diff --git a/001_002_scratch-run_exp.ipynb b/001_002_scratch-run_exp.ipynb
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+++ b/001_002_scratch-run_exp.ipynb
@@ -0,0 +1,1242 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b1e031e3",
+   "metadata": {},
+   "source": [
+    "- [x] try just one predictor\n",
+    "    - [ ] multi input, single output\n",
+    "- [x] comparem ulti\n",
+    "- losses:\n",
+    "    - try logp? nah\n",
+    "    - mae?\n",
+    "- [x] make my own csv with 5m data (maybe 10k rows)\n",
+    "- [ ] backtest?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7f9e3d73",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:58:24.757461Z",
+     "start_time": "2022-11-23T06:58:24.749861Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.simplefilter(\"ignore\")\n",
+    "\n",
+    "# autoreload import your package\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4e09086b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:58:25.961679Z",
+     "start_time": "2022-11-23T06:58:24.758565Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import join\n",
+    "import math\n",
+    "import logging\n",
+    "from typing import Callable, Optional, Union, Dict, Tuple\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import matplotlib.colors as mcolors\n",
+    "\n",
+    "import gin\n",
+    "from fire import Fire\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch import optim\n",
+    "from torch import nn\n",
+    "\n",
+    "from experiments.base import Experiment\n",
+    "from data.datasets import ForecastDataset\n",
+    "from models import get_model\n",
+    "from utils.checkpoint import Checkpoint\n",
+    "from utils.ops import default_device, to_tensor\n",
+    "from utils.losses import get_loss_fn\n",
+    "from utils.metrics import calc_metrics\n",
+    "\n",
+    "from experiments.forecast import get_data\n",
+    "gin.enter_interactive_mode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "66d7f095",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:58:25.987675Z",
+     "start_time": "2022-11-23T06:58:25.963849Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import logging\n",
+    "logging.root.setLevel(logging.INFO)\n",
+    "\n",
+    "from loguru import logger\n",
+    "logger.remove()\n",
+    "logger.add(os.sys.stdout, level=\"INFO\", colorize=True, format=\"<level>{time} | {message}</level>\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4df5270",
+   "metadata": {},
+   "source": [
+    "# auto"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "04499bef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:58:26.005030Z",
+     "start_time": "2022-11-23T06:58:25.988575Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def plot(model_name=\"deeptime\", save_path=Path(\"storage/experiments/Exchange/96M/repeat=0\"), i=200, title=None, plot=True):\n",
+    "\n",
+    "    gin.clear_config()\n",
+    "    gin.parse_config(open(save_path/\"config.gin\"))\n",
+    "\n",
+    "    train_set, train_loader = get_data(flag='train', batch_size=2)\n",
+    "\n",
+    "    model = get_model(model_name,\n",
+    "                        dim_size=train_set.data_x.shape[1],\n",
+    "                        datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
+    "    model.load_state_dict(torch.load(save_path/'model.pth'))\n",
+    "    model = model.eval()\n",
+    "\n",
+    "\n",
+    "    b = train_set[i]\n",
+    "    b = [bb[None, :] for bb in b]\n",
+    "    b2 = list(map(to_tensor, b))\n",
+    "    context_past_x, context_y, query_past_x, query_y, context_time, query_time = b2\n",
+    "    with torch.no_grad():\n",
+    "        forecast = model(*b2)\n",
+    "\n",
+    "    if title is None:\n",
+    "        title = str(save_path).split('/')[-3:]\n",
+    "        title = \"-\".join(title)\n",
+    "    \n",
+    "    colors = list(mcolors.BASE_COLORS.keys())\n",
+    "    l = x.shape[1]\n",
+    "    forecast2 = forecast[0].detach().cpu().numpy()\n",
+    "    x2 = x[0].cpu()\n",
+    "    y2 = y[0].cpu()\n",
+    "    l2 = y.shape[1]\n",
+    "    i_past = list(range(l))\n",
+    "    i_future = list(range(l, l+l2))\n",
+    "    \n",
+    "    if plot:\n",
+    "        plt.title(title)\n",
+    "        for i in range(x.shape[-1]):\n",
+    "            plt.plot(i_past, x2[:, i], c=colors[i])\n",
+    "        for i in range(x.shape[-1]):\n",
+    "            plt.plot(i_future, y2[:, i], c=colors[i])\n",
+    "        for i in range(x.shape[-1]):\n",
+    "            plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')\n",
+    "    return x2, y2, forecast2, i_past, i_future\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a51165b9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-22T13:28:35.849491Z",
+     "start_time": "2022-11-22T13:28:35.766453Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dd540a97",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:58:26.027691Z",
+     "start_time": "2022-11-23T06:58:26.006255Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(mcolors.BASE_COLORS.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8523787",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-22T13:38:15.786656Z",
+     "start_time": "2022-11-22T13:38:15.303577Z"
+    }
+   },
+   "source": [
+    "# view model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccf70857",
+   "metadata": {},
+   "source": [
+    "# run exps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "694f6ac4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T03:31:08.583641Z",
+     "start_time": "2022-11-23T03:31:08.558690Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b70f754",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:58:26.043794Z",
+     "start_time": "2022-11-23T06:58:26.028937Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# list the models we have run...\n",
+    "configs=sorted(Path(\"storage/experiments/Stocks\").glob(\"**/config.gin\"))\n",
+    "import random\n",
+    "random.shuffle(configs)\n",
+    "# print(configs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c09e8335",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:58:26.058417Z",
+     "start_time": "2022-11-23T06:58:26.044685Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from experiments.forecast import ForecastExperiment\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a4eba154",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.749Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "82dcfd19e8a74cf89d3d49aef4bff644",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/42 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INRPlus2,enc=transformer2,repeat=0/config.gin\n",
+      "receptive field [690 378 242]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 0.23\n",
+      "INFO:root:Validation loss decreased (inf --> 0.142). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss decreased (0.142 --> 0.024). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss decreased (0.024 --> 0.017). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.020). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.05\n",
+      "INFO:root:Validation loss decreased (0.017 --> 0.017). Saving model ...\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.019). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.017). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.022). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.023). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss decreased (0.017 --> 0.017). Saving model ...\n",
+      "INFO:root:epochs: 11, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss decreased (0.017 --> 0.016). Saving model ...\n",
+      "INFO:root:epochs: 12, iters: 100 | training loss: 0.02\n",
+      "INFO:root:Validation loss decreased (0.016 --> 0.016). Saving model ...\n",
+      "INFO:root:epochs: 13, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.016 --> 0.017). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 14, iters: 100 | training loss: 0.05\n",
+      "INFO:root:Validation loss increased (0.016 --> 0.016). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 15, iters: 100 | training loss: 0.02\n",
+      "INFO:root:Validation loss decreased (0.016 --> 0.015). Saving model ...\n",
+      "INFO:root:epochs: 16, iters: 100 | training loss: 0.02\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.016). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 17, iters: 100 | training loss: 0.02\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.019). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 18, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss decreased (0.015 --> 0.015). Saving model ...\n",
+      "INFO:root:epochs: 19, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss decreased (0.015 --> 0.015). Saving model ...\n",
+      "INFO:root:epochs: 20, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.016). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 21, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.016). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 22, iters: 100 | training loss: 0.05\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.017). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 23, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss decreased (0.015 --> 0.015). Saving model ...\n",
+      "INFO:root:epochs: 24, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.015). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 25, iters: 100 | training loss: 0.05\n",
+      "INFO:root:Validation loss decreased (0.015 --> 0.015). Saving model ...\n",
+      "INFO:root:epochs: 26, iters: 100 | training loss: 0.02\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.015). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 27, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.018). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 28, iters: 100 | training loss: 0.05\n",
+      "INFO:root:Validation loss increased (0.015 --> 0.015). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 29, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss decreased (0.015 --> 0.014). Saving model ...\n",
+      "INFO:root:epochs: 30, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.014). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 31, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.015). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 32, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.015). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 33, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.015). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 34, iters: 100 | training loss: 0.02\n",
+      "INFO:root:Validation loss decreased (0.014 --> 0.014). Saving model ...\n",
+      "INFO:root:epochs: 35, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.015). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 36, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.014). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 37, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss decreased (0.014 --> 0.014). Saving model ...\n",
+      "INFO:root:epochs: 38, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.015). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 39, iters: 100 | training loss: 0.02\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.014). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 40, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.014). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 41, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.015). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 42, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.014). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 43, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.014). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 44, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.014 --> 0.014). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=None,inr=INR,enc=lstm,repeat=0/config.gin\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 1.33\n",
+      "INFO:root:Validation loss decreased (inf --> 0.624). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.39\n",
+      "INFO:root:Validation loss decreased (0.624 --> 0.141). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.15\n",
+      "INFO:root:Validation loss decreased (0.141 --> 0.080). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.33\n",
+      "INFO:root:Validation loss increased (0.080 --> 0.095). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.17\n",
+      "INFO:root:Validation loss increased (0.080 --> 0.128). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.22\n",
+      "INFO:root:Validation loss decreased (0.080 --> 0.076). Saving model ...\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.15\n",
+      "INFO:root:Validation loss increased (0.076 --> 0.091). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.29\n",
+      "INFO:root:Validation loss increased (0.076 --> 0.166). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.16\n",
+      "INFO:root:Validation loss decreased (0.076 --> 0.059). Saving model ...\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.20\n",
+      "INFO:root:Validation loss increased (0.059 --> 0.083). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 11, iters: 100 | training loss: 0.39\n",
+      "INFO:root:Validation loss increased (0.059 --> 0.087). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 12, iters: 100 | training loss: 0.12\n",
+      "INFO:root:Validation loss increased (0.059 --> 0.060). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 13, iters: 100 | training loss: 0.16\n",
+      "INFO:root:Validation loss increased (0.059 --> 0.128). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 14, iters: 100 | training loss: 0.16\n",
+      "INFO:root:Validation loss increased (0.059 --> 0.103). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 15, iters: 100 | training loss: 0.13\n",
+      "INFO:root:Validation loss increased (0.059 --> 0.066). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 16, iters: 100 | training loss: 0.21\n",
+      "INFO:root:Validation loss increased (0.059 --> 0.111). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=Transformer,inr=INRPlus2,enc=mlp,repeat=0/config.gin\n",
+      "receptive field [690 378 242]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 1.23\n",
+      "INFO:root:Validation loss decreased (inf --> 0.367). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.16\n",
+      "INFO:root:Validation loss decreased (0.367 --> 0.056). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.08\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.059). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.13\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.059). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.058). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.09\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.057). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.10\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.060). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.12\n",
+      "INFO:root:Validation loss decreased (0.056 --> 0.056). Saving model ...\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.10\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.063). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.10\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.059). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 11, iters: 100 | training loss: 0.09\n",
+      "INFO:root:Validation loss increased (0.056 --> 0.062). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 12, iters: 100 | training loss: 0.10\n",
+      "INFO:root:Validation loss decreased (0.056 --> 0.055). Saving model ...\n",
+      "INFO:root:epochs: 13, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.055 --> 0.062). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 14, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.055 --> 0.059). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 15, iters: 100 | training loss: 0.08\n",
+      "INFO:root:Validation loss increased (0.055 --> 0.056). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 16, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.055 --> 0.063). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 17, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.055 --> 0.056). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 18, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.055 --> 0.061). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 19, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.055 --> 0.057). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=None,inr=INRPlus2,enc=none,repeat=0/config.gin\n",
+      "receptive field [690 378 242]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 1.18\n",
+      "INFO:root:Validation loss decreased (inf --> 0.529). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.89\n",
+      "INFO:root:Validation loss decreased (0.529 --> 0.332). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 1.60\n",
+      "INFO:root:Validation loss decreased (0.332 --> 0.314). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 1.19\n",
+      "INFO:root:Validation loss increased (0.314 --> 0.321). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 1.15\n",
+      "INFO:root:Validation loss increased (0.314 --> 0.318). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.67\n",
+      "INFO:root:Validation loss increased (0.314 --> 0.317). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.98\n",
+      "INFO:root:Validation loss increased (0.314 --> 0.323). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 1.16\n",
+      "INFO:root:Validation loss increased (0.314 --> 0.335). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 1.31\n",
+      "INFO:root:Validation loss increased (0.314 --> 0.333). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 1.44\n",
+      "INFO:root:Validation loss increased (0.314 --> 0.324). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=Transformer,inr=INRPlus2,enc=lstm,repeat=0/config.gin\n",
+      "receptive field [690 378 242]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 1.10\n",
+      "INFO:root:Validation loss decreased (inf --> 0.286). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss decreased (0.286 --> 0.059). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.18\n",
+      "INFO:root:Validation loss decreased (0.059 --> 0.057). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.18\n",
+      "INFO:root:Validation loss decreased (0.057 --> 0.057). Saving model ...\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.14\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.060). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.08\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.059). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss decreased (0.057 --> 0.057). Saving model ...\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.077). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.060). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.15\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.060). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 11, iters: 100 | training loss: 0.10\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.057). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 12, iters: 100 | training loss: 0.09\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.069). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 13, iters: 100 | training loss: 0.12\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.058). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 14, iters: 100 | training loss: 0.16\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.058). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=None,inr=INR,enc=lstm2,repeat=0/config.gin\n",
+      "storage/experiments/Stocks/96M2S/lrn=None,inr=INRPlus2,enc=lstm,repeat=0/config.gin\n",
+      "receptive field [690 378 242]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 1.80\n",
+      "INFO:root:Validation loss decreased (inf --> 0.379). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.67\n",
+      "INFO:root:Validation loss decreased (0.379 --> 0.134). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.21\n",
+      "INFO:root:Validation loss decreased (0.134 --> 0.065). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.16\n",
+      "INFO:root:Validation loss increased (0.065 --> 0.087). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.19\n",
+      "INFO:root:Validation loss increased (0.065 --> 0.125). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.15\n",
+      "INFO:root:Validation loss increased (0.065 --> 0.080). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.15\n",
+      "INFO:root:Validation loss increased (0.065 --> 0.116). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.28\n",
+      "INFO:root:Validation loss increased (0.065 --> 0.077). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.28\n",
+      "INFO:root:Validation loss increased (0.065 --> 0.123). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.20\n",
+      "INFO:root:Validation loss increased (0.065 --> 0.102). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=Transformer,inr=INRPlus2,enc=transformer2,repeat=0/config.gin\n",
+      "receptive field [690 378 242]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 0.76\n",
+      "INFO:root:Validation loss decreased (inf --> 0.377). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.20\n",
+      "INFO:root:Validation loss decreased (0.377 --> 0.060). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.13\n",
+      "INFO:root:Validation loss decreased (0.060 --> 0.057). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.08\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.065). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.12\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.067). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.062). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.08\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.062). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.09\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.059). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.09\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.069). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.12\n",
+      "INFO:root:Validation loss increased (0.057 --> 0.065). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INR,enc=none,repeat=0/config.gin\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 0.68\n",
+      "INFO:root:Validation loss decreased (inf --> 0.298). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.10\n",
+      "INFO:root:Validation loss decreased (0.298 --> 0.041). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss decreased (0.041 --> 0.031). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss decreased (0.031 --> 0.027). Saving model ...\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss decreased (0.027 --> 0.023). Saving model ...\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.023 --> 0.024). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.11\n",
+      "INFO:root:Validation loss increased (0.023 --> 0.024). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss decreased (0.023 --> 0.021). Saving model ...\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.021 --> 0.032). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.021 --> 0.024). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 11, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss decreased (0.021 --> 0.018). Saving model ...\n",
+      "INFO:root:epochs: 12, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.018 --> 0.022). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 13, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.018 --> 0.028). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 14, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.018 --> 0.023). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 15, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss increased (0.018 --> 0.021). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 16, iters: 100 | training loss: 0.07\n",
+      "INFO:root:Validation loss increased (0.018 --> 0.027). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 17, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.018 --> 0.020). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 18, iters: 100 | training loss: 0.05\n",
+      "INFO:root:Validation loss decreased (0.018 --> 0.017). Saving model ...\n",
+      "INFO:root:epochs: 19, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.032). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 20, iters: 100 | training loss: 0.09\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.036). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 21, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.044). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 22, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.033). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 23, iters: 100 | training loss: 0.06\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.028). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 24, iters: 100 | training loss: 0.03\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.025). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 25, iters: 100 | training loss: 0.04\n",
+      "INFO:root:Validation loss increased (0.017 --> 0.025). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=None,inr=INRPlus2,enc=lstm2,repeat=0/config.gin\n",
+      "receptive field [690 378 242]\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 1.62\n",
+      "INFO:root:Validation loss decreased (inf --> 0.531). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.61\n",
+      "INFO:root:Validation loss decreased (0.531 --> 0.278). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.22\n",
+      "INFO:root:Validation loss decreased (0.278 --> 0.156). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.59\n",
+      "INFO:root:Validation loss decreased (0.156 --> 0.136). Saving model ...\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.37\n",
+      "INFO:root:Validation loss increased (0.136 --> 0.171). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.35\n",
+      "INFO:root:Validation loss increased (0.136 --> 0.483). Early stopping counter: 2 out of 7\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.56\n",
+      "INFO:root:Validation loss increased (0.136 --> 0.188). Early stopping counter: 3 out of 7\n",
+      "INFO:root:epochs: 8, iters: 100 | training loss: 0.36\n",
+      "INFO:root:Validation loss increased (0.136 --> 0.312). Early stopping counter: 4 out of 7\n",
+      "INFO:root:epochs: 9, iters: 100 | training loss: 0.35\n",
+      "INFO:root:Validation loss increased (0.136 --> 0.210). Early stopping counter: 5 out of 7\n",
+      "INFO:root:epochs: 10, iters: 100 | training loss: 0.25\n",
+      "INFO:root:Validation loss increased (0.136 --> 0.284). Early stopping counter: 6 out of 7\n",
+      "INFO:root:epochs: 11, iters: 100 | training loss: 0.31\n",
+      "INFO:root:Validation loss increased (0.136 --> 0.418). Early stopping counter: 7 out of 7\n",
+      "INFO:root:Early stopping\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INR,enc=transformer,repeat=0/config.gin\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "INFO:root:epochs: 1, iters: 100 | training loss: 1.34\n",
+      "INFO:root:Validation loss decreased (inf --> 0.299). Saving model ...\n",
+      "INFO:root:epochs: 2, iters: 100 | training loss: 0.17\n",
+      "INFO:root:Validation loss decreased (0.299 --> 0.122). Saving model ...\n",
+      "INFO:root:epochs: 3, iters: 100 | training loss: 0.22\n",
+      "INFO:root:Validation loss decreased (0.122 --> 0.090). Saving model ...\n",
+      "INFO:root:epochs: 4, iters: 100 | training loss: 0.29\n",
+      "INFO:root:Validation loss decreased (0.090 --> 0.071). Saving model ...\n",
+      "INFO:root:epochs: 5, iters: 100 | training loss: 0.30\n",
+      "INFO:root:Validation loss increased (0.071 --> 0.084). Early stopping counter: 1 out of 7\n",
+      "INFO:root:epochs: 6, iters: 100 | training loss: 0.19\n",
+      "INFO:root:Validation loss decreased (0.071 --> 0.066). Saving model ...\n",
+      "INFO:root:epochs: 7, iters: 100 | training loss: 0.15\n"
+     ]
+    }
+   ],
+   "source": [
+    "for config in tqdm(configs):\n",
+    "    save_path = config.parent\n",
+    "\n",
+    "    exp = ForecastExperiment(config_path=config)\n",
+    "    print(config)\n",
+    "    try:\n",
+    "        exp.run()\n",
+    "    except KeyboardInterrupt:\n",
+    "        raise\n",
+    "    except Exception as e:\n",
+    "        raise\n",
+    "        print(e)\n",
+    "        pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d587ef65",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.749Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "%debug"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "c1565c46",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.749Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "exp.instance()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "43aacbb6",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# save_path = Path('storage/experiments/Stocks/96M2S/repeat=0')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "dd2a96b6",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# gin.clear_config()\n",
+    "# config_path = save_path/\"config.gin\"\n",
+    "# gin.parse_config(open(config_path))\n",
+    "# model_name = gin.query_parameter(\"instance.model_type\")\n",
+    "# model_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "61b1ac70",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# exp = ForecastExperiment(config_path=config_path)\n",
+    "# # exp.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "629ea87a",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def save_path2name(save_path: Path) -> str:\n",
+    "    \"\"\"\n",
+    "    Path('storage/experiments/Stocks/96M2S/base_learner=None,inr=INR,encoder=mlp,repeat=0')\n",
+    "    to \n",
+    "    '96M2S-None_INR_mlp_0'\n",
+    "    \"\"\"\n",
+    "    mtitle = str(save_path).split('/')[-2:]\n",
+    "    tags = mtitle[-1]\n",
+    "    tags = [x.split('=')[-1] for x in tags.split(',')]\n",
+    "    mtitle[-1] = '_'.join(tags)\n",
+    "    mtitle = \"-\".join(mtitle)\n",
+    "    return mtitle\n",
+    "\n",
+    "# save_path2name(save_path)\n",
+    "# save_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2269528b",
+   "metadata": {},
+   "source": [
+    "# view all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "768530be",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from torchsummaryX import summary\n",
+    "\n",
+    "def plot_multi(save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True, verbose=1,):\n",
+    "    assert len(save_paths)>0\n",
+    "    for j in range(len(save_paths)):\n",
+    "        save_path = save_paths[j]\n",
+    "\n",
+    "        gin.clear_config()\n",
+    "        gin.parse_config(open(save_path/\"config.gin\"))\n",
+    "        model_name = gin.query_parameter(\"instance.model_type\")\n",
+    "\n",
+    "        train_set, train_loader = get_data(flag='test', batch_size=3)\n",
+    "        seq_len = train_set[0][0].shape[0]\n",
+    "        model = get_model(model_name,\n",
+    "                            dim_size=train_set.data_x.shape[1],\n",
+    "                          seq_len=seq_len,\n",
+    "                            datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
+    "        model.load_state_dict(torch.load(save_path/'model.pth'))\n",
+    "        model = model.eval()\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "\n",
+    "        b = train_set[i]\n",
+    "        b = [bb[None, :] for bb in b]\n",
+    "        b2 = list(map(to_tensor, b))\n",
+    "        \n",
+    "#         b = next(iter(train_loader))\n",
+    "#         print([s.shape for s in b]\n",
+    "        \n",
+    "        if verbose>1:\n",
+    "            \n",
+    "#             print(model)\n",
+    "            summary(model, *b2)\n",
+    "            print(save_path)\n",
+    "        \n",
+    "        context_past_x, context_y, query_past_x, query_y, context_time, query_time = b2\n",
+    "        with torch.no_grad():\n",
+    "            forecast = model(*b2)\n",
+    "       \n",
+    "        colors = list(mcolors.BASE_COLORS.keys())\n",
+    "        l = context_time.shape[1]\n",
+    "        forecast2 = forecast[0].detach().cpu().numpy()\n",
+    "        x2 = context_y[0].cpu()\n",
+    "        y2 = query_y[0].cpu()\n",
+    "        l2 = query_time.shape[1]\n",
+    "        i_past = list(range(l))\n",
+    "        i_future = list(range(l, l+l2))\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        if plot:\n",
+    "        \n",
+    "            if j==0:\n",
+    "                plt.plot(i_past, x2[:, 0], c=colors[0], label=f\"past\")\n",
+    "                plt.plot(i_future, y2[:, 0], c=colors[0], label=\"future true\", alpha=0.3)\n",
+    "            mtitle = save_path2name(save_path)\n",
+    "            plt.plot(i_future, forecast2[:, 0], linestyle='--', label=f\"{mtitle}\") # c=colors[j], \n",
+    "            \n",
+    "\n",
+    "    plt.legend()\n",
+    "    plt.title(title)\n",
+    "    return x2, y2, forecast2, i_past, i_future\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "739ee5e3",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# list the models we have run...\n",
+    "m=sorted(Path(\"storage/experiments/Stocks/96M2S\").glob(\"**/_SUCCESS\"))\n",
+    "print(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "19fe898b",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "for mm in m:\n",
+    "    mtitle = save_path2name(mm.parent)\n",
+    "    print(mtitle)\n",
+    "    m3 = np.load(mm.parent/'metrics.npy', allow_pickle=1)\n",
+    "    m3 = eval(str(m3))\n",
+    "    print(m3['val']['mape'])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c141512",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "train_set, train_loader = get_data(flag='train')\n",
+    "train_set[0][1].shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "e6961094",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "save_paths = [mm.parent for mm in m]\n",
+    "for mm in m:\n",
+    "    try:\n",
+    "        plot_multi(\n",
+    "            save_paths=[mm.parent],\n",
+    "            i=600,\n",
+    "            verbose=2,\n",
+    "        )\n",
+    "    except:\n",
+    "        print('failed', mm)\n",
+    "#         mm.unlink()\n",
+    "        pass\n",
+    "1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "115467ad",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "save_paths = [mm.parent for mm in m]\n",
+    "plot_multi(\n",
+    "    save_paths=save_paths,\n",
+    "    i=200,\n",
+    "    verbose=0,\n",
+    ")\n",
+    "1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "985a9f4e",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.766Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "256/24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36cba856",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-22T13:17:31.585029Z",
+     "start_time": "2022-11-22T13:17:31.528806Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b85499d6",
+   "metadata": {},
+   "source": [
+    "# check positions in dl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "497e8e53",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.783Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": [
+    "train_set, train_loader = get_data(flag='test', batch_size=3)\n",
+    "b = context_past_x, context_y, query_past_x, query_y, context_time, query_time = train_set[100]\n",
+    "print([bb.shape for bb in b])\n",
+    "# context_y, query_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "277d7ea7",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.783Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "cx_start, cx_end, c_start, c_end, qx_start, qx_end, q_start, q_end = train_set.get_inds(100)\n",
+    "cx_start, cx_end, c_start, c_end, qx_start, qx_end, q_start, q_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "d9180330",
+   "metadata": {
+    "ExecuteTime": {
+     "start_time": "2022-11-23T06:58:24.783Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "plt.hlines(1, cx_start, cx_end, color='green', alpha=0.5, label='context_past_x')\n",
+    "plt.hlines(2, c_start, c_end, color='green', label='context_labels')\n",
+    "plt.hlines(3, qx_start, qx_end, alpha=0.5, label='query_past_x')\n",
+    "plt.hlines(4, q_start, q_end, label='query_labels/target')\n",
+    "plt.legend(loc='upper left')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3c8452ee",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "038f5c6b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7c66089e",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "deeptime",
+   "language": "python",
+   "name": "deeptime"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/001_003_scratch-run_exp-backtest.ipynb b/001_003_scratch-run_exp-backtest.ipynb
new file mode 100644
index 0000000..efbd5d5
--- /dev/null
+++ b/001_003_scratch-run_exp-backtest.ipynb
@@ -0,0 +1,1144 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "id": "b1e031e3",
+   "metadata": {},
+   "source": [
+    "- [x] try just one predictor\n",
+    "    - [ ] multi input, single output\n",
+    "- [x] comparem ulti\n",
+    "- losses:\n",
+    "    - try logp? nah\n",
+    "    - mae?\n",
+    "- [x] make my own csv with 5m data (maybe 10k rows)\n",
+    "- [ ] backtest?"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "id": "7f9e3d73",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:41.319249Z",
+     "start_time": "2022-11-23T06:59:41.308639Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "import warnings\n",
+    "warnings.simplefilter(\"ignore\")\n",
+    "\n",
+    "# autoreload import your package\n",
+    "%load_ext autoreload\n",
+    "%autoreload 2"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "id": "4e09086b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.608751Z",
+     "start_time": "2022-11-23T06:59:41.320536Z"
+    },
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "from os.path import join\n",
+    "import math\n",
+    "import logging\n",
+    "from typing import Callable, Optional, Union, Dict, Tuple\n",
+    "\n",
+    "from matplotlib import pyplot as plt\n",
+    "from pathlib import Path\n",
+    "import matplotlib.colors as mcolors\n",
+    "\n",
+    "import gin\n",
+    "from fire import Fire\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch import optim\n",
+    "from torch import nn\n",
+    "\n",
+    "from experiments.base import Experiment\n",
+    "from data.datasets import ForecastDataset\n",
+    "from models import get_model\n",
+    "from utils.checkpoint import Checkpoint\n",
+    "from utils.ops import default_device, to_tensor\n",
+    "from utils.losses import get_loss_fn\n",
+    "from utils.metrics import calc_metrics\n",
+    "\n",
+    "from experiments.forecast import get_data\n",
+    "gin.enter_interactive_mode()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "id": "66d7f095",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.632689Z",
+     "start_time": "2022-11-23T06:59:42.610493Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "1"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import logging\n",
+    "logging.root.setLevel(logging.INFO)\n",
+    "\n",
+    "from loguru import logger\n",
+    "logger.remove()\n",
+    "logger.add(os.sys.stdout, level=\"INFO\", colorize=True, format=\"<level>{time} | {message}</level>\")"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "d4df5270",
+   "metadata": {},
+   "source": [
+    "# auto"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "id": "04499bef",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.652862Z",
+     "start_time": "2022-11-23T06:59:42.634056Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "def plot(model_name=\"deeptime\", save_path=Path(\"storage/experiments/Exchange/96M/repeat=0\"), i=200, title=None, plot=True):\n",
+    "\n",
+    "    gin.clear_config()\n",
+    "    gin.parse_config(open(save_path/\"config.gin\"))\n",
+    "\n",
+    "    train_set, train_loader = get_data(flag='train', batch_size=2)\n",
+    "\n",
+    "    model = get_model(model_name,\n",
+    "                        dim_size=train_set.data_x.shape[1],\n",
+    "                        datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
+    "    model.load_state_dict(torch.load(save_path/'model.pth'))\n",
+    "    model = model.eval()\n",
+    "\n",
+    "\n",
+    "    b = train_set[i]\n",
+    "    b = [bb[None, :] for bb in b]\n",
+    "    b2 = list(map(to_tensor, b))\n",
+    "    context_past_x, context_y, query_past_x, query_y, context_time, query_time = b2\n",
+    "    with torch.no_grad():\n",
+    "        forecast = model(*b2)\n",
+    "\n",
+    "    if title is None:\n",
+    "        title = str(save_path).split('/')[-3:]\n",
+    "        title = \"-\".join(title)\n",
+    "    \n",
+    "    colors = list(mcolors.BASE_COLORS.keys())\n",
+    "    l = x.shape[1]\n",
+    "    forecast2 = forecast[0].detach().cpu().numpy()\n",
+    "    x2 = x[0].cpu()\n",
+    "    y2 = y[0].cpu()\n",
+    "    l2 = y.shape[1]\n",
+    "    i_past = list(range(l))\n",
+    "    i_future = list(range(l, l+l2))\n",
+    "    \n",
+    "    if plot:\n",
+    "        plt.title(title)\n",
+    "        for i in range(x.shape[-1]):\n",
+    "            plt.plot(i_past, x2[:, i], c=colors[i])\n",
+    "        for i in range(x.shape[-1]):\n",
+    "            plt.plot(i_future, y2[:, i], c=colors[i])\n",
+    "        for i in range(x.shape[-1]):\n",
+    "            plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')\n",
+    "    return x2, y2, forecast2, i_past, i_future\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a51165b9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-22T13:28:35.849491Z",
+     "start_time": "2022-11-22T13:28:35.766453Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "id": "dd540a97",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.677217Z",
+     "start_time": "2022-11-23T06:59:42.654061Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "list(mcolors.BASE_COLORS.keys())"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "e8523787",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-22T13:38:15.786656Z",
+     "start_time": "2022-11-22T13:38:15.303577Z"
+    }
+   },
+   "source": [
+    "# view model"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "ccf70857",
+   "metadata": {},
+   "source": [
+    "# run exps"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "694f6ac4",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T03:31:08.583641Z",
+     "start_time": "2022-11-23T03:31:08.558690Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "id": "0b70f754",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.694227Z",
+     "start_time": "2022-11-23T06:59:42.678376Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# list the models we have run...\n",
+    "configs=sorted(Path(\"storage/experiments/Stocks\").glob(\"**/config.gin\"))\n",
+    "import random\n",
+    "random.shuffle(configs)\n",
+    "# print(configs)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "id": "c09e8335",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.709998Z",
+     "start_time": "2022-11-23T06:59:42.695396Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from experiments.forecast import ForecastExperiment\n",
+    "from tqdm.auto import tqdm"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "id": "a4eba154",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.727019Z",
+     "start_time": "2022-11-23T06:59:42.710918Z"
+    },
+    "scrolled": false
+   },
+   "outputs": [],
+   "source": [
+    "# for config in tqdm(configs):\n",
+    "#     save_path = config.parent\n",
+    "\n",
+    "#     exp = ForecastExperiment(config_path=config)\n",
+    "#     print(config)\n",
+    "#     try:\n",
+    "#         exp.run()\n",
+    "#     except KeyboardInterrupt:\n",
+    "#         raise\n",
+    "#     except Exception as e:\n",
+    "#         raise\n",
+    "#         print(e)\n",
+    "#         pass"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "id": "43aacbb6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.759654Z",
+     "start_time": "2022-11-23T06:59:42.728744Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# save_path = Path('storage/experiments/Stocks/96M2S/repeat=0')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "id": "dd2a96b6",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.776760Z",
+     "start_time": "2022-11-23T06:59:42.760840Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# gin.clear_config()\n",
+    "# config_path = save_path/\"config.gin\"\n",
+    "# gin.parse_config(open(config_path))\n",
+    "# model_name = gin.query_parameter(\"instance.model_type\")\n",
+    "# model_name"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "id": "61b1ac70",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.793638Z",
+     "start_time": "2022-11-23T06:59:42.777742Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "\n",
+    "# exp = ForecastExperiment(config_path=config_path)\n",
+    "# # exp.run()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "id": "629ea87a",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.810376Z",
+     "start_time": "2022-11-23T06:59:42.794672Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def save_path2name(save_path: Path) -> str:\n",
+    "    \"\"\"\n",
+    "    Path('storage/experiments/Stocks/96M2S/base_learner=None,inr=INR,encoder=mlp,repeat=0')\n",
+    "    to \n",
+    "    '96M2S-None_INR_mlp_0'\n",
+    "    \"\"\"\n",
+    "    mtitle = str(save_path).split('/')[-2:]\n",
+    "    tags = mtitle[-1]\n",
+    "    tags = [x.split('=')[-1] for x in tags.split(',')]\n",
+    "    mtitle[-1] = '_'.join(tags)\n",
+    "    mtitle = \"-\".join(mtitle)\n",
+    "    return mtitle\n",
+    "\n",
+    "# save_path2name(save_path)\n",
+    "# save_path"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "2269528b",
+   "metadata": {},
+   "source": [
+    "# view all"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "id": "768530be",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.831687Z",
+     "start_time": "2022-11-23T06:59:42.811250Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "from torchsummaryX import summary\n",
+    "from torchinfo import summary\n",
+    "\n",
+    "def plot_multi(save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True, verbose=1,):\n",
+    "    assert len(save_paths)>0\n",
+    "    for j in range(len(save_paths)):\n",
+    "        save_path = save_paths[j]\n",
+    "\n",
+    "        gin.clear_config()\n",
+    "        gin.parse_config(open(save_path/\"config.gin\"))\n",
+    "        model_name = gin.query_parameter(\"instance.model_type\")\n",
+    "\n",
+    "        train_set, train_loader = get_data(flag='test', batch_size=3)\n",
+    "        seq_len = train_set[0][0].shape[0]\n",
+    "        model = get_model(model_name,\n",
+    "                            dim_size=train_set.data_x.shape[1],\n",
+    "                          seq_len=seq_len,\n",
+    "                            datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
+    "        model.load_state_dict(torch.load(save_path/'model.pth'))\n",
+    "        model = model.eval()\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "\n",
+    "        b = train_set[i]\n",
+    "        b = [bb[None, :] for bb in b]\n",
+    "        b2 = list(map(to_tensor, b))\n",
+    "        \n",
+    "#         b = next(iter(train_loader))\n",
+    "#         print([s.shape for s in b]\n",
+    "        \n",
+    "        if verbose>1:\n",
+    "            print(summary(model, input_data=b2, depth=2))\n",
+    "            print(save_path)\n",
+    "        \n",
+    "        context_past_x, context_y, query_past_x, query_y, context_time, query_time = b2\n",
+    "        with torch.no_grad():\n",
+    "            forecast = model(*b2)\n",
+    "       \n",
+    "        colors = list(mcolors.BASE_COLORS.keys())\n",
+    "        l = context_time.shape[1]\n",
+    "        forecast2 = forecast[0].detach().cpu().numpy()\n",
+    "        x2 = context_y[0].cpu()\n",
+    "        y2 = query_y[0].cpu()\n",
+    "        l2 = query_time.shape[1]\n",
+    "        i_past = list(range(l))\n",
+    "        i_future = list(range(l, l+l2))\n",
+    "\n",
+    "        if plot:\n",
+    "        \n",
+    "            if j==0:\n",
+    "                plt.plot(i_past, x2[:, 0], c=colors[0], label=f\"past\")\n",
+    "                plt.plot(i_future, y2[:, 0], c=colors[0], label=\"future true\", alpha=0.3)\n",
+    "            mtitle = save_path2name(save_path)\n",
+    "            plt.plot(i_future, forecast2[:, 0], linestyle='--', label=f\"{mtitle}\") # c=colors[j], \n",
+    "            \n",
+    "\n",
+    "    plt.legend()\n",
+    "    plt.title(title)\n",
+    "    return x2, y2, forecast2, i_past, i_future\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "id": "739ee5e3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.853593Z",
+     "start_time": "2022-11-23T06:59:42.832746Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[Path('storage/experiments/Stocks/96M2S/lrn=None,inr=INR,enc=lstm2,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=None,inr=INRPlus2,enc=inception,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INR,enc=inception,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INR,enc=mlp,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INRPlus2,enc=inception,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INRPlus2,enc=lstm2,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Transformer,inr=INR,enc=inception,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Transformer,inr=INR,enc=lstm2,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Transformer,inr=INRPlus2,enc=none,repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96M2S/lrn=Transformer,inr=INRPlus2,enc=transformer,repeat=0/_SUCCESS')]\n"
+     ]
+    }
+   ],
+   "source": [
+    "# list the models we have run...\n",
+    "m=sorted(Path(\"storage/experiments/Stocks/96M2S\").glob(\"**/_SUCCESS\"))\n",
+    "print(m)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "id": "19fe898b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.870036Z",
+     "start_time": "2022-11-23T06:59:42.854590Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "mape=3.13, 96M2S-None_INR_lstm2_0\n",
+      "mape=2.06, 96M2S-None_INRPlus2_inception_0\n",
+      "mape=1.41, 96M2S-Ridge_INR_inception_0\n",
+      "mape=1.83, 96M2S-Ridge_INR_mlp_0\n",
+      "mape=0.86, 96M2S-Ridge_INRPlus2_inception_0\n",
+      "mape=0.84, 96M2S-Ridge_INRPlus2_lstm2_0\n",
+      "mape=1.76, 96M2S-Transformer_INR_inception_0\n",
+      "mape=2.39, 96M2S-Transformer_INR_lstm2_0\n",
+      "mape=1.89, 96M2S-Transformer_INRPlus2_none_0\n",
+      "mape=1.94, 96M2S-Transformer_INRPlus2_transformer_0\n"
+     ]
+    }
+   ],
+   "source": [
+    "# show score\n",
+    "for mm in m:\n",
+    "    mtitle = save_path2name(mm.parent)\n",
+    "#     print(mtitle)\n",
+    "    m3 = json.load((mm.parent/'metrics.json').open())\n",
+    "    m3 = eval(str(m3))\n",
+    "    print(f\"mape={m3['val']['mape']:2.2f}, {mtitle}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "id": "27c162b3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.966712Z",
+     "start_time": "2022-11-23T06:59:42.870879Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ZeroDivisionError",
+     "evalue": "division by zero",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mZeroDivisionError\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [16], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;241;43m1\u001b[39;49m\u001b[38;5;241;43m/\u001b[39;49m\u001b[38;5;241;43m0\u001b[39;49m\n",
+      "\u001b[0;31mZeroDivisionError\u001b[0m: division by zero"
+     ]
+    }
+   ],
+   "source": [
+    "1/0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2c141512",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.967702Z",
+     "start_time": "2022-11-23T06:59:42.967694Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "# show size\n",
+    "save_paths = [mm.parent for mm in m]\n",
+    "for mm in m:\n",
+    "    try:\n",
+    "        plot_multi(\n",
+    "            save_paths=[mm.parent],\n",
+    "            i=600,\n",
+    "            verbose=2,\n",
+    "        )\n",
+    "    except Exception as e:\n",
+    "        print('failed', mm, e)\n",
+    "#         mm.unlink()\n",
+    "        pass\n",
+    "1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "115467ad",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.968371Z",
+     "start_time": "2022-11-23T06:59:42.968363Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "save_paths = [mm.parent for mm in m]\n",
+    "plot_multi(\n",
+    "    save_paths=save_paths,\n",
+    "    i=200,\n",
+    "    verbose=0,\n",
+    ")\n",
+    "1"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "985a9f4e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:59:42.969045Z",
+     "start_time": "2022-11-23T06:59:42.969037Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "256/24"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "36cba856",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-22T13:17:31.585029Z",
+     "start_time": "2022-11-22T13:17:31.528806Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b85499d6",
+   "metadata": {},
+   "source": [
+    "# check positions in dl"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 83,
+   "id": "497e8e53",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:37:25.264009Z",
+     "start_time": "2022-11-23T07:37:24.745744Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "[(138, 5), (46, 1), (138, 5), (46, 1), (46, 0), (46, 0)]\n"
+     ]
+    }
+   ],
+   "source": [
+    "train_set, train_loader = get_data(flag='test', batch_size=3)\n",
+    "b = context_past_x, context_y, query_past_x, query_y, context_time, query_time = train_set[0]\n",
+    "print([bb.shape for bb in b])\n",
+    "# context_y, query_y"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 84,
+   "id": "277d7ea7",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:37:25.280780Z",
+     "start_time": "2022-11-23T07:37:25.265210Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(0, 138, 138, 184, 46, 184, 184, 230)"
+      ]
+     },
+     "execution_count": 84,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "cx_start, cx_end, c_start, c_end, qx_start, qx_end, q_start, q_end = train_set.get_inds(0)\n",
+    "cx_start, cx_end, c_start, c_end, qx_start, qx_end, q_start, q_end"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "2ce2ccc1",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:38:51.088685Z",
+     "start_time": "2022-11-23T07:38:51.072932Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 89,
+   "id": "e45f5116",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:38:02.653780Z",
+     "start_time": "2022-11-23T07:38:02.634798Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<function matplotlib.pyplot.vlines(x, ymin, ymax, colors=None, linestyles='solid', label='', *, data=None, **kwargs)>"
+      ]
+     },
+     "execution_count": 89,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "plt.vlines"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 94,
+   "id": "d9180330",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:38:38.998210Z",
+     "start_time": "2022-11-23T07:38:38.886078Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.legend.Legend at 0x7fd91c18e7c0>"
+      ]
+     },
+     "execution_count": 94,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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\n",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plt.hlines(1, cx_start, cx_end, color='green', alpha=0.5, label='context_past_x')\n",
+    "plt.hlines(2, c_start, c_end, color='green', label='context_labels')\n",
+    "plt.hlines(3, qx_start, qx_end, alpha=0.5, label='query_past_x')\n",
+    "plt.hlines(4, q_start, q_end, label='query_labels/target')\n",
+    "o = train_set.horizon_len+train_set.lookback_len\n",
+    "plt.vlines(o, *plt.ylim(), alpha=0.3, ls='--', label='now')\n",
+    "plt.legend(loc='upper left')"
+   ]
+  },
+  {
+   "cell_type": "markdown",
+   "id": "b4a8e127",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:40:12.172290Z",
+     "start_time": "2022-11-23T06:40:12.157848Z"
+    }
+   },
+   "source": [
+    "# backtest"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "40832cab",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T06:57:00.483250Z",
+     "start_time": "2022-11-23T06:57:00.467819Z"
+    }
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "id": "8420bbb3",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:25:09.987756Z",
+     "start_time": "2022-11-23T07:25:09.925063Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "save_path = Path('storage/experiments/Stocks/96M2S/lrn=Ridge,inr=INRPlus2,enc=lstm2,repeat=0')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "id": "038f5c6b",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:26:24.445511Z",
+     "start_time": "2022-11-23T07:26:24.051121Z"
+    }
+   },
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "receptive field [690 378 242]\n"
+     ]
+    }
+   ],
+   "source": [
+    "gin.clear_config()\n",
+    "gin.parse_config(open(save_path/\"config.gin\"))\n",
+    "model_name = gin.query_parameter(\"instance.model_type\")\n",
+    "\n",
+    "train_set, train_loader = get_data(flag='train', batch_size=256)\n",
+    "test_set, test_loader = get_data(flag='test', batch_size=256)\n",
+    "seq_len = train_set[0][0].shape[0]\n",
+    "model = get_model(model_name,\n",
+    "                    dim_size=train_set.data_x.shape[1],\n",
+    "                  seq_len=seq_len,\n",
+    "                    datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
+    "model.load_state_dict(torch.load(save_path/'model.pth'))\n",
+    "model = model.eval()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 117,
+   "id": "57ca933d",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:44:19.083082Z",
+     "start_time": "2022-11-23T07:44:18.996891Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "42765    2020-09-01 16:10:00\n",
+       "42766    2020-09-01 16:20:00\n",
+       "42767    2020-09-01 16:35:00\n",
+       "42768    2020-09-01 16:40:00\n",
+       "42769    2020-09-01 16:45:00\n",
+       "                ...         \n",
+       "53256    2020-12-30 15:30:00\n",
+       "53257    2020-12-30 15:35:00\n",
+       "53258    2020-12-30 15:40:00\n",
+       "53259    2020-12-30 15:45:00\n",
+       "53260    2020-12-30 15:50:00\n",
+       "Name: date, Length: 10496, dtype: object"
+      ]
+     },
+     "execution_count": 117,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "test_set, test_loader = get_data(flag='test', batch_size=256)\n",
+    "test_set.index"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 118,
+   "id": "119180c2",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:44:20.752575Z",
+     "start_time": "2022-11-23T07:44:20.736233Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "10496"
+      ]
+     },
+     "execution_count": 118,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "id": "7c66089e",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:30:04.667930Z",
+     "start_time": "2022-11-23T07:30:04.649627Z"
+    }
+   },
+   "outputs": [],
+   "source": [
+    "def pred(test_loader, model):\n",
+    "    isinstance(test_loader.sampler, torch.utils.data.sampler.SequentialSampler)\n",
+    "    forecasts = []\n",
+    "    with torch.no_grad():\n",
+    "        for it, data in enumerate(tqdm(test_loader)):\n",
+    "            data2 = list(map(to_tensor, data))\n",
+    "            context_past_x, context_y, query_past_x, query_y, context_time, query_time  = data2\n",
+    "\n",
+    "            if context_past_x.shape[0] == 1:\n",
+    "                # skip final batch if batch_size == 1\n",
+    "                # due to bug in torch.linalg.solve which raises error when batch_size == 1\n",
+    "                continue\n",
+    "\n",
+    "            forecast = model(*data2)\n",
+    "            forecasts.append(forecast.cpu().numpy())\n",
+    "    return np.concatenate(forecasts, 0)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 131,
+   "id": "de7e44bd",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:45:51.816185Z",
+     "start_time": "2022-11-23T07:45:51.719101Z"
+    }
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "date\n",
+       "2020-09-01 16:10:00    12.480\n",
+       "2020-09-01 16:20:00    12.480\n",
+       "2020-09-01 16:35:00    12.500\n",
+       "2020-09-01 16:40:00    12.500\n",
+       "2020-09-01 16:45:00    12.500\n",
+       "                        ...  \n",
+       "2020-12-30 15:30:00    17.845\n",
+       "2020-12-30 15:35:00    17.875\n",
+       "2020-12-30 15:40:00    17.840\n",
+       "2020-12-30 15:45:00    17.800\n",
+       "2020-12-30 15:50:00    17.895\n",
+       "Name: close, Length: 10496, dtype: float64"
+      ]
+     },
+     "execution_count": 131,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import pandas as pd\n",
+    "df = pd.read_csv(Path('storage/datasets')/test_loader.dataset.data_path).set_index('date')\n",
+    "close = df.close.reindex(test_set.index)\n",
+    "close"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 133,
+   "id": "7fa14114",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:46:09.418984Z",
+     "start_time": "2022-11-23T07:46:04.568065Z"
+    },
+    "scrolled": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "application/vnd.jupyter.widget-view+json": {
+       "model_id": "6ca272f0e6964bd8b5bb099bee202c73",
+       "version_major": 2,
+       "version_minor": 0
+      },
+      "text/plain": [
+       "  0%|          | 0/41 [00:00<?, ?it/s]"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "text/plain": [
+       "array([[-0.6328174 , -0.63319993, -0.6346327 , ..., -0.5592192 ,\n",
+       "        -0.55776495, -0.5553882 ],\n",
+       "       [-0.63254887, -0.6316724 , -0.6318742 , ..., -0.55676687,\n",
+       "        -0.55493146, -0.55278534],\n",
+       "       [-0.6345463 , -0.6336551 , -0.6358172 , ..., -0.54483443,\n",
+       "        -0.54275066, -0.54060847],\n",
+       "       ...,\n",
+       "       [ 0.5262324 ,  0.5326949 ,  0.5357634 , ...,  0.6983865 ,\n",
+       "         0.7018202 ,  0.70224243],\n",
+       "       [ 0.53653884,  0.54718244,  0.5533827 , ...,  0.7077196 ,\n",
+       "         0.7094669 ,  0.7116887 ],\n",
+       "       [ 0.539682  ,  0.5489382 ,  0.5567719 , ...,  0.6913836 ,\n",
+       "         0.6931053 ,  0.6938973 ]], dtype=float32)"
+      ]
+     },
+     "execution_count": 133,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "y_pred = pred(test_loader, model)[:, :, 0]\n",
+    "y_pred"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 135,
+   "id": "53f1ace9",
+   "metadata": {
+    "ExecuteTime": {
+     "end_time": "2022-11-23T07:46:43.304700Z",
+     "start_time": "2022-11-23T07:46:43.273350Z"
+    }
+   },
+   "outputs": [
+    {
+     "ename": "ModuleNotFoundError",
+     "evalue": "No module named 'vectorbt'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m                       Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn [135], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[38;5;28;01mimport\u001b[39;00m \u001b[38;5;21;01mvectorbt\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m \u001b[38;5;21;01mvbt\u001b[39;00m\n\u001b[1;32m      2\u001b[0m pf \u001b[38;5;241m=\u001b[39m vbt\u001b[38;5;241m.\u001b[39mPortfolio\u001b[38;5;241m.\u001b[39mfrom_signals(\n\u001b[1;32m      3\u001b[0m     close,\n\u001b[1;32m      4\u001b[0m     entries\u001b[38;5;241m=\u001b[39mentries,\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m     10\u001b[0m     size\u001b[38;5;241m=\u001b[39msize \u001b[38;5;66;03m# size=1 and short leas to inf's\u001b[39;00m\n\u001b[1;32m     11\u001b[0m )\n",
+      "\u001b[0;31mModuleNotFoundError\u001b[0m: No module named 'vectorbt'"
+     ]
+    }
+   ],
+   "source": [
+    "import vectorbt as vbt\n",
+    "pf = vbt.Portfolio.from_signals(\n",
+    "    close,\n",
+    "    entries=entries,\n",
+    "    exits=exits,\n",
+    "    short_entries=short_entries,\n",
+    "    short_exits=short_exits,\n",
+    "    fees=fees,\n",
+    "    freq=\"5T\",\n",
+    "    size=size # size=1 and short leas to inf's\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "58a4e2bd",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "a3dba8a8",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "deeptime",
+   "language": "python",
+   "name": "deeptime"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.8.13"
+  },
+  "toc": {
+   "base_numbering": 1,
+   "nav_menu": {},
+   "number_sections": true,
+   "sideBar": true,
+   "skip_h1_title": false,
+   "title_cell": "Table of Contents",
+   "title_sidebar": "Contents",
+   "toc_cell": false,
+   "toc_position": {},
+   "toc_section_display": true,
+   "toc_window_display": false
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/data/datasets.py b/data/datasets.py
index f223adc..ff20ef7 100644
--- a/data/datasets.py
+++ b/data/datasets.py
@@ -100,14 +100,19 @@ class ForecastDataset(Dataset):
             data = df_data.values
 
         # is will be our past data, including the y col
-        self.data_x = data[border1:border2] 
+        self.data_x = data[border1:border2]
+        
+        self.dates = df_raw['date'][border1:border2]
         # y is just the col we predict
         self.data_y = data[border1:border2][:, [-1]]
-        self.timestamps = get_time_features(pd.to_datetime(df_raw.date[border1:border2].values),
+        self.timestamps = get_time_features(pd.to_datetime(self.dates.values),
                                             normalise=self.normalise_time_features,
                                             features=self.time_features)
         self.n_time = len(self.data_x)
         self.n_time_samples = self.n_time - self.lookback_len * 2 - self.horizon_len + 1 + self.gap
+        
+        o = self.horizon_len + self.lookback_len
+        self.index = self.dates.iloc[o:].iloc[:self.n_time_samples]
 
     def get_borders(self, df_raw: pd.DataFrame) -> Tuple[List[int], List[int], List[int], List[int]]:
         set_type = {'train': 0, 'val': 1, 'test': 2}[self.flag]
@@ -133,6 +138,9 @@ class ForecastDataset(Dataset):
         return self.n_time_samples
     
     def get_inds(self, idx):
+        
+        
+        
         cx_start = idx
         cx_end = cx_start + self.lookback_len
         c_start = cx_end + self.gap
@@ -144,6 +152,20 @@ class ForecastDataset(Dataset):
         qx_end = q_start
         qx_start = qx_end - self.lookback_len
         
+        ####
+        
+        # q_start = idx
+        # q_end = q_start + self.horizon_len
+        
+        # qx_end = q_start
+        # qx_start = qx_end - self.lookback_len
+        
+        # c_end = q_start - self.gap
+        # c_start = c_end - self.horizon_len
+        
+        # cx_end = c_start - self.gap
+        # cx_start = cx_end - self.lookback_len
+        
         return cx_start, cx_end, c_start, c_end, qx_start, qx_end, q_start, q_end
 
     def __getitem__(self, idx: int) -> Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray]:
diff --git a/experiments/configs/Stocks/96M2S.gin b/experiments/configs/Stocks/96M2S.gin
index 2e8cefe..0c2bb11 100644
--- a/experiments/configs/Stocks/96M2S.gin
+++ b/experiments/configs/Stocks/96M2S.gin
@@ -24,7 +24,7 @@ Checkpoint.patience = 7
 deeptime3.layer_size = 32
 deeptime3.inr_layers = 5
 deeptime3.dropout = 0.3
-deeptime3.base_learner = 'Ridge'
+deeptime3.lrn = 'Ridge'
 deeptime3.n_fourier_feats = 2048
 deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
 
diff --git a/experiments/configs/hp_search/Stocks.gin b/experiments/configs/hp_search/Stocks.gin
index 43c8610..59540c7 100644
--- a/experiments/configs/hp_search/Stocks.gin
+++ b/experiments/configs/hp_search/Stocks.gin
@@ -5,9 +5,9 @@ build.variables_dict = {
     # 'ForecastDataset.lookback_mult': [1, 3, 5, 7, 9],
     # 'ForecastDataset.horizon_len': [6, 12, 24, 48, 96, 192, 336, 720],
     # 'ForecastDataset.features': ['m', 'h', 'd'],
-    'deeptime3.base_learner': ['Ridge', 'None', 'Transformer'],
+    'deeptime3.lrn': ['Ridge', 'None', 'Transformer'],
     'deeptime3.inr': ['INR', 'INRPlus2'],
-    'deeptime3.encoder': ['inception', 'lstm', 'mlp', 'lstm2', 'transformer', 'transformer2', 'none'],
+    'deeptime3.enc': ['inception', 'lstm', 'mlp', 'lstm2', 'transformer', 'transformer2', 'none'],
     # 'deeptime3.dropout': [0.0, 0.1, 0.3, 0.5,],
 }
 
@@ -31,7 +31,7 @@ Checkpoint.patience = 7
 deeptime3.layer_size = 256
 deeptime3.inr_layers = 5
 deeptime3.dropout = 0.1
-deeptime3.base_learner = 'Ridge'
+deeptime3.lrn = 'Ridge'
 deeptime3.n_fourier_feats = 4096
 deeptime3.scales = [0.01, 0.1, 1, 5, 10, 20, 50, 100]
 
diff --git a/experiments/forecast.py b/experiments/forecast.py
index 04ca761..cba669a 100644
--- a/experiments/forecast.py
+++ b/experiments/forecast.py
@@ -34,7 +34,7 @@ class ForecastExperiment(Experiment):
         test_set, test_loader = get_data(flag='test')
 
         dim_size=train_set.data_x.shape[1]
-        seq_len = train_set[0][1].shape[0]
+        seq_len = train_set[0][0].shape[0]
         model = get_model(model_type,
                           dim_size=dim_size,
                           seq_len=seq_len,
@@ -51,7 +51,7 @@ class ForecastExperiment(Experiment):
         metrics = {'val': val_metrics, 'test': test_metrics}
         # np.save(join(self.root, 'metrics.npy'), {'val': val_metrics, 'test': test_metrics})
         metrics = serialize(metrics)
-        json.dump(metrics, open(join(self.root, 'metrics.npy'), 'w'))
+        json.dump(metrics, open(join(self.root, 'metrics.json'), 'w'))
 
         val_metrics = {f'ValMetric/{k}': v for k, v in val_metrics.items()}
         test_metrics = {f'TestMetric/{k}': v for k, v in test_metrics.items()}
diff --git a/mjc_notes.md b/mjc_notes.md
index 9586ffa..43afc9e 100644
--- a/mjc_notes.md
+++ b/mjc_notes.md
@@ -71,8 +71,8 @@ TODO:
 
 - [x] M2S mode
 - [ ] add other INR's
-- [ ] add None as learner
-- [ ] no encoder?
+- [ ] add None as lrn
+- [ ] no enc?
 
 ```
 python -m experiments.forecast --config_path=experiments/configs/hp_search/Stocks.gin build_experiment
diff --git a/models/DeepTIMe3.py b/models/DeepTIMe3.py
index abaad51..7d99755 100644
--- a/models/DeepTIMe3.py
+++ b/models/DeepTIMe3.py
@@ -20,38 +20,38 @@ from models.modules.encoders import LSTMEncoder, TransformerEncoder2, Transforme
 # from models.modules.regressors import RidgeRegressor
 
 @gin.configurable()
-def deeptime3(dim_size:int, datetime_feats: int, layer_size: int, inr_layers: int, n_fourier_feats: int, scales: float, dropout: float, base_learner: str, encoder:str, inr: str, seq_len: int):
-    return DeepTIMe3(dim_size, datetime_feats, layer_size, inr_layers, n_fourier_feats, scales, dropout, base_learner, encoder, inr, seq_len)
+def deeptime3(dim_size:int, datetime_feats: int, layer_size: int, inr_layers: int, n_fourier_feats: int, scales: float, dropout: float, lrn: str, enc:str, inr: str, seq_len: int):
+    return DeepTIMe3(dim_size, datetime_feats, layer_size, inr_layers, n_fourier_feats, scales, dropout, lrn, enc, inr, seq_len)
 
 
 class DeepTIMe3(nn.Module):
-    def __init__(self, dim_size: int, datetime_feats: int, layer_size: int, inr_layers: int, n_fourier_feats: int, scales: float, dropout: float=0.3, base_learner:str='Ridge', encoder:str='inception', inr:str='INR', seq_len: int=46):
+    def __init__(self, dim_size: int, datetime_feats: int, layer_size: int, inr_layers: int, n_fourier_feats: int, scales: float, dropout: float=0.3, lrn:str='Ridge', enc:str='inception', inr:str='INR', seq_len: int=46):
         super().__init__()
         
         # encode the past
         encoded_size = layer_size
         encoder_features = 24
         encoder_layers = 3
-        if encoder == 'inception':
-            self.encoder = InceptionEncoder(
+        if enc == 'inception':
+            self.enc = InceptionEncoder(
                 c_in=dim_size, c_out=encoded_size, dilation=6,
                 layer_size=17, layers=encoder_layers, dropout=dropout,
             )
-        elif encoder == 'lstm':
-            self.encoder = LSTMEncoder(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=24)
-        elif encoder == 'lstm2':
-            self.encoder = LSTMEncoder2(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=32, seq_len=seq_len)
-        elif encoder == 'mlp':
-            self.encoder = MLPEncoder(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=256)
-        elif encoder == 'transformer':
-            self.encoder = TransformerEncoder(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=256, seq_len=seq_len)
-        elif encoder == 'transformer2':
-            self.encoder = TransformerEncoder2(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=256, seq_len=seq_len)
-        elif encoder == 'none':
-            self.encoder = None
+        elif enc == 'lstm':
+            self.enc = LSTMEncoder(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=24)
+        elif enc == 'lstm2':
+            self.enc = LSTMEncoder2(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=32, seq_len=seq_len)
+        elif enc == 'mlp':
+            self.enc = MLPEncoder(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=256)
+        elif enc == 'transformer':
+            self.enc = TransformerEncoder(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=256, seq_len=seq_len)
+        elif enc == 'transformer2':
+            self.enc = TransformerEncoder2(c_in=dim_size, c_out=encoded_size, dropout=dropout, layers=encoder_layers, layer_size=128, seq_len=seq_len)
+        elif enc == 'none':
+            self.enc = None
             encoded_size = 0
         else:
-            raise NotADirectoryError(encoder)
+            raise NotADirectoryError(enc)
         
         # translate coords to a representation, given a summary of the past
         coord_size = 1
@@ -66,7 +66,7 @@ class DeepTIMe3(nn.Module):
             raise NotImplementedError(inr)
         
         # meta learn y given a representation
-        self.regressionhead = RegressionHead(base_learner=base_learner, d=layer_size, dropout=dropout)
+        self.regressionhead = RegressionHead(lrn=lrn, d=layer_size, dropout=dropout)
 
         self.datetime_feats = datetime_feats
         self.inr_layers = inr_layers
@@ -83,8 +83,8 @@ class DeepTIMe3(nn.Module):
         
         # we summarize the past into a single hidden layer. Then repeat it for each coordinate
         past_len = time.shape[1]
-        if self.encoder is not None:
-            encoded_x = self.encoder(past_x)
+        if self.enc is not None:
+            encoded_x = self.enc(past_x)
             encoded_x = repeat(encoded_x, "b f -> b t f", t=past_len)
         
 
@@ -93,7 +93,7 @@ class DeepTIMe3(nn.Module):
         coords = repeat(coords, "1 t 1 -> b t 1", b=time.shape[0])
         
         # combine and run INR to decode the representation
-        if self.encoder is not None:
+        if self.enc is not None:
             context_input = torch.cat([encoded_x, coords, time], dim=-1)
         else:
             context_input = torch.cat([coords, time], dim=-1)
diff --git a/models/modules/causalinception.py b/models/modules/causalinception.py
index 4f7681e..28fe90e 100644
--- a/models/modules/causalinception.py
+++ b/models/modules/causalinception.py
@@ -126,7 +126,8 @@ class CausalInceptionTimePlus(nn.Sequential):
         dilations = np.array([max(1, d*dilation) for d in range(depth)])
         d=np.array([dilations**i for i in range(3)]).T
         rf = ((ks-1)*d).sum(0)
-        print(f"receptive field {rf}={ks-1}*{d}")
+        # print(f"receptive field {rf}={ks-1}*{d}")
+        print(f"receptive field {rf}")
 
     def create_head(self, nf, c_out, seq_len, flatten=False, concat_pool=False, fc_dropout=0., bn=False, y_range=None):
         if flatten:
diff --git a/models/modules/encoders.py b/models/modules/encoders.py
index 59e8416..12d4e72 100644
--- a/models/modules/encoders.py
+++ b/models/modules/encoders.py
@@ -133,14 +133,14 @@ class TransformerEncoder2(nn.Module):
         super().__init__()
         # d_model (82) must be divisible by n_heads (4)
         layer_size = layer_size // n_heads * n_heads
-        d_model = layer_size // 2 
+        d_model = layer_size // 4 
         self.net = TSPerceiver(
             c_in=c_in,
             c_out=c_out,
             seq_len=seq_len,
             
             # cat_szs=0, n_cont=0, 
-            n_latents=layer_size, d_latent=layer_size//4, 
+            n_latents=layer_size, d_latent=d_model, 
             # d_context=None, 
             self_per_cross_attn=1,
                 #  share_weights=True, cross_n_heads=1, d_head=None, 
@@ -229,7 +229,7 @@ class LSTMEncoder2(nn.Module):
             depth=layers,
             lstm_dropout=conv_dropout,
             fc_dropout=dropout,
-            pre_norm=False, use_token=True, use_pe=True, 
+            pre_norm=False, use_token=False, use_pe=False, 
             use_bn=False, 
         )
 
diff --git a/models/modules/metareghead.py b/models/modules/metareghead.py
index 46a5882..5a045f0 100644
--- a/models/modules/metareghead.py
+++ b/models/modules/metareghead.py
@@ -63,17 +63,17 @@ class TransformerHead(nn.Module):
 
 
 class RegressionHead(nn.Module):
-    def __init__(self, base_learner='Ridge', d=512, enable_scale=True, dropout=0.1, num_heads=16):
+    def __init__(self, lrn='Ridge', d=512, enable_scale=True, dropout=0.1, num_heads=16):
         super().__init__()
-        if ('Ridge' in base_learner):
+        if ('Ridge' in lrn):
             # the regular DeepTime one
             self.head = RidgeRegressor()
-        elif ("None" in base_learner):
+        elif ("None" in lrn):
             self.head = SumHead(d=d, dropout=dropout)
-        elif ("Transformer" in base_learner):
+        elif ("Transformer" in lrn):
             self.head = TransformerHead(d=d, dropout=dropout, num_heads=num_heads)
         else:
-            raise NotImplementedError(base_learner)
+            raise NotImplementedError(lrn)
         
         # Add a learnable scale
         self.enable_scale = enable_scale
diff --git a/requirements/doc_reqs.sh b/requirements/doc_reqs.sh
old mode 100644
new mode 100755
diff --git a/requirements/environment.max.yaml b/requirements/environment.max.yaml
index 161ca70..1cd2aac 100644
--- a/requirements/environment.max.yaml
+++ b/requirements/environment.max.yaml
@@ -149,9 +149,11 @@ dependencies:
     - grpcio==1.50.0
     - imbalanced-learn==0.9.1
     - joblib==1.2.0
+    - json-tricks==3.16.1
     - kiwisolver==1.4.4
     - langcodes==3.3.0
     - llvmlite==0.39.1
+    - loguru==0.6.0
     - markdown==3.4.1
     - matplotlib==3.6.2
     - murmurhash==1.0.9
@@ -185,6 +187,8 @@ dependencies:
     - threadpoolctl==3.1.0
     - torch==1.10.0+cu113
     - torchaudio==0.10.0+cu113
+    - torchinfo==1.7.1
+    - torchsummaryx==1.3.0
     - torchvision==0.11.1+cu113
     - tqdm==4.64.1
     - tsai==0.3.4
diff --git a/requirements/pip.conda.txt b/requirements/pip.conda.txt
index 0c2524d..e522ca5 100644
--- a/requirements/pip.conda.txt
+++ b/requirements/pip.conda.txt
@@ -55,6 +55,7 @@ jedi @ file:///home/conda/feedstock_root/build_artifacts/jedi_1659959867326/work
 jeepney @ file:///home/conda/feedstock_root/build_artifacts/jeepney_1649085214306/work
 Jinja2 @ file:///home/conda/feedstock_root/build_artifacts/jinja2_1654302431367/work
 joblib==1.2.0
+json-tricks==3.16.1
 jsonschema @ file:///home/conda/feedstock_root/build_artifacts/jsonschema-meta_1667361745641/work
 jupyter_client @ file:///home/conda/feedstock_root/build_artifacts/jupyter_client_1668623095912/work
 jupyter_core @ file:///home/conda/feedstock_root/build_artifacts/jupyter_core_1668030817979/work
@@ -63,6 +64,7 @@ keyring @ file:///home/conda/feedstock_root/build_artifacts/keyring_166769673355
 kiwisolver==1.4.4
 langcodes==3.3.0
 llvmlite==0.39.1
+loguru==0.6.0
 Markdown==3.4.1
 MarkupSafe @ file:///home/conda/feedstock_root/build_artifacts/markupsafe_1666770195345/work
 matplotlib==3.6.2
@@ -134,6 +136,8 @@ tomlkit @ file:///home/conda/feedstock_root/build_artifacts/tomlkit_166686418860
 toolz @ file:///home/conda/feedstock_root/build_artifacts/toolz_1657485559105/work
 torch==1.10.0+cu113
 torchaudio==0.10.0+cu113
+torchinfo==1.7.1
+torchsummaryX==1.3.0
 torchvision==0.11.1+cu113
 tornado @ file:///home/conda/feedstock_root/build_artifacts/tornado_1666788592778/work
 tqdm==4.64.1
diff --git a/scratch-run_exp.ipynb b/scratch-run_exp.ipynb
deleted file mode 100644
index bb7fa66..0000000
--- a/scratch-run_exp.ipynb
+++ /dev/null
@@ -1,778 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b1e031e3",
-   "metadata": {},
-   "source": [
-    "- [x] try just one predictor\n",
-    "    - [ ] multi input, single output\n",
-    "- [x] comparem ulti\n",
-    "- losses:\n",
-    "    - try logp? nah\n",
-    "    - mae?\n",
-    "- [x] make my own csv with 5m data (maybe 10k rows)\n",
-    "- [ ] backtest?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7f9e3d73",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T05:42:46.869262Z",
-     "start_time": "2022-11-23T05:42:46.859832Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.simplefilter(\"ignore\")\n",
-    "\n",
-    "# autoreload import your package\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4e09086b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T05:42:48.073268Z",
-     "start_time": "2022-11-23T05:42:46.870484Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from os.path import join\n",
-    "import math\n",
-    "import logging\n",
-    "from typing import Callable, Optional, Union, Dict, Tuple\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from pathlib import Path\n",
-    "import matplotlib.colors as mcolors\n",
-    "\n",
-    "import gin\n",
-    "from fire import Fire\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader\n",
-    "from torch import optim\n",
-    "from torch import nn\n",
-    "\n",
-    "from experiments.base import Experiment\n",
-    "from data.datasets import ForecastDataset\n",
-    "from models import get_model\n",
-    "from utils.checkpoint import Checkpoint\n",
-    "from utils.ops import default_device, to_tensor\n",
-    "from utils.losses import get_loss_fn\n",
-    "from utils.metrics import calc_metrics\n",
-    "\n",
-    "from experiments.forecast import get_data\n",
-    "gin.enter_interactive_mode()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "66d7f095",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T05:42:48.097114Z",
-     "start_time": "2022-11-23T05:42:48.074670Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 3,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "import logging\n",
-    "logging.root.setLevel(logging.INFO)\n",
-    "\n",
-    "from loguru import logger\n",
-    "logger.remove()\n",
-    "logger.add(os.sys.stdout, level=\"INFO\", colorize=True, format=\"<level>{time} | {message}</level>\")"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4df5270",
-   "metadata": {},
-   "source": [
-    "# auto"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "04499bef",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T05:42:48.115157Z",
-     "start_time": "2022-11-23T05:42:48.098029Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot(model_name=\"deeptime\", save_path=Path(\"storage/experiments/Exchange/96M/repeat=0\"), i=200, title=None, plot=True):\n",
-    "\n",
-    "    gin.clear_config()\n",
-    "    gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "\n",
-    "    train_set, train_loader = get_data(flag='train', batch_size=2)\n",
-    "\n",
-    "    model = get_model(model_name,\n",
-    "                        dim_size=train_set.data_x.shape[1],\n",
-    "                        datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "    model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "    model = model.eval()\n",
-    "\n",
-    "\n",
-    "    b = train_set[i]\n",
-    "    b = [bb[None, :] for bb in b]\n",
-    "    b2 = list(map(to_tensor, b))\n",
-    "    context_past_x, context_y, query_past_x, query_y, context_time, query_time = b2\n",
-    "    with torch.no_grad():\n",
-    "        forecast = model(*b2)\n",
-    "\n",
-    "    if title is None:\n",
-    "        title = str(save_path).split('/')[-3:]\n",
-    "        title = \"-\".join(title)\n",
-    "    \n",
-    "    colors = list(mcolors.BASE_COLORS.keys())\n",
-    "    l = x.shape[1]\n",
-    "    forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "    x2 = x[0].cpu()\n",
-    "    y2 = y[0].cpu()\n",
-    "    l2 = y.shape[1]\n",
-    "    i_past = list(range(l))\n",
-    "    i_future = list(range(l, l+l2))\n",
-    "    \n",
-    "    if plot:\n",
-    "        plt.title(title)\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_past, x2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, y2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "6615bb09",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T13:28:35.849491Z",
-     "start_time": "2022-11-22T13:28:35.766453Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "ec47ae46",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T05:42:48.138242Z",
-     "start_time": "2022-11-23T05:42:48.115973Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "['b', 'g', 'r', 'c', 'm', 'y', 'k', 'w']"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "list(mcolors.BASE_COLORS.keys())"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "0a298ce2",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T13:38:15.786656Z",
-     "start_time": "2022-11-22T13:38:15.303577Z"
-    }
-   },
-   "source": [
-    "# view model"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "40002390",
-   "metadata": {},
-   "source": [
-    "# run exps"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "2e1e58e7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T03:31:08.583641Z",
-     "start_time": "2022-11-23T03:31:08.558690Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "91b92e80",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T05:42:48.154921Z",
-     "start_time": "2022-11-23T05:42:48.139190Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# list the models we have run...\n",
-    "configs=sorted(Path(\"storage/experiments/Stocks\").glob(\"**/config.gin\"))\n",
-    "import random\n",
-    "random.shuffle(configs)\n",
-    "# print(configs)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "id": "8bb001f7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-23T05:42:48.171079Z",
-     "start_time": "2022-11-23T05:42:48.155764Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from experiments.forecast import ForecastExperiment\n",
-    "from tqdm.auto import tqdm"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "029641dd",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.859Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [
-    {
-     "data": {
-      "application/vnd.jupyter.widget-view+json": {
-       "model_id": "49eccaa5eb6942e29ed4943d5b92cf89",
-       "version_major": 2,
-       "version_minor": 0
-      },
-      "text/plain": [
-       "  0%|          | 0/42 [00:00<?, ?it/s]"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    },
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "storage/experiments/Stocks/96M2S/base_learner=Transformer,inr=INR,encoder=none,repeat=0/config.gin\n"
-     ]
-    },
-    {
-     "name": "stderr",
-     "output_type": "stream",
-     "text": [
-      "INFO:root:epochs: 1, iters: 100 | training loss: 1.84\n"
-     ]
-    }
-   ],
-   "source": [
-    "for config in tqdm(configs):\n",
-    "    save_path = config.parent\n",
-    "\n",
-    "    exp = ForecastExperiment(config_path=config)\n",
-    "    print(config)\n",
-    "    try:\n",
-    "        exp.run()\n",
-    "    except KeyboardInterrupt:\n",
-    "        raise\n",
-    "    except Exception as e:\n",
-    "        raise\n",
-    "        print(e)\n",
-    "        pass"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "0472a84b",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "%debug"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "03a28a90",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "exp.instance()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "a31ae85a",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# save_path = Path('storage/experiments/Stocks/96M2S/repeat=0')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "1a9d823d",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# gin.clear_config()\n",
-    "# config_path = save_path/\"config.gin\"\n",
-    "# gin.parse_config(open(config_path))\n",
-    "# model_name = gin.query_parameter(\"instance.model_type\")\n",
-    "# model_name"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5e4983e7",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "# exp = ForecastExperiment(config_path=config_path)\n",
-    "# # exp.run()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "cdd0fbbf",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "def save_path2name(save_path: Path) -> str:\n",
-    "    \"\"\"\n",
-    "    Path('storage/experiments/Stocks/96M2S/base_learner=None,inr=INR,encoder=mlp,repeat=0')\n",
-    "    to \n",
-    "    '96M2S-None_INR_mlp_0'\n",
-    "    \"\"\"\n",
-    "    mtitle = str(save_path).split('/')[-2:]\n",
-    "    tags = mtitle[-1]\n",
-    "    tags = [x.split('=')[-1] for x in tags.split(',')]\n",
-    "    mtitle[-1] = '_'.join(tags)\n",
-    "    mtitle = \"-\".join(mtitle)\n",
-    "    return mtitle\n",
-    "\n",
-    "# save_path2name(save_path)\n",
-    "# save_path"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "321106ef",
-   "metadata": {},
-   "source": [
-    "# view all"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "768530be",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "from torchsummaryX import summary\n",
-    "\n",
-    "def plot_multi(save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True, verbose=1,):\n",
-    "    assert len(save_paths)>0\n",
-    "    for j in range(len(save_paths)):\n",
-    "        save_path = save_paths[j]\n",
-    "\n",
-    "        gin.clear_config()\n",
-    "        gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "        model_name = gin.query_parameter(\"instance.model_type\")\n",
-    "\n",
-    "        train_set, train_loader = get_data(flag='test', batch_size=3)\n",
-    "        seq_len = train_set[0][1].shape[0]\n",
-    "        model = get_model(model_name,\n",
-    "                            dim_size=train_set.data_x.shape[1],\n",
-    "                          seq_len=seq_len,\n",
-    "                            datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "        model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "        model = model.eval()\n",
-    "        \n",
-    "        \n",
-    "\n",
-    "\n",
-    "        b = train_set[i]\n",
-    "        b = [bb[None, :] for bb in b]\n",
-    "        b2 = list(map(to_tensor, b))\n",
-    "        \n",
-    "#         b = next(iter(train_loader))\n",
-    "#         print([s.shape for s in b]\n",
-    "        \n",
-    "        if verbose>1:\n",
-    "            \n",
-    "#             print(model)\n",
-    "            summary(model, *b2)\n",
-    "            print(save_path)\n",
-    "        \n",
-    "        context_past_x, context_y, query_past_x, query_y, context_time, query_time = b2\n",
-    "        with torch.no_grad():\n",
-    "            forecast = model(*b2)\n",
-    "       \n",
-    "        colors = list(mcolors.BASE_COLORS.keys())\n",
-    "        l = context_time.shape[1]\n",
-    "        forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "        x2 = context_y[0].cpu()\n",
-    "        y2 = query_y[0].cpu()\n",
-    "        l2 = query_time.shape[1]\n",
-    "        i_past = list(range(l))\n",
-    "        i_future = list(range(l, l+l2))\n",
-    "        \n",
-    "        \n",
-    "\n",
-    "        if plot:\n",
-    "        \n",
-    "            if j==0:\n",
-    "                plt.plot(i_past, x2[:, 0], c=colors[0], label=f\"past\")\n",
-    "                plt.plot(i_future, y2[:, 0], c=colors[0], label=\"future true\", alpha=0.3)\n",
-    "            mtitle = save_path2name(save_path)\n",
-    "            plt.plot(i_future, forecast2[:, 0], linestyle='--', label=f\"{mtitle}\") # c=colors[j], \n",
-    "            \n",
-    "\n",
-    "    plt.legend()\n",
-    "    plt.title(title)\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "739ee5e3",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# list the models we have run...\n",
-    "m=sorted(Path(\"storage/experiments/Stocks/96M2S\").glob(\"**/_SUCCESS\"))\n",
-    "print(m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f1ae652d",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "for mm in m:\n",
-    "    mtitle = save_path2name(mm.parent)\n",
-    "    print(mtitle)\n",
-    "    m3 = np.load(mm.parent/'metrics.npy', allow_pickle=1)\n",
-    "    m3 = eval(str(m3))\n",
-    "    print(m3['val']['mape'])"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "79bda4bf",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.875Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "train_set, train_loader = get_data(flag='train')\n",
-    "train_set[0][1].shape"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b06e53a9",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.892Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "save_paths = [mm.parent for mm in m]\n",
-    "for mm in m:\n",
-    "    try:\n",
-    "        plot_multi(\n",
-    "            save_paths=[mm.parent],\n",
-    "            i=600,\n",
-    "            verbose=2,\n",
-    "        )\n",
-    "    except:\n",
-    "        print('failed', mm)\n",
-    "#         mm.unlink()\n",
-    "        pass\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "25b9b9c5",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.892Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "save_paths = [mm.parent for mm in m]\n",
-    "plot_multi(\n",
-    "    save_paths=save_paths,\n",
-    "    i=200,\n",
-    "    verbose=0,\n",
-    ")\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e173ea6f",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.892Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "256/24"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9ca6f142",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T13:17:31.585029Z",
-     "start_time": "2022-11-22T13:17:31.528806Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "fce6db58",
-   "metadata": {},
-   "source": [
-    "# check positions in dl"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "5d574727",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.892Z"
-    },
-    "scrolled": true
-   },
-   "outputs": [],
-   "source": [
-    "train_set, train_loader = get_data(flag='test', batch_size=3)\n",
-    "b = context_past_x, context_y, query_past_x, query_y, context_time, query_time = train_set[100]\n",
-    "print([bb.shape for bb in b])\n",
-    "# context_y, query_y"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "74ebc120",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.892Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "cx_start, cx_end, c_start, c_end, qx_start, qx_end, q_start, q_end = train_set.get_inds(100)\n",
-    "cx_start, cx_end, c_start, c_end, qx_start, qx_end, q_start, q_end"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "d289f6bd",
-   "metadata": {
-    "ExecuteTime": {
-     "start_time": "2022-11-23T05:42:46.892Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "plt.hlines(1, cx_start, cx_end, color='green', alpha=0.5, label='context_past_x')\n",
-    "plt.hlines(2, c_start, c_end, color='green', label='context_labels')\n",
-    "plt.hlines(3, qx_start, qx_end, alpha=0.5, label='query_past_x')\n",
-    "plt.hlines(4, q_start, q_end, label='query_labels/target')\n",
-    "plt.legend(loc='upper left')"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "879d9d45",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "37d7641b",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "061b3b77",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "deeptime",
-   "language": "python",
-   "name": "deeptime"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/scratch-single-stocks.ipynb b/scratch-single-stocks.ipynb
deleted file mode 100644
index 26ebad9..0000000
--- a/scratch-single-stocks.ipynb
+++ /dev/null
@@ -1,683 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "markdown",
-   "id": "b1e031e3",
-   "metadata": {},
-   "source": [
-    "- [x] try just one predictor\n",
-    "    - [ ] multi input, single output\n",
-    "- [x] comparem ulti\n",
-    "- losses:\n",
-    "    - try logp? nah\n",
-    "    - mae?\n",
-    "- [x] make my own csv with 5m data (maybe 10k rows)\n",
-    "- [ ] backtest?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "7f9e3d73",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:31:36.717738Z",
-     "start_time": "2022-11-22T02:31:36.708540Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.simplefilter(\"ignore\")\n",
-    "\n",
-    "# autoreload import your package\n",
-    "%load_ext autoreload\n",
-    "%autoreload 2"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4e09086b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:31:38.523459Z",
-     "start_time": "2022-11-22T02:31:36.718873Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "import os\n",
-    "from os.path import join\n",
-    "import math\n",
-    "import logging\n",
-    "from typing import Callable, Optional, Union, Dict, Tuple\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from pathlib import Path\n",
-    "import matplotlib.colors as mcolors\n",
-    "\n",
-    "import gin\n",
-    "from fire import Fire\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader\n",
-    "from torch import optim\n",
-    "from torch import nn\n",
-    "\n",
-    "from experiments.base import Experiment\n",
-    "from data.datasets import ForecastDataset\n",
-    "from models import get_model\n",
-    "from utils.checkpoint import Checkpoint\n",
-    "from utils.ops import default_device, to_tensor\n",
-    "from utils.losses import get_loss_fn\n",
-    "from utils.metrics import calc_metrics\n",
-    "\n",
-    "from experiments.forecast import get_data\n",
-    "gin.enter_interactive_mode()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d7f095",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-19T23:55:34.939075Z",
-     "start_time": "2022-11-19T23:55:34.820277Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4df5270",
-   "metadata": {},
-   "source": [
-    "# auto"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "04499bef",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:31:38.542165Z",
-     "start_time": "2022-11-22T02:31:38.525217Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot(model_name=\"deeptime\", save_path=Path(\"storage/experiments/Exchange/96M/repeat=0\"), i=200, title=None, plot=True):\n",
-    "\n",
-    "    gin.clear_config()\n",
-    "    gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "\n",
-    "    train_set, train_loader = get_data(flag='train', batch_size=2)\n",
-    "\n",
-    "    model = get_model(model_name,\n",
-    "                        dim_size=train_set.data_x.shape[1],\n",
-    "                        datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "    model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "    model = model.eval()\n",
-    "\n",
-    "\n",
-    "    b = train_set[i]\n",
-    "    b = [bb[None, :] for bb in b]\n",
-    "    x, y, x_time, y_time = map(to_tensor, b)\n",
-    "    with torch.no_grad():\n",
-    "        forecast = model(x, x_time, y_time)\n",
-    "\n",
-    "    if title is None:\n",
-    "        title = str(save_path).split('/')[-3:]\n",
-    "        title = \"-\".join(title)\n",
-    "    \n",
-    "    colors = list(mcolors.BASE_COLORS.keys())\n",
-    "    l = x.shape[1]\n",
-    "    forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "    x2 = x[0].cpu()\n",
-    "    y2 = y[0].cpu()\n",
-    "    l2 = y.shape[1]\n",
-    "    i_past = list(range(l))\n",
-    "    i_future = list(range(l, l+l2))\n",
-    "    \n",
-    "    if plot:\n",
-    "        plt.title(title)\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_past, x2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, y2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 39,
-   "id": "768530be",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:39:04.239175Z",
-     "start_time": "2022-11-22T02:39:04.167600Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot_multi(save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True):\n",
-    "    for j in range(len(save_paths)):\n",
-    "        save_path = save_paths[j]\n",
-    "\n",
-    "        gin.clear_config()\n",
-    "        gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "        model_name = gin.query_parameter(\"instance.model_type\")\n",
-    "\n",
-    "        train_set, train_loader = get_data(flag='test', batch_size=3)\n",
-    "\n",
-    "        model = get_model(model_name,\n",
-    "                            dim_size=train_set.data_x.shape[1],\n",
-    "                            datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "        model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "        model = model.eval()\n",
-    "\n",
-    "\n",
-    "        b = train_set[i]\n",
-    "        b = [bb[None, :] for bb in b]\n",
-    "        \n",
-    "        b = next(iter(train_loader))\n",
-    "        print([s.shape for s in b])\n",
-    "        \n",
-    "        x, y, x_time, y_time = map(to_tensor, b)\n",
-    "#         print(b)\n",
-    "        with torch.no_grad():\n",
-    "            forecast = model(x, x_time, y_time)\n",
-    "       \n",
-    "        colors = list(mcolors.BASE_COLORS.keys())\n",
-    "        l = x.shape[1]\n",
-    "        forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "        x2 = x[0].cpu()\n",
-    "        y2 = y[0].cpu()\n",
-    "        l2 = y.shape[1]\n",
-    "        i_past = list(range(l))\n",
-    "        i_future = list(range(l, l+l2))\n",
-    "\n",
-    "        if plot:\n",
-    "            plt.plot(i_past, x2[:, 0], c=colors[0], label=f\"past\")\n",
-    "            plt.plot(i_future, y2[:, 0], c=colors[0], label=\"future true\", alpha=0.3)\n",
-    "            \n",
-    "            mtitle = str(save_path).split('/')[-2:-1]\n",
-    "            mtitle = \"-\".join(mtitle)\n",
-    "            plt.plot(i_future, forecast2[:, 0], c=colors[j], linestyle='--', label=f\"{mtitle}\")\n",
-    "    plt.legend()\n",
-    "    plt.title(title)\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 40,
-   "id": "739ee5e3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:39:04.324202Z",
-     "start_time": "2022-11-22T02:39:04.306581Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "[Path('storage/experiments/Stocks/96M/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96S/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96Splus/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96Splusshort/repeat=0/_SUCCESS'), Path('storage/experiments/Stocks/96Sshort/repeat=0/_SUCCESS')]\n"
-     ]
-    }
-   ],
-   "source": [
-    "# list the models we have run...\n",
-    "m=sorted(Path(\"storage/experiments/Stocks\").glob(\"**/_SUCCESS\"))\n",
-    "print(m)"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "83dca123",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T07:05:16.231276Z",
-     "start_time": "2022-11-22T07:05:15.779822Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n  In call to configurable 'ForecastDataset' (<class 'data.datasets.ForecastDataset'>)\n  In call to configurable 'get_data' (<function get_data at 0x7fb53c141160>)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [42], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m mm \u001b[38;5;129;01min\u001b[39;00m m:\n\u001b[0;32m----> 2\u001b[0m     \u001b[43mplot_multi\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m               \u001b[49m\u001b[43msave_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m                   \u001b[49m\u001b[43mmm\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mparent\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m               \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m               \u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m160\u001b[39;49m\n\u001b[1;32m      7\u001b[0m \u001b[43m              \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m     plt\u001b[38;5;241m.\u001b[39mshow()\n\u001b[1;32m      9\u001b[0m \u001b[38;5;241m1\u001b[39m\n",
-      "Cell \u001b[0;32mIn [39], line 9\u001b[0m, in \u001b[0;36mplot_multi\u001b[0;34m(save_paths, i, title, plot)\u001b[0m\n\u001b[1;32m      6\u001b[0m gin\u001b[38;5;241m.\u001b[39mparse_config(\u001b[38;5;28mopen\u001b[39m(save_path\u001b[38;5;241m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig.gin\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m      7\u001b[0m model_name \u001b[38;5;241m=\u001b[39m gin\u001b[38;5;241m.\u001b[39mquery_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minstance.model_type\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 9\u001b[0m train_set, train_loader \u001b[38;5;241m=\u001b[39m \u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m model \u001b[38;5;241m=\u001b[39m get_model(model_name,\n\u001b[1;32m     12\u001b[0m                     dim_size\u001b[38;5;241m=\u001b[39mtrain_set\u001b[38;5;241m.\u001b[39mdata_x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m     13\u001b[0m                     datetime_feats\u001b[38;5;241m=\u001b[39mtrain_set\u001b[38;5;241m.\u001b[39mtimestamps\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\u001b[38;5;241m.\u001b[39mto(default_device())\n\u001b[1;32m     14\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(torch\u001b[38;5;241m.\u001b[39mload(save_path\u001b[38;5;241m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel.pth\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1605\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m scope_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in scope \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(scope_str) \u001b[38;5;28;01mif\u001b[39;00m scope_str \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1604\u001b[0m err_str \u001b[38;5;241m=\u001b[39m err_str\u001b[38;5;241m.\u001b[39mformat(name, fn_or_cls, scope_info)\n\u001b[0;32m-> 1605\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment_exception_message_and_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_str\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/utils.py:41\u001b[0m, in \u001b[0;36maugment_exception_message_and_reraise\u001b[0;34m(exception, message)\u001b[0m\n\u001b[1;32m     39\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ExceptionProxy()\n\u001b[1;32m     40\u001b[0m ExceptionProxy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m proxy\u001b[38;5;241m.\u001b[39mwith_traceback(exception\u001b[38;5;241m.\u001b[39m__traceback__) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1582\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1579\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m   1581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1582\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1583\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m   1584\u001b[0m   err_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
-      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/experiments/forecast.py:115\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(flag, batch_size)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mno such flag \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mflag\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 115\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mForecastDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflag\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    116\u001b[0m data_loader \u001b[38;5;241m=\u001b[39m DataLoader(dataset,\n\u001b[1;32m    117\u001b[0m                          batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m    118\u001b[0m                          shuffle\u001b[38;5;241m=\u001b[39mshuffle,\n\u001b[1;32m    119\u001b[0m                          drop_last\u001b[38;5;241m=\u001b[39mdrop_last)\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dataset, data_loader\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1605\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m scope_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in scope \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(scope_str) \u001b[38;5;28;01mif\u001b[39;00m scope_str \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1604\u001b[0m err_str \u001b[38;5;241m=\u001b[39m err_str\u001b[38;5;241m.\u001b[39mformat(name, fn_or_cls, scope_info)\n\u001b[0;32m-> 1605\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment_exception_message_and_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_str\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/utils.py:41\u001b[0m, in \u001b[0;36maugment_exception_message_and_reraise\u001b[0;34m(exception, message)\u001b[0m\n\u001b[1;32m     39\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ExceptionProxy()\n\u001b[1;32m     40\u001b[0m ExceptionProxy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m proxy\u001b[38;5;241m.\u001b[39mwith_traceback(exception\u001b[38;5;241m.\u001b[39m__traceback__) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1582\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1579\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m   1581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1582\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1583\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m   1584\u001b[0m   err_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
-      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/data/datasets.py:71\u001b[0m, in \u001b[0;36mForecastDataset.__init__\u001b[0;34m(self, flag, horizon_len, scale, cross_learn, data_path, root_path, features, target, lookback_len, lookback_aux_len, lookback_mult, time_features, normalise_time_features)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimestamps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_time_samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_data()\n",
-      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/data/datasets.py:105\u001b[0m, in \u001b[0;36mForecastDataset.load_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_x \u001b[38;5;241m=\u001b[39m data[border1:border2] \n\u001b[1;32m    104\u001b[0m \u001b[38;5;66;03m# y is just the col we predict\u001b[39;00m\n\u001b[0;32m--> 105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_y \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mborder1\u001b[49m\u001b[43m:\u001b[49m\u001b[43mborder2\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimestamps \u001b[38;5;241m=\u001b[39m get_time_features(pd\u001b[38;5;241m.\u001b[39mto_datetime(df_raw\u001b[38;5;241m.\u001b[39mdate[border1:border2]\u001b[38;5;241m.\u001b[39mvalues),\n\u001b[1;32m    107\u001b[0m                                     normalise\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalise_time_features,\n\u001b[1;32m    108\u001b[0m                                     features\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_features)\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_x)\n",
-      "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n  In call to configurable 'ForecastDataset' (<class 'data.datasets.ForecastDataset'>)\n  In call to configurable 'get_data' (<function get_data at 0x7fb53c141160>)"
-     ]
-    }
-   ],
-   "source": [
-    "for mm in m:\n",
-    "    plot_multi(\n",
-    "               save_paths=[\n",
-    "                   mm.parent\n",
-    "               ],\n",
-    "               i=160\n",
-    "              )\n",
-    "    plt.show()\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 49,
-   "id": "cfc602db",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T07:08:30.855574Z",
-     "start_time": "2022-11-22T07:08:30.549156Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "AttributeError",
-     "evalue": "",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [49], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_multi\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m           \u001b[49m\u001b[43msave_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m               \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage/experiments/Stocks/96M/repeat=0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;43;03m#                Path(\"storage/experiments/Stocks/96Mplus/repeat=0\"),\u001b[39;49;00m\n\u001b[1;32m      5\u001b[0m \u001b[43m           \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m           \u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m60\u001b[39;49m\n\u001b[1;32m      7\u001b[0m \u001b[43m          \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;241m1\u001b[39m\n",
-      "Cell \u001b[0;32mIn [39], line 18\u001b[0m, in \u001b[0;36mplot_multi\u001b[0;34m(save_paths, i, title, plot)\u001b[0m\n\u001b[1;32m     14\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(torch\u001b[38;5;241m.\u001b[39mload(save_path\u001b[38;5;241m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel.pth\u001b[39m\u001b[38;5;124m'\u001b[39m))\n\u001b[1;32m     15\u001b[0m model \u001b[38;5;241m=\u001b[39m model\u001b[38;5;241m.\u001b[39meval()\n\u001b[0;32m---> 18\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[43mtrain_set\u001b[49m\u001b[43m[\u001b[49m\u001b[43mi\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m     19\u001b[0m b \u001b[38;5;241m=\u001b[39m [bb[\u001b[38;5;28;01mNone\u001b[39;00m, :] \u001b[38;5;28;01mfor\u001b[39;00m bb \u001b[38;5;129;01min\u001b[39;00m b]\n\u001b[1;32m     21\u001b[0m b \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mnext\u001b[39m(\u001b[38;5;28miter\u001b[39m(train_loader))\n",
-      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/data/datasets.py:145\u001b[0m, in \u001b[0;36mForecastDataset.__getitem__\u001b[0;34m(self, idx)\u001b[0m\n\u001b[1;32m    143\u001b[0m cx_start \u001b[38;5;241m=\u001b[39m idx\n\u001b[1;32m    144\u001b[0m cx_end \u001b[38;5;241m=\u001b[39m cx_start \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlookback_len\n\u001b[0;32m--> 145\u001b[0m c_start \u001b[38;5;241m=\u001b[39m cx_end \u001b[38;5;241m+\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgap\u001b[49m\n\u001b[1;32m    146\u001b[0m c_end \u001b[38;5;241m=\u001b[39m c_start \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mhorizon_len\n\u001b[1;32m    148\u001b[0m qx_start \u001b[38;5;241m=\u001b[39m cx_end \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgap\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/utils/data/dataset.py:83\u001b[0m, in \u001b[0;36mDataset.__getattr__\u001b[0;34m(self, attribute_name)\u001b[0m\n\u001b[1;32m     81\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m function\n\u001b[1;32m     82\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m---> 83\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mAttributeError\u001b[39;00m\n",
-      "\u001b[0;31mAttributeError\u001b[0m: "
-     ]
-    }
-   ],
-   "source": [
-    "plot_multi(\n",
-    "           save_paths=[\n",
-    "               Path(\"storage/experiments/Stocks/96M/repeat=0\"),\n",
-    "#                Path(\"storage/experiments/Stocks/96Mplus/repeat=0\"),\n",
-    "           ],\n",
-    "           i=60\n",
-    "          )\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 50,
-   "id": "b3b005c0",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T07:08:43.569996Z",
-     "start_time": "2022-11-22T07:08:39.011776Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> \u001b[0;32m/home/wassname/miniforge3/envs/deeptime/lib/python3.8/site-packages/torch/utils/data/dataset.py\u001b[0m(83)\u001b[0;36m__getattr__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m     81 \u001b[0;31m            \u001b[0;32mreturn\u001b[0m \u001b[0mfunction\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m     82 \u001b[0;31m        \u001b[0;32melse\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m---> 83 \u001b[0;31m            \u001b[0;32mraise\u001b[0m \u001b[0mAttributeError\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m     84 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m     85 \u001b[0;31m    \u001b[0;34m@\u001b[0m\u001b[0mclassmethod\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> u\n",
-      "> \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/data/datasets.py\u001b[0m(145)\u001b[0;36m__getitem__\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    143 \u001b[0;31m        \u001b[0mcx_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0midx\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    144 \u001b[0;31m        \u001b[0mcx_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcx_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mlookback_len\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 145 \u001b[0;31m        \u001b[0mc_start\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mcx_end\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mgap\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    146 \u001b[0;31m        \u001b[0mc_end\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mc_start\u001b[0m \u001b[0;34m+\u001b[0m \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mhorizon_len\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    147 \u001b[0;31m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> self.gap\n",
-      "*** AttributeError\n",
-      "ipdb> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "%debug"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 43,
-   "id": "b8843217",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T07:05:18.277499Z",
-     "start_time": "2022-11-22T07:05:18.146297Z"
-    }
-   },
-   "outputs": [
-    {
-     "ename": "IndexError",
-     "evalue": "only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n  In call to configurable 'ForecastDataset' (<class 'data.datasets.ForecastDataset'>)\n  In call to configurable 'get_data' (<function get_data at 0x7fb53c141160>)",
-     "output_type": "error",
-     "traceback": [
-      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
-      "\u001b[0;31mIndexError\u001b[0m                                Traceback (most recent call last)",
-      "Cell \u001b[0;32mIn [43], line 1\u001b[0m\n\u001b[0;32m----> 1\u001b[0m \u001b[43mplot_multi\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m      2\u001b[0m \u001b[43m           \u001b[49m\u001b[43msave_paths\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m[\u001b[49m\n\u001b[1;32m      3\u001b[0m \u001b[43m               \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage/experiments/Stocks/96S/repeat=0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      4\u001b[0m \u001b[43m               \u001b[49m\u001b[43mPath\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[38;5;124;43mstorage/experiments/Stocks/96Splus/repeat=0\u001b[39;49m\u001b[38;5;124;43m\"\u001b[39;49m\u001b[43m)\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      5\u001b[0m \u001b[43m           \u001b[49m\u001b[43m]\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m      6\u001b[0m \u001b[43m           \u001b[49m\u001b[43mi\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m60\u001b[39;49m\n\u001b[1;32m      7\u001b[0m \u001b[43m          \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m      8\u001b[0m \u001b[38;5;241m1\u001b[39m\n",
-      "Cell \u001b[0;32mIn [39], line 9\u001b[0m, in \u001b[0;36mplot_multi\u001b[0;34m(save_paths, i, title, plot)\u001b[0m\n\u001b[1;32m      6\u001b[0m gin\u001b[38;5;241m.\u001b[39mparse_config(\u001b[38;5;28mopen\u001b[39m(save_path\u001b[38;5;241m/\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mconfig.gin\u001b[39m\u001b[38;5;124m\"\u001b[39m))\n\u001b[1;32m      7\u001b[0m model_name \u001b[38;5;241m=\u001b[39m gin\u001b[38;5;241m.\u001b[39mquery_parameter(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124minstance.model_type\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 9\u001b[0m train_set, train_loader \u001b[38;5;241m=\u001b[39m \u001b[43mget_data\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflag\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[38;5;124;43mtest\u001b[39;49m\u001b[38;5;124;43m'\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbatch_size\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[38;5;241;43m3\u001b[39;49m\u001b[43m)\u001b[49m\n\u001b[1;32m     11\u001b[0m model \u001b[38;5;241m=\u001b[39m get_model(model_name,\n\u001b[1;32m     12\u001b[0m                     dim_size\u001b[38;5;241m=\u001b[39mtrain_set\u001b[38;5;241m.\u001b[39mdata_x\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m1\u001b[39m],\n\u001b[1;32m     13\u001b[0m                     datetime_feats\u001b[38;5;241m=\u001b[39mtrain_set\u001b[38;5;241m.\u001b[39mtimestamps\u001b[38;5;241m.\u001b[39mshape[\u001b[38;5;241m-\u001b[39m\u001b[38;5;241m1\u001b[39m])\u001b[38;5;241m.\u001b[39mto(default_device())\n\u001b[1;32m     14\u001b[0m model\u001b[38;5;241m.\u001b[39mload_state_dict(torch\u001b[38;5;241m.\u001b[39mload(save_path\u001b[38;5;241m/\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel.pth\u001b[39m\u001b[38;5;124m'\u001b[39m))\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1605\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m scope_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in scope \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(scope_str) \u001b[38;5;28;01mif\u001b[39;00m scope_str \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1604\u001b[0m err_str \u001b[38;5;241m=\u001b[39m err_str\u001b[38;5;241m.\u001b[39mformat(name, fn_or_cls, scope_info)\n\u001b[0;32m-> 1605\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment_exception_message_and_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_str\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/utils.py:41\u001b[0m, in \u001b[0;36maugment_exception_message_and_reraise\u001b[0;34m(exception, message)\u001b[0m\n\u001b[1;32m     39\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ExceptionProxy()\n\u001b[1;32m     40\u001b[0m ExceptionProxy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m proxy\u001b[38;5;241m.\u001b[39mwith_traceback(exception\u001b[38;5;241m.\u001b[39m__traceback__) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1582\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1579\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m   1581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1582\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1583\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m   1584\u001b[0m   err_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
-      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/experiments/forecast.py:115\u001b[0m, in \u001b[0;36mget_data\u001b[0;34m(flag, batch_size)\u001b[0m\n\u001b[1;32m    113\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    114\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mValueError\u001b[39;00m(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mno such flag \u001b[39m\u001b[38;5;132;01m{\u001b[39;00mflag\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m'\u001b[39m)\n\u001b[0;32m--> 115\u001b[0m dataset \u001b[38;5;241m=\u001b[39m \u001b[43mForecastDataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mflag\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    116\u001b[0m data_loader \u001b[38;5;241m=\u001b[39m DataLoader(dataset,\n\u001b[1;32m    117\u001b[0m                          batch_size\u001b[38;5;241m=\u001b[39mbatch_size,\n\u001b[1;32m    118\u001b[0m                          shuffle\u001b[38;5;241m=\u001b[39mshuffle,\n\u001b[1;32m    119\u001b[0m                          drop_last\u001b[38;5;241m=\u001b[39mdrop_last)\n\u001b[1;32m    120\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m dataset, data_loader\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1605\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1603\u001b[0m scope_info \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m in scope \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mformat(scope_str) \u001b[38;5;28;01mif\u001b[39;00m scope_str \u001b[38;5;28;01melse\u001b[39;00m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n\u001b[1;32m   1604\u001b[0m err_str \u001b[38;5;241m=\u001b[39m err_str\u001b[38;5;241m.\u001b[39mformat(name, fn_or_cls, scope_info)\n\u001b[0;32m-> 1605\u001b[0m \u001b[43mutils\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43maugment_exception_message_and_reraise\u001b[49m\u001b[43m(\u001b[49m\u001b[43me\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43merr_str\u001b[49m\u001b[43m)\u001b[49m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/utils.py:41\u001b[0m, in \u001b[0;36maugment_exception_message_and_reraise\u001b[0;34m(exception, message)\u001b[0m\n\u001b[1;32m     39\u001b[0m proxy \u001b[38;5;241m=\u001b[39m ExceptionProxy()\n\u001b[1;32m     40\u001b[0m ExceptionProxy\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mtype\u001b[39m(exception)\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__qualname__\u001b[39m\n\u001b[0;32m---> 41\u001b[0m \u001b[38;5;28;01mraise\u001b[39;00m proxy\u001b[38;5;241m.\u001b[39mwith_traceback(exception\u001b[38;5;241m.\u001b[39m__traceback__) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;28mNone\u001b[39m\n",
-      "File \u001b[0;32m~/miniforge3/envs/deeptime/lib/python3.8/site-packages/gin/config.py:1582\u001b[0m, in \u001b[0;36m_make_gin_wrapper.<locals>.gin_wrapper\u001b[0;34m(*args, **kwargs)\u001b[0m\n\u001b[1;32m   1579\u001b[0m new_kwargs\u001b[38;5;241m.\u001b[39mupdate(kwargs)\n\u001b[1;32m   1581\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[0;32m-> 1582\u001b[0m   \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mfn\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_args\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43mnew_kwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1583\u001b[0m \u001b[38;5;28;01mexcept\u001b[39;00m \u001b[38;5;167;01mException\u001b[39;00m \u001b[38;5;28;01mas\u001b[39;00m e:  \u001b[38;5;66;03m# pylint: disable=broad-except\u001b[39;00m\n\u001b[1;32m   1584\u001b[0m   err_str \u001b[38;5;241m=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m'\u001b[39m\n",
-      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/data/datasets.py:71\u001b[0m, in \u001b[0;36mForecastDataset.__init__\u001b[0;34m(self, flag, horizon_len, scale, cross_learn, data_path, root_path, features, target, lookback_len, lookback_aux_len, lookback_mult, time_features, normalise_time_features)\u001b[0m\n\u001b[1;32m     69\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimestamps \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     70\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[0;32m---> 71\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_time_samples \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n\u001b[1;32m     72\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mload_data()\n",
-      "File \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/data/datasets.py:105\u001b[0m, in \u001b[0;36mForecastDataset.load_data\u001b[0;34m(self)\u001b[0m\n\u001b[1;32m    103\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_x \u001b[38;5;241m=\u001b[39m data[border1:border2] \n\u001b[1;32m    104\u001b[0m \u001b[38;5;66;03m# y is just the col we predict\u001b[39;00m\n\u001b[0;32m--> 105\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_y \u001b[38;5;241m=\u001b[39m \u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43mborder1\u001b[49m\u001b[43m:\u001b[49m\u001b[43mborder2\u001b[49m\u001b[43m]\u001b[49m\u001b[43m[\u001b[49m\u001b[43m[\u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mtarget\u001b[49m\u001b[43m]\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m    106\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtimestamps \u001b[38;5;241m=\u001b[39m get_time_features(pd\u001b[38;5;241m.\u001b[39mto_datetime(df_raw\u001b[38;5;241m.\u001b[39mdate[border1:border2]\u001b[38;5;241m.\u001b[39mvalues),\n\u001b[1;32m    107\u001b[0m                                     normalise\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mnormalise_time_features,\n\u001b[1;32m    108\u001b[0m                                     features\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mtime_features)\n\u001b[1;32m    109\u001b[0m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mn_time \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mlen\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdata_x)\n",
-      "\u001b[0;31mIndexError\u001b[0m: only integers, slices (`:`), ellipsis (`...`), numpy.newaxis (`None`) and integer or boolean arrays are valid indices\n  In call to configurable 'ForecastDataset' (<class 'data.datasets.ForecastDataset'>)\n  In call to configurable 'get_data' (<function get_data at 0x7fb53c141160>)"
-     ]
-    }
-   ],
-   "source": [
-    "plot_multi(\n",
-    "           save_paths=[\n",
-    "               Path(\"storage/experiments/Stocks/96S/repeat=0\"),\n",
-    "               Path(\"storage/experiments/Stocks/96Splus/repeat=0\"),\n",
-    "           ],\n",
-    "           i=60\n",
-    "          )\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 44,
-   "id": "7486f672",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T07:07:06.097048Z",
-     "start_time": "2022-11-22T07:06:49.173386Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "> \u001b[0;32m/media/wassname/SGIronWolf/projects5/investing/DeepTime/data/datasets.py\u001b[0m(105)\u001b[0;36mload_data\u001b[0;34m()\u001b[0m\n",
-      "\u001b[0;32m    103 \u001b[0;31m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_x\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mborder1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mborder2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    104 \u001b[0;31m        \u001b[0;31m# y is just the col we predict\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m--> 105 \u001b[0;31m        \u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mdata_y\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mdata\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mborder1\u001b[0m\u001b[0;34m:\u001b[0m\u001b[0mborder2\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0;34m[\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mtarget\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m]\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\u001b[0;32m    106 \u001b[0;31m        self.timestamps = get_time_features(pd.to_datetime(df_raw.date[border1:border2].values),\n",
-      "\u001b[0m\u001b[0;32m    107 \u001b[0;31m                                            \u001b[0mnormalise\u001b[0m\u001b[0;34m=\u001b[0m\u001b[0mself\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mnormalise_time_features\u001b[0m\u001b[0;34m,\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
-      "\u001b[0m\n",
-      "ipdb> self.target\n",
-      "'RSMKs_18_144_72'\n",
-      "ipdb> data[border1:border2]\n",
-      "array([[-0.31080395],\n",
-      "       [-0.30072371],\n",
-      "       [-0.29116544],\n",
-      "       ...,\n",
-      "       [-0.14084614],\n",
-      "       [-0.15264537],\n",
-      "       [-0.16396183]])\n",
-      "ipdb> data\n",
-      "array([[ 0.08264075],\n",
-      "       [ 0.08548946],\n",
-      "       [ 0.08766026],\n",
-      "       ...,\n",
-      "       [-0.14084614],\n",
-      "       [-0.15264537],\n",
-      "       [-0.16396183]])\n",
-      "ipdb> data.shape\n",
-      "(53398, 1)\n",
-      "ipdb> q\n"
-     ]
-    }
-   ],
-   "source": [
-    "%debug"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 38,
-   "id": "d39ba7c5",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:36:49.885952Z",
-     "start_time": "2022-11-22T02:36:49.217176Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "torch.Size([1, 54, 1]) torch.Size([1, 54, 256]) 3 torch.Size([3, 54, 256])\n",
-      "receptive field [690 378 242]=[138  18   2]*[[ 1  1  1]\n",
-      " [ 1  2  4]\n",
-      " [ 1  4 16]\n",
-      " [ 1  6 36]\n",
-      " [ 1  8 64]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 38,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_multi(\n",
-    "           save_paths=[\n",
-    "               Path(\"storage/experiments/Stocks/96Sshort/repeat=0\"),\n",
-    "               Path(\"storage/experiments/Stocks/96Splusshort/repeat=0\"),\n",
-    "           ], i=60)\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "9af56ddc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:31:41.691079Z",
-     "start_time": "2022-11-22T02:31:41.691072Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# plot(\"deeptime\", Path(\"storage/experiments/Exchange/96S/repeat=0\"));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "7fee5ab7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T00:57:32.922740Z",
-     "start_time": "2022-11-20T00:57:32.919738Z"
-    }
-   },
-   "source": [
-    "# multi"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f59232cc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:31:41.691700Z",
-     "start_time": "2022-11-22T02:31:41.691693Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot_multiM(save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True):\n",
-    "    for j in range(len(save_paths)):\n",
-    "        save_path = save_paths[j]\n",
-    "\n",
-    "        gin.clear_config()\n",
-    "        gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "        model_name = gin.query_parameter(\"instance.model_type\")\n",
-    "        train_set, train_loader = get_data(flag='train', batch_size=2)\n",
-    "\n",
-    "        model = get_model(model_name,\n",
-    "                            dim_size=train_set.data_x.shape[1],\n",
-    "                            datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "        model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "        model = model.eval()\n",
-    "\n",
-    "\n",
-    "        b = train_set[i]\n",
-    "        b = [bb[None, :] for bb in b]\n",
-    "        x, y, x_time, y_time = map(to_tensor, b)\n",
-    "        with torch.no_grad():\n",
-    "            forecast = model(x, x_time, y_time)\n",
-    "\n",
-    "       \n",
-    "        colors = list(mcolors.BASE_COLORS.keys())\n",
-    "        l = x.shape[1]\n",
-    "        forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "        x2 = x[0].cpu()\n",
-    "        y2 = y[0].cpu()\n",
-    "        l2 = y.shape[1]\n",
-    "        i_past = list(range(l))\n",
-    "        i_future = list(range(l, l+l2))\n",
-    "\n",
-    "        linestyles =  [ '--', '-.', '-', ':']\n",
-    "        if plot:\n",
-    "            ls = linestyles[j]\n",
-    "            print(ls)\n",
-    "            if j==0:\n",
-    "                \n",
-    "                \n",
-    "                for k in range(x.shape[-1]):\n",
-    "                    plt.plot(i_past, x2[:, k], c=colors[k], label=f\"var {k}\")\n",
-    "                for k in range(x.shape[-1]):\n",
-    "                    plt.plot(i_future, y2[:, k], c=colors[k], alpha=0.3)\n",
-    "            \n",
-    "            mtitle = str(save_path).split('/')[-2:-1]\n",
-    "            mtitle = \"-\".join(mtitle)\n",
-    "            for k in range(x.shape[-1]):\n",
-    "                plt.plot(i_future, forecast2[:, k], c=colors[k], linestyle=ls, label=f\"{mtitle}\" if k==0 else None)\n",
-    "    plt.legend()\n",
-    "    plt.title(title)\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1d152b2",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-22T02:31:41.692264Z",
-     "start_time": "2022-11-22T02:31:41.692258Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "plot_multiM([Path(\"storage/experiments/Stocks/96M/repeat=0\")]);"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "e8680326",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "c20bb663",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "deeptime",
-   "language": "python",
-   "name": "deeptime"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/scratch-single.ipynb b/scratch-single.ipynb
deleted file mode 100644
index c482d3e..0000000
--- a/scratch-single.ipynb
+++ /dev/null
@@ -1,412 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 1,
-   "id": "4e09086b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T10:31:28.624566Z",
-     "start_time": "2022-11-20T10:31:27.336916Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "import os\n",
-    "from os.path import join\n",
-    "import math\n",
-    "import logging\n",
-    "from typing import Callable, Optional, Union, Dict, Tuple\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from pathlib import Path\n",
-    "import matplotlib.colors as mcolors\n",
-    "\n",
-    "import gin\n",
-    "from fire import Fire\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader\n",
-    "from torch import optim\n",
-    "from torch import nn\n",
-    "\n",
-    "from experiments.base import Experiment\n",
-    "from data.datasets import ForecastDataset\n",
-    "from models import get_model\n",
-    "from utils.checkpoint import Checkpoint\n",
-    "from utils.ops import default_device, to_tensor\n",
-    "from utils.losses import get_loss_fn\n",
-    "from utils.metrics import calc_metrics\n",
-    "\n",
-    "from experiments.forecast import get_data\n",
-    "gin.enter_interactive_mode()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d7f095",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-19T23:55:34.939075Z",
-     "start_time": "2022-11-19T23:55:34.820277Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4df5270",
-   "metadata": {},
-   "source": [
-    "# auto"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "04499bef",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T10:31:28.634463Z",
-     "start_time": "2022-11-20T10:31:28.626256Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot(model_name=\"deeptime\", save_path=Path(\"storage/experiments/Exchange/96M/repeat=0\"), i=200, title=None, plot=True):\n",
-    "\n",
-    "    gin.clear_config()\n",
-    "    gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "\n",
-    "    train_set, train_loader = get_data(flag='train', batch_size=2)\n",
-    "\n",
-    "    model = get_model(model_name,\n",
-    "                        dim_size=train_set.data_x.shape[1],\n",
-    "                        datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "    model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "    model = model.eval()\n",
-    "\n",
-    "\n",
-    "    b = train_set[i]\n",
-    "    b = [bb[None, :] for bb in b]\n",
-    "    x, y, x_time, y_time = map(to_tensor, b)\n",
-    "    with torch.no_grad():\n",
-    "        forecast = model(x, x_time, y_time)\n",
-    "\n",
-    "    if title is None:\n",
-    "        title = str(save_path).split('/')[-3:]\n",
-    "        title = \"-\".join(title)\n",
-    "    \n",
-    "    colors = list(mcolors.BASE_COLORS.keys())\n",
-    "    l = x.shape[1]\n",
-    "    forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "    x2 = x[0].cpu()\n",
-    "    y2 = y[0].cpu()\n",
-    "    l2 = y.shape[1]\n",
-    "    i_past = list(range(l))\n",
-    "    i_future = list(range(l, l+l2))\n",
-    "    \n",
-    "    if plot:\n",
-    "        plt.title(title)\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_past, x2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, y2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "768530be",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T10:31:28.667218Z",
-     "start_time": "2022-11-20T10:31:28.635969Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot_multi(save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True):\n",
-    "    for j in range(len(save_paths)):\n",
-    "        save_path = save_paths[j]\n",
-    "\n",
-    "        gin.clear_config()\n",
-    "        gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "        model_name = gin.query_parameter(\"instance.model_type\")\n",
-    "\n",
-    "        train_set, train_loader = get_data(flag='train', batch_size=2)\n",
-    "\n",
-    "        model = get_model(model_name,\n",
-    "                            dim_size=train_set.data_x.shape[1],\n",
-    "                            datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "        model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "        model = model.eval()\n",
-    "\n",
-    "\n",
-    "        b = train_set[i]\n",
-    "        b = [bb[None, :] for bb in b]\n",
-    "        x, y, x_time, y_time = map(to_tensor, b)\n",
-    "        with torch.no_grad():\n",
-    "            forecast = model(x, x_time, y_time)\n",
-    "       \n",
-    "        colors = list(mcolors.BASE_COLORS.keys())\n",
-    "        l = x.shape[1]\n",
-    "        forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "        x2 = x[0].cpu()\n",
-    "        y2 = y[0].cpu()\n",
-    "        l2 = y.shape[1]\n",
-    "        i_past = list(range(l))\n",
-    "        i_future = list(range(l, l+l2))\n",
-    "\n",
-    "        if plot:\n",
-    "            plt.plot(i_past, x2[:, 0], c=colors[0], label=f\"past\")\n",
-    "            plt.plot(i_future, y2[:, 0], c=colors[0], label=\"future true\", alpha=0.5)\n",
-    "            \n",
-    "            mtitle = str(save_path).split('/')[-2:-1]\n",
-    "            mtitle = \"-\".join(mtitle)\n",
-    "            plt.plot(i_future, forecast2[:, 0], c=colors[j], linestyle='--', label=f\"{mtitle}\")\n",
-    "    plt.legend()\n",
-    "    plt.title(title)\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f6da82f9",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T06:50:03.264769Z",
-     "start_time": "2022-11-20T06:50:03.262375Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "b8843217",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T10:31:32.788856Z",
-     "start_time": "2022-11-20T10:31:28.668594Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "receptive field [552 234 114]=[138  18   2]*[[ 1  1  1]\n",
-      " [ 1  2  4]\n",
-      " [ 1  4 16]\n",
-      " [ 1  6 36]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 4,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_multi(\n",
-    "           save_paths=[\n",
-    "               Path(\"storage/experiments/Exchange/96S/repeat=0\"),\n",
-    "               Path(\"storage/experiments/Exchange/96Splus/repeat=0\"),\n",
-    "           ],\n",
-    "           i=100\n",
-    "          )\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "id": "d39ba7c5",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T10:31:33.408193Z",
-     "start_time": "2022-11-20T10:31:32.789874Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "receptive field [690 378 242]=[138  18   2]*[[ 1  1  1]\n",
-      " [ 1  2  4]\n",
-      " [ 1  4 16]\n",
-      " [ 1  6 36]\n",
-      " [ 1  8 64]]\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 5,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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w1jg3wYFw5MVG5EoiuTzZmLe3GDm5ckUd+qxSpehz69YVq5ays0XQ06iRyFtRFLG6aMAAcV5Ghpo/07Fj8X0LDBRfKSli80ZtUjARkTnc3cUIiL1euzz0ej0Uo7mnbPlL1ALPq2y0YiM9PR19+/bF7NmzC51brVo1U7pc4vX1+cPo2r4Z9ys9PR3PPfccXnrppULXi5RFyBwQgxcbyMtTp4LkyiJjvr7AzZuGIydF5cdIEREiP0YGODL42btXDV7kSiNA3cuoOC1aAJs2AVu3lnweEVFJdDrTpm4cUUhICJLkXDuAtLS0QqMP7u7uyJXD45rnHTlyxOC+AwcOwL2UaKpFixb44YcfEBUVZfJqJw8Pj0KvXxw5zZWUlIQH8ofyjevZtGjRAseOHUMd431rHBynjWxAO/ond4Y2JlcWaVcMlRS8jBsnfkk89ZS4LfNjZD0ZQK3xUtQyaWMy4Ll8mQXriKhi6tatG7766iv88ccfOHz4MIYPH45KlSoZnBMVFYWNGzciOTkZt/N32+3WrRv27NmDL7/8EomJiZg+fXqhYKYoY8aMwa1btzBkyBDs3r0bZ86cwfr16zFy5MhiA5SoqCikp6dj48aNuHHjBjKKSnbMV6dOHURERGDGjBlITEzEL7/8grlz5xqc849//APbt2/H2LFjceDAASQmJuLHH390+IRdBi82IIvH6XSAv3/R58jgQ24RoL2vKC++KIIMmftVvbpotbtEy9ct6TrSsGGiVRTgxx9LP5+IyNVMmTIFnTt3xiOPPIKHH34Y/fv3R21ZAj3f3LlzsWHDBkRERCA2f0g7Pj4eU6dOxauvvorWrVvjzp07GCZ/qZYgPDwc27ZtQ25uLnr27IkmTZpg3LhxCAwMLJjyMdauXTs8//zzGDRoEEJCQgwSgI25u7vjm2++wYkTJ9C0aVPMnj0bb731lsE5TZs2xZYtW3Dq1Cl07NgRsbGxmDZtGsKNS8E7GJ1iPFHn5NLS0hAQEIDU1FT4Fxcp2NjbbwNvvAF4eam5L8YGDgS+/15M/8hzzHlnhg4Fli8HgoOB69fFfc2aiQCmdWtg167Sr1GlCnDrFvD448APP6j3X7okppViY4H1603vExG5vszMTJw9exbR0dEOuzKFHEdJPy/mfH5z5MUGZOXbosr9S3LVm6xyq9OZ9xr59Y5w4wbw2WfiWE7d5ievl0pW2zUOUCZPFgHR//4HJCSY1y8iIiJLY/BiA7KOUXBw8efIaR852mI0zVqqYcOAWrXE8ZgxYr8iU5ZJa8mE97t3gWXL1Pu1xescfBqUiIgqAAYvVpacDBw9Ko7/9rfiz5NVcqWybLPxv/+JAnNZWSIYkivi2rc37fmNG4uKuwAwc6ZoExPFVJJ09ChgQh4aERGR1TB4sTJZ7NDdXT0uinHxurIEL7VrA7JSdHq6aHW60pdJa8nnnzwJXLgg8nUAka8jp71GjTK/b0RERJbC4MWK8vKA//5XHPfpU3LZfaOE9jLXSZg9W5TmHjpUfK1aZV4gNGaMWFoNAP/3f8CaNeK4Qwe1ku+ffwLjx5etf0REROXFInVW9OmnagKu0dL6QkJDDW+Xp8jTlCllf65eDwwaBHz5pZqrAwATJgAPPSRyYS5cAObPF8u6tbkxREREtsCRFyv6z39EW7t24ZEVY3q94ciMHP2wh8WLxdJtycsL6N1bjOAkJorcGEAszdbstE5ERGQTDF6s6MoV0bZpY9r52krS3t6W74+p9Hrg22+Bw4dF0LJ8ufqYhwewf796+/Jl2/ePiIgqNk4bWVFqqmhNrbPi5QXcvy+OfXys0ydzNG4M/Ppr4fu1OTT51bGJiIhshiMvViTzXZo3N+18bRE7o81BHY4soqddRk1ERKY7d+4cdDpdoc0SqXQMXqzkwgX1WFauLY2fn3rs6MGLzM/hyAsROas7d+5g3LhxqFmzJry9vdGuXTvs1q5UAHD8+HE8+uijCAgIQOXKldG6dWtc0PyCP3jwIB599FGEhobCy8sLUVFRGDRoEK5pN6qzA1cPjBi8WIncS0inK7ySqDj5O5YDKHkrAUcgKwAzYZeInNWzzz6LDRs24KuvvsLhw4fRs2dP9OjRA5fzk/nOnDmDDh06ICYmBgkJCTh06BCmTp1asCfP9evX0b17dwQFBWH9+vU4fvw4Fi9ejPDwcNy9e9du31dWVpbdXttWGLxYycGDojVnn7IqVdTjgADL9sfSZPAi83qIiJzJvXv38MMPP+Ddd99Fp06dUKdOHcyYMQN16tTBwoULAQCvv/46+vTpg3fffRexsbGoXbt2wSgLAGzbtg2pqan47LPPEBsbi+joaHTt2hXz5s1DdHQ0AOD27dsYOnQoQkJC4O3tjbp162Lx4sUGffnrr7/QtWtX+Pj4oFmzZtixY4fB4z/88AMaNWoET09PREVFYa5R7Y2oqCjMnDkTw4YNg7+/P0aPHl3w+rGxsdDpdOjSpYs1/hnthsGLlRw/LlrtaEppqlZVjx1kQ+xiyZVRDF6IqCh37xb/JfMBTTn33j3TzjVXTk4OcnNzC+1s7O3tja1btyIvLw+//PIL6tWrh/j4eISGhqJt27ZYvXp1wblhYWHIycnBqlWroMiN6YxMnToVx44dw9q1a3H8+HEsXLgQwUYb3b3++ut45ZVXcODAAdSrVw9DhgxBTk4OAGDv3r144oknMHjwYBw+fBgzZszA1KlTsWTJEoNrzJkzB82aNcP+/fsxdepU7Mof/v/tt9+QlJSE/8qKqa5CcTGpqakKACU1NdWu/WjZUlEARWnRwvTnvPGGeA6gKDNmWK9vllCliujnqFH27gkR2dO9e/eUY8eOKffu3TO4X/4uK+qrTx/Da/j4FH9u586G5wYHF31eWcTFxSmdO3dWLl++rOTk5ChfffWVotfrlXr16ilJSUkKAMXHx0d5//33lf379yuzZs1SdDqdkpCQUHCN1157TXFzc1OCgoKUXr16Ke+++66SnJxc8Hjfvn2VkSNHFvn6Z8+eVQAon332WcF9R48eVQAox48fVxRFUZ588knloYceMnjepEmTlIYNGxbcrlmzptK/f/8ir71///6y/eNYSXE/L4pi3uc3R16sJDlZtDVrmv6cGjXU46Agy/bH0mQFYLmHEhGRs/nqq6+gKAqqV68OT09PfPjhhxgyZAj0ej3y8vIAAP369cP48ePRvHlzTJ48GY888ggWLVpUcI23334bycnJWLRoERo1aoRFixYhJiYGhw8fBgC88MILWLFiBZo3b45XX30V27dvL9SPpk2bFhxXq1YNAAoSfo8fP472Rrvrtm/fHomJicjNzS24r1WrVhb6V3EODF6sRCay1q9v+nPkjs6AedNN9iArANsxJ42IHFh6evFfP/xgeO61a8Wfu3at4bnnzhV9XlnUrl0bW7ZsQXp6Oi5evIhdu3YhOzsbtWrVQnBwMNzc3NCwYUOD5zRo0MBgtREAVKlSBQMHDsScOXNw/PhxhIeHY86cOQCA3r174/z58xg/fjyuXLmC7t274xW5A24+d02FUl1+HQoZPJmqsqMvUbUwBi9WkpEhWk1AXapatdRjbfKuI5LTxBx5sa/MTGDWLK76IsdTuXLxX8YLGUo617jaeHHnla+vlVGtWjXcvn0b69evR79+/eDh4YHWrVvj5MmTBueeOnUKNUsYUvfw8EDt2rUNVhuFhIRg+PDh+PrrrzF//nx88sknJvetQYMG2LZtm8F927ZtQ7169VBJrpwoph8ADEZnXAkr7FrACy8AmzcD+/aJyrg3bohZWMD0rQEAID85HABQvbpl+2hp8pePDNLIPsaOBT7/XGykKZPEicg069evh6IoqF+/Pk6fPo1JkyYhJiYGI0eOBABMmjQJgwYNQqdOndC1a1esW7cOP//8MxISEgAAa9aswYoVKzB48GDUq1cPiqLg559/xq+//lqwomjatGlo2bIlGjVqhPv372PNmjVo0KCByX2cOHEiWrdujZkzZ2LQoEHYsWMH/v3vf+Pjjz8u8XmhoaHw9vbGunXrUKNGDXh5eSHA0ZexmsPi2Th2Zo+EXb1eJIzNni1u//KLmkSWm2vetYYOVZQePSzfR0tr1058f82a2bsnFVurVurP2tWr9u4NVUQlJWA6upUrVyq1atVSPDw8lLCwMGXMmDFKSkqKwTmff/65UqdOHcXLy0tp1qyZsnr16oLHzpw5o4waNUqpV6+e4u3trQQGBiqtW7dWFi9eXHDOzJkzlQYNGije3t5KUFCQ0q9fP+Wvv/5SFKXopNrbt28rAJTNmzcX3Pf9998rDRs2VNzd3ZXIyEjlvffeM+hjzZo1lXnz5hX6/j799FMlIiJC0ev1SmfjzGc7sVTCrk5Rilnf5aTS0tIQEBCA1NRU+NtgvXFiIlCvnjgeOFBsaPj228Abb4i8EOMlga7ioYeA334TOT0nTti7NxVXnTrAmTPi+IUXgFL+GCOyuMzMTJw9exbR0dGFlh0TGSvp58Wcz2/mvJTT+vXq8alTopXD9640QmdMzjG7anDmLNLS1ONvv7VfP4iIbInBSzlpCyHmV5TG2bOiNXVbAGckty+Qu2CTfWhXe928KfKuiIhcHYOXcjp6VD2WKz5kEBMRYfPu2IzcRLICbKHh0GTwKHf5fuMN+/WFiMhWGLyU08WL6nFOjhjGv3JF3DaqK+RSZPCSnW3fflRkeXmAXAUpdy5fuxb46Sf79YmIyBYYvJSTcX2NL79UP9CffNLm3bEZmUuVv/0G2YGs4gwA33yj5lgNGKAm8RIRuSIGL+Vw/rz46xdQNyr8979F6+FhWLfF1QQGipbBi/3IBHFAbEOxYwfg5ibek65d7dcvIiJrY/BSDnKlkV4PhIeLY1mMUVvq3xXJ4MXMCtZkQadPi1YW2WzQAPjoI3F88aLhSiQiIlfC4KUc5P5agYGFR1nMqazrjBi82N/586LVlkp49lk1effLL23fJyIiW7BJ8PLRRx8hKioKXl5eaNu2LXbt2lXi+d999x1iYmLg5eWFJk2a4Ndff7VFN8125IhoIyOBJk0MH+vXz/b9sSW5caRrlTh0LjJZ3MdHvU+vV7eW+O9/bd8nIiJbsHrwsnLlSkyYMAHTp0/Hvn370KxZM8THxxds921s+/btGDJkCJ555hns378f/fv3R//+/XFERgoORP7l26gR0Lat4WOPPmr7/thScLB6zOXS9pGUJFrjYogdOoh2/37b9oeIyFasHry8//77GDVqFEaOHImGDRti0aJF8PHxwRdffFHk+R988AF69eqFSZMmoUGDBpg5cyZatGiBf8tMWAdy+7Zo27UzTJAMDCy8a6qr0e56feuW/fpRkd24IdqgIMP7n35atCkp3G2ayB7OnTsHnU6HAwcO2LsrLsuqwUtWVhb27t2LHj16qC+o16NHjx7YoS1Nq7Fjxw6D8wEgPj6+2PPv37+PtLQ0gy9byMlRa2y0aSMSdvX5/5oxMTbpgl3JnBdA/RAl25JBY0iI4f29eqk/i0uW2LRLREQ2YdXg5caNG8jNzUXVqlUN7q9atSqStUUqNJKTk806f9asWQgICCj4irBRWVtZiA4AatUSbbVqoh040CZdsCs3N/WYIy/2IeN0udJN0uuBGjXE8apVtu0TkbPo0qULxo4di7FjxyIgIADBwcGYOnUq5F7FX331FVq1agU/Pz+EhYXhySefNEh3uH37NoYOHYqQkBB4e3ujbt26WLx4MQAgOn8FR2xsLHQ6Hbp06WLz78/VuZV+imObMmUKJkyYUHA7LS3NJgGMtrKuHIXYvBn4/XfgmWes/vIOQacTCbty+oxs69490cpARatjR2DZMoCj1mRrigJkZNjntX181NV2pli6dCmeeeYZ7Nq1C3v27MHo0aMRGRmJUaNGITs7GzNnzkT9+vVx7do1TJgwASNGjChYQDJ16lQcO3YMa9euRXBwME6fPo17+f8pd+3ahTZt2uC3335Do0aN4OHhYY1vt0KzavASHByMSpUq4erVqwb3X716FWFhYUU+JywszKzzPT094enpaZkOm+HSJdHq9eoQfd264qui0OvF1Jl25OXXX4HFi4GlSw1XwZDlyX2N5MifVv/+Ini5c8emXSJCRoa6cautpaerO96bIiIiAvPmzYNOp0P9+vVx+PBhzJs3D6NGjcLf/va3gvNq1aqFDz/8EK1bt0Z6ejp8fX1x4cIFxMbGolWrVgCAqKiogvND8udyq1SpUuxnF5WPVaeNPDw80LJlS2zcuLHgvry8PGzcuBFxcXFFPicuLs7gfADYsGFDsefbi9x80c3px67KThZHS01V73vqKeD774G33rJPnyqKvDy1xk5RAbMMaBSFtXiIivPggw9CpxmqiYuLQ2JiInJzc7F371707dsXkZGR8PPzQ+fOnQEAFy5cAAC88MILWLFiBZo3b45XX30V22XhL7IJq3/0TpgwAcOHD0erVq3Qpk0bzJ8/H3fv3sXIkSMBAMOGDUP16tUxa9YsAMDLL7+Mzp07Y+7cuXj44YexYsUK7NmzB5988om1u2oWmYJTkUcD3dzEMmkZvOTlqVNI2tL1ZHnaacv69Qs/rq3wnJxcOC+GyFp8fMQIiL1e2xIyMzMRHx+P+Ph4LFu2DCEhIbhw4QLi4+ORlV8bonfv3jh//jx+/fVXbNiwAd27d8eYMWMwZ84cy3SCSmT14GXQoEG4fv06pk2bhuTkZDRv3hzr1q0rSMq9cOEC9Hp1AKhdu3ZYvnw53njjDbz22muoW7cuVq9ejcaNG1u7q2aReVve3vbthz3JUSeZOLp1q/pYMfnVZCEnTqjH2pVfknb59PnzDF7IdnQ686Zu7Gnnzp0Gt//880/UrVsXJ06cwM2bN/HOO+8U5FDu2bOn0PNDQkIwfPhwDB8+HB07dsSkSZMwZ86cghyXXLkklSzOJpMeMqO7KAkJCYXuGzhwIAY6+JIduTzYWf6TWoPcjFIGL2vXqo/dvGn7/lQkf/0l2uKmLfV6NaFaTnESkaELFy5gwoQJeO6557Bv3z4sWLAAc+fORWRkJDw8PLBgwQI8//zzOHLkCGbOnGnw3GnTpqFly5Zo1KgR7t+/jzVr1qBBgwYAgNDQUHh7e2PdunWoUaMGvLy8EGBcTZLKhXsblZEs/uXnZ9du2JXMk5ZDxNo/YlgczbrOnRNtScUQZWAjK/ESkaFhw4bh3r17aNOmDcaMGYOXX34Zo0ePRkhICJYsWYLvvvsODRs2xDvvvFNoOsjDwwNTpkxB06ZN0alTJ1SqVAkrVqwAALi5ueHDDz/Ef/7zH4SHh6Ofq+8XYwcVON20fGSeR0UOpo2DF7mjtvY+sg652q2kVR0eHkB2NqfwiIrj7u6O+fPnY+HChYUeGzJkCIYMGWJwn6LZzO2NN97AG2+8Uey1n332WTz77LOW6ywZ4MhLGcklqMal2SsSGbzImg7a7aoyM23fn4pEVhMoKXiWozLXr1u/P0REtsTgpYzu3hWtdo+fikYmK9+9KyoO5+Soj2mPqeyuXQOK2hlDBi8l/fzJlRfMPyIiV8NpozKS1U2NdjKoUOSH4717wM8/F3781q2KPTJlCTExYvn5li1Ap07q/XJ7inr1in+unFLi9g1EhRW1WIScB0deyih/qT8qcvFEGbxkZgLy90BwsPq4NgeGzKetm/PDD4aPyZyr1q2Lf76/v+G5RESugsFLGclpEbkZY0Ukl4lnZgKHDonjOnXUyruJifbpl6s4c0Y9PnZMPb51S93RvGvX4p8v679wiwAicjUMXsogL0/UzwAAG21i7ZDkMvGsLCC/YjZatVKrDstaJFQ2u3apx2fPqsdbtohWpyu6uq4k82FkfhYRkatg8FIGskAdANSsab9+2JvMqcjMVJdGx8erIzIyoKGykaNZgOFKLpnA6+OjbgpaFDmFJ/OziIhcBYOXMjh/Xj2uyDkvcuRFm1PRs6eaa8HiaOWj3R9KWzfn4EHRhoaW/Hz5OJetE5GrYfBSBnJEQacr+S9fVyeDFDmF5usrpozkCiPWFykfWUUXEP/GsjCdnI6TO0cXRwbW2dkW7xoRkV1V4I/espPLVIvbV6aieOABw9vR0aKVy8e5RLd8ZC0XSea6yPubNCn5+XIzRu4NR1SYoigYPXo0goKCoNPpcODAAXt3iczA4KUM5HSITEytqIyru7ZoIVr5oSk3bKSyMd4fSibwyimkBx8s+fmRkeoxiwYSGVq3bh2WLFmCNWvWICkpCY0bNzbpeTqdDqtXr7Zu50qwZMkSBBa1lXwFw+ClDOR0SEmb4lUExiMv8fGilR+actsAKhuZqyLr6Rw7JpZPy2m6kpZJA4Yr4bizNJGhM2fOoFq1amjXrh3CwsLgZuOh9CxZLMxJr29vDF7KQK42kh8qFZVx8NK3r2jl9NH9+7btjytJTlaDFPkH4dmzwObN4rhSpdITdmVOEmCYZE5U0Y0YMQIvvvgiLly4AJ1Oh6ioKABAVFQU5s+fb3Bu8+bNMWPGjILHAeCxxx4zeN6IESPQv39/g+eNGzcOXbp0KbjdpUsXjB07FuPGjUNwcDDi8//aO3LkCHr37g1fX19UrVoVTz/9NG5ol7RqJCQkYOTIkUhNTYVOp4NOpzPo28yZMzFs2DD4+/tj9OjRSEhIgE6nQ4pmGPfAgQPQ6XQ4p0mq27p1Kzp27Ahvb29ERETgpZdewl0Hr7HA4KUMZC6HXG1TUWmr6Xp7q0unZcn6vDwxXcHcF/Pt3ClanQ5o104cX7umTh1pA5OSyIRyjryQrSiKqP1kjy/Nps8l+uCDD/Dmm2+iRo0aSEpKwu7du016njxv8eLFZj1PWrp0KTw8PLBt2zYsWrQIKSkp6NatG2JjY7Fnzx6sW7cOV69exRNPPFHk89u1a4f58+fD398fSUlJSEpKwiuvvFLw+Jw5c9CsWTPs378fU6dONalPZ86cQa9evTBgwAAcOnQIK1euxNatWzF27Fizvjdbq+App2Ujg9iSdvStCLSbAtaooR5rC6f16gVs3AiMGwfMm2ezrjk9mTvo6SkK/wEi10VW2jW1srO7uxgBK8uy9fR04PnngTFjgLg4859PFVN2NvCvf9nntV97zbRcxICAAPj5+aFSpUoIM6PeRUhICAAgMDDQrOdJdevWxbvvvltw+6233kJsbCz+pfkH++KLLxAREYFTp06hntHmZR4eHggICIBOpyvy9bt164aJEycW3L548WKpfZo1axaGDh2KcePGFfTxww8/ROfOnbFw4UJ4OWh+BIOXMpDl1o2nTSoabc5Y8+bqsb+/GDFQFBG4AGpLpjl+XLSBgWpui6IA27eL4wYNTLuOh4cIXoxXLplizBhg2TLgjz847URkCS1btjS4ffDgQWzevBm+ctha48yZM4WCl9K0kn/pmOHgwYM4dOgQli1bVnCfoijIy8vD2bNn0cDUXzY2xuClDORUoHbapCLS1rjp3t3wMTc3w/oi2iJrVDq5HUDVqmL1ll6vbkvh7Q28/75p1/HyEsF2WWrubN0q2ps3zX8uVVzu7mIExF6vXR56vR6K0dxTtgmFkkx9XmVZfjxfeno6+vbti9mzZxc6t1oZNs4zvr4+/5e0tm/G/UpPT8dzzz2Hl156qdD1IrVLFh0Mg5cykOXWK3rwAoh8jHPngOHDDe/39jYMXhw898vhyGkeuf1EQIDYYfqBB4AjR9Tl6KWRSeVlyTuSI86s0Evm0Omct4xESEgIkjRzrGlpaTir3VgMgLu7O3KNiieFhITgyJEjBvcdOHAA7qVEUy1atMAPP/yAqKgok1c7eXh4FHr94shprqSkJDyQP1VgXM+mRYsWOHbsGOrUqWPSNR0FE3bLQK5Aq8hbA0jbtolkUONpUeNkZu6vY57bt0UrR41XrgSGDROBoqmBC6C+D+YGL4mJavCZm8s6MVQxdOvWDV999RX++OMPHD58GMOHD0elSpUMzomKisLGjRuRnJyM2/n/Ubt164Y9e/bgyy+/RGJiIqZPn14omCnKmDFjcOvWLQwZMgS7d+/GmTNnsH79eowcObLYACUqKgrp6enYuHEjbty4gYwSalLUqVMHERERmDFjBhITE/HLL79g7ty5Buf84x//wPbt2zF27FgcOHAAiYmJ+PHHHx0+YZfBSxnIX+rmfIhUNK+/LuqMjBghbrt4yYESpaQAb75Zct2bzz4D1q8Xx3l56rmyiu5DDwFLl5q+ykiSSeXa/aeMnT0LzJ0rXldavtzwnJMnzXtdImc0ZcoUdO7cGY888ggefvhh9O/fH7Vr1zY4Z+7cudiwYQMiIiIQGxsLAIiPj8fUqVPx6quvonXr1rhz5w6GDRtW6uuFh4dj27ZtyM3NRc+ePdGkSROMGzcOgYGBBVM+xtq1a4fnn38egwYNQkhIiEECsDF3d3d88803OHHiBJo2bYrZs2fjrbfeMjinadOm2LJlC06dOoWOHTsiNjYW06ZNQ7iDf8DpFOOJOieXlpaGgIAApKamwt/c3/Qm0ulEu2UL0KmTVV7CZSxbBjz1lMjZqKhl6vv0AdauFYGIdqdo6b33gFdfFT9XV64A338PvPiieCwpqXwjfI88Avzyi9gH6cyZos+JiRHBiXZFWKdOIlFXWrECGDSo7P0g15WZmYmzZ88iOjraYVemkOMo6efFnM9vjryYSVuyXeYjUPFklVftX/UVjVzefPiwyFfRSk5WkxsVBXjuORHMACKoKO/UpFzOXlLOkcxtWbNGve/oUcNzOPJCRI6EwYuZfv9dPdaWX6eiaf+NSltxdOGC+DB3NdrVOqNGGT7Ws6dhPsmaNequ5dOmlf+1ZVJ5STlH8jG5HFpbWFCOXMudrImIHAGDFzPJnX0rVzZcKkxF0wYvJdUKycgQ2wpERLhecqg21+XPP9XgZNUqMRoDAFOnqsuhAfHzNWRI+V9bjtwUt1XDrVtqVdLsbDGt9csv6uNyq4dLl8rfFyIiS+HHr5n27xdtGZbgV0ja1X/yQ7soZ8+65nYC6elqQCIXLTz3nGjnzBFtWJhI6O3TR32eJQIXQP05LS4gNK5uvnQp8MMP4jgwUH2+K46IEZHzYvBiptOnRWtm4cMKTQYwV64Uf452dEIuE3YFci8iQCx1BoANG8TqKxk4DBwo2v/8R/xbubkBRdSsKhO5YCA3F/j448Krvg4eNLy9cSPw3/+K49hYoHp1cexKASUROT8GL2aSlUpbt7ZvP5yJrNNU0v46cssFAEhLs25/bEmO1Hl4iKq4Op0IJPr2VZfcT54s2vBw8W909SoQFGSZ14+JUY/HjBFLrbW5R3IbAungQTW5d8ECddrIld4TInJ+DF7MkJmpVhvt1s2+fXEmnp6ivXat+HO0H6jaFV3OTq7a8fMT0zAtWojb//ufaMPCDOsFBQdbLnABxLXnzAGiosTt+/eBH39UH5fFQ0NDDZ/XpAnQqBEgi26yyi4RORIGL2aQybqAKItPpvH2Fm1J++tol/KWVFDN2cjaKvlVugtNB/Xvb/0+TJwoghRZsE7uWQSoU3nNmqk5OYCYYgKAxo1Fyyq7RORIGLyYISFBtD4+homoVDK5YWpJeRPanBftFJKzu3xZtHLVVffuau0VAJgyxXZ9qVFDtNo8lxs3RFu7tlq3KDoa6NBBHDdqpJ6bmGj9PhIRmYLBixn27BEt9zQyjyyUePu2mB7q18+wIBpgGLy4Un6FrPGi3fNs4kTR1qwJ2HLTVrmzvXafOTld17Ah8OWXQPv2ohqw5OurVpSWy7qJyLKWLFmCwMBAm7zWjBkz0Lx5c5u8ljUxeDGD/Muzbl379sPZyP+Td+6IarI//VS4WJt22siVRl5kcCD3KALEaMvGjcC+fbbty4MPilYGVDk5atJwbKwIXLZuBerXN3yezFk6dco2/SSylTt37mDcuHGoWbMmvL290a5dO+w2qh9w/PhxPProowgICEDlypXRunVrXNDUfTh48CAeffRRhIaGwsvLC1FRURg0aBCulZTk56JsGRgxeDGD/Fls2dK+/XA2+Tux484ddW8f42q72pGXkkrZO5OsLDVPpFUrw8e6dbNsYq4pHnpItNnZIin6xAn1MZlIXBQ57Vfc3khEzurZZ5/Fhg0b8NVXX+Hw4cPo2bMnevTogcv5871nzpxBhw4dEBMTg4SEBBw6dAhTp04t2JPn+vXr6N69O4KCgrB+/XocP34cixcvRnh4OO66yi8yEyiKghwbJ8UxeDFRVpZaRp0rjcwjk1Xv3VP30TH+OdeuZiltGwFnoc0tadbMfv2QGjdWp4B++w3Yu1cc6/Uij6s4MviU7x0gitl9/DFXIZHzunfvHn744Qe8++676NSpE+rUqYMZM2agTp06WLhwIQDg9ddfR58+ffDuu+8iNjYWtWvXLhhlAYBt27YhNTUVn332GWJjYxEdHY2uXbti3rx5iM6vM5CQkACdTodffvkFTZs2hZeXFx588EEcMd7oTGPEiBHob5TNP27cOHTp0qXg9vfff48mTZrA29sbVapUQY8ePQoCpoSEBLRp0waVK1dGYGAg2rdvj/NGJc6/+uorREVFISAgAIMHD8YdzZD3/fv38dJLLxWMJnXo0MFgREp+T2vXrkXLli3h6emJr7/+Gv/85z9x8OBB6HQ66HQ6LFmyxOz3xVQMXkykXaHRsaP9+uGM5DLczEx1xZFx8KLde8dVghf5f93NTdR5sTe9Xh1F2bJF3SSypMAFUN+/q1dFO2AAMGKEqBvj4wO0aWOV7pKTu5t1t9ivzJxMk8+9l33PpHPNlZOTg9zc3EI7G3t7e2Pr1q3Iy8vDL7/8gnr16iE+Ph6hoaFo27YtVq9eXXBuWFgYcnJysGrVKihyn41iTJo0CXPnzsXu3bsREhKCvn37IlvO25opKSkJQ4YMwd/+9jccP34cCQkJePzxxwtGQPr374/OnTvj0KFD2LFjB0aPHg2d/MsFYkRp9erVWLNmDdasWYMtW7bgnXfeKXj81VdfxQ8//IClS5di3759qFOnDuLj43HLaNXF5MmT8c477+D48eN46KGHMHHiRDRq1AhJSUlISkrCICtuRc81MybKzlarjTrCB5EzkSXms7LUUvm5uYbnaIOXkjYRdCYyOJABgyOoXl1MFx04oK56kiMrxZGrlE6fFkm/2ukmRRFB2tat6golIgDwnVX8D36fun3wy5PqJlqhc0KRkZ1R5Lmda3ZGwoiEgttRH0ThRsaNQucp00sOHoz5+fkhLi4OM2fORIMGDVC1alV888032LFjB+rUqYNr164hPT0d77zzDt566y3Mnj0b69atw+OPP47Nmzejc+fOePDBB/Haa6/hySefxPPPP482bdqgW7duGDZsGKpWrWrwetOnT8dD+XO3S5cuRY0aNbBq1So88cQTZvUbEMFLTk4OHn/8cdTMXybYJD+x7tatW0hNTcUjjzyC2rVrAwAayGz9fHl5eViyZAn8/PwAAE8//TQ2btyIt99+G3fv3sXChQuxZMkS9O7dGwDw6aefYsOGDfj8888xadKkguu8+eabBd8TAPj6+sLNzQ1hNljVwpEXE8XHi83puEGd+bT768jgxfiPFG3A4ipTxXIrCbmzsyOQFXf/+kvda6q03zNyK4zMTDVw6d5dvGcymVcW3SNyJl999RUURUH16tXh6emJDz/8EEOGDIFer0de/i+rfv36Yfz48WjevDkmT56MRx55BIsWLSq4xttvv43k5GQsWrQIjRo1wqJFixATE4PDRsvz4uLiCo6DgoJQv359HDcucW2iZs2aoXv37mjSpAkGDhyITz/9FLfz91UJCgrCiBEjEB8fj759++KDDz5AklF586ioqILABQCqVatWkGB85swZZGdno3379gWPu7u7o02bNoX628o4mc+GOPJCVlfccuCcHLVejnbXY1cZeTl3TrRyxM4RtGkDrF4t6rvIQFJW3y3O5MliPyaZsP7448B774njsDCxW7h2DyciAEifUvz8byV9JYPb114pfmWOXmf4N/a5l8+Vq19atWvXxpYtW3D37l2kpaWhWrVqGDRoEGrVqoXg4GC4ubmhYcOGBs9p0KABtmrzCABUqVIFAwcOxMCBA/Gvf/0LsbGxmDNnDpYuXVqmfun1+kLTUNoppkqVKmHDhg3Yvn07/ve//2HBggV4/fXXsXPnTkRHR2Px4sV46aWXsG7dOqxcuRJvvPEGNmzYgAfzlxy6yz1b8ul0uoJgzRyVK1cuw3dnGRx5IasrLni5oRn51SZ+OloS6Nq15m9MmJGhjrw4UoK3HOHNylJ3ii7tjycfH2DHDrHa6MwZNXAB1GXV2qkkIgCo7FG52C8vNy+Tz/V29zbp3HL1tXJlVKtWDbdv38b69evRr18/eHh4oHXr1jh58qTBuadOnSqYqimKh4cHateuXWi10Z9//llwfPv2bZw6darQdI4UEhJSaLTkwIEDBrd1Oh3at2+Pf/7zn9i/fz88PDywatWqgsdjY2MxZcoUbN++HY0bN8by5ctL/DeQateuDQ8PD2zbtq3gvuzsbOzevbtQIGfMw8MDucY5AVbCkReyuuJqL127pk5ZaEdeHCl4+fJLYPhwUSFXU9qhVLNmiakxvR549VXr9c9cxkuiW7YEXnml7Ndr00ZMGclkXiJnsn79eiiKgvr16+P06dOYNGkSYmJiMHLkSAAiyXbQoEHo1KkTunbtinXr1uHnn39GQn659TVr1mDFihUYPHgw6tWrB0VR8PPPP+PXX3/F4sWLDV7rzTffRJUqVVC1alW8/vrrCA4OLrSiSOrWrRvee+89fPnll4iLi8PXX3+NI0eOIDY2FgCwc+dObNy4ET179kRoaCh27tyJ69evo0GDBjh79iw++eQTPProowgPD8fJkyeRmJiIYXJb+1JUrlwZL7zwAiZNmoSgoCBERkbi3XffRUZGBp555pkSnxsVFYWzZ8/iwIEDqFGjBvz8/OAp55YtTXExqampCgAlNTXV3l0hDb1eUcTHufr122/q4x07qvc3amS/fhp7+mnRJ3d3855Xo4Z4XosW1ulXeTz6qKJUraooy5eX/1pbtqjv29275b8eOZ979+4px44dU+7du2fvrpht5cqVSq1atRQPDw8lLCxMGTNmjJKSkmJwzueff67UqVNH8fLyUpo1a6asXr264LEzZ84oo0aNUurVq6d4e3srgYGBSuvWrZXFixcXnLN582YFgPLzzz8rjRo1Ujw8PJQ2bdooBw8eLDhn8eLFSkBAgMHrTps2TalataoSEBCgjB8/Xhk7dqzSuXNnRVEU5dixY0p8fLwSEhKieHp6KvXq1VMWLFigKIqiJCcnK/3791eqVaumeHh4KDVr1lSmTZum5ObmKoqiKNOnT1eaNWtm8Frz5s1TatasWXD73r17yosvvqgEBwcrnp6eSvv27ZVdu3YV+p5u375tcJ3MzExlwIABSmBgoALA4N9Be+3ifl7M+fzWKUop67ucTFpaGgICApCamgp/WZee7M7TU0xVaK1cCchE+wcfBHbuFMd16zpONdcuXdQNOXNzxUhKcV57TWx78MorYrQGAFasAKy4WtDucnIAOX3+44/AH3+If4PVqwtX6iXXlJmZibNnzyI6OrrQsmMSNVG6du2K27dv22wLAEdW0s+LOZ/fzHkhmyhqebksUw8YBjbaKSR7006HGE19G0hOFlNFhw+rgYuXFzBwoHX7Z29uboDM2fvpJ2DuXJH/0rQpoJkyJyKyKAYvZBNF/UGmTYLVBi/GIzT2pO1jSXsRFZU30q1bySM1rkKuplq6VF0Cn5UFdOoEHD1qv34RkeuqAL9ayRFoV9TJiq6pqep92oCljEUnrUK7SWRx1bzz8oD//lccP/KISNDt1AlYtsz6/XMEjRqJVlZNbt5cjMjk5QGzZ9utW0QOoUuXLlAUhVNGFsbghWxCVpnV6wHv/JWPKSnq49rtAmy8v1eJtCuf5K7ixr74Qq1N8/774gN7y5biV1m5Gk3tLQDA8uVAnTriOH9/OyIii2LwQjYRECBaX191CiktTX1cO9riKMFLRoZhJWDjpdKjR4vqsy+9JG7Xri2SjSua/AriAIDoaLGFgNyM81rxtceIiMqMwQvZhKzpVKOGWlJeG7xoAxYb1TgqlXZXaMAweff4ceDTT8VojBx1ef112/XNkTRuDFTKL5g6ebJow8NFa25xP3JeLrZwlazEUj8nVitSd+vWLbz44ov4+eefodfrMWDAAHzwwQfwLWGXuk8++QTLly/Hvn37cOfOHS4tcyH//rfIexk3Dhg8WNynLUCpHXlxlODFONk0f+sQACJwAcSHdo8eQGwskF/XqkJauVIsbx89WtyWVZW1OUNaiYlim4Gnn3asIn5kPnd3d+h0Oly/fh0hISEGuxcTaSmKguvXr0On0xXaosBcVgtehg4diqSkJGzYsAHZ2dkYOXIkRo8eXWKJ4oyMDPTq1Qu9evXClClTrNU1soOgIPUDXybsaoMXbcDiKMGLca2ZDM2mt3IjwkaNgHXrbNcnRzVggOHt/M1si9ynKiMDaN1aJGxPncrgxdlVqlQJNWrUwKVLl3BObuhFVAydTocaNWqgUqVKpZ9cAqsEL8ePH8e6deuwe/fugl0nFyxYgD59+mDOnDkIl2PKRsaNGwcABaWXyTXJlUfFBS+OMvr811+i9fUF0tNFH7OyRM0ambzbq5f9+ufIZIE6uZO4dsm4DFwA8e+5cyfQtq3t+0iW4+vri7p16xpsHkhUFHd393IHLoCVgpcdO3YgMDDQYLvsHj16QK/XY+fOnXjssccs9lr379/HfU1VszRtIgU5JLkTu3YljyMGL3KlTHS0KD4HAIcOieBFLu1+9ln79M3RafdvS05Wc2CmTgWOHRPHer0IbBYtYvDiCipVqmSRDyUiU1glYTc5ORmhoaEG97m5uSEoKAjJcitbC5k1axYCAgIKviIiIix6fbK80oIXQHyo2ZtcKVO9upqQeuAA8Nln4tjLq2KuLjKF9r+/Nndo5UrRNmkCdOwojjdutF2/iMg1mBW8TJ48GTqdrsSvEydOWKuvRZoyZQpSU1MLvi5evGjT1yfzyWXT2m0AjIMVbX6JvcgE3Zo11TydY8eADRvEcTG72VM+mY+n3Vbh7FnR/t//AUOGiONLlxyrqjIROT6zpo0mTpyIESNGlHhOrVq1EBYWhmtGBR5ycnJw69YthIWFmd3Jknh6elpvy22yChm8aKfHjYOXW7fUwnb2InNyatcWBefu3AHOnBFfgGF9EyrM21u8x/Lfa9s2dUn8s8+KJO7nnxfThN98o+4JRURUGrOCl5CQEITI6lMliIuLQ0pKCvbu3YuWLVsCADZt2oS8vDy05eR2hffAA6LVBi/GeS4pKepyW3uRowGNGgFVqwIXL4opDtlvuSyYiubvL2r5yOJ+S5aI1tdXzYGpVg1ISgK+/prBCxGZzio5Lw0aNECvXr0watQo7Nq1C9u2bcPYsWMxePDggpVGly9fRkxMDHbt2lXwvOTkZBw4cACnT58GABw+fBgHDhzALVa6cilBQaItKUnX3nnX2oHDpk3VQEqOxtSvrxbeo6LJ9zkpSbSbN4u2SRP1nC5dRLt7t826RUQuwGoVdpctW4aYmBh0794dffr0QYcOHfDJJ58UPJ6dnY2TJ08iQ5PcsGjRIsTGxmLUqFEAgE6dOiE2NhY//fSTtbpJdmBK8KLd98getNV1w8PVvXoAMXLw55+275OzqVpVtDduiGlBWQKkb1/1nPh40aanW+51b90CoqKAF16w3DWJyLFYrUhdUFBQiQXpoqKiCpUJnjFjBmbMmGGtLpGDkDOPJS2J1u44bQ9yOa+7u1jS27s38O67YtXRtm0VZ9PF8qheXbQpKcDvv6vB6jPPqOfI6SNLFiacPRs4f16sClu40HLXJSLHwb2NyOa0aVNZWUVvxGjJv8TLQlbXlQX1unQBVq8WxemaNrVXr5xLVJRo09PVfBc/P8Nl1DVqqMeWWnEkR8VycriKichVMXghm9MGL9euGea3yHoq9s55+eMP0cqpDwDo108UrCPT1Ksn2vv31e0UYmMNz9GWZZJFActLVj8GgP37LXNNInIsDF7I5rRTLjduGI6yyNogxW3oZy3Tpondrv/zH/EXuyysJjeRJPPJOjh5eWrS7osvGp6jXQ5vqRJN16+rx5r1AETkQqyW80JUHO0+N9evqwELIErvZ2Ya7ntkrpwcMc3Tti0wd65pz1myREwxTJwo8i9k3ZkJE8rej4ouJsbwtru72EnamNwmwBIjL5cuGU5Dym0diMi1cOSF7EIGMMWNvJQnePnkE5FUO2+e6c+RCcJ376q7HNeqJWqVUNl4eQE6nXq7VSvDwFVyy/8T6urV8r+m8cJE7RQSEbkOBi9kFzK35ebNooOX8iTs/vabaBVFLZBWGu12BDJw4pRR+WmLX48ZU/Q5Hh6itUTwYrwh/aVL5b8mETkeBi9kF/Kv7ZQUNVjQ6dTg5d69sl/70CH1WCbeliQvr+gVT5Mmlb0PJMjVWm5u6l5GxmSAo81VKSs5TSSD4xs3yn9NInI8DF7ILmSQcvu2YfAiP8jKszGjNndi797Szz9/Xj2uVk20NWuyloslyJVlLVoUPWUEqJtelqeQ9o4dIgiVSb+NG4vW1onfRGQbDF7ILuRUQWqqGrzo9WrwUtaRl5QUkfAryWJzJTlwQLQ6nShh36kT8N//lu31ydBnnwEPPQT8+GPx58gVR3IXb3O9+irQrp0IlOTP0rBhos3NNfx5ICLXwOCF7EIGKWlpRQcvZf3AMU7YlCXpS3LihGg9PMSeRVu2iJECKr/27UWNl5I2k/fzE21Za/vIPZO0Izd//7t6zOXSRK6HwQvZhZeXaO/cUUdZ9Hr1/rIGL7IYmlRUEqhcBi2dOSNabc0Rsh05PVfWKZ7kZMPblSuLnyM5NblnT5m7RkQOisEL2YXMc0hPV/Nb9HrA21sc379ftuvKiqpyibP2A/HMGaBKFTG6o12FJI/lhpFkWw88INqyrjCT0009e4qcpfHjxW35M3DkSPn6R0SOh8EL2YUMXjIy1JGXSpXU4KWse9LI5NuHHhJtbq6Yjti9G2jUSEwt5OSIXAxJVn/V7rlDtlOlimjLOtomf36efhq4cgWYOVPclu/n6dOmX2vNGqB2bZEATESOi8EL2YVcQpuRoY68uLmVb+RFW5n3uefU+xMSgA4dDK+5dat6fPOmaLWbBJLtyCCjLMFLSoo6Ddi6teFjkZGiNady78SJwF9/Ge58TUSOh8EL2YVM0rx3Tw0q3NzUoCY72/xrrl2rHnftqubPTJ8uRnJ0OqB5c3GfTNIF1ETRWrXMf00qPxm8lOU91y6Fr1vX8LH69UVrTq2Xa9dEK/OgiMgxMXghu5D5CJmZhtNGMmm2LB9ku3eLtnJlEQjJHBa5FLpRI2DECHEsP6RkHwD1w45sKzxctLm55j933z7RengUriPTtKlo09MLJ2kXR+ZIZWWp1yYix8PghexCjrxkZanBg7u7mgtTVMXb0sgKrXLqqXp1w8fHjQMee0wc5+aKfW+ystQPzUaNzH9NKj8ZvCiK6UGGdPy4aOXPk9bDD4s2L09dTl2S9HTDAOrzz83rCxHZDoMXsgu5PNY4eJEfQmUJXmTuigyAtNMI7u7AyJEiD0JuTbBqleH0kazKSrYVEaEem1vO/6+/RCsr+WqFhak/Z59+Wvq1du40vL1xo3l9ISLbYfBCdiFXmGRnqyuLtMFLWaYQ5JJZOfXUrJn6WMeO6rRC1aqi3bJF3QtHW2OGbEu7ysvUjTQlmYxbXLJ1mzai3bKl9GsZF7OTgZEjmzVL/OzOmGHvnhDZFoMXsouaNUWbna0m7Lq7q4GHudMHgNhqAFADoPbt1cemT1eP5fTQkSPqyAsDF/vR60UyNSCWOptDjrbVqVP043KbgORksaqtXz/xc1HUz5cMZAMCRJudLfKoHHl7gYULxXTb/Pn27gmRbTF4IbuoXVu0iqIm7Hp4qB8cZQleZLKlnCpo317kPQwZIvYrkuRxcrL617VMICb7kLtAy5o7ppLvecOGRT8+aJAaGHXtKraP2L4dWLq08LlyhVFUlLrqLS5O5FDJukGORv57paaqielEFQGDF7KLevXUY/nXc3mDF1njRVspd80aYPlyw/P69xdtVhZw8mTh55DtyVL+xqX+S5KTo+ZGFbcXlZubWu9FOy20bFnhc+UUVK1aQJMm4lhOXzpi0bozZwxzw955x359IbI1Bi9kFzKpFlBzVTw8yjcCIkdwgoNLPq9RIzX/RdYJKWnjQLI+uSGnOQm72rL/sbHFn9ejR+H75LJ6LbmxY4MGwNdfi3yZjh3FfbKQoiP59lvD2+vX26cfRPbA4IXsRv61LUdMPD3VKR/A/C0CZO6MKWX+e/Y0vF3ctAPZhlzebk7wIjdcrFTJMBg2pt1hWlZeTksDLl0yPE/mtrRqJaY1d+4EVqwQ9ymKuvWEo5DLv2WeWEoKcPSo3bpDZFMMXshu5F/bsiCdp6e6SR+gVr41lbyOKaMoa9cCFy8CEyYA//d/wHvvmfdaZFkyx0SOfphCflDL5xanRQvgjTdE0vaiRepS+YUL1XPOnhUBCmCY6B0erubM/PGH6X2zBTny1KWLGrz961926w6RTTF4Ibsx/tDx8jIceZHTSaaS+Qmy6FlpatQA5s4FvvuOq43sTa4QkyvGTHHqlGi1AW9xZs5UlxPHxIj255/Vx7dvF61OV3jkTo5saLcicARXr4q2Z0+xdxcg9vEiqggYvJDdGOe3eHqKvBfJnL/Cc3LUv5xNDV7IccifBXNG2y5eFK2573ffvqLVFiiUWwEUNYojC+AdO2be65THY4+JoKS4xPUDB9THBg4Uoy+A+UX+iJwVgxeyG+O/mGXeg0ymNSfHQLtKRa4uIechfxZk/pMp5P5UUVHmvZbMgcnOBt56SxzLQEYWT9SSP0/nzpn3OmW1cyewejWwYUPx9Vt++EG0np5imlQmJWdliW0OiFwdgxeyG+NVQTJ4kYm85lQ4lX+FA4ZTT+QcZNBgzqoeOcUkp4FMVaOGWiRx6lSgd2+1Am9RlXrl9bWbeVqTNhfn7beLPse4vy1bqo9xWwOqCBi8kN1Uq2Z4WwYvstUGJKWRNTp0usK7C5Pjk8GLOdVs5eqy5s3Nf71Dh9Q9ldatU0d82rUrfK5chi0L4lnbpk3q8a1bYtm2MTmFJevb6PXqlJcpWyEQOTv+mie7Mc5VkEGLTN40p9qqPFeuJCHnIvebMnV5/KVLao6T3L/IHP7+YmSvVSsx0teoETB7dtGF3uTqo9xc81fAmSsrS13CLVfjTZ5seE5OjpoP9uij6v3y/9P+/dbtI5EjYPBCdiOH7iW53FNO+5gzTC9zXrQJv+Q8ZPBi6m7issicTlf2AoNubuI6WVli2fGrrxY9ateggXq8dWvZXstU33yjBmWLF4v28mW1pg0A/O9/6jmPP67eX7++aE+ftm4fiRwBgxeym+how9syeJFTCHLbAFPIc7nk2TnJQDYvz7QA5uBB0dri/dbr1VFB452nLU1OEVWrJvbkkq/73XfqOf/9r2j9/Q2L87VuLdrr163bRyJHwOCF7Ea7vxGgztnLv8LNqfkhl4iWVGmVHJc2b0Vb9r84cnWQ3AvL2uTeV3LnaWuRwZFc+iwTcmUdGu2xHGmRuncX7f37YnfuVq2AKVOs1lUiu2LwQnZjnPMiAw95vznLZmVBu9KqrZJj8vVVd5Y2ZXRDLluWga61Va8uWmtOyVy6pObUjB4tWhnUyYJ8gKgGDKgBjtS2rXrcoYMoqjd7dtk2OSVydAxeyG70esMcAxl4yLoa5qw8kaM0MtmXnI+sZGvK6IZM0JYrhqytbl3D183LA5o1A7p2tdxrfPCBaN3d1cBEjqbIkcXkZPX/xeDBhs93c1P/AJABjqIYVhImchUMXsiutAm28sNL5sLIvYpMIf9iZY0X5yVznU6eLP1cOdJmPPVoLY0aiVYul963Tyy3TkiwXFG4VatE26SJet9jj4k2L0+8ntwoslIldZm0VlHJy0UttSZydgxeyK5kQiKgBi916ohWUUwffZFTTDI3gZyPHEUxpb6PfL+bNrVef7TkaKBcyn3mjPqYOcUUi5OVpV5n6FD1/tBQNcBfvVpsKAoUrpEkafNgZDCozZchchUMXsiutNM8ctpIDtEDpucY3Lsn2qLKu5NzkEFraUvk09PVPA5tZVlrklsQ5OWJL+3WFdpApigJCSIH5fjx4s9ZskRd/vz884aPyUDljz/UfKCiRl0A4MknRRsRoe4wnZRk+hJ0ImfB4IXsSrtaRAYyHh6ifgcAJCaadh05QmO8IzA5j8aNRVtaJVu5iSIANGxovf5o1a6tHt+4oVZ0Bkrfg+uFF4Bt24CXXy7+nCVLRBsRUXjFnJxG2rwZSEkRx+PHF32dp54SOUOnT4tjQARFq1eX3EciZ8PghexKu7+Rdpdpub+RTDwsjcyPKWvBMrI/WackO7vkkQIZvLi7224rCG1QnJgIXL2q3i5tmkvWXSlpY0dZFfeRRwo/JpN3c3NF26xZ4ZVGWo0biz8AfHzUkcjly0vuI5GzYfBCdqX9UND+xSmLj5m6v5H8xc7gxXlpp4BKKnF/9KhobbmyTLsy7tw5w0JwpW1jIfNz5IohKSdHLIkODVVHDosaUdFW0QWAlStN7nZBQLhjh+nPIXIGDF7IrrS1XrT7EskPJu3wfHHy8tR8AVmPg5yPl5f6MyDL/xdFJrYa70pubTJx9uJFdW8hoPQcHbmBpPF02Jw5wKefqoFQVJRhvpcUHa3+u8THFy5OV5IBA0SrHSkicgUMXsiuZAVRYzIXxpT9jbTnyFUh5JxMqfUip19sHajK0cArVwyrP5e0jUV6uhpY5+QYTofJAK1yZVHyv6TE35kzgU6dgG+/Na/Pcom37AORq2DwQnbVrJloZXVVyZz9jbRTS7b+a5wsKyREtNqKslrXrqkjLz172qZPklwNl5xsWNtFJtEWxTgIO3BAPZaJvjExop5LSfk7kycDW7YY5oWZQlv3yNQdu4mcAYMXsqvu3UVdC7msU5K5MLIYWUkuXRKtTme7BE6yjtJqvUyaJFo3N+CVV2zTJ0lOZd64AWRkqPeXtDrq2DHD23v3qscyV8Z4d3VL0pYOKCnIInI2bqWfQmRdRVUAlbkwplQvlR8Cbvxpdnp16wKbNhW9M3Jenrq7cny87d/vBx4Q7c2bah4LoNYYKorxCJJ200kZTJiTw2IubdHGmzdZSoBcB3/dk0OSf4GbUmE3OVm0cnk1OS9ZMTc9vXC+07ffqoHCvHm27RegjmKkphrmrmgDGWPGNWC0dYvk9yKnTq1BG+Bx5IVcCYMXckhyfyNT5unlh5xMqCTnJZf25uQUv2N0rVpFr8qxNtkfuY+WJJfpF0VOaUpyOuzaNTWJ9sEHLdO/4uh04rUYvJArYYYAOSTt/kallTZPSBBtcfu9kPOIjVVXHBVn9mzb9MWY/PkyDl6AwjVcJBlYyxEQeVuW+Qesm/MCqNWqGbyQK7Fq8HLr1i0MHToU/v7+CAwMxDPPPIP0EpIYbt26hRdffBH169eHt7c3IiMj8dJLLyFVuy6RKgTtbsElLSHNyFDzCoYPt26fyPrc3MS0zPXrRX/duwf83//Zp29yKlOOtMigACj+Z1QmnMtl3TLwkVWCbTFaKJPYiwq6iJyVVYOXoUOH4ujRo9iwYQPWrFmD33//HaNHjy72/CtXruDKlSuYM2cOjhw5giVLlmDdunV45plnrNlNckA+PuqHw4kToh0/XtR/kSMtAPDvf4vRGZ0OePFFm3eTrECvF0vei/qy59Sg8QiJm5saGBS3s7T8W61VK9FmZYnEY/kzbe7S57KQZQgYvJArsVrOy/Hjx7Fu3Trs3r0brfL/5y5YsAB9+vTBnDlzEK4trZqvcePG+OGHHwpu165dG2+//Taeeuop5OTkwI3LSSqUypXFL/+JE8Uy1fnzxf3jx6vl4+WGdvXrM+eFrKtWLcPbHh5iFCYzs/jNGWUy78MPA/JXW2KiumdXcXk9luTmJvrB4IVcidVGXnbs2IHAwMCCwAUAevToAb1ej507d5p8ndTUVPj7+xcbuNy/fx9paWkGX+Qa5swR7ZkzQK9e6v2HDok8mKws9S/YYcNs3z+qWIxHXnx81IC5qG0sMjIMk3LlKM3u3eryfltUhJa/Ok0pO0DkLKwWvCQnJyPUqKiAm5sbgoKCkCzXtpbixo0bmDlzZolTTbNmzUJAQEDBV4ScmCan99xzQIsW4ljuGg2IYfcvvjCcMnr5Zfv0kSoODw/DPBc/PzW5uKjNGbXVdevWBby9xfGhQ+reSNrcLmuRJQQYvJArMTt4mTx5MnQ6XYlfJ+Sfw+WQlpaGhx9+GA0bNsSMGTOKPW/KlClITU0t+Lpo6jbE5BTWr1f/cnzsMbF5HQAsXAjIH4u6dQ13pCayFu0AcECAugdXUUX1ZHXdSpXE82TBuJMn1Z2mmzSxXl8lBi/kisxOIpk4cSJGjBhR4jm1atVCWFgYrhlVmcrJycGtW7cQFhZW4vPv3LmDXr16wc/PD6tWrYJ7CdXHPD094enpaXL/ybkEBwPbtwM//QT885/A668D77xjuEeMzHshsjZPT3UUMChIrUOk3WVakqvg5NRSeLio87Jtmxg9BNS6NtYkfz1qtzQgcnZmBy8hISEIkbunlSAuLg4pKSnYu3cvWrZsCQDYtGkT8vLy0LZt22Kfl5aWhvj4eHh6euKnn36CF7MwK7zWrdVf8pMmieBFGjIEiIuzT7+o4vHxUUcwQkPVhFxtNYdffwVWr1YDGrmi6B//AB5/3HCz0YYNrd7lguBFjvYQuQKr5bw0aNAAvXr1wqhRo7Br1y5s27YNY8eOxeDBgwtWGl2+fBkxMTHYlV+xKS0tDT179sTdu3fx+eefIy0tDcnJyUhOTkZuSWUsqcIIClKTHAMDgS+/tGt3qIKRmzMCQFiY+ALUgCYzE+jXD/j0U3V1kZwueuwxoHNn9fkeHrbZSFQGLyXtwUTkbKz6X2fZsmWIiYlB9+7d0adPH3To0AGffPJJwePZ2dk4efIkMvLHM/ft24edO3fi8OHDqFOnDqpVq1bwxVwWklasADp0AP74g5sxkm0FBqrH1asDNWqIYxkYTJxYuCK0dpZ8zRp1GkkbCFmTTBQ2ZZ8wImdh1V/9QUFBWL58ebGPR0VFQZFrCQF06dLF4DZRUeLiROBCZGvaXZojItT8F1l87osv1Mfk31vaXaN9fcUu6n/7GzBqlG36LIMXjryQK+HfrUREJtKm+0VHq6MseXnA9Onq6MaWLcDp0yKYmTXL8BoDBogvW5HBS0m7XxM5GwYvREQm0k4B1a6trhoCgLfeEm2rViKwiY4GHnrItv0rSuXKojVlh3YiZ8HghYjIRHKDRUCdQgoIUFcb6fXAf/5j+36VhMELuSIGL0REJmrfXrRyKgYQS6Ll3kZVqzpewUSZGKytUk3k7Bi8EBGZqG1bYPlyoEED9T69XkwROSq5hQGDF3IlDF6IiMwwZIi9e2AeGbwYL+EmcmY2KJFERET2IvdfKq7OZ3KyqD9D5EwYvBARuTC5PYF2ZZRWixZA377AlCm26xNReTF4ISJyYXLkpajgZc8eIClJHM+Zw52nyXkweCEicmEPPCDaooqXT5umHufkACNGFD7n0iWgbl1g7lyrdI+oTBi8EBG5MO1+TNrRl7w8YNMmcSynlv77X+DCBcPnv/KKqBY8aZIIZIgcAYMXIiIXpg1e0tLU45Ur1S0Ddu0Su1wrCvCPfxg+//Bh0SoKMHiwVbtKZDIGL0RELiw4WD2+fVs9fu890daoITaPbNNG3N6/3/D52tGWbdtEngyRvTF4ISJyYdqKvzdvinbHDjVIkXkuMnjRBit5eepoTaVKon36aat1lchkDF6IiCqI1FQRkPTtK277+QFTp4rjXr1Ee/euug/S3r3qc199VbSnT9umr0QlYfBCROTidDrR3r4NjB6tjsCsWCFyXQCga1f1/N9+E+369aL19AT69BHHOTms1kv2x+CFiMjF6fN/06emAosXi+PevdWABADc3NQdqGXQsmOHaENDRTE76cQJ6/aXqDQMXoiIXJzMV7l8WV0uvXBh4fMiIkS7e7doZZBSr57InZFB0L591usrkSkYvBARuTgZvBw7pt4nAxWtZs1EK/NaZPXd1q1F6+0t2qNHLd9HInMweCEicnFubqI9c0a9rS/it3/nzqK9eVMk7d67J2736CFaWTOGSbtkbwxeiIhcnLu7aOVIipdX0efJVUh5ecCSJer9HTuKNjRUtMZVeIlsjcELEZGLkyuKbt0SrZ9f0efVqKGO0vzzn6L18VGfL6earl61Tj+JTMXghYjIxcmRl8xM0crNGosSEiLaK1dEGxmpPla7tmhTUizaPSKzMXghInJxcppI7iwtp3+K0r+/aD08RNXd775TH2vUSLQZGRbvIpFZGLwQEbk4T0/D2+HhxZ/78cfAnTti08adO4HGjdXHYmNFm5urVuElsgcGL0RELs44eKlZs+TzfX2Lvl8byBw8WL4+EZUHgxciIhen3ZwRUHNXzOXhodaMMd59msiWGLwQEbk4WVxOiokp+7XkFgIsVEf2xOCFiMjFyYBDkom3ZSFXKsmCd0T2wOCFiMjFaaeNdDq1Um5ZVK0q2kuXytUlclI3bgA//AAkJNi3HwxeiIhcnDYBV9Z8KStZ9+XCBaB9e2DgwPJdj5xLYiKwYIH4sic3+748ERFZm7airnHyrrnq1hXt7dvA9u3iOCWlfKM55DzS0kRrnEdlaxx5ISJycdqRl4CA8l2refPC9x05Ur5rkvO4c0e05Q2Cy4vBCxGRi9OOvFSpUr5r/d//iY0aH3tMXTZ94kT5rknO4+5d0Rongdsap42IiFycdrRFJtyWlV4P/P67OPbzA9LTRR4EVQwceSEiIpvQBi81aljuunJE5/x5y12THFt6umiLq8JsKwxeiIhcnDZ4iY623HWDgkR7+bLlrkmOTU4bMXghIiKrkoXlAHW1kCXIKajr1y13TXJsDF6IiMgmtMFLgwaWu66cgrp923LXJMeWkSFabRK4PTBhl4jIxdWsKf5S1umA+vUtd105BSWTOMn1yeCFq42IiMiq3NyA1FRxrLfgeHudOqK9f99y1yTHJoMXf3/79oPTRkREFYBeb9nABQAaNhRtXh6QlWXZa5NjundPtOUtdlheDF6IiKhMZPACAKdO2a8fZBt5eUBmpjjmyAsRETklLy91NOfoUfv2hawvMxPIzRXH9t7LisELERGVmaenaE+etG8/yPpSUkSr03GpNBEROTG56uTsWeu+zqFDwMcfW/c1qGQy6Vs74mYvXG1ERERl9sADwI0b1q+y266dKJBWuTIwfLh1X4uKlpYmWm9v+/YD4MgLERGVQ0iIaJOTrfca69erlV1//NF6r0MlY/BCREQuITxctLduWe815s5Vj48ds97rUMlk8GLvHaUBKwcvt27dwtChQ+Hv74/AwEA888wzSJdbUhbjueeeQ+3ateHt7Y2QkBD069cPJ06csGY3iYiojCIjRSs/2Kxh61b1+NIl670OlUx+fLt88DJ06FAcPXoUGzZswJo1a/D7779j9OjRJT6nZcuWWLx4MY4fP47169dDURT07NkTuXJ9FhEROQxZZVcWL7O0TZsMr333LpCTY53XopLJbSDsvTUAYMWE3ePHj2PdunXYvXs3WrVqBQBYsGAB+vTpgzlz5iBcjjUa0QY3UVFReOutt9CsWTOcO3cOtWvXtlZ3iYioDORGjzk5ooiZpVehzJkj2oAAdbXLn38CHTpY9nWodHLkxRGCF6uNvOzYsQOBgYEFgQsA9OjRA3q9Hjt37jTpGnfv3sXixYsRHR2NiIiIIs+5f/8+0tLSDL6IiMg2mjZVj60xpbNli2j79BFLdAHgf/+z/OtQ6eS+Rvau8QJYMXhJTk5GaGiowX1ubm4ICgpCcilp6R9//DF8fX3h6+uLtWvXYsOGDfDw8Cjy3FmzZiEgIKDgq7ggh4iILC8oSBQtA8SISHGK2/soI0OM2BTlm2/UD8zXXwfCwsSxiX//koXJFV9OGbxMnjwZOp2uxK/yJtgOHToU+/fvx5YtW1CvXj088cQTyJQbKhiZMmUKUlNTC74uXrxYrtcmIiLzyGmE4oKKF14QlXhnzDC8//vvxXMbNSr6eW++KdqICHFO/friNqv52ocMJP387NsPoAw5LxMnTsSIESNKPKdWrVoICwvDtWvXDO7PycnBrVu3ECbD52LIUZS6deviwQcfxAMPPIBVq1ZhyJAhhc719PSEp6xPTURENhcaKvIhDh8u+nE5zbNggWEAM26caE+cEIFP27bqYxcuiPsBYOJE0bZpI2q+XL1qyd7bT3Ky+Lezd7VaUzl18BISEoIQWZWoBHFxcUhJScHevXvRsmVLAMCmTZuQl5eHttqf0FIoigJFUXD//n1zu0pERDYQHQ389VfxWwTIRM9bt8SoSf36wNq1hlV5X3kF+OMP9faECaL19ARefFEcx8cDM2eKDQIzMhxjyW5ZbdsmpsJ69ADeeMPevTGNDF7svaM0YMWclwYNGqBXr14YNWoUdu3ahW3btmHs2LEYPHhwwUqjy5cvIyYmBrt27QIA/PXXX5g1axb27t2LCxcuYPv27Rg4cCC8vb3Rp08fa3WViIjKoUkT0RY3IqJd6jxrlmjlqIscddi+Xf1wzMwEfv5ZHPftq54TF6de57ffyt1tuzpyRLQHDlj/tXbuBEaOFMFSecj30RFGXqw6WLVs2TLExMSge/fu6NOnDzp06IBPPvmk4PHs7GycPHkSGfk/sV5eXvjjjz/Qp08f1KlTB4MGDYKfnx+2b99eKPmXiIgcQ5s2oi2uBql24PyXX8Sow6lT4vb8+SI4yctTRyAGDFATfLXVdfV6NVl082aLdd8url8X7c2b1ivwl5EhApZ//EOMim3bBly5UvbrydTTgADL9K88rLoxY1BQEJYvX17s41FRUVAUpeB2eHg4fv31V2t2iYiILKxzZ9EqCnD+PFCzpuHj2qJyN24AXbqI44AAMSX07beiiu5HHwGVKgHyY2DkSLWCr1SligiSEhOt8q3YjNxOQVGAo0cNR5Usdf2JE9WpPA8PERDu2CGCQ3NlZqrvoyMEL06SJkRERI4qPFyd2klIMHwsL09dCi2XVOfkAG5uwE8/idvz5onHsrLUonRVqgCffVb4teQgfHlGEBzB7dvqsaX3a8rIAMaMEYGLl5dYtSUDzNKmqfbtEyM1Fy4Y3p+SIlqdzsVzXoiIqOKQeRD5KYwFbtxQj2VVXG9vYO9eoFMncbtVK/E8bZmuH38sehVOjRqildMuzkpWCwaA06cte+1Nm4CkJJHQPGeO+Hdu0UI8dvx4yc9dsEDkyMyeXXR/PT0dY3WUA3SBiIicnayAcfSo4f3aFUjr1gH/+hdw7pxhZV5ABDAXLgDLlwMbNwLt2xf9OtHRotV++DubvDzDPJfz5y17fTklVbs20LixOI6LE6MmN24UXwn5zBn1/Tp61HBDTNlfb2/L9rWsGLwQEVG51aol2nPnDO+XdUN1OjESMGWKOvVTlCFDgG7din9c7qUkVyY5o5QUwzyg5GQ1GdYSZKChXRUUGAhUry6Oi6uELFd4yem9RYvUKT8GL0RE5HLkcmnj6Rz5V767u2VfJzfXeXeXlvk6Pj7iKy/PsnkvclTKeEmzHIXZt6/wc/Ly1Do7Q4aIBN9Ll9SARuboOMKmjACDFyIisoB27URrvFdRUpJoi9mezmwyeAHUCrzORtbDCQhQc3gsGbzIJevGwUt+vdgi/9127xbLtj08gCefVEe/5JYPMgh1lKolDF6IiKjc5GoWwDApVH5QW6oaro+PmjB68KBlrmlrcuecwEB1um3VKqBfP+DVV4vfqNJUMngJDDS8v21bsRT91i0RrGj98otoY2NFLR05PSdHiWQ1ZBls2RuDFyIiKrfAQPHBCAC//67eL1cbWXK6wctLtM468iL/TR54QA0Sbt4U0z27domE5eKYEtjcuSNa4yXN/v5qQcGvvza8ppxK6tlTtLVri/bqVfG4DGKMa/jYC4MXIiKyCFm8TPtXvVz5YsnaIPJall5ibCs3b4q2ShWxX1OnTmKPIxlYfP550fk8b78ttksorUDf3buiDQoq/NiwYSIh9+BBdYRs3z4xWuPhAXTsKO6rVUucd++eGCmSuUxytZe9MXghIiKLqFZNtNoREVnczHgKozyqVBGtXMnkbGRAFxKiFpF74w1g8mSxmic5Gfjuu8LP+/NPEZh89VXJ15crsYr6N2/QQF2m/uWXopUjZY0aqblJPj5iZAgQVXmzskQww+CFiIhcSt26otVWZ5X5FzLgsIT8vX2L3QjS0cmALiTE8P6gIOCxx8TxypWGU0Rpaep00J9/Fr8fUl6euoFicQHjU0+JdudOscv33r3i9oMPGp4na/ds3672T07Z2RuDFyIisohmzUQrp0UAdQrD+IO6PGTehRzBcDZy2XHVqoUfe+opMcKRkqIm9gLqRpaAGAVZtUq9nZkpgpCcHBHUyKAnOLjo12/dGmjYUJw3Y4ZIxtXpgK5dDc+TdWEOHy6+v/Zi1Y0ZiYio4pBVceUmfm5uavE1+Ve8JcgRHhkYOZOcHHU0qqh/Ex8fkTuUkiIq78pz/vpLtDqd2Mxx7VoxCrJ9u8hdycoSgU/37uI8d/eSR0kmTwaefVZdyl6zZuFl0DJIlO+hHPFyBBx5ISIii9CW9N+/X7RZWaK15Adfw4aivX/fcte0lWvXRPCh0xVfM0WOUmm3DZDHLVqIwCQ5GVi4UCTeyn/jo0fVUa/SlqZHRqrTR4DYnsGYcX6Ldu8pe2PwQkREFuHjo1bSldVac3NFa8n6IM2bq8fJyZa7ri3I/vr6ipGpohS1c7YsEtegAfDQQ+K4enXg0UeBJ54Qt69fV6vrmrI0/amnRNE/Dw/g4YcLPy5HuCRHWSYNcNqIiIgsKDBQfIju3StGBBRF3G/JDz5tILR/P9C7t+WubW3a6rrFkbkm2uBFTu/UqgX87W/A2LHq6MqhQ8C334pcGpkMbErwotcDH3wgprKKqoAcHCxWP8kEYFlQzxFw5IWIiCxGfvCePGm4lNnSS2zlh60ly+rbgqyXIpchF0X+G8qE3awsdTqoTh0RdGinhWQwd/euGhz5+prWH72++K0b9Ho1Sdfd3XGq6wIMXoiIyILq1xft5cuGO0xbansASY4slFawzdHI4KWkpeNylEqee/68mH4rLoAIDAQ8PcXxmTOiNd7XqKxkrlJIiLotgyNwoK4QEZGzk/kot26p9V7ktgGWJEcutAGSM/i//wMmT1bg0/pb9P2mL17d8CoupV0yOEdOz9y9K5Y+y0rCoaFFBxB6vVpNVyb2Wip4kX2Ro0GOgjkvRERkMbK8fFaWGrzIJF5LksGL3CfIWdSsCbyy6wl8f+x7AMCaU2sw78956FSzE1qHt8bI5iNRP7g+fHxEpdxz59QAraQVW0FBIi9G/nuUlFNjjieeEEulHS2viCMvRERkMW3bqsfbtonWGlVZ5bSLXF3jTPrW64vK7pXxStwr6FyzM3LycrDp7CbM3jYb7b5ohzv37xQslz53Ts0dKinnxHjZtaWCF39/kRwsN2p0FBx5ISIii3FzE/kX9++LMvaA5fNdADWRtLgy+Y5saJOhiK8dj6q+4ps4dv0Ytl3Yhq8Pf403u7wJP08/hIaKKaDLl9VVR1FRxV/TOHix5EaYjojBCxERWdSDDwJbtqh78Vgq/0JLVp6VmxA6urw8UVnX3x+opK9UELgAQMOQhmgY0hCjWo4quE9ucrlvnzr9FhNT/PWNp5SK2lHalXDaiIiILGr5clFBVrLGKICs9uosVXanTxcBxfjxpp1/M3AD7vjuR2KiCHwaNlRXchXFOKHWkrt4OyIGL0REZFHh4cCgQepta4wCREaKNifH8tcuj48/FpVrN25U78vMBN59Vyx33rGj9GvsvbIX71/uj4OxXZASuAU6HTBmTMnPMc6HKamOTHkpsvKgHTF4ISIii/v8c3WVkQw0LEkmkCqKurePPe3bJ77PMWOAZcuAHj1EddoffhAbIGZlidGob78t/Vp1guogtmpr5Lql4VCzh1Cpw/to0DCvxOeEhhpuN2CtaaNhq4Zh6H+HWufiZmDwQkREFufjA/zyi9iH5913LX99ban6s2ctf31zPfiguipIFtDLzAQGDhTTaADQv79pgVyAVwB+G7EONVKegKLPxka3iXj0m0eRkV18go9er462eHkVv29SeVV2r4x1p9chNy/XOi9gIgYvRERkFQ89BPzvf9bJv9CuYJJVZe0lIwPIzhbH//mPSMw9dkwkKiuK+HJzA778Un1Obimf/T4eXjgyYwXe7fwxPCt54pfEXzD4+8HIySt+nkwuHzdlXyNT3M26i8m/TcbOSzsL7nul3SvYNWoX9Dr7hg8MXoiIyCnJ0QV7V9n96y/1eMQI0TZoIO6Xq4amTBH7DSkKMH8+EBcnRmZKEhCgw6QuL2DD0xvg5eaFn0/9jE/2flLs+bI2THmDl6zcLHy+73M0/LghZm+bjb//+nfkKWLaqnZQbdQJqgOdNiPbDrhUmoiInJKnp0jYvXSp9HOtSQZPOp3hJofBwaJGy40b4hgQGyz+619i36JXXgH+/W9x/927ouBeUVV0O9bsiG8GfIN1p9dhdMvR4vysu6j+fnV4VPLA1E5TMbbNWISGioCirMFLTl4OPt/3OWb+PhOX71wGANQMqInpnafbfaTFGIMXIiJySj4+4kNfFnGzF5nrUtweTjJwkcdffinK7X/0kajhkpMj6uL07y+SfaV//lNMBaWnAzpdf3Ss0R/b/hAjLDUivJB6X5QXfmndS9h6cSvcAqrjSPNj8I1shWt3X8Klk6GoXl0t6AeIkR/jQZPsbODHA7/jn3vG4Mi1IwCAcN9wPFZtPGY8/HcEB4g5ur/+EruD23nQBQCDFyIiclJ+fmIE49o1+/ZDjvzInZ1L06sXMHEiMHcu8PPP6v1HjoiaLnq9GM2ZMaP4a7z1ViWcHHsSv5z6BZM2TMK3R/OXMT0A/HpnA1Z8+3dMGQs0agRs3ixGY86dExtDjholgr67d0UgsnIlkB55DefaHEGQdxBeajodv775HD7a4YllgcBjjwF79wKHDgFXrxau5msPDF6IiMgpyURge2/OePWqaL29TX/OO++IHbjT0sSITYsWQKtW6qiGXg+8/LIY1fH3Fwm+Fy+KUabr14Hdu4HXX6+HenH1cHBtKyw9/m/gbihwsx4Un5t4OUHMPwUEKvhozwL0qNcB773eDHv3VsLevQAqZQGB54Cb9QAAQcn98dLf3sJrD72AaZOCsCu/Hk1KCrB4sTh2dxeBGoMXIiKiMpKra1JS7NoNXL8uWnNyTdzcRDG74kRGisReUwzt2BF3j3dErhdQKVIEGJcjxIjJiFePosVnLwMJQHiLGoiLeA43k3yRXP0TZLvdxmPX9qBWSHW8/LIbgoNfx/nzwPr1IpBasQI4fVoseW/aFHj8ccfZdoDBCxEROSW5ukbuoWQvN2+K1lI7OZvroYfEV1EOJOfgkXqPYMu5LbiSfglXvKcC+TVyAjwDMGpyIrpEqXsLBAUBixYBXbuKabDatYH4eBt8E2Zi8EJERE5Jrsy5e9e+/bh9W7TWLMlfVs3DmuPnIT/jfs59fH/se3y2/zNk52bjySZPYkjjIXjA27DTfn4iJ8fRMXghIiKnJPfzsffmjHLkR05jOSJPN08MbToUQ5vav7S/JTjWwm0iIiITyVL7srqtvciRH+2SZLIuBi9EROSUHGVzxnv3RBsWZr8+VDQMXoiIyClpN2c8f95+/ZCBU/XqJZ9HlsPghYiInJKvr3qs3V/I1uQmi1FR9utDRcPghYiInJZ2c8Yff1Rrvly5IlbOdOli3ddPT1ePGbzYDlcbERGR05KbM06ZIpYst2ghStkvXCgCi99/V0vuW4N2R+uICOu8BhXGkRciInJaPmLPwIJaKydOiHbfPtEqighmrKW4HaXJuhi8EBGR09LmvQBARoYYiUlMVO9bv956ry93lHbjPIZNMXghIiKnJTdn1PrzTyApyfC2tcgdpTnqYlsMXoiIyGk99ZTYlfnFF0X+CwD89pvhlgHHj1vv9cuyozSVH4MXIiJyWhMmiGmiDz8EQkPFfb/+KnJdJO0ojKXJHaWNp6/Iuhi8EBGRS5AVd/fvN7z/3r3yV+Bdv95wWbR044Zo/f3Ld30yD4MXIiJyCbGxos3JEa12o8TNm8t+3c8+Ezstx8QUfkzWlXHEHaVdmVWDl1u3bmHo0KHw9/dHYGAgnnnmGaQXFboWQVEU9O7dGzqdDqtXr7ZmN4mIyAV07mx4u2ZNdSn1xo1lv6587uXLwLffGj4md5QODi779cl8Vg1ehg4diqNHj2LDhg1Ys2YNfv/9d4wePdqk586fPx86nc6a3SMiIhfSvbvh7QYNgPBwcbxnT9mvK/NaAGDSJMPHZGKwzLch27Ba8HL8+HGsW7cOn332Gdq2bYsOHTpgwYIFWLFiBa5cuVLicw8cOIC5c+fiiy++sFb3iIjIxfj6Gi5ZbttWneo5dars1715Uz2+cAHYtEm9zR2l7cNqwcuOHTsQGBiIVq1aFdzXo0cP6PV67Ny5s9jnZWRk4Mknn8RHH32EMBN+Gu7fv4+0tDSDLyIiqphCQtTjbt2Adu3EsXb0xFypqYa3X3xRPZaJwNwawLasFrwkJycj1Ggczc3NDUFBQUhOTi72eePHj0e7du3Qr18/k15n1qxZCAgIKPiK4E8QEVGFVauWaHU6MW0kN2bMylITec0lUzUjI0UrtyAA1B2l5WNkG2YHL5MnT4ZOpyvx64T2nTXDTz/9hE2bNmH+/PkmP2fKlClITU0t+LooazUTEVGF07ataP39xWaMTZqoj509W7ZrZmSI9pFHRJuXBxw6pN4PcEdpWzN7N4aJEydixIgRJZ5Tq1YthIWF4dq1awb35+Tk4NatW8VOB23atAlnzpxBoFG95wEDBqBjx45ISEgo9BxPT094yrKKRERUoc2cKfJbhg0Tt319xSiMogDHjgF165p/zfv3Rdu4scipycoCVq82DIxq1ix318kMZgcvISEhCNFOKhYjLi4OKSkp2Lt3L1q2bAlABCd5eXloK0NjI5MnT8azzz5rcF+TJk0wb9489O3b19yuEhFRBePlBfz4o+F9Hh4iAClr0q6cGqpZE6hWDTh/HvjjD3X66IEHuDGjrVntn7tBgwbo1asXRo0ahUWLFiE7Oxtjx47F4MGDEZ6/du3y5cvo3r07vvzyS7Rp0wZhYWFFjspERkYiOjraWl0lIiIX5uMjgpeyTBtlZqpbDURHi9GX8+fFfkmnT4v7mzWzXF/JNFat87Js2TLExMSge/fu6NOnDzp06IBPPvmk4PHs7GycPHkSGdqJQyIiIgsKCBBtWVIiz5xRj6Ojga5dxfHVq+r1+vQpX//IfFYd6AoKCsLy5cuLfTwqKgqKdvesIpT2OBERUUmqVAHOnSvbBo1ytEanE1NSjz8OvPKK4cqlp5+2SDfJDNzbiIiIXJrMRtAWmzPVhQuirVRJtNHRhvktPj4sUGcPDF6IiMilyfJfxsXmTHHpkmi1i1q1Jczq1y97v6jsGLwQEZFLk+s9ypJeKaea5AaPgCh+Jxnvp0S2weCFiIhcmqztIkv5m0OWK/PzU+/r2FE9Hj687P2ismPwQkRELq1hQ9EqilrqvyTLlonRlU2b1DwZuWIJAEaOFNV7q1QRS6fJ9hi8EBGRS6tdWz0+frzkc995B3jqKVGAbtw4ICVF3B8UpJ4TGSnqxpSwTR9ZGYMXIiJyaW5uYqQEKDl4eecdYMoU9fa5c0Bamjg2Lizv5saquvbE4IWIiFyel5doZVXcosybJ9rKlUV75w5w96445nJox8LghYiIXJ6vr2jPny/+nBs3RPvWW+p9cuQlf1cbchAMXoiIyOU98IBoL18u+vHERCAvTxw/8QTg7W34eGSk9fpG5mPwQkRELk/mrMilz8Z+/lm0bm5ilMV4pKVWLev1jczH4IWIiFxetWqivXWr6Mf/+EO0Mshp1Mjwce2KJbI/Bi9EROTyatYUrcxhMXbkiGjr1RNthw6Gj2uXSpP9MXghIiKXJ6d97t0r+nGZC9O2rWgffVR9TG7KSI6DwQsREbk8uYFiTk7hxzIz1aCmd2/1fFkbxt3d+v0j8zB4ISIil6ct43/ypOFj69erx9rpIjlVZLzyiOyPwQsREbm80FDAw0Mcf/214WP/+59o/fwMq+bKJF1ZtI4cB4MXIiKqEKKjRSuDFWnPHtEa13J57TVR3O6556zfNzIPd2YgIqIKoVMnMWWk3d/o99+BvXvFcbNmhuc/+qjYIoAcD0deiIioQnjySdHeuSPqvXz/PdC1K5CbK6aLZsywa/fIDAxeiIioQujUSV1B9J//AIMHiy0BvLyA3buBunXt2z8yHYMXIiKqEPR6tez/tGlixEWvB06cAJo3t2vXyEwMXoiIqMJo00a0st7L8OFq9V1yHgxeiIiownj8cfXYywtYtMh+faGyY/BCREQVxoABgE4njqdMUWu/kHPhUmkiIqow5GhLYqLIeyHnxOCFiIgqlNGj7d0DKi9OGxEREZFTYfBCREREToXBCxERETkVBi9ERETkVBi8EBERkVNh8EJEREROhcELERERORUGL0RERORUGLwQERGRU2HwQkRERE6FwQsRERE5FQYvRERE5FQYvBAREZFTcbldpRVFAQCkpaXZuSdERERkKvm5LT/HS+JywcudO3cAABEREXbuCREREZnrzp07CAgIKPEcnWJKiONE8vLycOXKFfj5+UGn01n02mlpaYiIiMDFixfh7+9v0WuTZfA9cnx8jxwb3x/H56rvkaIouHPnDsLDw6HXl5zV4nIjL3q9HjVq1LDqa/j7+7vUD4wr4nvk+PgeOTa+P47PFd+j0kZcJCbsEhERkVNh8EJEREROhcGLGTw9PTF9+nR4enrauytUDL5Hjo/vkWPj++P4+B65YMIuERERuTaOvBAREZFTYfBCREREToXBCxERETkVBi9ERETkVBi8mOijjz5CVFQUvLy80LZtW+zatcveXaqwZsyYAZ1OZ/AVExNT8HhmZibGjBmDKlWqwNfXFwMGDMDVq1ft2GPX9/vvv6Nv374IDw+HTqfD6tWrDR5XFAXTpk1DtWrV4O3tjR49eiAxMdHgnFu3bmHo0KHw9/dHYGAgnnnmGaSnp9vwu3Btpb1HI0aMKPT/qlevXgbn8D2ynlmzZqF169bw8/NDaGgo+vfvj5MnTxqcY8rvtgsXLuDhhx+Gj48PQkNDMWnSJOTk5NjyW7EJBi8mWLlyJSZMmIDp06dj3759aNasGeLj43Ht2jV7d63CatSoEZKSkgq+tm7dWvDY+PHj8fPPP+O7777Dli1bcOXKFTz++ON27K3ru3v3Lpo1a4aPPvqoyMffffddfPjhh1i0aBF27tyJypUrIz4+HpmZmQXnDB06FEePHsWGDRuwZs0a/P777xg9erStvgWXV9p7BAC9evUy+H/1zTffGDzO98h6tmzZgjFjxuDPP//Ehg0bkJ2djZ49e+Lu3bsF55T2uy03NxcPP/wwsrKysH37dixduhRLlizBtGnT7PEtWZdCpWrTpo0yZsyYgtu5ublKeHi4MmvWLDv2quKaPn260qxZsyIfS0lJUdzd3ZXvvvuu4L7jx48rAJQdO3bYqIcVGwBl1apVBbfz8vKUsLAw5b333iu4LyUlRfH09FS++eYbRVEU5dixYwoAZffu3QXnrF27VtHpdMrly5dt1veKwvg9UhRFGT58uNKvX79in8P3yLauXbumAFC2bNmiKIppv9t+/fVXRa/XK8nJyQXnLFy4UPH391fu379v22/AyjjyUoqsrCzs3bsXPXr0KLhPr9ejR48e2LFjhx17VrElJiYiPDwctWrVwtChQ3HhwgUAwN69e5GdnW3wfsXExCAyMpLvl52cPXsWycnJBu9JQEAA2rZtW/Ce7NixA4GBgWjVqlXBOT169IBer8fOnTtt3ueKKiEhAaGhoahfvz5eeOEF3Lx5s+Axvke2lZqaCgAICgoCYNrvth07dqBJkyaoWrVqwTnx8fFIS0vD0aNHbdh762PwUoobN24gNzfX4IcBAKpWrYrk5GQ79apia9u2LZYsWYJ169Zh4cKFOHv2LDp27Ig7d+4gOTkZHh4eCAwMNHgO3y/7kf/uJf0fSk5ORmhoqMHjbm5uCAoK4vtmI7169cKXX36JjRs3Yvbs2diyZQt69+6N3NxcAHyPbCkvLw/jxo1D+/bt0bhxYwAw6XdbcnJykf/P5GOuxOV2lSbX17t374Ljpk2bom3btqhZsya+/fZbeHt727FnRM5r8ODBBcdNmjRB06ZNUbt2bSQkJKB79+527FnFM2bMGBw5csQgl48MceSlFMHBwahUqVKhjO6rV68iLCzMTr0ircDAQNSrVw+nT59GWFgYsrKykJKSYnAO3y/7kf/uJf0fCgsLK5QAn5OTg1u3bvF9s5NatWohODgYp0+fBsD3yFbGjh2LNWvWYPPmzahRo0bB/ab8bgsLCyvy/5l8zJUweCmFh4cHWrZsiY0bNxbcl5eXh40bNyIuLs6OPSMpPT0dZ86cQbVq1dCyZUu4u7sbvF8nT57EhQsX+H7ZSXR0NMLCwgzek7S0NOzcubPgPYmLi0NKSgr27t1bcM6mTZuQl5eHtm3b2rzPBFy6dAk3b95EtWrVAPA9sjZFUTB27FisWrUKmzZtQnR0tMHjpvxui4uLw+HDhw2CzA0bNsDf3x8NGza0zTdiK/bOGHYGK1asUDw9PZUlS5Yox44dU0aPHq0EBgYaZHST7UycOFFJSEhQzp49q2zbtk3p0aOHEhwcrFy7dk1RFEV5/vnnlcjISGXTpk3Knj17lLi4OCUuLs7OvXZtd+7cUfbv36/s379fAaC8//77yv79+5Xz588riqIo77zzjhIYGKj8+OOPyqFDh5R+/fop0dHRyr179wqu0atXLyU2NlbZuXOnsnXrVqVu3brKkCFD7PUtuZyS3qM7d+4or7zyirJjxw7l7Nmzym+//aa0aNFCqVu3rpKZmVlwDb5H1vPCCy8oAQEBSkJCgpKUlFTwlZGRUXBOab/bcnJylMaNGys9e/ZUDhw4oKxbt04JCQlRpkyZYo9vyaoYvJhowYIFSmRkpOLh4aG0adNG+fPPP+3dpQpr0KBBSrVq1RQPDw+levXqyqBBg5TTp08XPH7v3j3l73//u/LAAw8oPj4+ymOPPaYkJSXZsceub/PmzQqAQl/Dhw9XFEUsl546dapStWpVxdPTU+nevbty8uRJg2vcvHlTGTJkiOLr66v4+/srI0eOVO7cuWOH78Y1lfQeZWRkKD179lRCQkIUd3d3pWbNmsqoUaMK/YHG98h6inpvACiLFy8uOMeU323nzp1TevfurXh7eyvBwcHKxIkTlezsbBt/N9anUxRFsfVoDxEREVFZMeeFiIiInAqDFyIiInIqDF6IiIjIqTB4ISIiIqfC4IWIiIicCoMXIiIicioMXoiIiMipMHghIiIip8LghYiIiJwKgxciIiJyKgxeiIiIyKkweCEiIiKn8v8AZrfUobzXlQAAAABJRU5ErkJggg==\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_multi(\n",
-    "           save_paths=[\n",
-    "               Path(\"storage/experiments/Exchange/96Sshort/repeat=0\"),\n",
-    "               Path(\"storage/experiments/Exchange/96Splusshort/repeat=0\"),\n",
-    "           ])\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "id": "9af56ddc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T10:31:33.539388Z",
-     "start_time": "2022-11-20T10:31:33.409217Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(\"deeptime\", Path(\"storage/experiments/Exchange/96S/repeat=0\"));"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1e031e3",
-   "metadata": {},
-   "source": [
-    "- [x] try just one predictor\n",
-    "    - [ ] multi input, single output\n",
-    "- [x] comparem ulti\n",
-    "\n",
-    "- try logp? nah\n",
-    "- mae?\n",
-    "- make my own csv with 5m data (maybe 10k rows)\n",
-    "- backtest?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "42214089",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T00:57:32.922740Z",
-     "start_time": "2022-11-20T00:57:32.919738Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f59232cc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1d152b2",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "deeptime",
-   "language": "python",
-   "name": "deeptime"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}
diff --git a/scratch.ipynb b/scratch.ipynb
deleted file mode 100644
index bb5ec84..0000000
--- a/scratch.ipynb
+++ /dev/null
@@ -1,869 +0,0 @@
-{
- "cells": [
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "id": "4e09086b",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:40:00.581454Z",
-     "start_time": "2022-11-20T04:39:59.525562Z"
-    },
-    "lines_to_next_cell": 0
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "import os\n",
-    "from os.path import join\n",
-    "import math\n",
-    "import logging\n",
-    "from typing import Callable, Optional, Union, Dict, Tuple\n",
-    "\n",
-    "from matplotlib import pyplot as plt\n",
-    "from pathlib import Path\n",
-    "import matplotlib.colors as mcolors\n",
-    "\n",
-    "import gin\n",
-    "from fire import Fire\n",
-    "import numpy as np\n",
-    "import torch\n",
-    "from torch.utils.data import DataLoader\n",
-    "from torch import optim\n",
-    "from torch import nn\n",
-    "\n",
-    "from experiments.base import Experiment\n",
-    "from data.datasets import ForecastDataset\n",
-    "from models import get_model\n",
-    "from utils.checkpoint import Checkpoint\n",
-    "from utils.ops import default_device, to_tensor\n",
-    "from utils.losses import get_loss_fn\n",
-    "from utils.metrics import calc_metrics\n",
-    "\n",
-    "from experiments.forecast import get_data\n",
-    "gin.enter_interactive_mode()"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "66d7f095",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-19T23:55:34.939075Z",
-     "start_time": "2022-11-19T23:55:34.820277Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "id": "6ca906be",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:40:03.647641Z",
-     "start_time": "2022-11-20T04:40:01.373201Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "\n",
-    "\n",
-    "gin.clear_config()\n",
-    "gin.parse_config(open(\"storage/experiments/Exchange/96M2/repeat=0/config.gin\"))\n",
-    "\n",
-    "train_set, train_loader = get_data(flag='train', batch_size=16)\n",
-    "\n",
-    "model = get_model(\"deeptime\",\n",
-    "                    dim_size=train_set.data_x.shape[1],\n",
-    "                    datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "model.load_state_dict(torch.load('storage/experiments/Exchange/96M2/repeat=0/model.pth'))\n",
-    "model = model.eval()\n",
-    "\n",
-    "\n",
-    "b = train_set[1]\n",
-    "b = [bb[None, :] for bb in b]\n",
-    "x, y, x_time, y_time = map(to_tensor, b)\n",
-    "with torch.no_grad():\n",
-    "    forecast = model(x, x_time, y_time)\n",
-    "\n",
-    "\n",
-    "plt.title('mlp inr2')\n",
-    "import matplotlib.colors as mcolors\n",
-    "colors = list(mcolors.BASE_COLORS.keys())\n",
-    "l = x.shape[1]\n",
-    "forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "x2 = x[0].cpu()\n",
-    "y2 = y[0].cpu()\n",
-    "l2 = y.shape[1]\n",
-    "i_past = list(range(l))\n",
-    "i_future = list(range(l, l+l2))\n",
-    "for i in range(x.shape[-1]):\n",
-    "    plt.plot(i_past, x2[:, i], c=colors[i])\n",
-    "for i in range(x.shape[-1]):\n",
-    "    plt.plot(i_future, y2[:, i], c=colors[i])\n",
-    "for i in range(x.shape[-1]):\n",
-    "    plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "d4df5270",
-   "metadata": {},
-   "source": [
-    "# auto"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "id": "04499bef",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:40:03.653663Z",
-     "start_time": "2022-11-20T04:40:03.648765Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot(model_name=\"deeptime\", save_path=Path(\"storage/experiments/Exchange/96M/repeat=0\"), i=200, title=None, plot=True):\n",
-    "\n",
-    "    gin.clear_config()\n",
-    "    gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "\n",
-    "    train_set, train_loader = get_data(flag='train', batch_size=2)\n",
-    "\n",
-    "    model = get_model(model_name,\n",
-    "                        dim_size=train_set.data_x.shape[1],\n",
-    "                        datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "    model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "    model = model.eval()\n",
-    "\n",
-    "\n",
-    "    b = train_set[i]\n",
-    "    b = [bb[None, :] for bb in b]\n",
-    "    x, y, x_time, y_time = map(to_tensor, b)\n",
-    "    with torch.no_grad():\n",
-    "        forecast = model(x, x_time, y_time)\n",
-    "\n",
-    "    if title is None:\n",
-    "        title = str(save_path).split('/')[-3:]\n",
-    "        title = \"-\".join(title)\n",
-    "    \n",
-    "    colors = list(mcolors.BASE_COLORS.keys())\n",
-    "    l = x.shape[1]\n",
-    "    forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "    x2 = x[0].cpu()\n",
-    "    y2 = y[0].cpu()\n",
-    "    l2 = y.shape[1]\n",
-    "    i_past = list(range(l))\n",
-    "    i_future = list(range(l, l+l2))\n",
-    "    \n",
-    "    if plot:\n",
-    "        plt.title(title)\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_past, x2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, y2[:, i], c=colors[i])\n",
-    "        for i in range(x.shape[-1]):\n",
-    "            plt.plot(i_future, forecast2[:, i], c=colors[i], linestyle='--')\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 54,
-   "id": "768530be",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:49:59.301321Z",
-     "start_time": "2022-11-20T04:49:59.294905Z"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "\n",
-    "\n",
-    "def plot_multi(model_names=[\"deeptime\"], save_paths=[Path(\"storage/experiments/Exchange/96M/repeat=0\")], i=200, title=None, plot=True):\n",
-    "    for j in range(len(model_names)):\n",
-    "        model_name = model_names[j]\n",
-    "        save_path = save_paths[j]\n",
-    "\n",
-    "        gin.clear_config()\n",
-    "        gin.parse_config(open(save_path/\"config.gin\"))\n",
-    "\n",
-    "        train_set, train_loader = get_data(flag='train', batch_size=2)\n",
-    "\n",
-    "        model = get_model(model_name,\n",
-    "                            dim_size=train_set.data_x.shape[1],\n",
-    "                            datetime_feats=train_set.timestamps.shape[-1]).to(default_device())\n",
-    "        model.load_state_dict(torch.load(save_path/'model.pth'))\n",
-    "        model = model.eval()\n",
-    "\n",
-    "\n",
-    "        b = train_set[i]\n",
-    "        b = [bb[None, :] for bb in b]\n",
-    "        x, y, x_time, y_time = map(to_tensor, b)\n",
-    "        with torch.no_grad():\n",
-    "            forecast = model(x, x_time, y_time)\n",
-    "\n",
-    "#         if title is None:\n",
-    "#             title = str(save_path).split('/')[-3:]\n",
-    "#             title = \"-\".join(title)\n",
-    "\n",
-    "\n",
-    "       \n",
-    "        colors = list(mcolors.BASE_COLORS.keys())\n",
-    "        l = x.shape[1]\n",
-    "        forecast2 = forecast[0].detach().cpu().numpy()\n",
-    "        x2 = x[0].cpu()\n",
-    "        y2 = y[0].cpu()\n",
-    "        l2 = y.shape[1]\n",
-    "        i_past = list(range(l))\n",
-    "        i_future = list(range(l, l+l2))\n",
-    "\n",
-    "        linestyles =  [ '--', '-.', '-', ':']\n",
-    "        if plot:\n",
-    "            ls = linestyles[j]\n",
-    "            print(ls)\n",
-    "            if j==0:\n",
-    "                \n",
-    "                \n",
-    "                for k in range(x.shape[-1]):\n",
-    "                    plt.plot(i_past, x2[:, k], c=colors[k], label=f\"var {k}\")\n",
-    "                for k in range(x.shape[-1]):\n",
-    "                    plt.plot(i_future, y2[:, k], c=colors[k])\n",
-    "            \n",
-    "            mtitle = str(save_path).split('/')[-2:-1]\n",
-    "            mtitle = \"-\".join(mtitle)\n",
-    "            for k in range(x.shape[-1]):\n",
-    "                plt.plot(i_future, forecast2[:, k], c=colors[k], linestyle=ls, label=f\"{mtitle}\" if k==0 else None)\n",
-    "    plt.legend()\n",
-    "    plt.title(title)\n",
-    "    return x2, y2, forecast2, i_past, i_future\n"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 62,
-   "id": "b8843217",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:51:00.720388Z",
-     "start_time": "2022-11-20T04:51:00.083720Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--\n",
-      "receptive field [690 378 242]=[138  18   2]*[[ 1  1  1]\n",
-      " [ 1  2  4]\n",
-      " [ 1  4 16]\n",
-      " [ 1  6 36]\n",
-      " [ 1  8 64]]\n",
-      "-.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 62,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_multi(model_names=[\"deeptime\", \"deeptime2\"],\n",
-    "           save_paths=[\n",
-    "               Path(\"storage/experiments/Exchange/96M/repeat=0\"),\n",
-    "               Path(\"storage/experiments/Exchange/96Mplus/repeat=0\"),\n",
-    "           ],\n",
-    "           i=100\n",
-    "          )\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 64,
-   "id": "d39ba7c5",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:51:14.490633Z",
-     "start_time": "2022-11-20T04:51:14.197106Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "--\n",
-      "receptive field [690 378 242]=[138  18   2]*[[ 1  1  1]\n",
-      " [ 1  2  4]\n",
-      " [ 1  4 16]\n",
-      " [ 1  6 36]\n",
-      " [ 1  8 64]]\n",
-      "-.\n"
-     ]
-    },
-    {
-     "data": {
-      "text/plain": [
-       "1"
-      ]
-     },
-     "execution_count": 64,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot_multi(model_names=[\"deeptime\", \"deeptime2\"],\n",
-    "           save_paths=[\n",
-    "               Path(\"storage/experiments/Exchange/96M2/repeat=0\"),\n",
-    "               Path(\"storage/experiments/Exchange/96Mplus2/repeat=0\"),\n",
-    "           ])\n",
-    "1"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 67,
-   "id": "9af56ddc",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:52:00.890235Z",
-     "start_time": "2022-11-20T04:52:00.783659Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "image/png": 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vv/5a6GfNmjUL8+fPR6dOnVChQgX8/fff+Omnn/Dkk0/igQceMDl38uTJuPvuu3H77bdj2LBhyMjIwEcffYRbbrkFw4cPN57Xq1cv1K5dGz179kTdunVx9epV/P333/j1119x6623Gr8bexXnelNSUnDvvfciKSkJycnJmDVrFh555BHjYPC6devirbfewvjx43Hs2DHcd999qFixIlJSUrBw4UIMHToUzz//POLi4lC3bl08//zzOH36NEJCQjB//nyL3XSq6vXss8+ia9eu8PX1Rb9+/QAUf0zOxIkTMWLECDz44IPo2rUr1q5di1mzZuHtt99GRESE3e9JVGpcc1MXkWcp7BZyAOKrr74St956q6hWrZrJ7d1CCPHJJ58IAOLHH380Hjt+/Lh4/PHHRVRUlAgICBB16tQRI0aMMM7Sa23GY3V7s7qNWwgh1q9fL9q3by+CgoJElSpVxIsvvij+/PPPAud17txZNG7cuMC1DRgwQNSsWdPk2NGjR0WPHj1EUFCQiIqKEmPHjhXz588XAMSGDRtMzt2+fbvo3bu3qFSpkggICBA1a9YUffv2FcuXLy/y97px40bRqVMnER4eLgIDA0Xz5s3FjBkzTG7R1lu2bJlo3769CAwMFBEREeKxxx4TZ8+eNTnnhx9+EP369RN169YVQUFBIjAwUDRq1Ej85z//EZmZmUW2Sf2Of/75Z4vP23K96hbyvXv3ij59+oiKFSuK8PBwMXLkSHHt2rUC7zl//nxx++23i/Lly4vy5cuLuLg4MWLECHHgwAHjOXv37hWJiYmiQoUKIjIyUgwZMkT8+++/AoD49ttvjefl5uaKZ555RkRFRQmDweCw2Y+/+OIL0aBBA+Hv7y/q1q0rJk+ebPV7InIXBiFK8E8+IiozPv74Y4wePRqnTp1C1apVXd0ct/b666/jjTfewIULF4xda0RU+risAxEVYD5u4/r16/j8889Rv359Bhwi8hgck0NEBfTu3Rs1atRAixYtkJGRgVmzZmH//v2YPXu2q5tGRGQzhhwiKqBr16746quvMHv2bOTl5aFRo0aYO3cuHnroIVc3jYjIZhyTQ0RERF6JY3KIiIjIKzHkEBERkVfyujE5+fn5OHPmDCpWrGjXzLFERETkOkIIZGVloUqVKvDxcUwNxutCzpkzZ1C9enVXN4OIiIiK4eTJk6hWrZpD3svrQk7FihUByF9SSEiIi1tDREREtsjMzET16tWNf8cdwetCjuqiCgkJYcghIiLyMI4casKBx0REROSVGHKIiIjIKzHkEBERkVdiyCEiIiKvxJBDREREXokhh4iIiLwSQw4RERF5JYYcIiIi8koMOUREROSVGHKIiIjIKzHkEBERkVdiyCEiIiKvxJDjJFevAh99BBw4IB9v2ACMHg28/jpw5YpLm0ZERFQmeN0q5O6iSxfgn3+ANWuARYuA4cOBHTvkc8HBwIsvurJ1RERE3o+VHCeYN08GHAD45Re5vXhRe/7330u/TURERGUNQ44TfP+9th8TI7fZ2dqx9euB9PSi3+fiReDIEYc2jYiIqMxgyHGw3Fxg9Wrt8fnz8pgKOcHBQF4esGxZ0e/Vti1Qrx5w4oRz2kpEROTNGHIcbMsWIDMTCA2Vj4UAUlOB69fl4wcekNuiQs7Jk0BKitxfudI5bSUiIvJmDDkO9vffcpuYCFSuLPdVWAGAhAS53bat8PdZu1bbP3nSce0jIiIqKxhyHGz9erm96y4gNlbu68fVdOggt7t2ATdvWn8ffcjZt8+xbSQiIioLGHIc7PRpua1XT6vkqJATGCiPh4YCN27I8LJ7t2mgUdas0fYZcoiIiOzHkONg587JbUyMFnKOHpXb4GDAYABatJCPt22T3Vp33SUHKCvHjwN792qP9+8H8vOd3nQiIiKvwpDjQHl52nw4MTEFu6uCg+W2ZUu5XbJEhqLcXODQIe19vvhCbjt3Bvz9gWvXZPAhIiIi2zHkONCFC7LiYjAAkZEFu6vMQ85vv2mvVbeJ37gBfPWV3H/2WeCWW+T+7t3ObTsREZG3YchxINVVFRkJ+PlpIUdVd1TIadVKbq9d016rQs769bLrKjYWuPdebaDy1187t+1ERETehiHHgfTjcQCtu0pRIScuTg5C1lO3iavxOy1byqA0ZoysDP3yi7wji4iIiGzDkONAKuSocFOpkunzKuT4+QFNm5o+pyo5x47Jba1actugAdCnj9xnNYeIiMh2DDkOZF7JCQ83fV6FHEDrslKshRwAuP9+uVWLfhIREVHRGHIcyJ6QowYfK6q7Ss2OrA858fFyu3276TgeIiIiso4hx4HMQ065ckD58trzlkKOz/++gbQ04MoVy5WcmjVlF1huLrB1qzNaTkRE5H0YchzIPOQAQFiYtq8POa1bAwMHAhMnAiEh8tiRI8CZM3K/dm3tXINBq+YkJ8vt1ava7MpERERUEEOOA1kKOfouK33I8fUFvv0WePlloEYNeey33+Sq5cHB8jZ0PRVy1BIQffoAderILiwiIiIqiCHHQW7e1Lqa9LeOWws5ej16yO0rr8htrVqyeqPXtavc/vkncOAAsHSpnDjw1VdL2nIiIiLvxJDjIH/9BWRkANHRQJMm2nFbQs748fJ1Ss2aBc9p1kzedn7jBvDkk9rxv/8GLl0qWduJiIi8EUOOg8ycKbcPPyznwVFsCTmhocDnnwN168pVyocNs3zeY4/J7bp12rGcHGDoUDkomYiIiDQMOQ5w7ZqckRjQgohibeCxufvuAw4flgt19upl+ZzHHjMNTW+9JRfwXLAA+PjjYjSciIjIi/kVfQoV5dgx4Pp1eZeU+SR/tlRybBUbC2zZAowYIQcujxsnxwK98Qawc2fJ3puIiMjbMOQ4gLqVu1q1ggOGHRlyAHlH1R9/aI/VXVicJJCIiMgUu6sc4NQpua1WreBzjg455oKC5JYhh4iIyBRDjgOoSk7VqgWfY8ghIiJyDYYcB9B3V5ljyCEiInINhhwHUN1Vlio5tt5dVVwMOURERJYx5JSAEHJrayVHBRJH8vaQk5wM7N/v6lYQEZEnYsgppu+/l3c2rVljeyUnMNDx7fDmkHP2LNChA9CwoZzpmYiIyB68hbyYfv4ZSEsDFi4Ezp+XxyxVcoKDgS+/lDMTmy+66QjeHHLUWmAAsHKltn4XERGRLRhyiunQIbldv15uAwKASpUsn6tfa8rRvDnkXL6s7S9YwJBDRET2YXdVMeTmAkePyv3Nm+W2atWCEwGWBm8OOfqFRxctkrM7ExER2YohpxhOnCj4B7d+fde0RYWcvDzvCwH6kHP+PPD66y5rChEReSCGnGJQXVV65mtWlRb9HVveVs1RISc6Wm4nTQJ27XJde4iIyLOUSsiZOnUqatWqhcDAQLRr1w6bNm0q9Pyff/4ZcXFxCAwMRNOmTbFkyZLSaKbNLIWcli1Lvx2A6R1b3hpyhg0DkpLkLft//unaNhERkedwesj58ccfMWbMGEyYMAHbtm1D8+bN0bVrV5xXtySZ+eeff/Dwww9j8ODB2L59O+677z7cd9992L17t7ObajN3CjkGgxZ0vDXkVKoE3Hmn3E9Odl17iIjIszg95Hz00UcYMmQIBg0ahEaNGmHGjBkIDg7GN998Y/H8Tz75BElJSXjhhRfQsGFDTJw4Ea1atcJnn33m7KbazFLIqVOn9NuheOvgY33IiY+X+8nJ2iSMREREhXFqyLlx4wa2bt2KxMRE7QN9fJCYmIhkK/8kT05ONjkfALp27Wr1/JycHGRmZpr8OJua4VjPx4Wjm8pCyGnTBvDzkxMEnjjh2nYREZFncOqf5osXLyIvLw8xMTEmx2NiYpCammrxNampqXadP2nSJISGhhp/qlev7pjGFyIjQ24nT5bLNnz+udM/slDmIcdbKh1paXJbqZK8xhYt5GN2WRERkS08/u6q8ePHIyMjw/hz8uRJp3+mCjlJSbLaMHSo0z+yUPqQ06cP0LgxcP26a9vkCPpKDqCFnIMHXdIcIiLyME6d8TgyMhK+vr44d+6cyfFz584hNjbW4mtiY2PtOj8gIAABAQGOabANhABUj1hoqGsmADSnQs7Fi8D8+XL/4EGgWTPXtamkcnKAq1flvgo5arHTUuiRJCIiL+DUSo6/vz9at26N5cuXG4/l5+dj+fLliFcjSc3Ex8ebnA8Ay5Yts3p+abtyBcjPl/uhoa5ti6JCzrZt2jH9RHqeSLXf11f7PYeEyC1DDhER2cLpa1eNGTMGAwYMQJs2bdC2bVt8/PHHuHr1KgYNGgQAePzxx1G1alVMmjQJADBq1Ch07twZ//d//4cePXpg7ty52LJlC7744gtnN9UmqqvKz890Ij5XUu3YulU7dvGia9riKCrkRERo1TKGHCIisofTQ85DDz2ECxcu4LXXXkNqaipatGiBpUuXGgcXnzhxAj66W5M6dOiAOXPm4JVXXsHLL7+M+vXrY9GiRWjSpImzm2oT9Qc2JMQ9uqoAyyHHWyo5+kVPGXKIiMgepbIK+ciRIzFy5EiLz61atarAsQcffBAPPvigk1tVPKqS4y5dVYAWclTbAM8MOT/+CKxeDXz6KXDhgjwWGak9r37n+uskIiKyplRCjjdx55Cj54ndVePGAceOyTvEUlLksZo1tedZySEiInt4/C3kpc1TQo6nVXLy84FTp+R+Sgpw9Kjc188kzZBDRET2YMixk6eEHE+r5Jw/D+Tmyv1jx7RKDkMOEREVF0OOndwx5AQHa/tqMLSnVXL0S2XoKzm1a2vH1e88M9N7ZnUmIiLnYcixk/7uKnehr+Q0bSq3nlbJUV1VAHDkCHD8uNy3VMnJz9cmCiQiIrKGIcdO7ljJ0YecTp3k1pMrORs2ADdvAuXKAVWqaMeDguTkgAC7rIiIqGgMOXbylJCTkSGDgqewtLJ7rVpaqAFkVxzH5RARka0Ycuzk7iHnttu0cTkVKwL//OOaNtlL312l6LuqlKJCjhDAlCmA2cogRERUBjHk2MmdQ05EBFC5MlC+vHyckwN89JHr2mUPS5WcuLiCx4qaEPDPP4FnnwUSEx3XNiIi8kwMOXZyx5Cj7kDq0EFWca5c0Z6LiHBNm+ylKjkVKmjHnn++4HlFVXLWrtX21UKqRERUNjHk2Mkd765q3VquQD5zpvZYuX7dNW2y5OjRgt1SGzYAWVlaJeezz4BBg+S51aoVfA9LIScrC0hOll1V+ve/fNmx7SciIs/CkGMnd6zkAEDLlkBYmNyfORNo21buu8s6T1lZso3Vq2uVpunTgfh44K675LFy5YAHHwS++cZ0fhw9SyHnkUdkFevHH4E9e7Tj588751qIiMgzMOTYIS8PSE+X+ypQuKOGDYExY+S+ebfO9evAzp2lP5nekSNaW+bMkbe4P/20fLxli9x27246saEl+gkBld9+k9vXXwd27dKOnztX4mYTEZEHY8ixw4kTcukBf385wNedqYqHeSVn9GigefPSv/tIP7B42jRg8uSC5zz6aNHvY+26AODAAeDGDe0xKzlERGUbQ44dDh2S27p1TedvcUeWKh6AtlzCkSOl2x59yPn3X2DqVLn/wANyGxoK3HNP0e+jQs7MmTLo5ORYP5eVHCKiss3P1Q3wJCrk1K/v2nbYwlrFQ1U6CgsHzmA+4Dg9HfDxAWbMADp2BBo3BgIDi34fNQPy+fNAnz7Al19aP5eVHCKiso2VHDt4UsixVslRIae077pSlZwOHbRj7dsDkZHAqFG2z2vz6KPAxIlyf9Mmy/PrjB0rtww5RERlG0OOHTwp5KhKzo0bwJIl2oKXrgo5qpIzcCAQHS33u3e3/338/eX8OQaDDHD//mv6fJ8+QL16cp/dVUREZRtDjh08KeRUrKjt9+ghxxEBruuuUhWXWrWADz8E7rwTGDy4eO8VGAjUqCH3V6+W227d5Did77/XQhQrOUREZRtDjo1yc4GUFLnvCSHHx8c06OTlya2rKzlVqwKPPQasWAHExhb//VS1ZtUquW3QQHZlBQVpIefcOTkmafFiTgxIRFQWMeTY6NgxGXQCA+Ufak9gacJCV4Scq1e1AdCWZjEuDhU0VbVGDUgGgJgYuT16FLj9dqBXLxmofv7ZMZ9NRESegSHHRvn58nbn7t1llcQTWFp64uZNuS3N7irVVVWhguOWwzCvpumDp6rkAMDu3XJ74wYwd65jPpuIiDyDh/y5dr1bbgHmzQPmz3d1S2xnXsnJz3deJaew91Nz8jiyAmYecvSVnJAQoF8/uW3RQluJ/cABx30+ERG5P4YcL2ZeNbl2zTkDjxculFWa776z/Pzvv8ut/vbxktKHnLAwoFkz7bHBAPzwg+wi274duO8+efzwYW1sEhEReT+GHC9mXsnJznZOJWflShkeVqwwPZ6RAfz3v3LBTUCb3dgR6teXkwh26ACsXQtERFg/t0YNICBABrvp04F//nFcO4iIyH1xxmMv5mf27Tor5Kj5aM6eNT3+1FPaOJjAQNsn/LOFry+wZo3t59avL8fnPPOMPLZgAXD//Y5rDxERuR9WcryY+WzHV644Z+CxCjlnzmjH0tNN72bq109WU1ylQQPTx/37ayvKExGRd2LI8WLmf8T161g5s5KTkSFnJc7LA+rUAX75BfjsM8d9XnGoeXWUa9e0xUqJiMg7sbvKi5mPU9GHHkeGnNRUub18WXaJtW6t3VE1bBhw772O+6zi0t9WHhcH7N8PXLjguvYQEZHzsZLjxSZPlpPhKfpZfx3VXZWTYxqeVq/WAk58PPDEE475nJIaPBjo2hX4+mttQkIVcg4dkt1ZY8a4rn1EROR4DDlerE4deeeRCjrOqOSYrw+l5hHq2FHexRQZ6ZjPKanQUGDpUhm6oqLksQsX5EDshATg4EEZCtXAbCIi8nwMOWVAcLDcOiPkmK/0rUJOfLxj3t8ZVMg5f14u6nnypPbc1q2uaRMRETkeQ04ZUL683OpDjqO6q8xDjvoMTwg5Fy5oq5gra9eWfnuIiMg5GHLKgNKs5CieEnI2bJD7d98tt7bOvUNERO6PIacMsBRycnIAIUr+3pZCTosW2krg7kiFnP375aBjAHjhBbldt06u8WWLnTsLzkVERETugyGnDLAUcgDHDLJVIUcfar7+uuTv60wq5Bw8KLdxccBtt8n9jAzg6tWi32PtWqB5c6BbN+e0kYiISo7z5JQBlsbkALLLqqSzEKsJAIcPB3btAvr2BVq1Ktl7OpsKOUp8PBAUJJd/yMuT1ZmKFQt/jxkz5JbrYBERuS+GnDJAVXL08+QAMuSYL+Kpp7qzDIaCz40ZI7u81q+Xj9u1AyZMKHlbS4N5yOnUSV5jSIj8HWVmAlWrFv4eWVnafl6eDEhEROReGHLKAGvdVYXdYZWTA7RpI//YL11q+tylS3JOGSUoCOjc2SFNLRXh4aaPk5LktmJFGXL0AcYafWA8dw6oUsVx7SMiIsfgmJwyoLDuKms2bJCrdv/5Z8ExKmoZB+Wuu2TQ8RQ+uv/qfX2B2Fi5HxIit7YMJlbjeQDTeXaIiMh9MOSUAaqSk5dnerywSk5KirZ//Ljpc+Z3VCUkFL9trqa/1d2WkCOEvCtLP9MzQw4RkXtiyCkDVMgxV1glZ/dubd885OgrOdHRwMMPF79trjJ+PFCpEvDNN9oxNdi4sO6qiROBhg1Nj5065fj2ERFRyTHklAGOCjmqu0tVcvr2lYFHdfd4knfekZMB1q+vHbOlkrNwYcFjrOQQEbknhpwyQI3JMVdYd9WePdr+sWPA00/LAbs//KCFnMqVLd955SnM225LyLl5U9uvU0durVVyDh4EDh8ufvuIiKhkeHdVGWBvJSc93fQP93vvafuLFmnv586zGheHLSHn9Gm53bxZVrj69LFcybl2DWjQQO5fvWr9OyAiIudhJacMsDfk7N1r/b2OHLE8y7E3KGpMTna21mVXrx5QrZrct1TJOXFC29+2zWFNJCIiOzDklAH2dlepEGNpgru9e7VZjr0t5BRVyVFVnOBgOYlirVracfPX6Ks7yckObSYREdmIIacMsLeSo/5gN22qHXv4YbkExLVrwI4d8lhZDTlVq8rxPDExQO3ackFP8yCjr+Q4K+TcuCHvEuPSEkREljHklAHqj7e5okJOvXrasaSkgrdOe2vIsdZdpQ85SqdOcjtgAJCYCFy8KB/rQ87ChcC8ebavbm6rzz4D3n1XW1yUiIhMMeSUAf7+lrusrHVXqZATFgZ89BEwZIis5DRubHpedLRDm+lyakyOtUrOmTNyaynknDsHLF+uzbtjPhj5wQeB+fMd11ZALoiq6CdvJCIiiSGnjIiIKHisqEpOSAgwejTwxRdAuXJAkybaORUrlnwFc3djT3eVokKOsmmT3KpKjp/u/kV9KHEE/SSNf/zh2PcmIvIGDDllRHFDjl6fPtrcMPrlELxFcbqr6tYFGjXSHq9dK5d+UJWcv/4C3n5b7uu7sEpKCG1sFAAsWeK49yYi8hacJ6eMsBRyiuquMg859erJye0OHvTOVbeL6q6yFHIMBhlsLl+W3Xnnz8vfjwo01atr3Vzmy2PYY9Mm+TtXt62fPGm6Evrq1TL4ePLkjEREjsZKThmhDzn+/nJ75Yrlc62FHED+EW3QQAsE3kRd77VrpjMbK5ZCDiB/t3XrAu3by8dffy3fA5ChpGZNuV/ckLN7t3zvdu20RVb//Vdu4+LkqupXrhRcHZ6IqKxjyCkj9CFHrTWVlmb53MJCjjfTBzfzLqv8fMsDj/V695bbDz6Q2+hoIDBQCzknTxZcCd4Wv/wiqzRnzmjdUqqr6tZbtfc/dMj+9yYi8mYMOWVEpUrafuXKcsuQY8rfX4YSoGDIuXAByM2VlSxrC5KOHAkMHqw9HjBAbqtUkRMr5uZqEynaQz8PzvTpcqtCTosW2iKjDDlERKYYcsoIS5WcS5csn1tWQw6gVXMyMkyPq66q6Gh5p5klPj7yTrTly2XV5v335XFfX20sjb2Dj3Nz5ZgfZelSubQEQw4RUdEYcsoIfchhJce6qCi5VUtbKNbG45jz8QHuuksLNUpxx+Vs2yarSuHhsiIkhJxB+ehR+Xzz5lrIee89oHNn698rEVFZw5BTRnBMjm2qV5db80U3bQ051uhDzhtvAD/+aNvrVq2S206dgGbN5P6cOXJbrZrshlQhBwDWrAFmzixeG4mIvA1DThlhqZKTkSG7Q/RycuSaSEDZDDnWVhYvatBxUVR4Wr4ceP114JlnbHvdypVye8cd2ozTKuS0aCG3+pADaHdeERGVdQw5ZYQ+5OiXY9DPtQKYzhFToYJz2+SOVBgxX5ahpJUcNfD7wAG5vXjRdC2r3Fzg1Ve1yg0gb2Nft07u33mnNuO0ep0KOWo1dIWrnhMRSQw5ZYQ+5AQGynWpgIKDj1XIqVBBDpgta6xVckoactTvW72vEKaDm1esAN56Sy6joWzbJue/iYiQK8Kbrx12551yW66cXBfrzTfl4/37C4ZXIqKyiCGnjNCHnJwc7bH5uJyyPB4HMJ1RWK+kISc8XG6F0I7pf/fq/dXA5I0b5S3pgBxM7ONjunxErVpayAHkHD2vvqqtHL9xY/HaSUTkTRhyyoigIG3/yhWt+8RaJaeshhxnDTxWIUdPH3LU3VyXLwPZ2cD48cCWLXJenn795HPly8uZlQFZ9bG0hMOtt8qtoxcDJSLyRAw5ZVBoKCs51qhKTnq6tuyF6v7x8wNq1Cje+1oKOfoupfPntf3Tp4Fjx+T+r78Cfftqz/3yC7BoEdC/v+XPUd1iV68Wr51kn127gKlTTSt0ROQ+uEBnGfLjj3Khx+7dgR9+kMdYyTEVEiJ/MjNlNScuTrslOymp+Gt2FVXJ0YecU6e0ypH5OJzGjQse01MzNltbYZ4cJy9Pu62/Zk3gnntc2x4iKoghpwzp21erCrCSY121asDevbJLqHp14L//lccffbT476kqLHr6So5+8sF//9Vu47d3tXeGnNLzxx/avpqckYjci1O7q9LS0tC/f3+EhIQgLCwMgwcPxhVrS1//7/xnnnkGDRo0QFBQEGrUqIFnn30WGeZz7FOJWRuToyoIlv4olxVq8O7s2cC778r1pkJCgHvvLf57VqxY8G41a5WcTZvkNjpaWzHeVmrsFUOO802bpu3z/6KI3JNTKzn9+/fH2bNnsWzZMty8eRODBg3C0KFDMUfNZmbmzJkzOHPmDD788EM0atQIx48fx1NPPYUzZ85g3rx5zmxqmaOv5KxbJwe7dukC/PWXPH777a5rm6u9/77sprp5Uzt2zz2mg7ftZTDI4KgPlZYGHgPanVHmS0PYQlVyrl2z/7Vku5s3gWXLtMepqa5rCxFZ57SQs2/fPixduhSbN29GmzZtAABTpkxB9+7d8eGHH6KKhTp8kyZNMH/+fOPjunXr4u2338ajjz6K3Nxc+Pmxd81RYmLkdv164Kef5P6mTcDmzXI/Kck17XIHDRrIdaAcLTzcNOSo7qr8fLnKuaK6PopzJxe7q0pHSorpbOEMOUTuyWndVcnJyQgLCzMGHABITEyEj48PNtoxiUdGRgZCQkKsBpycnBxkZmaa/FDRunWT3SGqewoARo2S21attKUfyHHMBx+rSk5amunsxwpDjvsyX/FdhZz8fGDAANuX7SAi53JayElNTUW0fv0AAH5+foiIiECqjf/suXjxIiZOnIihQ4daPWfSpEkIDQ01/lRXE51QoUJCgHfeMT2mlgPo3r3021MWmIccVckxX/FcKU53FcfklA4VctRit+o7XL0a+P574LPP5N1XRORadoeccePGwWAwFPqzf//+EjcsMzMTPXr0QKNGjfD6669bPW/8+PHIyMgw/pw0n6qWrBo0SI4/ee017ZjBAAwZ4ro2eTPzwdxpaTLoTJ0qH0dFmT5fkkoOx+Q4lwo5HTvKrfp324oV2jnqDjkich27B7mMHTsWAwcOLPScOnXqIDY2Fuf1t4wAyM3NRVpaGmLVP3+syMrKQlJSEipWrIiFCxeiXLlyVs8NCAhAQECAze0njY8P8MILciKzt9+W//IcNar4E95R4Sx1Vw0fLucvAuRdXd26yUoAwO4qd6YPOT//LCdfvHIFWLtWO+fGjZINVieikrM75ERFRSHK/J+cFsTHxyM9PR1bt25F69atAQArVqxAfn4+2rVrZ/V1mZmZ6Nq1KwICArB48WIEqv/XJqcxGOScH6tWyfWPyDnMQ86ZM1rAAeRM1JMmaSGnfn37P4Mhp3SokNOihVxu4+pVue6YfrghKzlErue0MTkNGzZEUlIShgwZgk2bNmH9+vUYOXIk+vXrZ7yz6vTp04iLi8Om/00MkpmZiS5duuDq1av4+uuvkZmZidTUVKSmpiKPHdxOdffdsprDTOk8KuSoOYr02raVg1WrVJGrjy9ZIhfhtBfH5DhfTg5w4oTcr19fG5fTvr3p7z0np/TbRkSmnHpP9uzZszFy5EgkJCTAx8cHDzzwAD799FPj8zdv3sSBAweQnZ0NANi2bZvxzqt6aka2/0lJSUGt4vy/PpGbUCGnZk35r/ysLPl4zhzg4Ye181q2LP5nsJLjfCkp8i6qChXkVAwxMcCRI7K7KjBQ+92zkkPkek4NOREREVYn/gOAWrVqQehWtrvjjjtMHhN5k1tukdsGDYAPPwSWL5eVm4cectxncOCx86nFU2vXll29+sVQ164FunaV460Ycohcj7PrEZWSTp3kbfoNG8rxN3fe6fjPYCXH+VTIUYXlwYOB556Td8m1aaMtxcGQQ+R6Tl27iog0BoMctxEa6rzPsDQmZ/x4OYO16h6jkjEPOc88A6SnA089JR+rkMMxOUSux5BD5EVUJSc3V/6cOycXGf3zT+Djj7Xzjh+Xf5jJfuYhB5ALsCqs5BC5D4YcIi+ivzvu+nXgl1+0x599JsfqnDwpxwXFx5uuv0S20Y/JsURN28WQQ+R6DDlEXkQ/L+b168CCBdrj8+eBxYuBf/6RXSn792uLs1Lhbt6Ud1ABlis5eqzkELkPhhwiL+LrC6gJwg8dkndwAcAdd8jt7t3aH2tArl/GGxqLNnasnJF6zBhtnaqiQg7H5BC5HkMOkZdRg49HjZLdUZ06Ab16yWP79gEHDmjn7tmjrbtElgkhu/oAYPJkufX1LbgWmcJKDpH74C3kRF4mMBDIzAQ2b5Z3dH38seyqAmTI0Q+SBYCzZ4HKlUu9mR5j//6C1a5bbpG/W0sYcojcBys5RF5GP/i4XTs5g3LDhvLxwYOyegNof4zPnCnd9nka1eV3113AX3/J3+no0dbP58BjIvfBSg6Rl9GHnJgYua1eXVtI8soVWYXo2FH+AT971jXt9BQq5CQkyDXe7r678PNZySFyH6zkEHkZNSYHAKKi5NZgAOLitOM1awJ16sh9VnIK988/cqsGbxeFA4+J3AdDDpGX0VdyoqO1fdVlBQCNG8t1swBWcgpz7pwcz2QwAM2b2/YaVnKI3AdDDpGX0YccVckBgEGDgLp1gQcfBN5/XxtszEqOdTt3ym29erK7zxYck0PkPjgmh8jLWAs5d90FHD6sPVb7rORY9++/cmtrFQdgJYfInbCSQ+RlLI3JsUR1V7GSY52q5DRrZvtrOCaHyH0w5BB5GWuVHHOqu+rcOSAvz7lt8lQlCTms5BC5HkMOkZfRr19VWMhRt5fn5cnumJQU57bL0+TmAnv3yn17Qg7H5BC5D4YcIi9z/bq2X1jI8fMDfP73/wB79gALFzq3XZ4mM1MuzAkA1arZ/jpWcojcB0MOkZfJytL29VUdSwYO1PbV0g8kZWbKbVCQtuipLTgmh8h9MOQQeRl9yCnKV18Bb7wh9y9ccE57PJUKOeZrfRWFlRwi98GQQ+Rl7Ak5BoM2AJmVHFMq5ISE2Pc6hhwi98GQQ+Rl7Ak5gDYrMkOOqeKGHA48JnIfDDlEXmb6dLlV3VBFYcixjJUcIs/HGY+JvMzdd8s/0LaOJVF3YDHkmCppyOHAYyLXYyWHyAvZM1hWVXKys4GrV53THk/ESg6R52PIISrjKlbUxpHwDisNx+QQeT6GHKIyzmAo/ric3buByEjg3Xcd3y5XYyWHyPMx5BBRsUPOwIHApUvA+PEOb5LLcUwOkedjyCEi4+Bje7urtm/X9vPzHdced8BKDpHnY8ghomJVco4cMQ02p045tk2uxjE5RJ6PIYeIjCHn3DnbX7NkienjAwcc1x53wEoOkedjyCEi49IOp09rx37/HRgyRN5Wnp0t92fN0p6fO9f0PRhyJI7JIXIfnAyQiFC7ttympGjH7rlHbsuVAxo2lIt5/vgj0Ls3kJoK/PMP4OMD9O8PzJxZuiFn1Srg5Engscec9xms5BB5PlZyiMhiyFHmzgWmTZP7WVnAr79qFZ2EBOCOO+T+wYNOb6bRnXcCjz8ObN5s/2sXLgQefRQ4caLw8xhyiDwfKzlEZAw5Fy8CV64AFSpoz12+LH+UyZOBQ4fk/uOPa68trZBz/bq2v3UrcOuttr1OCGDUKGDKFPm4Rg3gnXcsn5ufry10WtyBx3l58sfX177XE5HjsJJDRAgNBcLD5X5KigwE5n+ce/SQ240bgbQ0oHFjoF8/oHp1efzMGfk6Z9PfAaYfQ1SU8eO1gAPI7ja9NWtkCMrMlEFPKW4lBwBu3rTvtUTkWAw5RARAq8gcOwZcuyarEEqXLsD8+cBHHwF+/6v/fvyx3I+JkY9v3ADS053fTv0dYHv22Paa/HxZgQKAsWPldvNmIDdXO+fuu4FPPwXGjNG6qvz8gMBA+9qnDzkcfEzkWgw5RAQAqFVLblNSgIwM7fgvvwCLF8tumNGjgb17geRkIDFRPh8QoFWBUlOd3079Z9gacrKytDEyb74pK1fZ2cDOnfLYvn3a819/DRw/LvdDQuSyF/YoV07b57gcItdiyCEiAKaDj1VFJjwcuPdebZwJANSvD7Rvb/ra2Fi5LY2Qo6/kHD5sOkbHGnU9AQFAcDDQrp18nJwstwsWmJ7/4YdyGxZmf/t8fLSgw5BD5FoMOUQEwDTkqEpOaKhtr1VdVqUdcvLzgaAgecdXYdTAaVVx6tBBbpctk1sVcurUkdulS+VWVbfsxTusiNwDQw4RAbBcybG1kuGqSo7y/PPaGKKvvwYGDZLjihTz6+nTR25/+02uv7Vtm3z83HNyq6pD6ndiL04ISOQeGHKICIDlkGNrJccVIWf0aGDqVLl/8KAcGA0ATz4JfPedHCSt6LvfAHlnWKdOMhj16yePNWumVXgUVdmxF0MOkXtgyCEiAFrXTFaWNimgO1dybr0VePpp4LXX5OPZs7W7ogBg0SJ5K/jy5XIxUcD0ep5+Wm7V/D6JiUDduqafVdyQo+7IYsghci1OBkhEAOTYlthYGVR27JDH3LmSoz4zPl5uU1K0MAPIiQKjo027rfQh54EHgKZNgV275OOEBPl8RIScBwgofncVQw6Re2Alh4iM1B/14oYce1YxLy71GWqwc82acnv8uGnIEcI04ABadxUg58BRc+cYDLL7CgDq1dPOKWklx5Y7v4jIeRhyiMjIfIkGW7urSuvuqhs3tDul1GfWqCG3mZnaAOIOHYAPPpALiuqZX09CghzLs2yZtpSF6rKqUAGIjCxeOxlyiNwDu6uIyMi8e8beSs6FC85dr0lN0ufvr1VlypcHKlUCLl0CVqyQxxIT5R1XV6+avl5fyVF69zZ9rEJO7dr2TwSoqHmFGHKIXIuVHCIyMg85tlZyoqJkIMjPl4t8OsvKlXLbrp2cdE9RXVYbN8qtCirly5tegy3X07Kl3DZvXvx2spJD5B4YcojIqLiVHF9f7Vz9iuWOtny53KolJRQVchT9uJpq1bR9S5Ucc716AUuWyLW5ioshh8g9MOQQkdEtt5g+tjXkAFqAcFbIyc/XuqMSEkyfMw85+lvBq1bV9m2p5Pj6At26yS6w4mLIIXIPDDlEZFStGpCUpD22Z+0mFXLU7deOkJ0t16cSQs5MfPGiHBDctq3pefqQ06KFvHVc0VdyirMWVXHwFnIi98CQQ0Qm/vMfbd+VlZx164C4OLkgaNOmwOOPy+Pdupmu9A2YVmteftl0wLD+OVu6qxyBlRwi98C7q4jIxO23yzuTzp2TAcNWjgw5QgAPPqjdkr5nj/bcSy8VPL9tWxl86tQpeLeUvqpT2pUchhwi12LIIaICPvjA/tdERMitI0LOlStawDl8GHj0UWDDBtmV1rp1wfNr1gQOHJBtML99Xc1/A9hXmSoJ3kJO5B4YcojIIRxZyVELaqrqzJ9/ArNmFazS6FlbgkFNFgg4b/4ec6zkELkHhhwicghHhhz1HmFhcnxNSIi2oKa97rgDeOEFoGHDkrfLVgw5RO6BIYeIHMKRd1epSo4jxtAYDMD775f8fexhHnKuXQO6dAFuuw14993SbQtRWca7q4jIIZzRXVVad0M5mvkt5Bs2yLvFpkyR8/0QUelgJYeIHMKRA48dWclxBRVyUlOBb77RqlvZ2cDp00D16q5rG1FZwpBDRA7hjEqOp4ecdevkj96BA64LOadOyTvMKlZ0zecTlTZ2VxGRQzDkaNQt5JYcOFB67dBLSZFreulntCbydgw5ROQQKuRcuwYcOgTcvAm89hqwfr397+XpIUdVcixxVcj56y85Ruiff4CdO13TBqLSxpBDRA4REqItp3DLLcAzzwATJwKjRtn/Xgw5jnf0qLY/c6Zr2kBU2hhyiMghfHzkcgzK55/L7b599t9RVJZCzuXLQF6ec9sDmC6NMWcO7/KisoEhh4icSt1RZA9vDjknTsjfCQBs2gRUrgw89ZR8vGAB8NBDQFaW49u0e7e2f+YMsHev4z+DyN0w5BCRw3z0keXj9nbReMs8OeaiomS1SwWO++6T42S++ko+fuAB4KefgDfecGx7srKA48flfqtWcrtmjWM/g8gdOTXkpKWloX///ggJCUFYWBgGDx6MK1eu2PRaIQS6desGg8GARYsWObOZROQgo0fLBTXNFRVy1q0DhgwBLl2Sj72tktO5M7BqlRYwtm+Xc+icPaudo+/q27LFse1RVZvKlWWwAoC1ax37GUTuyKkhp3///tizZw+WLVuG3377DWvWrMHQoUNteu3HH38MgxrFSEQeo2ZNwM9sBq6iQk6fPrKaof4Ae3rIMb+FfN48GXRattQed+9ueo4KeIBp+HGEXbvktnFjoGNHub9mjWmwIvJGTpsMcN++fVi6dCk2b96MNm3aAACmTJmC7t2748MPP0SVKlWsvnbHjh34v//7P2zZsgWVK1d2VhOJyAn8/OTK4QcPaseKCjnnzsntunXA1aueH3LMKzkhIXKrKjl//y23ERHabMgnTmjnp6Y6tj0bN8ptmzZAu3ZydfczZ+TcOXXqOPaziNyJ0yo5ycnJCAsLMwYcAEhMTISPjw82qv/FWZCdnY1HHnkEU6dORWxsbJGfk5OTg8zMTJMfInKt+vXl1ud//w9TVMipW1fb//prrcLgLSHH319uVSUHAHx9ZbdU69by8b592nOZmcCNGyVvxzffAHfeCcyfLx/HxwNBQUCDBvLxkSMl/wwid+a0kJOamoro6GiTY35+foiIiEBqIf9MGT16NDp06IBevXrZ9DmTJk1CaGio8ac6F4UhcjkVcm69VW5Pniz8luWMDG1/wQK5DQgo/C4ld6Zvd7ly2r6+apKUBNSuDVSrJh+rLiXF0tgmew0eLMcCqVmo27eXW1Ugd3S3GJG7sTvkjBs3DgaDodCf/fv3F6sxixcvxooVK/Dxxx/b/Jrx48cjIyPD+HPy5MlifTYROc599wGRkcDw4fJxfr7WLWNOCNOlIDZskNvISKc20an0Y5L043N8fICxY+VkidOny2Pq32XmsxDv21eyMTPnz5s+rlwZUP/uVEVyhhzydnaPyRk7diwGDhxY6Dl16tRBbGwszpv9ryw3NxdpaWlWu6FWrFiBI0eOIMysRv3AAw+gY8eOWLVqVYHXBAQEIKCwhWKIqNR17iz/yBoMwHPPyTE2Fy5YDi5Xr5pOhpeTI7ctWpRCQ0uB+f89ffih/FGsVXImTpQhsU8fYNo0+z83Odn0sVolHtAqOY4e+0PkbuwOOVFRUYiKiiryvPj4eKSnp2Pr1q1o/b9O5xUrViA/Px/t2rWz+Jpx48bhySefNDnWtGlTTJ48GT179rS3qUTkQurmyKgoLeQ0bFjwPFXF8fWVXTvXr8vHqqvL0xX1bzBVyTl1Sm4rVpQVn3//lY+//rpkISc4WL7f5Mnac+yuorLCaWNyGjZsiKSkJAwZMgSbNm3C+vXrMXLkSPTr1894Z9Xp06cRFxeHTZs2AQBiY2PRpEkTkx8AqFGjBmrXru2sphKRE6l/E124YPl5FXIqVQIaNdKOl5WQoyo5SseOwJ9/ArVqycc3bxZvCQYVcqZMkZMB3n239hy7q6iscOo8ObNnz0ZcXBwSEhLQvXt33H777fjiiy+Mz9+8eRMHDhxAtprjnIi8jhoHYj5GRFEhJzxczuOi6G7M9GhFhZwaNUwfh4fL27zV0EbzMUu2UpUgS2GR3VVUVjhtnhwAiIiIwJw5c6w+X6tWLYgiRtYV9TwRubfCKjnZ2docOWFhWsipUUMLR57Olu6qgABtLJJayiIgQO5fvix/R5Uq2f6ZubnaHWuWphpjdxWVFVy7ioicylrI2bNHzo/z0EPycXg4cM89ck6Z/v1Lt43OpObIscbXF6hXT3usv+8iJkZuVRC0lZpM0fz9FNVdlZUlB34TeSuGHCJyKkshJz0dSEgw7S5R3VXXrgFvv12qTXQqW+b6ueUWbV+/KGlxQ466Xb9ixYJLbKjjwcFyn11W5M0YcojIqSyFnFWrCv7hVn/cfXy0O7O8gS2zNqsZiAHHhBz9OCdLDAZ2WVHZwJBDRE5lKeTolzBQrP1B9lTTpsluKFvmNi1JyJk2Deja1XSBz6JCDsA7rKhsYMghIqeydHeVCjn68Sqeuk6VNcOHA4cO2bYAZklCzogRwF9/AfopxlTI0U8AaI53WFFZwJBDRE6lKjkXL2rzvaiQ07u3dp63VXLsoQ85+rWuVMixJYgsWiTvqgJsq+Swu4rKAoYcInIqtZRDXp4ccJyfz5BjLiJC+z3p5wqypZITEqLtL1kit+yuIpIYcojIqdR8LwBw4oRcvuDqVXnXT7du2nlqOYey6uhRWbEJDdWO2RJy9Ot+7d0rt+ruKlsqOeyuIm/GkENETqcW29y6Vavi1KsHVKgAVK0qH992m0ua5jYqVtRCjaIPOZbmRRVCTqioqPlx7BmTc/Ik8N57BRcIJfIGDDlE5HRqaYFly4Bx4+S+Cj579sgqRs2aLmmaW1OVmJs3LVe6rl83DT9qlmN7uqv27JHfSbNmloMUkSdz6rIORESAFnJ+/FFuo6OBN96Q+6Ghpl00pNEvCZGTAwQFmT5vvuyfeSXHlu4qvWrV5N1gS5cC5cvb3Vwit8NKDhE5nfkikQsXms7yS5bpb7FXa1vpmYcceyo5kZFySQm9M2eAdeuA99+3v61E7oghh4icTr/Sdmws0KGD69riSQwGrZpjS8g5dgxo3x7YuVM+Lizk+PpaXwT1/feB06ftbi6R22HIISKnMxiAp56S6zgtXOjq1ngWFXIsjckxDzn79gEbN2qPCxt4DMiB38rjj8vZmVu2lJ+1enWxmkvkVjgmh4hKxdSpskJQsaKrW+JZ7KnkmCtq7iF1qzkA/Pe/crtnD7B9u+WlN4g8DSs5RFQqfHwYcIpDrWJeWMixNki4qAHd+vWulIYN5ZYhh7wBQw4RkRuzpZJj6U6p/v0LDiw2N3q03D70kHaMIYe8CUMOEZEbs2VMjnnImTYNmDWr6Pd+6y1gwQLgq6+0YyrkHDqkrYVF5KkYcoiI3JgtlZzQUCA4WDtu7a4pc8HBwP33mw5Arl5dHr95EzhypHhtJnIXDDlERG7MljE5wcGm429sDTmW+PgAcXFyn11W5OkYcoiI3JgtlZzgYCAsTDtuvgaWvVTIOXSoZO9D5GoMOUREbsyWMTnBwaZLQJSkkgNoY3zOny/Z+xC5GkMOEZEbs7WSo3++pGuBqZDEkEOejiGHiMiN2TomR/+8wVCyz4yKkluGHPJ0DDlERG6sOJWckrJUyfnmG+DFF4H8fMd9DpGzcVkHIiI3ZuuYHGeEnAsXtGODB8tthw7Affc57rOInImVHCIiN2ZrJScxUe5XrVryz9RXcoQw/ez160v+/kSlhZUcIiI3ZuuYnKlT5a3fjz9e8s9UY3JycoCsLODKFe25nTtL/v5EpYUhh4jIjdlayYmIACZMcMxnBgfLRT+vXpXVnKtXteeSk4G8vKLXxSJyB+yuIiJyY7aGHEfTd1ldvKgdz8oCduxw/OcROQNDDhGRG7N14LGj6Qcf6wcgA6YLehK5M4YcIiI3ZuuYHEezVMlRx775Bjh92vGfSeRoDDlERG7MHbqrVCWnd2/g9tuBGzeA7793/GcSORpDDhGRG1Mh58IFYN484JdfZNdVXp42INgZIUfdYXXunFbJiYoC7r5b7qekOP4ziRyNd1cREbkxFXLWr9fmqHnzTeDkSeDmTXkXVKVKjv/c+vXlds4cbVXyyEit++zsWcd/JpGjMeQQEbkxFSr0pk0DUlMBHx85PsYZlZxHH5Vz72zbpoWrqCigQgW5n5rq+M8kcjR2VxERuTFVydFTAaNbN6BvX+d8rr+/DFB6UVFA5cpyn5Uc8gQMOUREbsxSyFE6d3buZzdrBtSpoz2OjARiY+X+uXNcrJPcH0MOEZEbMw85detq+506OfezDQZZLVKiooCYGHk8N9d0kkAid8SQQ0TkxszH5HTpIrfBwUCrVs7//I4dtf3ISKBcObkFOC6H3B9DDhGRG9NXcipW1G7hTkiQgcPZevUC2reXc+SotqguK47LIXfHu6uIiNyYPuRUqgTcd5+cK6dt29L5/MBAuSinXuXKwK5dDDnk/hhyiIjcmD7kRETI8TD33uu69gDaHVbsriJ3x+4qIiI3ph+T44xJ/4qD3VXkKRhyiIjcmL6SExLiunboca4c8hQMOUREbkwfcsqXd1079NTdVZcuubYdREVhyCEicmP6kKOWVHC18HC5vXzZte0gKgpDDhGRG/PR/b+0u4Wc9HSXNoOoSAw5REQewt1CDis55O4YcoiIPIS7jMkJC5PbjAyuX0XujSGHiMhDNGzo6hZIqpIjhAw6RO6KkwESEbm5pUuBf/8FkpJc3RIpIAAICgKuXZNdVir0ELkbhhwiIjfXtav8cSfh4VrIIXJX7K4iIiK7lXTwcVaW49pCZA1DDhER2U0NPrb3NvIjR2RAqlnT0S0iKoghh4iI7FbcSk7lyjIYXb4MpKU5vFlEJhhyiIjIbsUNOcHB2tpXR444tk1E5hhyiIjIbirkjB8PvPOOfa+tW1duGXLI2RhyiIjIbirk5OcD//kPcOWK7a+tV09uy2rIEQKYORM4etTVLfF+DDlERGQ3NfBYuXrV9teqSs7hww5rjkeZMgV4/HGgWzdXt8T7MeQQEZHdzCcAzM62/bVlvbtqyhS5PXgQyM11bVu8HUMOERHZzbySw5BTkBDAW28Bv/2mHcvKAo4f1x7v3Vv67SpLOOMxERHZzc/sr8e1a0W/ZuNG2U2jQs6ZM/J1QUGOb587WL0aePVVuZ+SAtSqBfzxB3DzpnbOli1As2YuaV6ZwEoOERHZLTER6NlTe2xLJSczU3bRnD4NhIbKY8eOOaV5bmHXLm1/7FhZ2VmyxPSczZtLt01lDUMOERHZLSAAWLwYaNFCPrYl5OTkaK+NjJT73rz21Z492v6CBcArrwDLl8vHQ4fKLUOOczHkEBFRsQUHy60t3VU3bsitvz8QEiL3MzOd0y53sHu33N52m9y+8w5w6pQMec8/L49t2ya7ssg5GHKIiKjYVMixt5Lj7SFHCC3kTJsGjBmjPdemDVC/PtClizzv889d08aywGkhJy0tDf3790dISAjCwsIwePBgXLFhtqjk5GTcddddKF++PEJCQtCpUydcs+WfCEREVOrUoGFvruTk5wNffgkcOmR6fONGoE4d4MUXtQCnnDkDZGQAvr5AgwbAhAnac507y+3w4XL79dfa74Ycy2khp3///tizZw+WLVuG3377DWvWrMFQ1QlpRXJyMpKSktClSxds2rQJmzdvxsiRI+Hjw4ITEZE7sqeSo/6Q6ys5GRnOaZcjvfWWHEOTmGh6fPp02dX0wQeyOrNzp/acquLUr69d7+rVwIABchAyANxzjxyAffEicOBA6VxLWeOUW8j37duHpUuXYvPmzWjTpg0AYMqUKejevTs+/PBDVKlSxeLrRo8ejWeffRbjxo0zHmvQoIEzmkhERA5QnO4qf3/t7ipPqOTMmCG3J07I7e7dcsDw779r5+zeLUPQsWPyd6IGHTdurJ3TqZP8Ufz8gFtuke916BDQtKlTL6NMckqJJDk5GWFhYcaAAwCJiYnw8fHBxo0bLb7m/Pnz2LhxI6Kjo9GhQwfExMSgc+fOWLduXaGflZOTg8zMTJMfIiIqHd7eXSUEcPas9jgvT946/8QTsgJToQJw8qScA+fCBWDuXHmequQ0aVL4+9evL7fmXWHkGE4JOampqYiOjjY55ufnh4iICKSmplp8zdH/rVT2+uuvY8iQIVi6dClatWqFhIQEHCrk2580aRJCQ0ONP9WrV3fchRARUaFK2l3l7iFHfxs4APz8s+ncPu3bA9WqAU8/LR9PnSqDkXodQ45r2RVyxo0bB4PBUOjP/v37i9WQ/Px8AMCwYcMwaNAgtGzZEpMnT0aDBg3wzTffWH3d+PHjkZGRYfw5efJksT6fiIjsV9zuKk8Zk6PvkgK0GYwVNXh40CB5Xdu2yfE1lrqrLDEPObm5wLhxwN9/l6zdJNk1Jmfs2LEYOHBgoefUqVMHsbGxOH/+vMnx3NxcpKWlITY21uLrKleuDABo1KiRyfGGDRvihOoItSAgIAABAQE2tJ6IiBytuN1VnjAmJy1NDirWUyunHzgABAYCNWrIx5GRQKtWwIYNwC+/yFXZ/f2BevUK/wzzkLN8OfDee8DSpcCOHQ67lDLLrpATFRWFqKioIs+Lj49Heno6tm7ditatWwMAVqxYgfz8fLRr187ia2rVqoUqVarggNkQ84MHD6Ib16MnInJL3jpPTn4+MGoUcOmSrMaEhgL//COfi4yUA4bNNWsmQ86cOfJxgwZAuXKFf44KOWfPAleuaIuWnjrlmOso65wyJqdhw4ZISkrCkCFDsGnTJqxfvx4jR45Ev379jHdWnT59GnFxcdi0aRMAwGAw4IUXXsCnn36KefPm4fDhw3j11Vexf/9+DB482BnNJCKiEvLWgccTJgCzZgE+PsCUKYB+uKe1cTZqoU11K3lR43EAIDwcqFRJ7h8+rK1QfumS6UKeVDxOW4V89uzZGDlyJBISEuDj44MHHngAn376qfH5mzdv4sCBA8jWxf/nnnsO169fx+jRo5GWlobmzZtj2bJlqKuWrCUiIrfirfPkfPut3E6bBtx5J/Drr9pzRYUcpVUr2z4rLg5Yv16GIxVyAOD8eaBqVdvbTAU5LeRERERgjqrZWVCrVi0IIQocHzdunMk8OURE5L5KOvDYXSs5KnypCQD/N2wUgPXBxObz3Dz6qG2f1a6dDDnJydpcPACQmsqQU1JOCzlEROT9Sjrw+MoVOfeMr69z2lcceXmyXYAWxvQhx1olJyxM269dG7Byn00B8fFym5ws59pRrMy4QnbgeglERFRsJe2uArRA4S707VHtrFBBO1bYbeHPPSeD308/2f55KuT8+69c80qxFnLefRd4+205Hw8VjiGHiIiKTYUcWyo5+u6qgAC5BdxvXI7qQlPtBIAWLbTnw8Otv/ajj+St57oJ/4tUtarpwGbFUshZuxYYPx545RUZiqhwDDlERFRsqrvKnkqOCjfuOi5HtUdfbapVC9iyxXTMjCUGg5w/x1633VbwmKWQ8/bb2v7MmfZ/TlnDkENERMVm3l2Vnw/88INcnducfp4cwH0nBLQUcgCgdWvLFRdHePHFgsfMQ87+/cCff2qP58yRMySTdQw5RERUbPqBx0IAkycDjzwCPP54wXOtVXLOnQNeesl9Zvi1FnKcqWVL4OOP5b66Fd085CxfLredO8u5dVJTTUMPFcSQQ0RExaYqOULIrpw335SP1683vVMIMB14DGgh4u23gfffl3/oXcF8AK8rQg4gZ1g+c0ZOPggUDDlr18ptQoIWIqdPL732eSKGHCIiKjZVyQGAMWO0gCBEwSqDfuAxoN1ivXWrdo6aLbi0TJgAREcDR49qx1wVcgB5q7q6Xf3sWfl7FEIu97B6tTzeqRPw1FNyf8kS01XRyRRDDhERFVu5coDf/2ZcW7BAbhMS5HbJEtNzzburLI1vmTULWLRIBqZduxze3ALefBO4eFHeraS4MuQAQLVqcjmJq1dlNefjj+VCn6qy07atXDsrMVEGoG++cU07PQFDDhERlYjqsgLkeJwJE+T+ihWm51nrrtKbNw/o21eO7WnWDBg6tOAt5llZcnBzerpDmg/AdH4aV4ecoCBtAdCdO4G//9aeu/12rXo2aJDczp7NOXOsYcghIqISUSEnOBh47z1tsrxz57QuKsC0u+rKFeDLLwu+V1qarOYoX34JtG8v15I6cEAee+UVGaYaNJBVo5Ejizdo+epVbV8fmFwdcgDTxT5375b7DRvK369y331yksKjR+VsyVQQQw4REZWICgPjxsmulvBwrUvq3DntPH0lZ/hw4OTJgu+VmQk8+KCsTKxaJSfK278feOIJuZzCli3Ahx/K8Tznz8tq0dSpcv2nVavsa/fp09r+yZNaNUSFnIoV7Xs/R1IhZ/VqbW6ef/4BOnTQzgkOBnr3lvs//1y67fMUDDlERFQikyYBY8cCL7wgHxsM2qDis2e181QlZ9kyWa2xtF6VENqyCp07A5s3yy6r+Hjgrrvkyt7lysnBtvPmAd99J6s5N24AvXoBe/aYvpeSlycH7+qP6UNOWpo25kVfyTlyRAayw4e1c/fulXdA/f67fF9nUCHn99/ltmZN07WxlDvvlFvOfmwZQw4REZVI796yuqKf6Vd/h5CiKjkHD8rtyy9rr4mI0AYw68fgREUBn38ub0lfuFAOyAVkNeiBB4ABA4DffpN3HGVmynWdAODQISAuTo7dAWSXVr162nghoOBdSZs3y60KOUFBwP33AzNmAMOGyWPnzgF33AE8+yxwzz1Av37AzZu2/qZsp0KOtceKWixUdWmRKYYcIiJyOFXJUdURIbSQ89prsmvp1VeBGjXkserVtRmQVchZtEiGoK+/ltUh/QBnvcBAYP582V321Vfy2ODBMkw98ojszpoxQx6/4w65zc8HPvnE9H169ZJ3W6nPnzNHu8NrxQo5T01IiBwYDciK0rx5ciJDR6tRA4iM1B5bCzkNG8rthQuy+45MMeQQEZHDmVdycnO1riJ/f9kVVa6cdhu5ecgRQlZR8vKAp58u+vMiI2W3mbpzSx+IRo6U2/vv17p3pk+33MWzaJEWclatkuFKTVJ48aKs7nz2mRy0PHeuPD5lCrBtW9FttIfBIMOdup727S2fV748UKeO3Nd31ZHEkENERA5nHnJUFQfQBiUDppUcNYA5IwPYsEE7R/2hL8r163LNrNOnTW+7LldO3pL9yScyPBw4INfaMhjk861ayW3TpnK8kOqu8vUF/vtf4Ndfgf795d1Mihr0e9ddMsDdfnvhbdu0SYahH34w/V0U5t575Z1Tf/wB9Ohh/Tx1NxtDTkF+rm4AERF5H/OBx/pbyfWhpVcvGSJ69pQDegEZMhYu1M7JypI/Rd3tNGaMrND07Gk6IDghQQYFQN6hdNttMoSFh8sBx507y0rMxYty7plTp+S5K1cCHTvKff1t7XrvvSfPCQyU4aVcOfkZP/4oK0FHjsgwpW5Xr1FDVmVq1y78WpQqVeRPYZo0kb9DhpyCWMkhIiKHU5UcNSZHVS8MBtO7qnr1kmNJunUz7a4y7/5RAagwah2nX3+V2/795XbNGhlmAOCLL+T27FntWOfO2jH1WgCoW7foz2zTRl7j2bNahWrPHlm12bVLVoyuXpXh5+675S3htgYcWzVqJLf79jn2fb0BQw4RETmceSVHP0eO6iZS1GMVctLT5dw4gDZmR3/30D//AN27m3ZpqcCiN2qUHJibnQ0MGSK38+ebnlOvnlwewVKXmK2TAYaGaq/fsUMOrB4wQH7WwYPyWlJTgb/+AmrV0l736KNyEPMbb8hutuKqWlVu9XMSkcTuKiIicjhVyTl3Tt7JZL44pyUq5OzZI6sffn5yXMrUqfLYtWvyfaZMkd1Pf/whF/ds1QrYvt30vcLD5fGZM+UcOwsWyOB15YqspBw6JENPcLCsLNWpo1VCDAbgmWfkbML2atmy4Orhlpw9K+/eEkJO5PfGG3LMz5NPAklJ2q3yGRny2lq0kPPkXL4s2xcaqoXDqCi5NV/1nVjJISIiJ4iJkX+Ec3OBadMKLs5piaqcqCUK6teXf9wBGQSqV5dh5Phx7TWjRsmteci5804ZXlq3ljMoA1p31kMPyecqVtS6zqpV01578GDB28sdrVIluYDp5MlyzJAQchxSjx5yTNG1a/JY797yWqKi5KSIVavKAKe/M0xVyy5dct7khJ6KIYeIiByuXDlg4EC5/8wzsosJKPxOKVXJOXRIbhs2lGN2qlaVg4EvXZKVDNWVBQDr1skKh5ptuWdPWcEZO1Y7JzFRbtUt7PfcU/Cz1R1VgG1jcUrK319WbJ57Tt4Jtnu3/D0FBcnwM2uW7I5Ti5zm5sp1vK5dk483btTea84cbX/wYHnruRCygqan1v4CZIXt3XcLLqJalOxs2S5PWRCUIYeIiJziq6+0u5O2bJFbW7qrlIYNZQXj339lAFAuX5ZbtcyBfobk4cNlF5Z+jaeEBG0/PFyuc2VOBZ/q1QuOGSoNjRsDn34K/PmnDD5PPinvwvrzT9k993//J7vveveWd4E99pj22tdf1ypS//2vfG3LlnKgc3a2PP7dd/L32bu3nB26Xj1g/Hj5u1ETJQKySvbtt/J3nJMj7zb79lvt+W+/lbfN60OhWxNeJiMjQwAQGRkZrm4KEVGZ98QTQgBC3H233DZoYP3cH36Q56ifWbNMn+/QwfT5yZPlNjZWiF69hGjbVogrVyy/d/368tx+/Sw/f+OGEDNmCHHqVHGusnRYuzYhhKhTR15fixZC+Plpv6MZM+Tzzz5r+rsD5O/Ex0duc3KEOHZMCINBPte6tRBdusj95s2FyM+X7/PKK0KMGOGc63PG328OPCYiIqdRd/6ou4ds6a5SzJcyiIgwfdyzp6x21Kql3c1lzYABchmJJ56w/Hy5ctr6VO6qfHnrz8XGyokDX31V7i9fDnTtCrRtK5//5BN5m/7mzbKKU6+eHK904YIcC+XvL9cIU91QW7fKbVCQnAtIVbcmTnTe9TkDQw4RETmNmshOLYZpy8BjQHZFqZl8lUqVTB+Hh9s+fmb8eNnlZett4Z5GrXN14YLsktJ31ylJSfJHLyZG23/nHeA//5HjgZ56SnYVLl5sfUkJT8CQQ0RETqNCTm6u3NpayenYUbuNWjGv5JhXfgrj4+O9AQdw3G3k5cvLilbTprLaEx1d8ra5EgceExGR06juKsXWgcdqwLKevpITEmI6c3JZp0LOxYsFn/vzT/snG+zQwfMDDsBKDhEROZH5uku2hhw1lkRPX8kJDy9Zu7yNvrtKb8kSOfeOug2/rGElh4iInCY62rTiUlh3VcWKcvHMli0tjynRV3IYckxZq+R8/73cnj4tFzldvFjOrFxWloBgJYeIiJzG11fe7XP6tHxcWCXHYJCLaRoMlueqYSXHOmuVHP1M0Nu2yckVAfk9/PRT6bTNlVjJISIip1LrWAFFhxMfH+uT8THkWGdp4PHhw3KJCuWvv7T9xYsLzojsjRhyiIjIqa5f1/afe67478PuKutUJefSJe3Y8uWm50yerO3n5ABr1zq/Xa7GkENERE711FNySYJvvgEaNSr+++grOWpJB5LU7+PaNW0xVLWQaVyc9pzetGlyyQxbVk33VAw5RETkVCNGyLWOBg0q2ftUqCBnJgZYyTGnnwNIreWlxkHdd5/83SnPPy+3P/0kV3nv0aM0WugaDDlEROR0QUElfw+DQavmMOSY8vWVd6cBQHq63J45I7eNGgG//QbUqCHPGTtWLomhbNvmOauK24shh4iIPIYal8OQU5D5quyqklOlCtC5sxyIfPKkvNvtnXdMxzhduVKqTS01DDlEROQx2rSRd2A1b+7qlrgfFXLMKzlq1uly5bQJF5s0kXPqqApbSZeDcFcMOURE5DG++QY4e7ZkA5i9lQow6enA1ataRcd81mk9tXTD+fNObZrLMOQQEZHH8PX1jjWVnEFfyVFVnAoVCl+YtDghJyenOK1zDYYcIiIiL6Afk6NCTmFVHEALObZ2V12+LN9z0CAgO7tYzSxVDDlEREReQF/J0Q86Loy9lZzvvgPS0uRyEY64Y87ZGHKIiIi8gH5MjvmgY2vUchC2hJz8fGD6dLn/9NPWl99wJww5REREXkDfXVXcSk5WFvD++5ZXKV+5Ejh0SM6188gjDmmy0zHkEBEReQEVci5f1hbjLOouNPMxOW+/Dbz0EhAfX/DcJUvk9qGHTGdQdmcMOURERF5AdVetWgXs3QsEBMglHQpjXsn5/Xe5TUnR1r5S1qyR2zvvdERrSwdDDhERkRdQlRw1e/G99xa9kKl5yMnN1Z6rVQv47DO5n5UlBxsDQMeODmhsKWHIISIi8gLmgaZ//6Jfo++uun5djrnRe/ZZ4NgxIDkZyMsDatcGqld3RGtLB0MOERGRF9CHnMBA4O67i36NursqNxdYt04GmbAwOf4mIEAu3PnFF1pXVadOjm61czHkEBEReQE1JgeQYSQ4uOjX+PvL6gwAzJsnt02aAN26AXPmyMdffQUsXiz377jDYc0tFQw5REREXkAfcuwZN9O4sdz++KPp43vvlfPsXLgA7Nolj3XrVvJ2liaGHCIiIi8QEKDt9+5t++uaNJFbtXp506Zy6+cHDB2qndemDRATU6ImljqGHCIiIi+xaxewfr19q7Sryo2iH8vz5JPafvv2JWubKzDkEBEReYkmTYAOHex/jVKnDlC/vva4ShV5h1VICPDMM45pY2liyCEiIirD4uK0/VatCq5J9fHHsivrlltKs1WOwZBDRERUhgUGAuXKyf2HHir4vMHgGYtxWuLn6gYQERGRa23dCuzZA/Tp4+qWOBZDDhERURnXtKl2V5U3YXcVEREReSWGHCIiIvJKDDlERETklRhyiIiIyCsx5BAREZFXYsghIiIir8SQQ0RERF6JIYeIiIi8ktNCTlpaGvr374+QkBCEhYVh8ODBuHLlSqGvSU1NxWOPPYbY2FiUL18erVq1wvz5853VRCIiIvJiTgs5/fv3x549e7Bs2TL89ttvWLNmDYYOHVroax5//HEcOHAAixcvxq5du9C7d2/07dsX27dvd1YziYiIyEsZhBDC0W+6b98+NGrUCJs3b0abNm0AAEuXLkX37t1x6tQpVKlSxeLrKlSogOnTp+Oxxx4zHqtUqRLee+89PPnkkxZfk5OTg5ycHOPjzMxMVK9eHRkZGQgJCXHgVREREZGzZGZmIjQ01KF/v51SyUlOTkZYWJgx4ABAYmIifHx8sHHjRquv69ChA3788UekpaUhPz8fc+fOxfXr13HHHXdYfc2kSZMQGhpq/KlevbojL4WIiIg8lFNCTmpqKqKjo02O+fn5ISIiAqmpqVZf99NPP+HmzZuoVKkSAgICMGzYMCxcuBD16tWz+prx48cjIyPD+HPy5EmHXQcRERF5LrtWIR83bhzee++9Qs/Zt29fsRvz6quvIj09HX///TciIyOxaNEi9O3bF2vXrkVTK8ujBgQEICAgwPhY9b5lZmYWux1ERERUutTfbUeOorEr5IwdOxYDBw4s9Jw6deogNjYW58+fNzmem5uLtLQ0xMbGWnzdkSNH8Nlnn2H37t1o3LgxAKB58+ZYu3Ytpk6dihkzZtjUxqysLABgtxUREZEHysrKQmhoqEPey66QExUVhaioqCLPi4+PR3p6OrZu3YrWrVsDAFasWIH8/Hy0a9fO4muys7MBAD4+pj1ovr6+yM/Pt7mNVapUwcmTJ1GxYkUYDAabX2cLNaj55MmTXj+omdfqfcrKdQK8Vm/Fa/VO6lpPnDgBg8Fg9eak4rAr5NiqYcOGSEpKwpAhQzBjxgzcvHkTI0eORL9+/YyNP336NBISEvD999+jbdu2iIuLQ7169TBs2DB8+OGHqFSpEhYtWmS8Bd1WPj4+qFatmjMuyygkJMTr/6NTeK3ep6xcJ8Br9Va8Vu8UGhrq8Gt12jw5s2fPRlxcHBISEtC9e3fcfvvt+OKLL4zP37x5EwcOHDBWcMqVK4clS5YgKioKPXv2RLNmzfD999/jv//9L7p37+6sZhIREZGXckolBwAiIiIwZ84cq8/XqlWrwOCi+vXrc4ZjIiIicgiuXWWHgIAATJgwweRuLm/Fa/U+ZeU6AV6rt+K1eidnXqtTZjwmIiIicjVWcoiIiMgrMeQQERGRV2LIISIiIq/EkENEREReiSGHiIiIvBJDjo2mTp2KWrVqITAwEO3atcOmTZtc3aQSe/3112EwGEx+4uLijM9fv34dI0aMQKVKlVChQgU88MADOHfunAtbbLs1a9agZ8+eqFKlCgwGAxYtWmTyvBACr732GipXroygoCAkJibi0KFDJuekpaWhf//+CAkJQVhYGAYPHowrV66U4lXYpqhrHThwYIHvOSkpyeQcT7jWSZMm4dZbb0XFihURHR2N++67DwcOHDA5x5b/Zk+cOIEePXogODgY0dHReOGFF5Cbm1ual1IkW671jjvuKPC9PvXUUybneMK1Tp8+Hc2aNTPO7BsfH48//vjD+Ly3fKdA0dfqLd+puXfffRcGgwHPPfec8Vipfa+CijR37lzh7+8vvvnmG7Fnzx4xZMgQERYWJs6dO+fqppXIhAkTROPGjcXZs2eNPxcuXDA+/9RTT4nq1auL5cuXiy1btoj27duLDh06uLDFtluyZIn4z3/+IxYsWCAAiIULF5o8/+6774rQ0FCxaNEi8e+//4p7771X1K5dW1y7ds14TlJSkmjevLnYsGGDWLt2rahXr554+OGHS/lKilbUtQ4YMEAkJSWZfM9paWkm53jCtXbt2lV8++23Yvfu3WLHjh2ie/fuokaNGuLKlSvGc4r6bzY3N1c0adJEJCYmiu3bt4slS5aIyMhIMX78eFdcklW2XGvnzp3FkCFDTL7XjIwM4/Oecq2LFy8Wv//+uzh48KA4cOCAePnll0W5cuXE7t27hRDe850KUfS1est3qrdp0yZRq1Yt0axZMzFq1Cjj8dL6XhlybNC2bVsxYsQI4+O8vDxRpUoVMWnSJBe2quQmTJggmjdvbvG59PR0Ua5cOfHzzz8bj+3bt08AEMnJyaXUQscw/8Ofn58vYmNjxQcffGA8lp6eLgICAsQPP/wghBBi7969AoDYvHmz8Zw//vhDGAwGcfr06VJru72shZxevXpZfY2nXuv58+cFALF69WohhG3/zS5ZskT4+PiI1NRU4znTp08XISEhIicnp3QvwA7m1yqE/IOo/6NhzlOvVQghwsPDxVdffeXV36mirlUI7/tOs7KyRP369cWyZctMrq00v1d2VxXhxo0b2Lp1KxITE43HfHx8kJiYiOTkZBe2zDEOHTqEKlWqoE6dOujfvz9OnDgBANi6dStu3rxpct1xcXGoUaOGx193SkoKUlNTTa4tNDQU7dq1M15bcnIywsLC0KZNG+M5iYmJ8PHxwcaNG0u9zSW1atUqREdHo0GDBhg+fDguXbpkfM5TrzUjIwOAXEIGsO2/2eTkZDRt2hQxMTHGc7p27YrMzEzs2bOnFFtvH/NrVWbPno3IyEg0adIE48ePN64FCHjmtebl5WHu3Lm4evUq4uPjvfo7Nb9WxZu+0xEjRqBHjx4m3x9Quv9bddraVd7i4sWLyMvLM/lFA0BMTAz279/volY5Rrt27fDdd9+hQYMGOHv2LN544w107NgRu3fvRmpqKvz9/REWFmbympiYGKSmprqmwQ6i2m/pO1XPpaamIjo62uR5Pz8/REREeNz1JyUloXfv3qhduzaOHDmCl19+Gd26dUNycjJ8fX098lrz8/Px3HPP4bbbbkOTJk0AwKb/ZlNTUy1+7+o5d2TpWgHgkUceQc2aNVGlShXs3LkTL730Eg4cOIAFCxYA8Kxr3bVrF+Lj43H9+nVUqFABCxcuRKNGjbBjxw6v+06tXSvgXd/p3LlzsW3bNmzevLnAc6X5v1WGnDKsW7duxv1mzZqhXbt2qFmzJn766ScEBQW5sGXkSP369TPuN23aFM2aNUPdunWxatUqJCQkuLBlxTdixAjs3r0b69atc3VTnM7atQ4dOtS437RpU1SuXBkJCQk4cuQI6tatW9rNLJEGDRpgx44dyMjIwLx58zBgwACsXr3a1c1yCmvX2qhRI6/5Tk+ePIlRo0Zh2bJlCAwMdGlb2F1VhMjISPj6+hYY9X3u3DnExsa6qFXOERYWhltuuQWHDx9GbGwsbty4gfT0dJNzvOG6VfsL+05jY2Nx/vx5k+dzc3ORlpbm8ddfp04dREZG4vDhwwA871pHjhyJ3377DStXrkS1atWMx235bzY2Ntbi966eczfWrtWSdu3aAYDJ9+op1+rv74969eqhdevWmDRpEpo3b45PPvnEK79Ta9dqiad+p1u3bsX58+fRqlUr+Pn5wc/PD6tXr8ann34KPz8/xMTElNr3ypBTBH9/f7Ru3RrLly83HsvPz8fy5ctN+lG9wZUrV3DkyBFUrlwZrVu3Rrly5Uyu+8CBAzhx4oTHX3ft2rURGxtrcm2ZmZnYuHGj8dri4+ORnp6OrVu3Gs9ZsWIF8vPzjf/H46lOnTqFS5cuoXLlygA851qFEBg5ciQWLlyIFStWoHbt2ibP2/LfbHx8PHbt2mUS6pYtW4aQkBBjl4E7KOpaLdmxYwcAmHyvnnCtluTn5yMnJ8ervlNr1LVa4qnfaUJCAnbt2oUdO3YYf9q0aYP+/fsb90vte3XECGpvN3fuXBEQECC+++47sXfvXjF06FARFhZmMurbE40dO1asWrVKpKSkiPXr14vExEQRGRkpzp8/L4SQt/jVqFFDrFixQmzZskXEx8eL+Ph4F7faNllZWWL79u1i+/btAoD46KOPxPbt28Xx48eFEPIW8rCwMPHLL7+InTt3il69elm8hbxly5Zi48aNYt26daJ+/fpud1u1EIVfa1ZWlnj++edFcnKySElJEX///bdo1aqVqF+/vrh+/brxPTzhWocPHy5CQ0PFqlWrTG6xzc7ONp5T1H+z6rbULl26iB07doilS5eKqKgot7sFt6hrPXz4sHjzzTfFli1bREpKivjll19EnTp1RKdOnYzv4SnXOm7cOLF69WqRkpIidu7cKcaNGycMBoP466+/hBDe850KUfi1etN3aon5nWOl9b0y5NhoypQpokaNGsLf31+0bdtWbNiwwdVNKrGHHnpIVK5cWfj7+4uqVauKhx56SBw+fNj4/LVr18TTTz8twsPDRXBwsLj//vvF2bNnXdhi261cuVIAKPAzYMAAIYS8jfzVV18VMTExIiAgQCQkJIgDBw6YvMelS5fEww8/LCpUqCBCQkLEoEGDRFZWlguupnCFXWt2drbo0qWLiIqKEuXKlRM1a9YUQ4YMKRDQPeFaLV0jAPHtt98az7Hlv9ljx46Jbt26iaCgIBEZGSnGjh0rbt68WcpXU7iirvXEiROiU6dOIiIiQgQEBIh69eqJF154wWROFSE841qfeOIJUbNmTeHv7y+ioqJEQkKCMeAI4T3fqRCFX6s3faeWmIec0vpeDUIIYXctioiIiMjNcUwOEREReSWGHCIiIvJKDDlERETklRhyiIiIyCsx5BAREZFXYsghIiIir8SQQ0RERF6JIYeIiIi8EkMOEREReSWGHCIiIvJKDDlERETklf4fgWWmFMgk/yQAAAAASUVORK5CYII=\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(\"deeptime\", Path(\"storage/experiments/Exchange/96S/repeat=0\"));"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 57,
-   "id": "7e3685e7",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T04:50:23.052127Z",
-     "start_time": "2022-11-20T04:50:22.937090Z"
-    }
-   },
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "(tensor([[ 0.5292,  1.7510,  0.7053,  ..., -0.8215, -0.8000, -0.0716],\n",
-       "         [ 0.4468,  1.6824,  0.6341,  ..., -0.8088, -0.7295, -0.0506],\n",
-       "         [ 0.5728,  1.7182,  0.6890,  ..., -0.8497, -0.7756,  0.0018],\n",
-       "         ...,\n",
-       "         [ 0.5457,  0.0489,  0.8019,  ..., -1.4872, -0.9826, -0.3539],\n",
-       "         [ 0.5340,  0.0770,  0.7975,  ..., -1.4627, -0.9620, -0.3570],\n",
-       "         [ 0.5195,  0.0113,  0.8092,  ..., -1.4745, -0.9472, -0.3507]]),\n",
-       " tensor([[ 5.4374e-01,  1.3666e-01,  8.0406e-01, -8.8760e-01,  1.9074e+00,\n",
-       "          -1.4300e+00, -9.5311e-01, -3.3919e-01],\n",
-       "         [ 5.5538e-01,  2.2618e-01,  8.1282e-01, -8.4952e-01,  1.9074e+00,\n",
-       "          -1.4273e+00, -8.8462e-01, -3.0561e-01],\n",
-       "         [ 5.7478e-01,  2.4110e-01,  8.2086e-01, -8.3329e-01,  1.9123e+00,\n",
-       "          -1.3801e+00, -8.4853e-01, -3.2345e-01],\n",
-       "         [ 5.8739e-01,  2.4588e-01,  8.2966e-01, -8.3030e-01,  1.9123e+00,\n",
-       "          -1.3864e+00, -8.5757e-01, -3.2240e-01],\n",
-       "         [ 5.7769e-01,  1.6949e-01,  8.3553e-01, -8.8549e-01,  1.9123e+00,\n",
-       "          -1.4164e+00, -8.3038e-01, -3.2870e-01],\n",
-       "         [ 5.8739e-01,  1.3964e-01,  8.3480e-01, -9.1691e-01,  1.9057e+00,\n",
-       "          -1.4373e+00, -8.2736e-01, -3.5388e-01],\n",
-       "         [ 5.9127e-01,  1.5934e-01,  8.3406e-01, -8.9600e-01,  1.9057e+00,\n",
-       "          -1.4200e+00, -8.3341e-01, -3.4759e-01],\n",
-       "         [ 5.7769e-01,  1.3069e-01,  8.4067e-01, -9.1274e-01,  1.9057e+00,\n",
-       "          -1.4191e+00, -8.2736e-01, -3.2555e-01],\n",
-       "         [ 5.7381e-01,  9.4885e-02,  8.5245e-01, -9.3146e-01,  1.9057e+00,\n",
-       "          -1.4236e+00, -8.2736e-01, -3.3395e-01],\n",
-       "         [ 5.3889e-01,  9.7869e-02,  8.5984e-01, -9.5007e-01,  1.9057e+00,\n",
-       "          -1.4164e+00, -8.2736e-01, -3.3290e-01],\n",
-       "         [ 5.3017e-01, -5.7301e-02,  8.5320e-01, -1.0214e+00,  1.8953e+00,\n",
-       "          -1.4545e+00, -8.5457e-01, -3.3185e-01],\n",
-       "         [ 5.4374e-01, -2.4828e-01,  8.5320e-01, -1.0635e+00,  1.8835e+00,\n",
-       "          -1.5045e+00, -9.4125e-01, -3.6018e-01],\n",
-       "         [ 5.6799e-01, -2.0232e-01,  8.6945e-01, -1.0535e+00,  1.8636e+00,\n",
-       "          -1.4645e+00, -9.3532e-01, -3.1296e-01],\n",
-       "         [ 5.9224e-01,  2.9236e-02,  8.5689e-01, -9.5007e-01,  1.8699e+00,\n",
-       "          -1.4327e+00, -8.4853e-01, -3.0981e-01],\n",
-       "         [ 6.2230e-01,  1.5158e-01,  8.5910e-01, -8.7495e-01,  1.8734e+00,\n",
-       "          -1.4146e+00, -8.3643e-01, -3.0771e-01],\n",
-       "         [ 6.0678e-01, -6.0420e-04,  8.5541e-01, -9.8697e-01,  1.8734e+00,\n",
-       "          -1.4473e+00, -8.3643e-01, -3.2135e-01],\n",
-       "         [ 5.9709e-01,  4.7140e-02,  8.5172e-01, -9.6858e-01,  1.8734e+00,\n",
-       "          -1.4427e+00, -8.3825e-01, -3.2870e-01],\n",
-       "         [ 5.9321e-01,  5.0124e-02,  8.7242e-01, -9.4884e-01,  1.8734e+00,\n",
-       "          -1.4427e+00, -8.3643e-01, -3.3395e-01],\n",
-       "         [ 5.9709e-01,  1.0980e-01,  8.7909e-01, -9.2648e-01,  1.8734e+00,\n",
-       "          -1.4227e+00, -8.3341e-01, -3.2240e-01],\n",
-       "         [ 5.9418e-01,  5.3108e-02,  8.8058e-01, -9.6036e-01,  1.8734e+00,\n",
-       "          -1.4427e+00, -8.1219e-01, -3.1296e-01],\n",
-       "         [ 6.0388e-01,  5.0124e-02,  8.6871e-01, -9.7268e-01,  1.8734e+00,\n",
-       "          -1.4354e+00, -8.1219e-01, -3.1926e-01],\n",
-       "         [ 5.3501e-01,  1.1577e-01,  8.7984e-01, -9.6448e-01,  1.8734e+00,\n",
-       "          -1.3946e+00, -8.0609e-01, -3.4129e-01],\n",
-       "         [ 6.0000e-01,  1.3069e-01,  8.7242e-01, -9.5337e-01,  1.8734e+00,\n",
-       "          -1.3592e+00, -8.0609e-01, -3.1821e-01],\n",
-       "         [ 6.0775e-01,  1.2174e-01,  8.7761e-01, -9.6776e-01,  1.8734e+00,\n",
-       "          -1.3873e+00, -8.0913e-01, -2.6259e-01]]),\n",
-       " array([[ 0.52890754,  0.04601173,  0.818106  , -0.9959081 ,  1.9366491 ,\n",
-       "         -1.5069696 , -0.9808905 , -0.36548147],\n",
-       "        [ 0.528438  ,  0.03981169,  0.8184106 , -0.9991272 ,  1.937193  ,\n",
-       "         -1.5084361 , -0.9828327 , -0.36484447],\n",
-       "        [ 0.5307723 ,  0.04257247,  0.8204612 , -1.0004759 ,  1.9423875 ,\n",
-       "         -1.5103797 , -0.9852797 , -0.36599845],\n",
-       "        [ 0.5303809 ,  0.04465088,  0.8191587 , -0.9979849 ,  1.9372461 ,\n",
-       "         -1.505586  , -0.9807858 , -0.36503443],\n",
-       "        [ 0.5305283 ,  0.04264332,  0.8198152 , -1.0008417 ,  1.9394246 ,\n",
-       "         -1.5105733 , -0.9837851 , -0.3655746 ],\n",
-       "        [ 0.5307227 ,  0.04202083,  0.8191189 , -0.99933463,  1.9369458 ,\n",
-       "         -1.506729  , -0.98293924, -0.36503708],\n",
-       "        [ 0.5283215 ,  0.04141821,  0.81586105, -0.9971103 ,  1.9277518 ,\n",
-       "         -1.5040027 , -0.978884  , -0.36278751],\n",
-       "        [ 0.5252694 ,  0.04255188,  0.8136384 , -0.9928628 ,  1.9213691 ,\n",
-       "         -1.4981959 , -0.9740302 , -0.36309323],\n",
-       "        [ 0.5275271 ,  0.04352546,  0.814939  , -0.9949809 ,  1.9263753 ,\n",
-       "         -1.5013223 , -0.9757192 , -0.3638668 ],\n",
-       "        [ 0.5276257 ,  0.04390374,  0.8143943 , -0.9922678 ,  1.9258269 ,\n",
-       "         -1.5007619 , -0.97613275, -0.36400792],\n",
-       "        [ 0.52339524,  0.04113823,  0.81086344, -0.9905842 ,  1.9132272 ,\n",
-       "         -1.4942402 , -0.9708606 , -0.36080262],\n",
-       "        [ 0.5248644 ,  0.042987  ,  0.81305283, -0.9912248 ,  1.9210671 ,\n",
-       "         -1.4969778 , -0.9736485 , -0.3629068 ],\n",
-       "        [ 0.52518415,  0.04741649,  0.81146616, -0.98756695,  1.9176267 ,\n",
-       "         -1.4939798 , -0.97169137, -0.36096278],\n",
-       "        [ 0.5220071 ,  0.04414371,  0.81153566, -0.98984647,  1.9137857 ,\n",
-       "         -1.4954246 , -0.97107446, -0.36085606],\n",
-       "        [ 0.52268267,  0.0460408 ,  0.8109568 , -0.9874458 ,  1.9127601 ,\n",
-       "         -1.4930701 , -0.9692728 , -0.3606753 ],\n",
-       "        [ 0.5223365 ,  0.04568218,  0.8103212 , -0.98693126,  1.9110385 ,\n",
-       "         -1.4933776 , -0.96929896, -0.3607128 ],\n",
-       "        [ 0.52153313,  0.04434089,  0.8080093 , -0.9862203 ,  1.9069732 ,\n",
-       "         -1.4901737 , -0.96673113, -0.3586038 ],\n",
-       "        [ 0.5211224 ,  0.04329763,  0.80827016, -0.9870948 ,  1.9067513 ,\n",
-       "         -1.4907483 , -0.96722066, -0.35898498],\n",
-       "        [ 0.51958317,  0.0411768 ,  0.80564517, -0.9852966 ,  1.90139   ,\n",
-       "         -1.4851948 , -0.96316063, -0.35826832],\n",
-       "        [ 0.52038646,  0.04568981,  0.80709517, -0.9835636 ,  1.9032961 ,\n",
-       "         -1.4881977 , -0.9650438 , -0.35861585],\n",
-       "        [ 0.5194442 ,  0.04407027,  0.80520827, -0.98266006,  1.8993053 ,\n",
-       "         -1.4852973 , -0.9634341 , -0.35824707],\n",
-       "        [ 0.51898205,  0.04415236,  0.8051576 , -0.9825941 ,  1.897731  ,\n",
-       "         -1.4861436 , -0.9634547 , -0.3574056 ],\n",
-       "        [ 0.5183343 ,  0.04260498,  0.80339235, -0.9823522 ,  1.893536  ,\n",
-       "         -1.4827589 , -0.9612396 , -0.3563275 ],\n",
-       "        [ 0.5182621 ,  0.03967791,  0.8052563 , -0.9854379 ,  1.8969235 ,\n",
-       "         -1.4861064 , -0.9637438 , -0.3569042 ]], dtype=float32),\n",
-       " [0,\n",
-       "  1,\n",
-       "  2,\n",
-       "  3,\n",
-       "  4,\n",
-       "  5,\n",
-       "  6,\n",
-       "  7,\n",
-       "  8,\n",
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-       "  10,\n",
-       "  11,\n",
-       "  12,\n",
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-       "  214,\n",
-       "  215])"
-      ]
-     },
-     "execution_count": 57,
-     "metadata": {},
-     "output_type": "execute_result"
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(\"deeptime\", Path(\"storage/experiments/Exchange/96M2/repeat=0\"))"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 42,
-   "id": "9c6bf6c3",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T00:01:09.186170Z",
-     "start_time": "2022-11-20T00:01:08.989739Z"
-    }
-   },
-   "outputs": [
-    {
-     "name": "stdout",
-     "output_type": "stream",
-     "text": [
-      "receptive field [690 378 242]=[138  18   2]*[[ 1  1  1]\n",
-      " [ 1  2  4]\n",
-      " [ 1  4 16]\n",
-      " [ 1  6 36]\n",
-      " [ 1  8 64]]\n"
-     ]
-    },
-    {
-     "data": {
-      "image/png": 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\n",
-      "text/plain": [
-       "<Figure size 640x480 with 1 Axes>"
-      ]
-     },
-     "metadata": {},
-     "output_type": "display_data"
-    }
-   ],
-   "source": [
-    "plot(\"deeptime2\", Path(\"storage/experiments/Exchange/96Mplus2/repeat=0\"))"
-   ]
-  },
-  {
-   "cell_type": "markdown",
-   "id": "b1e031e3",
-   "metadata": {},
-   "source": [
-    "- try just one predictor\n",
-    "- try logp? nah\n",
-    "- mae?\n",
-    "- make my own csv with 5m data (maybe 10k rows)\n",
-    "- backtest?"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "42214089",
-   "metadata": {
-    "ExecuteTime": {
-     "end_time": "2022-11-20T00:57:32.922740Z",
-     "start_time": "2022-11-20T00:57:32.919738Z"
-    }
-   },
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "f59232cc",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  },
-  {
-   "cell_type": "code",
-   "execution_count": null,
-   "id": "b1d152b2",
-   "metadata": {},
-   "outputs": [],
-   "source": []
-  }
- ],
- "metadata": {
-  "jupytext": {
-   "cell_metadata_filter": "-all",
-   "main_language": "python",
-   "notebook_metadata_filter": "-all"
-  },
-  "kernelspec": {
-   "display_name": "deeptime",
-   "language": "python",
-   "name": "deeptime"
-  },
-  "language_info": {
-   "codemirror_mode": {
-    "name": "ipython",
-    "version": 3
-   },
-   "file_extension": ".py",
-   "mimetype": "text/x-python",
-   "name": "python",
-   "nbconvert_exporter": "python",
-   "pygments_lexer": "ipython3",
-   "version": "3.8.13"
-  },
-  "toc": {
-   "base_numbering": 1,
-   "nav_menu": {},
-   "number_sections": true,
-   "sideBar": true,
-   "skip_h1_title": false,
-   "title_cell": "Table of Contents",
-   "title_sidebar": "Contents",
-   "toc_cell": false,
-   "toc_position": {},
-   "toc_section_display": true,
-   "toc_window_display": false
-  }
- },
- "nbformat": 4,
- "nbformat_minor": 5
-}