diff --git a/README.md b/README.md
index 0bc0c3a..af5d7f4 100644
--- a/README.md
+++ b/README.md
@@ -1,3 +1,10 @@
+Fork note:
+
+Interested by the results on the Exchange data I'm digging deeper, plotting, extending, and replicating the exchange reults. See `scratch.ipynb`
+
+
+----
+
 # DeepTime: Deep Time-Index Meta-Learning for Non-Stationary Time-Series Forecasting
 
 <p align="center">
@@ -109,4 +116,4 @@ Please consider citing if you find this code useful to your research.
     author={Gerald Woo and Chenghao Liu and Doyen Sahoo and Akshat Kumar and Steven C. H. Hoi},
     year={2022},
     url={https://arxiv.org/abs/2207.06046},
-}</pre>
\ No newline at end of file
+}</pre>
diff --git a/scratch.ipynb b/scratch.ipynb
new file mode 100644
index 0000000..aec61dd
--- /dev/null
+++ b/scratch.ipynb
@@ -0,0 +1,332 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "4e09086b",
+   "metadata": {
+    "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",
+    "\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": "516c4869",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "gin.clear_config()\n",
+    "# gin.parse_config(open(\"storage/experiments/Exchange/96M/repeat=0/config.gin\"))\n",
+    "gin.parse_config(open(\"storage/experiments/Exchange/96Mplus/repeat=0/config.gin\"))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "3e056c6f",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "train_set, train_loader = get_data(flag='train', batch_size=16)\n",
+    "# x, _, _, _ =train_set[0]\n",
+    "# x = x * 1.0"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "07f093f2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": [
+    "# x -= x[0]\n",
+    "# x /= x.std()\n",
+    "# plt.plot(x)\n",
+    "# # %%"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "7aeee9df",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "model = get_model(\"deeptime2\",\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/96Mplus/repeat=0/model.pth'))\n",
+    "model = model.eval()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "b6e8ca1f",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
+    "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)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "638da5c2",
+   "metadata": {
+    "lines_to_next_cell": 2
+   },
+   "outputs": [],
+   "source": []
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "66d7f095",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "plt.title('inception inr')\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",
+    "i_past = list(range(l))\n",
+    "i_future = list(range(l, l*2))\n",
+    "for i in range(x.shape[-1]):\n",
+    "    plt.plot(range(l), x2[:, i], c=colors[i])\n",
+    "for i in range(x.shape[-1]):\n",
+    "    plt.plot(range(l, l*2), y2[:, i], c=colors[i])\n",
+    "for i in range(x.shape[-1]):\n",
+    "    plt.plot(range(l, l*2), forecast2[:, i], c=colors[i], linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "47bf8d52",
+   "metadata": {
+    "lines_to_next_cell": 0
+   },
+   "outputs": [],
+   "source": [
+    "gin.clear_config()\n",
+    "gin.parse_config(open(\"storage/experiments/Exchange/96M/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/96M/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 inr')\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",
+    "i_past = list(range(l))\n",
+    "i_future = list(range(l, l*2))\n",
+    "for i in range(x.shape[-1]):\n",
+    "    plt.plot(range(l), x2[:, i], c=colors[i])\n",
+    "for i in range(x.shape[-1]):\n",
+    "    plt.plot(range(l, l*2), y2[:, i], c=colors[i])\n",
+    "for i in range(x.shape[-1]):\n",
+    "    plt.plot(range(l, l*2), forecast2[:, i], c=colors[i], linestyle='--')"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "28eca411",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "gin.clear_config()\n",
+    "gin.parse_config(open(\"storage/experiments/Exchange/96Mplus2/repeat=0/config.gin\"))\n",
+    "\n",
+    "train_set, train_loader = get_data(flag='train', batch_size=16)\n",
+    "\n",
+    "model = get_model(\"deeptime2\",\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/96Mplus2/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('inception inr')\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": "code",
+   "execution_count": null,
+   "id": "6ca906be",
+   "metadata": {},
+   "outputs": [],
+   "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": "code",
+   "execution_count": null,
+   "id": "8f8c075b",
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "jupytext": {
+   "cell_metadata_filter": "-all",
+   "formats": "auto:percent,ipynb",
+   "main_language": "python",
+   "notebook_metadata_filter": "-all"
+  },
+  "kernelspec": {
+   "display_name": "Python 3 (ipykernel)",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.9.9"
+  },
+  "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
+}