlIh7F1ZwOqhZYpd2Ae2AI7YqysEG/P5xY36gyrBfUBI4LX+Id5y+iTRGEpJZ9QY2+1nY0vrw3apheC83jFuZLl/vHvaqlj3XbEZebyKIhvpR5tApiKO4FRit9xZM31z04FKXcmr0XkNvAFKayshgjI2PhnK5KOjsHUFUVXwJhgiAmD8rKQiLxBEoB/BBiKBiEe2AQPTCm+JlB+cwXlVsuSQFQXvdYOD2TqABpWaCknNjxkqn9jA0Nq5YVdQ8Ooq81otAqfQYB4Pxrh8L5T1PFxYOHYcvhh5qlipZVSHjvGxtD+xn1qHUWCqHrwiVD+1EqcWPDI3CVlxnZMKzUd2pUqzr54ssxre3KkYGgPz4Ltk/DcutWKcV74eARjBhKA2cOtWtFStPxk5ixdLFhy6tyPb2XiZaTp1G/WN31oedyM3rQHHZHiAUpr1OYqqpidHYOai7v6hrEkqUNaZOHIIiJw9Hnd8QdGKPqA5lC1JQTzYezdF1NA5q2VcporlwjaBUeOPuqfiQ2wPsdqwULmUHrpUUMmpMG7smTDUQmtNJ2qZUkBaIrNvHtyScvHjyCukULUF5fq9pGuK1gCP1tHeHgJDXMZH8AeB9pv0+/X3ubWtX/GyZM2cNGriMNq6ne/pRyKf8Hg51dqJ0/14iIAPgX2GS4H5mFlNcpTFVViWppWJHOzkGyvBLEJCaRZ048iquZIK54GO0fgLO0BD2X1SuFSWVuPdWIvPzo8qed5y/AMzysaSECgAsHDkdnAhA68+LBI+YF1yIDSoGU0b4BDPf0wlVRLpuvFjQnVZRCQgS6lqp28sWX4VemtRLoa25FWW2NommxvUjVLWYgJZM49J3M7AOawX2Sc+UdG0PHWXVLbzJJZv5i8YUsFAiGK70ZwefxaOfqjdqHsTaNKMOkvE5hYiuvA6iqKk6fQARBZCXDPX2GAosyTW9zq/qwqPAslFoBxbRKSvzjXvS1tqN+8UJN5cA/7k04eCcW/e0d6NcJKEoXFw4cxsJrrgq7SRjFO+bRtLrqWTtDqqmXmOyLR1/BaTp+EsPdiQ27D3R0oaS60tC6FiO+n8l+GVFcn3q6YW9LmyybgJKmoydhd+ZrLj+qKHogMmA0Jy0QdyCfGqS8TmGqqkrQpaO8dnT0o7q6NI0SEQSRDRzfsQt1C+eHH9xBvx8tJxMM2sog8foomiHZekk41+cEQanon3opujJbstoGItY5rTKmWi8nZjBzTvvbOsBZ9DfIpCFdu5IZ37ee4WHN7BXDvX2a1msz1t/+9g5DmSCMvAiQ8jqFqaoqQXOztl9NV9cgCgrscDodGB2Nr/4wQRDZi1ZwRcDrRdfFy8l72Kbpqa31IA34fCl3WZjypKEEr/T89jQ1IxgIxB00mGz6Wtt0U2+lAuX1nooyyKd27VEdZTi37wDmrlttroJwKCQrmJIIlCprClNZVazrNuD1+tHfP4LqavJ7JYiphmd4ODqv5JTD+JM5Dbpb5jBSuclEX5kl3LeSTh4fdaP9zLmU7RNA0l+6eptbU6bgDnZ1o+eyicwdBi9Yr3tM1eoqq9SXAUh5ncLE8nkFKF0WQUxqMhwQlGwSLXaQEJNYew0JOWK1gqwAgGkUojCLZ3gYl48eVzSemb5NdlGHsaFhw7llzcBxHC4dOiorrGBElrbGs3Fba8WXlUzdQshtYApjRHnt6BggyytBTFI8I5m1niSTky/uhs+jrVzFg5kH+2BXNwrLJmeMwPn9h2C12RDw+2CxqqsNyRyyFgsVKNtOxbC4Hq2nGs1ZMzOEdjEN/W26L6ln5TC0T+FlJaoqWpq0WbK8TmGMKa/9ZHkliEmK1z2WFl/QdDzPkq24mmV8ZDRmAviJStDvh8/jQSgQNJQ71mh5VsNkyPIaCgQzPjxuBK1gqlSORDDGcPi5bYYrcSUbsrxOUYqLC5CXl4Pu7ujKHlK6Ogcp4wBBTHIGO7vg0xkSJgijhEIh04n/Y5FKf9qJztm9+zUV7M7zF9HflnjWhWyELK9TlKqqEvT2DiMQoyZ4R0c/KsnyShDZwqfARxGVx1rRDJcOH0PbafUynklhgvrWTmI31pSRaQv4VMM9MKi5jIVC6gUlUsj46Gha3DtIeZ2i1NdXoKUldvm5zk7yeSWILKEewE0Ast8JjyCSCFMtXEBkI/5xL47854W4t/eMjMA9OBhzPXIbmKJMn16hm+NVRAzYsjsLkGO3I8eeh5w8/mOxWWGxWGGxWmCx8t+cxRr+bbFYwFksfCk/xsBCIYRCDIyFwEJMPj8YRMDnQ8Dn57/9fgR8fgTFeX5/8v2oCGJi8RCAzwJ4OtOCmCUTtc8TYaLJa4bGPftSaghPZd9N5vNC8DS+vNfQeqS8ThE4iwX5RS4UFBehoLgIq65ZBV+OE9fd9Q4UFBcjv7gIBSVFKCgqQkFJMRyuQuQ67Kh02TBnzgjuf/UFBPx+viyi14uA14dgIAAWCoW/Q0FeCQ2FgmDBEEKhEO/MzXHgLBwsnAWc1QKOs4CzcOAsFn6ehYPFZoMtNwe23FzYcoRvYVpKtILrE5Tc6HkBnw9BQQkO+HzwjnngGR7B2BBfSWQs/HsEY8PDGB9Jz3AHQcTBbQDaABw1sO49wgfl5Un1Lpg6iDrSJLwfaFVRymom4XkgEoOU1wkGZ7HAUeiEw+VCvqsQDlch8otc/LfLxSuoJUXIL5Iro/ZCJwBgbHAIY0PDWLWIQ1PHCCoaZmBscAg9l5tw+cgw3IOD4XV8nnHYczncee7neODGW9HdpZ+ZIFVYbbawMmsVlVqFgmvLzYHVliOfVijCefkOlNRWoXbhvHB/8d98P4ZCIYyPjMItHP/Y8HC4L/hP5Ld7cDj8e3xkhJReIhm8AKBKZf6XAHwRvMuAER4WPujt7c2KC3OiWcxCgSD8Xh+N9mQJdH8llJDymkYsNivy8gtgL8hHnrMA9oIC5BXkw+4U5hVIvp0FMgVLqmQBCFsLx4ZH4BE+Y8PD8AwNo/PcRbiHhuAeGMLY4BBGBwfhHhiSKVn/feiH+N39v8d//nMwptzbtx/FXe+5AQ888FRK+0eLYCCAYCAAbwr9zi1Wq/BSwL8M5BcX8S8CRUVhi3VFw/TwC0J+sfDtciEUCoUtumODQ/x5GRqGe3AIHkEJliq74jpk6SUU3KgxfymAmYhYXesAHAKwFkCnxjZEgpzY8VKmRSAIQoNJq7xyHAfOaoHFaoPVauX9M638RzltsfHrcBYLLDZhudUKW26uzMdT/G2zy6dlvzXm5eXnI9dhBwCMu93wusfgdY9hfJT/Pe5287/H+Hl9re28Mjo8wg9xD4kK6ggfzRdnbrUlS2bgv//7asyfX4e9exsNbfO1rz6BbS98ExcudOCpp/bEtd9sJxQMwj04BPegfuowJZzFwr9UFLlkym5Y+S0twbSZM3jruKAEiy8kAB+Z6x8fh9fjgX/cC5/HA9/4OD/fw3/7xsfh93plLhBBfyDsJhH0+RAQp328b7DoPhHZRpzPr8OCITDGu3rwvsihjOXrI2JyHMA0yfRlAKsB9GZEmjgI0bVFJIMJZsEnUkfKldcvbf07r0hyHO/7CA7gEJkWLkbZOpywDhTTHAeAi6wLyNsS27bwwUJqBPx+hAJB3jczGEQwEAj/DgWDCAWCCEqW+T284uD3esP+nuK3z+OBe2AwPC8gXUexflhhHfNkVEmori7FtGnFuP0N92Nw0G1om/37z+HeD/wMDkdu7JWnGCwUikvptVitsDsLkGu3I8dhR67Djly7HbkOB3LsduTm89M5dn5Zjj0PNlsO7M4C2HJLBHeIHFhzciK/BTcJq+hWIf4W54vr2bT/9qFQSK7YShRcxhjAmGa1m/C0dLk4CyrLZNtI24teFmmPofVUI/7wua+Z6m8is/Q1t2J8ZDTTYhBpwOs29lwxS397x6SqCEckBmdm2PLAgQNszZo1pnZQu3Be+MHE74vxzy9xWvEwFOfx08J6kKzHIg8xpmwLkd/BQBChYCCskIaCQRqiJQjwL3echQtniuCEQDoxO4TFYgFntcIijl5YrOEAO0Duvxh5ieQ0psNrypZLXz7lbUqWK9sQfvjGPOg8f9H0cTPGDoK3WE4Z4rlnE0S8WG02GsUhkobePTvllteUJr4mCMI0LBQCCwEh6BeoIAiCMEMwEMi0CMQUgYoUEARBEARBEBMGUl4JgiAIgiCICYMpn1cAPQCaUiSLknJkbzRtNssGkHyJks3yZbNsQHbLNwNARaaFSDMjAM5kWog0k83XYKqgY578TLXjBXTu2WaV13RyANkbXJHNsgEkX6Jks3zZLBuQ/fJNNabi+aBjnhpMtWOeaserC7kNEARBEARBEBMGUl4JgiAIgiCICUM2K68PZ1oAHbJZNoDkS5Rsli+bZQOyX76pxlQ8H3TMU4OpdsxT7Xh1yWafV4IgCIIgCIKQkc2WV4IgCIIgCIKQQcorQRAEQRAEMWGYCMrrpwAw8DnOsolvADgG4AiA5wHUZFSaaL4HoBG8jP8AUJzCfV2G+RQebwFwEkAIwCcB/FiY3wDgg8kSzCS3gM+JeR7A5zMkgxa/AdAN4ESmBdGgHsBOAKfAn9ePZVacKU82X8tmUbv2SwFsA3BO+C4R5nPg7yXnwd/7Vkm2ebew/jnhdzaj9X9K1nFfRmJpl16P5N+z7QBeA3AU/DH/rzB/JoB94I/tLwByhfl5wvR5YXmDpK0vCPPPALg5CbKlEiuAwwD+JUxP9uNNDoyxbP7UM8a2MsaaGGPlWSCP9OOS/P4oY+yXWSCT9HMTY8wm/H5A+KRqX5cZY6tNbrOQMTafMfaiYttNjLETGegvK2PsAmNsFmMslzF2lDG2KAvOo/i5ljG2KkN9Y+RTLcgHxlghY+xslvXfVPpk+7Vs9qN27X+XMfZ54ffnWeT+ditj7DnGGMcYW88Y2yfML2WMXRS+S4TfJVlwbFofrf9Tso77MjN/z9b6bGLJuS9xjDGn8DtHOIb1jLEnGWN3CPN/yRj7H+H3B1nkuXsHY+wvwu9FjL/m8xhjMxn/X7BmwTnV+nySMfZHxti/hOnJfrxJ+WS75fUhAJ8Fb3nNNoYlvwuQfTI+DyAg/N4LoC4N+5wFYCv4N//jAN4vWTYfwCvg36ifBvBdAP8lLHsdIm+djwCYDd6ivTvG/hiArwE4BN6q8KkEZF8L/s31IgAfgD8DuC2B9pLNLgD9mRZChw7w5wHgqzqdBlCbOXGmNNl+LZtF7dq/DcBjwu/HALxBMv9x8PeGveBHnKrBW6O2Ce0MCL9vSaHMiaL1f0r2cZu5Z/8TwKeFZe9B8u/ZDMCo8DtH+DAA1wN4SuOYxb54CsAN4C3Qt4G/5r0ALoH/L6yNIVemqAPw/8D3IcDLP5mPN2lks/J6G4A28EMI2cr9AFoAvAPAVzMsix53A3guDft5AsCzAJYB2AzgPgBXCcv+AP7muhjAJwBs0mjjfQAuAFgB4BoD+3SCHyK7CvzQ2tXxCA7+wdAimW4FKV/x0gBgJfihLSL9TIVruRK8ggcAncI0oH3sE7lPGhD5PyX7uLPtnm0FrwR3g1e0LwAYRMQQI5VfemwBAEMAyjCxzvUPwRvoQsJ0GSb38SaNTCuvL4D3Y1J+bgPwRWReIdSTDwC+BN436QkAH85C+QBexgB4GVOJA/zb3i+F6U4AQQD/B95vazWAjwjyLQWwPUn7/ZXw3QPg7+BvwETmcAL4G4CPQz46QRCpgiH7Rr6Shd7/KdHjVrtnPwXgJgAu8Armb4RlF5Gee3YQvBJcJ8i2IEn7zEZeB15JP5hpQSYitgzv/0aN+UvBOy2LVtc68MMMa8H/wdKFlnxKxLfXr6VQFjViyfce8H+QG5C+m7t0P/8AP1T1A+F7JfhhTFG2bKIN/IuISJ0wjzBODvgH7RPgH0pEZpgK13IX+GHxDuG7W5ivdextkFsO6wC8mGohE0Tt/5SK4zb6bEjnC8Ig+IC1K8G7QNjAG2Gk17J4zK3C8iIAfZg41/8G8IFvt4IPVnMB+BEm7/EmlUxbXrU4DmAa+OGSBvAnaxXSq7jGYq7k923gI/uziVvAD0e8HsBYGvbnAR8p+gFhuhLAG8H7Uw2Dj6Z8j7CsAbxCrcYQ+D+lUUQfrTIAt4MfaoqH/eDP6Uzw0Z13AHgmzramIhyAR8H75v0gw7JMdabCtfwMIpHz7wbvkynOfxf463E9+PtJB/j70E3go/NLhN9b0yivWbT+T8k87my7Z1cgkhXHAd4iexq8EvtmYb7ymMW+eDOAHeAV7GfAX/N54P8Dc8EfZ7bxBfCKZgN4eXeAd0GcrMebXDIdMWbwc5llX7aBvzE+wvIYY+yfjLHaLJBJ+jnPGGthjB0RPqnMhnCZ8ZGrsxifHeIYY+w4Y+z9knUWMsZeZYydZHxU5RHGWD9jzMsYG2KMdQvrWRljTwvr7Y6xX8YY+ypj7BBj7Bxj7FMJHsetjI/qvcAY+1IWnEPp50+MsQ7GmJ8x1soYe28WyCT9XC2cj2Mscs3dmgVyTdVPNl/LZj9q134ZY2w74//3LzA+mh6Mj1j/mXDcx5k8ov5uxt8XzzPG7sqC49L7aP2fknXcl5n5e/Y2xtj7hGXvYZHo+GTds5cxxg4LspwQ1oMg42uC/H9lfFQ9GGN2Yfq8sHyWpK0vCX1xhjG2JQvOZ6zPJkl/ToXjTfhD5WGJdFEA/k0/BN6ZfD94K0AieUsZ+Lf13oSlIwiCIKTQPZvIWjLt80pMHdYjMvyVAz4BdbYm3CcIgpjq0D2byFrI8kpkO88AmK4y/0rwVgGCIAgie6B7NpFySHklCIIgCIIgJgym3AZ6enpYU1NTqmQhCIJIGatXr+4F7283ZaB7NkEQExW9e7Yp5bWpqQlr1qxJilAEQRDphDE25bQ4umcTBDFR0btnZ2ueV4IgCIIgCIKIgrINEBmHs1iQV5APjuMwPjIK8sMmCIIgCEILUl6JtMNxHOZvWIcl12/E7NUrUVpXA1tODgAg4PdjqKsHXRcuoenYCTQfP4mmYyfhdaejSBhBEJlmwdXr4R/34sKBw5kWhSCILIWUVyJtcByHVf/vZtz0P+8FOODwc9vw1NcfQPelJrgHh8AYg7OkGMXVVaiZPwczli3Byi2bUT6jHk3HTuDMy/tw5pW9aDt9lqyzBDFJcRQWwlFYmGkxCILIYkh5JdJCWV0t3vatr8JZWoJ//eCnOLFzN1goFLXeSF8/Rvr60XLiFPb9jS/H7iwtwbyr1mL+VetwzZ3/DQA4++prOLFzN069+DICPl9aj4UgiOwjLz8fDSuX4cyevZkWhSCIFEPKK5FyVtxyI978lc9iz5//hud/+RsE/X5T24/2D+DQv7bi0L+2guM41CyYiwUbrsSN73s33vK1z+HIcy9g9xNPovvSlAsmJwhCwFlWinwXWWyzlYKSYsxbvwaHn9uWaVEICeUz6tHX3DrhRjNJeSVSypaP3ou1t78Oj33yizi370DC7THG0Hb6LNpOn8X2Rx5Dzfy5WPfG/8LH//wbHH/hJfznpw9joKMzCZITBEEQycJBLxZZSf2iBfAMDcM9OJRpUUxBqbKIlGCxWXHHN7+MZTduwo/f8b6kKK5qtJ85h398+wf4zuveCu/YGD711ONY/+bbUrIvgiAIgphsZNLqas3JQVldrentSHklko4tNxd3//i7qGiYjp+88x4MtKfeEjrc04u/3/8gHv3wp3HdXXfirh99B7kOe8r3SxAEQSSHvIJ8WG00IBwvefn5cW2XSY+BkpoqTF+6yPR2pLwSScWWm4v3/Og7sObk4Jfv+wjGhobTuv9Lh4/h+29+FyxWGz7425+jsKw0rfsnCEKfvIL4HrAxmWA+e0Q0i67dgPol5hUZM7gqylN3DWaYRRs3INfhML1dfpErBdIYJM7/LSmvRNKw5uTg3Q99C7acHPzmI5+Bf9ybETl8Hg9++7HPofn4KXz497+Cq6I8I3IQxFShfskilE+vi7mes6wEi67dkBIZbHm52styczP7gCYMY7VZU9r+7NUrMXPlspTuI5NY4ui/TBp54n3nJOWVSApWmw3v/v79yLXbM6q4ioSCQfz9/gdxetcr+MDDP0JBcVFG5SGIyUx5fS2q5syKuV5Obh4AvqpeshELnagxc+UyzL9qXdL3SRDZBsdxptc1sUnWQMorkTAcx+GO+7+C/OIiPPrhz8DnGc+0SGGefuCHaD5xCu//5UPkA0sQGYaz8o8czpKCp6VOk6lQlokJjI62VlpbnUZBko8Z5TXcD5nUXsltgMgUb/j8J1A1ZxYe+dCn4PN4Mi2ODMYY/nrfdzA6MIi3fuPLmRaHINR4C4CTAEIAVuusdxnAcQBHAKQmfUeK8afhxVbt4W3qgU6khgngk2y12TBj2RLYcnkXlJKaqgnnH2vmRU18iUzG/6OgpDit6dBIeSUS4qZ778bCa6/Cwx/4OMZHRjMtjiqhYBB/+OxXUTNvDm5437szLQ5BKDkB4I0AdhlY9zoAK6Cv5Moon16XNZbHgFCghNMzk8aA4zhUzp4ZNX98dExcIe62iakNg6BgC9dQw/KlqFu0IIMSGUdUQO1OJ1Zu2WxsGyTP8jpv/Ros2LDe9HbhPjdJdtzRiAnJhjvehCv/+3b86v0fw0hvX6bF0WV8ZBS/+ehnsemut2PRxqszLQ5BSDkN4EyqGq9fvBDO0pJUNS/B+APQWRa/PA5XIWrmzYma7x1z81KoPYhJoU0JdmeBYUUpm+DAIS8/P+paSeSlKlswFZgo+rymSBYzmLX+kvJKxMWS6zfilo/cg4fv/QT6WtsyLY4hei4344+f/1+87f6voKSmKtPiEIRZGIDnARwEcI/OeveAdys4UF6enZk2qlWUT6PEHH0m3TVhLFaroah1R2F8w8TV8+Zg5qrl6gvTdLIWbdyAmvlzY4pgVqmy5ebCqhM8mDIEOS1WE24DovKagpEZ0zEmpLwSqWb60kW445tfxuOf+jI6zp7PtDimOL37Fez7+z9x5wNfjyulCEHEyQvg3QOUHzPl4K4GsArAFgAfAnCtxnoPg3crWN3b28vP0dD47M4CE7uPH47jhAc6/4ByOJ1YcDU/xFhcVWmyNRZuU2W2qvXMJmQ5IHjyCvKx5PqNmsvnb1iPRddqj1CFs7eo6Bvzrlwb09JfMaMexZXTDMmaSvTSqxllwdXrZcUB5m9Yh8WbMje6ZzGjiIZdDZJ/H1i86RpjIyxM4/8cA1JeCVOU1dXi7p98D08/8BDO7d2faXHi4rkf/xIWqxU3f/D9mRaFmDrcCGCJyudpE22IQxzdAP4BYG2sDSwa1YrE4d6F11xlYvfxU7NgLpbduEk2z1FYCIfLZTrnJguFAEQyF0QhPASdpSUoKCkGAOQkQUmZTDhLSnT7xF6Qr7m8sLwM865cC3uhE7n2aOtaQXERiqsyr5jqIlwjUWVR47D6OgoLZcp6rt2ekSphovKn9Z9X34b/zsmL7+WusKxU123ESLvhc2Cy60l5JQzjcLnwvp9/H68++Q/sf/rZTIsTN8FAAL//7Fdw1Vtvx7wr12RaHIIwQgGAQsnvm8BbbnWxa0RK5yvyHttyc1OaqFyr6k9BsfnCAeLDzmJRHzkRH+Jz163GvPXy/7eZ4dHpSxcZ6hNemZs69xGrle/3hVdfqTnsHqUUZisacpqOvo9H6XUVYukNG1FYXmZ6W3UZ+C+XifaUx1k1ZxYqZzVErSd1AcgvciHHziulBQoLu5HzXlBSrDraUjFjuhGRw5DyShjClpuLu378HTQfP4WtP38k0+IkTH9rO/729e/ibd/6WkLBIwSRBG4H0ArgSgD/BrBVmF8DQHxLrATwMoCjAF4T1vuP2R3Nv2odnKUl4Dj5rb9+8QLMWXuFqbYKSorDDzFA+/ldLhkiVq5Tv3ihqX0CkWpAFqtCeRXb1lEkpJZC0SqrRVldLSoaYj9Q6xbOR0GxflvZgLOsJJwCKhG/UnuK0yFxHJemAENdKVK+hwUb1sOWm5tS9wmO42DLy8OyzdepLi+trQEADLR3AgCq585WfSFZvOma8H9h/lXrMGvVCqF98zLNumKF6mhLzbw5sBc6DbdDyithiLd+/YsIBYJ48mvfyrQoSePI1u049eLLePv9X6U8kEQm+QeAOgB54JXUm4X57QBuFX5fBLBc+CwGcH88O8ovcqGosiJ6gcr1by90YtrMGZptzVu/JvwQ06OkOhIcmYz/majwWnPkw6Oir6veLqSuBvPWrwnn8bTFOWwKpM9v2CzKkrhz165G/RK+73Id8R9vPGV2lQYCPb/MwrJSzF1nOBNcUslIjuAkta/m692wYimWXn+tphtD9dzZAAC/L3ZFTOmLX0IFfxTW2Xj7l5RXIibX3fUO1C9ZhMc++UUEA4FMi5NU/u+Bh1BUVYmN73pbpkUhiPRh4AFSM28OahfM02/HyIPHwFBiPA+wuoXzZdNieVozbVksViy6dgPmrF2lujzVw98rt2xOWeDojOVLokriKvsm1r5teXlRqQWHunpMyVFYVoq5a+XKqBH3jSpBsZJisVkj1mOTWGzWsMUv3A8a14rZ6zER/TMl1eYEHK7Ii4Y4SlI5e2bUqIWR45X+FyLnwLzsUX8pyb5N/XdN75mYUszfsB43vO/d+O1HPwvP8EimxUk6/nEv/vDZr2LzvXdPmGTUBJEWwnXPtR8oHIdIWiAjD5446q7XLpynmlorRxEsFKmEpCevfJmYIF0zwCYNvps5ikwIC6/dgNqFMV4aDGAk6lxprcvLz5cpNvlFLuTlq/srx6K0tlpVSZ2xfEnUvOlLF6Gsvk42r0Dhlw0Ac9ZcgaU3yLMkFJQUx1R65m9Yj9mrV8WR2SL1JM2yq2o1jvxech2fnKRm3hy4KpQp9Mwpr+OjblOiFVVWoKSmCtXzZkcFAsqvQVJeiSRQPr0Odz7wv/jTl7+BrouXMy1Oyug4ex7P/viXuPO7X5elPCGIyYjRJP7inGkqARzhdSzWqCwCSgz5L+o8wKc1zECVSkWtkb5+2XQoKGQhULRVPl2uFGULEeuffL69IB+uChXXjiQgKiDeMfUy3os2bgi7FiTKjGVLVPNpl9ZUR80rq6tF1ewGmYxAtGVYLfBv3vo1KBPOcdXc2VHBc/OvWod8VyGcKj7OUf8FjXMSkwQU0JQWRtCQS3zhHHePxWxCzUUgGOAr5SlfkMT+VJ6nWatWoGH5UlTNnhVbZoOQ8kqokpefj7t+9AB2/+EvOLlzd6bFSTl7/vQUui9exu1f/FSmRSGItJJXkI+iKEtMBL2USloJ0esWLTCVLshR6IRrmkJhi6EQjCqU1/ALtmIzqc+tWa1EzfBqxFLmcBXGDD4RrZLp9LMU2/V7xsUZUevYJAn21ayfWkgVFtEIwHGWsHU7dnEJQZZwNgkLlm++XhbQxBj/glI9T+5SICpRpTXVUcFzaj66sbo3rTEQyfJ5NXEdWYWXguHuHt31hFai1hEDPrVSopnKc0s+r0Sy4DgOb//2V9HT1IJtv/ptpsVJG3/56v2Yt34NVr3u5tgrE8REQmO4HNBOYxVxG9B+TGgtq5hRr5rFQ+shOf+qdZh9xQrZPOXwcHRj8slQMKC6D6Xlt7iqMno426B7wOzVK7HkenltiLGh4aj1FmxYj4VXXxktsnS/YV0tea4J0uwP8SKVpry+FgDk1bA05F286epwXxcpX0RgXEcRWxd9QXMlbgviubUXRAfJWW024y4OWRSgK/V5nXflmijFPBb6fsvqxxmVf1XRH2qZOGTKq/DSqnbvmKX4H0e9lEa1q7tYE1JeiSg233s3yqfX409f/PrEydeXBNyDQ3jiC/fhjV/4FMrqajMtDkEkDV2/VQ3fyHB0scq2Ym5K6YM3kTrxaj50ZhO9xwrCEZm5cpnhJPquaXKLtKuiPK6AofwiF+ZftQ4rbr4hPE+0cLJQcu6xheVlYb9GLSIJ4XX6SHLPF38aTeeklyDfaI5d0YoqWlCNurmY8WeN6Q8sad9ZWoLZa9QD+kTUcqNKg6X0kPZtQXExSmtrDPU3Z7HAWVqC5ZuvR/W8ObFf9iSEs4hwkaIfUuT5kaOvT4dTe1RB+eJitghJWV2tob4j5ZWQseT6a3H129+C33z0c/COxfaHmWxc2H8IL//5Kdz53a9npEoKQaSC2atXyqal13ZIJYPIvCvXhFNAqekOc4SHuVQhsdpscquLqtKhLl9I5QEKQLd6j6a/og75yhylMV7OrcpcskoZLJaw/+DsNatQVFmh6jc//6p1UcPX1hxeCXa4nEnJ9RlPFbHYw+OJK9ZiIGytRkGDmKTArUJ8AVG+YGk1Gyvxv1qlMWucWSQ4jsPMVctVr32L1YriqkqU1ddhWsP0cEoxrXRtsbrJYrGCxVgv3gpY4e01/tsWm5W/fyh2Pn3pIkw34HdNyisRpnL2TNzxjS/jic99DX0trZkWJ2M8/4tHEQoGseUjH8i0KASRdIqmVcjSTAUDwah1pH6DnMWCaTNnqCY6Vyp3UqtLsv0GV27ZLM87q2Hp1duvuH1U1gHGt2+V+HsaoWH5kvBQtau8DKU11Vi0cUPUeuoPcF4pmLFsiXxYPk7yFEPp8Sb6lyrZanIzPYVW5WVAtNLFsrxqnTeL1RreNt4ypmaJde2u3LJZPV+yhHhL5Er3Lb4INSxfCoB3x5m5chmmL1kYM80Zx3Extdfy6XW8FdrAf5XjuLiCH7VGbxdduwHzr1pLeV6JxHC4XLj7x9/F9kcew5lX9mVanIwSCgTxh899Feve9HrM37A+0+IQRFJRppiKBQcOropy8yMRqoZX9QdVlEVUg5h5Z7V2LC4RlKAZy/h0TaJ7kKiAmvUZzVOW31U8iBdecxXshU6ZuqdUOoJ+v6F9FRQXoWH5UtTMn6taVEEtI4MW+unPIsv6Wtujl+v0bzxuZlGV0hRUzZ4ZVea3aFqFzH1DMzApxssIZ7WgYflSlaHt2AqVs0T/5cCuM7RulILSYlhtNpTUVEUdS3FlbDcJo4qh0fWq5pjzx9V7z8nJy4OjsNB0WVgRUl4JWGxWvOv738Tlo8ex87dPZFqcrGCgvRNPfu1beNv9X4npcE4QE4GF10ZbBI1QUFoc13ZqlrZk5C8Ntw8OtQvnRRSPcICZ8TairJMK5Wu0f0B931rJ7RXHbHcWwFVeJlPqlm++XlCS9fPnKimtq0VJTRUqZzWgrDY63ZQUZdCMoR0oCPh8CAWjrfLqzfHtaQ0R67H8pusB6FtVHSovN8qhcjWLcCxrb3HlNJTUVIX9Zc1YAfWqzwGRvrDm5Bj291WiFywpqzCnnhYjKa5vkT6J129A/4UmTxaQp/yhDSmvBG777MeRl5+Pv973nUyLklUc3/4Sjm7djjsf+N+Y1gGCyHbsSithjPkiuXZ7XEN7ahY6R2FsC6ta9L4WFTOmSxQPfl4y/6tjw+qyhJUppfuBhrKndK8w654AyBVDURlScw3gLBaZ+4bu8LYgf8PypeGKVnoBaRabVd0HVZAnlQG+etdgjZY/bZzyJMPjJejnfcmX3bjJVNCStP+nL1mIHCHPqp4CbLb4wlBPr2zaSHClXv/r+aYbySUbD6S8TnHWv+UNWHr9Rvz2Y59DwOfLtDhZxzMP/gS5+Q7c9MH3ZloUgkgKyodQw4qlRjYK/4ylHIYfsnFqAIZLUHOc6gN17rrVKK2rUd0kFJIrlwPtnarraW0voiy7KsJCLEqBi6n4G+wnMccpwNekn7tuNeauWx2lOEQFhkmsb1qZDUpqqqItiSpyLd98PWrmR1c7E3Wf2gXmgrLMDGuvuOVG2Tyli8eMpYtN7VsN8aVEOz+qTVdRA4DRgcGoecoIfGWAXl5+vqaVVPQZdlXIg8Zi+f/q9W2UhdxAnlfeh9fcy0DDiqWmKnNSeVjCELNXr8R/ffLD+M1HP4uR3r5Mi5OVBP1+/P7TX8HVd7w5qnILQUwVpM8UcZhXiwah/GfaU2lKdqiWBxQAepvlgahej7pVqGZefJHxqsFMMTrCsKVY0bRWQJbSP1S6f7Faknr2KWXkvXy6sKwUgLyIgcicNVcAQFSBgFgUV8dfrlX0W9bF5EVYXq8fkGTE7cWIEUgZoLdo4wbVamRS1DIa6DFXeR0gYoU3oiSKyrS4rtIib9QVomJGvaH1hJ0ZXpWU1ylK7cJ5eM+PvoMn7/s2Wk81ZlqcrKavpRV//foDePu37wvntySIqYXxh0r4PxKH9mqxWk1Y45TTsbcznlOVXy/X4cCijVdHpRoL71PRL4wxw0PVorhqymCuw6GoDBZ7SL63pU1z2SxBfrFiVmFZmUwGQOUMK/pzztorNNs3U4lLSqygJ10M9LPZS1AvD27tgnlR+b9Vq1oJSl1JTVX4HAaEoLziqkrV4h1A7JeYgpJiU+4makGQs1atEISUj1rwiQnkx7Js83VwuFyq2TMA47l/UwUpr1OQaTNn4P2/eAjP/vCXOLp1e6bFmRAc3bodx7btxDu+c1/czvcEMRFQ8x80M5yntNiYwVVeZjzFk46lUDOgSjG7cpZ+hH5x5TTk5TvCVkdDYhm4PxjpG6U7RyzllYW0g6uk5X9nr1kVtobJXhZiWF5TQaI+srEUqGQeg2qAlpryKvmtPIczVy7DnNXqBQ9iycpxnGoxhHiZvyHi+uJwubD4umui1lmwQd09BoCsQEmyILcBQpOS6ip84OEfYdfv/4xX//qPTIszoXjmez9GvsuFzR+4K9OiEETKUMvlqBbtHQuO42JWJlJSt3iB6f0AJuSLV0HTWi8B5Ui578qYqa60FT3eIqehsCvmSxPuL7p2A+o1EsLHrEJlgHoT59NspgLOYjGQH9fc+RF9U6NcLzRl0La8KueJllWtlxutoDMxq4BWIYJ44CAPnszLdxjKoStdx5DbRjyCAXAUOmO+xJLyOoVwlpXgAw//CIeefR47Hv19psWZcAR8Pjz+6S/h2jvfqjuERhAmeQuAkwBCAFbrrFcM4CkAjQBOA7gy2YJoWyzjU9JiVSZSYiYJvVSmBRvWG8oBG+s4REugcghXbbvlN98gS/MDGM9Xa8vNhV2ReaFm3pyoB7bUXUHPSGmxxv8oF5UYtWNM1DKaDuttJvcvfTHIK8jHyi2bVfdptVpj+oprUb+Yf7lIV4GGbIDjuHD1MC1IeZ0i2AuduOeXP8T5/Yfw74d+nmlxJiy9za342ze/h3d85z5TQ4kEocMJAG8EsCvGej8C8B8ACwAsB6/AJpUVt9yomxtyspdMXmQiF67FYolSVNRSgdXMi47On7NmlWoJTOUD21VRHilqEK8imWEFUp/kpNYaH3XLph2uQr4srYlDV1pEDV3r0mA4u5jSKpv7WyCOayJoMOdvIiizMuhByusUINdhx/t+9n10X2rC3775vUyLM+E5/Nw2nHzpZbyd/F+J5HAawJkY6xQBuBbAo8K0D8BgCmVShTORQzXV/w1brk7wisazWVoWd6IRS5mK18qoTK2VMRJRshXbltfXoWJGvTkfSsX1On3pIgO7jbQvusiYzbiQCeLK25yGl6BpDfqFH6TQk3eSY83JwXse+jY8IyP40xe/HlcFFCKa//vOD+EsLcEN7393pkUhpgYzAfQA+C2AwwAeAaDlBHcPgAMADhQaKApgBocJv7tUW2mrZs/SXKaVKmsiwoGDxWbVrehkpI59PKRT2U9EObIorJ3htFMm2lS+mBlJYSa1siZLufOMGM+Lmk4y7QKihJTXSYzFasU7vnMfbPY8PP6pLxlP/k3EJOD14vef/jI2vfvtmml0CELCC+DdA5Sf2wxubwOwCsAvAKwE4AbweY11HwbvO7t6JMkPwmT5entGR5PSjiaJPmhVto+VnD5V5OY7wnlU9UhBTFnKSVpFLsVBRjJeGFdx4glQc1Ukv3T4+GhqKlJJyVbLqxlIeZ3EvPmrn0NpXQ1+8+HPwD/uzbQ4k47uS034+/3fw53f/TpcJnx1iCnJjQCWqHyeNrh9q/DZJ0w/BV6ZzVp0H3YpLCMqtm+mzKySbHpQF02rMJZHVUNmZW7SyYiWkqrrWqIgJ0+ehD/X4dBYM4Ka33KipNtndrCrO637SxakvE5SXv+Zj6JhxVL8+t5PRDmzE8nj0L+fx9Hnd+A9P/hWXPXKCcIgnQBaAIjjuDcAOJU5cQyQQd2V3z+H9rPn49o0GWmikoVRZUZL4c4an1YV8pLk3qH1rjHHRKo2TpGxIZmpqcyQjvMldYkwU75VSfflpmSIExfZ8w8lksaNH7gLS2/YhF/d81G4VeosE8nlmQd/DL/Ph9u/+MlMi0JMTG4Hb1W9EsC/AWwV5tcAeFay3kcAPAHgGIAVAL6VPhHNoywnmU4Y+LRVY4NDprfNJqsroO/bG4GLStuVSvze2CVQjSAtnpAIyQgOzHTFKBGzZWDjQaYgJ3K9J+klNJ5MBqS8TjKueusbseGON+FX93wUQ109mRZnShAKBPH7T38ZC6++Euvf8oZMi0NMPP4BoA5AHoBKADcL89sB3CpZ7wh4X9ZlAN4AYCBtEsZBaU216vyWU424dOhIancumHbjKedcXF2ZbGlSzqzVK9Ia5d52OlZyDHOM9PXLpi+avD6SERxYMWN6wm1MSNIyDKLPQFuH6W1IeZ1ELLn+Wtz60XvxyP98Er3NrZkWZ0ox2j+A337s83jdJz5EAVwEocNo/wB8nvG07Cue5P0W68TLZetwOtO6v6QFWmkQCqQ+pyiROFYTPsXJhpTXScKM5Utwxze/gsc//WW0NZ7NtDhTktZTjXjyvm/j3Q99WzetDUFMZdIxLD/c0wsgPiUry7wGshLGkptyMdXKcCL4PJ5Mi5AwvS1tKWm3rLZGNj3arz4YFDP9Vxx/OlJeJwHlM+px94+/i//7zkM4++prmRZnSnPs+R148Xd/xHt/9iAKSoozLQ4xxQn4kuObONFISBci7TUmieqaIUW+8WxVXlkohEuHj2VaDE2MZgpgIW1LdjIzEWkFhzOW/KwGpLxOcJylJXj/L36A3U88iQPPPBt7AyLl7Hj0cVzYfxh3/eiBjAatEES6hucnE/aC/EyLkPXkJHhfazpyXHe5b3wco/0DGGjvTGg/RhnqVo8P4SyWbHAJ1SRR98D2M+fgHUt9XtlUQMrrBCbXYcd7f/ogzu07gBce/l2mxSEk/O0b30XA58Nbv/GlTItCEGnB+PBqbMtm4569CcmS68iLmmdUvikbuGMCZVopKX2t5oeomSLaPBgI4ty+Awgl2T1BZEyRHipTlSeVFmjTGNSsPSPqFtGE92+CZLsLkfI6QbFYrXjn976J0YEB/P2bD2ZaHEJBMBDAY5/8ImoXzMOWj3wg0+IQRFyc2rXH8LrJrOCXqDIRTi8lebYHfH7T7ailhBql9IMYHx2Fd0z9ZcBIcQimyLEUde0ISpnXbdwqGAqFTF2vst2FtJXAeIL+jNK4+9WEtlf2oxZa56R2wbyE9q/En0Y3JVJeJyhv/PKn4Swrwe8//RWE4siRRqQez/AIfv0/n8Da21+HK99ye6bFIQjTpCK4ykiTtjz1Yenh3r649yv1q2w6dsLQNqqyZuE4stFyuz1NLUnZn7t/EONubf9GJbECdkRfSd/4uNBGPIF2xq9V5aohHZ9QM1W6zJKwr6/hzdVXTPT/7R4chN8b8Zk1m56zcc++2CtpQMrrBOSG978bc9etxqMf/vSkiISczAy0d+KRD34K/+/j/4PF112TaXGIKciFg0fi3tbnGTesGBmtI2/kgTnap5HCNqGHfWTbRHyB460KlcosMCxozFLd09ScnP3pnQeVZUF/INYqAIDxkVHZCmLWCCNwHGdYmVOeQ70cual9V5E3btaH1ajyq7VewOcz4sWj+dIz2BkJwuJl15Ynvyi6xLFnmLcIx6NDk/I6wVj9+ltx7Z1vxa//55PaN3giq2hrPIvff+YreNv9X8X0ZYszLQ4xxRjWCEYxSvcl9RKQytKr0iFMpU+hGh3nLiQklxGkD23p89twkIrKU9Vis6qsGJuUFo0x+vRPpiKmoRAF/dHuGbGULHF5+1n+mhCvpURKl+qhLP8rLQWrtO6n0h9W2S+i5dkoRksHa52rlpONMWUCdALaJNfdcG+v7nnO0RhNiRdSXicQ865cgzd8/hP4zUc/i94kDf8Q6eHMK/vw9AMP4b0/+R7Kp9dlWhyCMARjDJyGaWagvROHn9umusyrkTIHQFjR0ho1aj5+Slse7VbV15cqrxIlxD/ujVnFye/1qlqJ+zUCkuIpcZksDOuuKXZ5CPh8GFUpyWt2v3o+qKlGae11Dw4mre2O8xcT2l55zeY51DNjRKUi0+pPlfNy5D8vRM0b1xh9kT7L0l3Rk5TXCUL94oV414P3489f/gaajhrz1yKyi/1PP4vdTzyJ9//yITjLSjItDkEYQ0MzMuOypBbco6bQnHvtQFzR6kZQpl2SKuXnXzsYtb7f61M99qBW9aeY1sUURnZnSW7a49tfQsAbO29o0bQK2bR4LYQPI04l26jlMqBiHRaxK6qVJbPal1fwE46ZtF9AGTCoDDoULdTjisC2UEDppqHdn1ovp0bglXHJ9mn0ByfldQIwbeYMvO/n38czD/4EJ3bsyrQ4RAK88PDvcG7vAbz3pw8i1+HItDgEoY/Kw0irio5sM4WN9NRLL6O/Xb9+ecvJ0zFdoZSPWaUfqV+hOEkf2vZCuZ9jSLJspK8/emeMJTVgLRRihiLx48G4ApJE5UKnb5pPnNZVJLWCoMT+jmWplb44nX45ErFvdIjflqMdhFVYVho1z2i+WbXsFFKULz6xdb0YKwgNxM5NnNh515Kzv7U9oXYTgZTXLKe4chru+dUP8eJjf8Rr//hnpsUhksDf738QIz19eOeD34DFGp//HEGki2Tpb6JC4k5iqinPiHw4U+mfy4LBiG+u4gEcjJHWh7FQlG+k/vqx0z8plWs9+ts7DCljWhZitfXSVcmqr6UVraei/Sm1CKfEUjkOZRDTyRdfhnsg4powPmIsoNAoan1++ah+UQUR94D+y5fof+4oLAzP0wqGaj5xOsqFRvkypTyfzSdO49y+A7h46KjueqnCzH5kLhRUHnZykV/kwvt/9UMc3boDO3/zh0yLQySJUDCIP3zuqygoLsabvvyZTItDEPokSXvtvnhZP0grCftRPjwZY/ArLIAndvKjV+7BIZx5ZR/O7Tug0ZZ8OpZVTdxgsLMLgHreW2WQmx6j/YMYVrEIK0twhoJBVQvx6d2vyKbPv3YgY1m+gorhbqW8I719OLJ1u+pxRA+xs5S6STAWMpxhIwoduVRfXBhDp4YfbF9La+xRDsX5HOzoxGj/ANwDg+hpag6PTOied4nMl2NUPjPz8hWL0X6V0Q4Yd/0g5TVLcbhc+MDDP0bz8ZP41w9+mmlxiCTj84zjNx/5DOasvQKb77070+IQUxTPyAhGBwYx0NGlupwxhjyD7i3SIX81C8z4qFv+oI5DkYpqN4Y2NjYcGaYXg1ikfoNjQ8OaCkKPwoorRtFr5dUWRem+3AzP6Cj626KHVMdHRo1bpzTWC0fij46ChUJ8UJ2gf0hdM5RKLmexJNUn0adRpCC8P8GVoa3xLJpPyi2IatkejA75hwymBYsXxoDz+6J9oJUc3/5S1DzOoKVe6nOrdz3Eulb8PoWbjORP1XrqDNxi8JyiHa2UaQMdGu4RwvbSFzjVe4aOuFF+xhrrKl82tSDlNQtxuFy499c/Ruf5i3jya99Om8mfSC+j/QN4+N5PYMMdb8LaN7wu0+IQUxGOw7m9+9HWeEZzFWuOTTbdfblJNR+lLMVQHPesQQ0FWspIXz/O7z8Uno6Vtmq0byB8/xSVVC0/1rOvviab1rIyaStZ/H7GhobRuPtVdJyVW5lFOcTvWDlM1aoneUZGwkph66lGHNm6HRarJex+lJMXXRY3QnKtlR1nz6P5xOnwdJRfqNDP4+6xqKCnvhaNoDwDIgYSrOI02NWtvwJjqum+jMghXltqAYqyXLdigJoi1VXUtSmsF7biKxaL7hOiUqkXXCb93yRFp4jRhnJEY6ir21CgmlHRSHnNMqSK65+/8s2M1Vwm0kNfSyse/fBn8PrPfgwLrl6faXGISUxb41mcfHG3fKaoUOmkJlIqce6BIbScPK2xtnHEB6iY/seoUjIiUZJtuXJlTVUxFQ5NTHmkdU91K1I8RT3gJW1Lg4Q019dA3H/M9Q22l2u3w5bL59BUpkiSWmItVktSDSHBQACDgqXu7KuvGfYLBeTHLnUlSSTy3SiXDh1Fy8nTmi4cjLG4+0lUUM/s2RuVG7XtzLnwb/E/xXEWQ4qyph7AGI5u2xHT15YxJvvfSN+LzAQlDnZ0ouP8Rb49tSBHSVPKEY3m46fQ+PJenHkluqqWTAah7y8cOKwrCymvWQQprlOTlhOn8MTn78OdD3wddYvmZ1ocIv28BcBJACEAqzXWmQ/giOQzDODjZnbiG/NEVZbqPH/JwJbmFQozz37/uHE/OqWCayRJ+2BnFy4cPIJQIIjDz20zUZVIPs1xHPxeH0b6BtSDhMQNtNpXWF7bz57XTQvGGIPDWaCYB3iFSPugP9rKplSEZAFsjMVlEVfz3VUSUnn5GTFYyjcTI4u9za3ouqB+7SeSEUI6ghEVXKXibiJ92Wg6fjJqebhvOA5+rxfjI9H5k0OBoLavraJvOy9cRPflJnScN1cgRJQj4POj89wFHPnPC+FrV7prI4pwrMITYruxzgMpr1mCs7QE//PoT0hxnaKc3rUH//zBT/Hen30fpbXVmRaHSC8nALwRgF4evDMAVgifKwCMAfhHIjvtvtwUDi4yglZUtIhuYQIVhrq6YxYKkNLX1h6dq9XAw5IxFmeVMfmDn+OAEzteCpe0bJdY0vj9GG1W8B/0jEelD5NNM6im07t06ChOv/xqWA7dXQnPkeYTp+EeHAq7Ipit5BSb6IMXlV5VH0aNvnIPDRkqkZrKtLbShPxSF4OWk6fDIw5aw98yf1ylkHItD0e37ZDlxOXAaSryHMfhxI5dUS9v4fy4sXxthfU6zl5A2+mzcvcCE52p5soik1NDDqllWHmMl48el/2XhnvVA7mUkPKaBRRXVeJDv/sFmo+fIsV1CrPvb89g71NP495HfoKyutpMi0Okj9PglVOj3ADgAgD1uq0a6D14lA8UdaVSe/vxUTe8Y2OaFbe09jnU1aPpLqD0CR1S8VVUKq+xgojMoOc2AEQrgInWmTfaBguFNNNDaW0/NiQG7ui3bcQdRK1csNZ+j2zdbqrEaygQTIpLijRYjYVCMaPoRY5t2ylTnqWW7N7m1vAyrUIVQb8fHecuIBgIRKVZU16rUf6pHKf5MqYMvlPiKHSqLxDz5iaa39fgta0m/7h7DK2nom9v4jUz0N4pG30RFXqtwEgRUl4zTPmMenz48V/i5M7deOrrD5DiOsXZ+rNf48Azz+HDj/8S1fNmZ1ocIju5A8CfdJbfA+AAgAPl5eVRC9XKPyoZ7u5Fi5CnU5k4Xk1RiXo4mhgK9nnGVZVeqc/biZ27VMtPKh+Wg13dGIoRCGUYprS8GrNuaS6G6F8cuccrfTwH2jvDVihVhdCkn6zynMVSjtV8n5XbSAtDqB2TvD2N+ZI24ykEoZe2rLelDc2SIfix4RHN1FcXDhzGudcigUVKn+GiymmmZQtn1FAcl3QYXM23l7NwmkPlymIcUdtq9KHoVsN0MjTodf/p3a9EpVzTY9ztxnBPr6xP43FVOfzcNlJes5nqebPxod/9Aq8++X/410M/y7Q4RJbw/C8exQu/fgwf/O3PsXjT1ZkWh0gOL4B3D1B+bjPZTi6A1wP4q846D4P3nV3d2xtR5ERLjxGliDGGXsFNQHwwikOiRgJMpJzatcfU+mYQhylDoRDGBAtfyB/bR9MI0n4KhUIY7tF3PTBa/lXsRzVrGGOhSAqiZPqCMrmSGfD5DfmyArF9FPmG45YsLh+AtsazsvMjBuMBfAyBPPhOu1LacE+vblU3zUpcKufmkqIwgLhLUXGWjTCoyMNxnHYGipilhzXcDWCsYhmg7goxPuqOafWVlqYNBYK4cOCwPG2ewYvDrO+zLfYqRCqYsXwJ3vvTB7H1549gz5+eyrQ4RJax509Poa+1DW//1tfw6pP/wNafPxLzTZTIam5MUjtbABwCYNxZFcCZV/ZFW3WSpBuFQiGMaiTTtzsLItWTksjp3a/waaEEJWBscEiz2EA8BAMBWZaFky++LPNPBKL9+/zjXjQdO6n9EGZi20K+WEmgDRNL0bLIimoP/VjD36MDg3KXo3AMmbytUDCIUDAEq0QDuHzkOCy2yIyephZ0nLuAUDCIFTffoLtf80FXUsuryU3BK+GDnd0oqa40v7Fuw+aOY3RgEM6SYgBqgW3qB9bT1CLLPxxeW68jYnTSUFcPnGUl0ZvF8IX1jY8j124HADS+vDc8f7CzC8VVkb7VU0Av7D8Ul/U8oLCeuweHwn1pBLK8ZoAl11+L9//iITz93R+R4kpo0rj7VfzwbXdj/oZ1+OgfHyE3AgIA3gZ9lwFV1IYj1R6gavS3d+jmxjy6dXuUTxtjDB3nLuhG05tBqVOMj7ox0tcfjmiXKk9Gj0uPY9t2yn0SVZQa98Ag/F4vOi9ECi8YsVJKmxKVi6ajJ8Lz1CL3AT6Prlpyfymjff3qQ8yKJtVehJXBe62nGhH0+w25shm1OovD/YwxU/7RAG/BHJWUFm46ehxHn99hQDZTu5HRqZGRQLzezu3dHz4OpYKnpdC1nmpEf2t0AYt4FEBpm427o9O36WXjOPKfF/j7gtp+TcgSCgZ1rfgOZ7Q/7vEdu6KvZZMnipTXNHP129+Mt37jS3j8U1/EwX8+l2lxiCynv7UdP37H+3Hs+Z348OO/wus/+zHkF7kyLRaRXG4H0ArgSgD/BrBVmF8D4FnJegUANgP4e6I7PLZtpyxyX8+y4hkeiRoSNcJgZ1dUbfZUIVVe1QKKktm+iNc9hhM7doWLEcR69KopgW4hF2aB1OIk7Mul4q9sVlYtX1cWCqnrLAZSj8nWF4eldfIEy2VTU3KN7bNx96s4t3e/pC1maDQqbSNWyi4QOtho7trkZ4DQ37d4TaS6f9T8k5WjGPFAymuasFiteP1nPorr7n4nfn7XB3H21f2xNyII8DeXHY8+jof++z0omlaBLz77FK5/77uQY9erpkNMIP4BoA5AHoBKADcL89sB3CpZzw2gDIA8m34cGPV3lGLOMpQcn4Qxo5HqSfIPVSaWTyaishCO/EfkPIhWT8ZYRMmNyxKntY3SbSBaiWSMaft4CrSellvYg4EAui81GS4wEfG7jsyLdV0NtHeGgwe1UAumE7cxUrlNJPolRf26Ug1ajArw47+NWOOPbtsRXaFM1lZ8Vlkj5WqHe3rRuGev/krCodkLCvTXU8Goctzf3mkqxy4pr2nAWVaCe371Q8y6YgV+8o73R5UNJAgj9Da34vef/jIevvfjmL9hHb7w77/iyv++HVYbua4TqWewsyuqClWquSCWtExTInvVqkFJ3n/b6bM4snV7ePrwc9vC1Yj8496wFXyou0c3qt4MUT6vIY1UT4IyrTWk33O5OWperEh4Ea0qXLEUs2AgEA4e1ELNYtzb1ILDz21LmuuKaUR/bANuLHplXUf6+k2lG5My7jaWe1nZftQ5ESbFMsRmMOoP3dfSqlp9SwtSXlPMzFXL8cm/PIbellb89F33xq6rTBAxaD5+Cr+4+0P4y1fux7o3/hc+988/44r/2mLoLZsgVDHwfBkbGsbZV18z1lySlD3DuVMV050XLqLr4uWE9398h17dCKUQxo9ZzYXg8HPb4PN4wkPwo/0KRVqjfTGafbinl99etp7oNiDfxt0/KPMfFem53Izj218ydhAmcQ8ORfJ5ykyvibd96fAxnH/toM4a/E40X05MomohVHTycHdyUradf+2gbKREloYqBp7hEdO+xWqIynU2BQ2TySZF5NjzsOWj92LtG16Hpx94CPuffjb2RgRhgjOv7MOZV/Zh6Q0bcctHPoDr3/tO/Ocnv0rZw4eYvGSiRGcqiXd0a6S3T5YaSDRAxds/0mh0o7BQCEf+84LhfZ55eS9W3HIj2hQVv+SNRtoSlZm+1jbMXbcazlJ5lLpRF4B4UDumRAKVRLzuMUNZLfQV3GgKy8rQAfm1dGTrdtWXD6+iQEbnhYuqFdISRS+1V9LQyhsbV2qI1NxbyFSTApZtvg6f+ccTqJ4zC99/0ztJcSVSyvHtL+HBN96JHY/+Hrd97uO451c/RPmM+kyLRUxAsrVISkxFLkkPyPFRtywpu+hLaaRf1B7s5/bux3nR9cEEyXqZEEdjtHyclUn5U06GXpI8w8OGq2xJkfoni2hZzZVKfygQxOUjx0wrfM0nTofdSLKOOHRXu9O8n6wRyPKaRBpWLMOtH78XJVVV+PdDP5P5NRFEKmGhEA7+8zkcf2EnbrznLnziL7/FS7/7I1545DFdfyqCAPjr5+i2HZi3fg0chYVJaDDxJrIBsSCDEWVSyx8wEctiTl6u4XW5iJk4Ms9iXPlOB6KyLPe5TYLfgAEGOrQDoi4eOoJZq1bI5jXu2Ru3r6mU3uZWBEwUzuhraUVfS6v+Oq1tKS0hrnZGLh06ipEsUqpJeU0QjuOw4JqrcMN734mKhul46fE/YdcfnkxKKgiCMIvPM45nf/QLHPznc3jrN76MRZuuxp+++PWk+P8Rk5tQIIhz+w7CajMflBHVlkZAUMpIwtCzGsFAwLDPYKb8AfUUa2nNeDW6L16Gq7ws2SJpwkKhqP5MhttAogR80VXjkqG4Avw1FEsZNUvLyUZTkflm8YyMwlUhT9WWbfE6pLzGicVmxcpbNuO6u+9EXn4+Xnzsj3jtH/+MebMgiHTQdfEyfvou3g/2o088gud/8Sh2/eEvWWOBIbKToN9vuvyrkkuHjmK4ry85AomKWQzLZ1FFfDlRk8XZvfujfB5FfB71+Wax5emnxhOVWKnPbtDv11W+zQQwJSvzgZJsCALKBgXaDCwUQm9zchViKe1nzqHz/MXYK8bg1K49CCapXLMSUl5NYsvLw7o3/hc2veftGB91Y8ejj+PI1u00NEtkHaFgEC88/Duc3vUK3vatr2Lxddfgz1/+BvrbOjItGjGJSaqFxqBSEcywAuRWidwXGR91xx3x3XnhEiwWC6bNnIF8lzF3jtH+AdP7cw8OxiFd4pze/Yqm0p9OUhmkNlFJxktFKkpDi5DyapC8gnxc9dY3YuO73obe5lb8/ZsPyhz7CSJbaWs8i4feehdu+fD78cknH8O/fvhz7Hvq6UkXYU5MXbyjxvJZTjQ6zp4HAEybOSPDkqSG8Sw5b+OjbhzbtjPTYhAmIOU1BvlFLlxz51tx9dvfjJYTp/H4p7+MiwcOZ1osgjBF0O/Hvx/6OU7ufBlv/foXserWm/Dkfd+OmfybICYEE2zYNxUkMvRt7D12cr/sxlN1jsgcpLxqUFhehk3vfjvWv/k2nNt3AA/f83G0nDydabEIIiEuHzmG77/5XbjxA+/BJ/7yW2z/9eN48bEnyO2FmNCQ7ppaLh0+Bv/4eKbFIIgwpLwqKKmpwnV33YnVr78VJ3fuwk/eeU9SHJcJIlsI+Hz4z08extGt2/GWr30Ba9/w//DvH/6cihsQExfSXhNzA4qx7WBnV/xtE0QKIOVVoHrebFx3151Ycv1GHH72eXz/ze9KenoLgsgmOs5ewE/ufD9WbNmM13/mY9j0nnfghV8/hsbdr5A/LDGhCKQoEn6qEEgwwwRBpJspr7zOXLUc17/3nZi1agVe/ev/4Tv/9VYMd/dkWiyCSAuMMRx+9nkc27YTV77lNtz+hU8i8KkPY/cTT+LIf7bDM5y6XIIEkQwa9+xLWjqqqcipXXtI+ScmHFNSec2x52HlLZtx5VtvR0l1FXY/8ST++IX/TVpSYoKYaAT9frz8x6fwyl/+gSU3bMRV/307bvvMx9C4Zy+OvbATZ199LT01tQnCJPSClRipTGdEEKliyiivnMWCmSuXYcUtN2LVrTeh69Jl7PnT33Bk63aqhkUQAqFgEMee34Fjz+9AUWUFVt6yGevfdBve+vUvoevCJTQdPYHW02fQeqoRXReb6L9DEARBpJ1Jrby6Ksoxe80qzF17BRZeexVCwSCObtuJn9/9IbSfOZdp8Qgiqxnq6sGLj/0RLz72R+Tl52PW6pWYvmQhFm+6Bjd/8H0omlaBwa5u9LW0ob+tA0PdPRju7sFQdw+Gunsx1N2D0b7+rKigk+W8BcB9ABYCWAvggMZ6nwDwPvA5i44DuAsAhYATGB0YhC8Lkv0TRLqYcMorZ7Eg12FHXn4+cvMdyHM4YHcWwDWtAkXTKlBUWYGq2TNRNXc28otcaDp6Auf3HcDvPvEFNB87SYEoBBEH3rExnN61B6d37QnPyyvIR3l9Hcqm16G0phquaeWYtXoliirKhf9jOSxWK0b6+nnFtqcXQ128Qjs2PAz34DDGhoTP4BC8njEEvD4EfP6pVvHmBIA3AviVzjq1AD4KYBEAD4AnAdwB4HepFi6ThAIBuAcHKQcngL62ds1l5/buT6MkBJF5Uq68fupvvwfHcfzHYol8WzhwHP9tsVjCv7XW4TgLLFYLcoT6zgGfDz7POLxjY/C6x/gHY3cPhrp6cOCZ59Bx7gK6LzVNtYcgQaQNr3sMbY1n0dZ4VnOdguKisCJbJLxgFpSWoGLmDBQUueAocqGguAj5RUXIddhhtUVuSQEfr8gGAwGAMYRCITDG0Hz8JH770c+l4xDThdEE0jYADgB+APkAtLWZSQJjDGdfJcUMIN9UgpCScuX1/77zEBhjYKEQWIiBsZDid+ShxM8PqazPT4cYg8/jgc/joaTqBDEBcA8OwT04FC5zGQuL1Qpbbg6sObnIycuFLTcHFqs18lLLcfBPzRfSNgAPAmgGb3l9XviocY/wQXl5eVqEI1KLZ3QUwz19mRaDILKGlCuvF/YfSvUuCIKYJISCQfg8QcAzjknmwfcCgCqV+V8C8LSB7UsA3AZgJoBBAH8FcCeAP6is+7DwQW9vL/lJTQIad7+aaREIIquYcD6vBEEQE5Abk7D9JQBiEuq/A7gK6sorQRDEpMaSaQEIgiCImDQDWA/e15UDcAOM+8oSBEFMKkh5JQiCyCy3A2gFcCWAfwPYKsyvAfCs8HsfgKcAHAKfJssCwTWAIAhiqsGZTB3VA6DJ5D7KAfSa3CZdkGzxk83ykWzxk83yJSrbDAAVSZJlojAC4EymhUgz2XwNpwo65snPVDteQOeebVZ5jYcDAFaneidxQrLFTzbLR7LFTzbLl82yZStTsc/omKcGU+2Yp9rx6kJuAwRBEARBEMSEgZRXgiAIgiAIYsKQDuU1m4MKSLb4yWb5SLb4yWb5slm2bGUq9hkd89Rgqh3zVDteXdLh80oQBEEQBEEQSYHcBgiCIAiCIIgJAymvBEEQBEEQxIQhWcrrWwCcBBCCfiqHTwjrnQDwJwB2Yf5M8Em4zwP4C4DcJMllVLb5AI5IPsMAPi4suw9Am2TZrVkkWymAbQDOCd8laZYNAIrBJ09vBF/x50ph/n1IXb8lQ75s6LvL4BPOHwGfBkXkPmRH32nJlw19BwBWAIcB/Esy73fgy6geET4rkijbROIW8PldzwP4fIZlSZTfAOgG/9wQ0boGOQA/Bn/cxwCskmzzbmH9c8LvbKYewE4Ap8D/Hz4mzJ/Mx20H8BqAo+CP+X+F+Vr6QZ4wfV5Y3iBp6wvC/DMAbk6x3ImivI9N9uNNDoyxZHwWMsbmM8ZeZIyt1linljF2iTHmEKafZIy9R/L7DuH3Lxlj/5MkuYzKJv1YGWOdjLEZwvR9jLFPJ1GeZMr2XcbY54Xfn2eMPZAB2R5jjL1P+J3LGCtOQ78lQ75s6LvLjLFylfnZ0nda8mVD34Ex9knG2B8ZY/+SzPsdY+zNKey7ifCxMsYuMMZmMf6aP8oYW5QFcsX7uZYxtooxdkIyT+savJUx9hxjjGOMrWeM7RPmlzLGLgrfJcLvkiw4Nq1PtXDMYIwVMsbOCudwMh83xxhzCr9zhGNYz7T1gw8K0xCW/0X4vYjx13weY2wm4/8L1iw4Pq2P8j422Y83KZ9kWV5Pw1gVFxsAh/CdD6Ad/Bvj9eCtYwDwGIA3JEkuM7KJ3ADgAsxXEouHRGW7DXx/AZnptyIA1wJ4VJj2ARhMogx6JCpfpvsukyQqXzb0XR2A/wfgkSTue7KwFrwV5iL4a/7P4M/ZRGUXgH7FPK1r8DYAjwNgAPaCH3mpBm+N2ia0MyD8viWFMidKB/hSwABfJe00gFpM7uNmAEaF3znCh0FbP5D2xVPgn4+cMP/PALzgR2HOg/9PZCPK+5iePjQZjjdppNPntQ3AgwCawf8xhwA8D6AMvEIRENZrBf8nzRR3gHdpkPJh8EMxv0Fyh0jNopStEnxfAkCnMJ1OZoIvGfxb8MMejwAokCzPdL/pyZfpvgP4G/PzAA4CuEexLNN9B2jLlw1990MAnwXvXqDkfvB99xD4obapRi2AFsl0pu+pqUDrGtQ69oncJw0AVoIfKp7sx20F7+7TDV7RvgBt/UB6bAHwOkUZJtYx/xDy+5iePjQZjjdpmFFeXwDvc6T8GH2jLxHWnQmgBrwScaeJ/adSNpFcAK8H8FfJvF8AmA3ed64DwPezSDYpTPikUzYbeN+qX4C/uboR8a9LtN9SLZ+UTPQdAFwtyLcFwIfAW4mB7Og7PfmkZKLvXgf+4XZQZdkXACwAsAa8f+DnTMpGTDziuQYnCk4AfwMf5zCsWDYZjzsI/r5XB956uCCj0qQWvfsYEQObiXVvTHBfN4I3afcI038HcBWAJ8APcdjAv03UgbfSmm07GWwBP1TTJZkn/f1ryINDjJBq2arBKzjV4P8IZkhUtlbhs0+YfgoR5TDRfgNSL18m+w6IXOfdAP4B/ma9C9nRd4C+fJnsuw3gX+RuBR/k4QLwB/Avw6JVygve4v7pBPc1EWkDH/AjEs89NdvRuga1jr0NwCbF/BdTLWSC5IBXXJ8A/7wEpsZxA7z1cSf4ANtiqOsH4jG3CsuLAPRh4lz/avexH2HyHm9SSafbQDOA9eB9XTnw/hqnwb857gTwZmG9dwN4Oo1ySXkbol0GqiW/b4c84jWdqMn2DCLRo5not07wwxXzhekbwEfHAtnRb3ryZbrvCgAUSn7fhEgfZUPf6cmX6b77AvgbdAN4V5odiIziiH3HgfcVy9T/NZPsBzAX/ChXLvg+eiajEiUfrWvwGQDvAn/+14MfWu0AsBX8NVwifG4S5mUrHHhf/dMAfiCZP5mPuwK84gbwsTGbwR+/ln4g7Ys3g78PMGH+HeBdhmaC/y+8llrR40LtPvYOTN7jTS5Jivy6nTHWyhjzMsa6GGNbhfk1jLFnJev9L2OskfFRo78XouPA+KjY1xhj5xljf5XMT6dsBYyxPsZYkWL73zPGjjPGjjHGnmF8FGi2yFbGGNvOGDvHGHuB8RGl6ZZtBWPsgNA//8cikayp7LdkyJfpvpvF+AjRo4yxk4yxL0m2z4a+05Mv030n/Wxi8mwDO4S+O8EY+wOLRC9Ptc+tjI9Qv6A4dxPx8yfGWAdjzM/4a+O9TPsa5BhjPxOO+ziTZ6y4m/HPmPOMsbuy4Lj0PlcznmOMsSPC59ZJftzLGGOHhWM+wRj7qjBfSz+wC9PnheWzJG19SeiLM4yxLVlwbLE+m1jkPjYVjjfhD5WHJQiCIAiCICYMVGGLIAiCIAiCmDCQ8koQBEEQBEFMGEh5JQiCIAiCICYMpLwSBEEQBEEQEwZSXgmCIAiCIIgJAymvBEEQBEEQxISBlFeCIAiCIAhiwvD/AZ9/k2RSnumgAAAAAElFTkSuQmCC","text/plain":["<Figure size 864x288 with 4 Axes>"]},"metadata":{"needs_background":"dark"}}],"metadata":{}}],"nbformat":4,"nbformat_minor":2,"metadata":{"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},"orig_nbformat":4}}
week-05-fixed.ipynb
{"cells":[{"cell_type":"code","execution_count":1,"source":["# INFO: IPython extension for auto reloading modified custom packages\"\"\"\n","%load_ext autoreload\n","%autoreload 2\n","\n","\n","# INFO: IPython extension for package/system spec output\n","%load_ext watermark\n","\n","\n","# INFO: Core imports for practicaly any data science activity\n","import numpy as np\n","import pandas as pd\n","\n","\n","# INFO: Customize settings for Pandas\n","pd.options.display.max_columns = 500\n","pd.options.display.max_rows = 500\n","pd.options.display.max_colwidth = 500\n","\n","# INFO: to display dataframes as tables on call\n","from IPython.display import display\n","\n","\n","# INFO: Plotting setup (matplotlib is only for compatibility with legacy code)\n","# import matplotlib.pyplot as plt\n","%matplotlib inline\n","import plotly.io as pio\n","import plotly.express as px\n","import plotly.graph_objects as go\n","\n","\n","# INFO: Customize plotting backend for Pandas (matplotlib for compat)\n","pd.options.plotting.backend = \"plotly\"\n","# pd.options.plotting.backend = \"matplotlib\"\n","\n","\n","# INFO: Customize Plotly theme\n","pio.templates.default = \"plotly_dark\"\n","# pio.templates.default = \"plotly\"\n","\n","\n","# INFO: Logging setup (replaces 'print' in development & seamlessly transitions to production code)\n","import logging\n","import sys\n","\n","root = logging.getLogger()\n","root.setLevel(logging.INFO)\n","\n","handler = logging.StreamHandler(sys.stdout)\n","handler.setLevel(logging.INFO)\n","formatter = logging.Formatter(\"%(asctime)s - %(name)s - %(levelname)s - %(message)s\")\n","handler.setFormatter(formatter)\n","root.addHandler(handler)\n","\n","# INFO: Use logging like this:\n","logging.info(\"Logging is set!\")\n","\n","\n","# INFO: Call for package/system spec output\n","%watermark --iversions"],"outputs":[{"output_type":"stream","name":"stdout","text":["The autoreload extension is already loaded. To reload it, use:\n"," %reload_ext autoreload\n","2022-04-26 03:27:07,836 - root - INFO - Logging is set!\n","matplotlib: 3.5.1\n","sys : 3.10.4 (main, Mar 25 2022, 00:00:00) [GCC 11.2.1 20220127 (Red Hat 11.2.1-9)]\n","plotly : 5.6.0\n","logging : 0.5.1.2\n","numpy : 1.22.3\n","pandas : 1.4.1\n","\n"]}],"metadata":{}},{"cell_type":"code","execution_count":2,"source":["# INFO: PPL specific imports\n","\n","import jax.numpy as jnp\n","from jax import random\n","\n","import numpyro\n","import numpyro.distributions as dist\n","import numpyro.optim as optim\n","from numpyro.infer import SVI, Trace_ELBO, Predictive\n","from numpyro.infer import MCMC, NUTS\n","from numpyro.infer.autoguide import AutoLaplaceApproximation, AutoNormal\n","\n","from jax import lax, random\n","from jax.scipy.special import expit\n","\n","import arviz as az"],"outputs":[],"metadata":{}},{"cell_type":"code","execution_count":3,"source":["data_uri = \"https://raw.githubusercontent.com/rmcelreath/rethinking/master/data/NWOGrants.csv\"\n","df_dev = pd.read_csv(data_uri, sep=\";\")\n","df_dev.head()\n","df_dev[\"gender\"] = df_dev[\"gender\"] == \"m\"\n","df_dev[\"gender\"] = df_dev[\"gender\"].astype(int)\n","# df_dev[\"gender\"] += 1 # <- злая штука INFO: ломалось из-за нее + из-за того, что вместо гендера-индексера указал кардинальность в deterministic\n","\n","df_dev.head()"],"outputs":[{"output_type":"execute_result","data":{"text/html":["<div>\n","<style scoped>\n"," .dataframe tbody tr th:only-of-type {\n"," vertical-align: middle;\n"," }\n","\n"," .dataframe tbody tr th {\n"," vertical-align: top;\n"," }\n","\n"," .dataframe thead th {\n"," text-align: right;\n"," }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n"," <thead>\n"," <tr style=\"text-align: right;\">\n"," <th></th>\n"," <th>discipline</th>\n"," <th>gender</th>\n"," <th>applications</th>\n"," <th>awards</th>\n"," </tr>\n"," </thead>\n"," <tbody>\n"," <tr>\n"," <th>0</th>\n"," <td>Chemical sciences</td>\n"," <td>1</td>\n"," <td>83</td>\n"," <td>22</td>\n"," </tr>\n"," <tr>\n"," <th>1</th>\n"," <td>Chemical sciences</td>\n"," <td>0</td>\n"," <td>39</td>\n"," <td>10</td>\n"," </tr>\n"," <tr>\n"," <th>2</th>\n"," <td>Physical sciences</td>\n"," <td>1</td>\n"," <td>135</td>\n"," <td>26</td>\n"," </tr>\n"," <tr>\n"," <th>3</th>\n"," <td>Physical sciences</td>\n"," <td>0</td>\n"," <td>39</td>\n"," <td>9</td>\n"," </tr>\n"," <tr>\n"," <th>4</th>\n"," <td>Physics</td>\n"," <td>1</td>\n"," <td>67</td>\n"," <td>18</td>\n"," </tr>\n"," </tbody>\n","</table>\n","</div>"],"text/plain":[" discipline gender applications awards\n","0 Chemical sciences 1 83 22\n","1 Chemical sciences 0 39 10\n","2 Physical sciences 1 135 26\n","3 Physical sciences 0 39 9\n","4 Physics 1 67 18"]},"metadata":{},"execution_count":3}],"metadata":{}},{"cell_type":"code","execution_count":4,"source":["def model(data: pd.DataFrame, observed=True):\n"," applications = data[\"applications\"].values\n"," awards = data[\"awards\"].values\n"," discipline = data[\"discipline\"].values\n"," discipline_card = np.unique(discipline).shape[0]\n"," gender = data[\"gender\"].values\n"," gender_card = np.unique(gender).shape[0]\n"," observations_card = data.shape[0]\n"," # INFO: non-plate version\n"," # alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1).expand([gender_card]))\n"," # assert alpha_gender.shape == (2,)\n"," # link_p = numpyro.deterministic(\"link_p\", alpha_gender[gender])\n"," # assert link_p.shape == (18,)\n"," # numpyro.sample(\"awards\", dist.Binomial(total_count=applications, logits=link_p), obs=awards if observed else None)\n"," # /\n"," # INFO: good plate version\n"," with numpyro.plate(\"plate_gender\", gender_card):\n"," alpha_gender = numpyro.sample(\"alpha_gender\", dist.Normal(-1, 1))\n"," assert alpha_gender.shape == (2,)\n"," link_p = numpyro.deterministic(\"link_p\", alpha_gender[gender])\n"," assert link_p.shape == (18,)\n"," with numpyro.plate(\"plate_observations\", observations_card):\n"," numpyro.sample(\n"," \"awards\", dist.Binomial(total_count=applications, logits=link_p), obs=awards if observed else None\n"," )\n"," # /\n","\n","kernel = NUTS(model)\n","mcmc = MCMC(\n"," kernel,\n"," # num_warmup=500,\n"," num_warmup=1000,\n"," # num_warmup=2000,\n"," # num_samples=2000,\n"," num_samples=5000,\n"," # num_samples=10_000,\n"," num_chains=1,\n"," # num_chains=4,\n"," progress_bar=True,\n",")\n","\n","mcmc.run(random.PRNGKey(0), df_dev)\n","# mcmc.run(random.PRNGKey(1), df_dev)\n","\n","samples = mcmc.get_samples()\n","numpyro.diagnostics.print_summary(samples, prob=0.89, group_by_chain=False)"],"outputs":[{"output_type":"stream","name":"stdout","text":["2022-04-26 03:27:24,428 - absl - INFO - Unable to initialize backend 'tpu_driver': NOT_FOUND: Unable to find driver in registry given worker: \n","2022-04-26 03:27:24,429 - absl - INFO - Unable to initialize backend 'gpu': NOT_FOUND: Could not find registered platform with name: \"cuda\". Available platform names are: Interpreter Host\n","2022-04-26 03:27:24,430 - absl - INFO - Unable to initialize backend 'tpu': INVALID_ARGUMENT: TpuPlatform is not available.\n"]},{"output_type":"stream","name":"stderr","text":["sample: 100%|██████████| 6000/6000 [00:07<00:00, 854.77it/s, 3 steps of size 8.69e-01. acc. prob=0.92] \n"]},{"output_type":"stream","name":"stdout","text":["\n"," mean std median 5.5% 94.5% n_eff r_hat\n","alpha_gender[0] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n","alpha_gender[1] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[0] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[1] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[2] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[3] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[4] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[5] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[6] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[7] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[8] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[9] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[10] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[11] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[12] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[13] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[14] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[15] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n"," link_p[16] -1.53 0.06 -1.53 -1.64 -1.43 5586.17 1.00\n"," link_p[17] -1.74 0.08 -1.74 -1.88 -1.62 3062.69 1.00\n","\n"]}],"metadata":{}},{"cell_type":"code","execution_count":5,"source":["az.plot_trace(az.from_numpyro(mcmc))"],"outputs":[{"output_type":"execute_result","data":{"text/plain":["array([[<AxesSubplot:title={'center':'alpha_gender'}>,\n"," <AxesSubplot:title={'center':'alpha_gender'}>],\n"," [<AxesSubplot:title={'center':'link_p'}>,\n"," <AxesSubplot:title={'center':'link_p'}>]], dtype=object)"]},"metadata":{},"execution_count":5},{"output_type":"display_data","data":{"image/png":"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","text/plain":["<Figure size 864x288 with 4 Axes>"]},"metadata":{"needs_background":"dark"}}],"metadata":{}}],"nbformat":4,"nbformat_minor":2,"metadata":{"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},"orig_nbformat":4}}