{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "5bae3ab7",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sbi import analysis as analysis\n",
    "\n",
    "# sbi\n",
    "from sbi import utils as utils\n",
    "from sbi.inference import NPE, simulate_for_sbi\n",
    "from sbi.utils.user_input_checks import (\n",
    "    check_sbi_inputs,\n",
    "    process_prior,\n",
    "    process_simulator,\n",
    ")\n",
    "\n",
    "import torch\n",
    "import torch.nn as nn\n",
    "import torch.nn.functional as F\n",
    "from sbi.inference import SNPE, SNLE\n",
    "from sbi.neural_nets import posterior_nn\n",
    "from sbi import analysis as analysis\n",
    "from sbi.utils import MultipleIndependent\n",
    "\n",
    "\n",
    "from kestrel.dustbi_simulator import *\n",
    "from kestrel.Functions import *\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "09ac40e9",
   "metadata": {},
   "outputs": [],
   "source": [
    "infos = load_kestrel(\"../posteriors/sims_DEBASS.v5.MIX.yml.bk\")\n",
    "#infos = load_kestrel(\"KESTREL_GAMMA.yml\")\n",
    "\n",
    "\n",
    "\n",
    "dicts = [infos['Functions'], infos['Splits'], infos['Priors'], infos['Correlations']]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d3164f31",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "1bd3f3d9",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Ensuring only valid log masses.\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/cfs/cdirs/lsst/www/DESC_TD_PUBLIC/users/bastienc/kestrel/src/kestrel/dustbi_simulator.py:751: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df[\"MU\"] = Planck18.distmod(df.zHD.values).value\n"
     ]
    }
   ],
   "source": [
    "\n",
    "simfilename = infos['Simbank_File'][0]\n",
    "simfilename = \"/global/homes/b/bastienc/duStBI/DEBASSDUSTBI/FITOPT000.FITRES.gz\"\n",
    "datfilename = infos['Data_File'][0]\n",
    "#datfilename = \"SIMS_FOR_TESTING/FITOPT000.FITRES.gz\"\n",
    "#datfilename = \"G10_SHIAM.FITRES.gz\"\n",
    "\n",
    "df, dfdata = load_data(simfilename, datfilename)\n",
    "\n",
    "#"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "69123593-4e88-4a46-bcf1-8e5ff2ce518c",
   "metadata": {},
   "outputs": [],
   "source": [
    "df = df.sample(frac=0.67, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "825f81ae-17b2-4fb8-a7f3-c8689ef79fdc",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da428c85-c461-444c-b9d6-a0168910708d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5281856c-6b20-44fa-ab41-ddbb6334964d",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "49fdc858-8c45-42ac-b489-78e0759a700a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "0c56eebd",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Added 15 priors\n"
     ]
    }
   ],
   "source": [
    "param_names = infos['param_names']\n",
    "\n",
    "params_to_fit = parameter_generation(param_names, dicts)\n",
    "priors = prior_generator(param_names, dicts)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "9ccb84f9",
   "metadata": {},
   "outputs": [],
   "source": [
    "layout = build_layout(params_to_fit, dicts)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "b9683b98",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_to_condition_on = infos['parameters_to_condition_on']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "85e81e44-eb65-403d-8b5f-68527e1d0651",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'c': tensor([-0.0216, -0.1508,  0.4811,  ...,  0.0308, -0.2078, -0.0392]),\n",
       " 'mB': tensor([16.0296, 17.9071, 19.6222,  ..., 17.6628, 16.2479, 14.1694]),\n",
       " 'x1': tensor([ 2.7931,  1.8447,  4.0569,  ...,  2.8707,  3.6968, -0.5719]),\n",
       " 'zHD': tensor([0.0324, 0.0719, 0.1062,  ..., 0.0686, 0.0459, 0.0094]),\n",
       " 'cERR': tensor([0.0269, 0.0291, 0.0401,  ..., 0.0304, 0.0358, 0.0211]),\n",
       " 'mBERR': tensor([0.0281, 0.0337, 0.0571,  ..., 0.0506, 0.0413, 0.0260]),\n",
       " 'x1ERR': tensor([0.3953, 0.3637, 0.4047,  ..., 0.2395, 0.4697, 0.1827]),\n",
       " 'MU': tensor([35.8365, 37.6299, 38.5276,  ..., 37.5233, 36.6129, 33.1183]),\n",
       " 'HOST_LOGMASS': tensor([10.3270, 10.6570, 10.6080,  ..., 11.2070,  9.6990, 11.0930]),\n",
       " 'x0': tensor([0.0070, 0.0012, 0.0003,  ..., 0.0015, 0.0057, 0.0386]),\n",
       " 'x0ERR': tensor([1.7989e-04, 3.8257e-05, 1.3361e-05,  ..., 7.1933e-05, 2.1638e-04,\n",
       "         9.2224e-04])}"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "output_distribution"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "6018670e-f92d-4884-b05b-a1f9081dc36b",
   "metadata": {},
   "outputs": [],
   "source": [
    "def add_distance(df_tensor):\n",
    "    x1_obs = df_tensor['x1'] ; c_obs = df_tensor['c'] ; mB_obs = df_tensor['mB']\n",
    "    beta = 3.1 ; alpha = 0.16 ; M0 = -19.3\n",
    "    correction = alpha * x1_obs - beta * c_obs + M0 + mB_obs\n",
    "    MURES = df_tensor['MU'] - correction\n",
    "    return MURES"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "8c61453a-6f4a-44f6-86d6-b27948bad6f2",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_524916/2020536348.py:6: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  df['MURES'] = MURES_sims\n"
     ]
    }
   ],
   "source": [
    "output_distribution = preprocess_input_distribution(\n",
    "    df, parameters_to_condition_on[:-1]+['x0', 'x0ERR', 'MU'])\n",
    "\n",
    "MURES_sims = add_distance(output_distribution)\n",
    "\n",
    "df['MURES'] = MURES_sims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "44ac4ea5-bd34-430a-ac94-555576a7129b",
   "metadata": {},
   "outputs": [],
   "source": [
    "output_distribution = preprocess_input_distribution(\n",
    "    dfdata, parameters_to_condition_on[:-1]+['x0', 'x0ERR', 'MU'])\n",
    "\n",
    "MURES_sims = add_distance(output_distribution)\n",
    "\n",
    "dfdata['MURES'] = MURES_sims"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "52ae6e17-1b0a-4084-83c1-49681ddc4710",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d64e6db2-6601-49ab-b046-66853ca7aa54",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5307a8af-ae59-47d9-9a22-ea924e835c52",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "['SIM_c', 'SIM_RV', 'SIM_beta', 'SIM_x1', 'SIM_EBV', 'STEP', 'SCATTER']"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "param_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "116a9b79-d9cd-4388-bf0f-ef398833fea6",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "750342c2-d9d6-4af4-bc32-25fffee99b62",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Found that there is a split and correlation entry for ['SIM_x1']. Continuing.\n"
     ]
    }
   ],
   "source": [
    "sim_for_training = make_batched_simulator(layout, df,\n",
    "                        param_names,parameters_to_condition_on,\n",
    "                        dicts, dfdata, sub_batch=20, device='cpu', )\n",
    "batched = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "69800f06",
   "metadata": {},
   "outputs": [],
   "source": [
    "inp_vals = priors.sample()\n",
    "blegh = sim_for_training(inp_vals)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8e868d64-b1e9-428f-98ab-6951bb7c3889",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "2fe58fe2-c994-4733-9fb8-da09f27b51b4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "6b8e9129",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "10\n"
     ]
    }
   ],
   "source": [
    "ndim = len(parameters_to_condition_on)\n",
    "\n",
    "print(ndim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9e8dbc56",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "6b11a895",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'dustbi_nn'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[22]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m dustbi_nn \u001b[38;5;28;01mimport\u001b[39;00m PopulationEmbeddingFull\n",
      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'dustbi_nn'"
     ]
    }
   ],
   "source": [
    "from dustbi_nn import PopulationEmbeddingFull"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "eb61ad1d",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'PopulationEmbeddingFull' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[23]\u001b[39m\u001b[32m, line 3\u001b[39m\n\u001b[32m      1\u001b[39m density_estimator = posterior_nn(\n\u001b[32m      2\u001b[39m     model=\u001b[33m\"nsf\"\u001b[39m, \u001b[38;5;66;03m#switch to nsf if interested\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m3\u001b[39m     embedding_net=PopulationEmbeddingFull(input_dim=ndim)\n\u001b[32m      4\u001b[39m )\n\u001b[32m      5\u001b[39m \n\u001b[32m      6\u001b[39m inference = SNPE(\n",
      "\u001b[31mNameError\u001b[39m: name 'PopulationEmbeddingFull' is not defined"
     ]
    }
   ],
   "source": [
    "density_estimator = posterior_nn(\n",
    "    model=\"nsf\", #switch to nsf if interested \n",
    "    embedding_net=PopulationEmbeddingFull(input_dim=ndim)\n",
    ")\n",
    "\n",
    "inference = SNPE(\n",
    "    prior=priors,\n",
    "    density_estimator=density_estimator, \n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "137ca6a8-6185-479e-adf7-06a6c7def051",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "db43ceeb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  1%|          | 209/30000 [00:02<06:36, 75.19it/s]\n"
     ]
    },
    {
     "ename": "KeyboardInterrupt",
     "evalue": "",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mKeyboardInterrupt\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[24]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# generate simulations and pass to the inference object\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m theta, x = simulate_for_sbi(\n\u001b[32m      3\u001b[39m     sim_for_training, proposal=priors, num_simulations=\u001b[32m30_000\u001b[39m, num_workers=\u001b[32m1\u001b[39m\n\u001b[32m      4\u001b[39m )\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.miniconda3/envs/dustbi/lib/python3.13/site-packages/sbi/utils/simulation_utils.py:113\u001b[39m, in \u001b[36msimulate_for_sbi\u001b[39m\u001b[34m(simulator, proposal, num_simulations, num_workers, simulation_batch_size, seed, show_progress_bar)\u001b[39m\n\u001b[32m    111\u001b[39m     batches = torch.split(theta, simulation_batch_size)\n\u001b[32m    112\u001b[39m     \u001b[38;5;28;01mfor\u001b[39;00m batch \u001b[38;5;129;01min\u001b[39;00m tqdm(batches, disable=\u001b[38;5;129;01mnot\u001b[39;00m show_progress_bar):\n\u001b[32m--> \u001b[39m\u001b[32m113\u001b[39m         simulation_outputs.append(\u001b[43msimulator\u001b[49m\u001b[43m(\u001b[49m\u001b[43mbatch\u001b[49m\u001b[43m)\u001b[49m)\n\u001b[32m    115\u001b[39m \u001b[38;5;66;03m# Correctly format the output\u001b[39;00m\n\u001b[32m    116\u001b[39m x = torch.cat(simulation_outputs, dim=\u001b[32m0\u001b[39m)\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/global/cfs/cdirs/lsst/www/DESC_TD_PUBLIC/users/bastienc/kestrel/src/kestrel/dustbi_simulator.py:448\u001b[39m, in \u001b[36mmake_batched_simulator.<locals>.batched_simulator\u001b[39m\u001b[34m(theta_batch)\u001b[39m\n\u001b[32m    446\u001b[39m B = theta_batch.shape[\u001b[32m0\u001b[39m]\n\u001b[32m    447\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m B <= sub_batch:\n\u001b[32m--> \u001b[39m\u001b[32m448\u001b[39m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43m_simulate_sub_batch\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta_batch\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    450\u001b[39m results = []\n\u001b[32m    451\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m start \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mrange\u001b[39m(\u001b[32m0\u001b[39m, B, sub_batch):\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/global/cfs/cdirs/lsst/www/DESC_TD_PUBLIC/users/bastienc/kestrel/src/kestrel/dustbi_simulator.py:389\u001b[39m, in \u001b[36mmake_batched_simulator.<locals>._simulate_sub_batch\u001b[39m\u001b[34m(theta)\u001b[39m\n\u001b[32m    385\u001b[39m     joint_weights = f * _compute_joint_weights(\n\u001b[32m    386\u001b[39m         theta_A, B, dev, extra_tensors=extra_tensors\n\u001b[32m    387\u001b[39m     ) + (\u001b[32m1\u001b[39m - f) * _compute_joint_weights(theta_B, B, dev, extra_tensors=extra_tensors)\n\u001b[32m    388\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m--> \u001b[39m\u001b[32m389\u001b[39m     joint_weights = \u001b[43m_compute_joint_weights\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtheta\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mB\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mdev\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mextra_tensors\u001b[49m\u001b[43m=\u001b[49m\u001b[43mextra_tensors\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    391\u001b[39m \u001b[38;5;66;03m# --- Normalise ---\u001b[39;00m\n\u001b[32m    392\u001b[39m weight_sum = joint_weights.sum(dim=\u001b[32m1\u001b[39m, keepdim=\u001b[38;5;28;01mTrue\u001b[39;00m)  \u001b[38;5;66;03m# (B, 1)\u001b[39;00m\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/global/cfs/cdirs/lsst/www/DESC_TD_PUBLIC/users/bastienc/kestrel/src/kestrel/dustbi_simulator.py:339\u001b[39m, in \u001b[36mmake_batched_simulator.<locals>._compute_joint_weights\u001b[39m\u001b[34m(theta_pop, B, dev, extra_tensors)\u001b[39m\n\u001b[32m    336\u001b[39m weights = torch.ones(batch_size, N, device=dev)\n\u001b[32m    338\u001b[39m x_sub = x[:, mask]\n\u001b[32m--> \u001b[39m\u001b[32m339\u001b[39m density_sub = \u001b[43mfunction_dict\u001b[49m\u001b[43m[\u001b[49m\u001b[43mname\u001b[49m\u001b[43m]\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx_sub\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mtheta_i\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcorrelation\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m    340\u001b[39m density_sub = torch.clamp(density_sub, \u001b[38;5;28mmin\u001b[39m=\u001b[32m0.0\u001b[39m)\n\u001b[32m    342\u001b[39m \u001b[38;5;28;01mif\u001b[39;00m density_sub.ndim == \u001b[32m1\u001b[39m:\n",
      "\u001b[36mFile \u001b[39m\u001b[32m/global/cfs/cdirs/lsst/www/DESC_TD_PUBLIC/users/bastienc/kestrel/src/kestrel/Functions.py:35\u001b[39m, in \u001b[36mDistGaussian\u001b[39m\u001b[34m(x, theta, correlation)\u001b[39m\n\u001b[32m     32\u001b[39m mu = theta[:, \u001b[32m0\u001b[39m].unsqueeze(\u001b[32m1\u001b[39m)      \u001b[38;5;66;03m# (batch_size, 1)\u001b[39;00m\n\u001b[32m     33\u001b[39m sigma = theta[:, \u001b[32m1\u001b[39m].unsqueeze(\u001b[32m1\u001b[39m)   \u001b[38;5;66;03m# (batch_size, 1)\u001b[39;00m\n\u001b[32m---> \u001b[39m\u001b[32m35\u001b[39m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mtorch\u001b[49m\u001b[43m.\u001b[49m\u001b[43mexp\u001b[49m\u001b[43m(\u001b[49m\u001b[43m-\u001b[49m\u001b[32;43m0.5\u001b[39;49m\u001b[43m \u001b[49m\u001b[43m*\u001b[49m\u001b[43m \u001b[49m\u001b[43m(\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m \u001b[49m\u001b[43m-\u001b[49m\u001b[43m \u001b[49m\u001b[43mmu\u001b[49m\u001b[43m)\u001b[49m\u001b[43m/\u001b[49m\u001b[43msigma\u001b[49m\u001b[43m)\u001b[49m\u001b[43m*\u001b[49m\u001b[43m*\u001b[49m\u001b[32;43m2\u001b[39;49m\u001b[43m)\u001b[49m / (sigma * math.sqrt(\u001b[32m2.0\u001b[39m * math.pi))\n",
      "\u001b[31mKeyboardInterrupt\u001b[39m: "
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "\n",
      "KeyboardInterrupt\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# generate simulations and pass to the inference object\n",
    "theta, x = simulate_for_sbi(\n",
    "    sim_for_training, proposal=priors, num_simulations=30_000, num_workers=1\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1597ec5f-99ef-4dfe-b7f9-679b0fde6fc8",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "032b9ee8-926d-4a6a-bbfc-b3767f10f8f6",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inference' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[25]\u001b[39m\u001b[32m, line 2\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;66;03m# Create inference object. Here, NPE is used.\u001b[39;00m\n\u001b[32m----> \u001b[39m\u001b[32m2\u001b[39m inference = inference.append_simulations(theta, x)\n\u001b[32m      3\u001b[39m \n\u001b[32m      4\u001b[39m \u001b[38;5;66;03m# train the density estimator and build the posterior\u001b[39;00m\n\u001b[32m      5\u001b[39m density_estimator = inference.train()\n",
      "\u001b[31mNameError\u001b[39m: name 'inference' is not defined"
     ]
    }
   ],
   "source": [
    "# Create inference object. Here, NPE is used.\n",
    "inference = inference.append_simulations(theta, x)\n",
    "\n",
    "# train the density estimator and build the posterior\n",
    "density_estimator = inference.train()\n",
    "posterior = inference.build_posterior(density_estimator)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d008311b-85c0-48cb-b195-f67b8b28d159",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1b25197b-23cb-4bf6-9d10-9abe431eb531",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "405fda6e-1c4b-4cfc-965c-8a017d358dbd",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "8a1c9ad3-716a-42db-a44e-d3c771013391",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "7128e03e",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_524916/212307759.py:8: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
      "  return lambda b: torch.load(io.BytesIO(b), map_location='cpu')\n"
     ]
    }
   ],
   "source": [
    "import pickle\n",
    "import io\n",
    "\n",
    "#https://stackoverflow.com/questions/57081727/load-pickle-file-obtained-from-gpu-to-cpu\n",
    "class CPU_Unpickler(pickle.Unpickler):\n",
    "    def find_class(self, module, name):\n",
    "        if module == 'torch.storage' and name == '_load_from_bytes':\n",
    "            return lambda b: torch.load(io.BytesIO(b), map_location='cpu')\n",
    "        else:\n",
    "            return super().find_class(module, name)\n",
    "\n",
    "with open(\"../posteriors/posterior_DEBASS.v5.MIX.pt\", \"rb\") as f:\n",
    "    posterior = CPU_Unpickler(f).load()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "32ecfaf7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "bfdb2315-dc52-4d6b-a2af-21513dc631ff",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels = unspool_labels(param_names, dicts, infos['Latex_Names'], infos['Functions'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "fa3f6e31-b4ba-4eb1-b3e1-064165be43f7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 29,
   "id": "094fac29-0e26-4ea7-8457-9ef851e9390f",
   "metadata": {},
   "outputs": [],
   "source": [
    "posterior.to(device=\"cpu\")\n",
    "#posterior.log_prob(theta_hat, x=x)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "92205ec3-c307-4254-b118-9d4650379c02",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_params = priors.sample()\n",
    "\n",
    "new_x = sim_for_training(true_params)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9107fa5f-fccf-45b2-b739-b60337eb5deb",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "4c02c846-94cc-4f0f-a9cb-73e82fb893e4",
   "metadata": {},
   "outputs": [],
   "source": [
    "x = preprocess_data(parameters_to_condition_on, dfdata)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 32,
   "id": "95c52e44-76a8-424c-a27a-428020f2500a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "torch.Size([306, 10])"
      ]
     },
     "execution_count": 32,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x.shape"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 33,
   "id": "d2865d70-0971-4656-8c59-de8bed6996a3",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/homes/b/bastienc/.miniconda3/envs/dustbi/lib/python3.13/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at ../aten/src/ATen/native/BatchLinearAlgebra.cpp:2190.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "50150it [00:10, 4887.84it/s]                           \n"
     ]
    }
   ],
   "source": [
    "\n",
    "posterior_samples = posterior.sample((50000,), x=x)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 42,
   "id": "3874e6d4-fe80-4412-bdf1-ef2ba8c9590f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "torch.Size([306, 10])\n"
     ]
    }
   ],
   "source": [
    "print(posterior.posterior_estimator.condition_shape)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 43,
   "id": "8430da8b-6b15-4a05-a4c5-92124460b58e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.0359,  0.0295,  3.9522,  1.4273,  3.3954,  0.9338,  1.5913,  0.0658,\n",
       "        -0.9359,  0.2392, -0.0558,  0.4500,  0.0933,  0.0816,  0.0842])"
      ]
     },
     "execution_count": 43,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "true_params"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 41,
   "id": "262214d3-d035-4704-988e-9ab8ab08a5be",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x1000 with 225 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = analysis.pairplot(\n",
    "    posterior_samples,\n",
    "    labels=labels,\n",
    "    upper_kwargs={\n",
    "        \"mpl_kwargs\": {\n",
    "            \"cmap\": \"viridis\" #matter_r, solar ?\n",
    "        }\n",
    "    },\n",
    "    diag_kwargs={\n",
    "        \"mpl_kwargs\": {\n",
    "            \"edgecolor\": \"darkorange\",  \n",
    "            \"linewidth\": 1.5,\n",
    "            \"facecolor\": \"none\"  \n",
    "        }\n",
    "    },\n",
    "\n",
    ");\n",
    "\n",
    "fig.savefig(\"posteriors.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 38,
   "id": "dd63a3f7-fea5-45c3-8982-d819700baf8d",
   "metadata": {},
   "outputs": [],
   "source": [
    "theta_hat = posterior_samples.mean(0)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 39,
   "id": "701472bc-cefc-482d-9f37-6eaac09efe31",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "tensor([-0.0528,  0.0761,  2.3965,  1.8643,  3.8169,  1.0442,  1.8940,  0.1902,\n",
       "        -0.2711,  0.9945, -0.0175,  0.1269,  0.1106,  0.0065,  0.0258])"
      ]
     },
     "execution_count": 39,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "theta_hat"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 45,
   "id": "19ed9da6-cd8c-4f27-9a9b-efdeaa510e5d",
   "metadata": {},
   "outputs": [],
   "source": [
    "# c_int mu, c_int std, Hi Rv_mu, Hi Rv_std, low Rv_mu, low Rv_std, x1 mean, x1 slope, x1 std, Hi EBV, low EBV\n",
    "#INP: -0.013 0.038 3.252 0.934 3.751 1.226 0.000 -0.040 0.328 0.112 0.063"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "39ff3e59-ed3a-4874-b3b4-b9455ae02167",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 46,
   "id": "09056f40-ec99-42a6-ac5f-c81b7ec7e02f",
   "metadata": {},
   "outputs": [],
   "source": [
    "original_errors = [0.006, 0.005, 0.25, 0.429, 0.375, .418, 0, 0,0,0, 0.018, 0.018, 0,0]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 47,
   "id": "f3e965f8-f4f9-491a-b27c-a7f8519153c1",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Old Errors: 0.006 0.005 0.250 0.429 0.375 0.418 0.000 0.000 0.000 0.000 0.018 0.018 0.000 0.000\n",
      "New Errors: 0.009 0.006 0.542 0.152 0.158 0.379 0.058 0.074 0.984 0.004 0.098 0.023 0.026 0.004 0.002\n"
     ]
    }
   ],
   "source": [
    "print(\"Old Errors:\",\" \".join(f\"{f:.3f}\" for f in original_errors))\n",
    "print(\"New Errors:\",\" \".join(f\"{f:.3f}\" for f in posterior_samples.std(0)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 48,
   "id": "952ac5f2-b562-47e7-ae6c-57f0ee0eec41",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_params = torch.tensor([-0.07, 0.053, 1.66, 0.95, 3.25, 0.93, 2.2, -0.1, 0.75, 0, 0.15, 0.12, 0, 0])\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 49,
   "id": "c90d79ab-754a-4810-b7aa-28937bd184fe",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/tmp/ipykernel_524916/1515167181.py:8: DeprecationWarning: __array_wrap__ must accept context and return_scalar arguments (positionally) in the future. (Deprecated NumPy 2.0)\n",
      "  err = np.sqrt(new_err**2 + old_err**2)\n"
     ]
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"c_{int}\" r'\\mu' = -0.053 \\pm 0.009~:~1.637 \\sigma~from~-0.070$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"c_{int}\" r'\\sigma' = 0.076 \\pm 0.006~:~2.997 \\sigma~from~0.053$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" Hi r'\\mu' = 2.396 \\pm 0.542~:~1.234 \\sigma~from~1.660$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" Hi r'\\sigma' = 1.864 \\pm 0.152~:~2.009 \\sigma~from~0.950$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" r'\\mu' = 3.817 \\pm 0.158~:~1.393 \\sigma~from~3.250$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" r'\\sigma' = 1.044 \\pm 0.379~:~0.202 \\sigma~from~0.930$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"\\beta_{int}\" r'\\mu' = 1.894 \\pm 0.058~:~-5.321 \\sigma~from~2.200$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"\\beta_{int}\" r'\\sigma' = 0.190 \\pm 0.074~:~3.935 \\sigma~from~-0.100$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"x_1\" r'\\mu' = -0.271 \\pm 0.984~:~-1.037 \\sigma~from~0.750$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"x_1\" r'\\sigma' = 0.995 \\pm 0.004~:~231.829 \\sigma~from~0.000$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"x_1\" r'\\mu slope' = -0.018 \\pm 0.098~:~-1.686 \\sigma~from~0.150$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"EBV\" Hi r'\\tau' = 0.127 \\pm 0.023~:~0.238 \\sigma~from~0.120$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"EBV\" r'\\tau' = 0.111 \\pm 0.026~:~4.231 \\sigma~from~0.000$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle STEP \\gamma = 0.006 \\pm 0.004~:~1.554 \\sigma~from~0.000$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "ename": "IndexError",
     "evalue": "list index out of range",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mIndexError\u001b[39m                                Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[49]\u001b[39m\u001b[32m, line 6\u001b[39m\n\u001b[32m      2\u001b[39m \n\u001b[32m      3\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m n \u001b[38;5;28;01min\u001b[39;00m range(len(theta_hat)):\n\u001b[32m      4\u001b[39m \n\u001b[32m      5\u001b[39m     new_err = posterior_samples.std(\u001b[32m0\u001b[39m)[n]\n\u001b[32m----> \u001b[39m\u001b[32m6\u001b[39m     old_err = original_errors[n]\n\u001b[32m      7\u001b[39m \n\u001b[32m      8\u001b[39m     err = np.sqrt(new_err**\u001b[32m2\u001b[39m + old_err**\u001b[32m2\u001b[39m)\n\u001b[32m      9\u001b[39m \n",
      "\u001b[31mIndexError\u001b[39m: list index out of range"
     ]
    }
   ],
   "source": [
    "from IPython.display import display, Math\n",
    "\n",
    "for n in range(len(theta_hat)):\n",
    "\n",
    "    new_err = posterior_samples.std(0)[n]\n",
    "    old_err = original_errors[n]\n",
    "\n",
    "    err = np.sqrt(new_err**2 + old_err**2)\n",
    "\n",
    "    delta = theta_hat[n] - true_params[n]\n",
    "    sigma = delta/err\n",
    "\n",
    "    label = labels[n]\n",
    "    label = label.replace(\"$\",\"\")\n",
    "\n",
    "\n",
    "    \n",
    "    string = rf\"{label} = {theta_hat[n]:.3f} \\pm {new_err:.3f}~:~{sigma:.3f} \\sigma~from~{true_params[n]:.3f}\"\n",
    "    #string = rf\"{labels[n]} = {theta_hat[n]:.3f} +/- {posterior_samples.std(0)[n]:.3f}\"\n",
    "    \n",
    "    #print(string)\n",
    "    display(Math(string))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 50,
   "id": "9f8c0b6d-8568-4d10-a466-e672791a36cf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"c_{int}\" r'\\mu' = -0.053 \\pm 0.009$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"c_{int}\" r'\\sigma' = 0.076 \\pm 0.006$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" Hi r'\\mu' = 2.396 \\pm 0.542$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" Hi r'\\sigma' = 1.864 \\pm 0.152$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" r'\\mu' = 3.817 \\pm 0.158$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"R_V\" r'\\sigma' = 1.044 \\pm 0.379$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"\\beta_{int}\" r'\\mu' = 1.894 \\pm 0.058$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"\\beta_{int}\" r'\\sigma' = 0.190 \\pm 0.074$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"x_1\" r'\\mu' = -0.271 \\pm 0.984$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"x_1\" r'\\sigma' = 0.995 \\pm 0.004$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"x_1\" r'\\mu slope' = -0.018 \\pm 0.098$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"EBV\" Hi r'\\tau' = 0.127 \\pm 0.023$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle r\"EBV\" r'\\tau' = 0.111 \\pm 0.026$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle STEP \\gamma = 0.006 \\pm 0.004$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/latex": [
       "$\\displaystyle SCATTER \\sigma_{\\rm int} = 0.026 \\pm 0.002$"
      ],
      "text/plain": [
       "<IPython.core.display.Math object>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from IPython.display import display, Math\n",
    "\n",
    "for n in range(len(theta_hat)):\n",
    "\n",
    "    new_err = posterior_samples.std(0)[n]\n",
    "\n",
    "\n",
    "    label = labels[n]\n",
    "    label = label.replace(\"$\",\"\")\n",
    "\n",
    "\n",
    "    string = rf\"{label} = {theta_hat[n]:.3f} \\pm {new_err:.3f}\"\n",
    "    #string = rf\"{labels[n]} = {theta_hat[n]:.3f} +/- {posterior_samples.std(0)[n]:.3f}\"\n",
    "    \n",
    "    display(Math(string))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "021c79ae-dcc3-4f50-bfa0-b1deb4a1ecbc",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "#true_params = torch.tensor([-0.07, 0.053, 2.6, 0.95, -0.1, 0.75, 0.09])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "aa0429e7-ba3d-41b4-8473-bf8987cb05f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "infos[\"Functions\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c334e092-aba4-42c2-ba3f-f0be90144141",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ea646bcb-e32c-4ba3-9601-539f9ca1d208",
   "metadata": {},
   "outputs": [],
   "source": [
    "sim_for_training = make_batched_simulator(layout, df,\n",
    "                        param_names,parameters_to_condition_on,\n",
    "                        dicts, dfdata, sub_batch=20, device='cpu')\n",
    "batched = True"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ac8a79d1-7641-4186-8e2a-b56ebb4984ad",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_values = priors.sample()\n",
    "\n",
    "sim_x = sim_for_training(true_values)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "9a14355c-8671-401d-8157-0a61934b1425",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bb6ea8d3-328e-4efe-bc2f-e3dbd7cc831d",
   "metadata": {},
   "outputs": [],
   "source": [
    "true_values"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "f04a6956-f9b5-4be0-8012-426b0cdbf6c2",
   "metadata": {},
   "outputs": [],
   "source": [
    "labels"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 51,
   "id": "5e718ced-3c76-4549-9bdf-317c1be75d44",
   "metadata": {},
   "outputs": [],
   "source": [
    "nbins = 30\n",
    "ranges_dict = {\n",
    "    \"c\":  np.linspace(-0.3,0.3,nbins),\n",
    "    \"mB\": np.linspace(18,25,nbins),\n",
    "    \"x1\": np.linspace(-3,3,nbins),\n",
    "    \"zHD\": np.linspace(0,1,nbins),\n",
    "    \"cERR\": np.linspace(0,.6,nbins),\n",
    "    \"mBERR\":  np.linspace(0,.6,nbins),\n",
    "    \"x1ERR\": np.linspace(0,.6,nbins),\n",
    "    \"MU\": np.linspace(38,45,nbins),\n",
    "    \"HOST_LOGMASS\": np.linspace(8,14,nbins),\n",
    "    \"MURES\": np.linspace(37,41,nbins)\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "07bc986e-099f-438b-b215-d8fd62b78303",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'plt' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[52]\u001b[39m\u001b[32m, line 7\u001b[39m\n\u001b[32m      3\u001b[39m \n\u001b[32m      4\u001b[39m     \u001b[38;5;66;03m#if \"ERR\" in templabel:\u001b[39;00m\n\u001b[32m      5\u001b[39m     \u001b[38;5;66;03m#    continue\u001b[39;00m\n\u001b[32m      6\u001b[39m \n\u001b[32m----> \u001b[39m\u001b[32m7\u001b[39m     plt.figure()\n\u001b[32m      8\u001b[39m     plt.title(templabel)\n\u001b[32m      9\u001b[39m     plt.hist(x[:,l], bins=ranges_dict[templabel], histtype=\u001b[33m\"step\"\u001b[39m, label=\u001b[33m\"Data\"\u001b[39m)\n\u001b[32m     10\u001b[39m     plt.hist(sim_x[\u001b[32m0\u001b[39m,:,l], bins=ranges_dict[templabel], histtype=\u001b[33m\"step\"\u001b[39m, label=\u001b[33m\"Simulated\"\u001b[39m)\n",
      "\u001b[31mNameError\u001b[39m: name 'plt' is not defined"
     ]
    }
   ],
   "source": [
    "for l in range(10):\n",
    "    templabel = parameters_to_condition_on[l]\n",
    "\n",
    "    #if \"ERR\" in templabel:\n",
    "    #    continue\n",
    "    \n",
    "    plt.figure()\n",
    "    plt.title(templabel)\n",
    "    plt.hist(x[:,l], bins=ranges_dict[templabel], histtype=\"step\", label=\"Data\")\n",
    "    plt.hist(sim_x[0,:,l], bins=ranges_dict[templabel], histtype=\"step\", label=\"Simulated\")\n",
    "    plt.legend()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0e1d87e0-6400-455b-b43e-b6defe0ab8e7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "76e746c9-51cf-49c9-a718-ab8456aa4ddb",
   "metadata": {},
   "outputs": [],
   "source": [
    "plt.hist(df.x1.values)\n",
    "plt.hist(dfdata.x1.values)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "20165399",
   "metadata": {},
   "source": [
    "### Calibrate those Posteriors"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1cd69d41-3737-40aa-a32b-c02cf024247a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "548449ac-57ae-42a7-b197-04edf93861f2",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "4bc85ea6-f9f2-484a-af15-ae994c0e7fa9",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sbi.diagnostics import run_sbc\n",
    "from sbi.analysis.plot import sbc_rank_plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 55,
   "id": "c296ae48-67dc-4346-ab20-f9159f2a96b7",
   "metadata": {},
   "outputs": [],
   "source": [
    "# Obtain your `posterior_estimator` with NPE, NLE, NRE.\n",
    "#posterior = inference.build_posterior()\n",
    "\n",
    "num_sbc_samples = 200  # choose a number of sbc runs, should be ~100s\n",
    "prior_samples = priors.sample((num_sbc_samples,))\n",
    "prior_predictives = sim_for_training(prior_samples)\n",
    "\n",
    "num_posterior_samples = 4000"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ae6154-f291-4374-bd16-397f1914cfc1",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d9037b2b-cad6-47e4-b448-9b428eaa87f5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 56,
   "id": "5baeb52d-8954-484b-b7e8-94df328053a4",
   "metadata": {},
   "outputs": [
    {
     "ename": "FileNotFoundError",
     "evalue": "[Errno 2] No such file or directory: '/Users/bpopovic/Documents/SBI_Testing/Objectivity/Objectivity-Bold.otf'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mFileNotFoundError\u001b[39m                         Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[56]\u001b[39m\u001b[32m, line 5\u001b[39m\n\u001b[32m      1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m matplotlib.pyplot \u001b[38;5;28;01mas\u001b[39;00m plt\n\u001b[32m      2\u001b[39m \u001b[38;5;28;01mfrom\u001b[39;00m matplotlib \u001b[38;5;28;01mimport\u001b[39;00m font_manager\n\u001b[32m      3\u001b[39m \n\u001b[32m      4\u001b[39m font_path = \u001b[33m\"/Users/bpopovic/Documents/SBI_Testing/Objectivity/Objectivity-Bold.otf\"\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m5\u001b[39m font_manager.fontManager.addfont(font_path)\n\u001b[32m      6\u001b[39m \n\u001b[32m      7\u001b[39m prop = font_manager.FontProperties(fname=font_path)\n\u001b[32m      8\u001b[39m font_name = prop.get_name()\n",
      "\u001b[36mFile \u001b[39m\u001b[32m~/.miniconda3/envs/dustbi/lib/python3.13/site-packages/matplotlib/font_manager.py:1136\u001b[39m, in \u001b[36mFontManager.addfont\u001b[39m\u001b[34m(self, path)\u001b[39m\n\u001b[32m   1134\u001b[39m     \u001b[38;5;28mself\u001b[39m.afmlist.append(prop)\n\u001b[32m   1135\u001b[39m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[32m-> \u001b[39m\u001b[32m1136\u001b[39m     font = \u001b[43mft2font\u001b[49m\u001b[43m.\u001b[49m\u001b[43mFT2Font\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[32m   1137\u001b[39m     prop = ttfFontProperty(font)\n\u001b[32m   1138\u001b[39m     \u001b[38;5;28mself\u001b[39m.ttflist.append(prop)\n",
      "\u001b[31mFileNotFoundError\u001b[39m: [Errno 2] No such file or directory: '/Users/bpopovic/Documents/SBI_Testing/Objectivity/Objectivity-Bold.otf'"
     ]
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from matplotlib import font_manager\n",
    "\n",
    "font_path = \"/Users/bpopovic/Documents/SBI_Testing/Objectivity/Objectivity-Bold.otf\"\n",
    "font_manager.fontManager.addfont(font_path)\n",
    "\n",
    "prop = font_manager.FontProperties(fname=font_path)\n",
    "font_name = prop.get_name()\n",
    "\n",
    "\n",
    "def DISCRETE_CMAP(CMAP, bins):\n",
    "    import matplotlib\n",
    "    cmap = plt.get_cmap(CMAP, bins)\n",
    "    colours = [matplotlib.colors.rgb2hex(cmap(i)) for i in range(cmap.N)]\n",
    "    return colours"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 57,
   "id": "990f233b-83f1-4e83-881b-5a22633f3051",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/homes/b/bastienc/.miniconda3/envs/dustbi/lib/python3.13/site-packages/sbi/diagnostics/sbc.py:66: UserWarning: Found 15 NaNs and 0 Infs in the data. These will be ignored below. Beware that only 185 / 200 samples are left.\n",
      "  thetas, xs = remove_nans_and_infs_in_x(thetas, xs)\n",
      "/global/homes/b/bastienc/.miniconda3/envs/dustbi/lib/python3.13/site-packages/sbi/utils/diagnostics_utils.py:45: UserWarning: Capping max_sampling_batch_size from 10000 to 540 to avoid excessive memory usage.\n",
      "  posterior_samples = posterior.sample_batched(\n",
      "Drawing 4000 samples for 185 observations: 4046it [01:04, 63.07it/s]                          \n",
      "Calculating ranks for 185 SBC samples: 100%|██████████| 185/185 [00:00<00:00, 1318.87it/s]\n"
     ]
    }
   ],
   "source": [
    "ranks, dap_samples = run_sbc(\n",
    "    prior_samples,\n",
    "    prior_predictives,\n",
    "    posterior,\n",
    "    num_posterior_samples=num_posterior_samples,\n",
    "    use_batched_sampling=True, # `True` can give speed-ups, but can cause memory issues.\n",
    ")\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 58,
   "id": "3da0f4d0-6d9c-459a-8941-366aba94a245",
   "metadata": {},
   "outputs": [
    {
     "ename": "ModuleNotFoundError",
     "evalue": "No module named 'cmasher'",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mModuleNotFoundError\u001b[39m                       Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[58]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m \u001b[38;5;28;01mimport\u001b[39;00m cmasher\n",
      "\u001b[31mModuleNotFoundError\u001b[39m: No module named 'cmasher'"
     ]
    }
   ],
   "source": [
    "import cmasher"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 62,
   "id": "7a84750a-ec3b-410d-bad5-be8bdd401d84",
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "\n",
    "\n",
    "from kestrel.dustbi_plotting import sbc_rank_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 65,
   "id": "5a0c8ae9-b025-44fe-93b7-6d482fba22bf",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "fig, ax = sbc_rank_plot(\n",
    "    ranks,\n",
    "    num_posterior_samples,\n",
    "    plot_type=\"cdf\",\n",
    "    num_bins=50,\n",
    "    figsize=(6,6),\n",
    "    parameter_labels = labels,\n",
    "    legend_kwargs={\"ncol\":2}\n",
    ")\n",
    "\n",
    "    \n",
    "\n",
    "#Aesthete\n",
    "ax.spines[['bottom', 'left']].set_linewidth(2)\n",
    "ax.spines[['top', 'right']].set_visible(False)\n",
    "plt.tick_params(axis='both', labelsize=14, length=6, width=2)\n",
    "\n",
    "fig.savefig(\"rank_cdf.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 66,
   "id": "198089c3-6b5c-4c3e-98e9-da3a4c107368",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = sbc_rank_plot(\n",
    "    ranks=ranks,\n",
    "    num_posterior_samples=num_posterior_samples,\n",
    "    plot_type=\"hist\",\n",
    "    num_bins=None, # by passing None we use a heuristic for the number of bins.\n",
    "    figsize=(10, 6),  # width, height in inches\n",
    "    parameter_labels = labels,\n",
    "    colors=['darkorange']\n",
    ")\n",
    "\n",
    "f.subplots_adjust(\n",
    "    wspace=0.5,  # horizontal spacing between subplots\n",
    "    hspace=0.6   # vertical spacing\n",
    ")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "f.savefig(\"rank.png\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "bd4864af-8598-469a-a610-684c0ed0df72",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e75de4f1-5bab-4836-90dc-7329aa285f70",
   "metadata": {},
   "outputs": [],
   "source": [
    "#Flat histogram → well-calibrated.\n",
    "\n",
    "#U-shaped → posteriors too narrow.\n",
    "\n",
    "#Bell-shaped → posteriors too wide."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 69,
   "id": "0008b895-ae89-4f62-81f1-d187cce1ff99",
   "metadata": {},
   "outputs": [],
   "source": [
    "num_tarp_samples = 200  # choose a number of sbc runs, should be ~100s\n",
    "# generate ground truth parameters and corresponding simulated observations for SBC.\n",
    "thetas = priors.sample((num_tarp_samples,))\n",
    "xs = sim_for_training(thetas)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "eb40c393-6dc6-44a4-b116-2535317069fb",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/global/homes/b/bastienc/.miniconda3/envs/dustbi/lib/python3.13/site-packages/sbi/diagnostics/tarp.py:71: UserWarning: Found 10 NaNs and 0 Infs in the data. These will be ignored below. Beware that only 190 / 200 samples are left.\n",
      "  thetas, xs = remove_nans_and_infs_in_x(thetas, xs)\n",
      "/global/homes/b/bastienc/.miniconda3/envs/dustbi/lib/python3.13/site-packages/sbi/utils/diagnostics_utils.py:45: UserWarning: Capping max_sampling_batch_size from 10000 to 526 to avoid excessive memory usage.\n",
      "  posterior_samples = posterior.sample_batched(\n",
      "Drawing 4000 samples for 190 observations: 4024it [00:38, 105.52it/s]                          \n"
     ]
    }
   ],
   "source": [
    "from sbi.diagnostics import check_sbc, check_tarp, run_sbc, run_tarp\n",
    "# the tarp method returns the ECP values for a given set of alpha coverage levels.\n",
    "ecp, alpha = run_tarp(\n",
    "    thetas,\n",
    "    xs,\n",
    "    posterior,\n",
    "    references=None,  # will be calculated automatically.\n",
    "    num_posterior_samples=4000,\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "bd1d003a-8a73-472f-a34d-7733ea70821f",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "0.13852575421333313 Should be close to 0\n",
      "1.0 Should be larger than 0.05\n"
     ]
    }
   ],
   "source": [
    "atc, ks_pval = check_tarp(ecp, alpha)\n",
    "print(atc, \"Should be close to 0\")\n",
    "print(ks_pval, \"Should be larger than 0.05\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4aab5e86-fb2e-46fe-90a6-f14a50cd040c",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "id": "7e1ea0d0-c00d-4828-85eb-f630d1fa29d0",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tarp(ecp, alpha, savename):\n",
    "    \n",
    "    \n",
    "    fig = plt.figure(figsize=(6, 6))\n",
    "    ax: Axes = plt.gca()\n",
    "\n",
    "    ax.plot(alpha, ecp, color=\"darkorange\", label=\"TARP\", lw=3)\n",
    "    ax.plot(alpha, alpha, color=\"black\", linestyle=\"--\", label=\"IDEAL\", lw=2)\n",
    "    ax.set_xlabel(r\"Credibility Level $\\alpha$\", fontsize=15)\n",
    "    ax.set_ylabel(r\"Expected Coverage Probability\", fontsize=15)\n",
    "    ax.set_xlim(0.0, 1.0)\n",
    "    ax.set_ylim(0.0, 1.0)\n",
    "    ax.legend(frameon=False, labelcolor='linecolor', fontsize=\"x-large\")\n",
    "\n",
    "    #Aesthete\n",
    "    ax.spines[['bottom', 'left']].set_linewidth(2)\n",
    "    ax.spines[['top', 'right']].set_visible(False)\n",
    "    plt.tick_params(axis='both', labelsize=14, length=6, width=2)\n",
    "\n",
    "    ax.text(x=0.4,y=0.01, s=\"Over Confident\", color=\"darkorange\", fontsize=12)\n",
    "    ax.text(x=0.01,y=0.4, s=\"Under Confident\", color=\"darkorange\", fontsize=12, rotation=-90)\n",
    "    \n",
    "    plt.savefig(savename, bbox_inches=\"tight\")\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "id": "c794c91a-be7b-48e9-86d3-4d797671c36e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 600x600 with 1 Axes>,\n",
       " <Axes: xlabel='Credibility Level $\\\\alpha$', ylabel='Expected Coverage Probability'>)"
      ]
     },
     "execution_count": 78,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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5vLy8WLt2LTNnzuTbb78lderUVv4ORETiwbbhcHgyAFf/CznHbpi6bt4O4u7du2TKlMmGBSYdenQldi9HjhzMnz+fOnXqcPHiRb799tt4u/bkyaZvJL169aJz584EBgaydOnSeLu+iMgr2/8b7BgFmEJO3d+fhpz8+fMTEBBAwYIFbVhg0qKgI0mCg4MDX3zxBQDz5s3DaDTG+ZrXr19nxYoVFCtWjGrVqpnvBk2aNCnO1xYRscrxhbDpfwBcCYI6v8Pxm6au/Pnz4+/vr5DzivToyk6MHz+e8ePHv3RcxYoVWbFihUVbixYt2Ldv30vfO2jQIAYNGmR+fe/evVjvnrl8+XIqVaoUq7EJpUaNGjg5OXHjxg3OnTsX5Q/7smXLOHfuXLTv7dChAyVKlLBomz59OmFhYeaAU6ZMGSpVqoSfnx+nTp2iSJEiCfHbEBGJ3vmNsKYLYORykOlOzsn/ts0pUKAAfn5+FChQwJYVJkkKOnYiODiYy5cvv3Rc3rx5o7TdvHkzVu8NDg62eG00GmP1PoDQ0NBYjUtIzs7OZMmShevXr3Pz5s0oQWf58uUsX7482vdWqFDBIug8mYTs4OBAt25Pz4zx8vJi7969TJ48me+//z5hfiMiIs+7vs98tMOlu1D3Dzj1X8gpWLAgfn5+5M+f36YlJlUKOnbCzc0Nd3f3l47Lli1btG2xea+bm5vFa4PBEKv3AXYzOffJI6volqhPnz491pORN23axOnTp/H09LT4d9CpUycGDx7MjBkz+Prrr0mVKlW81C0iEqM7p0wbAobeA+DHzU9DTqFChfDz8yNfvnw2LDBpU9CxE88/VnoVzz/Kiq306dNz6dIlq95rCyEhIdy+fRuIPvC9iifzcJ4PRpkzZ6Z58+Z4e3uzfPly2rRpE6fPERF5oQfXwNsTHt4wN33XBC6E52Hv1dT4+/tHeydfYk+TkSXJ2LJlC+Hh4eTIkSNOz6lv3rxp3km0Y8eOUTYX9Pb2BjQpWUQS2OMg8G4MQWcsmp0K1GXu+qNs27ZNISce6I6OJAmRkZF88803gOnxUlzMnDmT0NBQKlWqFONGgitWrGDDhg2cPXtWKxxEJP4Fn4fVneHmAc7fhpBwKJ4dyFYBWi4jlbMrOdK42rrKZEFBR+zejRs36N+/P/7+/uTLl4+hQ4fG6XpP9s6ZOHEiVapUiXbMsGHD+Prrr5kyZYo5YImIxFlkOOz7GbZ9CWEPOHfbtLrqcQT4f5yHYq19wNnt5deRWFPQEbvyZGfkyMhI8xEQW7ZsITQ0lCpVqjB37lyyZs0a7XtftLy8QIECeHl54e/vz4kTJyhbtmyMIQfg3Xff5ZtvvmH69Ol89dVXODk9/aOyZcuWGCc9V6xYkQEDBsTq9yoiKcz1vbCuN9wwbQdy7rZpn5zzd0zdfdflYdMXOW1YYPKkoCN25auvvgJMq7zSp09P/vz56datG61bt6Zhw4Y4OMQ8rexFy8tr166Nl5eX+W5Oz549X1hHgQIFaNCgAevXr2flypW0atXK3Hf69GlOnz4d7fvu3r2roCMilkLvw9ZhsP9nMEYCcPaWKeRcuGsaUrxIAebM97ZdjcmYwRgfW8ymUHny5OHy5cu4u7snqdVLIiKSSE6vgo3vw70L5qYzt0yPq56EnBLFi7HJz59cuXLZpsZkTnd0RERE4tv9q+A3AE4stmg+HWjaDPDiXdPrkiVLsmnTJnLm1COrhKKgIyIiEl+MkXDwT9g8BEItd6M/9V/IuXTX9LpUqVJs2rSJHDlyJH6dKYiCjoiISHwIPALr+8CVbVG6bj+EOpOcuXz3MQClS5dm48aNCjmJQBsGioiIxEXYI9jyOcx+LdqQg0sWMr89k17/GwKYDhDWnZzEozs6IiIi1jq/ETb0hbunou8v3R1qjYW0WRleGrLnyEGbNm3ifIyNxJ6CjoiIyKt6eBMCBsO/s6Pvz1iEsDq/kapwQ4vmfv36JUJx8iw9uhIREYktoxH+mQnTS0YfchycoOrnHKuyiBIN+7Fx48bEr1EsKOiIiIjExp2TsLgBrPWCkFtR+3NXg64HOJqlM3UbNubMmTM0b96crVu3Jnqp8pRVQSembfZFRESSnYhQ2PE1zCwLFzZF7XfOAA1+hw6b+feGgbp163Lt2jUASpQoQcmSJRO5YHmWVUGnSJEiNGrUCG9vb8LDw+O7JhEREftwaYtpNdXWYRDxOGp/sXbgdRTK9+Wff49St25drl+/DpjOvtuwYQOZM2dO5KLlWVYFncKFC7Nu3TratWtHnjx5GDJkCCdPnozv2kRERGwj5K5pT5wFNeHWv1H70+eDVqug+QJwzcWRI0eoW7cuN27cAKBSpUoKOXbCqqBz/Phx/Pz86NixI8HBwYwZM4YSJUpQr1495s+fT2hoaHzXKSIikvCMRji2AKaXgEOTovYbHKDSIPD6Bwo1BeDw4cPUq1ePmzdvAlC5cmU2bNhApkyZErNyiUGcD/W8e/cus2fPZsqUKRw+fBiDwUCmTJno1q0bvXr1StbPJnWop4hIMhJ0znQA59k10ffnqAQekyBHRXPToUOHqF+/PoGBgQC8/vrrrFu3jowZMyZ8vRIrcV51lTFjRv73v/9x8OBBduzYQY8ePQgNDeWnn36iTJky1KxZk9mzZ/P4cTTPNkVERGwtMhz2jIMZpaMPOanSQZ3x0GmHRcgBOH36NHfu3AGgatWqrF+/XiHHzsT5js7zjh8/zrhx45gyZcrTDzEYyJo1K8OGDaN///7x+XE2pTs6IiJJ3M1DpuXiN/ZH31+oGdT/DdzyxXiJRYsW8fPPP7Nq1SoyZMiQMHWK1eIl6ISEhLBo0SImT57M1q1bMRqN5MyZkx49elCvXj0WLFjAnDlzCAkJYcSIEQwbNiw+arc5BR0RkSTs7mmYUwkeB0XtS5cL6v0MRVuDwfDSS0VGRuLgoK3p7FGcgs6hQ4eYPHkyc+fOJSjI9D9K3bp16du3L2+99RZOTk9PmDh//jxvvPEGTk5OXLx4Me6V2wEFHRGRJMpohMUecOH5nYsNUL4v1Bxt2h/nOfv27WPfvn307NkzceqUOLPqrKspU6YwefJk9uzZg9FoJEuWLAwcOJA+ffpQtGjRaN+TP39+PDw8mDt3bpwKFhERibN/Z0UNOVnLmCYb534z2rfs3bsXDw8P7ty5Q3h4OH379k2EQiWurAo6vXv3BqBatWr07duXtm3b4uzs/NL3lStXLtnczRERkSTq4U3wH2TZ5poHOm6D1OmjfcuePXvw8PDg7t27APz111/06tULR0fHBC5W4sqqR1f/+9//6NOnD2XKlEmImpIMPboSEUmC1nSBo889XXhrBRRuHu3w3bt307BhQ3PIqVmzJmvWrMHV1TWBC5X4YNXMqY8//pjcuXO/dNydO3e4cOGCNR8hIiIS/875Rg05xdrGGHJ27dplcSenVq1aCjlJjFVBp2DBgnz88ccvHffJJ59QqFAhaz5CREQkfoU9gPXPzatxzmhaXRWNnTt34uHhYV5sU6dOHYWcJMiqoGM0GontE6+4rF7fvXs3TZo0IWPGjKRLl4433niDhQsXvtI1rly5wgcffECpUqVIly4dOXLkoEaNGsyePZuIiAiraxMRkSRm63AIPmfZVusHSJczytDt27fj4eFBcHAwYFpRvGrVKtKlS5cIhUp8smoycmwFBgaSJk0aq97r5+eHp6cnLi4udOjQgfTp0+Pt7U379u25ePEigwcPfuk1zpw5Q9WqVbl16xaenp40b96c4OBgli1bRrdu3di0aRPTp0+3qj4REUlCru+FfRMs2/LUgrI9ogwNDQ2lY8eO3Lt3D4B69eqxcuVK0qZNmxiVSjyL9WTkv//+2/zrOnXq0KhRI4YMGRLt2PDwcI4fP87gwYMpXbo0u3fvfqWiwsPDKVGiBJcuXWLHjh1UqFABgKCgIKpUqcK5c+c4ceIE+fPnf+F13nvvPX7//Xd+/PFHPvjgA3P73bt3KV++PBcuXODcuXMvvU5MNBlZRCQJiAyHuVUsdz92dIZuByFz8WjfsmfPHho0aEDlypVZsWKFQk4SFus7OnXq1MHwzO6Qvr6++Pr6xjjeaDRiMBhidefleZs2beL06dO888475pADkCFDBoYOHYqXlxczZ87kyy+/fOF1zpw5A0CTJk0s2jNmzEiNGjX466+/CAwMtDroiIhIErD3x6hHPLzxRYwhB0wnkG/ZsoVChQop5CRxsQ463bp1MwedmTNnUrhwYapXrx7t2NSpU5M7d26aN29OxYoVox3zIv7+/gA0bNgwSp+npycAAQEBL71OmTJl8PX1Zc2aNVHu6GzdupWcOXNSqlSpV65PRESSiLtnYNtzPxRnKQ2vf2LRdOLECYoWLWrxA31K30IluYh10JkxY4b51zNnzqRGjRpMmzYtIWri5MmTANHuspwzZ05cXV3NY17k448/ZuXKlQwcOJC1a9dSrlw58xydtGnTsnTpUqvnEImIiJ0zGmFDXwh/9EyjARpOBsfU5pa///6bJk2a0L17d3799VeLsCNJn1WTkSMjI+O7DgtPlvLFdAqsm5ubecyL5MiRg+3bt9OlSxd8fHxYu3YtAGnSpKFv376UL1/+pdcYP34848ePj7bv6tWrL32/iIjYyNE5cH69ZVuF9yyOeAgICKBJkyY8fPiQiRMnUrZsWR3tkMwk6KorWzt16hTNmzfH1dWVzZs3U6FCBe7evcucOXP44osv8PX1ZfPmzS/cwjs4OJjLly8nYtUiIhJnDwPBb6Blm6s71PjW/NLf35+mTZvy8OFDABo3boyXl1ciFimJIVZBZ+TIkRgMBt5//30yZ87MyJEjY/0BBoOBYcOGvVJRT+7kxHTXJjg4mEyZMr30Ol5eXpw/f54zZ86QM6dpnwRXV1eGDBnC9evX+fHHH5k/fz6dO3eO8Rpubm64u7tH23f16tUEv7slIiJWCBgEIbcs2+r/Bs5ugGkLk6ZNm/LokemxVtOmTfH29o7VuY2SxBhjwWAwGB0cHIzHjx+3eG0wGF765eDgEJuPsPDZZ58ZAeO8efOi9F29etUIGOvVq/fCawQHBxsBY8WKFaPtX758uREwDh48+JXre8Ld3d0IGN3d3a2+hoiIxLOzvkbjWCy/lrc2d2/cuNGYJk0aI2AEjM2aNTOGhITYsGBJSLG6o/NkU71cuXJZvE4otWvXZvTo0axbt44OHTpY9D1Z0l67du0XXiM0NBQwbVoYnZs3bwIovYuIJCdhD00TkJ/lnAHq/QLAxo0badasGSEhIQA0b96cRYsW6e+CZMyq08sTWnh4OMWLF+fy5csxbhh4/PhxChQoAJgeIQUFBZErVy6LCcwlSpTg+PHjTJ48mZ49e5rb7969y5tvvsmxY8dYv349DRo0sKpObRgoImJnAj6BPT9YtjX4A8r3ISAggEaNGplDTsuWLVm4cCGpU6eO5kKSXFh11lVCc3JyYsqUKURGRlKrVi169+7N4MGDKV++PCdOnODbb781hxyAzz77jJIlS7J06VKL60yYMAEnJyd69epFgwYN+Pjjj+nZsyfFihXj2LFjtG7d2uqQIyIidub6ftj73CpZ95pQrhdg2rIkb968ALz11lsKOSmE3a66qlu3Llu2bGH48OEsWLCAsLAwypYty/fff0/79u1jdY3GjRuzbds2fvjhB7Zs2UJAQAAuLi6ULFmSL7/8kn79+iXw70JERBJFZDis6wnGZw5rdkwNHn+CwfQzfe7cufHz8+OHH35gzJgxCjkpRKweXfXoEfXQs1h/gMHA1KlTrX6/PdOjKxERO7FnPAQ8d+TQmyMwvvmlNgBM4WIVdBwcrH/CZTAYiIiIePnAJEhBR0TEDgSdhRllIPzh07bMJVmTdTS/T5rCwoULtQt+CharR1d+fn4JXYeIiMirMxphQz/LkAOsdnyHt9u0IzQ0lJYtW7J8+XKFnRQqVkHnZUu5RUREEl3YQ9j6BZzztWhe+bARrXt+TlhYGACZMmUiVapUtqhQ7IDdTkYWERGJ0Tlf052coLMWzStOZ6bNlI3mkNOhQwdmz56Nk5P+ukup9F9eRESSjoc3TGdYHfsrStfyI9B2bjBhYeEAdOzYkVmzZinkpHCx+q9fr149DAYDM2fOJE+ePNSrVy/WH2AwGNi4caPVBYqIiGA0wpFp8PfHEHInSvfSI460m20kPMIUcjp16sTMmTMVciR2Qcff3x+DwWA+4dXf3z/WH6BlfSIiEie3jsGGPnDp72i7l1wuQfs5pwj/b4Vvly5dmDFjBo6OjolZpdipWAWds2dNz0CfnOL95LWIiEiCCX8Mu0abviJCo/Y7Z8RY83smD1lCePgxALp27cr06dMVcsTMLs+6Siq0j46ISAK59Des7wO3j0XfX7wD1J0A6XLy8OFDmjVrRt68eZk2bZpCjljQw0sREbEfj27D35/AkRh21HcrAA1+h4KNzE1p06Zl9erVpE6dWiFHoohT0Hn8+DHe3t5s3ryZK1euAKazRGrUqEHr1q1xcXGJlyJFRCSZMxrh2DzwH2haWfU8gyNUGgTVhrPCZyOVna+QO3duc7c2A5SYWP3oasOGDXh5eXH16lWev4TBYCBnzpzMmDEDDw+PeCnUHunRlYhIPAi+AOt6wfl10ffnfB08JkH2CsyfP5/OnTtTpEgR/P39yZUrV+LWKkmOVXd0du7cSbNmzQgNDaVq1ap07NiRAgUKAHD+/HnmzZvHjh07aN68OQEBAVStWjU+axYRkeTi4U2YVx3uR/PDYipXqPEtVHgPHBz566+/6Nq1K5GRkZw4cYJJkyYxfPjwxK9ZkhSr7ug0bNiQjRs3MnHiRPr06RPtmEmTJtG3b188PDzw9fWNdkxSpzs6IiJxYDTCspZwZmXUviJvQb1fIH0eAObOnUu3bt2IjIwEoE+fPkycODFOh05LymBV0MmQIQMlSpRg586dLxxXtWpVjh07RlBQkNUF2jMFHRGRODj4J2zoa9nmmhvq/QpFW5mbZs+ejZeXlznk9O3bl99++00hR2LFqv9LHBwcKFKkyEvHFSlSRBsGiohIVLePmyYePyttduiyzyLkzJw5k+7du5tDznvvvac7OfJKrPo/pUqVKhw6dOil4w4dOkSVKlWs+QgREUmuIsJgTRcIf2TZ7jkd0uUwv5wxYwbvvPOOecHL+++/z6+//qofoOWVWBV0Ro0axcmTJxk+fLg5ZT/LaDQyfPhwTp48yahRo+JcpIiIJCPbR8D1PZZt5d+DQk3ML7dt20aPHj3MIed///sfv/zyi0KOvLJYzdGZNWtWlLatW7cyZcoU8ufPT+vWrcmfPz9gWnW1ZMkSzp07R69evahWrRrdunWL/8rtgOboiIi8okubYUFt4Jm/ejKXgC57IVVac1NkZCTvv/8+f/zxBx988AETJkxQyBGrxCroODg4RPs/2LNvfdIf3Z46Ef8dtJbcKOiIiLyCx0EwqzwEn3/a5pAKOu2AHBWjDI+MjMTb25s2bdoo5IjVYrWPzpdffqn/yUREJG429rcMOQDVR5lDzu3bt8mcObO5y8HBgbZt2yZmhZIM6VDPONAdHRGRWDo2H1Z3tGzLUxvabgQHR/7880+GDh3K+vXrqVgx6t0dEWtpfZ6IiCSs4AtR98txzgCNZ4GDI3/88Qd9+/bl9u3bNGjQQD84SrxS0BERkYQTGQE+3Uzzc55V/3dwy8fEiRPp16+fublXr164u7sncpGSnMXp9PItW7awfPlyTp48yb1796JMRAbTZOSNGzfG5WNERCSp2jMOLgVYtpXsDCU78ttvv9G/f39z86effsro0aM1J1TilVVBx2g08u677zJz5kxzuDEYDFFWYRmNRv0PKyKSUl3fB1u/sGxzyw/1f+PXX3/lf//7n7l56NChfP311/o7Q+KdVY+u/vjjD2bMmEGlSpVYv349b7/9NgDHjx/Hx8cHLy8vHBwc+Pjjjzlz5ky8FiwiIklA2ENY0xkiw55pNEDj2fz850yLkPP5558r5EiCseqOzowZM0iXLh0+Pj5kyZKFOXPmAFC0aFGKFi2Kp6cnTZo0oX379lSrVs28maCIiKQQAR/D7WOWbVWG8JP3Pj788ENz07Bhw/jqq68UciTBWHVH5+jRo1SrVo0sWbIATzcLfHZjwDZt2lCpUiXGjh0bD2WKiEiScWYNHJxo2ZajElQbgZPT05+vv/zyS4UcSXBW3dGJjIw0hxyAtGlN23bfuXOHrFmzmtuLFi3K6tWr41iiiIgkGQ9vgO87lm1OaaHJXHBMzfvvvw/AzZs3GTFiROLXJymOVUHH3d2dK1eumF8/eTS1f/9+PDw8zO0nTpywSO8iIpKMGY3g+64p7DyrznjIXNz88knYEUkMVj26qlixIv/++6/5UVXDhg0xGo188sknHDt2jHv37vHDDz+wd+9eXnvttXgtWERE7NShP+HMKoumcYdL4X0ySwxvEEl4VgWdFi1aEBgYaH4sVb58eTp06MDBgwcpXbo0GTNmZMiQITg5OfHNN9/Ea8EiImKHbh8H/0EWTd9vScdHM/+lQ8eOLFmyxEaFSUpn1XOljh078vbbb1s8lpo5cyblypVj2bJl3Llzh2LFivHJJ59QpUqVeCtWRETsUESoaSl5+CNz0+iNMNTnAQDh4eGcOHHCVtVJCqdDPeNAh3qKSIoXEQprusKJheambzfC5z5Ph4wePZohQ4bYoDiROB4BISIiKVjYI1jZGs4+TTVfb4Bha58O+f777/nkk09sUJyISZyCzuPHj/H29mbz5s3mVVi5c+emRo0atG7dGhcXl3gpUkRE7MzjYFjWwuIcq5HrYPi6p0PGjBnDxx9/bIPiRJ6y+tHVhg0b8PLy4urVq1EO8zQYDOTMmZMZM2ZYLDdPbvToSkRSpEe3YEljuLbb3PTVOhjxTMgZO3YsgwcPtkFxIpasuqOzc+dOmjVrRmhoKFWrVqVjx44UKFAAgPPnzzNv3jx27NhB8+bNCQgIoGrVqvFZs4iI2Mr9q+DdEAKPmJvO3ILv/J4OGT9+PAMHDrRBcSJRWXVHp2HDhmzcuJGJEyfSp0+faMdMmjSJvn374uHhga+vb5wLtUe6oyMiKUrQOVjcAO6etmxPm511WUbQsvsgRo8ebXGWlYitWRV0MmTIQIkSJdi5c+cLx1WtWpVjx44RFBRkdYH2TEFHRFKM28dhUQO4/9z3Otc80HYDZC7OxYsXyZs3r23qE4mBVRsGOjg4UKRIkZeOK1KkiA5rExFJ6m4cgPk1zSHHaAS/U0DGItBxi/l4B4UcsUdWBZ0qVapw6NChl447dOiQNgwUEUnKrmyHhXXg0U3AFHKG+kC9P+CbC63BLb9t6xN5CauCzqhRozh58iTDhw8nMjIySr/RaGT48OGcPHmSUaNGxblIERGxgfMbYbEHPDZNPzAaYchq+G6TqfuLkd+zb98+GxYo8nKxWnU1a9asKG3du3fn66+/Zvbs2bRu3dp8gvn58+dZsmQJ586do1evXhw/flyrrkREkppTy2FVO9POx5hCzierYOzTbXOYOHEiFStWtFGBIrETq8nIDg4O0c61efatT/qj21PnySnnyY0mI4tIsnR0Lvh0B6Ppe7fRCB+vgnHPhJw//vgjxlW3IvYkVnd0vvzyS00qFhFJCQ7+CRv6AaYfWo1GGLwSJvz9dMiff/5J7969bVOfyCvSoZ5x8Mp3dB7ehAsbIfg8hIe8YKARqo2IrzJFRGJn9w/w99NzqYxGGLgCftr8dMjkyZPp2bOnDYoTsY4O9UwsO7+F7SMhMuzlY40KOiKSiCIjYMtQ2D3GovmrdU9DjsFgYMqUKfTo0cMGBYpYL85B5/Lly2zdupXLly8D4O7uTvXq1XF3d49zccnG0XmwdRgUfRvK94NMxcEpja2rEhGBh4GwphOcXx+lq8M77/PnYW+uX7/O1KlTeeedd2xQoEjcWB10bt68yfvvv8/SpUujLDE3GAy0bt2aX3/9lWzZssW5yCTvwK+Quxo0Wwia6yQi9uL6Pljxtulx+vNqj6NE5UH4vdafffv20alTp8SvTyQeWBV0goKCqFWrFsePHydNmjQ0bNiQAgUKYDAYOHfuHL6+vixatIhDhw6xY8cOMmTIEN91Jy2Bh+GNYQo5ImI/jsyADX0h4rG5KTISIg2OOHn+CWXfBaBEiRKUKFHCRkWKxJ1VQee7777j+PHjtG3bNtq7NoGBgfTv35+FCxfy/fff8+2338ZLsUlWZDg4pbV1FSIiEP4Y/D+Eg39YNEdGwvsr0xKUuRqzPuyuCZySbFi16qpEiRI8evSIU6dOkSpVqmjHhIWFUaRIEVxcXDh+/HicC7VHsV51NaM0ZCsPTf9KvOJERJ537zKsbANXd1g0R0ZCP9+cTNp4DYCuXbtGu1GsSFJk1REQ58+fp3r16jGGHIBUqVJRvXp1Lly4YHVxyUaJTnBsPuz9EcIe2boaEUmJLgbAnIrRhpy+AaXNIcfBwYFGjRrZokKRBGHV3ck0adIQGBj40nGBgYGkSaPVRbz+CVzZCgGDTXtUpM8LqV7wKMtoBK8jiVefiCRfRiPs+xECPjbvdPxEpIMLfXa8wZTV/oAp5MyZM4eOHTsmfp0iCcSqoFOpUiUCAgLYs2cPlStXjnbM3r178ff3p06dOnGpL3lwTAVvr4HTq+DsGtMKh4gQ0zcgEZGEEnof1vWE4wuidEWmL0ivgPJMW7wMAEdHR+bOnUv79u0TuUiRhGVV0Bk4cCAbN26kfv36DBgwgE6dOlGgQAHA9Fhr3rx5/Pzzz0RERDBw4MD4rDdpK9zM9CUiktDunITlreDWP1G6IvI1pufKjMz4ax5gCjl//fUX7dq1S+wqRRKc1UdAjB49mmHDhkU5xNN8YYOBUaNG8dlnn8WpQHumQz1FxC6dXglrukBocJSuiCrDeHfqeWbONE02dnR0ZP78+bRp0yaxqxRJFFavIPzss8/w8PDgl19+YcuWLVy5cgWA3LlzU7NmTd5//31ef/31eCs0SbsY8PIxAHlrJ2wdIpK8RUbA9hGw4+uofc4ZoPEcHmSrxaFDdQBwcnJi/vz5tG7dOlHLFElMVt3RWbFiBalSpaJx48YJUVOSEes7OuMdYzcfZ3Dky8eIiEQn7AGsbAtnfaL2ZS0LLZZApiIA3L59m8aNGzNkyBBatWqVyIWKJC6rgo6joyMNGzbExyeaP1ApSKyDzsklUdsiI+DeRTgyDRydocoQKN424YoVkeTLaIRVHeDEwqh9JTpBw0mQKp1Fc2RkJA4OVu0wIpKkWPXoKlu2bGTKlCm+a0m+ir4dc1/FD2Dem3DnROLVIyLJy55xUUOOwRHqjCO87Ht8O3o0H3zwgcVxPAo5klJY9X96nTp12LVrV4wTkeUVODhCyS5RtmMXEYmV8xtg86eWbc4ZoN0mwsu9T9du3Rg+fDgNGzYkKCjINjWK2JBVQWfUqFEEBgYycOBAQkJC4rumlOdxEIS8fANGERELQedMj6yMz87vM0CTvwjPWY0uXbowf/58AA4cOMD+/fttUqaILVn16GrevHk0adKEX375hfnz59OgQQPy5cuHi4tLlLEGg4Fhw4bFudAkLfh89O0hd+DSZtj9PeSunrg1iUjSFvYIVrwNIbcs26uPJCyvB507dWLRokUApE6dmiVLlmgDV0mRrJqM7ODggMFgiNWjK4PBQERExEvHJUXxsurKYICcVaDJHMhYOGEKFZHkxWiEtd3h39mW7YVbEtZkAZ06d2Hx4sWAKeQsXbqUJk2a2KBQEduz6o7O9OnT47uO5M0zmn9fBgM4Z4KMhSBLqcSvSUSSrv2/Rg05mYoT1mAqHTp2YskS00pPZ2dnli1bpkM6JUWzemdk0c7IImIDl/6GhfUsD+hMnZ7QNlvo8P4Ili5dCphCzvLly/H09LRRoSL24ZXu6ISHh7Nr1y4uXryIs7Mz5cqVo1ChQglVW/ITGQ7X9jyds+OWH3JWBgerN6gWkZTk3iXTpoDPnUJOo1n88td6c8hxcXFh+fLlNGzY0AZFitiXWP8Nu2rVKvr27cvVq1ct2j08PJg1axbZs2eP9+KSlUOTYesX8PCm6bEVmJ6zp80ONb6Bsu/atj4RsW/hj2FFa3h4w7L9jS+g6FsMGBDG1q1b8fHxYcWKFXh4eNimThE7E6tHV/v37+eNN94gLCwMV1dXihUrxr179zhz5gyRkZG8/vrr7NixIzHqtSuxfnR18E/Y+B5kqwDl+0Kmoqb2O6fg0B9wfR94/AHleidK3SKSBK3rBYenWLYVbAxvrTTtxwWEhoZy+PBhKlWqZIMCRexTrPbR+eGHHwgLC6NXr15cu3aNPXv2cPz4cQ4ePEjRokXZvXs369evT+hak649P0DuatB5J5TrBXnrmL7K9YROO8C9BuweY+sqRcReHZoUJeQ8TleIC2W+N4ccMK2wUsgRsRSroLN582YKFCjAxIkTSZs2rbm9VKlS/PbbbxiNRjZv3pxgRSZ5969A8fbRz8VxcDL13b+S+HWJiP27sh029rdoCjGm4W1vd6rXb8Lp06dtVJhI0hCroHP9+nUqV66Mo6NjlL4333zTPEZikLUsBJ2NuT/oDGQrl3j1iEjScP+qaV5OZJi5KSQM3l5ZnDUbNnPp0iWaNWtGeHi4DYsUsW+xCjrh4eG4urpG2/fkDk9YWFi0/QLUGQ//TDdNSI585htSZDgcnmrqqzXWdvWJiP2JCDWtsHrwdAFISBi0WloAn78PAKbvv3/88QdOTlq5KRIT/elIDFuHgUsWWN/HdPieW0FTe9AZ0zlXGQvBtueOyTAaob1/opcqInbCfxBc2Wp+GRIGb83PjO/BcwCkS5eONWvWUKtWLRsVKJI0xDrobNmyhR49erxyv8FgYOrUqdZVl1wYDJA+r+nrWdlfe/F7RCRlOjIDDvxmfvkoDN6a7cK6f28DppDj4+NDzZo1bVSgSNIRq+XlDg5WHXJu+oA4nHW1e/duhg8fzrZt2wgLC6Ns2bIMGjSIdu3avdJ1bty4wejRo1m1ahUXL14kXbp0FCtWjG7dutGvXz+ragPtjCwiCeDaHphfAyIeA6aQ03KGA+uPm04od3V1xcfHhxo1atiySpEkI1Z3dGxxtpWfnx+enp64uLjQoUMH0qdPj7e3N+3bt+fixYsMHjw4Vtc5cOAADRs25M6dOzRt2pQ2bdpw//59jh49ysqVK+MUdERE4tXDm6YTyf8LOWER0GIabDj5NOSsXbuW6tWr27JKkSTFLs+6Cg8Pp0SJEly6dIkdO3ZQoUIFAIKCgqhSpQrnzp3jxIkT5M+f/4XXCQ4OpmzZsjx69IgNGzZQrpzlyqbw8PA4TeJ7pTs6RqPpEL4TC+HuaTA4QMaiUKwNlOysR1UiKd35DbChr+n7w3+MRhh2oDLfzN1D+vTpWbt2LdWqVbNhkSJJj/XPpBLQpk2bOH36NJ06dTKHHIAMGTIwdOhQQkNDmTlz5kuvM3HiRC5cuMB3330XJeQAibdSITICljWHtV5w8xA4OsPtY6bJyL7vgHcj0woLEUl5Ht4En26w2MMi5AAY8tRk1IytjBw5El9fX4UcESvY5aorf39/gGgPpHtyEm9AQMBLr7NgwQIMBgOtW7fm+PHjrFu3jkePHlGiRAkaNWpE6tSp47XuGO2dAGfXgsefpp2RL/jBovrQzg/unIDFDWHHN1D9q8SpR0Rsz2iEf2ZCwEcQcitqv6s7NF+EwSk1w4YNi9ovIrFil0Hn5MmTABQtWjRKX86cOXF1dTWPicmTM1+yZcvGL7/8wvDhw4mMjDT3FypUiGXLllG2bNkXXmf8+PGMHz8+2r7nDziN0b+zoHg7U8gB4JmnhbnfhHJ94NhfCjoiKcXtE6bHVBf9LJrvP4b2s+GTtqWp/e5ySJfDRgWKJB92+egqKCgIMD2qio6bm5t5TExu375NREQEt27dYuTIkYwZM4br169z6dIlhg0bxtmzZ2nevDkhISEvvE5wcDCXL1+O9uvZ4PRCd09B7hdMHsxcAu5djN21RCTpigiFHV/DrHLRhpwm0xxZcwyajDvL34cu26hIkeTFLu/oxIcnISQiIoL+/ftbrNIaOXIkx48fZ+HChSxevJguXbrEeB03Nzfc3d2j7bt69Wrswo5TWjC+YIl94GFIm/3l1xGRpOvSFtjQB279G6XrXgg0+SsrW04HAqbDOZ89V1BErGeXd3Se3MmJ6a5NcHBwjHd7nr8GQIsWLaL0P2nbs2fPC68zaNAgLl26FO1Xrly5Xvhes4yF4PbRqO0ht02nEh/8HUp0jN21RCRpCblj2hV9Qc3oQ06qPDReWpIt/5pCTqZMmdiwYQOVK1dO7EpFkiW7DDpP5uZENw/n2rVr3L9/P9r5O89Kly6d+U5MxowZo/Q/aXv06FHcio2Nwi3h+CIIf+YxmcEA04rDhn5QqBlUH5XwdYhI4jEa4dgCmF7S9APN8wwOBJd4j0bz3dm61/SD0JOQU6lSpUQuViT5ssugU7t2bQDWrVsXpc/X19dizIvUq1cPgH//jfpT1JO2AgUKWFtm7FX8ANqsA+N/j7nS54XXBkCNb6DDVmi5BBwTaQWYiCS8oHOwtBms7gAPr0ftz16R4BabaPTNfrZt3wlA5syZ2bhxIxUrVkzcWkWSuThtGPjvv/8yefJkdu3aRWBgIC1btmTMmDEAbNu2jT179tClSxcyZ878StcNDw+nePHiXL58OcYNA48fP24OKVevXiUoKIhcuXJZPLLatm0b1atXp3Tp0mzZssV8F+fatWtUrlyZq1evcvToUYoVK2bV719HQIhIFAcmQsDHEP4wal+qdFB9FEGFutGoSTN27NgBPA05z+4bJiLxw+rJyOPHj2fIkCGEh4cDpjOtAgMDLcYMHDgQZ2dn+vTp82pFOTkxZcoUPD09qVWrlsUREOfPn2fs2LEWd2I+++wzZs6cyfTp0/Hy8jK3V6tWjUGDBjF+/HjKlStH8+bNCQsLY/ny5dy4cYNvv/3W6pAjIhLF4amw8f3o+wo1g/q/glt+tvn4sGvXLgCyZMnCxo0bKV++fCIWKpJyWBV0Vq9ezUcffUTBggUZN24cNWrUIHt2y1VD1apVI1u2bCxfvvyVgw5A3bp12bJlC8OHD2fBggXmQz2///572rdvH+vrjBs3jrJly/Lbb78xY8YMDAYDr732Gn/88QetWrV65bqsMt7R9Lz+ZQbHcrm6iNifOydh04Co7elyQb2foWhr81EvjRs3Zvbs2QwaNIh169ZFu3O7iMQPqx5d1a9fn127dnHw4EEKFSoEmE449/LyYtq0aeZxjRo14uTJk5w+fTqmSyVpsX50tekDLDYJBAh/ZHqOf8kfMhWDfPVN3wxFJOmJCIP51eHabsv28n2h5nfgHP0q0Xv37pE+ffpEKFAk5bLqjs7evXt54403zCEnJlmzZmXz5s1WFZas1Psp5r7bx2FBbcj1RuLVIyLxa8eoqCGnXB9o8DsAd+7cYdOmTbRu3dpiiEKOSMKzatVVaGhorP6A3rhxI/EOzkyqMheH1/rDzm9tXYmIWOPyVtj5jWVbpmJQZxxg2qW9QYMGtGnThqlTp9qgQJGUzaqgU7BgQQ4ePPjCMaGhoRw6dEiTfWMjTVa4ezJ283hExH48DoY1XZ5uHQHg4ARN5kKqdOaQs2/fPgCGDRvGvXv3bFSsSMpkVdBp0aIF586di/GwS4AxY8Zw8+ZN3n77bauLSzFyV4Nmi0zn4IhI0rHpfxB8zrKt2leQszK3bt2ifv367N+/H4AcOXKwceNGPa4SSWRWTUa+c+cO5cuX5/Lly7Rp04ZWrVrRqVMnGjduTM+ePVm6dClz586lYMGC7N+/P9n+wdY+OiIp2PGFsOq5FaDuNaGdH4G379CgQQPzne+cOXPi5+dHiRIlbFCoSMpm9YaBJ06coE2bNhw5cgSDwYDRaMTw39JJo9FIqVKlWLZsGUWKFInXgu2Jgo5ICnXvEswsC4/vPm1L7QbdDxEYmo769etz6NAhAHLlyoWfnx/Fixe3Ta0iKZzVM4WLFSvGgQMHWLlyJevWrePcuXNERkaSJ08ePDw8aN26NY6OjvFZq4iI7RkjYW13y5AD0GAiNx+npX79ehw+fBiA3Llz4+fnp7mKIjYUpyVRDg4OtGzZkpYtW8ZXPSIi9m3PeLiwybKteAeMxTvydu3aFiHH39//pQcQi0jCsstDPUVE7NKNA7BlqGVb+rzQ4HcMDg6MHTuW9OnT4+7uTkBAgEKOiB2w6o7O33//HatxqVOnJkuWLBQpUsQ8fyfFC3sEl7fA7WMQGmx6rp+lJLjXACcXW1cnIjEJewSrO0Fk2DONBmg8G1wyAlC1alXWr19v/r4nIrZnVdCpU6fOKwUXV1dXOnbsyDfffEOWLFms+cikz2iE3T/A7u8h5I75zBtzn0tmqDIEXv/IdjWKSMw2fwq3j1o03S0xgAx5avHsd8OqVasmbl0i8kJWrbry8vLi7t27rFixAgcHBypUqEC+fPkAuHjxIvv378doNNKsWTMePXrEgQMHCAwMpEiRIuzcuZNMmTLF+2/EFl5p1dVaL/hnFmQpBSU6QtaykDo9hN6DW0fg6F9w618o3R0aTU+U+kUkls6uhSWNLZqupi5DvYlheHo2YsKECbprLWKnrAo6N2/epGrVqhQvXpyff/45ynPoU6dOMWDAAI4dO8aOHTtIly4dvXr1YsGCBXz66ad8+23yOO4g1kHn5FJY2QYqDYJaYyzv5jxhNELAx7B3PLTwhqKJdLK6iLzYw5umpeQPr5ubrj5wpu7s3Bw/dRaAr7/+ms8//9xWFYrIC1gVdHr27ImPjw+nT5/GxSX6eSWPHj2iSJEiNGrUiKlTpxIcHEyhQoXImTMnR44ciXPh9iDWQce7MTy6CV32vPyicypD2uzw9pr4K1RErGM0wvK34PQKc9OVIKg7Kzsnzt8AIH/+/Pj7+1OgQAHb1CgiL2TVqqvVq1dTq1atGEMOQJo0aahZsyZr1pj+wnZzc+O1117j7Nmz1lWalF3fA0XbxG5s0TZRT0EWEds4PMUi5FwOgjpT0ppDToECBQgICFDIEbFjVgWdoKAggoKCXjouODjYYlzWrFmt+bikLzQY0uWM3dh0OU3jRcS2bp8Avw/NLy8HQd0/HTl59SFgOtzY39+f/Pnz26hAEYkNq4JO0aJF8fPz4+jRozGOOXr0aJQdQa9evZoyw06abPDgWuzGPrhqOs1cRGwnIgx8ukC4KdRcugt1foeTNyIAKFSokEKOSBJhVdB5//33efz4MbVq1WL06NGcPHmSkJAQQkJCOHnyJN999x21a9cmNDSU999/HzDN2dm7dy+VKlWK199AkpCnFpxeFruxp5ZC7hoJWo6IvMT2r8yPkC/eNYWcU4GmrsKFC+Pv729eaSoi9s2qfXR69+7N0aNH+emnn/jiiy/44osvoowxGo18+OGH9OrVC4Dz58/TpUsXWrduHbeKk6Ly78HC2nB0LpTsHPO4o3Ph+l6o+X3i1SYili5tgV2jLZqMDqmAMHPIyZMnj21qE5FXZvXp5QBbt27l999/Z9u2bVy9ehUwndRbvXp1+vTpQ40ayfvOxCvto3N1JxgcIWflmMdc2wOR4ZD7jfgtVERiJyIUZpaBOyeftjk4caHWMnp//gtTp07F3d3ddvWJyCuLU9BJ6V4p6IiI/ds9Fv7+2LKtxrdQ9TPb1CMicRan08vlFTy8Abu+gzNr4N55U1v6/FCwsembaNrstq1PJKV7cB12jOTcbfhmI/zyFrjkrQKvf2LrykQkDhR0EkPQOZj3pins5Kzy9NHU7eOw/2c4Ng86bYcMBW1apkiKtvULzl69R53f4cJd03LyJWt+wMXB0daViUgcWB10Hj58yPjx41m+fDknT57k3r170Y4zGAyEh4dbXWCy8PenEB4CHbdBrucO/Luy3XSGzt+fQPNFtqlPJKW7vo8z/lOo87tplRXA2QcZCE5Xgpi3RRWRpMCqoBMUFETNmjX5559/cHR0JHXq1BiNRnLlysW1a9d4Mu1He0z858IGeG1A1JADkPtNeO0DOPBr4tclImA0cnp+b+pMhEv/7W9aMoeBTX7+ZM+uR8oiSZ1V++h89913HDlyhN69exMcHEybNm0wGAxcvnyZBw8eMGPGDHLmzEnVqlU5c+ZMfNec9ISHQJrMMfenyWwaIyKJ7tS6n6kzfK855JTKAX7ThpCzcAWb1iUi8cOqoLNs2TJy587Nzz//jIuLC4ZnTuN2cXGhW7dubNiwgaVLlzJu3Lh4KzbJylYOjsyAsAdR+8IewD8zTWNEJFGd/PcQdToMNoec0jlg0yf5yNHwS9sWJiLxxqqgc/78eSpWrEiqVKlMF3EwXSYsLMw8plSpUtSuXZsZM2bEvcqk7s3hEHgYZpaDXd/DqRWmr13fm9puHoI3htm6SpEU5eTJk9SpXYPLd03HOpTJCZv6Qo7mP4KTZuaIJBdWzdFxcXGxOLnczc0NgGvXrpE3b15ze+bMmdm6dWscS0wGCjaCFksgYBBsGWrZ51YAWnhDoSY2KU0kpfrys4+4EmhaRFE2F2zsA9lK1YMib9m2MBGJV1YFnbx583Lx4kXz6xIlSgAQEBBAly5dAAgPD2f37t1kyZIlHspMBoq0MH3d+heC/9tHxy0/ZCll27pEUqjJ7VNxeR8EhcCGPpAtvQPU+RGeeRQvIkmfVUGnZs2aTJ8+nXv37pE+fXqaN2/OgAEDGDBgAPfv38fd3Z2pU6dy7tw5OnXqFN81J21ZSinciNjapS24XvBmTU8ICYes6YByfSBbWVtXJiLxzKqg06FDB/bu3cu2bdvw9PQkd+7cjB49mo8++sh8WrnRaCRnzpx8/70OqORiQOzG5a2dsHWIpGDHjh0jU6ZM5MieDfw+AMDV2fSFc0aoNtKm9YlIwojXs6527tzJ0qVLuXPnDsWKFeOdd94hc+YXLKtO4mJ91tV4R4jNv+bBkfFXnIiY/fvvv9SrV48sWbLg91svsu8daDmg7k9QcYBtihORBGVV0AkODsZgMJA+ffqEqCnJiHXQObkkaltkBNy7CEemgaMzVBkCxdsmXLEiKdQ///xDvXr1uHHjBgCdX3dmTvvHTwdkLgndDoJjKhtVKCIJyapHVxkzZqRq1aps3749vutJnoq+HXNfxQ9M52DdOZF49YikEEeOHKFevXrcvHkTgErFcvBz8+uWg+pOUMgRScas2kcnQ4YMFCpUKL5rSZkcHKFkFzj4h60rEUlWDh8+TN26dc0hp/JrZVjf7RaZ0z4zqFBzKOBpmwJFJFFYFXRee+01Tp8+Hd+1pFyPgyAk0NZViCQbhw4dol69egQGmv5cvf7666wflIdMLs8cMOyQCupo53aR5M6qoPPpp5+ye/duFi9eHN/1JE/B56P/unEA9v0Cu7+H3NVtXaVIsnDw4EGLkFOlShXWTxlCxutrLQdW/BAyFU38AkUkUVk1RydNmjT07NmT9u3b06xZM5o3b06+fPksdkt+Vq1ateJUZJI3pVDMq64MBshZBTz+TNyaRJKh8+fPU79+fW7dugVA1apV8V2zigzLn/selDY7vPGFDSoUkcRm1aorBwcHDAYDT95qeMlOohEREdZVZ+diverqn1lR2wwGcM4EGQtpA0GReBIZGUnv3r2ZOnUqb7zxBr6+vridmgl+zy0dbzgFyr5rmyJFJFFZdUenW7duLw038ozS3WxdgUiK4ODgwKRJkyhatCj9+vXDzSkUtj13Enn2ilDayyb1iUjii9cNA1OaWN/ReZlTK0w7tfY6G3/FiaQQ4eHhODnF8DPbhvfh4ETLtvabIU+NhC9MROyCVXd05BVt++rF/YGHTJOTt4+EtDkgbx3IXDxRShNJyvbs2UPHjh1ZvHgx5cuXt+y8eRgOPbdtQ/EOCjkiKUycg87t27fZu3cvgYGB5M+fn2rVqsVHXcnLjlicoWMwwPYngcgAjaZDqa4JWpZIUrZ79248PDwICgqifv36bNmyhRIlSpg6jUbTXVLjM8eqOKWBWjp7TySlsTro3Lx5kw8++IDFixebJxt3797dHHSmTJnCJ598wooVK6hRI4X/BNXv5ov7T3rD+j7w3k0IDYZNH8DObxV0RGKwa9cuPDw8CA4OBqB06dLkyZPn6YBTy+Cin+WbXv8U3PIlXpEiYhes2kfn9u3bVKtWjfnz51OmTBnee+89np/q8/bbb3Pv3j3ttQOQJvOLv1Kl+29cFshQEIq1gbvakFEkOjt37rQIObVr12bNmjW4urqaBoQ9gIDBlm9Knxde/ziRKxURe2BV0Pnmm284ffo0X375Jfv27eOXX36JMiZz5syUK1eOgICAOBeZ7GUrB9VGPH2dKh2kdrVZOSL2aseOHRYhp06dOqxevZp06f77YSEiDFa2g6DnJvbX+gFSpUVEUh6rHl0tW7aMYsWKMWLEiBeOK1y4MP7+/tZ8RMqStYzp64lirU1fImK2fft2PD09uXfvHgD16tVj5cqVpE37X4AxGmFdTzi7xvKN7jWgeLtErlZE7IVVQefy5cu0bNnypeMMBoP5J68UbWHdl48xGqG9P0SGw2KPp69FhG3btuHp6cn9+/cBqF+/PitWrHgacgD+/hT+fW5zTpfMps0Bte+XSIplVdBxc3Pj6tWrLx13+vRpsmXLZs1HJC8Gh5iPgDCPeeYbscEBiIxxqEhKs3//fnPI8fDwYPny5aRJk+bpgD3jYM8Plm9ySgOtVmmrBpEUzqqg8/rrr7Np0ybOnj1LwYIFox1z8OBBDhw4QJs2beJUYLLQdmPsxzo4vdp4kRTg/fffJzQ0lLVr17Js2TLLkPPvbAj4yPINBkdovghyv5m4hYqI3bFqMvL//vc/Hj9+TKtWrTh69GiU/lOnTtG1a1eMRiP9+/ePc5EiIgMHDmTNmjWWIeesD/j2iDrYcxoUapp4xYmI3bLqjk6jRo345JNPGDNmDGXKlKFo0aIYDAZ8fX0pX748//77LxEREXz++efaQ+eJsEdweLJpomTweVObW34o2BjK9QGn6E9+F0mJAgICCAoKokWLFhbtjo6OT19c3Qkr2pjmtT2r1hidLyciZnE662rRokV88803HDp0yKK9RIkSDBs2jI4dO8a5QHsW67Ou7l2CRfVMe+Okyw0ZC5va756CB1chYxFouwnSuydO4SJ2zN/fn6ZNmxIWFoa3tzfNmzePOujWUZhfA0JuW7ZXGgS1x2rysYiYxcuhnjdv3uTcuXNERkaSJ08e3N1Txl/YsQ46y1vBOV9oPDvqsvGTS2BNV8jvAW8tS9B6Reydn58fTZs25dGjRwC0atUKb29vDM8Gl3uXYF41uHfR8s0lu0Djmf9N5hcRMYmXQz2zZcum1VUvcn4DVPwg+r1xir4NFffA/p8Tvy4RO7Jp0yaaNWtmDjnNmjVj3rx5liHn0W3w9owacgo0Ms3LUcgRkedY9V2hcuXK/PTTT1y7di2+60meUqWFdLli7k+X6+kxECIp0IYNGyzu5DRv3pzFixfj7Oz8dFDYQ1jWHG79a/nmnFVMK6wcUyVixSKSVFgVdPbt28egQYPImzcvnp6ezJ4927zHhUSjZBfTEtiwh1H7wkPg6Gwo2Tnx6xKxA+vXr6d58+aEhIQA0LJly6ghJzIcVrWHK9ss35ypOLRarSNTRCRGVs3ROXLkCHPmzGH+/PlcuHABg8GAi4sLLVq0oEuXLjRq1MhydUQyFes5OufWw9+fQPgDKNsLMpcwtd8+DocngVNa00oRR2fL9+WtnXDFi9gBX19fWrZsyePHjwF46623WLBgAalTp346yGgE33fhn+mWb3Z1h45bTasXRURiEOfJyJs3b2bOnDksXryYO3fuYDAYyJw5M+3ataNz585Uq1Ytvmq1O7EOOuNjEfqMRsuVIkYjDNbuyJJ83b17lwIFChAUFASYJh7Pnz/fMuQAbP4Mdn1n2eacETpstjwjTkQkGvGy6gogLCyMNWvWMHfuXFatWkVISAgGg4ECBQpw+vTp+PgIuxProHPpbys/oJZ17xNJItasWUOrVq1o3rw58+bNI1Wq5+bZ7P0R/Adatjm5QOv1kEd7dInIy8Vb0HnWvXv3+PTTT/njjz8wGAxERETE90fYhVgHHRGJ0a5du3jttdeihpyjc2FNF8s2gyO0WAJFLDcSFBGJSbwsL3/i5MmTzJ07l3nz5nHq1CkAXFy046+Z0QjX95o2DjQ4QKaikL2CrasSSTRnzpyhUKFCFm1VqlSxHPQw0LTdwq7RUS/gMUkhR0ReSZyDzrVr15g/fz5z585l3759GI1GHBwcqFevHp07d6Z162j2jkmJLgbAup6mkPOsTEWhwe+Qr55t6hJJJCtXrqRNmzaMGjWKTz75JOqA4Iuwdxwcmgzh0axQrPEtlI3mXCsRkRewKugEBwfj7e3NX3/9hb+/P5GRkRiNRl577TU6d+5Mx44dyZXrBfvGpDQ3D8GSJpA+r+kn0rB74D8YGs+CY/NMfe0DIFdVW1cqkiBWrFhBmzZtCAsL49NPP6VkyZJPj3a4dQx2j4GjcyAyLPoLVPwAqgxJvIJFJNmwKujkzJmTx48fYzQaKVCgAJ06daJz586ULFkyvutLHnaMgrTZoOte08aAFzaZ2gs2hlJdYF4N2DYcWq+1bZ0iCWD58uW0bduWsDBTiOnYsSONGzeGa3tMj6dOLgVeMFWwQn+oM17nV4mIVawKOmnTpsXLy4vOnTtTvXr1+K4p+bkYABXej3n342JtYPuIRC1JJDEsXbqUdu3aER5uOmG8U6dOzBzlhdPSxnBhQ8xvNDhCyU7w+idaQi4icWJV0Ll27RpOTvE6jzl5C7tv2twsJgYHiIjhlr1IEvV8yOnSojYzGp3EcWnDmN/k5AJl3oXKH0GGAolTqIgka1allehCzp07dwDIlClT3CpKjtLlhPuXo7YbjRD+GI5Mh5yvJ35dIgnE29ubDh06mENO1zczML1GAI43YnhDajfTXc+KH0C6HIlXqIgke3E66nfNmjV4enri6upK1qxZyZo1K66urjRq1Ig1a9bEV41JX86qcNEvavuWoTCjFNw+alpRIpIMLFu2jPbt25tDTrdKML1VEI7RfbdJmwNqfge9L0DNbxVyRCTeWR10Bg4cSPPmzVm/fj0PHz7Ezc2NDBky8PDhQ9atW0fz5s0ZNGhQfNaadJXvA84ZTPuDPGFwMK0yyVQUOmwB9+R7VIakLEWzOZI5nelbi1dlmNaeqCEnQ0GoPxF6noUqn5r+fIiIJACrdkZesGABHTt2JHv27HzxxRd07dqVDBlM36iCg4OZPXs2X3/9NTdu3GDevHm0a9cu3gu3B1bvjBwZYZq3o2/ukpyEPzadSbXrW/65HMq0XfBDM3B4NuRkLWNaJl68PThonp+IJDyrgk7t2rXZvXs3Bw4coFixYtGOOXHiBBUqVKBKlSr4+/vHtU67pCMgRP5z6W9Y3wduH4u+P3c1qPIZFGpiupspIpJIrPqR6uDBg9SrVy/GkANQrFgx6tWrx5YtW6wuLtnY9lUsBhmh2oiErkQkfj26zbyvWrNuoz9T2kXziMqtANT/zbRnlPbBEREbsCrohIaGki5dDHvCPCNdunSEhoZa8xHJy46RMfc9e0NNQUeSCqMRjs3jrx/60XVGMJFGiDQ+Mx/H4AiVBkG14THvHyUikgisCjqFCxcmICCABw8exBh4Hj58SEBAAIULF45TgclCv5tR24wRcO8CHPzTdNBn84WJX5eINe6egY3vMcfbl+7zTQEHIG1qMIBpqwSPSTqwVkTsglUPy9u1a8eNGzd46623OHnyZJT+06dP8/bbb3Pz5k3at28f5yKTvDSZo36lzQY5KkHDSZCxkOmYCBF7FhEGu76HmWWYtdiXbs+EnH5vwm/t0uFQ/yfouF0hR0TshlVB56OPPqJixYps3LiRUqVKUbVqVdq3b0/79u154403KFGiBOvWraNSpUoMHjzY6uJ2795NkyZNyJgxI+nSpeONN95g4ULr73zcuXMHd3d3DAYDjRo1svo68S5fAzi13NZViMTs6k6YWxk2D2Hm9kd4LXj61PX9avDbxy1w6HEUKg4AB0fb1ioi8gyrHl2lSZMGf39/PvvsM6ZNm8bu3bvZvXu3RX+PHj0YPXo0adKksaowPz8/PD09cXFxoUOHDqRPnx5vb2/at2/PxYsXrQpQ/fv3JygoyKp6EtSNfbzwUEMRW3kcDFs+hwO/AUZm7IYeC5+GnP/VTcdPf8zCUOxtm5YpIhITq5aXP+vhw4fs3buXK1euAJA7d24qVapE2rRprb5meHg4JUqU4NKlS+zYsYMKFSoAEBQURJUqVTh37hwnTpwgf/78sb6mt7c3bdq04ddff6V///54enqydm3cTguP9fLyf2ZG3x5yBy5vhlPLoFR3aDQtTvWIxKtre2B5K7hv+n972i7ouehpyPng7fJMmBuAwUX7QYmI/Yrzjl1p06alZs2a8VGL2aZNmzh9+jTvvPOOOeQAZMiQgaFDh+Ll5cXMmTP58ssvY3W9mzdv0q9fP7p27UrTpk3p379/vNb7Ur49Yu5zyWI6ofnN4YlXj8jLXPobljaD0HsAhEXAz1uehpwPe3Vg/J9/YdCScRGxc7EOOps2beLSpUtUrlyZUqVKvXDsv//+y549e8ibNy9169Z95aKebDDYsGHUU449PT0BCAgIiPX1+vbti6OjIz/99JNtHl31PBtNowFcMkFq10QvR+SFzvrAirchPMTclMoR1r/nQv0ZGWnQtD3jJkxQyBGRJCFWQefixYs0bdqUvHnzsnfv3peOz5s3L61ateLSpUucPHmS3Llzv1JRT1ZyFS1aNEpfzpw5cXV1jXa1V3TmzJnDkiVLWLZsGZkyZXrloDN+/HjGjx8fbd/Vq1djdxG3fK/0mSI2c3wRrOkMkWGW7Xlqkc1zOlsHZMPV1VUhR0SSjFitupoyZQqhoaGMGTOG9OnTv3R8+vTp+eGHH3j06BFTp0595aKehJEn52c9z83NLVaB5cqVKwwYMICOHTvSsmXLV64DTGd3Xb58OdqvyMhIq64ZxdG/YJy2xRcbOzwNVncwhxzvQxAcAhRpBa3XQcZCpE+fXiFHRJKUWN3RWb9+PdmyZeOtt96K9YVbtGhBjhw58PHxYdiwYdbWFyc9e/YkVapU/Pzzz1Zfw83NDXd392j7rl69Gruwc/sEBAwybQwY9sByN2QAY7hpe/yf/wuRVYdC1c+srlnkle37Cfw+NL+cuBXeXwrVSmZj7fappHdytl1tIiJxEKugc+zYMapXr/7KF69cuTLbtm175fc9uZMT012b4OBgMmXK9MJrzJw5Ex8fHxYtWkTWrFlfuYYnBg0axKBBg6Lte7Lq6qU29DGFnHwNwDlj1P6gM6bJn8Xaml5nefEcKJF4YzTCzm9g69MfRn7bCv2Xmn697ehN/pq/kD59+tioQBGRuIlV0Hnw4EGMj5FeJEOGDNy/f/+V3/dkbs7JkyepVKmSRd+1a9e4f/8+VapUeeE19u/fD0Dbtm2j7ff19cVgMFC+fHkOHDjwyjW+kmu7ofooqDQw+v6jf5mCjpaXS2IyGuHvT2DPWHPTL1tgwLKnQz777DN69+6d+LWJiMSTWAWdTJkycf369Ve++PXr11965yU6tWvXZvTo0axbt44OHTpY9Pn6+prHvMibb74Zbci6f/8+CxYsIE+ePHh6epIvXyJMFA5/BOlebUK2SIKKjICN78GhSeamnzbDh89s0P35558zatQozckRkSQtVhsG1qtXj927d3Pjxo1Y73T88OFDsmfPTpUqVdi0adMrFRUeHk7x4sW5fPlyjBsGHj9+nAIFCgCmuTJBQUHkypXrpXeezp07R8GCBRN3w8C1PaB8X8gVw12oqztNh3vqjo4khogwWNsdjs0zN034GwateDpk2LBhfPXVVwo5IpLkxWqpT7NmzXjw4AFff/11rC/89ddf8+jRI5o3b/7KRTk5OTFlyhQiIyOpVasWvXv3ZvDgwZQvX54TJ07w7bffmkMOmG6vlyxZkqVLl77yZyWKRtNiDjkAuaoq5EjiCA+BFa0tQs74AMuQ8+WXXyrkiEiyEaug07dvX3LkyMF3333H119//cKVRpGRkYwaNYrvvvuOHDlyWD2JsW7dumzZsoXq1auzYMECfv/9d3LkyMH8+fPjdFCoSIoVeh+WNoUzK81NSw/D4KcvGT58uEKOiCQrsT7ratu2bTRo0IDHjx+TJ08e2rZtS8WKFcmWLRtgOmZh3759LFq0iEuXLuHs7MzGjRt58803E/Q3YEuxfnQlYmshd2BJE7i6w6I5FBfa+FRg5cYdjBgxguHDdRSJiCQvr3So54EDB+jatSv//PNPtD/xPblU6dKlmTNnDuXLl4+/Su2Qgo4kCQ+ug3dDuHnIsj21G7RazeNsr7N06dIoE/9FRJIDq04vX7t2LatXr+bAgQPcunULgCxZslChQgWaNm1Ko0aN4r1Qe6SgI3Yv+AIs9oA7J8xNQY8gQ+as0NoXclS0YXEiIgnPqqAjJgo6Ytce3Ya/qsDd0+ambzfCpJ2O+G9YS4HXGtiwOBGRxKEDlkSSo8gIWN3RIuR8swE+94HztyOo+3YvgoODbVigiEjiUNARSY62fgHn15lfjloPXzyzbVS/fv1wc3OzQWEiIokrVjsji0gScsIbdn1nfvnVOhjxNPPwww8/8NFHH9mgMBGRxKegI5KcBP5j2vX4P8+HnLFjx2ofKhFJURR0RJKLkLuwohWEPcBoNAWckeufdo8fP56BA2M4WFZEJJlS0BFJDoyR4NMV7pzEaIThvjBqw9PuCRMm8OGHH9qsPBERW1HQEUkOto+EM6vMLx+HP+366aefGDBggA2KEhGxPQUdkaTu9ErY/pX5pcEA37XJirFca/IWKc3//vc/GxYnImJbsQo6I0eOtPoDDAYDw4YNs/r9IvICt0/Ami6WbQZHDC0WMSZvHZuUJCJiT2K1M7KDgwMGg4Hnhz573tWTvufbDAYDERER8VWvXdHOyGJTofdgblWMt44yYh14FIMaBYE646GSJh2LiEAs7+hMnz49Stv27duZNGkSefLkoU2bNhQoUACA8+fP4+3tzYULF+jdu3eyPr1cxGaMRljrhfHWUT5dDT/4w/i/Ye0oD6pX/NDW1YmI2I1YBZ3u3btbvN61axd9+/ZlyJAhjBw5Eicny8uMGTOGL7/8knHjxvHOO+/EX7UiYrLrO4wnlvDxKhgXYGq6/xiOur1F9WfuqoqIpHRWHerZqFEjLl26xJEjR144rkyZMri7u+Pr62t1gfZMj67EJs75YlzciMErYcLfT5sn/fgNvT4Yaru6RETskFVnXe3cuZPy5cu/dFz58uXZtWuXNR8hItG5ewbjqg4MWmEZciaP/lAhR0QkGlYtL4+IiODMmTMvHXfmzJlkOxFZJNGFPcC4/C0+XHCXn7eYmgwGmDK0NT2GTLBtbSIidsqqOzpVqlRh165dzJo1K8Yxs2bNYufOnVSpUsXq4kTkP0YjRt+eDJh82CLkTO1fiR6jFtm2NhERO2bVHJ2tW7dSt25dIiIiqF27Nu3btyd//vyAadXVwoUL8ff3x9HRET8/P6pXrx7vhdsDzdGRRLN3AjtnDuLNX00LrgwGmN4jN90nHoPU6W1dnYiI3bIq6ACsWrWKHj16EBgYaLF3Dpj2z8mSJQtTp06lRYsW8VKoPVLQkURxwQ8We4Axgpm7oecimNrJhW4/HYTMxWxdnYiIXbM66ADcv3+fxYsXs3nzZq5cuQJArly5qFmzJm3btsXV1TXeCrVHCjqS4K5sh2Ut4FGguenMLSj07koo3MyGhYmIJA1xCjopnYKOJJjHQUQGDGHHyj+oVuC5vjdHQLXhNihKRCTpsWoysogkEKMRTngTObUEfb/8gxq/waw9z/QXag5v6uw4EZHYilPQWbduHa1atcLd3R1nZ2d69Ohh7vP19WXQoEHmR1oi8hLBF2BZSyKXt6H3zGtM3mnKPT0XwYU7QK43oMlsMOjnExGR2LJqHx2ADz74gF9//RWj0YirqythYWEW/bly5eLHH38kb968DByoAwZFYhQZAft/ga1fEPn4Ab0Ww7T/9tl0dIDZnVOTr+U3UOlDcLD6j6yISIpk1Y+Gs2bN4pdffqFSpUrs27eP4ODgKGPKlStH3rx5WblyZZyLFEm2ru+Dv6qC/0AiHj/g3YWWIWfewAq0//k4vP6RQo6IiBWs+s75+++/kzFjRlavXk22bNliHFeuXDkOHz5sdXEiyVbofdg2HPb9CMZIIiLh3YUw87/5OI4OMH/8h7QZMN60aY6IiFjFqqBz5MgRateu/cKQA5AhQwauX79uVWEiydaZ1bDhPbh3AYCISHhnAczea+p2cjQwf/Z0WnfsbsMiRUSSB6vvhT+/SWB0rly5Qpo0aaz9CJHk5f5V8PsATlge2dB/6TMhx8mRBQsW8vbbb9ugQBGR5MeqOTpFixZl3759USYgP+vevXscOHCA0qVLW12cSLJgjISDf8CMklFCDkCXyk64pk2Nk5MTCxcuUsgREYlHVgWdtm3bcvXqVYYMGRLjmM8++4ygoCA6dOhgdXEiSV7YI1j2FmzoB4+DovbnrUP1Ef/g47uRxYsX06pVq0QvUUQkObNqZ+RHjx7xxhtvcOTIEapUqULLli0ZOnQoNWvW5K233mLp0qVs2bKFihUrsm3bNlKnTp0QtducdkaWFwq9B0ubw6UAi+aISHBIkwlDnXFQ2kuTjUVEEpDVR0DcvHkTLy8vfHx8MBgMPH8ZDw8P5syZ89IJy0mZgo7E6NFtWNIYru2yaA6PgM6r8pO/UjO+H/9LrOa6iYiI9ayejJwtWzZWr17NwYMHWbduHefOnSMyMpI8efLg4eFBlSpV4rNOkaTjwTXTaeOBRyyaw5zc6LypPIs2b4bNv5E6XUa+/vprGxUpIpIyxHkHsvLly1O+fPn4qEUk6Qs+D4sawN1TFs1hqbPScV15vNdsBCB16tRUq1bNFhWKiKQoVk1GrlevHmPGjHnpuLFjx1KvXj1rPkIk6bl9AubXjBpy0rjTcX1FvFeZQo6zszPLli2jSZMmtqhSRCRFseqOjr+/PwUKFHjpuOPHjxMQEPDScSJJ3o2D4N0QHt6waA51LUSHVUVZusoXeBpyGjVqZIsqRURSnAQ9PCckJAQnJ53PI8nclR2miceP71o0h2YsRftl+Vi2ai1gCjnLly/H09PTBkWKiKRMCZZCgoOD2bZtG7ly5UqojxCxvfMbYXlLCHtg0RyatRLtFmdn+SofAFxcXFixYgUeHh62qFJEJMWKddApVKiQxevFixfj7+8f7djw8HCuX79OeHg4/fv3j1OBInbr9EpY2RYiHlu256lFYJVJHBhuunPj4uLCypUradCggQ2KFBFJ2WIddM6dO2f+tcFg4P79+9y/fz/asalSpSJ37ty0aNGC0aNHx7lIEbtzdB74dAVjhGV7wcbQfDG5U6XF39+fJk2a8Msvv1C/fn3b1CkiksLFOuhERkaaf+3g4ICXlxfTpk1LkKJE7NqhSbC+L/DcXpvF2kKTOeBo2gm8QIECHDp0SPPURERsyKrvwNOnT6dIkSLxXYuI/dszDgI+itIcUqwb4w4W4aOGRpwdn7Yr5IiI2JZV34W7d+8e33WI2DejEbYNhx2jonSFlHmfVj+eYu3aWezctZvFixcn2/PdRESSGqs2DFyyZAkVK1Zk48aNMY7ZsGEDFStWZPny5VYXJ2IXjEbwHxh9yHltKG9NOMXataZ9cjZt2sTRo0cTu0IREYmBVUFn+vTpnD9/nho1asQ4pmbNmpw7d07zeCTp2zwE9v0UpflR1dG0/H4Pvr6mkJMuXTp8fHx0JIqIiB2xKugcPHiQ8uXL4+zsHOMYZ2dnKlSowIEDB6ytTcT29oyH3c8fd2LgYY1faPHVRtatWweAq6sra9eupWbNmolfo4iIxMiqoHPjxg1y58790nG5cuXixo0bLx0nYpf+nQMBgy3bDI48rDuNFp8vY8OGDcDTkPOiO5wiImIbVk1GzpgxIxcuXHjpuIsXL+Lq6mrNR4jY1tm14PtOlOYHNSfS/KNZ+Pn5AZA+fXrWrl2rk8hFROyUVXd0qlSpwvbt2zl8+HCMYw4fPsz27dt5/fXXrS5OxCau7oQVrSEy3LK95vd8Of+4Rcjx9fVVyBERsWNWBZ333nuPiIgImjZtyuLFi6P0L168mKZNmxIZGcl7770X5yJFEs2tY7CkKYQ/tGyvNAhe/5gRI0ZQrVo13NzcWLduHW+++aZt6hQRkVgxGI1G48uHRTV48GAmTJiAwWAgY8aM5rOwzpw5w927dzEajQwYMIAff/wxPuu1K3ny5OHy5cu4u7tz6dIlW5cjcXXvEsyrDveeeyxbsjM0ngUG088FwcHBnDlzhgoVKiR+jSIi8kqsDjoAc+bM4dtvv+XYsWMW7SVLlmTIkCF07do1zgXaMwWdZCTkDsyvCbf+sWi+n6M+IR6zyJrj5ZPvRUTE/sQp6Dxx9epVLl68CEDevHnJlStXnAtLChR0komwh7C4IVzZatF8L0MlmsxITVDwfTZt2kTWrFltVKCIiFgrXg7iyZUrV4oJN5LMRIbDqg5RQ45LURpPc2Tr9u0AtG3blk2bNmEwGGxRpYiIWCnOQefgwYPs2rWLwMBASpcuTYsWLQB4/Pgxjx8/xs3NLc5FiiQIoxHW94EzKy2agx1z0ni2G9t27gIgU6ZMjBs3TiFHRCQJsmrVFcDx48epVq0aFStWpG/fvnzxxRcsW7bM3P/XX3+RKVMm1q5dGx91isS/LZ/DEcsjSoIjM9Dorxxs27kXgMyZM7Nx40YqVqxoiwpFRCSOrAo6Fy9epFatWuzYsYPmzZszZswYnp/q065dO1KnTo23t3e8FCoSr/b9BLtGWzQFhTnjOT8P23cfBJ6GnNdee80WFYqISDywKuiMHDmSwMBApkyZwrJlyxg8eHCUMenSpaNChQrs3LkzzkWKxKuj88DvQ4umoBADnvPzs2OfadVVlixZ2LRpk5aQi4gkcVYFnbVr11KuXDl69OjxwnEFChTg8uXLVhUmkiDOrYO13S2a7oVAw/kF2HnwBABZs2Zl06ZNOoVcRCQZsPpQz+LFi790XFhYGA8fPnzpOJFEcW03rHgbIsMsmtPW/Zrir5kO5HwScsqVK2eLCkVEJJ5ZteoqS5YssTrU88SJE1p2Lvbh9glY0gTCHli2vzYAxzeHMr1qJJkyZaJXr16UKVPGNjWKiEi8s+qOTvXq1dm9ezcHDhyIcUxAQABHjhyhTp06VpYmEk9u/QuL6sKjQMv2Eh2h7gQwGHB0dOSnn35SyBERSWasCjofffQRRqORli1b4uPjQ0REhEX/pk2b6Nq1K05OTnz44YfxUaeIda7vhfm14P4Vc9Pth9B4Thb+yfuJ+fwqERFJnqw+AmLixIl88MEHREZGkjZtWh4+fIirqysODg4EBwdjMBiYOHEivXv3ju+a7YaOgLBzlzbD0mYQGmxuuvUAGkxLw4Hzj8iePTt+fn6UKlXKhkWKiEhCsvrH2ffee4/NmzfTvHlzDAYDRqORe/fu8fjxYzw9PQkICEjWIUfs3Dlf8Pa0CDmBD6D+1LQcOP8IAAcHBxwcdEdHRCQ5i5dDPY1GI4GBgURGRpI1a1YcHR3joza7pzs6durkEtP5Vc+srgp8AA2muXLw/H3AdD6bn59frFYPiohI0hUvh3oaDAayZcsWH5cSiZt/ZoHvO2CMNDfdvA/1p7tx+Lzp7k7u3Lnx8/OjWLFitqpSREQSSZyDzrZt29i8eTNXrpgme+bOnZsaNWpQvXr1OBcn8kr2/wab+ls03bgH9Wdk4Mj5IEAhR0QkpbE66Bw+fBgvLy/zEvMnT8CenPBcvnx5ZsyYoY3XJHHsHA1bhlo03bgH9WZk4p/zdwBwd3fHz8+PokWL2qJCERGxAauCzvHjx6lduzZ3794lT548tGnThgIFCgBw/vx5vL29OXDgAHXq1GHbtm2UKFEiPmsWecpohM2fwe7vo3StfOjJP+d9AdN8Kj8/P4oUKZLYFYqIiA1ZFXSGDh3K3bt3GTJkCCNHjsTJyfIyY8aM4csvv2T06NF8/vnnOsFcEoYxEjb2h4O/R+2r/jXvVh1KYJYx/Pbbb/j5+VG4cOHEr1FERGzKqlVXmTNnxt3dncOHD79wXNmyZbl8+TK3b9+2ukB7plVXNhQZDr494N/ZUfvq/gQVB5hfBgUFkSFDhkQsTkRE7IVVm4iEhYXFau5NuXLlCAsLe+k4kVcS/hhWtosScq7eM7DBbbBFyAEUckREUjCrgk758uU5ffr0S8edPn2a8uXLW/MRItELewDLmsOppRbNV+45UWdmTpr2/wUfHx8bFSciIvbGqqDz+eefs3v3bqZNmxbjmOnTp7N7926GDh0a4xiRVxJyFxZ7wvn1Fs2X7ztTZ2Z2Tpy7SmhoKIMGDSI8PNw2NYqIiF2xajJyunTp6NevH7169WLGjBm0b9+e/PnzA6ZVVwsXLmTLli3069cPV1dX/v77b4v316pVK+6VS8ryOBgW1Ycb+yyaLz1IR90ZGTl17jIABQsWZO3atVEmyIuISMpk1WRkBwcH8/lW8HTvnCdian/i+dPOY7J7926GDx/Otm3bCAsLo2zZsgwaNIh27dq99L1Go5G1a9eyYsUKtm7dyvnz5wkLC6No0aK0b9+eQYMG4eLiEqs6YqLJyInIp1uUOTkXH2Wg7tT0nD5n+ndfqFAh/Pz8yJcvny0qFBERO2TVj73dunWLMcTEFz8/Pzw9PXFxcaFDhw6kT58eb29v2rdvz8WLFxk8ePAL3//48WOaNGmCs7MzderUwdPTk5CQEHx9ffn8889ZtmwZ/v7+pE2bNkF/HxIPji2IEnIuPM5G3anOnHkm5Pj7+5M3b15bVCgiInYqXg71jG/h4eGUKFGCS5cusWPHDipUqACYlglXqVKFc+fOceLECfPjsuiEhYUxZswY3nvvPTJlymTR3rp1a1auXMmYMWP4+OOPra5Td3QSQfBFmFUOHt81N12470qd6Rk5e97077xw4cL4+/uTJ08eGxUpIiL2yqrJyAlt06ZNnD59mk6dOplDDpiWCQ8dOpTQ0FBmzpz5wmukSpWKzz//3CLkPGn/7LPPAAgICIj32iUeGSNhbTeLkBMWAQ1npjeHnCJFihAQEKCQIyIi0bIq6Gzbti3WY3/66adXvr6/vz8ADRs2jNLn6ekJxC2kpEqVCkATVu3dnnFw0d+iKVXpjoz58Q9SpUpF0aJF8ff3x93d3SbliYiI/bMq6NSuXZvhw4cTGRkZ45hr167h6enJoEGDXvn6J0+eBIj28MWcOXPi6upqHmONJ8viowtSzxs/fjx58uSJ9uvq1atW1yAvcX0/bPncsi19Pqg/kRYtWrBixQqFHBEReSmrbmlky5aNr7/+mnXr1jFnzpwoZwgtXbqU3r17c+vWLWrUqPHK1w8KCgJi3tHWzc3NPOZV+fj48Oeff1KyZEnefffdl44PDg7m8uXLVn2WWCnsIazpDJGmXbXvhUB6FwM0ngUuGQFo1KiRDQsUEZGkwqo7OocPH6ZFixbs3LmTChUqMHXqVAAePnxIz549adOmDcHBwXzzzTd2NQ9m9+7dtG/fngwZMrBo0SKcnZ1f+h43Nzfc3d2j/XJwsMspTknf35/C7aMAnLkFZcbChLN1IW9tGxcmIiJJTZxWXU2ePJmBAwfy6NEjGjduzPHjxzl9+jQlSpRgzpw5VKxY0arrtm3blsWLF7Nnzx4qVaoUpT99+vRkypSJCxcuxPqae/bswcPDA6PRyPr163n99detqu1ZWnWVAM76wJImAJwOhLp/wMW7pq4ZM2bQvXt329UmIiJJTpxuSfTq1Ys9e/aQPn16fHx8OHPmDK1bt2b//v1Whxx4Ojcnunk4165d4/79+9HO34nJk5ATGRmJr69vvIQcSQAPb8DadwA4FQh1fn8ackqVKqXHVSIi8sriFHTOnz9Pz549CQ4OxtnZ2bwb8ezZs1/+5heoXdv0iGLdunVR+nx9fS3GvMyTkBMREcHatWupWrVqnGqTBGI0gm9PeHjdHHIu/TcNq3Tp0mzatIkcOXLYtkYREUlyrA46s2fPpnz58mzbto2mTZty7tw5/vzzTwD69OlDq1atuHXrllXXrl+/PoUKFeKvv/7iwIED5vagoCC+/fZbUqdOTbdu3cztV69e5dixY1EmKO/duxcPDw/Cw8Px8fHhzTfftKoeSQSHJ8OZlZy8CbUnwuX//lOWKVNGIUdERKxm1RydDh06sGjRIlxcXBg7diz9+vUz9508eZIuXbqwe/ducuXKxbRp08x737yKmI6AOH/+PGPHjrU4AsLLy4uZM2cyffp0vLy8ALh9+zZFihThzp07NGrUKNo7ORkzZuTDDz985dqe0BydeHL7BMx+jRNXH1L3d7gSbGouW7okG/0CyJYtm23rExGRpMtoBYPBYKxYsaLx6NGj0faHh4cbhw0bZnRycjI6Ojpa8xFGo9Fo3Llzp7FRo0ZGNzc3Y5o0aYxVqlQxzp8/P8q47t27GwHj9OnTzW1nz541Ai/8yp8/v9W1GY1Go7u7uxEwuru7x+k6KVp4qNE4u7Lx2CcYc7k9/W9TrkQB440bN2xdnYiIJHFW3dEZMmQIo0aNMu8wHJPt27fTtWtXTp069aofkSTojk482PI57PyWQ1eg3h9w6yGUL5SFDTuPkTVrVltXJyIiSVyCH+r54MED0qVLl5AfYTMKOnF0aTMsqI3pJg4cvAIfrU3L/I3HyJJTp5CLiEjcJfhhT8k15EgcPQ4Cn648CTkA5fM4sX7T35BDIUdEROJHrFZd9ejRw3w+1PNWrFhhsTLqWcOHD492wz+Rf6d34aO557E4Lq3aKMih/19ERCT+xCrozJgxgy1btkTb99Zbb/Hzzz9H23fhwoUYQ5CkXEdW/ECdj1YxLgB6L8YUdvLUgtc/tnVpIiKSzOiwJklUh7evo17nT7n5wPT64BV4YHAzHdjp4Gjb4kREJNlR0JFEc+jAfup5NuPmfdO8nNfzwvo+kL7p7+CW38bViYhIcqSgI4ni4MGD1KtTg8B7YQBUyQvre0PG1zpByU42rk5ERJIrBR1JcAcOHKB+nRrcCnoIQNV8sK43ZMieD+r/ZuPqREQkOVPQkQS1f/9+6tetxa279wF4I/9/ISeNwTQvxyWjbQsUEZFkTUFHEozRaOSDfj24ffceAG/mB99e4OYC1Poe8sbuBHoRERFrxWpnZAcHBwwGg9UfEhERYfV77Zl2Rn6JW0e5PqkadX+8S+a04NMT0rsAlQZB7bEQh/+nREREYiPWOyNbe1JEXAKSJGH3LoG3JzlS38WvH6RN9V/IKdkFav+gkCMiIokiVkEn0mL7WpGYHT58mII5M+C6ugncuwhAjvT/dRZoBJ7TwKAnpiIikjj0N47Em127dlGzZk2a1irLgyv/WHbmrALNF4Hji0+8FxERiU8KOhIvdu7ciYeHB0FBQfx9LJiv1j/Tmak4tFoNqV1tVp+IiKRMCjoSZzt27MDDw4Pg4GAA6haG4R7/dbrmhja+kDar7QoUEZEUS0FH4mT79u00bNiQe/dMS8jrFYFV70I6Z8A5I7T21fEOIiJiMwo6YrWtW7dahJz6RWFlD0ibGnBygbdWQtYyti1SRERSNAUdscqWLVto1KgR9++bdjxu8GzIMThC0wWQp4ZtixQRkRQv1vvoiDxx8OBBGjVqxIMHDwDwKAbL34E0TxZUeUyCIi1sV6CIiMh/dEdHXlnx4sWpVbU8AJ7Fnws5Nb6Fsj1sV5yIiMgzdEdHXpnLw/MsaXGc753g07rg8iTkvDYAqgyxaW0iIiLPUtCRWImIiMDR0RHuXwFvT1zCbzG84TMDSnSEuhN0tIOIiNgVPbqSl9q0aRMVKlTg4umj4N0Igs9bDsjXABrN0NEOIiJid/Q3k7zQxo0badasGUeOHKFO9de5euaw5YAclaHlEnBMbZsCRUREXkBBR2K0YcMGmjVrxqNHjwAoneUBmdM+MyBTUXh7NaROH/0FREREbExBR6K1fv16mjdvTkhICAAtSsPibuD8ZFaXS2Z4ey2kzW67IkVERF5CQUeiWLdunUXIaVnWiUVdIfWTkGNwgKbzIGMh2xUpIiISCwo6YsHX15cWLVrw+PFjAFpVcmNh5/CnIQeg+jdQoGH0FxAREbEjCjpitnbtWlq2bGkOOa2r5WFBu2DLkFOsDVT51DYFioiIvCIFHTHz8/Mzh5w29csxr+UlUjk+MyBLKfCcpr1yREQkydCGgWL23Xff8fjxY66cOsDc2n+T6tkY7JwBWi7TCisREUlSFHTEzGAwMOGrD4mYVRmnUKNlZ+M5puXkIiIiSYgeXaVgq1atYseOHU8bwh5hWNEap9BblgOrfQWFmyVucSIiIvFAQSeFWr58OW+//Taenp7s3LkTjEbY2A9u7LMcWLgFvPGFbYoUERGJIz26SoGWLVtG27ZtCQ8PJywsjBkzZlA19R74Z6blwEzFoPEsnWElIiJJloJOCrNkyRLat29PeHg4AF26dOHXIe1hiYflwFSu0HKpaRKyiIhIEqWgk4J4e3vToUMHc8jp2rUr03/+Gsd5VSAy3HJw45mm5eQiIiJJmIJOCrFo0SI6duxIREQEAN27d2fqnxNx9K4PD69bDq46FIq+bYMqRURE4pcmX6QACxcutAg5Xl5eTJ06Fce/B8LVHZaDC3hCtZE2qFJERCT+Kegkc+fPn6dLly7mkPPOO++YQs4/0+HQJMvBGQpBk7/AwTGaK4mIiCQ9CjrJXP78+Zk4cSIA7777LlOmTMHh+m7Y9L7lQKe0psnHaTLboEoREZGEoTk6KUDPnj0pWrQoNWvWxOHRTVjRGiJCLQd5ToVs5WxToIiISALRHZ1k6MKFC1HaateujcPD67CoPty/bNlZaTCU6JBI1YmIiCQeBZ1kZvbs2RQpUoR58+ZZdgSdgwU14dY/lu356kGt7xKtPhERkcSkoJOMzJo1i+7duxMWFkaXLl3YvXu3qePWMZhfA+6etnyDWwFoOh8c9ARTRESSJwWdZGLmzJl4eXlhNJpOHe/Xrx+VK1eG6/thQa2oj6syFYX2AZA2mw2qFRERSRwKOsnA9OnTeeedd8whp3///vzyyy8YrmyHRXXh0U3LN2QtC+03g1s+G1QrIiKSeBR0krhp06bx7rvvmkPOgAED+PnnnzFc2AiLPeBxkOUbclWFdv6QLkfiFysiIpLIFHSSsKlTp1qEnA8++IAff/wRw6nlsLQphD+0fEPeutBmvfbKERGRFENBJ4maNm0aPXv2NL8eOHAgEyZMwHB0LqxsE3WfnEJNodVqSJ0+kSsVERGxHQWdJKpw4cKkTZsWgEGDBjFu3DgMh/4En25gjLAcXLwDtFgKqdLYoFIRERHb0briJKp27dqsXr2aDRs2MGrUKAy7f4DNn0YdWLYnNPhD51eJiEiKZDA+meAhryxPnjxcvnwZd3d3Ll26ZJsijEbY+gXs/DZqX6VBUHssGAyJX5eIiIgd0KOrJOK3337j888/xyKXGiNh04DoQ061rxRyREQkxdOjqyTgl19+YcCAAQAYDAbToypjBKzrCf/MjPqGOuOh0sBErlJERMT+KOjYuZ9//pkPPvjA/NpoNEL4Y/DpAie9nxttAI9JUK4nIiIioqBj13788UcGDnx6Z+aLL75g5LAhGFa0gnNrLQc7OEHjOVCifSJXKSIiYr8UdOzUhAkTGDRokPn1l19+yYjPBmJY0hgub7Yc7OQCzReb9soRERERMwUdOzRu3Dg++ugj8+sRI0Yw/OP3YVF9uLHPcnAqV2i1EvLWSdwiRUREkgAFHTszduxYPv74Y/Prr776ii8H9YSFteHWv5aDXTLB2z6m86tEREQkCgUdOxIUFMRPP/1kfj1y5EiGDegC82tC0BnLwWlzmM6tylY2kasUERFJOrSPjh3JkCEDfn5+uLu78/XXXzPsvTYwv0bUkJM+H3TYrJAjIiLyErqjY2eKFCnCkSNHyPj4DCyoBY8CLQdkKgptNoBbPtsUKCIikoTojo6NLV68mLCwMIu2jPePwMK6UUNOtnLQfrNCjoiISCwp6NjQqFGjaNu2LR06dHgads6tA++GEBpsOTjXG9DOH9LlSPQ6RSQOQu+Db0/4PSeMM4DfhxB0zvTrIzNe/v61XjC5QIKWKJKc6dGVjYwcOZLhw4cDsGTJEtasWUPLUpGwugNEhFoOzlcPWi6H1K42qFTkOYH/wK7RcNHPdNfRJQvkrQtVh0LW0rauLnqPg2HvBDi1BO6eBmMEZChs2nuq4gfgmjvhPnvnt/DPDHhjGGQsDFlKJtxnWevWv3B8IZT2ggwFbF2NSLxS0LGBESNG8NVXX5lfjx07lpZFgmBlD9M34GcVag7NF5o2BRSxtZNLYHVHcMkMZd6FDAUh+BwcngonF0PT+VC0la2rtHT3DCxuAMEXoFhbKNsbHFPDzUOmuk8thR4nEu7zL24y3ZGtNvxpm9EIHzwCh1QJ97mv4ta/sP0r035cCjqSzCjoJCKj0ciIESMYOXKkuW3cuHEMqusMa7tHfUOJjtBoJjjayTdDSdnunoY1XSFDIWj/N6TN9rTvtQ9gQU3w6QrZDkHGQolXV9gDSJUu+r7IcFjxNjy4bnr0m6eGZX+Nb2D39wlb38MbkKWUZZvBoB9eRBKJ5ugkEqPRyPDhwy1CzoQJExhU/TFs6h/1DWV7QePZCjliP3b/AOEPTQfHPhtyANJmBY8/TaFj9xhT24nFpnkoFwOiXuvgn6a+wCNP224dgxVt4LfM8KMLzKkMp1ZYvu/IjKfX3PAeTMwOf+aJueYT3nDzILzxedSQA+DsZgo7zzq+CGZXgp/SwG9ZYU0XuHfZcsxaL/jZ1dS+7C3TrydmA/+PIPK/u7IX/U21Bp2FM6tNvx5nMM3PiWmOzsllMKOM6fc/owycXBr978sYCXt/hBmlTWN/zwHr+0DIHctxkwvA0mZwaQvMrWIaO6UQ/DPr6ZgjM2BlW9OvF9Z9WudF/+g/WySJUdBJBEajkWHDhjFq1Chz248TJvBhpeuwZWjUN1QabPpLw8ExEasUeYkzK8GtAOSpGX1/nlqm/jOrTa8LNjUdUXJ8YdSxxxdAltKQtYzpdeA/MO8NuH0UXh8CtceZ7tIsfyv6v+w3vmd63PLml1BlSMw1n/4vKJXsGrvf45EZsKqd6c9ejdFQrpfpcd38GhBy13KsMQK8PSFNFqg9FvLUhr3j4NAkU3/mkqYfVtJkhWwVTL9uPDtqSHzi3DpY2dp0t6fmaCjyFvi+A9f2RB27vg/8/THkrg51f4LS78DRuaZ6IixXcXLnFKxsA/k9TP9enTOZglrgP6b+PLXgtQGmX1cd+rTOzHY4l0jECnp0lQgmTZrEN988/anx559+5H+lT8Ku36IOrjYS3vjC9M1OxF48DoL7V6BwyxePy1bOFC5C70Hq9FC4uWnuTr2fnwb3B9fgUgC8OeLp+/w+MG2E2Xk3ODmb2iq8ZwoYmz+NOu/HJTO03fjyHwZuHwXnDOCW9+W/x4gw02dlLWN6NPfk0ZJ7DdNdkb0ToPrTuXWEh0Dx9vDmMNPr8n1hdkU4MhUq9DOtkCzVBbZ+AendTb82uxn18//+1LTjeYctpprBFJ68G4Jb/qfjLm2Bw1OgyVwo2elpe7664N0ITiyybL9z3PT7eRJQi7eDSXnhyHSoM9b0mDFPTdj/sykM6dw8SWZ0RycRdOjQgSpVqgDwyzef8L/Mc+BANCGn7o+mb5oKOWJvQu+Z/pk6/YvHPel//N/2CMXbm+aoXPR/OubEYtOjl+LtTa8f3YYLm0x/AYfeg4eBpq9Ht6CAJ9w5GfXRUdlesbvjGRoMqV5S8xPX95hqLf+e5fyZQk0hcwk4uzrqe8r3tXztHs1xLbFx/yrcPACluz8NOQAFPKLO7zmxyDQmv8fTf1cPAyF7JdMdtIt+luOzlLK8C5c2G2Qubl2dIkmQ7ugkggwZMuC7agnrfu1LO5dxcP25lVUGB/CYDGV72KbA/7d372FRVesfwL97YGBguCsCoiIQoqLmzwsIqGgo0FHzBBresTAzj6cSPWb1U0jLfnl88HiozOMNj1qYJVbnEREzfRK8cbROliJ44WheQOWiyEWZ9/fHdiZwZoAZNgMM7+d55inWWrP2u9fgzMvea61hrDHqBEad8OjzZELUM0r8UM7bBXiFi2V5u8RbOS69xJ9LCwAQkL1MfOjyoEi8KqLm6N3EuB2AyiZ+oJcXiv918deuc+kN/Ha0fpmlQvs2lMJZe56MIcd28tOuc/YHik7//nNpvniFbX0X3X09KKr/s72ODUatnYFqI+JkrB3iRKcFEBEqKipgZ/d435tL++D03Xy84FAI0BONZZbiJWj/F0weJ2NNZu0IKD2A2/9puF3xfwA7T3GSLyDehnrqj+IS7jGfiKuffssGhq/6/TmkEv87ZLF4BUcX56fq/2xp07S4XXoDRWeA8qtNu31lCKGV5tCRCrDtIr5v6GLzRPKl78oXPflmxJh54kRHYkSExYsX4/vvv8fBr3fC5ad3xb9gdXHpDURsAjxDTRskY8bwGQ/8vFGcI6JrBdO1H8Q9dQa8Ur/cPxb4ZRvw3++AO+cAENA79vd69VJ0mRzwGiNxzBOA858D53YAQW813FY9D+ZunrhJZ1138+rPk5Gauu/SfO26krz6Pzv6AoUHxYnI8iYmfI3i2+XMfPEcHQkRERYtWoTk5GScOXMGEaH98fBXHUmOhRUQ8i4w80dOclj7MfQv4pWUg6+I82fqqrwLHJwHWNqK7erqMUacPHx+l5j0uwfWv/Vk20WcAPufDeJclSc90DFxt6l6TQI69wdOvA9cP6ZdX3MPOPqO+P9uQ8RY/vMp8Kj69zaXM8RJzd7jjI+jMXYe4u28X7aJt6XUrmSJq8vq8n9BXPF1fCW0qB5prw5rCvU+RMY8l7E2rk1f0Tl16hQSExORk5ODhw8fon///khISMALLzT9Nk91dTU+/PBDbN++HVevXoWLiwvGjx+P9957D1266LnHbQQiwsKFC7Fu3TpN2bxhtZA/edW4W5i4dFzXPADG2jJnP3EDy33TgW39gf7xgMPjnZHPbha/DmLc5+LXHNRlIQf8ooHzaeI+O2FrtPsO/1hcYbWtv7ik29EHeHBLTE7uXwNm/WRczBZy4Lk94s7Iu0YCvV4Q/7iQyYE7vwDnPhPn1Qx/X2w74kNxSfcXYeKGnRW3gNPrxGXzgxcaF0NTjfgASB8njkO/l4Cqu8CZFHEZ/sP7v7frHiZeNTv5gTiB2StCPJ/SfHGi8uh1YoJniC4DxVtxpz4EasoAC2vxqpatdO+RjLWWNpvofP/994iMjIRCocCUKVNgb2+Pr776CrGxsbh69SoWLVrUaB8qlQoTJ05EZmYmhg0bhpiYGOTn52PTpk347rvvcPz4cbi66tnTwkBvvLYAf//oEwDioqmNk4D4oDoNFM7iHhYBs3lVFWu//CeLt1xPfiB+fULlbXEfGc13XfXT87xYcUk0BN3z0Tr1Babnil9D8EuqeMXItgvQ5X+AYcubF7PzU+LV03+vFecKFewFoAKcngL6zwEGvfZ7236zAbktcPL/xOXecqW4tH3Eh4DCqXlxNMY7CpiwGzj6v8APb4kJY+RW4OLX2pv3jf0UcBssXgU7+rY418+hJ9BnhnhLy1BKd2DMp+LrmhkvXjF64XtOdJhZEIja3oy0R48eoXfv3rh27RqOHz+OgQMHAgDKysoQGBiIK1eu4MKFC/Dyavie+datW/HSSy9h6tSp2LlzJ4THCcann36KV199FXPnzsWGDRuMjrNbt2747bffoLRVoOJBFQAxh9k0GXgpsE7DPtOBUcn8psEYY4yZWJuco3Po0CFcvHgR06ZN0yQ5gLhM++2330ZNTQ22bdvWaD8bN24EAHzwwQeaJAcAXnnlFfj4+GDnzp2orKxsdrx1k5wtL9RJchx9gJhM4A87OMlhjDHGWkGbTHQOHz4MAIiIiNCqi4wUl58eOaLj+3PqqKqqwokTJ+Dv76915UcQBIwdOxYVFRXIzdWxvboRBAFIjQVmD4V4GTlwKRD3M9BT+xwYY4wxZhptco5Ofr64xNLPT3vzLHd3d9jZ2Wna6HPx4kWoVCqdfdTtOz8/HyNG6PnuHgDJyclITk7WWffbb7/v1uqkAN7OAN7OlIvzcWTbAWxvMEbGGGOso3N3d5fsooMubTLRKSsTl1c6OjrqrHdwcNC0aU4fddvpU15eXi+h0aekUnwADwEUNdKaMcYYYwBw//79xhs1Q5tMdNoSBwcHeHp66qyrmwDpa8OkdePGDahUKshkMnh4eLR2OB0Cj7np8ZibHo+56anH/MGDBy16nDaZ6Kivwui72lJeXg5nZ+dm91G3nT4JCQlISEjQWadedeXp6Ylr16412A+ThnrMPTw8eMxNhMfc9HjMTY/H3PTUYy7lnna6tMnJyHXnzzzp5s2buH//vt65N2o+Pj6QyWR65/I0NA+IMcYYY+ahTSY6YWFhAIADBw5o1WVmZtZro4+NjQ0CAwORl5eHwsLCenVEhKysLCiVSgwZMkSiqBljjDHW1rTJRCc8PBw+Pj747LPP8OOPP2rKy8rKsGrVKlhZWWHWrFma8hs3buD8+fNat6nmzp0LAHjrrbdQd1/EDRs24NKlS5g+fTpsbKT6UjzGGGOMtTVtMtGxtLTEpk2boFKpMHLkSMydOxeLFi3C008/jQsXLmDVqlXo2bOnpv1bb72FPn36ID09vV4/cXFxiIyMxOeff46QkBAsXboUkyZNwvz58+Ht7Y333nvPxGfGGGOMMVNqk4kOAIwePRpHjx5FaGgodu3ahfXr18PNzQ1paWlN+p4rAJDJZPj666+RlJSE4uJirF27FtnZ2YiPj8exY8ck+54rxhhjjLVNbXLVlVpgYCAyMjIabZeamorU1FSdddbW1khMTERiYqLE0YkrssrLyzV78rCWx2Nuejzmpsdjbno85qZnqjFvk1/qyRhjjDEmhTZ764oxxhhjrLk40WGMMcaY2eJEhzHGGGNmixMdxhhjjJktTnQYY4wxZrY40XnCqVOn8Ic//AFOTk5QKpUYNmwYvvjiC4P6qK6uxooVK+Dn5weFQoGuXbti7ty5KCoqaqGo26/mjDcRISMjA6+++ioGDBgAR0dH2Nra4umnn8aqVatQVVXVwtG3T1L8jtdVUlICT09PCIKAqKgoCSM1H1KNeVFRERYuXKh5b+nUqROCg4Oxfv36Foi6fZNizK9fv47XX38dffv2hVKphJubG4YPH47t27ejtra2hSJvn3bs2IFXXnkFQ4YMgbW1NQRB0LvtS0NUKhVSUlLQv39/2NjYwNXVFVOnTsWlS5eMD46YxqFDh0gul5O9vT29/PLLlJCQQF5eXgSA1qxZ06Q+amtrKTIykgDQsGHD6M0336To6GgSBIF8fHyoqKiohc+i/WjueFdWVhIAsra2psjISFq8eDEtWLCA/Pz8CAANHTqUKioqTHAm7YcUv+NPmjZtGimVSgJAkZGREkfc/kk15mfOnCFXV1eytLSkiRMn0tKlS2nBggUUHh5Ozz77bAueQfsjxZhfvHiROnfuTIIgUFRUFC1ZsoTmzZtH7u7uBIBmz57dwmfRvqjHt3Pnzpr/37p1q8H9zJkzhwBQQEAALVmyhGbMmEFWVlbk4uJCFy5cMCo2TnQee/jwIfn6+pK1tTWdOXNGU15aWkq9evUiKysrunLlSqP9bNmyhQDQ1KlTSaVSacrXr19PAGju3LktEX67I8V419TU0HvvvUd3797VKp8wYQIBoNWrV7dE+O2SVL/jdX355ZcEgD766CNOdHSQaszLysqoR48e5OrqSj/99JPO4zCRVGP+6quvEgD629/+Vq+8pKSEevToQQAM/vdizrKysjTj8cEHHxiV6Bw6dIgA0MiRI6m6ulpTvm/fPgJAERERRsXGic5jmZmZBIBefPFFrbrU1FQCQO+++26j/QQHB+v8B6BSqcjHx4eUSiU9ePBAsrjbK6nGW5+cnBwCQOPGjWtOmGZF6jEvKioiV1dXmjlzJl2+fJkTHR2kGnP1B8fmzZtbIkyzItWYq6/M67qKMG3aNAJAubm5ksRsboxNdKZOnUoA6MiRI1p1o0aNIgBUWFhocDw8R+exw4cPAwAiIiK06iIjIwEAR44cabCPqqoqnDhxAv7+/vDy8qpXJwgCxo4di4qKCuTm5koTdDsmxXg3RC6XAxC/IJaJpB7zefPmwcLCAuvWrZMkPnMk1Zjv2rULgiAgJiYGeXl5SElJwerVq/HNN9+gpqZG0pjbO6nGvF+/fgCAffv21SsvLS1FdnY23N3d0bdv32ZGy+o6fPgwlEolQkNDteqa87nAnwKP5efnAwD8/Py06tzd3WFnZ6dpo8/FixehUql09lG37/z8fIwYMaKZEbdvUox3Q7Zs2QJA95tdRyXlmO/YsQN79uzB3r174ezsjLKyMkljNRdSjHlNTQ1+/vlnuLq6IiUlBYmJiVCpVJp6Hx8f7N27F/3795c2+HZKqt/zv/zlL/j222+xcOFC7N+/HwMGDEB5eTn27t0LW1tbpKenw8bGRvL4O6qKigrcuHED/fr1g4WFhVZ93c9PQ/EVncfUb9SOjo466x0cHBp9M29KH3XbdWRSjLc+GRkZ2LBhA/r06YP4+HijYzQ3Uo359evX8dprr2Hq1KmYOHGipDGaGynG/O7du6itrcWdO3ewYsUKrF69Grdu3cK1a9ewbNkyXL58GRMmTOBVho9J9Xvu5uaGY8eOISoqCvv378fq1avx6aefoqysDLNmzcLTTz8tadwdXUt+fnKiw8zKqVOnEBsbC0dHR+zevRvW1tatHZLZmTNnDuRyOf7+97+3digdgvrqTW1tLebPn49FixahS5cu8PT0xIoVKzB58mQUFhbiyy+/bOVIzUtBQQFCQ0NRXFyMH374Affu3cPVq1exfPlyrFy5EuHh4bzEvJ3gROcxdRapL1ssLy/Xm2ka0kfddh2ZFOP9pNzcXEREREAmkyEzMxMBAQHNjtOcSDHm27ZtQ0ZGBj7++GN07txZ8hjNjZTvKwDw3HPPadWry3jun0iq95bZs2ejsLAQ3377LYYPHw47Ozt069YNS5cuxZ///GccO3YMaWlpksbekbXk5ycnOo81dP/v5s2buH//vt65N2o+Pj6QyWR67yE2dO+4o5FivOvKzc3F2LFjoVKpkJmZiaFDh0oWq7mQYszPnDkDAJg8eTIEQdA8vL29AQCZmZkQBAEDBw6UNvh2SooxVyqV8PT0BAA4OTlp1avLKisrmxesmZBizO/du4fs7Gz06dMH7u7uWvWjR48G8Pu/B9Z8SqUSHh4euHz5ss4rZc35/ORE57GwsDAAwIEDB7TqMjMz67XRx8bGBoGBgcjLy0NhYWG9OiJCVlYWlEolhgwZIlHU7ZcU462mTnJqa2uxf/9+BAUFSReoGZFizIODgxEfH6/1iI2NBQB069YN8fHxiI6Oljj69kmq3/NnnnkGAPDrr79q1anLevbsaWyYZkWKMVevZLt9+7bO+uLiYgDgW+MSCwsLQ0VFBbKzs7Xq1K/dyJEjDe/Y4AXpZurhw4fk4+PT4CZTly9f1pRfv36dzp07R6WlpfX64Q0Dm0aq8c7NzSUnJyeys7Ojo0ePmij69kmqMdeF99HRTaoxz87O1uwWW1JSoim/ceMGeXp6kkwmo7y8vBY+m/ZBqjH39/cnALRx48Z65SUlJdS7d28CQFlZWS15Ku1WY/voFBcX07lz56i4uLheOW8YaAKGbBseFxen84XU9RUQMTExJAgCeXt781dA1NHc8b5z5w45OzsTAIqKiqLExEStx9q1a017Um2cFL/junCio59UY56QkEAAqHv37jR//nx6+eWXqUuXLgSAVq1aZaKzaR+kGPN9+/aRpaUlAaDw8HBavHgxxcfHk6urKwGgmJgYE55R27dx40aKi4ujuLg4GjRoEAGg0NBQTVndhDExMZEAUGJiolY/T34FxMyZMzVfAWFsMs+JzhNOnDhBUVFR5ODgQDY2NhQYGEhpaWla7Rp6Q6qqqqKkpCTy9fUlKysrcnd3pzlz5tDNmzdNcAbtS3PGW/3h2tDDy8vLdCfTTkjxO/4kTnQaJtWYb926lYYMGUK2trakVCpp+PDhtGfPnhaOvn2SYsxPnjxJkydPJg8PD7K0tCQ7OzsaOnQopaSk0KNHj0xwFu2Hehz1PeLi4jRtG0p0amtrad26dRQQEEDW1tbUqVMnio2NpYKCAqNjE4iIDL/hxRhjjDHW9vFkZMYYY4yZLU50GGOMMWa2ONFhjDHGmNniRIcxxhhjZosTHcYYY4yZLU50GGOMMWa2ONFhjDHGmNniRIcxxhhjZosTHcYYY4yZLU50GGOMMWa2ONFhrBVUVFQgOTkZo0ePhpubG6ysrODs7Izg4GAsX74c//3vf1s7RCQlJUEQBKSmptYrFwQBPXv2rFd25coVCIKAUaNGGXQMXX3pK9fX1pQEQYAgCK0agxTawlgyZiqc6DBmYjk5OXjqqaewaNEinDx5Ev369cOkSZMQEhKCixcvYuXKlejVqxcOHjzY2qG2ecYmWIyxjsOytQNgrCP58ccfER4ejqqqKrz55ptYtmwZlEqlpl6lUmHv3r1YsmQJrl271oqR6nfu3DnI5XKT9yXlcRljHQcnOoyZCBFh5syZqKqqQlJSEhITE7XayGQyREdHIzw8HFevXm2FKBvXu3fvVulLyuMyxjoOvnXFmIns378fZ8+eRbdu3fDOO+802NbR0RH9+vUDUP/2THl5ORISEuDt7Q25XI433nij3vOuXr2KBQsWwNfXFwqFAi4uLhg/fjxycnL0Huubb75BcHAwbG1t0alTJ8TExODChQt62zc2v6O8vByvv/46unfvDoVCgT59+mDt2rVQqVQG99VQ26SkJHh7ewMAjhw5opk/IwgCZs+ejdzcXAiCgJCQEL19rlq1CoIg6Ew6pdLU1+T06dMQBAFBQUF6+0pJSYEgCEhISDDqGM2VkZGBiIgIODo6wsXFBePGjUNBQQEAYO3atRAEAcePH5f0mIw1GzHGTOJPf/oTAaCFCxca9LzLly8TAAoMDKSBAweSs7Mz/fGPf6To6GhKSkrStMvJySFnZ2cCQP7+/hQdHU0jRowgS0tLsrCwoLS0NK2+169fTwBIEAQaOXIkxcbGkpeXFzk6OtKMGTMIAG3durXecwCQl5eXzhiHDRtGgwcPJicnJ4qOjqbx48eTjY0NAaC4uDit4+vqS1/5k2Xp6ekUExNDAMjNzY3i4uI0j40bNxIR0aBBgwgAnT17VusYKpWKfHx8SCaTUWFhoVa9LgDIkLdNQ1+T3r17EwAqKCjQ2V9QUBABoH//+99GH0N9HrrGXR+VSqX5/XVzc6PJkydTv379CAANGjSIKisrqWvXrhQZGdnkPhkzFU50GDOR0NBQAkDbt2836HnqJAIABQcHU0lJiVabsrIy8vDwIAsLC9qxY0e9ulOnTpGzszPZ2dlRUVGRpvzKlSukUChILpfT/v37NeU1NTU0ffp0zTENSXQA0IABA6i4uFhTV1BQQF27diUAlJ6e3mhf+sobOm5YWJhWH0RE//jHPwgAvf7661p1WVlZBICeffZZnc/VxZBEx5jXZOXKlQSAVqxYodVfQUEBAaDevXs36xjq8zAk0VHHNXz4cCovLycioocPH9LgwYMJACUlJREAysnJaXKfjJkKJzqMmYj6r/W6SUVT1E0iTp06pbPN2rVrCQAtWrRIZ31ycjIBoOTkZE3Z8uXLCQDNmjVLq/3t27fJ1tbWqETnwIEDWv2prxyFh4c32pe+cmMSnfv375ODgwO5uLhQVVVVvbrY2FgCQHv27NH5XF0MSXSMeU0uXbqklcyovfvuuwSAVq5c2axjqM+jqYnOrVu3SKFQkLW1tdaVpnfeeYcAkEKhoIiIiCb1x5ip8RwdxtoJDw8PDBkyRGfdgQMHAADR0dE660eMGAEAOHnypKbshx9+AABMmTJFq32nTp0QERFhcIwuLi4YO3asVvnUqVMBiEvrdc3VaSlKpRIzZszA3bt38dVXX2nKb9++jfT0dLi7u2PChAktcmxjXhNvb2+EhITg/PnzOH36dL32O3fuBABMnz69Wccw1I4dO1BVVYXZs2fD19e3Xp2TkxMAoKqqqkXnOTHWHJzoMGYinTp1AgAUFxcb9fwePXrorbty5QoAIDQ0tN6kXPVj6NChAMQPeLXr168DALy8vHT2acyGcvr6cnR0hJOTEyorK1FSUmJwv80xb948AMDGjRs1Zf/85z9RU1ODF198EZaWLbP41JjXBPg9kVEnNgCQm5uLCxcuICQkRDMBuznHMIQ6mXr++ef1thkzZkyDk74Za028vJwxExk4cCCys7Nx+vRpzJgxw+DnKxQKvXXqqySTJk2qty/PkzriEu3+/fsjJCQEhw8fRn5+Pvz8/LB582YIgoA5c+a02HGNfU1iY2PxxhtvIC0tDX/9618hk8l0Xs1pzjEMob6ypCuRKS8vBwC8+eabRvfPWEvjRIcxExk3bhw+/vhj7N69G6tXr5b0SkK3bt2Ql5eHpUuXYvDgwU16joeHB/Ly8lBYWIi+fftq1RcWFhoch76vrigvL0dpaSlsbGw0tztMad68ecjJycGmTZvw3HPP4ddff8WYMWPg4+PTYsc05jUBxCt/kZGR+Ne//oXDhw8jLCwMaWlpkMvliI2NleQYTVVTU4Pi4mLY2dnB3t6+Xl1paSk++eQTTRyMtVV864oxE4mKikJAQACuXbuG999/v8G25eXl+OWXX5rct3peTHp6epOfo56/8cUXX2jV3b17V3PLwhB37tzBd999p1WelpYGAAgODoaFhYXB/epjZWUFAHj06FGD7SZPnoxOnTohNTVV8+H88ssvSxaHLsa8JmrqKzefffYZDh06hJs3byIyMlJz+1OKYzSF+hZYZWUlqqqq6tUtW7YMd+7cAQBJX1PGJNfas6EZ60jOnDlDCoWCANDSpUvp/v379epVKhV9/fXX5Ofnp1nt1NjKIiKikpIS6tKlC8nlctqwYQPV1tbWq3/48CHt37+ffv75Z03ZpUuXyNramuRyOWVlZWnKa2pqaNasWUYvLx84cCDdvn273nE8PT0JAH311VeN9qWvXFdZdXU1yeVycnd3p0ePHukZHVFCQoImRldXV6qurm6wvS4wYNWVMa+J2oMHD8je3p6cnJxoypQpBIA+//xzyY6hb9x18fX1JQCUkpKiKdu9ezfJZDKysrIiAJSRkdGkvhhrDZzoMGZiR48eJTc3NwJAtra2FB4eTtOmTaNx48ZpyhUKBR08eJCImpboEBEdO3aMOnfuTACoe/fu9Oyzz9K0adPomWeeIScnJ5372Hz00UcEgGQyGY0aNYqmTJlCPXv2JEdHR81eOoZuGDho0CDNhoETJkzQLFOfMWOGVszNTXSIiCZMmEAAKCAggGbOnEnx8fG0ZcsWrXZ5eXkkCAIBoMWLF+sawkapE52goCC9D/VmhUTGvSZqdZNNe3t7evDggc52xhzDkERHvYRdJpNRZGQkjRkzhgRBoF69elFKSgoBoK5du9Jrr73WpP4YMzVOdBhrBffu3aM1a9ZQWFgYubq6kqWlJTk5OVFQUBAlJibS1atXNW2bmugQEd24cYOWLFlCAQEBZGtrS7a2tuTr60sTJ06k1NRUunfvntZz0tPTKSgoiGxsbMjZ2ZkmTpxI586do8TERIMTnbCwMCotLaX58+dT165dycrKivz9/WnNmjU6r7hIkejcunWLZs6cSe7u7mRhYaF3F2Yiou7duxMAOn/+vM76xqgTj4YeiYmJ9Z5jzGtCRJSZmanpU9deR805hiGJjkqlouTkZPL39ycrKytydHSk559/noqKiqi6uppmz55NCoWiSb+fjLUGgYioube/GGOsrTt27BhCQkIQFhaGw4cPt3Y4jDET4cnIjLEOQT0BfMGCBa0cCWPMlPiKDmPMbOXk5GDz5s04e/YsTp48iUGDBuHUqVOQyfhvPMY6Ct5HhzFmti5cuIAtW7bA3t5es48RJzmMdSx8RYcxxhhjZov/tGGMMcaY2eJEhzHGGGNmixMdxhhjjJktTnQYY4wxZrY40WGMMcaY2eJEhzHGGGNmixMdxhhjjJktTnQYY4wxZrY40WGMMcaY2eJEhzHGGGNmixMdxhhjjJmt/wesYG0iqyMaOwAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "\n",
    "plot_tarp(ecp, alpha, \"TARP.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da0a480b-55b2-420e-87f7-98007aa22de5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "ca370e7e-3a33-4658-8a42-7be69e90c065",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "5250it [00:00, 5389.73it/s]                          \n"
     ]
    }
   ],
   "source": [
    "\n",
    "# A PPC is performed after we trained a neural posterior `posterior`\n",
    "posterior.set_default_x(x) # x_o loaded from disk for example\n",
    "\n",
    "# We draw theta samples from the posterior. This part is not in the scope of SBI\n",
    "posterior_samples = posterior.sample((5_000,))\n",
    "\n",
    "# We use posterior theta samples to generate x data\n",
    "x_pp = sim_for_training(posterior_samples)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 80,
   "id": "19f12996-8b0a-4e8c-869a-0c5b2b6e17e8",
   "metadata": {},
   "outputs": [],
   "source": [
    "mask = [True, True, True, False, False, False, False, True, False, True]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "id": "13f65345-35a7-4def-8f46-6dd6bc30b272",
   "metadata": {},
   "outputs": [],
   "source": [
    "#mask = [(\"ERR\" not in p) for p in parameters_to_condition_on]\n",
    "\n",
    "mask = torch.tensor(mask)\n",
    "\n",
    "x_pp_filtered = x_pp[:, :, mask]   # (5000, 1635, new_dim)\n",
    "x_filtered    = x[:, mask]         # (1635, new_dim)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "id": "8505632e-d3b9-4333-9550-b02ccd05ab6f",
   "metadata": {},
   "outputs": [],
   "source": [
    "import torch\n",
    "\n",
    "# mean per dataset\n",
    "sim_means = x_pp_filtered.mean(dim=(1, 2))   # (5000,)\n",
    "obs_mean  = x_filtered.mean()\n",
    "\n",
    "# variance per dataset\n",
    "sim_vars = x_pp_filtered.var(dim=(1, 2))\n",
    "obs_var  = x_filtered.var()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "88f1c4ea-f902-44d8-b830-ef196cfba96f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 83,
   "id": "4d42b84a-841e-413a-a2ce-144161e0af88",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.lines.Line2D at 0x7f5ee4d9a490>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.hist(sim_means.numpy(), bins=50, alpha=0.5)\n",
    "plt.axvline(obs_mean.item(), color='red')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 85,
   "id": "00853197-6e87-493a-9057-4e9c7db63cbd",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 500x2000 with 5 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "from scipy.stats import wasserstein_distance\n",
    "import numpy as np\n",
    "\n",
    "valid_indices = [i for i in range(len(parameters_to_condition_on)) if mask[i]]\n",
    "N = len(valid_indices)\n",
    "\n",
    "fig, axes = plt.subplots(N, 1, figsize=(5, 4*N), sharex=False)\n",
    "\n",
    "\n",
    "if N == 1:\n",
    "    axes = [axes]\n",
    "\n",
    "for ax, n in zip(axes, valid_indices):\n",
    "    label = parameters_to_condition_on[n]\n",
    "\n",
    "    x_obs = x[:, n]\n",
    "    x_sim = x_pp[:, :, n].reshape(-1)\n",
    "\n",
    "    was = wasserstein_distance(x_sim.numpy(), x_obs.numpy())\n",
    "\n",
    "    #x_obs_np = x_obs.numpy()\n",
    "    #x_sim_np = x_sim.numpy()\n",
    "\n",
    "    # Remove NaN/inf\n",
    "    #x_obs_np = x_obs_np[np.isfinite(x_obs_np)]\n",
    "    #x_sim_np = x_sim_np[np.isfinite(x_sim_np)]\n",
    "\n",
    "    #was = wasserstein_distance(x_sim_np, x_obs_np)\n",
    "\n",
    "    ax.hist(x_sim.numpy(), bins=50, alpha=1, density=True,\n",
    "            label=\"simulated\", color=\"darkorange\", zorder=1)\n",
    "    ax.hist(x_obs.numpy(), bins=50, alpha=0.5, density=True,\n",
    "            label=\"data\", color=\"k\", zorder=2)\n",
    "\n",
    "    # Title without Wasserstein\n",
    "    ax.set_xlabel(label, fontsize=15)\n",
    "    ax.set_yticks([])\n",
    "\n",
    "    ax.spines[['top', 'right', 'left', 'bottom']].set_visible(False)\n",
    "\n",
    "\n",
    "    ax.tick_params(\n",
    "        axis='x',\n",
    "        labelsize=12,   # size of tick labels\n",
    "        length=6,       # tick length\n",
    "        width=1.5       # tick thickness\n",
    "    )\n",
    "\n",
    "    quantiles = [0, 0.05, 0.25, 0.5, 0.75, 0.95, 1]\n",
    "    \n",
    "    # choose which distribution to base ticks on\n",
    "    #q_vals = np.quantile(x_obs.numpy(), quantiles)\n",
    "    \n",
    "    #ax.set_xticks(q_vals)\n",
    "    #ax.set_xticklabels([f\"{int(q*100)}%\" for q in quantiles])\n",
    "            \n",
    "    # Wasserstein inside plot (top-right corner)\n",
    "    ax.text(\n",
    "        0.95, 0.9,\n",
    "        f\"Wasserstein Distance\\n{was:.3f}\",\n",
    "        transform=ax.transAxes,\n",
    "        ha='right',\n",
    "        va='top',\n",
    "        fontsize=10,\n",
    "        bbox=dict(facecolor='white', alpha=0.7, edgecolor='none')\n",
    "    )\n",
    "\n",
    "# Single legend (cleaner than repeating)\n",
    "axes[0].legend(frameon=False, labelcolor='linecolor')\n",
    "\n",
    "# Super y-axis label\n",
    "fig.supylabel(\"Density\", fontsize=20)\n",
    "\n",
    "plt.tight_layout()\n",
    "#plt.savefig(\"PPC.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "c9593495-9792-4514-aa21-36c842786e98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b799311d-525f-4698-90cc-9c90925ffac7",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "0143b0d6-0c19-4c77-bf92-b7afeae17e98",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "86226f38-7fb8-49b0-9676-ced1364b7dae",
   "metadata": {},
   "outputs": [],
   "source": [
    "parameters_to_condition_on"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 86,
   "id": "3c0e745a-ecf6-46fb-a302-7c4913176226",
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'inference' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[31m---------------------------------------------------------------------------\u001b[39m",
      "\u001b[31mNameError\u001b[39m                                 Traceback (most recent call last)",
      "\u001b[36mCell\u001b[39m\u001b[36m \u001b[39m\u001b[32mIn[86]\u001b[39m\u001b[32m, line 1\u001b[39m\n\u001b[32m----> \u001b[39m\u001b[32m1\u001b[39m plt.plot(inference._summary[\u001b[33m'training_loss'\u001b[39m])\n\u001b[32m      2\u001b[39m plt.plot(inference._summary[\u001b[33m'validation_loss'\u001b[39m])\n",
      "\u001b[31mNameError\u001b[39m: name 'inference' is not defined"
     ]
    }
   ],
   "source": [
    "plt.plot(inference._summary['training_loss'])\n",
    "plt.plot(inference._summary['validation_loss'])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "4436e7b2-8486-4699-a9c4-2c6ef9a5beee",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "20ddc994-af9e-42ff-9b12-3cd8a365c2e4",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "e7856e99-f4ce-44a7-aef7-20cc7588ff81",
   "metadata": {},
   "outputs": [],
   "source": [
    "def change_cosmo(df, w):\n",
    "\n",
    "    from astropy.cosmology import wCDM\n",
    "\n",
    "    cosmo = wCDM(\n",
    "        H0=67.66,        # Hubble constant (km/s/Mpc)\n",
    "        Om0=0.31,      # Omega matter\n",
    "        Ode0=1-.31,     # Omega dark energy\n",
    "        w0=w       # your chosen w value\n",
    "    )\n",
    "\n",
    "    df['MU'] = cosmo.distmod(df.zHD.values).value\n",
    "\n",
    "\n",
    "    return df"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "883c419e-80a3-4b24-a8ec-9fccfb08d12f",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 52,
   "id": "020fa121-71ca-493a-99d8-1f4f14498987",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "d562a7d0cadb4734b03d1302fcab4f49",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 3000 samples for 198 observations:   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/diagnostics/tarp.py:71: UserWarning: Found 1 NaNs and 0 Infs in the data. These will be ignored below. Beware that only 199 / 200 samples are left.\n",
      "  thetas, xs = remove_nans_and_infs_in_x(thetas, xs)\n",
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/utils/diagnostics_utils.py:45: UserWarning: Capping max_sampling_batch_size from 10000 to 502 to avoid excessive memory usage.\n",
      "  posterior_samples = posterior.sample_batched(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "3bb645ae4c8a4c27b7e092f777d6d14f",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 3000 samples for 199 observations:   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "de9148605c874ac2b416589931d102b1",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 3000 samples for 199 observations:   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "c8501ca243aa49968d749e69487ea14a",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 3000 samples for 199 observations:   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/diagnostics/tarp.py:71: UserWarning: Found 4 NaNs and 0 Infs in the data. These will be ignored below. Beware that only 196 / 200 samples are left.\n",
      "  thetas, xs = remove_nans_and_infs_in_x(thetas, xs)\n",
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/utils/diagnostics_utils.py:45: UserWarning: Capping max_sampling_batch_size from 10000 to 510 to avoid excessive memory usage.\n",
      "  posterior_samples = posterior.sample_batched(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "b30e8903fb084416a99d062a662a67f5",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 3000 samples for 196 observations:   0%|          | 0/3000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "WARNING:root:Only 0.000% proposal samples are\n",
      "                    accepted. It may take a long time to collect the remaining\n",
      "                    3000 samples. Consider interrupting (Ctrl-C) and switching to\n",
      "                    `build_posterior(..., sample_with='mcmc')`.\n"
     ]
    }
   ],
   "source": [
    "ws = [-0.90,-0.95, -1, -1.05,-1.1]\n",
    "ecp_list = []\n",
    "alpha_list = []\n",
    "\n",
    "for w in ws:\n",
    "\n",
    "    dft = change_cosmo(df, w)\n",
    "    \n",
    "    output_distribution = preprocess_input_distribution(\n",
    "        dft, parameters_to_condition_on[:-1] + ['x0', 'x0ERR', 'MU']\n",
    "    )\n",
    "    \n",
    "    dft['MURES'] = add_distance(output_distribution)\n",
    "    \n",
    "    sim_for_training = make_batched_simulator(\n",
    "        layout, dft,\n",
    "        param_names, parameters_to_condition_on,\n",
    "        dicts, dfdata,\n",
    "        sub_batch=20, device='cpu'\n",
    "    )\n",
    "    \n",
    "    \n",
    "    num_tarp_samples = 200  # choose a number of sbc runs, should be ~100s\n",
    "    # generate ground truth parameters and corresponding simulated observations for SBC.\n",
    "    thetas = priors.sample((num_tarp_samples,))\n",
    "    xs = sim_for_training(thetas)\n",
    "    \n",
    "    from sbi.diagnostics import check_sbc, check_tarp, run_sbc, run_tarp\n",
    "    # the tarp method returns the ECP values for a given set of alpha coverage levels.\n",
    "    ecp, alpha = run_tarp(\n",
    "        thetas,\n",
    "        xs,\n",
    "        posterior,\n",
    "        references=None,  # will be calculated automatically.\n",
    "        num_posterior_samples=3000,\n",
    "    )\n",
    "    \n",
    "    ecp_list.append(ecp)\n",
    "    alpha_list.append(alpha)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "b3dfe59a-fce7-4824-bb10-7e68a9c69358",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 53,
   "id": "50c338ae-0455-4024-92b8-050ca5a0f4ab",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_tarp_w(ecp_list, alpha_list, ws, savename):\n",
    "    \n",
    "    plt.rcParams['font.family'] = font_name\n",
    "    \n",
    "    fig = plt.figure(figsize=(6, 6))\n",
    "    ax: Axes = plt.gca()\n",
    "\n",
    "    colors = DISCRETE_CMAP('cmo.solar', len(ws)+1)\n",
    "    \n",
    "    for i in range(len(ws)):\n",
    "        alpha = alpha_list[i]\n",
    "        ecp = ecp_list[i]\n",
    "        w = ws[i]\n",
    "        ax.plot(alpha, ecp, color=colors[i], label=f\"$w=${w}\", lw=3)\n",
    "\n",
    "\n",
    "\n",
    "    ax.plot(alpha, alpha, color=\"black\", linestyle=\"--\", label=\"IDEAL\", lw=2)\n",
    "    #Aesthete\n",
    "    ax.spines[['bottom', 'left']].set_linewidth(2)\n",
    "    ax.spines[['top', 'right']].set_visible(False)\n",
    "    plt.tick_params(axis='both', labelsize=14, length=6, width=2)\n",
    "    ax.legend(frameon=False, labelcolor='linecolor', fontsize=\"x-large\")\n",
    "    ax.set_xlabel(r\"Credibility Level $\\alpha$\", fontsize=15)\n",
    "    ax.set_ylabel(r\"Expected Coverage Probability\", fontsize=15)\n",
    "    ax.set_xlim(0.0, 1.0)\n",
    "    ax.set_ylim(0.0, 1.0)\n",
    "\n",
    "    \n",
    "    ax.text(x=0.4,y=0.01, s=\"Over Confident\", color=\"darkorange\", fontsize=12)\n",
    "    ax.text(x=0.01,y=0.4, s=\"Under Confident\", color=\"darkorange\", fontsize=12, rotation=-90)\n",
    "    \n",
    "    plt.savefig(savename, bbox_inches=\"tight\")\n",
    "    return fig, ax"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "5ad6a5d6-30ea-47c0-ac75-01f44bcc190a",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "fbadcbdd-416b-4673-8884-04198d538767",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "(<Figure size 600x600 with 1 Axes>,\n",
       " <Axes: xlabel='Credibility Level $\\\\alpha$', ylabel='Expected Coverage Probability'>)"
      ]
     },
     "execution_count": 54,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 600x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_tarp_w(ecp_list, alpha_list,ws,\"Tarp_w.pdf\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 54,
   "id": "d106e6b6-00c8-49ae-8a79-23987531dadb",
   "metadata": {},
   "outputs": [],
   "source": [
    "dft = change_cosmo(df, -1)\n",
    "\n",
    "output_distribution = preprocess_input_distribution(\n",
    "    dft, parameters_to_condition_on[:-1] + ['x0', 'x0ERR', 'MU']\n",
    ")\n",
    "\n",
    "dft['MURES'] = add_distance(output_distribution)\n",
    "\n",
    "sim_for_training = make_batched_simulator(\n",
    "    layout, dft,\n",
    "    param_names, parameters_to_condition_on,\n",
    "    dicts, dfdata,\n",
    "    sub_batch=20, device='cpu'\n",
    ")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 59,
   "id": "756a39db-5e62-4a02-9df1-aa5c6e079436",
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/utils/diagnostics_utils.py:45: UserWarning: Capping max_sampling_batch_size from 10000 to 500 to avoid excessive memory usage.\n",
      "  posterior_samples = posterior.sample_batched(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "bdd110bcfb3748149ddacc24a323eb83",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 4000 samples for 200 observations:   0%|          | 0/4000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "a785d5246af241de8bc84eb78e5dc754",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Calculating ranks for 200 SBC samples:   0%|          | 0/200 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/diagnostics/sbc.py:66: UserWarning: Found 4 NaNs and 0 Infs in the data. These will be ignored below. Beware that only 196 / 200 samples are left.\n",
      "  thetas, xs = remove_nans_and_infs_in_x(thetas, xs)\n",
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/utils/diagnostics_utils.py:45: UserWarning: Capping max_sampling_batch_size from 10000 to 510 to avoid excessive memory usage.\n",
      "  posterior_samples = posterior.sample_batched(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "f0bbafaade104048bef4dab4887d4005",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 4000 samples for 196 observations:   0%|          | 0/4000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "ad7a01e53d2645f59796aab10b449f96",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Calculating ranks for 196 SBC samples:   0%|          | 0/196 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/diagnostics/sbc.py:66: UserWarning: Found 3 NaNs and 0 Infs in the data. These will be ignored below. Beware that only 197 / 200 samples are left.\n",
      "  thetas, xs = remove_nans_and_infs_in_x(thetas, xs)\n",
      "/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/sbi/utils/diagnostics_utils.py:45: UserWarning: Capping max_sampling_batch_size from 10000 to 507 to avoid excessive memory usage.\n",
      "  posterior_samples = posterior.sample_batched(\n"
     ]
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "7bd1c211dd204e4897f5e5bab01af624",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Drawing 4000 samples for 197 observations:   0%|          | 0/4000 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "application/vnd.jupyter.widget-view+json": {
       "model_id": "38c5239a6ce647f4816f5834861caf37",
       "version_major": 2,
       "version_minor": 0
      },
      "text/plain": [
       "Calculating ranks for 197 SBC samples:   0%|          | 0/197 [00:00<?, ?it/s]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "ws = [ -0.95, -1, -1.05,]\n",
    "ranks_list = []\n",
    "\n",
    "\n",
    "for w in ws:\n",
    "\n",
    "    dft = change_cosmo(df, w)\n",
    "    \n",
    "    output_distribution = preprocess_input_distribution(\n",
    "        dft, parameters_to_condition_on[:-1] + ['x0', 'x0ERR', 'MU']\n",
    "    )\n",
    "    \n",
    "    dft['MURES'] = add_distance(output_distribution)\n",
    "    \n",
    "    sim_for_training = make_batched_simulator(\n",
    "        layout, dft,\n",
    "        param_names, parameters_to_condition_on,\n",
    "        dicts, dfdata,\n",
    "        sub_batch=20, device='cpu'\n",
    "    )\n",
    "\n",
    "    # Obtain your `posterior_estimator` with NPE, NLE, NRE.\n",
    "    #posterior = inference.build_posterior()\n",
    "    \n",
    "    num_sbc_samples = 200  # choose a number of sbc runs, should be ~100s\n",
    "    prior_samples = priors.sample((num_sbc_samples,))\n",
    "    prior_predictives = sim_for_training(prior_samples)\n",
    "    \n",
    "    num_posterior_samples = 4000\n",
    "\n",
    "    ranks, dap_samples = run_sbc(\n",
    "        prior_samples,\n",
    "        prior_predictives,\n",
    "        posterior,\n",
    "        num_posterior_samples=num_posterior_samples,\n",
    "        use_batched_sampling=True, # `True` can give speed-ups, but can cause memory issues.\n",
    "    )\n",
    "\n",
    "    ranks_list.append(ranks)\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 72,
   "id": "c4d8ceeb-6576-46db-860d-3704513db603",
   "metadata": {},
   "outputs": [],
   "source": [
    "import importlib\n",
    "import dustbi_plotting\n",
    "\n",
    "importlib.reload(dustbi_plotting)\n",
    "\n",
    "from dustbi_plotting import sbc_rank_plot"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 70,
   "id": "391b3f1b-7af7-43a2-9684-81a162793c88",
   "metadata": {
    "scrolled": true
   },
   "outputs": [],
   "source": [
    "min_runs = min(r.shape[0] for r in ranks_list)\n",
    "ranks_list = [r[:min_runs] for r in ranks_list]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "id": "b6ff46ac-a55e-45bc-8214-303feae95531",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 1000x600 with 16 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "f, ax = sbc_rank_plot(\n",
    "    ranks=ranks_list,\n",
    "    num_posterior_samples=num_posterior_samples,\n",
    "    plot_type=\"hist\",\n",
    "    num_bins=None,  # by passing None we use a heuristic for the number of bins.\n",
    "    figsize=(10, 6),  # width, height in inches\n",
    "    parameter_labels = labels,\n",
    "    colors=DISCRETE_CMAP(\"cmo.solar\", len(ranks_list)+1),\n",
    "    ranks_labels=ws,\n",
    "    histtype=\"step\",     \n",
    "    lw=2,   \n",
    "\n",
    ")\n",
    "\n",
    "f.subplots_adjust(\n",
    "    wspace=0.25,  # horizontal spacing between subplots\n",
    "    hspace=0.6   # vertical spacing\n",
    ")\n",
    "\n",
    "plt.show()\n",
    "\n",
    "f.savefig(\"rank_cosmology.pdf\", bbox_inches=\"tight\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "67ab60c3-293d-41a4-9be5-7dea188e6df5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 94,
   "id": "dc3c103c-4117-4d5b-9552-480066ab8604",
   "metadata": {},
   "outputs": [],
   "source": [
    "from dustbi_calibration import * "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 104,
   "id": "eaa0a75d-7ca7-4525-b965-0ec078519627",
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "def run_sbc(SIMS, GENPDFS, posterior, Nsamples=5000, timeout=60, parameters_to_condition_on=None):\n",
    "    num_sims = len(SIMS)\n",
    "    num_params = 13\n",
    "    ranks = np.full((num_sims, num_params), np.nan)  # preallocate\n",
    "\n",
    "    truth_list = []\n",
    "    values_list = []\n",
    "\n",
    "    # Convert the 0% acceptance warning into an exception\n",
    "    warnings.filterwarnings(\n",
    "        \"error\",\n",
    "        message=r\"Only 0\\.000% proposal samples are accepted.*\"\n",
    "    )\n",
    "\n",
    "    for i in range(num_sims):\n",
    "        simfile = SIMS[i]\n",
    "        truths = load_truth(GENPDFS[i])\n",
    "\n",
    "        dfdata = load_just_data(simfile, 2201, parameters_to_condition_on)\n",
    "        x = preprocess_data(parameters_to_condition_on, dfdata)\n",
    "\n",
    "        try:\n",
    "            samples = sample_with_timeout(posterior, x, Nsamples, timeout)\n",
    "            if samples is None:\n",
    "                print(f\"Iteration {i}: timeout or sampling failed → leaving NaNs\")\n",
    "                continue\n",
    "        except Warning:\n",
    "            print(f\"Iteration {i}: 0% proposal acceptance → skipping\")\n",
    "            continue\n",
    "\n",
    "        # Vectorized rank computation\n",
    "        samples_np = samples.detach().cpu().numpy()\n",
    "        truths_np = np.array(truths)\n",
    "        truth_list.append(truths_np)\n",
    "        values_list.append(samples_np)\n",
    "        \n",
    "        n = min(samples_np.shape[1], len(truths_np))\n",
    "\n",
    "        valid_mask = ~np.isnan(samples_np[:, :n])\n",
    "\n",
    "\n",
    "        # convert to float so we can assign NaN\n",
    "        rank_values = (samples_np[:, :n] < truths_np[:n]).sum(axis=0).astype(float)\n",
    "\n",
    "        # handle all-NaN columns\n",
    "        all_nan_cols = ~valid_mask.any(axis=0)\n",
    "        rank_values[all_nan_cols] = np.nan\n",
    "\n",
    "        ranks[i, :n] = rank_values\n",
    "\n",
    "        print(f\"Iteration {i}: done\")\n",
    "\n",
    "    return ranks, truth_list, values_list"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 106,
   "id": "d77a7863-35d9-4236-9d5c-beda83cc49ec",
   "metadata": {
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/bpopovic/Documents/SBI_Testing/dustbi_calibration.py:103: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfdata['MU'] = cosmo.distmod(dfdata.zHD.values).value\n",
      "/Users/bpopovic/Documents/SBI_Testing/dustbi_calibration.py:114: PerformanceWarning: DataFrame is highly fragmented.  This is usually the result of calling `frame.insert` many times, which has poor performance.  Consider joining all columns at once using pd.concat(axis=1) instead. To get a de-fragmented frame, use `newframe = frame.copy()`\n",
      "  dfdata['MURES'] = MURES_sims\n",
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4041it [00:00, 78841.11it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 0: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4280it [00:00, 62627.98it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 1: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4037it [00:00, 86018.10it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 2: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4063it [00:00, 82626.85it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 3: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4048it [00:00, 75754.12it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 4: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4087it [00:00, 78814.35it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 5: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4072it [00:00, 80434.43it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 6: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4036it [00:00, 75029.74it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 7: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4113it [00:00, 79975.76it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 8: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4076it [00:00, 77452.37it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 9: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4036it [00:00, 82239.66it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 10: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4085it [00:00, 76123.19it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 11: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4037it [00:00, 81938.00it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 12: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4051it [00:00, 79451.62it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 13: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4335it [00:00, 66913.63it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 14: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4215it [00:00, 14929.51it/s]                          \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 15: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4049it [00:00, 80505.22it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 16: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4040it [00:00, 78515.18it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 17: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4083it [00:00, 78484.62it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 18: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4129it [00:00, 72602.99it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 19: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4034it [00:00, 78678.55it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 20: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4061it [00:00, 79627.64it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 21: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4045it [00:00, 82053.91it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 22: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4040it [00:00, 81845.61it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 23: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4797it [00:00, 56131.56it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 24: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4036it [00:00, 89846.78it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 25: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4039it [00:00, 85319.12it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 26: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4034it [00:00, 78021.15it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 27: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4045it [00:00, 82218.94it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 28: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4036it [00:00, 82110.81it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 29: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4056it [00:00, 80895.19it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 30: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4051it [00:00, 81138.47it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 31: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4056it [00:00, 78105.22it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 32: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4046it [00:00, 83184.58it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 33: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4037it [00:00, 79542.29it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 34: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4032it [00:00, 75963.43it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 35: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4539it [00:00, 62210.43it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 36: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4059it [00:00, 78818.69it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 37: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4041it [00:00, 78485.88it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 38: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4100it [00:00, 77297.68it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 39: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4032it [00:00, 84846.92it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 40: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4041it [00:00, 82489.82it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 41: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4035it [00:00, 83908.54it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 42: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4072it [00:00, 73564.00it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 43: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4165it [00:00, 68685.82it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 44: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4046it [00:00, 71614.60it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 45: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4056it [00:00, 85484.92it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 46: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4084it [00:00, 80890.52it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 47: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4346it [00:00, 63720.67it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 48: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4048it [00:00, 79316.75it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 49: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4820it [00:00, 53893.83it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 50: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4035it [00:00, 82733.75it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 51: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4089it [00:00, 78979.28it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 52: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4032it [00:00, 81162.55it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 53: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4033it [00:00, 81581.65it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 54: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4048it [00:00, 84310.97it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 55: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4039it [00:00, 66988.26it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 56: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4060it [00:00, 82857.50it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 57: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4078it [00:00, 78607.90it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 58: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4036it [00:00, 83567.63it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 59: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4036it [00:00, 84369.39it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 60: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4084it [00:00, 81333.36it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 61: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4047it [00:00, 80972.12it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 62: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4048it [00:00, 82444.12it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 63: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4657it [00:00, 61442.10it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 64: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4086it [00:00, 81947.50it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 65: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4037it [00:00, 83356.25it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 66: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4043it [00:00, 84649.03it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 67: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4205it [00:00, 67629.31it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 68: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4105it [00:00, 77355.09it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 69: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4144it [00:00, 72663.86it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 70: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4050it [00:00, 65427.96it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 71: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4094it [00:00, 79766.44it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 72: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4051it [00:00, 81409.04it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 73: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4121it [00:00, 30969.61it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 74: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4448it [00:00, 58933.44it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 75: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4071it [00:00, 71533.65it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 76: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4041it [00:00, 79711.72it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 77: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4042it [00:00, 76411.31it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 78: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4048it [00:00, 83186.15it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 79: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4862it [00:00, 38513.94it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 80: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4051it [00:00, 81602.58it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 81: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4045it [00:00, 79321.70it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 82: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4205it [00:00, 60865.26it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 83: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4055it [00:00, 75706.76it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 84: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4031it [00:00, 77852.91it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 85: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4122it [00:00, 71935.86it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 86: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4472it [00:00, 53972.58it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 87: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4049it [00:00, 63290.49it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 88: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4040it [00:00, 85187.08it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 89: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4034it [00:00, 81938.57it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 90: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4106it [00:00, 77797.56it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 91: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4047it [00:00, 69027.51it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 92: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4035it [00:00, 84899.40it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 93: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4042it [00:00, 85969.16it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 94: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4129it [00:00, 73872.11it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 95: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4304it [00:00, 63952.37it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 96: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4039it [00:00, 77411.43it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 97: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4070it [00:00, 85966.17it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 98: done\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "  0%|          | 0/4000 [00:00<?, ?it/s]/Users/bpopovic/anaconda3/envs/SBI/lib/python3.11/site-packages/nflows/transforms/lu.py:80: UserWarning: torch.triangular_solve is deprecated in favor of torch.linalg.solve_triangularand will be removed in a future PyTorch release.\n",
      "torch.linalg.solve_triangular has its arguments reversed and does not return a copy of one of the inputs.\n",
      "X = torch.triangular_solve(B, A).solution\n",
      "should be replaced with\n",
      "X = torch.linalg.solve_triangular(A, B). (Triggered internally at /Users/runner/work/pytorch/pytorch/pytorch/aten/src/ATen/native/BatchLinearAlgebra.cpp:2196.)\n",
      "  outputs, _ = torch.triangular_solve(\n",
      "4054it [00:00, 78558.30it/s]            \n"
     ]
    },
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iteration 99: done\n"
     ]
    },
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<Figure size 1600x1200 with 13 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "GENPDFS, SIMS = load_assignments('CALIB_DATA/output/ASSIGNMENTS')\n",
    "ranks, truths, values = run_sbc(SIMS, GENPDFS, posterior, Nsamples=4000, \n",
    "    timeout=40, \n",
    "    parameters_to_condition_on=parameters_to_condition_on)\n",
    "plot_sbc_ranks(ranks)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "d8498891-ddf7-4d8d-b6ee-7e03e38ee9e5",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "id": "7a44feec-432e-4d0c-8cd8-c0be7094b3eb",
   "metadata": {},
   "outputs": [],
   "source": [
    "def plot_sbc_ranks(ranks, num_bins=20, param_names=None):\n",
    "    \"\"\"\n",
    "    Plot SBC rank histograms for each parameter.\n",
    "\n",
    "    Parameters\n",
    "    ----------\n",
    "    ranks : np.ndarray\n",
    "        Shape (num_sims, num_params)\n",
    "    num_bins : int\n",
    "        Number of histogram bins\n",
    "    param_names : list of str, optional\n",
    "        Names of parameters\n",
    "    \"\"\"\n",
    "    num_sims, num_params = ranks.shape\n",
    "\n",
    "    # Default parameter names\n",
    "    if param_names is None:\n",
    "        param_names = [f\"param_{i}\" for i in range(num_params)]\n",
    "\n",
    "    cols = 4\n",
    "    rows = int(np.ceil(num_params / cols))\n",
    "\n",
    "    fig, axes = plt.subplots(rows, cols, figsize=(4*cols, 3*rows))\n",
    "    axes = axes.flatten()\n",
    "\n",
    "    for j in range(num_params):\n",
    "        ax = axes[j]\n",
    "\n",
    "        # Remove NaNs\n",
    "        param_ranks = ranks[:, j]\n",
    "        param_ranks = param_ranks[~np.isnan(param_ranks)]\n",
    "\n",
    "        if len(param_ranks) == 0:\n",
    "            ax.set_title(f\"{param_names[j]} (no data)\")\n",
    "            continue\n",
    "\n",
    "        ax.hist(param_ranks, bins=num_bins, alpha=0.7, color=\"steelblue\")\n",
    "\n",
    "        # Expected uniform line\n",
    "        #ax.axhline(1.0 / num_bins, color=\"red\", linestyle=\"--\")\n",
    "\n",
    "        ax.set_title(param_names[j])\n",
    "        ax.set_xlabel(\"Rank\")\n",
    "        ax.set_ylabel(\"Density\")\n",
    "\n",
    "    # Remove unused subplots\n",
    "    for k in range(num_params, len(axes)):\n",
    "        fig.delaxes(axes[k])\n",
    "\n",
    "    plt.tight_layout()\n",
    "    #plt.savefig(\"bla.pdf\")\n",
    "    plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ad651b2f-7d01-4861-a4d5-ad04b6d48c6e",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "\n",
    "arr = np.array(values)   # shape: (100, 4000, 14)\n",
    "\n",
    "mean_vals = arr[:, 0, :].mean(axis=0)  # shape: (14,)\n",
    "std_vals = arr[:, 0, :].std(axis=0)  # shape: (14,)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "1e4ce2c6-87b5-4e24-92d1-19119c4c5f77",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 126,
   "id": "5c053d88-89ea-499a-81f5-0347fa492c84",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "e986cf5a-e514-4574-9fd6-70011444e89a",
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 144,
   "id": "0bff43eb-cf71-4e36-a47c-10eab41765e7",
   "metadata": {},
   "outputs": [],
   "source": [
    "truth_labels = [\n",
    "    \"c_int mu\", \"c_int std\",\n",
    "    \"Hi Rv_mu\", \"Hi Rv_std\",\n",
    "    \"low Rv_mu\", \"low Rv_std\",\n",
    "    \"x1 mean\", \"x1 slope\", \"x1 std\",\n",
    "    \"Hi EBV\", \"low EBV\"\n",
    "]\n",
    "\n",
    "mean_labels = [\n",
    "    \"c_int mu\", \"c_int std\",\n",
    "    \"Hi Rv_mu\", \"Hi Rv_std\",\n",
    "    \"low Rv_mu\", \"low Rv_std\",\n",
    "    \"beta mean\", \"beta std\",\n",
    "    \"x1 mean\", \"x1 std\",\n",
    "    \"Hi EBV\", \"low EBV\"\n",
    "]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 145,
   "id": "19fd2cc0-b60a-4d9a-8465-7b466c936b28",
   "metadata": {},
   "outputs": [],
   "source": [
    "index_map = {name: mean_labels.index(name) for name in truth_labels if name in mean_labels}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 154,
   "id": "047c2778-2207-4e15-8e61-c7e911009ab8",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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f//Vfw9577x1mzZoVqqurm3z+xIkTw+rVq0vLsmXLyt0kAKC1Vjy25eOPPw6jR48ODz74YOjVq1eYO3du+OxnP7vN18RQsq1gAgC0HWULHmvWrAmjRo0KTzzxROjXr19af1LoAAAqS9m6Wk455ZQUNorjPw444IB07Y64PPfcc+U6DQDQipWt4rF48eJG4zzi0nCqLQDA/zl49OnTp1GgWLJkiXcTANgmN4kDALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AADBAwBoe1Q8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACCbDvlOBQBN6zthjrenAqh4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAALTd43HnnnaFjx46hqqoqLF26tLT9wQcfDCeccELo2rVrOPTQQ8MNN9wQNm7cWO72AgCtWIftfWIMEZdddlmYOnXqFvtiyJg0aVLp5wULFqTl+eefDz/96U9Du3YKKwDADlQ8brnllhQ62rdv32j74sWLw7XXXpsex2DywQcfhB//+MehQ4cOYe7cuekxAMAOBY+1a9eG3r17hxkzZjTafscdd4QNGzaEvn37hptuuil069YtnHPOOeGss85K+2+99VbvNACwY8Fj0KBBYf78+eGwww5rtH3hwoVpPWzYsDTuo2j48OFp/Yc//CEUCoUmj1tfXx/q6uoaLQBAhY/xGDlyZFo3HFAaLVu2LK179OjRaHvPnj3T+k9/+lN47733tthfNGXKlDB58uQdbzmUUd8Jc1rl+1l740nN3QRaqNb63zRt36ce9VlTU5PW7777bqPt77zzTlrvsssuoXv37k2+fuLEiWH16tWlpRhkAIC251MHjwEDBqT1k08+2ahL5fHHH0/rAw88sFEXzOaqq6vDnnvu2WgBANqmTx08Lr744jSDpba2NowfPz6sWrUqDUD9yU9+kvZfcskl5WgnANAGfOrg8bnPfS5cc8016XFxVkuc0RJnuowYMSKMHj26HO0EANqAslzZK148bObMmeG4445LXSWDBw8O1113XZgzZ46LhwEAOz6rpahPnz5bnR57+umnpwUAoCmuZQ4AZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBA6w0eDzzwQDj88MPDHnvsEXr16hXOOOOMsGjRonKfBgBohTqU82APPfRQChpFH330UQoi8+bNCwsXLgxdunQp5+kAgEqueMTgEY0aNSq888474ZVXXgl77rlneOutt8JLL71UzlMBAJUePA4++OC03rRpU6iqqgqFQiEt0cCBA8t5KgCg0rtaxo4dG55++unw6KOPpqXo+9//fhrvsTX19fVpKaqrqytnkwCAtho8Zs2alcZzbO573/teOPnkk0Pv3r232DdlypQwefLkcjYDKkbfCXNCa1N740nN3QSgLXS1xIGkF198capenHbaaaG2tjY899xzoUePHuG///u/w1VXXbXV102cODGsXr26tCxbtqxcTQIA2mrweP3110vdJOPGjQt9+vQJxxxzTPjqV7+atr388stbfV11dXUagNpwAQDaprJ1tey3336lAaW333576NevX3jzzTfD7Nmz0/7+/fuX61QAQKVXPPbaa69U6YhmzpwZ9t133zBkyJDw9ttvh06dOoUrr7yyXKcCAFqpsk6nvfnmm8O0adPCF77whdC5c+c0mHTkyJHhF7/4RTjiiCPKeSoAoNJntbRr1y5ceOGFaQEA2JybxAEA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBk0yHfqagUfSfMae4mANBCqXgAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQOsPHieffHKoqqoKBx98cFi3bt3OOg0AUOnB49577w2zZ88Ou+66a5g5c2ZaAwCUPXgsXbo0XHrppenx1KlTw0EHHeRdBgDKHzwKhUIYM2ZMqKurC507dw7PPPNM+O53vxvWrl1bztMAAK1UWYPHHXfckcJG9NFHH4X7778/TJgwIRx77LFh48aNW31NfX19CioNFwCgbepQrgPFqsZ1112XHl9wwQXh5ptvDo8++mg4//zzw4IFC9JYj7PPPnuL102ZMiVMnjy5XM0AWri+E+Y0dxOAtlDxePnll8O7776bHo8fPz507do1nHvuuWHfffdN2371q19t9XUTJ04Mq1evLi3Lli0rV5MAgLZa8Yihoahht0rxcVMzW6qrq9MCALR9Zat4DB48OF23I4rdLGvWrAnTp08PK1asSNuGDBlSrlMBAJUePGpqasK4ceNK1/GIs1ri+I5o+PDh4Stf+Uq5TgUAVHpXS7HS0b9//zBt2rSwePHi0KdPn/DXf/3X4dvf/nY5TwMAtFJlDR7t2rUL//AP/5AWAIDNuUkcAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAIDgAQC0PSoeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcA0LqDx5IlS0LHjh1DVVVVWn70ox/tjNMAAK3MTgkeN9xwQ9iwYcPOODQA0Iq12xnVjljh6NevX7kPDQC0cu12VrXjyiuvLPehAYBWrt3OqHb07t07nH/++dv1mvr6+lBXV9doAQDapnY7o9rxrW99K1RXV2/Xa6ZMmRK6dOlSWmpqasrZJACgLQaPYrWje/fu4e/+7u+2+3UTJ04Mq1evLi3Lli0rV5MAgBamQ7kOdPvtt6dqx3vvvRd23333Rvtit0ttbW245pprtnhdrIxsb3UEAGjdyhY8Nm3aFPbZZ59G21asWJHWsQulc+fO5ToVAFDpwePWW29NS0Px4mHRbbfdFs4777xynQoAaKVcMh0AaH0Vj60pFAo78/AAQCuj4gEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAAK07eDz88MPh0EMPDXvssUc45JBDwr333hsKhcLOOBUA0Ip0KPcB77vvvnDBBReUfv7tb38bvva1r4XVq1eHSy+9tNynAwAqteKxfv36UriI6w8++CBMnDgx/Xz99deX81QAQKUHj2XLloWDDjoodO/ePVx99dWhW7duYfz48WlfDCErV64s5+kAgEruatlvv/3CCy+80GjbK6+8ktZ9+/ZNQQQAqFxlH+PR0IoVK8KFF16YHjcc99FQfX19Worq6up2ZpMAgLY4nba2tjYce+yxYcmSJWHUqFFh0qRJW33elClTQpcuXUpLTU3NzmoSANAWg8drr70WjjrqqPDGG2+EU089NcyYMSNUVVVt9blx8Gmc8VJc4jgRAKBtKntXy4svvhhOOumkNJh0zJgxYdq0aaF9+/ZNPr+6ujotAEDbV9aKx6JFi8KwYcNS6IimT58edtlll9CxY8fQv3//cp4KAKj0isfy5cvDmjVrSj9v3Lhxq48BgMpU1uAxdOhQl0YHAJrkJnEAQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAtM7gsW7dunDFFVeEgQMHhr333jt8+ctfDs8880w5TwEAtGIdynWgDz/8MAwdOjS8+uqrpW2PPfZYeOKJJ8I999wTzj333HKdCgCo9IrHLbfckkJH586dw+OPPx7eeeedcOaZZ4YNGzaESy65JKxatapcpwIAKj143HbbbWl92WWXhRNPPDH06NEj3H333WG33XYLK1euDNOnTy/XqQCASg4ey5cvT+EiGj58eGn7HnvsEYYMGZIeL1y4sBynAgAqfYzHsmXLSo9jpaOhnj17bvGchurr69NStHr16rSuq6sL5bapfm3ZjwkArUndTvh+bXjcQqGw84NHTU1N6fG7774bPv/5z5d+jmM9Nn9OQ1OmTAmTJ0/e5jEBgPLocmvYqeJkky5duuzc4NGrV6/QtWvXNIA0zmI56qijSid/8cUX0+MBAwZs9bUTJ05M40KKNm3aFJYuXRoGDRqUqiR77rlnOZrIThRTbgyKPq/Ww2fW+vjMWpdK/LwKhUL63u/du3ee6bTjxo1LlYs4u+WYY45JweGb3/xmWLNmTejWrVuT02mrq6vT0lC7dv9/6En8sCrlA2sLfF6tj8+s9fGZtS6V9nl12Ualo+zB4/LLLw+zZs1KU2qHDRtW2t6+ffvwve99L1VEAIDKVrbptHEGywsvvBAuvfTS1K0SqxwjRoxI1/Q477zzynUaAKAVK1vFI9p1111TV8unFbterrnmmi26YGiZfF6tj8+s9fGZtS4+r6ZVFT5p3gsAQJm4Oy0AkI3gAQBkI3gAANm06OARh59cddVVoaqqKi20POvWrQtXXHFFGDhwYNh7773Dl7/85fDMM880d7P4BHfeeWfo2LFj+ncVL9hHy/bwww+HQw89NM0ePOSQQ8K99977iZelpnkUv6+2ttDCB5euXbs2XXTsoYceKm1roU2tWPEKdUOHDk3XbmmoQ4cO4Z577mnyonE0n40bN6YrBU+dOrW0rba2NvTp08fH0kLdd9994YILLthie5xBGC9fQMsSA31DGzZsSOu+ffuGJUuWNFOrWpYWW/H41re+lUJHvAAZLVP8H18MHZ07d07Xa4n35TnzzDPTP7RLLrkkXUKflveZxdDh31XrsH79+lK4iOsPPvgg3WYiuv7665u5dTT1mRWX//iP/0j/1rp37x7mzp3rDWvpwSNWPA488MBw1113NXdTaMJtt92W1vEv6BNPPDHdmfjuu+8Ou+22W1i5cmWYPn26964F/ruK91GYMWNGczeF7RDv83HQQQelL66rr746XZhx/PjxaV8MIfHfGS3TvHnzwllnnZWu5zF79uxGN0+tdC02eMTLrscbzPXv37+5m8JWLF++vPQ/veHDh5e2xz7oIUOGpMcLFy703rUw8R5K8+fPD4cddlhzN4XtsN9++6UrQse7fhdvO/HKK6+USvcxiNAyjR07No2B69SpU3jkkUfCxx9/3NxNajFabPA455xzKurGOq3xL7GiWOloqGfPnls8h5Zh5MiRn3jnSFquFStWhAsvvDA93tq4D1qOeJPUs88+OwWOG2+8MXz9619v7ia1GO1aQrl+a6N/f/7znzd309iGeLvnovjXWENxrMfmzwE+nTgI+Nhjj00DFEeNGhUmTZrkLW2B4hi3OL5tzJgx4d///d/DAw88kLb/27/9W+n/jZWu2YNHLBUecMABWyxxnAAtV69evUql3yeeeKLRTJfYRRbFmwUCn95rr70WjjrqqPDGG2+EU089NY3RMT2z5bn//vvD7rvvHk4++eTStjj9uTgrUxW4hQSPOOXyD3/4wxaLPuiWb9y4caWZEk899VR4//33w0UXXRTWrFmTAqXptPDpxSAfKx1vvfVW+is6/gUdxw3Q8sSQEbtW4sDSeO2VWOGYPHly2hdnt+y///7N3cQWocVex6Modrkcd9xx6XELb2rFaeo6HvEfWLyOx3nnnddsbWPb4kXD4uDEyHU8Wq5FixaFwYMHpzBf/LdVrHTEa68sXry4mVvI5uIfX1ubjRm7xr7zne94w1pCxYPWK85giSPu4/UFYrdKrHKMGDEiXdND6IDyzB4rho7iBeDiGIK4xMe0PHfccUe46aab0jTo2O0SqyDf//73w3XXXdfcTWsxWnzFAwBoO1Q8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDyHKH1RNOOCF079493eSs4bJq1SqfAFQQwQPYqX7zm9+kO0zHm2U999xz4Wc/+1nYa6+9wvHHHx9mzpwZunbt6hOACuImccBONXTo0LDPPvukkFH0j//4j+Gll14K8+fP9+5DhenQ3A0A2q633347zJs3Lzz99NONtsfbhcduFqDy6GoBdpoFCxaETZs2pW6Wzbcfeuih3nmoQIIHsNPE0BGtW7eutO13v/tdGusxevRo7zxUIMED2GkOP/zwsOuuu4bx48eH119/PcyZMyeMHDkyXHTRReHII4/0zkMFMrgU2Klmz54dLr/88rBkyZJQU1MTvvGNb4QrrrgitGvn7x6oRIIHAJCNPzkAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAABCLv8PCS7ptb+s0xcAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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+fHmp/M477wzHHnts6NWrVxgxYkT4+te/HjZs2FDu/QUAKli3bX1hDBEXXXRRuPnmmzd7LoaMK664ovR44cKFaXriiSfC/fffH7p0UbECAGxHjceMGTNS6OjatWur8qVLl4arr746LcdgsmrVqvDjH/84dOvWLTz44INpGQBgu4LH2rVrw8CBA8OcOXNalX/7298O69evD4MHDw7XXXdd6N27d/jMZz4TPv3pT6fnb7zxRkcaANi+4DFs2LCwYMGCcNhhh7UqX7x4cZofd9xxqd9H0ejRo9P8D3/4QygUCltcb3Nzc2hqamo1AQCdvI/H2LFj07xlh9KooaEhzfv169eqvH///mn+l7/8Jbz22mubPV80ffr0cM0112z/nkMZDb58XkUez/prT9zVu9ApVOK/D/82aK92uNdnbW1tmq9cubJV+YoVK9J8t912C3379t3i++vq6kJjY2NpKgYZAKDj2eHgccABB6T5/PnzWzWpPPTQQ2m+//77t2qC2VR1dXXYa6+9Wk0AQMe0w8Fj0qRJaQRLfX19mDp1anj99ddTB9Q77rgjPX/hhReWYz8BgA5gh4PHBz7wgTBt2rS0XBzVEke0xJEuY8aMCWeddVY59hMA6ADKcmWvePGwuXPnhmOOOSY1lQwfPjx89atfDfPmzXPxMABg+0e1FA0aNKjN4bF///d/nyYAgC1xLXMAIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGy65dsUALkMvnyeg027pMYDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAqNzg8W//9m9h5MiRoWfPnmHAgAHhjDPOCM8//3y5NwMAVKBu5VzZXXfdlYJG0ZtvvpmCyJNPPhkWL14campqyrk5AKAz13jE4BGNGzcurFixIixatCjstdde4aWXXgrPPvtsOTcFAHT24HHQQQel+caNG0NVVVUoFAppig4++OBybgoA6OxNLeeff3545JFHwj333JOmou9+97upv0dbmpub01TU1NRUzl0CADpq8Lj33ntTf45N3XDDDeGkk04KAwcO3Oy56dOnh2uuuaacuwGdxuDL5+3qXYBOrRL/BuuvPbFjNLXEjqSTJk1KtRenn356qK+vD48//njo169f+OMf/xiuvPLKNt9XV1cXGhsbS1NDQ0O5dgkAaGfKFjyWLFlSaiaZPHlyGDRoUDjyyCPDySefnMp++ctftvm+6urq1AG15QQAdExla2rZb7/9Sh1Kv/nNb4YhQ4aEF198Mdx3333p+aFDh5ZrUwBAZ6/x6NOnT6rpiObOnRv23XffMGrUqPDqq6+G97znPeGyyy4r16YAgApV1uG0119/fZg5c2b4m7/5m9CjR4/UmXTs2LHhF7/4RTj88MPLuSkAoLOPaunSpUuYOHFimgAANuUmcQBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2XTLtynejcGXz3PgAOgw1HgAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAAlR88TjrppFBVVRUOOuigsG7dup21GQCgswePWbNmhfvuuy/svvvuYe7cuWkOAFD24LF8+fIwZcqUtHzzzTeHAw880FEGAMofPAqFQpgwYUJoamoKPXr0CI8++mj4xje+EdauXVvOzQAAFaqswePb3/52ChvRm2++GX7yk5+Eyy+/PBx11FFhw4YNbb6nubk5BZWWEwDQMZUteMRaja9+9atp+dxzzw2rV68Ot912W3q8cOHC1NejLdOnTw81NTWlqba2tly7BAB01ODxy1/+MqxcuTItT506NfTq1SucffbZYd99901lv/rVr9p8X11dXWhsbCxNDQ0N5dolAKCd6VauFcXQUNSyWaW4vKWRLdXV1WkCADq+stV4DB8+PF23I7r++uvDmjVrwuzZs8Mrr7ySykaNGlWuTQEAnT14xL4ZkydPLl3HI45qOeecc9Lj0aNHh09+8pPl2hQA0NmbWoo1HUOHDg0zZ84MS5cuDYMGDQqnnXZa+Md//MdybgYAqFBlDR5dunQJX/rSl9IEALApN4kDALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgMoOHsuWLQvdu3cPVVVVabr99tt3xmYAgAqzU4LH17/+9bB+/fqdsWoAoIJ12Rm1HbGGY8iQIeVeNQBQ4brsrNqOyy67rNyrBgAqXJedUdsxcODAcM4552zTe5qbm0NTU1OrCQDomLrsjNqOSy+9NFRXV2/Te6ZPnx5qampKU21tbTl3CQDoiMGjWNvRt2/f8IUvfGGb31dXVxcaGxtLU0NDQ7l2CQBoZ7qVa0Xf/OY3U23Ha6+9Fvbcc89Wz8Vml/r6+jBt2rTN3hdrRra1dgQAqGxlCx4bN24M++yzT6uyV155Jc1jE0qPHj3KtSkAoLMHjxtvvDFNLcWLh0U33XRTGD9+fLk2BQBUKJdMBwAqr8ajLYVCYWeuHgCoMGo8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPAKCyg8fdd98dRowYEXr27BkOOeSQMGvWrFAoFHbGpgCACtKt3Cv84Q9/GM4999zS49/97nfhc5/7XGhsbAxTpkwp9+YAgM5a4/H222+XwkWcr1q1KtTV1aXHX/va18q5KQCgswePhoaGcOCBB4a+ffuGq666KvTu3TtMnTo1PRdDyOrVq8u5OQCgMze17LfffuGpp55qVbZo0aI0Hzx4cAoiAEDnVfY+Hi298sorYeLEiWm5Zb+Plpqbm9NU1NTUtDN3CQDoiMNp6+vrw1FHHRWWLVsWxo0bF6644oo2Xzd9+vRQU1NTmmpra3fWLgEAHTF4PPfcc+GII44IL7zwQjj11FPDnDlzQlVVVZuvjZ1P44iX4hT7iQAAHVPZm1qeeeaZcOKJJ6bOpBMmTAgzZ84MXbt23eLrq6ur0wQAdHxlrfF4/vnnw3HHHZdCRzR79uyw2267he7du4ehQ4eWc1MAQGev8Xj55ZfDmjVrSo83bNjQ5jIA0DmVNXgcffTRLo0OAGyRm8QBANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQDaCBwCQjeABAGQjeAAA2QgeAEA2ggcAkI3gAQBkI3gAANkIHgBANoIHAJCN4AEAZCN4AADZCB4AQGUGj3Xr1oVLLrkkHHzwwWHvvfcOJ5xwQnj00UfLuQkAoIJ1K9eK3njjjXD00UeHX//616WyBx54IDz88MPhBz/4QTj77LPLtSkAoLPXeMyYMSOFjh49eoSHHnoorFixIpx55plh/fr14cILLwyvv/56uTYFAHT24HHTTTel+UUXXRSOP/740K9fv3DrrbeGPfbYI6xevTrMnj27XJsCADpz8Hj55ZdTuIhGjx5dKu/Zs2cYNWpUWl68eHE5NgUAdPY+Hg0NDaXlWNPRUv/+/Td7TUvNzc1pKmpsbEzzpqamUG4bm9eWfZ0AUEmadsL3a8v1FgqFnR88amtrS8srV64MH/zgB0uPY1+PTV/T0vTp08M111zzjusEAMqj5sawU8XBJjU1NTs3eAwYMCD06tUrdSCNo1iOOOKI0safeeaZtHzAAQe0+d66urrUL6Ro48aNYdWqVWk4blVVVegIYgqMQSrW+uy11167enfYRs5b5XHOKpPz1jHOWazpiN/7AwcOzDOcdvLkyanmIo5uOfLII8OwYcPCBRdcENasWRN69+69xeG01dXVaWophpiOKJ4cwaPyOG+VxzmrTM5b5Z+zd6rpKHvwuPjii8O9996bhtQed9xxpfKuXbuGG264ocOGCQBgFwynjSNYnnrqqTBlypTUrBJrOcaMGZOu6TF+/PhybQYAqGBlq/GIdt9999TUQmuxKWnatGmbNSnRvjlvlcc5q0zOW+c6Z1WFrY17AQAoE3enBQCyETwAgGwEDwAgG8Ejk9iVZubMmeHggw9OI4BGjhyZhh9TGefuyiuvTBe06ygXteuI1q1bFy655JL0NxYvQHjCCSeERx99dFfvFtvglltuCd27d09/X8uXL3fMKsDdd98dRowYkb7PDjnkkDBr1qytXiq9JHYuZee76qqr4hnZbLrrrrsc/nZszZo1hdNOO63VOaP9aWpqKhx66KGb/X1169atcPvtt+/q3WML1q9fX7jgggtanbP6+nrHq52bNWtWm99nM2bM2Kb3+180g5deeqnQpUuX0olZuXJl4ZxzzkmP43+WtF+TJk1K56lr166CRzt29dVXp/PTo0ePwkMPPVRYsWJF4cwzz0xlvXv3LqxevXpX7yJt+Od//ufN/r4Ej/btrbfeKtTU1KRzNWXKlMKqVasKdXV16XGfPn22aR2aWjKIVYfDhw8P73//+9Nl5Pv27RsuvPDC9Nyf/vSnHLvAu7R27dqw//77h+9973uOYTt20003pXm879Pxxx+f7pJ96623hj322COsXr06zJ49e1fvIlv4+4r39ZgzZ47jUyHivVkOPPDA9D121VVXpYuFTp06NT0X77MW/962RvDI4PDDDw8LFiwIS5cuTZeQjxYuXJjmMZDQfsXL/8cbHQ4dOnRX7wpb8PLLL5f+sxs9enSpPLY9jxo1Ki0vXrzY8WuH4j294v+Nhx122K7eFbbRfvvtl65SHu9EX7wVyqJFi9J88ODBKYhkvXIp22bJkiXh0ksvTcvnnnuuw9aOfeYzn9nVu8A2/AIrijUdLfXv33+z19B+jB07Ns11KK1cr7zySpg4ceJ2fZ+p8ShzdW9x5EPL6b/+679Kr4k30Tv66KNTlVRsbvnsZz9bzl1gJ5472qd4a+6i+CuspRUrVmz2GqA86uvrw1FHHRWWLVsWxo0bF6644optep8ajzKKVUwf/vCHNyuP7cxR/BI7+eSTQ1NTUzj//PPd16aCzh3t14ABA1KV7+uvvx4efvjhcMQRR6TyN954IzWTRfHGlUD5PPfcc6lp86WXXgqnnnpquOOOO7b9cgM7vQssyT333FPYbbfdUs/fOLSWyvLYY48Z1dKOTZs2LZ2fnj17FubPn1947bXXCv/wD/9gVEuFiCNZjGqpHE8//XQawRLP2YQJE9Kw6O3hJnEZPP744+HYY48N69evT4+7dfv/FU2x2WX+/Pk5doMdEGurjjnmmLTsvortT6zdiH9LsSmzpdiZ+wc/+EEYP378Lts3ti728YgdE4vV94MGDXLY2qnnn38+DYpYs2ZN6W+sWNMRz1scRLE1+nhkENu/iqEjisvFacOGDTl2ATq0OIIl9rSfMmVKalaJTWdjxowJDz30kNABZR5FVgwdUfwO297vMzUeAEA2ajwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAALIRPACAbAQPACAbwQPY6eLt6eONEvv27ZtuKNVyirezBzoPwQPYqX7729+mO/secsgh6U7NP//5z0OfPn3Cxz72sTB37tzQq1cvZwA6ETeJA3aqeLv6ffbZJ4WMoi9/+cvh2WefDQsWLHD0oZPptqt3AOi4Xn311fDkk0+GRx55pFX5nnvumZpZgM5HUwuw0yxcuDBs3LgxNbNsWj5ixAhHHjohwQPYaWLoiNatW1cq+/3vf5/6epx11lmOPHRCggew04wcOTLsvvvuYerUqWHJkiVh3rx5YezYseGLX/xi+MhHPuLIQyekcymwU913333h4osvDsuWLQu1tbXhvPPOC5dcckno0sXvHuiMBA8AIBs/OQCAbAQPACAbwQMAyEbwAACyETwAgGwEDwAgG8EDAMhG8AAAshE8AIBsBA8AIBvBAwDIRvAAAEIu/w9kpeaBHXM0FgAAAABJRU5ErkJggg==",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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zvn37yv33328eT5o0yfQS0rHS6X369PHV7gEAAD/ms8CiDWx37dolw4cPN41m4+LiZMSIEfLpp59Ko0aNzDLaGFevy1JaWupaT6+38vzzz5veRFqto+NFixaZ6QAAACrIod16AoB2a9beQlqtRHsWAIDNmk9NE3+TOXdIjf5+c/NDAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGC90JreAVSP5lPT/O7UZs4dUtO7AACwFCUsAADAegQWAABgPQILAAAI7MCyePFiCQsLk6CgIDly5IiZpuPbbrtNYmNj5eqrr5YHH3xQvv/+e6/bWLlypVm//PDwww9fzq4BAIDa3ui2pKREJk+eLAsXLnSbnp2dLd27dzdjpyVLlsiuXbtkx44dJtyUl5OTY8bBwcFmcCr7GAAA1G6XlArmzZtnwkpISIjb9JkzZ5qwcs0118g333wjW7dulXr16pnA8t5773ncljPc/OMf/5CioiLXsGjRokvZNQAAEIAuKbCcO3dO4uPjZfXq1W7THQ6Hma6lLwkJCdKzZ0+56aabzLxDhw553JazhGX48OGmBKZ9+/Yyffp0KS4uvpRdAwAAAeiSqoQSExNl586dpiSkfJsWHZxKS0tl9+7d5nHXrl0rDSxazaT2798vTz31lBw9elSWLl3qdR8KCwvN4JSfn38phwIAAAK1hGXo0KGmJOVCJkyYIJmZmdKhQwe5/vrrPS7zzjvvmJIZHU6fPi2PPfaYmb58+XJTreTNnDlzJDo62jVoiQ4AAAhM1dKyVUtWHnroIfnLX/4iDRs2lLffflvCw8MrLPfjjz+aEhhtkKtiYmLk2WeflTp16pht7Nmzx+vfSElJkby8PNeQlZVVHYcCAAAC8dL8GkLuueceWbt2rcTFxcn69eulVatWHpfdtGmTDBo0yJSOaHdo7c5cUFBgSltURESE17+jAchTCAIAAIHHpyUsZ8+elV/96lcmrLRo0cL0EurUqVOlbWG0oa2WjmjbF23MqyUsGlquuOIKMx8AAMCngWXYsGGyceNG81hLTNq1a2cCiQ5btmwxPX969+4tHTt2lNzcXGnSpIkkJyeb5R999FGJjIw0DW7VtGnTzMXnAAAAfFoldPDgQddjbYOig5OzmkdDiw7O57NnzzYNeLVHkHZ9btu2rQkvY8eO5dUBAACXH1iaNWvmCh7q8OHDF1znww8/dHuu7VbGjx9vBgAAAE+4/j0AALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6oTW9AwAQqJpPTRN/kzl3SE3vAuARJSwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAAAjOwLF68WMLCwiQoKEiOHDnimr527Vrp37+/xMTESFJSksyePVtKSkouuL1NmzbJ4MGDpWHDhtKpUydJTk6W8+fPX8quAQCAABRalYU1fEyePFkWLlxYYZ6GkyeeeML1fNeuXWbYunWrvPvuuxIc7DkbvfLKK3L//fdLcXGxeX7q1Cn5/PPPTYjZsmWL1KtXr+pHBQAAam8Jy7x580xYCQkJcZt+8OBBmTFjhnmsgUZDx9/+9jcJDQ2V9evXm8ee5ObmyqRJk0xYufPOOyUnJ8csryElIyPD/D0AAIAqBZZz585JfHy8rF692m36okWLTOho3ry5PPvss9KgQQO5++67TQhRCxYs8Fq6cvr0aYmMjJQXXnhBYmNjZeDAgSbEqNTUVF4hAABQtcCSmJgoO3fulG7durlN37dvnxnfdNNNpl2Lk4YP9cUXX4jD4aiwPed6PXr0kPr161dYT0tqjh8/7nFfCgsLJT8/320AAACBqUptWIYOHWrGZRvaqqysLDPWEpKyGjdubMYFBQVy8uTJCvMvtJ5zmSZNmlTYlzlz5sjMmTPlp9B8atpP8neAn4o/vqcz5w6p6V2oFXhvIKC7NSckJJjxiRMn3KZrmxQVEREhjRo1qvJ6ZZcpLyUlRfLy8lyDM/wAAIDA45PA0qFDBzNOT093q/rZsGGDGbdv396tqqj8ejt27JAffvihwnraFsZT6YoKDw+XqKgotwEAAAQmnwSWRx55xPQIyszMlClTppjeP9owd9WqVWb+xIkTPa43atQoE0rOnj0rDz30kHz//fcm9Dgb6XpbDwAA1C4+CSxt2rSR6dOnm8fOXkLaQ0h7Dt18881yzz33mHl6TRat4tELwyldbv78+aab9GuvvWaqjQYMGGBKW7p06eLqLQQAAGo3n12aXy8at2bNGunbt6+pnunatas89dRTkpaW5rponFYXFRUVuV39dvTo0aYKSIONBpiOHTuaa7ls377drecQAACovarUS8ipWbNmHrsp33777Wbwpnfv3h67Kffr188MAAAAnnDzQwAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAeqE1vQOAU/OpaZwMAFXGd0ftQAkLAACwHoEFAABYj8ACAACsR2ABAAC1M7DceOONEhQU5HHYvHmz27Lbt2/3uNygQYOqY9cAAIAfqpZeQiEhIRIa+n+bLi0tNYOKjo52WzYnJ8eMNaToek7BwRT+AACAf6uWVJCeni5FRUWu4aGHHjLT27ZtK4mJiW7LZmdnm3FqaqrbOmlpdHEFAAD/Vu3FGIWFhbJ69WrzePTo0RXmO0tYJk6cKGFhYdKyZUuZMGGCnD17trp3DQAA+Ilqv3DcP//5Tzl16pSp4hk5cqTXwOKsNjp8+LA8//zzsn//ftm4cWOlQUgHp/z8/Go6AgAAEPAlLK+88ooZ9+vXTxISEirM13DicDjMcObMGXnmmWdc1Urbtm3zut05c+aY9jDOwdO2AQBAYKjWwKLtU9atW+e1OqikpET27dsnmzZtMs8jIyNlypQp0rp1a/M8IyPD67ZTUlIkLy/PNWRlZVXbcQAAgACuElq5cqUUFxdLvXr15NZbb60w/8CBA9KxY0eJiIiQEydOmOW0pKWgoMDM1+nehIeHmwEAAAS+4J+iOuj222+XunXrVpivJSkxMTEmoMyePds0tH3xxRfl22+/NfOvu+666tw9AABQ2wOLVufs3bu3QnXQiBEjTE8gLV3Ra7U426zMnTvXlLCMGzfOPB8zZox07ty5unYPAAD4kdDqLl1p0aKF9OrVyzVdq4j0Oita9aMeeOABU8rypz/9yfQMatasmelNlJycXF27BgAA/EyQw5kc/Jx2a9beQtoANyoqyqfbbj6Vi9gBNS1z7hDxN3x3IJBkVtNn8GJ/v7n+PQAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAIDaFVi2b98uQUFBFYZBgwZ5XefUqVPywAMPSNu2beWqq66S4cOHy969e325WwAAwM+F+nJjOTk5ZqwhJSQkxDU9ONhzLvr222+ld+/ecvjwYde0f/zjH7JhwwYzHjBggC93DwAA+CmflrBkZ2ebcWpqqhQVFbmGtLQ0j8v//ve/N2ElLi5OPvnkExNg+vbtK+fOnZNHH31UiouLfbl7AADATwVXRwnLxIkTJSwsTFq2bCkTJkyQs2fPeqwKevXVV83j2bNnyy9+8Qv52c9+JsuWLTMlNAcOHJB3333Xl7sHAAD8VLUEltLSUlM6oqUnzz//vAwbNqzCsl9++aVZTg0cONA1vVWrViboqH379nn9W4WFhZKfn+82AACAwOTTwKLhxOFwmOHMmTPyzDPPmOnp6emybds2t2WzsrJcj2NjY93mNW7cuMIy5c2ZM0eio6NdQ0JCgi8PBQAABGJgKSkpMSUimzZtMs8jIyNlypQp0rp1a/M8IyPDbfmyAePEiRMeS2oqCyEpKSmSl5fnGioLNwAAwL/5LLBom5OOHTvKkCFDTOmK0pKWgoIC8zgiIsJt+WuuucbVe2jjxo2u6YcOHZKvv/7aPO7QoYPXvxceHi5RUVFuAwAACEw+CyxakhITE2MCijai1Ya2L774oun5o6677jq35a+88koZOXKkeTxt2jRTAnP06FEZN26cCTpt2rSRwYMH+2r3AACAH/NZYAkNDXW1WZk7d67Uq1fPhA81ZswYU/qi11zRcW5urpk+a9YsadGihQkqXbt2Nb2E3n//falbt64sWrTIbBMAAMCnjW71irVr1qyRpKQkE1g0nGh4Wbp0qZmvPYd00BIU1bRpU9m5c6eMHTvW9A7SxrZDhw6VDz/8kIvGAQAAF58XYdx+++1m8ESDSHkNGzY0114BAADwhpsfAgAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAAKh9d2sGgOrQfGoaJxaoxShhAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAA1N7A8t///d+SlJQk9evXl86dO8vy5cvF4XB4XHbAgAESFBRUYfj444+ra/cAAIAfCa2Ojb700kty3333uZ7v3btXxo4dK3l5eTJp0qQKy+fk5JhxSEiICSoAAADVWsJSVFTkCiU6PnXqlKSkpJjns2bN8rhOdna2XHnllVJcXGzWdw7du3f39e4BAAA/5PPAkpWVJR07dpRGjRrJk08+KQ0aNJApU6aYeRpeTp8+7bZ8aWmpnDx50swLCwszVUg9e/aUTZs2+XrXAACAn/J5lVDLli1l+/btbtM+/fRTM27evLkJMGVpUCkpKTGPtYTlzJkzZn1t1/LBBx9Ir169PP6dwsJCMzjl5+f7+lAAAEBt6SV0/Phxuf/++83jsu1anLQkRhvjOofPP/9c2rdvb0peZs6c6XW7c+bMkejoaNeQkJBQrccBAAACNLBkZmZK79695fDhwzJ8+HB54oknKizz/fffy/vvvy/fffedea7VSdOmTTOPMzIyvG5b28VoI17noFVRAAAgMFVbYPnXv/4lN9xwgxw4cEBuvfVWWb16tcceQI8++qj069dP/vrXv7qmnTt3zowjIiK8bj88PFyioqLcBgAAEJiqJbB89NFHpmTl6NGjMmbMGHn99dfliiuu8Lhst27dXF2h9+3bJ8eOHZPFixebafQSAgAA1RJYvvrqK7nppptMY1r16quvmpIS7QHUunVr2bp1q2lvkpycbOY/8sgj0qpVK1MlpNVB8fHxpiqoTp06pp0KAACAzwOLlpCcPXvW9Vx7AGnvHx30sTas1WusOHsGaTDZsWOHjB49Wpo2bWoa4Q4ZMkQ++eQTueaaa3iFAACA77s19+nTx+sl+Mv2HCorNjZWXn75ZV4OAADgETc/BAAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAYD0CCwAAsB6BBQAAWI/AAgAArEdgAQAA1iOwAAAA6xFYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwAAKB2BZbz589LcnKydOrUSRo2bCiDBw+W999/v9J1SkpKZNasWZKUlCQxMTHSv39/Wbt2rS93CwAA+Lkgh8Ph8MWGfvjhB+nTp49kZGS4TQ8NDZUXX3xRRo0aVWGd0tJSE2rWr19fYZ6GmGnTpl3038/Pz5fo6GjJy8uTqKgo8aXmU9N8uj0AAPxN5twh1bLdi/399lkJy7x580xYqVevnmzYsEFycnJkxIgRUlxcLBMnTpTc3NwK66xYscKEFQ01K1eulFOnTsnkyZPNvJkzZ8qhQ4d8tXsAAMCP+SywpKammrEGjgEDBkhsbKwsWbJE6tatK6dPn5ZXX321wjoLFiww47vuussMDRo0kD/96U/SrFkzKSoqkkWLFvlq9wAAQG0PLMeOHTOhRA0cONA1vX79+tKjRw/zeN++fRWqg/bv319hneDgYLnppps8rgMAAGqnUF9sJCsry/VYS1bKaty4cYVl1MmTJ6WgoKBK65RVWFhoBiet+3LWhflaaeE5n28TAAB/kl8Nv69lt3uhJrU+CSwJCQmuxydOnJC2bdu6nmtblvLLqEaNGklERIQJLbpOWd7WKWvOnDmmnUtl+wIAAHwj+t+tOKqNdt7RxrfVGlji4uJMl2RtWLtx40a54YYbXH/8o48+Mo87dOjgto5W/bRr1052795t1rn77rtdCSs9Pd3jOmWlpKS4Gug6q5i00a52pw4KCpKfiiZDDUlaGuTr3kn+orafg9p+/IpzwDngfcBn4VK/D/R3X/NCfHz8BRf0ienTp2tZjqN+/fqO9PR0x8mTJx133XWXmdagQQPH6dOnK6zz0ksvmfmhoaGOVatWmWWSk5Nd0w4cOOCwXV5entlfHddWtf0c1PbjV5wDzgHvAz4L1f194LNeQr/97W+lS5cuJiVpo1mt8nnttdckJCRE5s+fb0pg/uu//sukrq1bt5p19NosN998s+n6fOedd5peQs8++6yZN2PGDGndurWvdg8AAPgxnwUW7RG0fft2mTRpkqnK0fChYUSvyTJ69GizjAYT7a7sbFij1UJpaWny1FNPSdeuXU3dVd++fWXNmjVVumgcAAAIbD5pw+JUp04dcwE5b7SkRYeytATm97//vRn8UXh4uEyfPt2Ma6vafg5q+/ErzgHngPcBn4Xq/j7w2aX5AQAAqgt3awYAANYjsAAAAOsRWAAAgPUILBdBm/ksXbpUOnXqZHpDde/eXd5+++0Lrrd27Vrp37+/6dKdlJQks2fPlpKSEvH3c6ENpPXifJVdoE/vvu1cpuzw8MMPS204fqUXMnzggQfMlZ+vuuoqGT58uOzdu1f83aUcl/Yg9PR+GDRokPiL8+fPS3Jysvke0AtUDh48WN5///1K19HP+6xZs8znX78H9PtAvxf8VVXPwXfffefxdW/fvr34u8WLF0tYWJg5niNHjvjsPRNI52C7rz/3Pr2qS4B68sknzUVwyg9///vfva4za9Ysj+vcfPPNjpKSEoc/Onv2rOPXv/612/F4M2/ePDM/ODjYXATQOTzyyCMOf1WV48/KynK0aNGiwutft25dx4YNGxz+6lKP68033zTLBQUFub0fBg8e7PAH+fn5ji5dulQ4bj2GV155xeM6+jnXz7un7wH9fvA3l3IOPv30U4+ve8eOHR3+qri42PHYY4+5nYPMzEyfnK9AOwdv+vhzT2C5gKNHj5ofXT3p+iN84sQJx7333mue65vRE71Cr74ouszkyZMdp06dcvztb39zTfPXN6uGDd3/kJCQC/5g/+53vzPz3377bUegqMrxO98jcXFxjk8++cTx7bffOvr27WumtWnTxlFUVOTwR5d6XIsXLzbLLFy40OGPZsyYYfa/Xr16Jpjl5OQ4RowYUemVvF9++WXXD9TKlSvN94B+H+i0sLAwx8GDBx2Bfg7ee+891/dgoPjjH/9Y4XvA04/1pZyvQDsHi338uSewXMCOHTscSUlJjlatWplUqXbv3m1ehKioKI/rTJw40cxv3ry5o7S01DV95MiRlQYdf/ixat++vWPZsmUX/MEeM2aM6w2tX9jt2rUzJVX++kNdleP//vvvXSF3+fLlrun6A6X/aej0t956y+FvLue4Zs6c6VbipqU048ePd5w5c8bhD/QHRvdf38Nl/4PWkiWdnpqaWmGdxMREM2/UqFFupS7NmjUz0ydNmuTwJ5dyDvSfs7Kve3x8vPke1H/8/JUGET2OtWvXVvpjfSnnK9DOwUwff+5pw3IB119/vezcuVMOHjxoLnKndu3aZcZ6dV5P9u3bZ8Z6i4Ky7RwGDhxoxl988cUFb6NtIz0evZnlxdwywXnHba3D1ysc79+/31zR2J/bsFzs8X/55ZfmZpxlX3PVqlUradmypdt7xJ9cznE53w+6vr4fDh8+LM8//7wMGzZMbHfs2DE5ffp0hePW9mw9evTweNx6nPqeL7+OXt1b30ee1gm0c+DpdT969KisWLFCevfuba567o8SExPNb0K3bt18fr4C6RxUx+eewFJF+iWk90RS9913n8dl9A6VKjY21m1648aNzbigoEBOnjwp/kbvqH2xdyN+5513TCjTQT+4jz32mJm+fPly+eabb8QfXezxO1//yt4DZZfxF5dzXPol5Xw/nDlzRp555hkzXe/Mvm3bNgm049bPt37Oq7JOIL722uDU+brr+dCwov/E6T9tegsWfzR06NAL3lU4UL8DqnIOquNzT2D5/1JTUz22Zt68ebPrZGVkZEifPn1ML4mJEyfKyJEjPZ5UvcGjOnHihMe0GRERYW4O6c/nojI//vij7N69W3bs2GGea+8Ivaml3rpBk/aePXvEZpd7/M7Xv7L3QNll/OX4tUfQpRyXlrLpf5ObNm0yzyMjI2XKlCmukir9XNnsUl5P/Xzr57wq6wTaOdAfKS2Zfu+998xzvUz7PffcY3pK+cPrfjn8/TvAF6rjc+/Tewn5M71ZY7t27SpMr1u3rhnrj9V//ud/Sn5+vkyYMKHSeybpzR/1po+aIvVD66wW0mlKu/RdqEuszefiQvQNqt3W9AOpXd30WPW/K2c1mPOLPFCP/5prrjFF/xrONm7cKPfee6+ZfujQIfn6669d7xF/O3593S7luA4cOCAdO3Y06+uXd7169Vz/cTu3a7O4uDgTunNzc81x33DDDWa63pleqwg9HbeeJz2HGtx1HS2dU3rc+r3gaZ1AOwfapVe7vjuDi1YdqnPnzvnF6/5Tn69Ac6A6Pvc+aH8T8LQhYURERIUGVN589dVXrh5BycnJpjX4qlWr/L6XkNMHH3xQaaPTY8eOmV4QOn/RokWmO7Cza/gVV1xhWssH8vGr0aNHm/naME27dn733XeOG2+80e97CV3Kcem0mJgYs8zUqVNNg7ulS5e6zqE2Yrfd9OnTzb7Wr1/fkZ6e7jh58qTjrrvuqrTHx0svveTqJaSff11Gvw+c07Q3oT+5lHNw7bXXmvna0DYvL8+xbt06v254XpY2Mq2swemlnK9AOgdF1fC5J7BcwObNm11Bw/lF4xz69+9vllmzZo3p5jl//nzXek8//XTAXYfF2w+2vjF79erl6NChg+tDmJKS4vH4tdW4v7uY46/t12G54447zHIa3tULL7zg8f2gvcn8gbdramgvOO2+rDSMNG3a1LFly5ZadR2Wys6Bdmt2BpSyQ79+/RyB9mNd/j1/Mecr0M/BCz7+3NOG5QK0VbO2bnbSx87BedVaLSLXFu/OHhTqiSeeMI3K+vbtaxpqao8i7SWTlpZmiosDjfOcOKt99Kq+zz33nPz85z83dZddunSRZcuWyZNPPimBqPzxN23a1LSiHzt2rCkK14Z22lDtww8/lAEDBoi/utjj0nOhnwnn+dAr4+rnQa/4qkXDWlQ8d+5ccwVpf6C9O/SqnZMmTTJF+VptdvPNN5tq3tGjR3s8Zv2c6+ddP/f6+Y+OjjbfB3oepk2bJv7mUs7BLbfcYqrAevbsab4H27RpY9ox6HkJNOWP/WLOV6AprubPfZCmFh/vMwAAgE8F3r/6AAAg4BBYAACA9QgsAADAegQWAABgPQILAACwHoEFAABYj8ACAACsR2ABAADWI7AAAADrEVgAAID1CCwArPXRRx9J//79pVGjRhIUFOQ25Obm1vTuAfgJEVgAWGnPnj3mZoGdO3eWLVu2yLp16+TKK6+UG2+80dxQLSYmpqZ3EcBPiJsfArBSnz59pEmTJiacOI0fP14+/vhjc8doALVLaE3vAACUl52dLdu2bZNNmza5TY+MjDTVQQBqH6qEAFhn165dUlpaaqqDyk9PSkqqsf0CUHMILACso2FFnT9/3jXts88+M21Z7rnnnhrcMwA1hcACwDrdu3eXOnXqyJQpU2T//v2SlpYmQ4cOlYceekh++ctf1vTuAagBNLoFYKV33nlHfvvb38rhw4clISFBHnzwQUlOTpbgYP7PAmojAgsAALAe/6oAAADrEVgAAID1CCwAAMB6BBYAAGA9AgsAALAegQUAAFiPwAIAAKxHYAEAANYjsAAAAOsRWAAAgPUILAAAwHoEFgAAILb7f/ggHznkUghnAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "for j, name in enumerate(truth_labels):\n",
    "    if name not in index_map:\n",
    "        continue  # skips anything unmatched (e.g., x1 slope)\n",
    "\n",
    "    i = index_map[name]\n",
    "\n",
    "    color_mean = np.array([sublist[j] for sublist in truths])\n",
    "    spread = (color_mean - mean_vals[i]) / std_vals[i]\n",
    "\n",
    "    plt.figure()\n",
    "    plt.hist(spread)\n",
    "    plt.title(f\"{name}; {np.mean(spread):.3f}\")\n",
    "    plt.xlabel(r\"$\\sigma$\")\n",
    "    #print(f\" The average recovery for {name} is {np.mean(spread):.3f} $\\sigma$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 151,
   "id": "8da5d35c-6265-45f2-a954-caba45fb65b9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.32 , 0.459, 0.352, 0.214, 0.405, 0.411, 0.156, 0.452, 0.241,\n",
       "       0.306, 0.364, 0.397, 0.368, 0.373, 0.091, 0.384, 0.305, 0.469,\n",
       "       0.352, 0.219, 0.218, 0.366, 0.364, 0.356, 0.34 , 0.496, 0.336,\n",
       "       0.1  , 0.293, 0.459, 0.22 , 0.473, 0.13 , 0.139, 0.215, 0.305,\n",
       "       0.113, 0.21 , 0.358, 0.483, 0.354, 0.241, 0.444, 0.284, 0.178,\n",
       "       0.477, 0.144, 0.498, 0.42 , 0.443, 0.145, 0.131, 0.294, 0.118,\n",
       "       0.299, 0.375, 0.111, 0.125, 0.22 , 0.23 , 0.428, 0.49 , 0.059,\n",
       "       0.483, 0.425, 0.441, 0.063, 0.179, 0.137, 0.333, 0.432, 0.364,\n",
       "       0.309, 0.362, 0.306, 0.374, 0.089, 0.413, 0.184, 0.382, 0.096,\n",
       "       0.264, 0.288, 0.3  , 0.359, 0.079, 0.112, 0.255, 0.202, 0.062,\n",
       "       0.231, 0.493, 0.083, 0.433, 0.304, 0.479, 0.404, 0.121, 0.492,\n",
       "       0.132])"
      ]
     },
     "execution_count": 151,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": []
  },
  {
   "cell_type": "code",
   "execution_count": 157,
   "id": "751f4fc5-b99d-413c-867e-e9ba25147876",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "c_int mu: 0.001 +/- 0.003 $\\sigma$\n",
      "c_int std: -0.009 +/- 0.002 $\\sigma$\n",
      "Hi Rv_mu: -0.036 +/- 0.070 $\\sigma$\n",
      "Hi Rv_std: 0.366 +/- 0.047 $\\sigma$\n",
      "low Rv_mu: -0.083 +/- 0.069 $\\sigma$\n",
      "low Rv_std: 0.402 +/- 0.042 $\\sigma$\n",
      "x1 mean: 0.009 +/- 0.000 $\\sigma$\n",
      "x1 std: -0.636 +/- 0.003 $\\sigma$\n",
      "Hi EBV: -0.023 +/- 0.014 $\\sigma$\n",
      "low EBV: 0.002 +/- 0.013 $\\sigma$\n"
     ]
    }
   ],
   "source": [
    "for j, name in enumerate(truth_labels):\n",
    "    if name not in index_map:\n",
    "        continue  # skips anything unmatched (e.g., x1 slope)\n",
    "\n",
    "    i = index_map[name]\n",
    "\n",
    "    color_mean = np.array([sublist[j] for sublist in truths])\n",
    "    spread = np.std(color_mean - mean_vals[i])/10\n",
    "    mean_mean = np.median(color_mean - mean_vals[i])\n",
    "\n",
    "\n",
    "    print(f\"{name}: {mean_mean:.3f} +/- {np.mean(spread):.3f} $\\sigma$\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "deb43b08-f440-4e5e-89c4-f360ef690e62",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "duStBI",
   "language": "python",
   "name": "dustbi"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.13.13"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}