diff --git a/scripts/InitializationNotebook.ipynb b/scripts/InitializationNotebook.ipynb
index a3572bd..a23371a 100644
--- a/scripts/InitializationNotebook.ipynb
+++ b/scripts/InitializationNotebook.ipynb
@@ -2,7 +2,7 @@
  "cells": [
   {
    "cell_type": "code",
-   "execution_count": 4,
+   "execution_count": 15,
    "metadata": {},
    "outputs": [],
    "source": [
@@ -16,6 +16,8 @@
     "import re\n",
     "import os, os.path\n",
     "from os.path import splitext\n",
+    "import pathlib\n",
+    "import tqdm\n",
     "import pydicom as dicom\n",
     "import numpy as np\n",
     "from pydicom.uid import UID, generate_uid\n",
@@ -33,30 +35,31 @@
     "import math\n",
     "import torch\n",
     "import torchvision\n",
+    "import scipy\n",
     "\n",
     "sys.path.append(\"..\")\n",
     "import echonet\n",
     "\n",
     "import wget \n",
     "\n",
-    "#destinationFolder = \"/Users/davidouyang/Dropbox/Echo Research/CodeBase/Output\"\n",
-    "destinationFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\Output\"\n",
-    "#videosFolder = \"/Users/davidouyang/Dropbox/Echo Research/CodeBase/a4c-video-dir\"\n",
-    "videosFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\a4c-video-dir\"\n",
-    "#DestinationForWeights = \"/Users/davidouyang/Dropbox/Echo Research/CodeBase/EchoNetDynamic-Weights\"\n",
-    "DestinationForWeights = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\EchoNetDynamic-Weights\""
+    "destinationFolder = \"\"\n",
+    "#destinationFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\Output\"\n",
+    "videosFolder = \"\"\n",
+    "# videosFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\a4c-video-dir\"\n",
+    "DestinationForWeights = \"\"\n",
+    "# DestinationForWeights = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\EchoNetDynamic-Weights\""
    ]
   },
   {
    "cell_type": "code",
-   "execution_count": 5,
+   "execution_count": 16,
    "metadata": {},
    "outputs": [
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "The weights are at C:\\Users\\Windows\\Dropbox\\Echo Research\\CodeBase\\EchoNetDynamic-Weights\n",
+      "The weights are at /Users/akn/Drive/Developer/TTE-ML/weights\n",
       "Segmentation Weights already present\n",
       "EF Weights already present\n"
      ]
@@ -91,24 +94,32 @@
   },
   {
    "cell_type": "code",
-   "execution_count": 6,
+   "execution_count": 17,
    "metadata": {},
    "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  0%|          | 0/1 [00:00<?, ?it/s]"
+     ]
+    },
     {
      "name": "stdout",
      "output_type": "stream",
      "text": [
-      "loading weights from  C:\\Users\\Windows\\Dropbox\\Echo Research\\CodeBase\\EchoNetDynamic-Weights\\r2plus1d_18_32_2_pretrained\n",
-      "cuda is available, original weights\n",
-      "external_test ['0X1A05DFFFCAFB253B.avi', '0X1A0A263B22CCD966.avi', '0X1A2A76BDB5B98BED.avi', '0X1A2C60147AF9FDAE.avi', '0X1A2E9496910EFF5B.avi', '0X1A3D565B371DC573.avi', '0X1A3E7BF1DFB132FB.avi', '0X1A5FAE3F9D37794E.avi', '0X1A6ACFE7B286DAFC.avi', '0X1A8D85542DBE8204.avi', '23_Apical_4_chamber_view.dcm.avi', '62_Apical_4_chamber_view.dcm.avi', '64_Apical_4_chamber_view.dcm.avi']\n"
+      "loading weights from  /Users/akn/Drive/Developer/TTE-ML/weights/r2plus1d_18_32_2_pretrained\n",
+      "cuda is not available, cpu weights\n",
+      "external_test ['0X1A0A263B22CCD966.avi', '0X1A2A76BDB5B98BED.avi', '0X1A2C60147AF9FDAE.avi', '0X1A2E9496910EFF5B.avi']\n"
      ]
     },
     {
      "name": "stderr",
      "output_type": "stream",
      "text": [
-      "100%|██████████████████████████████████████████████████████████████████████████████████| 10/10 [00:10<00:00,  1.00s/it]\n",
-      "100%|████████████████████████████████████████████████████████| 13/13 [00:29<00:00,  2.26s/it, 3122.29 (3440.26) / 0.00]\n"
+      "100%|██████████| 1/1 [00:00<00:00,  4.14it/s]\n",
+      "100%|██████████| 4/4 [18:51<00:00, 282.84s/it, 3602.54 (3684.90) / 0.00]\n"
      ]
     }
    ],
@@ -145,9 +156,11 @@
     "#Weights = \"final_r2+1d_model_regression_EF_sgd_skip1_32frames.pth.tar\"\n",
     "\n",
     "\n",
-    "output = os.path.join(destinationFolder, \"cedars_ef_output.csv\")\n",
+    "output = os.path.join(destinationFolder, \"ef_output.csv\")\n",
     "\n",
-    "ds = echonet.datasets.Echo(split = \"external_test\", external_test_location = videosFolder, crops=\"all\")\n",
+    "#changed crops to clips\n",
+    "#have to set a length or will get runtime error w/ utils.get_mean_and_stds(ds) b/c videos have different lengths\n",
+    "ds = echonet.datasets.Echo(split = \"external_test\", external_test_location = videosFolder, length=frames, clips=1)\n",
     "print(ds.split, ds.fnames)\n",
     "\n",
     "mean, std = echonet.utils.get_mean_and_std(ds)\n",
@@ -159,10 +172,13 @@
     "          \"period\": period,\n",
     "          }\n",
     "\n",
-    "ds = echonet.datasets.Echo(split = \"external_test\", external_test_location = videosFolder, **kwargs, crops=\"all\")\n",
+    "#changed crops to clips\n",
+    "#clips=\"all\" will get EF predictions for every clip of length \"frames\" with overlap\n",
+    "ds = echonet.datasets.Echo(split = \"external_test\", external_test_location = videosFolder, **kwargs, clips=\"all\")\n",
     "\n",
     "test_dataloader = torch.utils.data.DataLoader(ds, batch_size = 1, num_workers = 5, shuffle = True, pin_memory=(device.type == \"cuda\"))\n",
-    "loss, yhat, y = echonet.utils.video.run_epoch(model, test_dataloader, \"test\", None, device, save_all=True, blocks=25)\n",
+    "#changed third argument to False\n",
+    "loss, yhat, y = echonet.utils.video.run_epoch(model, test_dataloader, False, None, device, save_all=True, block_size=25)\n",
     "\n",
     "with open(output, \"w\") as g:\n",
     "    for (filename, pred) in zip(ds.fnames, yhat):\n",
@@ -172,16 +188,41 @@
   },
   {
    "cell_type": "code",
-   "execution_count": null,
+   "execution_count": 19,
    "metadata": {},
-   "outputs": [],
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\r",
+      "  0%|          | 0/4 [00:00<?, ?it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "loading weights from  /Users/akn/Drive/Developer/TTE-ML/weights/deeplabv3_resnet50_random\n",
+      "cuda is not available, cpu weights\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4/4 [01:21<00:00, 20.29s/it]\n",
+      "100%|██████████| 4/4 [00:01<00:00,  2.49it/s]\n"
+     ]
+    }
+   ],
    "source": [
     "# Initialize and Run Segmentation model\n",
     "\n",
     "torch.cuda.empty_cache()\n",
     "\n",
     "\n",
-    "videosFolder = \"C:\\\\Users\\\\Windows\\\\Dropbox\\\\Echo Research\\\\CodeBase\\\\View Classification\\\\AppearsA4c\\\\Resized2\"\n",
+    "# videosFolder = \"C:\\\\Users\\\\Windows\\\\Drop  box\\\\Echo Research\\\\CodeBase\\\\View Classification\\\\AppearsA4c\\\\Resized2\"\n",
     "\n",
     "def collate_fn(x):\n",
     "    x, f = zip(*x)\n",
@@ -189,9 +230,29 @@
     "    x = torch.as_tensor(np.swapaxes(np.concatenate(x, 1), 0, 1))\n",
     "    return x, f, i\n",
     "\n",
-    "dataloader = torch.utils.data.DataLoader(echonet.datasets.Echo(split=\"external_test\", external_test_location = videosFolder, target_type=[\"Filename\"], length=None, period=1, mean=mean, std=std),\n",
-    "                                         batch_size=10, num_workers=0, shuffle=False, pin_memory=(device.type == \"cuda\"), collate_fn=collate_fn)\n",
-    "if not all([os.path.isfile(os.path.join(destinationFolder, \"labels\", os.path.splitext(f)[0] + \".npy\")) for f in dataloader.dataset.fnames]):\n",
+    "model = torchvision.models.segmentation.deeplabv3_resnet50(pretrained=False, aux_loss=False)\n",
+    "model.classifier[-1] = torch.nn.Conv2d(model.classifier[-1].in_channels, 1, kernel_size=model.classifier[-1].kernel_size)\n",
+    "\n",
+    "print(\"loading weights from \", os.path.join(DestinationForWeights, \"deeplabv3_resnet50_random\"))\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    print(\"cuda is available, original weights\")\n",
+    "    device = torch.device(\"cuda\")\n",
+    "    model = torch.nn.DataParallel(model)\n",
+    "    model.to(device)\n",
+    "    checkpoint = torch.load(os.path.join(DestinationForWeights, os.path.basename(segmentationWeightsURL)))\n",
+    "    model.load_state_dict(checkpoint['state_dict'])\n",
+    "else:\n",
+    "    print(\"cuda is not available, cpu weights\")\n",
+    "    device = torch.device(\"cpu\")\n",
+    "    checkpoint = torch.load(os.path.join(DestinationForWeights, os.path.basename(segmentationWeightsURL)), map_location = \"cpu\")\n",
+    "    state_dict_cpu = {k[7:]: v for (k, v) in checkpoint['state_dict'].items()}\n",
+    "    model.load_state_dict(state_dict_cpu)\n",
+    "\n",
+    "ds = echonet.datasets.Echo(split=\"external_test\", external_test_location = videosFolder, target_type=[\"Filename\"], length=None, period=1, mean=mean, std=std)\n",
+    "test_dataloader = torch.utils.data.DataLoader(ds, batch_size = 1, num_workers = 5, shuffle = False, pin_memory=(device.type == \"cuda\"), collate_fn=collate_fn)\n",
+    "\n",
+    "if not all([os.path.isfile(os.path.join(destinationFolder, \"labels\", os.path.splitext(f)[0] + \".npy\")) for f in test_dataloader.dataset.fnames]):\n",
     "    # Save segmentations for all frames\n",
     "    # Only run if missing files\n",
     "\n",
@@ -200,16 +261,16 @@
     "    model.eval()\n",
     "\n",
     "    with torch.no_grad():\n",
-    "        for (x, f, i) in tqdm.tqdm(dataloader):\n",
+    "        for (x, f, i) in tqdm.tqdm(test_dataloader):\n",
     "            x = x.to(device)\n",
     "            y = np.concatenate([model(x[i:(i + block), :, :, :])[\"out\"].detach().cpu().numpy() for i in range(0, x.shape[0], block)]).astype(np.float16)\n",
     "            start = 0\n",
     "            for (filename, offset) in zip(f, i):\n",
     "                np.save(os.path.join(destinationFolder, \"labels\", os.path.splitext(filename)[0]), y[start:(start + offset), 0, :, :])\n",
     "                start += offset\n",
-    "                \n",
-    "dataloader = torch.utils.data.DataLoader(echonet.datasets.Echo(split=\"external_test\", external_test_location = videosFolder, target_type=[\"Filename\"], length=None, period=1, segmentation=os.path.join(destinationFolder, \"labels\")),\n",
-    "                                         batch_size=1, num_workers=8, shuffle=False, pin_memory=False)\n",
+    "\n",
+    "dataloader = torch.utils.data.DataLoader(echonet.datasets.Echo(split=\"external_test\", external_test_location = videosFolder, target_type=[\"Filename\"], length=None, period=1),\n",
+    "                                         batch_size=1, num_workers=0, shuffle=False, pin_memory=False)\n",
     "if not all(os.path.isfile(os.path.join(destinationFolder, \"videos\", f)) for f in dataloader.dataset.fnames):\n",
     "    pathlib.Path(os.path.join(destinationFolder, \"videos\")).mkdir(parents=True, exist_ok=True)\n",
     "    pathlib.Path(os.path.join(destinationFolder, \"size\")).mkdir(parents=True, exist_ok=True)\n",
@@ -219,15 +280,18 @@
     "        for (x, filename) in tqdm.tqdm(dataloader):\n",
     "            x = x.numpy()\n",
     "            for i in range(len(filename)):\n",
+    "                file = pathlib.Path(filename[i]).stem\n",
+    "                y = np.load(os.path.join(destinationFolder, \"labels\", file + \".npy\"))\n",
+    "                \n",
     "                img = x[i, :, :, :, :].copy()\n",
-    "                logit = img[2, :, :, :].copy()\n",
+    "                logit = y.copy()\n",
     "                img[1, :, :, :] = img[0, :, :, :]\n",
     "                img[2, :, :, :] = img[0, :, :, :]\n",
     "                img = np.concatenate((img, img), 3)\n",
     "                img[0, :, :, 112:] = np.maximum(255. * (logit > 0), img[0, :, :, 112:])\n",
-    "\n",
     "                img = np.concatenate((img, np.zeros_like(img)), 2)\n",
-    "                size = (logit > 0).sum(2).sum(1)\n",
+    "                size = (logit > 0).sum((1, 2))\n",
+    "                \n",
     "                try:\n",
     "                    trim_min = sorted(size)[round(len(size) ** 0.05)]\n",
     "                except:\n",
@@ -260,8 +324,15 @@
     "                            img[:, x, a + int(round(115 + 100 * y)), b + int(round(x / len(size) * 200 + 10))] = 255.\n",
     "                    if x in peaks:\n",
     "                        img[:, :, 200:225, b + int(round(x / len(size) * 200 + 10))] = 255.\n",
-    "                echonet.utils.savevideo(os.path.join(destinationFolder, \"videos\", filename[i]), img.astype(np.uint8), 50)                "
+    "                echonet.utils.savevideo(os.path.join(destinationFolder, \"videos\", filename[i]), img.astype(np.uint8), 50)  "
    ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
   }
  ],
  "metadata": {
@@ -280,7 +351,7 @@
    "name": "python",
    "nbconvert_exporter": "python",
    "pygments_lexer": "ipython3",
-   "version": "3.7.4"
+   "version": "3.7.6"
   }
  },
  "nbformat": 4,