diff --git a/.github/workflows/black.yaml b/.github/workflows/black.yaml
new file mode 100644
index 0000000..42f4d5c
--- /dev/null
+++ b/.github/workflows/black.yaml
@@ -0,0 +1,17 @@
+name: Python Black
+
+on: [push, pull_request]
+
+jobs:
+  lint:
+    name: Python Lint
+    runs-on: ubuntu-latest
+    steps:
+      - name: Setup Python
+        uses: actions/setup-python@v5
+      - name: Setup checkout
+        uses: actions/checkout@master
+      - name: Lint with Black
+        run: |
+          pip install black
+          black --diff --check src/embed_time
diff --git a/.github/workflows/tests.yaml b/.github/workflows/tests.yaml
new file mode 100644
index 0000000..bd8481f
--- /dev/null
+++ b/.github/workflows/tests.yaml
@@ -0,0 +1,28 @@
+name: Test
+
+on:
+  push:
+
+jobs:
+  test:
+    runs-on: ubuntu-latest
+    strategy:
+      fail-fast: false
+      matrix:
+        python-version: ["3.10"]
+
+    steps:
+    - uses: actions/checkout@v4
+    - name: Set up Python ${{ matrix.python-version }}
+      uses: actions/setup-python@v5
+      with:
+        python-version: ${{ matrix.python-version }}
+    - name: Install dependencies
+      run: |
+        pip install ".[dev]"
+    - name: Test with pytest
+      run: |
+        pytest --color=yes 
+    # Coverage should work out of the box for public repos. For private repos, more setup is likely required.
+    - name: Coverage
+      uses: codecov/codecov-action@v4
\ No newline at end of file
diff --git a/.gitignore b/.gitignore
index 51611d2..184398f 100644
--- a/.gitignore
+++ b/.gitignore
@@ -2,6 +2,13 @@
 *.zarr
 *.tiff
 *.tif
+**/data/
+**/mnist_data/
+**/data/
+**/mnist_data/
+/notebooks/splits
+/scripts/embed_time_runs
+/scripts/graphs
 
 # Byte-compiled / optimized / DLL files
 __pycache__/
@@ -126,4 +133,8 @@ pyrepo
 .vscode/
 
 # OS Files
-.DS_Store
\ No newline at end of file
+.DS_Store
+
+#logs
+embed_time_runs/
+embed_time_static_runs/
\ No newline at end of file
diff --git a/README.md b/README.md
index 1bc3e1c..1d96157 100644
--- a/README.md
+++ b/README.md
@@ -5,6 +5,8 @@
 Create a new environment
 ```bash
 conda create -n embed_time python=3.10
-conda activate 3.10
+conda activate embed_time
 pip install -e .
+conda install -y pytorch-gpu cuda-toolkit=11.8 torchvision -c nvidia -c conda-forge -c pytorch
+
 ```
diff --git a/backup.py b/backup.py
new file mode 100644
index 0000000..40e8f30
--- /dev/null
+++ b/backup.py
@@ -0,0 +1,188 @@
+import torch
+from torch import nn, optim
+import torch.nn.functional as F
+import numpy as np
+
+class ResizeConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, mode='nearest'):
+        super().__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size//2)
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+        x = self.conv(x)
+        return x
+
+class ResizeArbitrary(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, out_size, mode='nearest'):
+        super().__init__()
+        self.out_size = out_size
+        self.mode = mode
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size//2)
+
+    def forward(self, x):
+        x = F.interpolate(x, size=self.out_size, mode=self.mode)
+        x = torch.relu(self.conv(x))
+        return x
+
+class BasicBlockEnc(nn.Module):
+
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+
+        planes = in_planes*stride
+
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+
+        if stride == 1:
+            self.shortcut = nn.Identity()
+        else:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes)
+            )
+
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class BasicBlockDec(nn.Module):
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+        planes = int(in_planes/stride)
+
+        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(in_planes)
+        # self.bn1 could have been placed here, 
+        # but that messes up the order of the layers when printing the class
+
+        if stride == 1:
+            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential()
+        else:
+            self.conv1 = ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential(
+                ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride),
+                nn.BatchNorm2d(planes)
+            )
+    def forward(self, x):
+        out = torch.relu(self.bn2(self.conv2(x)))
+        out = self.bn1(self.conv1(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+
+class ResNet18Enc(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3, linear_downsample_factor = 8):
+        super().__init__()
+        self.in_planes = 64
+        self.z_dim = z_dim
+        self.conv1 = nn.Conv2d(nc, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(BasicBlockEnc, 64, num_Blocks[0], stride=1)
+        self.layer2 = self._make_layer(BasicBlockEnc, 128, num_Blocks[1], stride=2)
+        self.layer3 = self._make_layer(BasicBlockEnc, 256, num_Blocks[2], stride=2)
+        self.layer4 = self._make_layer(BasicBlockEnc, 512, num_Blocks[3], stride=2)
+        self.linear = nn.Linear(int(512*(128/2**len(num_Blocks))**2), 2 * z_dim, bias = False)
+
+    def _make_layer(self, BasicBlockEnc, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in strides:
+            layers += [BasicBlockEnc(self.in_planes, stride)]
+            self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = torch.relu(self.bn1(self.conv1(x)))
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = torch.flatten(x, start_dim=1, end_dim=-1).unsqueeze(1)
+        x = torch.relu(self.linear(x))
+        mu, logvar = torch.chunk(x, 2, dim=2)
+        return mu, logvar
+
+class ResNet18Dec(nn.Module):
+
+    def __init__(self, spatial_dim_bottle, num_Blocks=[2,2,2,2], z_dim=10, nc=3, linear_downsample_factor =8):
+        super().__init__()
+        self.in_planes = 512
+        self.nc = nc
+        self.z_dim = z_dim
+
+        
+        self.linear = nn.Linear(z_dim, 256)
+        self.firstconv = nn.Conv2d(1, 512, kernel_size=1)
+
+        self.layer4 = self._make_layer(BasicBlockDec, 256, num_Blocks[3], stride=2)
+        self.layer3 = self._make_layer(BasicBlockDec, 128, num_Blocks[2], stride=2)
+        self.layer2 = self._make_layer(BasicBlockDec, 64, num_Blocks[1], stride=2)
+        self.layer1 = self._make_layer(BasicBlockDec, 64, num_Blocks[0], stride=1)
+        self.conv1 = ResizeConv2d(64, nc, kernel_size=3, scale_factor=1)
+
+    def _make_layer(self, BasicBlockDec, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in reversed(strides):
+            layers += [BasicBlockDec(self.in_planes, stride)]
+        self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, z):
+        x = torch.relu(self.linear(z))
+        x= x.view(-1, 1, 16,16)
+        x = self.firstconv(x)
+        x = self.layer4(x)
+        x = self.layer3(x)
+        x = self.layer2(x)
+        x = self.layer1(x)
+        x = torch.sigmoid(self.conv1(x))
+        return x
+
+
+class VAEResNet18_Linear(nn.Module):
+    def __init__(self, nc, z_dim, input_spatial_dim):
+        super().__init__()
+        self.in_spatial_shape = input_spatial_dim
+        self.spat_shape_bottle = self.compute_spatial_shape(4)
+        self.spat_shape_bottle = (self.spat_shape_bottle[0],self.spat_shape_bottle[1])
+        self.encoder = ResNet18Enc(nc=nc, z_dim=z_dim)
+        self.decoder = ResNet18Dec(nc=nc, z_dim=z_dim, spatial_dim_bottle=self.spat_shape_bottle)
+        self.enc_linear = nn.Sequential(
+
+        )
+
+    def forward(self, x):
+        mean, logvar = self.encoder(x)
+        z = self.reparameterize(mean, logvar)
+        x = self.decoder(z)
+        return x, z, mean, logvar
+
+    @staticmethod
+    def reparameterize(mean, logvar):
+        std = torch.exp(logvar / 2) # in log-space, squareroot is divide by two
+        epsilon = torch.randn_like(std)
+        return epsilon * std + mean
+
+    def compute_spatial_shape(self, level: int) -> tuple[int, int]:
+        # TODO Add warning when shape is odd before maxpool
+        spatial_shape = np.array(self.in_spatial_shape)
+        if level == 0:
+            return spatial_shape
+        spatial_shape = np.array(self.compute_spatial_shape(level-1)) // 2 
+        if any([s%2 != 0 for s in spatial_shape]):
+            raise ValueError("Can't Decode Because Input Dimension is Lost during Downsampling")
+        return spatial_shape
diff --git a/notebooks/MNIST_VAE.ipynb b/notebooks/MNIST_VAE.ipynb
new file mode 100644
index 0000000..bc48eae
--- /dev/null
+++ b/notebooks/MNIST_VAE.ipynb
@@ -0,0 +1,250 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./data/MNIST/raw/train-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 9912422/9912422 [00:00<00:00, 77828862.26it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/train-images-idx3-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 28881/28881 [00:00<00:00, 2239314.06it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/train-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1648877/1648877 [00:00<00:00, 19711818.15it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./data/MNIST/raw\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4542/4542 [00:00<00:00, 5626263.66it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./data/MNIST/raw\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "from torchvision.datasets import MNIST \n",
+    "from torchvision import transforms\n",
+    "transform = transforms.Compose([transforms.ToTensor(), \n",
+    "                                        transforms.Normalize((0.5,), (0.5,))])\n",
+    "dataset = MNIST(root = './data', train = True, transform = transform, download=True)\n",
+    "train_set, val_set = torch.utils.data.random_split(dataset, [50000, 10000])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# train_dataset.data = train_dataset.data.type(torch.FloatTensor) \n",
+    "# test_dataset.data = test_dataset.data.type(torch.FloatTensor)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Initialising dataloaders:\n",
+    "train_loader = torch.utils.data.DataLoader(train_set,batch_size=16, shuffle=True)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/home/S-ab/miniforge3/envs/embed_time/lib/python3.10/site-packages/tqdm/auto.py:21: TqdmWarning: IProgress not found. Please update jupyter and ipywidgets. See https://ipywidgets.readthedocs.io/en/stable/user_install.html\n",
+      "  from .autonotebook import tqdm as notebook_tqdm\n",
+      "Checking train dataset...\n"
+     ]
+    },
+    {
+     "ename": "AttributeError",
+     "evalue": "'tuple' object has no attribute 'keys'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[5], line 33\u001b[0m\n\u001b[1;32m     27\u001b[0m \u001b[38;5;66;03m# Build the Pipeline\u001b[39;00m\n\u001b[1;32m     28\u001b[0m pipeline \u001b[38;5;241m=\u001b[39m TrainingPipeline(\n\u001b[1;32m     29\u001b[0m     training_config\u001b[38;5;241m=\u001b[39mmy_training_config,\n\u001b[1;32m     30\u001b[0m     model\u001b[38;5;241m=\u001b[39mmy_vae_model\n\u001b[1;32m     31\u001b[0m )\n\u001b[0;32m---> 33\u001b[0m \u001b[43mpipeline\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m     34\u001b[0m \u001b[43m    \u001b[49m\u001b[43mtrain_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mtrain_set\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\n\u001b[1;32m     35\u001b[0m \u001b[43m    \u001b[49m\u001b[43meval_data\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43m \u001b[49m\u001b[43mval_set\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/pythae/pipelines/training.py:186\u001b[0m, in \u001b[0;36mTrainingPipeline.__call__\u001b[0;34m(self, train_data, eval_data, callbacks)\u001b[0m\n\u001b[1;32m    183\u001b[0m     train_dataset \u001b[38;5;241m=\u001b[39m train_data\n\u001b[1;32m    185\u001b[0m logger\u001b[38;5;241m.\u001b[39minfo(\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mChecking train dataset...\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m--> 186\u001b[0m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_check_dataset\u001b[49m\u001b[43m(\u001b[49m\u001b[43mtrain_dataset\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    188\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m eval_data \u001b[38;5;129;01mis\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28;01mNone\u001b[39;00m:\n\u001b[1;32m    189\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(eval_data, np\u001b[38;5;241m.\u001b[39mndarray) \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(eval_data, torch\u001b[38;5;241m.\u001b[39mTensor):\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/pythae/pipelines/training.py:126\u001b[0m, in \u001b[0;36mTrainingPipeline._check_dataset\u001b[0;34m(self, dataset)\u001b[0m\n\u001b[1;32m    118\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:\n\u001b[1;32m    119\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m DatasetError(\n\u001b[1;32m    120\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mError when trying to collect data from the dataset. Check `__getitem__` method. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    121\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe Dataset should output a dictionnary with keys at least [\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m]. \u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    122\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mPlease check documentation.\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    123\u001b[0m         \u001b[38;5;124mf\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mException raised: \u001b[39m\u001b[38;5;132;01m{\u001b[39;00m\u001b[38;5;28mtype\u001b[39m(e)\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m with message: \u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;241m+\u001b[39m \u001b[38;5;28mstr\u001b[39m(e)\n\u001b[1;32m    124\u001b[0m     ) \u001b[38;5;28;01mfrom\u001b[39;00m \u001b[38;5;21;01me\u001b[39;00m\n\u001b[0;32m--> 126\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m\"\u001b[39m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;129;01min\u001b[39;00m \u001b[43mdataset_output\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mkeys\u001b[49m():\n\u001b[1;32m    127\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m DatasetError(\n\u001b[1;32m    128\u001b[0m         \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mThe Dataset should output a dictionnary with keys [\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mdata\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m]\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[1;32m    129\u001b[0m     )\n\u001b[1;32m    131\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'tuple' object has no attribute 'keys'"
+     ]
+    }
+   ],
+   "source": [
+    "from pythae.pipelines import TrainingPipeline\n",
+    "from pythae.models import VAE, VAEConfig\n",
+    "from pythae.trainers import BaseTrainerConfig\n",
+    "\n",
+    "# Set up the training configuration\n",
+    "my_training_config = BaseTrainerConfig(\n",
+    "    output_dir='./output',\n",
+    "    num_epochs=50,\n",
+    "    learning_rate=1e-3,\n",
+    "    per_device_train_batch_size=200,\n",
+    "    per_device_eval_batch_size=200,\n",
+    "    steps_saving=None,\n",
+    "    optimizer_cls=\"AdamW\",\n",
+    "    optimizer_params={\"weight_decay\": 0.05, \"betas\": (0.91, 0.995)},\n",
+    "    scheduler_cls=\"ReduceLROnPlateau\",\n",
+    "    scheduler_params={\"patience\": 5, \"factor\": 0.5}\n",
+    ")\n",
+    "# Set up the model configuration\n",
+    "my_vae_config = model_config = VAEConfig(\n",
+    "    input_dim=(1, 28, 28),\n",
+    "    latent_dim=10\n",
+    ")\n",
+    "# Build the model\n",
+    "my_vae_model = VAE(\n",
+    "    model_config=my_vae_config\n",
+    ")\n",
+    "# Build the Pipeline\n",
+    "pipeline = TrainingPipeline(\n",
+    "    training_config=my_training_config,\n",
+    "    model=my_vae_model\n",
+    ")\n",
+    "\n",
+    "pipeline(\n",
+    "    train_data=train_set, \n",
+    "    eval_data= val_set)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/MNIST_VAE_2.ipynb b/notebooks/MNIST_VAE_2.ipynb
new file mode 100644
index 0000000..cab58dc
--- /dev/null
+++ b/notebooks/MNIST_VAE_2.ipynb
@@ -0,0 +1,741 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Downloading http://yann.lecun.com/exdb/mnist/train-images-idx3-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw/train-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 9912422/9912422 [00:00<00:00, 58353922.94it/s]\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./mnist_data/MNIST/raw/train-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/train-labels-idx1-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/train-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 28881/28881 [00:00<00:00, 2189331.17it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./mnist_data/MNIST/raw/train-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw\n",
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 1648877/1648877 [00:00<00:00, 19387613.70it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./mnist_data/MNIST/raw/t10k-images-idx3-ubyte.gz to ./mnist_data/MNIST/raw\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "\n",
+      "Downloading http://yann.lecun.com/exdb/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Failed to download (trying next):\n",
+      "HTTP Error 403: Forbidden\n",
+      "\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz\n",
+      "Downloading https://ossci-datasets.s3.amazonaws.com/mnist/t10k-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "100%|██████████| 4542/4542 [00:00<00:00, 3891834.27it/s]"
+     ]
+    },
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Extracting ./mnist_data/MNIST/raw/t10k-labels-idx1-ubyte.gz to ./mnist_data/MNIST/raw\n",
+      "\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# prerequisites\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.nn.functional as F\n",
+    "import torch.optim as optim\n",
+    "from torchvision import datasets, transforms\n",
+    "from torch.autograd import Variable\n",
+    "from torchvision.utils import save_image\n",
+    "\n",
+    "bs = 100\n",
+    "# MNIST Dataset\n",
+    "train_dataset = datasets.MNIST(root='./mnist_data/', train=True, transform=transforms.ToTensor(), download=True)\n",
+    "test_dataset = datasets.MNIST(root='./mnist_data/', train=False, transform=transforms.ToTensor(), download=False)\n",
+    "\n",
+    "# Data Loader (Input Pipeline)\n",
+    "train_loader = torch.utils.data.DataLoader(dataset=train_dataset, batch_size=bs, shuffle=True)\n",
+    "test_loader = torch.utils.data.DataLoader(dataset=test_dataset, batch_size=bs, shuffle=False)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class VAE(nn.Module):\n",
+    "    def __init__(self, x_dim, h_dim1, h_dim2, z_dim):\n",
+    "        super(VAE, self).__init__()\n",
+    "        \n",
+    "        # encoder part\n",
+    "        self.fc1 = nn.Linear(x_dim, h_dim1)\n",
+    "        self.fc2 = nn.Linear(h_dim1, h_dim2)\n",
+    "        self.fc31 = nn.Linear(h_dim2, z_dim)\n",
+    "        self.fc32 = nn.Linear(h_dim2, z_dim)\n",
+    "        # decoder part\n",
+    "        self.fc4 = nn.Linear(z_dim, h_dim2)\n",
+    "        self.fc5 = nn.Linear(h_dim2, h_dim1)\n",
+    "        self.fc6 = nn.Linear(h_dim1, x_dim)\n",
+    "        \n",
+    "    def encoder(self, x):\n",
+    "        h = F.relu(self.fc1(x))\n",
+    "        h = F.relu(self.fc2(h))\n",
+    "        return self.fc31(h), self.fc32(h) # mu, log_var\n",
+    "    \n",
+    "    def sampling(self, mu, log_var):\n",
+    "        std = torch.exp(0.5*log_var)\n",
+    "        eps = torch.randn_like(std)\n",
+    "        return eps.mul(std).add_(mu) # return z sample\n",
+    "        \n",
+    "    def decoder(self, z):\n",
+    "        h = F.relu(self.fc4(z))\n",
+    "        h = F.relu(self.fc5(h))\n",
+    "        return F.sigmoid(self.fc6(h)) \n",
+    "    \n",
+    "    def forward(self, x):\n",
+    "        mu, log_var = self.encoder(x.view(-1, 784))\n",
+    "        z = self.sampling(mu, log_var)\n",
+    "        return self.decoder(z), mu, log_var\n",
+    "\n",
+    "# build model\n",
+    "vae = VAE(x_dim=784, h_dim1= 512, h_dim2=256, z_dim=2)\n",
+    "if torch.cuda.is_available():\n",
+    "    vae.cuda()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "VAE(\n",
+       "  (fc1): Linear(in_features=784, out_features=512, bias=True)\n",
+       "  (fc2): Linear(in_features=512, out_features=256, bias=True)\n",
+       "  (fc31): Linear(in_features=256, out_features=2, bias=True)\n",
+       "  (fc32): Linear(in_features=256, out_features=2, bias=True)\n",
+       "  (fc4): Linear(in_features=2, out_features=256, bias=True)\n",
+       "  (fc5): Linear(in_features=256, out_features=512, bias=True)\n",
+       "  (fc6): Linear(in_features=512, out_features=784, bias=True)\n",
+       ")"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "vae"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "optimizer = optim.Adam(vae.parameters())\n",
+    "# return reconstruction error + KL divergence losses\n",
+    "def loss_function(recon_x, x, mu, log_var):\n",
+    "    BCE = F.binary_cross_entropy(recon_x, x.view(-1, 784), reduction='sum')\n",
+    "    KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())\n",
+    "    return BCE + KLD"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def train(epoch):\n",
+    "    vae.train()\n",
+    "    train_loss = 0\n",
+    "    for batch_idx, (data, _) in enumerate(train_loader):\n",
+    "        data = data.cuda()\n",
+    "        optimizer.zero_grad()\n",
+    "        \n",
+    "        recon_batch, mu, log_var = vae(data)\n",
+    "        loss = loss_function(recon_batch, data, mu, log_var)\n",
+    "        \n",
+    "        loss.backward()\n",
+    "        train_loss += loss.item()\n",
+    "        optimizer.step()\n",
+    "        \n",
+    "        if batch_idx % 100 == 0:\n",
+    "            print('Train Epoch: {} [{}/{} ({:.0f}%)]\\tLoss: {:.6f}'.format(\n",
+    "                epoch, batch_idx * len(data), len(train_loader.dataset),\n",
+    "                100. * batch_idx / len(train_loader), loss.item() / len(data)))\n",
+    "    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def test():\n",
+    "    vae.eval()\n",
+    "    test_loss= 0\n",
+    "    with torch.no_grad():\n",
+    "        for data, _ in test_loader:\n",
+    "            data = data.cuda()\n",
+    "            recon, mu, log_var = vae(data)\n",
+    "            \n",
+    "            # sum up batch loss\n",
+    "            test_loss += loss_function(recon, data, mu, log_var).item()\n",
+    "        \n",
+    "    test_loss /= len(test_loader.dataset)\n",
+    "    print('====> Test set loss: {:.4f}'.format(test_loss))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Train Epoch: 1 [0/60000 (0%)]\tLoss: 544.164922\n",
+      "Train Epoch: 1 [10000/60000 (17%)]\tLoss: 193.912617\n",
+      "Train Epoch: 1 [20000/60000 (33%)]\tLoss: 174.921172\n",
+      "Train Epoch: 1 [30000/60000 (50%)]\tLoss: 170.975918\n",
+      "Train Epoch: 1 [40000/60000 (67%)]\tLoss: 173.846172\n",
+      "Train Epoch: 1 [50000/60000 (83%)]\tLoss: 170.784238\n",
+      "====> Epoch: 1 Average loss: 177.2868\n",
+      "====> Test set loss: 161.6675\n",
+      "Train Epoch: 2 [0/60000 (0%)]\tLoss: 167.997852\n",
+      "Train Epoch: 2 [10000/60000 (17%)]\tLoss: 154.081719\n",
+      "Train Epoch: 2 [20000/60000 (33%)]\tLoss: 156.469199\n",
+      "Train Epoch: 2 [30000/60000 (50%)]\tLoss: 163.095010\n",
+      "Train Epoch: 2 [40000/60000 (67%)]\tLoss: 154.786533\n",
+      "Train Epoch: 2 [50000/60000 (83%)]\tLoss: 162.840674\n",
+      "====> Epoch: 2 Average loss: 157.5373\n",
+      "====> Test set loss: 155.1577\n",
+      "Train Epoch: 3 [0/60000 (0%)]\tLoss: 161.682773\n",
+      "Train Epoch: 3 [10000/60000 (17%)]\tLoss: 152.506504\n",
+      "Train Epoch: 3 [20000/60000 (33%)]\tLoss: 156.820830\n",
+      "Train Epoch: 3 [30000/60000 (50%)]\tLoss: 155.602979\n",
+      "Train Epoch: 3 [40000/60000 (67%)]\tLoss: 152.807510\n",
+      "Train Epoch: 3 [50000/60000 (83%)]\tLoss: 146.297461\n",
+      "====> Epoch: 3 Average loss: 152.3861\n",
+      "====> Test set loss: 150.9044\n",
+      "Train Epoch: 4 [0/60000 (0%)]\tLoss: 148.072490\n",
+      "Train Epoch: 4 [10000/60000 (17%)]\tLoss: 152.754180\n",
+      "Train Epoch: 4 [20000/60000 (33%)]\tLoss: 144.985850\n",
+      "Train Epoch: 4 [30000/60000 (50%)]\tLoss: 148.697627\n",
+      "Train Epoch: 4 [40000/60000 (67%)]\tLoss: 148.061777\n",
+      "Train Epoch: 4 [50000/60000 (83%)]\tLoss: 146.494746\n",
+      "====> Epoch: 4 Average loss: 149.4665\n",
+      "====> Test set loss: 148.6927\n",
+      "Train Epoch: 5 [0/60000 (0%)]\tLoss: 148.376240\n",
+      "Train Epoch: 5 [10000/60000 (17%)]\tLoss: 142.598486\n",
+      "Train Epoch: 5 [20000/60000 (33%)]\tLoss: 147.947910\n",
+      "Train Epoch: 5 [30000/60000 (50%)]\tLoss: 148.503955\n",
+      "Train Epoch: 5 [40000/60000 (67%)]\tLoss: 147.785225\n",
+      "Train Epoch: 5 [50000/60000 (83%)]\tLoss: 150.265088\n",
+      "====> Epoch: 5 Average loss: 147.4510\n",
+      "====> Test set loss: 146.8640\n",
+      "Train Epoch: 6 [0/60000 (0%)]\tLoss: 146.762188\n",
+      "Train Epoch: 6 [10000/60000 (17%)]\tLoss: 142.818838\n",
+      "Train Epoch: 6 [20000/60000 (33%)]\tLoss: 146.569326\n",
+      "Train Epoch: 6 [30000/60000 (50%)]\tLoss: 145.410723\n",
+      "Train Epoch: 6 [40000/60000 (67%)]\tLoss: 141.979746\n",
+      "Train Epoch: 6 [50000/60000 (83%)]\tLoss: 141.481426\n",
+      "====> Epoch: 6 Average loss: 146.0570\n",
+      "====> Test set loss: 145.7999\n",
+      "Train Epoch: 7 [0/60000 (0%)]\tLoss: 143.514092\n",
+      "Train Epoch: 7 [10000/60000 (17%)]\tLoss: 148.404189\n",
+      "Train Epoch: 7 [20000/60000 (33%)]\tLoss: 144.985342\n",
+      "Train Epoch: 7 [30000/60000 (50%)]\tLoss: 142.209678\n",
+      "Train Epoch: 7 [40000/60000 (67%)]\tLoss: 144.193555\n",
+      "Train Epoch: 7 [50000/60000 (83%)]\tLoss: 146.088838\n",
+      "====> Epoch: 7 Average loss: 144.8901\n",
+      "====> Test set loss: 144.8688\n",
+      "Train Epoch: 8 [0/60000 (0%)]\tLoss: 142.200059\n",
+      "Train Epoch: 8 [10000/60000 (17%)]\tLoss: 150.785859\n",
+      "Train Epoch: 8 [20000/60000 (33%)]\tLoss: 138.127822\n",
+      "Train Epoch: 8 [30000/60000 (50%)]\tLoss: 136.159687\n",
+      "Train Epoch: 8 [40000/60000 (67%)]\tLoss: 141.459814\n",
+      "Train Epoch: 8 [50000/60000 (83%)]\tLoss: 141.425889\n",
+      "====> Epoch: 8 Average loss: 144.0388\n",
+      "====> Test set loss: 144.6195\n",
+      "Train Epoch: 9 [0/60000 (0%)]\tLoss: 149.004639\n",
+      "Train Epoch: 9 [10000/60000 (17%)]\tLoss: 143.493252\n",
+      "Train Epoch: 9 [20000/60000 (33%)]\tLoss: 153.884766\n",
+      "Train Epoch: 9 [30000/60000 (50%)]\tLoss: 149.214629\n",
+      "Train Epoch: 9 [40000/60000 (67%)]\tLoss: 143.256035\n",
+      "Train Epoch: 9 [50000/60000 (83%)]\tLoss: 134.390068\n",
+      "====> Epoch: 9 Average loss: 143.2551\n",
+      "====> Test set loss: 143.7028\n",
+      "Train Epoch: 10 [0/60000 (0%)]\tLoss: 143.705967\n",
+      "Train Epoch: 10 [10000/60000 (17%)]\tLoss: 145.422148\n",
+      "Train Epoch: 10 [20000/60000 (33%)]\tLoss: 136.606582\n",
+      "Train Epoch: 10 [30000/60000 (50%)]\tLoss: 143.651240\n",
+      "Train Epoch: 10 [40000/60000 (67%)]\tLoss: 142.328467\n",
+      "Train Epoch: 10 [50000/60000 (83%)]\tLoss: 136.443350\n",
+      "====> Epoch: 10 Average loss: 142.7348\n",
+      "====> Test set loss: 143.1938\n",
+      "Train Epoch: 11 [0/60000 (0%)]\tLoss: 136.261299\n",
+      "Train Epoch: 11 [10000/60000 (17%)]\tLoss: 136.775508\n",
+      "Train Epoch: 11 [20000/60000 (33%)]\tLoss: 138.462646\n",
+      "Train Epoch: 11 [30000/60000 (50%)]\tLoss: 147.047471\n",
+      "Train Epoch: 11 [40000/60000 (67%)]\tLoss: 144.471240\n",
+      "Train Epoch: 11 [50000/60000 (83%)]\tLoss: 142.198076\n",
+      "====> Epoch: 11 Average loss: 142.3032\n",
+      "====> Test set loss: 143.2594\n",
+      "Train Epoch: 12 [0/60000 (0%)]\tLoss: 150.473496\n",
+      "Train Epoch: 12 [10000/60000 (17%)]\tLoss: 141.016660\n",
+      "Train Epoch: 12 [20000/60000 (33%)]\tLoss: 146.733711\n",
+      "Train Epoch: 12 [30000/60000 (50%)]\tLoss: 138.005176\n",
+      "Train Epoch: 12 [40000/60000 (67%)]\tLoss: 142.438223\n",
+      "Train Epoch: 12 [50000/60000 (83%)]\tLoss: 136.287764\n",
+      "====> Epoch: 12 Average loss: 141.6889\n",
+      "====> Test set loss: 143.2200\n",
+      "Train Epoch: 13 [0/60000 (0%)]\tLoss: 146.469951\n",
+      "Train Epoch: 13 [10000/60000 (17%)]\tLoss: 141.283047\n",
+      "Train Epoch: 13 [20000/60000 (33%)]\tLoss: 144.385156\n",
+      "Train Epoch: 13 [30000/60000 (50%)]\tLoss: 148.255273\n",
+      "Train Epoch: 13 [40000/60000 (67%)]\tLoss: 140.869189\n",
+      "Train Epoch: 13 [50000/60000 (83%)]\tLoss: 139.777744\n",
+      "====> Epoch: 13 Average loss: 141.4734\n",
+      "====> Test set loss: 142.9337\n",
+      "Train Epoch: 14 [0/60000 (0%)]\tLoss: 150.782432\n",
+      "Train Epoch: 14 [10000/60000 (17%)]\tLoss: 139.598730\n",
+      "Train Epoch: 14 [20000/60000 (33%)]\tLoss: 150.871533\n",
+      "Train Epoch: 14 [30000/60000 (50%)]\tLoss: 137.223262\n",
+      "Train Epoch: 14 [40000/60000 (67%)]\tLoss: 145.604326\n",
+      "Train Epoch: 14 [50000/60000 (83%)]\tLoss: 140.652373\n",
+      "====> Epoch: 14 Average loss: 140.9149\n",
+      "====> Test set loss: 142.2797\n",
+      "Train Epoch: 15 [0/60000 (0%)]\tLoss: 132.435313\n",
+      "Train Epoch: 15 [10000/60000 (17%)]\tLoss: 130.340869\n",
+      "Train Epoch: 15 [20000/60000 (33%)]\tLoss: 141.396934\n",
+      "Train Epoch: 15 [30000/60000 (50%)]\tLoss: 141.940723\n",
+      "Train Epoch: 15 [40000/60000 (67%)]\tLoss: 137.135771\n",
+      "Train Epoch: 15 [50000/60000 (83%)]\tLoss: 133.582998\n",
+      "====> Epoch: 15 Average loss: 140.4572\n",
+      "====> Test set loss: 142.0402\n",
+      "Train Epoch: 16 [0/60000 (0%)]\tLoss: 141.297988\n",
+      "Train Epoch: 16 [10000/60000 (17%)]\tLoss: 144.038818\n",
+      "Train Epoch: 16 [20000/60000 (33%)]\tLoss: 140.762432\n",
+      "Train Epoch: 16 [30000/60000 (50%)]\tLoss: 133.840830\n",
+      "Train Epoch: 16 [40000/60000 (67%)]\tLoss: 135.744004\n",
+      "Train Epoch: 16 [50000/60000 (83%)]\tLoss: 141.050352\n",
+      "====> Epoch: 16 Average loss: 140.3003\n",
+      "====> Test set loss: 141.9543\n",
+      "Train Epoch: 17 [0/60000 (0%)]\tLoss: 133.224873\n",
+      "Train Epoch: 17 [10000/60000 (17%)]\tLoss: 127.450537\n",
+      "Train Epoch: 17 [20000/60000 (33%)]\tLoss: 138.199727\n",
+      "Train Epoch: 17 [30000/60000 (50%)]\tLoss: 137.564336\n",
+      "Train Epoch: 17 [40000/60000 (67%)]\tLoss: 132.303438\n",
+      "Train Epoch: 17 [50000/60000 (83%)]\tLoss: 143.162969\n",
+      "====> Epoch: 17 Average loss: 139.7848\n",
+      "====> Test set loss: 141.2916\n",
+      "Train Epoch: 18 [0/60000 (0%)]\tLoss: 133.366777\n",
+      "Train Epoch: 18 [10000/60000 (17%)]\tLoss: 130.578262\n",
+      "Train Epoch: 18 [20000/60000 (33%)]\tLoss: 142.652041\n",
+      "Train Epoch: 18 [30000/60000 (50%)]\tLoss: 140.695908\n",
+      "Train Epoch: 18 [40000/60000 (67%)]\tLoss: 134.455312\n",
+      "Train Epoch: 18 [50000/60000 (83%)]\tLoss: 143.282061\n",
+      "====> Epoch: 18 Average loss: 139.6111\n",
+      "====> Test set loss: 141.1797\n",
+      "Train Epoch: 19 [0/60000 (0%)]\tLoss: 152.087246\n",
+      "Train Epoch: 19 [10000/60000 (17%)]\tLoss: 135.492119\n",
+      "Train Epoch: 19 [20000/60000 (33%)]\tLoss: 133.975762\n",
+      "Train Epoch: 19 [30000/60000 (50%)]\tLoss: 141.946562\n",
+      "Train Epoch: 19 [40000/60000 (67%)]\tLoss: 148.952490\n",
+      "Train Epoch: 19 [50000/60000 (83%)]\tLoss: 137.981289\n",
+      "====> Epoch: 19 Average loss: 139.4519\n",
+      "====> Test set loss: 141.2781\n",
+      "Train Epoch: 20 [0/60000 (0%)]\tLoss: 145.538887\n",
+      "Train Epoch: 20 [10000/60000 (17%)]\tLoss: 140.559658\n",
+      "Train Epoch: 20 [20000/60000 (33%)]\tLoss: 137.326328\n",
+      "Train Epoch: 20 [30000/60000 (50%)]\tLoss: 143.877734\n",
+      "Train Epoch: 20 [40000/60000 (67%)]\tLoss: 135.418105\n",
+      "Train Epoch: 20 [50000/60000 (83%)]\tLoss: 132.068535\n",
+      "====> Epoch: 20 Average loss: 138.9621\n",
+      "====> Test set loss: 140.5945\n",
+      "Train Epoch: 21 [0/60000 (0%)]\tLoss: 141.377715\n",
+      "Train Epoch: 21 [10000/60000 (17%)]\tLoss: 139.690117\n",
+      "Train Epoch: 21 [20000/60000 (33%)]\tLoss: 140.509521\n",
+      "Train Epoch: 21 [30000/60000 (50%)]\tLoss: 135.971475\n",
+      "Train Epoch: 21 [40000/60000 (67%)]\tLoss: 139.169746\n",
+      "Train Epoch: 21 [50000/60000 (83%)]\tLoss: 145.604434\n",
+      "====> Epoch: 21 Average loss: 138.6230\n",
+      "====> Test set loss: 140.2344\n",
+      "Train Epoch: 22 [0/60000 (0%)]\tLoss: 138.073340\n",
+      "Train Epoch: 22 [10000/60000 (17%)]\tLoss: 139.963955\n",
+      "Train Epoch: 22 [20000/60000 (33%)]\tLoss: 141.226240\n",
+      "Train Epoch: 22 [30000/60000 (50%)]\tLoss: 130.647725\n",
+      "Train Epoch: 22 [40000/60000 (67%)]\tLoss: 130.806650\n",
+      "Train Epoch: 22 [50000/60000 (83%)]\tLoss: 135.986143\n",
+      "====> Epoch: 22 Average loss: 138.4372\n",
+      "====> Test set loss: 140.3150\n",
+      "Train Epoch: 23 [0/60000 (0%)]\tLoss: 140.100059\n",
+      "Train Epoch: 23 [10000/60000 (17%)]\tLoss: 140.942422\n",
+      "Train Epoch: 23 [20000/60000 (33%)]\tLoss: 143.546289\n",
+      "Train Epoch: 23 [30000/60000 (50%)]\tLoss: 129.863242\n",
+      "Train Epoch: 23 [40000/60000 (67%)]\tLoss: 139.449463\n",
+      "Train Epoch: 23 [50000/60000 (83%)]\tLoss: 138.488057\n",
+      "====> Epoch: 23 Average loss: 138.2641\n",
+      "====> Test set loss: 140.5326\n",
+      "Train Epoch: 24 [0/60000 (0%)]\tLoss: 139.339902\n",
+      "Train Epoch: 24 [10000/60000 (17%)]\tLoss: 135.619990\n",
+      "Train Epoch: 24 [20000/60000 (33%)]\tLoss: 133.345596\n",
+      "Train Epoch: 24 [30000/60000 (50%)]\tLoss: 140.409600\n",
+      "Train Epoch: 24 [40000/60000 (67%)]\tLoss: 142.274736\n",
+      "Train Epoch: 24 [50000/60000 (83%)]\tLoss: 134.194727\n",
+      "====> Epoch: 24 Average loss: 137.8957\n",
+      "====> Test set loss: 139.8484\n",
+      "Train Epoch: 25 [0/60000 (0%)]\tLoss: 130.720830\n",
+      "Train Epoch: 25 [10000/60000 (17%)]\tLoss: 141.392051\n",
+      "Train Epoch: 25 [20000/60000 (33%)]\tLoss: 141.862646\n",
+      "Train Epoch: 25 [30000/60000 (50%)]\tLoss: 136.984521\n",
+      "Train Epoch: 25 [40000/60000 (67%)]\tLoss: 134.085225\n",
+      "Train Epoch: 25 [50000/60000 (83%)]\tLoss: 134.991191\n",
+      "====> Epoch: 25 Average loss: 137.8541\n",
+      "====> Test set loss: 139.8952\n",
+      "Train Epoch: 26 [0/60000 (0%)]\tLoss: 136.687285\n",
+      "Train Epoch: 26 [10000/60000 (17%)]\tLoss: 144.857070\n",
+      "Train Epoch: 26 [20000/60000 (33%)]\tLoss: 132.880625\n",
+      "Train Epoch: 26 [30000/60000 (50%)]\tLoss: 144.919502\n",
+      "Train Epoch: 26 [40000/60000 (67%)]\tLoss: 139.122197\n",
+      "Train Epoch: 26 [50000/60000 (83%)]\tLoss: 140.860254\n",
+      "====> Epoch: 26 Average loss: 137.6863\n",
+      "====> Test set loss: 139.7801\n",
+      "Train Epoch: 27 [0/60000 (0%)]\tLoss: 137.929277\n",
+      "Train Epoch: 27 [10000/60000 (17%)]\tLoss: 134.196045\n",
+      "Train Epoch: 27 [20000/60000 (33%)]\tLoss: 132.861016\n",
+      "Train Epoch: 27 [30000/60000 (50%)]\tLoss: 138.858799\n",
+      "Train Epoch: 27 [40000/60000 (67%)]\tLoss: 142.534336\n",
+      "Train Epoch: 27 [50000/60000 (83%)]\tLoss: 139.505879\n",
+      "====> Epoch: 27 Average loss: 137.5176\n",
+      "====> Test set loss: 139.8137\n",
+      "Train Epoch: 28 [0/60000 (0%)]\tLoss: 142.752939\n",
+      "Train Epoch: 28 [10000/60000 (17%)]\tLoss: 126.742568\n",
+      "Train Epoch: 28 [20000/60000 (33%)]\tLoss: 136.344141\n",
+      "Train Epoch: 28 [30000/60000 (50%)]\tLoss: 143.768389\n",
+      "Train Epoch: 28 [40000/60000 (67%)]\tLoss: 134.033984\n",
+      "Train Epoch: 28 [50000/60000 (83%)]\tLoss: 134.149238\n",
+      "====> Epoch: 28 Average loss: 137.4278\n",
+      "====> Test set loss: 139.6661\n",
+      "Train Epoch: 29 [0/60000 (0%)]\tLoss: 145.913936\n",
+      "Train Epoch: 29 [10000/60000 (17%)]\tLoss: 137.999619\n",
+      "Train Epoch: 29 [20000/60000 (33%)]\tLoss: 136.657090\n",
+      "Train Epoch: 29 [30000/60000 (50%)]\tLoss: 138.576543\n",
+      "Train Epoch: 29 [40000/60000 (67%)]\tLoss: 152.194326\n",
+      "Train Epoch: 29 [50000/60000 (83%)]\tLoss: 140.850000\n",
+      "====> Epoch: 29 Average loss: 137.2702\n",
+      "====> Test set loss: 139.5759\n",
+      "Train Epoch: 30 [0/60000 (0%)]\tLoss: 141.086152\n",
+      "Train Epoch: 30 [10000/60000 (17%)]\tLoss: 144.545449\n",
+      "Train Epoch: 30 [20000/60000 (33%)]\tLoss: 142.445088\n",
+      "Train Epoch: 30 [30000/60000 (50%)]\tLoss: 131.631299\n",
+      "Train Epoch: 30 [40000/60000 (67%)]\tLoss: 136.712412\n",
+      "Train Epoch: 30 [50000/60000 (83%)]\tLoss: 135.151377\n",
+      "====> Epoch: 30 Average loss: 136.9955\n",
+      "====> Test set loss: 139.6126\n",
+      "Train Epoch: 31 [0/60000 (0%)]\tLoss: 134.430654\n",
+      "Train Epoch: 31 [10000/60000 (17%)]\tLoss: 133.727979\n",
+      "Train Epoch: 31 [20000/60000 (33%)]\tLoss: 138.989102\n",
+      "Train Epoch: 31 [30000/60000 (50%)]\tLoss: 136.309541\n",
+      "Train Epoch: 31 [40000/60000 (67%)]\tLoss: 136.662949\n",
+      "Train Epoch: 31 [50000/60000 (83%)]\tLoss: 134.954766\n",
+      "====> Epoch: 31 Average loss: 137.0014\n",
+      "====> Test set loss: 139.2414\n",
+      "Train Epoch: 32 [0/60000 (0%)]\tLoss: 138.750967\n",
+      "Train Epoch: 32 [10000/60000 (17%)]\tLoss: 140.846807\n",
+      "Train Epoch: 32 [20000/60000 (33%)]\tLoss: 133.447148\n",
+      "Train Epoch: 32 [30000/60000 (50%)]\tLoss: 138.229531\n",
+      "Train Epoch: 32 [40000/60000 (67%)]\tLoss: 135.208955\n",
+      "Train Epoch: 32 [50000/60000 (83%)]\tLoss: 135.242949\n",
+      "====> Epoch: 32 Average loss: 136.9224\n",
+      "====> Test set loss: 138.9689\n",
+      "Train Epoch: 33 [0/60000 (0%)]\tLoss: 139.320820\n",
+      "Train Epoch: 33 [10000/60000 (17%)]\tLoss: 133.899219\n",
+      "Train Epoch: 33 [20000/60000 (33%)]\tLoss: 126.494170\n",
+      "Train Epoch: 33 [30000/60000 (50%)]\tLoss: 139.751455\n",
+      "Train Epoch: 33 [40000/60000 (67%)]\tLoss: 136.482900\n",
+      "Train Epoch: 33 [50000/60000 (83%)]\tLoss: 136.772461\n",
+      "====> Epoch: 33 Average loss: 136.7664\n",
+      "====> Test set loss: 139.1480\n",
+      "Train Epoch: 34 [0/60000 (0%)]\tLoss: 146.146611\n",
+      "Train Epoch: 34 [10000/60000 (17%)]\tLoss: 125.146094\n",
+      "Train Epoch: 34 [20000/60000 (33%)]\tLoss: 129.583203\n",
+      "Train Epoch: 34 [30000/60000 (50%)]\tLoss: 137.947617\n",
+      "Train Epoch: 34 [40000/60000 (67%)]\tLoss: 132.695430\n",
+      "Train Epoch: 34 [50000/60000 (83%)]\tLoss: 135.116689\n",
+      "====> Epoch: 34 Average loss: 136.6376\n",
+      "====> Test set loss: 139.8886\n",
+      "Train Epoch: 35 [0/60000 (0%)]\tLoss: 133.947158\n",
+      "Train Epoch: 35 [10000/60000 (17%)]\tLoss: 135.491230\n",
+      "Train Epoch: 35 [20000/60000 (33%)]\tLoss: 137.643896\n",
+      "Train Epoch: 35 [30000/60000 (50%)]\tLoss: 134.401758\n",
+      "Train Epoch: 35 [40000/60000 (67%)]\tLoss: 136.452363\n",
+      "Train Epoch: 35 [50000/60000 (83%)]\tLoss: 138.564443\n",
+      "====> Epoch: 35 Average loss: 136.4567\n",
+      "====> Test set loss: 139.3285\n",
+      "Train Epoch: 36 [0/60000 (0%)]\tLoss: 136.526855\n",
+      "Train Epoch: 36 [10000/60000 (17%)]\tLoss: 134.814717\n",
+      "Train Epoch: 36 [20000/60000 (33%)]\tLoss: 136.859766\n",
+      "Train Epoch: 36 [30000/60000 (50%)]\tLoss: 133.516143\n",
+      "Train Epoch: 36 [40000/60000 (67%)]\tLoss: 139.642002\n",
+      "Train Epoch: 36 [50000/60000 (83%)]\tLoss: 140.215947\n",
+      "====> Epoch: 36 Average loss: 136.5817\n",
+      "====> Test set loss: 140.3029\n",
+      "Train Epoch: 37 [0/60000 (0%)]\tLoss: 134.555645\n",
+      "Train Epoch: 37 [10000/60000 (17%)]\tLoss: 129.540977\n",
+      "Train Epoch: 37 [20000/60000 (33%)]\tLoss: 134.059561\n",
+      "Train Epoch: 37 [30000/60000 (50%)]\tLoss: 131.584814\n",
+      "Train Epoch: 37 [40000/60000 (67%)]\tLoss: 135.059502\n",
+      "Train Epoch: 37 [50000/60000 (83%)]\tLoss: 144.307090\n",
+      "====> Epoch: 37 Average loss: 136.5823\n",
+      "====> Test set loss: 139.0703\n",
+      "Train Epoch: 38 [0/60000 (0%)]\tLoss: 129.503838\n",
+      "Train Epoch: 38 [10000/60000 (17%)]\tLoss: 146.899199\n",
+      "Train Epoch: 38 [20000/60000 (33%)]\tLoss: 131.092695\n",
+      "Train Epoch: 38 [30000/60000 (50%)]\tLoss: 145.776553\n",
+      "Train Epoch: 38 [40000/60000 (67%)]\tLoss: 138.340088\n",
+      "Train Epoch: 38 [50000/60000 (83%)]\tLoss: 136.187520\n",
+      "====> Epoch: 38 Average loss: 136.1273\n",
+      "====> Test set loss: 139.1507\n",
+      "Train Epoch: 39 [0/60000 (0%)]\tLoss: 139.990391\n",
+      "Train Epoch: 39 [10000/60000 (17%)]\tLoss: 133.320303\n",
+      "Train Epoch: 39 [20000/60000 (33%)]\tLoss: 143.438350\n",
+      "Train Epoch: 39 [30000/60000 (50%)]\tLoss: 139.409990\n",
+      "Train Epoch: 39 [40000/60000 (67%)]\tLoss: 128.474736\n",
+      "Train Epoch: 39 [50000/60000 (83%)]\tLoss: 134.751191\n",
+      "====> Epoch: 39 Average loss: 136.0704\n",
+      "====> Test set loss: 139.2231\n",
+      "Train Epoch: 40 [0/60000 (0%)]\tLoss: 132.115557\n",
+      "Train Epoch: 40 [10000/60000 (17%)]\tLoss: 133.703047\n",
+      "Train Epoch: 40 [20000/60000 (33%)]\tLoss: 135.489209\n",
+      "Train Epoch: 40 [30000/60000 (50%)]\tLoss: 127.974082\n",
+      "Train Epoch: 40 [40000/60000 (67%)]\tLoss: 140.691904\n",
+      "Train Epoch: 40 [50000/60000 (83%)]\tLoss: 135.959111\n",
+      "====> Epoch: 40 Average loss: 135.8923\n",
+      "====> Test set loss: 138.8125\n",
+      "Train Epoch: 41 [0/60000 (0%)]\tLoss: 144.612012\n",
+      "Train Epoch: 41 [10000/60000 (17%)]\tLoss: 133.035176\n",
+      "Train Epoch: 41 [20000/60000 (33%)]\tLoss: 131.542148\n",
+      "Train Epoch: 41 [30000/60000 (50%)]\tLoss: 133.615273\n",
+      "Train Epoch: 41 [40000/60000 (67%)]\tLoss: 132.573496\n",
+      "Train Epoch: 41 [50000/60000 (83%)]\tLoss: 133.333496\n",
+      "====> Epoch: 41 Average loss: 135.8863\n",
+      "====> Test set loss: 138.5408\n",
+      "Train Epoch: 42 [0/60000 (0%)]\tLoss: 135.187744\n",
+      "Train Epoch: 42 [10000/60000 (17%)]\tLoss: 142.488740\n",
+      "Train Epoch: 42 [20000/60000 (33%)]\tLoss: 133.951953\n",
+      "Train Epoch: 42 [30000/60000 (50%)]\tLoss: 140.704082\n",
+      "Train Epoch: 42 [40000/60000 (67%)]\tLoss: 138.756250\n",
+      "Train Epoch: 42 [50000/60000 (83%)]\tLoss: 124.417930\n",
+      "====> Epoch: 42 Average loss: 135.7866\n",
+      "====> Test set loss: 138.7493\n",
+      "Train Epoch: 43 [0/60000 (0%)]\tLoss: 139.769297\n",
+      "Train Epoch: 43 [10000/60000 (17%)]\tLoss: 135.749326\n",
+      "Train Epoch: 43 [20000/60000 (33%)]\tLoss: 135.231748\n",
+      "Train Epoch: 43 [30000/60000 (50%)]\tLoss: 138.814219\n",
+      "Train Epoch: 43 [40000/60000 (67%)]\tLoss: 129.218350\n",
+      "Train Epoch: 43 [50000/60000 (83%)]\tLoss: 136.839766\n",
+      "====> Epoch: 43 Average loss: 135.9188\n",
+      "====> Test set loss: 138.6863\n",
+      "Train Epoch: 44 [0/60000 (0%)]\tLoss: 136.887998\n",
+      "Train Epoch: 44 [10000/60000 (17%)]\tLoss: 131.674199\n",
+      "Train Epoch: 44 [20000/60000 (33%)]\tLoss: 132.054590\n",
+      "Train Epoch: 44 [30000/60000 (50%)]\tLoss: 142.951719\n",
+      "Train Epoch: 44 [40000/60000 (67%)]\tLoss: 130.735020\n",
+      "Train Epoch: 44 [50000/60000 (83%)]\tLoss: 133.462617\n",
+      "====> Epoch: 44 Average loss: 135.5582\n",
+      "====> Test set loss: 138.2285\n",
+      "Train Epoch: 45 [0/60000 (0%)]\tLoss: 127.332354\n",
+      "Train Epoch: 45 [10000/60000 (17%)]\tLoss: 134.488799\n",
+      "Train Epoch: 45 [20000/60000 (33%)]\tLoss: 137.454844\n",
+      "Train Epoch: 45 [30000/60000 (50%)]\tLoss: 134.311699\n",
+      "Train Epoch: 45 [40000/60000 (67%)]\tLoss: 141.198701\n",
+      "Train Epoch: 45 [50000/60000 (83%)]\tLoss: 133.080830\n",
+      "====> Epoch: 45 Average loss: 135.6693\n",
+      "====> Test set loss: 139.1968\n",
+      "Train Epoch: 46 [0/60000 (0%)]\tLoss: 136.480625\n",
+      "Train Epoch: 46 [10000/60000 (17%)]\tLoss: 129.481973\n",
+      "Train Epoch: 46 [20000/60000 (33%)]\tLoss: 134.049463\n",
+      "Train Epoch: 46 [30000/60000 (50%)]\tLoss: 136.012480\n",
+      "Train Epoch: 46 [40000/60000 (67%)]\tLoss: 135.849199\n",
+      "Train Epoch: 46 [50000/60000 (83%)]\tLoss: 137.215996\n",
+      "====> Epoch: 46 Average loss: 135.3708\n",
+      "====> Test set loss: 138.6502\n",
+      "Train Epoch: 47 [0/60000 (0%)]\tLoss: 140.179971\n",
+      "Train Epoch: 47 [10000/60000 (17%)]\tLoss: 139.023750\n",
+      "Train Epoch: 47 [20000/60000 (33%)]\tLoss: 138.523594\n",
+      "Train Epoch: 47 [30000/60000 (50%)]\tLoss: 136.274932\n",
+      "Train Epoch: 47 [40000/60000 (67%)]\tLoss: 140.434072\n",
+      "Train Epoch: 47 [50000/60000 (83%)]\tLoss: 135.592344\n",
+      "====> Epoch: 47 Average loss: 135.5034\n",
+      "====> Test set loss: 138.4385\n",
+      "Train Epoch: 48 [0/60000 (0%)]\tLoss: 132.643633\n",
+      "Train Epoch: 48 [10000/60000 (17%)]\tLoss: 131.829033\n",
+      "Train Epoch: 48 [20000/60000 (33%)]\tLoss: 134.565566\n",
+      "Train Epoch: 48 [30000/60000 (50%)]\tLoss: 134.528027\n",
+      "Train Epoch: 48 [40000/60000 (67%)]\tLoss: 135.975840\n",
+      "Train Epoch: 48 [50000/60000 (83%)]\tLoss: 134.181445\n",
+      "====> Epoch: 48 Average loss: 135.1339\n",
+      "====> Test set loss: 138.4194\n",
+      "Train Epoch: 49 [0/60000 (0%)]\tLoss: 141.378506\n",
+      "Train Epoch: 49 [10000/60000 (17%)]\tLoss: 125.414883\n",
+      "Train Epoch: 49 [20000/60000 (33%)]\tLoss: 133.328389\n",
+      "Train Epoch: 49 [30000/60000 (50%)]\tLoss: 138.380625\n",
+      "Train Epoch: 49 [40000/60000 (67%)]\tLoss: 130.013760\n",
+      "Train Epoch: 49 [50000/60000 (83%)]\tLoss: 132.245508\n",
+      "====> Epoch: 49 Average loss: 135.0934\n",
+      "====> Test set loss: 138.3904\n",
+      "Train Epoch: 50 [0/60000 (0%)]\tLoss: 135.915293\n",
+      "Train Epoch: 50 [10000/60000 (17%)]\tLoss: 131.462734\n",
+      "Train Epoch: 50 [20000/60000 (33%)]\tLoss: 141.539639\n",
+      "Train Epoch: 50 [30000/60000 (50%)]\tLoss: 130.894375\n",
+      "Train Epoch: 50 [40000/60000 (67%)]\tLoss: 135.221924\n",
+      "Train Epoch: 50 [50000/60000 (83%)]\tLoss: 138.299229\n",
+      "====> Epoch: 50 Average loss: 135.0783\n",
+      "====> Test set loss: 138.4603\n"
+     ]
+    }
+   ],
+   "source": [
+    "for epoch in range(1, 51):\n",
+    "    train(epoch)\n",
+    "    test()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "with torch.no_grad():\n",
+    "    z = torch.randn(64, 2).cuda()\n",
+    "    sample = vae.decoder(z).cuda()\n",
+    "    \n",
+    "    save_image(sample.view(64, 1, 28, 28), './samples/sample_' + '.png')"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/VAE_from_unet.ipynb b/notebooks/VAE_from_unet.ipynb
new file mode 100644
index 0000000..c2dc2e0
--- /dev/null
+++ b/notebooks/VAE_from_unet.ipynb
@@ -0,0 +1,166 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import math\n",
+    "import torch\n",
+    "from torch import nn\n",
+    "import torch.nn.functional as F\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "class Encoder(nn.Module):\n",
+    "    def __init__(self, input_shape, x_dim, h_dim1, h_dim2, z_dim):\n",
+    "        \"\"\"\n",
+    "        Basic encoding model.\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        input_shape: tuple\n",
+    "            shape of the input data in spatial dimensions (not channels)\n",
+    "        x_dim: int\n",
+    "            input channels in the input data\n",
+    "        h_dim1: int\n",
+    "            number of features in the first hidden layer\n",
+    "        h_dim2: int\n",
+    "            number of features in the second hidden layer\n",
+    "        z_dim: int\n",
+    "            number of latent features\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        # encoder part\n",
+    "        self.conv1 = nn.Conv2d(x_dim, h_dim1, kernel_size=3, stride=1, padding=1)\n",
+    "        # o = [(i(input) + 2*p(padding) - k(kernel_size)) / s(stride)] + 1\n",
+    "        output_shape = [(s + 2 * 1 - 3) + 1 for s in input_shape]\n",
+    "        self.conv2 = nn.Conv2d(h_dim1, h_dim2, kernel_size=3, stride=1, padding=1)\n",
+    "        self.output_shape = [(s + 2 * 1 - 3) + 1 for s in output_shape]\n",
+    "        # Computing the shape of the data at this point\n",
+    "        linear_h_dim = h_dim2 * math.prod(output_shape)\n",
+    "        self.fc31 = nn.Linear(linear_h_dim, z_dim)\n",
+    "        self.fc32 = nn.Linear(linear_h_dim, z_dim)\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        \"\"\"\n",
+    "        x: torch.Tensor\n",
+    "            input tensor\n",
+    "\n",
+    "        Returns\n",
+    "        -------\n",
+    "        mu: torch.Tensor\n",
+    "            mean tensor\n",
+    "        log_var: torch.Tensor\n",
+    "            log variance tensor\n",
+    "        \"\"\"\n",
+    "        h = F.relu(self.conv1(x))\n",
+    "        h = F.relu(self.conv2(h))\n",
+    "        #get the input dimensions for the fully connected layer\n",
+    "        batch_size = h.size(0)\n",
+    "        #flatten the hiddenlayer before the fully connected layer\n",
+    "        h = h.view(batch_size, -1)\n",
+    "        return self.fc31(h), self.fc32(h)  # mu, log_var"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class Decoder(nn.Module):\n",
+    "    def __init__(self, z_dim, h_dim1, h_dim2, x_dim, output_shape):\n",
+    "        \"\"\"\n",
+    "        Basic decoding model\n",
+    "\n",
+    "        Parameters\n",
+    "        ----------\n",
+    "        z_dim: int\n",
+    "            number of latent features\n",
+    "        h_dim1: int\n",
+    "            number of features in the first hidden layer\n",
+    "        h_dim2: int\n",
+    "            number of features in the second hidden layer\n",
+    "        x_dim: int\n",
+    "            number of output channels\n",
+    "        output_shape: tuple\n",
+    "            shape of the output data in the spatial dimensions\n",
+    "        \"\"\"\n",
+    "        super().__init__()\n",
+    "        # decoder part\n",
+    "        self.z_spatial_shape = (h_dim1, *output_shape)\n",
+    "        spatial_shape = math.prod(self.z_spatial_shape)\n",
+    "        # \"Upsample\" the data back to the amount we need for the output shape\n",
+    "        self.fc = nn.Linear(z_dim, spatial_shape)\n",
+    "        # Here there will be a reshape\n",
+    "        self.conv1 = nn.Conv2d(h_dim1, h_dim2, kernel_size=3, padding=\"same\")\n",
+    "        self.conv2 = nn.Conv2d(h_dim2, x_dim, kernel_size=3, padding=\"same\")\n",
+    "\n",
+    "    def forward(self, z):\n",
+    "        z = F.relu(self.fc(z))\n",
+    "        h = z.view(-1, *self.z_spatial_shape)\n",
+    "        h = F.relu(self.conv1(h))\n",
+    "        return F.sigmoid(self.conv2(h))\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class VAE(nn.Module):\n",
+    "    def __init__(self, encoder, decoder):\n",
+    "        super(VAE, self).__init__()\n",
+    "        self.encoder = encoder\n",
+    "        self.decoder = decoder\n",
+    "\n",
+    "    def check_shapes(self, data_shape, z_dim):\n",
+    "        with torch.no_grad():\n",
+    "            try:\n",
+    "                output, mu, var = self.forward(torch.zeros(data_shape))\n",
+    "                input_shape = data_shape\n",
+    "                assert (\n",
+    "                    output.shape == input_shape\n",
+    "                ), f\"Output shape {output.shape} is not the same as input shape {input_shape}\"\n",
+    "                assert (\n",
+    "                    mu.shape[-1] == z_dim\n",
+    "                ), f\"Mu shape {mu.shape} is not the same as latent shape {z_dim}\"\n",
+    "                assert (\n",
+    "                    var.shape[-1] == z_dim\n",
+    "                ), f\"Var shape {var.shape} is not the same as latent shape {z_dim}\"\n",
+    "                print(\"Model shapes are correct\")\n",
+    "            except AssertionError as e:\n",
+    "                raise (e)\n",
+    "            except Exception as e:\n",
+    "                print(\"Error in checking shapes\")\n",
+    "                raise (e)\n",
+    "\n",
+    "    def sampling(self, mu, log_var):\n",
+    "        std = torch.exp(0.5 * log_var)\n",
+    "        eps = torch.randn_like(std)\n",
+    "        return eps.mul(std).add_(mu)  # return z sample\n",
+    "\n",
+    "    def forward(self, x):\n",
+    "        mu, log_var = self.encoder(x)\n",
+    "        z = self.sampling(mu, log_var)\n",
+    "        return self.decoder(z), mu, log_var\n"
+   ]
+  }
+ ],
+ "metadata": {
+  "language_info": {
+   "name": "python"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/classifier_tests.ipynb b/notebooks/classifier_tests.ipynb
new file mode 100644
index 0000000..8642a2c
--- /dev/null
+++ b/notebooks/classifier_tests.ipynb
@@ -0,0 +1,1425 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 22,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import numpy as np\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import functional as F\n",
+    "from torchvision.transforms import v2\n",
+    "import pandas as pd\n",
+    "import matplotlib.pyplot as plt\n",
+    "from sklearn.decomposition import PCA\n",
+    "from matplotlib.colors import ListedColormap\n",
+    "import umap\n",
+    "from embed_time.model_VAE_resnet18 import VAEResNet18\n",
+    "from datasets.neuromast import NeuromastDatasetTest, NeuromastDatasetTrain, NeuromastDatasetTrain_T10\n",
+    "from sklearn.ensemble import RandomForestClassifier\n",
+    "from sklearn.metrics import confusion_matrix\n",
+    "from sklearn.preprocessing import LabelEncoder\n",
+    "from sklearn.model_selection import train_test_split\n",
+    "from sklearn.metrics import classification_report, balanced_accuracy_score\n",
+    "import seaborn as sns"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def load_checkpoint(checkpoint_path, model, device):\n",
+    "    checkpoint = torch.load(checkpoint_path, map_location=device)\n",
+    "    model.load_state_dict(checkpoint['model_state_dict'])\n",
+    "    return model, checkpoint['epoch']"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "def reparameterize(mean, logvar):\n",
+    "        std = torch.exp(logvar / 2) # in log-space, squareroot is divide by two\n",
+    "        epsilon = torch.randn_like(std)\n",
+    "        return epsilon * std + mean"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 10,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "# Model Evaluation Function\n",
+    "def LS_sampling(model, dataloader, device):\n",
+    "    model.eval()\n",
+    "    total_loss = total_mse = total_kld = 0\n",
+    "    all_latent_vectors = []\n",
+    "    all_metadata = []\n",
+    "    \n",
+    "    with torch.no_grad():\n",
+    "        for idx, (batch, label) in enumerate(dataloader):\n",
+    "            data = batch.to(device)\n",
+    "            \n",
+    "            recon_batch, mu, logvar = model(data)\n",
+    "            for i in range(5):\n",
+    "                z = reparameterize(mu, logvar)\n",
+    "                all_latent_vectors.append(z.cpu())    \n",
+    "\n",
+    "                all_metadata.extend(label.tolist())\n",
+    "\n",
+    "            mse = F.mse_loss(recon_batch, data, reduction='sum')\n",
+    "            kld = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
+    "            loss = mse + kld * 1e-7\n",
+    "            \n",
+    "            total_loss += loss.item()\n",
+    "            total_mse += mse.item()\n",
+    "            total_kld += kld.item()\n",
+    "            print(f'[{idx}/{len(dataloader)}] Loss: {loss.item():.3f} | MSE: {mse.item():.3f} | KLD: {kld.item():.3f}', end='\\r')\n",
+    "            \n",
+    "\n",
+    "    \n",
+    "    avg_loss = total_loss / len(dataloader.dataset)\n",
+    "    avg_mse = total_mse / len(dataloader.dataset)\n",
+    "    avg_kld = total_kld / len(dataloader.dataset)\n",
+    "    latent_vectors = torch.cat(all_latent_vectors, dim=0)\n",
+    "    \n",
+    "    return avg_loss, avg_mse, avg_kld, latent_vectors, all_metadata"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_412548/687843937.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  checkpoint = torch.load(checkpoint_path, map_location=device)\n"
+     ]
+    }
+   ],
+   "source": [
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "    \n",
+    "# Model initialization and loading\n",
+    "model = VAEResNet18(nc = 1, z_dim = 22 ).to(device)\n",
+    "checkpoint_dir = \"/mnt/efs/dlmbl/G-et/checkpoints/static/Akila/20240903z_dim-22_lr-0.0001_beta-1e-07/_epoch_6/\"\n",
+    "\n",
+    "checkpoint_path = os.path.join(checkpoint_dir, \"checkpoint.pth\")\n",
+    "model, epoch = load_checkpoint(checkpoint_path, model, device)\n",
+    "model = model.to(device)\n",
+    "\n",
+    "dataset_train = NeuromastDatasetTrain_T10()\n",
+    "\n",
+    "\n",
+    "dataloader_train = DataLoader(dataset_train, batch_size=2, shuffle=True, num_workers=8)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 12,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluating on training data...\n",
+      "Training - Loss: 23.6150, MSE: 23.6150, KLD: 156.5760\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Model evaluation\n",
+    "print(\"Evaluating on training data...\")\n",
+    "train_loss, train_mse, train_kld, train_latents, train_metadata = LS_sampling(model, dataloader_train, device)\n",
+    "print(f\"Training - Loss: {train_loss:.4f}, MSE: {train_mse:.4f}, KLD: {train_kld:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1530, 22, 16, 16) (1530,)\n",
+      "(1530, 5632)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert lists to numpy arrays\n",
+    "latent_vectors = np.array(train_latents)\n",
+    "metadata = np.array(train_metadata)\n",
+    "print(latent_vectors.shape, metadata.shape)\n",
+    "\n",
+    "# Encode metadata if not already done\n",
+    "label_encoder = LabelEncoder()\n",
+    "metadata_encoded = label_encoder.fit_transform(metadata)\n",
+    "\n",
+    "# Flatten each latent vector to combine the channels with spatial dimensions\n",
+    "latent_vectors_reshaped = latent_vectors.reshape(latent_vectors.shape[0], -1)\n",
+    "print(latent_vectors_reshaped.shape)\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Split data into training and testing sets\n",
+    "X_train, X_test, y_train, y_test = train_test_split(latent_vectors_reshaped, metadata_encoded, test_size=0.3, random_state=42)\n",
+    "\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 20,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-1 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-1 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-1 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-1 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 20,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "# Initialize and train the RandomForestClassifier\n",
+    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "rf.fit(X_train, y_train)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 26,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.841564958339152\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "          SC       0.94      0.67      0.79       150\n",
+      "          MC       0.70      0.96      0.81       154\n",
+      "          HC       0.99      0.89      0.94       155\n",
+      "\n",
+      "    accuracy                           0.84       459\n",
+      "   macro avg       0.88      0.84      0.84       459\n",
+      "weighted avg       0.88      0.84      0.84       459\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "y_pred = rf.predict(X_test)\n",
+    "print(\"Accuracy:\", balanced_accuracy_score(y_test, y_pred)) #The best value is 1 and the worst value is 0 when adjusted=False\n",
+    "print(\"\\nClassification Report:\\n\", classification_report(y_test, y_pred, target_names=[\"SC\", \"MC\", \"HC\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 53,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate the confusion matrix\n",
+    "conf_mat = confusion_matrix(y_test, y_pred)\n",
+    "# Calculate percentages\n",
+    "conf_mat_percent = conf_mat / conf_mat.sum(axis=1, keepdims=True) * 100\n",
+    "# Create a custom annotation format to add % symbol\n",
+    "labels = np.asarray([f\"{value:.2f}%\" for value in conf_mat_percent.flatten()]).reshape(conf_mat.shape)\n",
+    "# Plot the confusion matrix\n",
+    "# Plot the confusion matrix\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(conf_mat_percent, annot=True, fmt='.2f', cmap='Greens', xticklabels=label_encoder.classes_, yticklabels=label_encoder.classes_)\n",
+    "plt.xlabel('Predicted Labels')\n",
+    "plt.ylabel('True Labels')\n",
+    "plt.title('Confusion Matrix')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "dataset_test = NeuromastDatasetTest()\n",
+    "\n",
+    "\n",
+    "dataloader_test = DataLoader(dataset_test, batch_size=2, shuffle=True, num_workers=8)\n",
+    "# Model evaluation\n",
+    "print(\"Evaluating on testing data...\")\n",
+    "test_loss, test_mse, test_kld, test_latents, test_metadata = LS_sampling(model, dataloader_test, device)\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 31,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Testing - Loss: 27.3370, MSE: 27.3370, KLD: 165.5264\n"
+     ]
+    }
+   ],
+   "source": [
+    "\n",
+    "print(f\"Testing - Loss: {test_loss:.4f}, MSE: {test_mse:.4f}, KLD: {test_kld:.4f}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 33,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7161839187167981\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "          SC       0.85      0.54      0.66      1405\n",
+      "          MC       0.41      0.78      0.53       550\n",
+      "          HC       0.80      0.83      0.82       460\n",
+      "\n",
+      "    accuracy                           0.65      2415\n",
+      "   macro avg       0.69      0.72      0.67      2415\n",
+      "weighted avg       0.74      0.65      0.66      2415\n",
+      "\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Convert lists to numpy arrays\n",
+    "latent_vectors_test = np.array(test_latents)\n",
+    "metadata_test = np.array(test_metadata)\n",
+    "\n",
+    "\n",
+    "# Encode metadata if not already done\n",
+    "label_encoder_test = LabelEncoder()\n",
+    "metadata_encoded_test = label_encoder_test.fit_transform(metadata_test)\n",
+    "\n",
+    "# Flatten each latent vector to combine the channels with spatial dimensions\n",
+    "latent_vectors_reshaped_test = latent_vectors_test.reshape(latent_vectors_test.shape[0], -1)\n",
+    "\n",
+    "y_pred_test = rf.predict(latent_vectors_reshaped_test)\n",
+    "print(\"Accuracy:\", balanced_accuracy_score(metadata_encoded_test, y_pred_test)) #The best value is 1 and the worst value is 0 when adjusted=False\n",
+    "print(\"\\nClassification Report:\\n\", classification_report(metadata_encoded_test, y_pred_test, target_names=[\"SC\", \"MC\", \"HC\"]))"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Generate the confusion matrix\n",
+    "conf_mat = confusion_matrix(metadata_encoded_test, y_pred_test)\n",
+    "# Calculate percentages\n",
+    "conf_mat_percent = conf_mat / conf_mat.sum(axis=1, keepdims=True) * 100\n",
+    "# Create a custom annotation format to add % symbol\n",
+    "labels = np.asarray([f\"{value:.2f}%\" for value in conf_mat_percent.flatten()]).reshape(conf_mat.shape)\n",
+    "# Plot the confusion matrix\n",
+    "# Plot the confusion matrix\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(conf_mat_percent, annot=True, fmt='.2f', cmap='Greens', xticklabels=label_encoder_test.classes_, yticklabels=label_encoder_test.classes_)\n",
+    "plt.xlabel('Predicted Labels')\n",
+    "plt.ylabel('True Labels')\n",
+    "plt.title('Confusion_Matrix_Test')\n",
+    "plt.show()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 37,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Evaluating on training data...\n",
+      "Training_big - Loss: 23.5543, MSE: 23.5543, KLD: 157.3459\n"
+     ]
+    }
+   ],
+   "source": [
+    "dataset_train_big = NeuromastDatasetTrain()\n",
+    "\n",
+    "\n",
+    "dataloader_train_big = DataLoader(dataset_train_big, batch_size=2, shuffle=True, num_workers=8)\n",
+    "\n",
+    "# Model evaluation\n",
+    "print(\"Evaluating on training data...\")\n",
+    "train_loss_big, train_mse_big, train_kld_big, train_latents_big, train_metadata_big = LS_sampling(model, dataloader_train_big, device)\n",
+    "print(f\"Training_big - Loss: {train_loss_big:.4f}, MSE: {train_mse_big:.4f}, KLD: {train_kld_big:.4f}\")\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "\n",
+    "\n",
+    "#save the train_latents_big \n",
+    "file_path = os.path.join(save_dir, 'train_latents_big.npy')\n",
+    "# Assuming latent_train and metadata_train are numpy arrays\n",
+    "np.save(file_path, train_latents_big)\n",
+    "#save the train_metadata_big\n",
+    "file_path_1 = os.path.join(save_dir, 'train_metadata_big.npy')\n",
+    "np.save(file_path_1, train_metadata_big)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 39,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/html": [
+       "<style>#sk-container-id-2 {\n",
+       "  /* Definition of color scheme common for light and dark mode */\n",
+       "  --sklearn-color-text: black;\n",
+       "  --sklearn-color-line: gray;\n",
+       "  /* Definition of color scheme for unfitted estimators */\n",
+       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
+       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
+       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
+       "  --sklearn-color-unfitted-level-3: chocolate;\n",
+       "  /* Definition of color scheme for fitted estimators */\n",
+       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
+       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
+       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
+       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
+       "\n",
+       "  /* Specific color for light theme */\n",
+       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
+       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
+       "  --sklearn-color-icon: #696969;\n",
+       "\n",
+       "  @media (prefers-color-scheme: dark) {\n",
+       "    /* Redefinition of color scheme for dark theme */\n",
+       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
+       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
+       "    --sklearn-color-icon: #878787;\n",
+       "  }\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 pre {\n",
+       "  padding: 0;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-hidden--visually {\n",
+       "  border: 0;\n",
+       "  clip: rect(1px 1px 1px 1px);\n",
+       "  clip: rect(1px, 1px, 1px, 1px);\n",
+       "  height: 1px;\n",
+       "  margin: -1px;\n",
+       "  overflow: hidden;\n",
+       "  padding: 0;\n",
+       "  position: absolute;\n",
+       "  width: 1px;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
+       "  border: 1px dashed var(--sklearn-color-line);\n",
+       "  margin: 0 0.4em 0.5em 0.4em;\n",
+       "  box-sizing: border-box;\n",
+       "  padding-bottom: 0.4em;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-container {\n",
+       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
+       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
+       "     so we also need the `!important` here to be able to override the\n",
+       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
+       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
+       "  display: inline-block !important;\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
+       "  display: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-parallel-item,\n",
+       "div.sk-serial,\n",
+       "div.sk-item {\n",
+       "  /* draw centered vertical line to link estimators */\n",
+       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
+       "  background-size: 2px 100%;\n",
+       "  background-repeat: no-repeat;\n",
+       "  background-position: center center;\n",
+       "}\n",
+       "\n",
+       "/* Parallel-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item::after {\n",
+       "  content: \"\";\n",
+       "  width: 100%;\n",
+       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
+       "  flex-grow: 1;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel {\n",
+       "  display: flex;\n",
+       "  align-items: stretch;\n",
+       "  justify-content: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  position: relative;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
+       "  align-self: flex-end;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
+       "  align-self: flex-start;\n",
+       "  width: 50%;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
+       "  width: 0;\n",
+       "}\n",
+       "\n",
+       "/* Serial-specific style estimator block */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-serial {\n",
+       "  display: flex;\n",
+       "  flex-direction: column;\n",
+       "  align-items: center;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  padding-right: 1em;\n",
+       "  padding-left: 1em;\n",
+       "}\n",
+       "\n",
+       "\n",
+       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
+       "clickable and can be expanded/collapsed.\n",
+       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
+       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
+       "*/\n",
+       "\n",
+       "/* Pipeline and ColumnTransformer style (default) */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable {\n",
+       "  /* Default theme specific background. It is overwritten whether we have a\n",
+       "  specific estimator or a Pipeline/ColumnTransformer */\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable label */\n",
+       "#sk-container-id-2 label.sk-toggleable__label {\n",
+       "  cursor: pointer;\n",
+       "  display: block;\n",
+       "  width: 100%;\n",
+       "  margin-bottom: 0;\n",
+       "  padding: 0.5em;\n",
+       "  box-sizing: border-box;\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
+       "  /* Arrow on the left of the label */\n",
+       "  content: \"▸\";\n",
+       "  float: left;\n",
+       "  margin-right: 0.25em;\n",
+       "  color: var(--sklearn-color-icon);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "}\n",
+       "\n",
+       "/* Toggleable content - dropdown */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content {\n",
+       "  max-height: 0;\n",
+       "  max-width: 0;\n",
+       "  overflow: hidden;\n",
+       "  text-align: left;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
+       "  margin: 0.2em;\n",
+       "  border-radius: 0.25em;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
+       "  /* Expand drop-down */\n",
+       "  max-height: 200px;\n",
+       "  max-width: 100%;\n",
+       "  overflow: auto;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
+       "  content: \"▾\";\n",
+       "}\n",
+       "\n",
+       "/* Pipeline/ColumnTransformer-specific style */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific style */\n",
+       "\n",
+       "/* Colorize estimator box */\n",
+       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  /* The background is the default theme color */\n",
+       "  color: var(--sklearn-color-text-on-default-background);\n",
+       "}\n",
+       "\n",
+       "/* On hover, darken the color of the background */\n",
+       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Label box, darken color on hover, fitted */\n",
+       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Estimator label */\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label label {\n",
+       "  font-family: monospace;\n",
+       "  font-weight: bold;\n",
+       "  display: inline-block;\n",
+       "  line-height: 1.2em;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-label-container {\n",
+       "  text-align: center;\n",
+       "}\n",
+       "\n",
+       "/* Estimator-specific */\n",
+       "#sk-container-id-2 div.sk-estimator {\n",
+       "  font-family: monospace;\n",
+       "  border: 1px dotted var(--sklearn-color-border-box);\n",
+       "  border-radius: 0.25em;\n",
+       "  box-sizing: border-box;\n",
+       "  margin-bottom: 0.5em;\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-0);\n",
+       "}\n",
+       "\n",
+       "/* on hover */\n",
+       "#sk-container-id-2 div.sk-estimator:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-2);\n",
+       "}\n",
+       "\n",
+       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
+       "\n",
+       "/* Common style for \"i\" and \"?\" */\n",
+       "\n",
+       ".sk-estimator-doc-link,\n",
+       "a:link.sk-estimator-doc-link,\n",
+       "a:visited.sk-estimator-doc-link {\n",
+       "  float: right;\n",
+       "  font-size: smaller;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1em;\n",
+       "  height: 1em;\n",
+       "  width: 1em;\n",
+       "  text-decoration: none !important;\n",
+       "  margin-left: 1ex;\n",
+       "  /* unfitted */\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted,\n",
+       "a:link.sk-estimator-doc-link.fitted,\n",
+       "a:visited.sk-estimator-doc-link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
+       ".sk-estimator-doc-link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover,\n",
+       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
+       ".sk-estimator-doc-link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "/* Span, style for the box shown on hovering the info icon */\n",
+       ".sk-estimator-doc-link span {\n",
+       "  display: none;\n",
+       "  z-index: 9999;\n",
+       "  position: relative;\n",
+       "  font-weight: normal;\n",
+       "  right: .2ex;\n",
+       "  padding: .5ex;\n",
+       "  margin: .5ex;\n",
+       "  width: min-content;\n",
+       "  min-width: 20ex;\n",
+       "  max-width: 50ex;\n",
+       "  color: var(--sklearn-color-text);\n",
+       "  box-shadow: 2pt 2pt 4pt #999;\n",
+       "  /* unfitted */\n",
+       "  background: var(--sklearn-color-unfitted-level-0);\n",
+       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link.fitted span {\n",
+       "  /* fitted */\n",
+       "  background: var(--sklearn-color-fitted-level-0);\n",
+       "  border: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "\n",
+       ".sk-estimator-doc-link:hover span {\n",
+       "  display: block;\n",
+       "}\n",
+       "\n",
+       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link {\n",
+       "  float: right;\n",
+       "  font-size: 1rem;\n",
+       "  line-height: 1em;\n",
+       "  font-family: monospace;\n",
+       "  background-color: var(--sklearn-color-background);\n",
+       "  border-radius: 1rem;\n",
+       "  height: 1rem;\n",
+       "  width: 1rem;\n",
+       "  text-decoration: none;\n",
+       "  /* unfitted */\n",
+       "  color: var(--sklearn-color-unfitted-level-1);\n",
+       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
+       "  /* fitted */\n",
+       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
+       "  color: var(--sklearn-color-fitted-level-1);\n",
+       "}\n",
+       "\n",
+       "/* On hover */\n",
+       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
+       "  /* unfitted */\n",
+       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
+       "  color: var(--sklearn-color-background);\n",
+       "  text-decoration: none;\n",
+       "}\n",
+       "\n",
+       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
+       "  /* fitted */\n",
+       "  background-color: var(--sklearn-color-fitted-level-3);\n",
+       "}\n",
+       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=42)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;RandomForestClassifier<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.5/modules/generated/sklearn.ensemble.RandomForestClassifier.html\">?<span>Documentation for RandomForestClassifier</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>RandomForestClassifier(random_state=42)</pre></div> </div></div></div></div>"
+      ],
+      "text/plain": [
+       "RandomForestClassifier(random_state=42)"
+      ]
+     },
+     "execution_count": 39,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "#convert lists to numpy arrays\n",
+    "latent_vectors_big = np.array(train_latents_big)\n",
+    "metadata_big = np.array(train_metadata_big)\n",
+    "\n",
+    "# Encode metadata if not already done\n",
+    "label_encoder_big = LabelEncoder()\n",
+    "metadata_encoded_big = label_encoder_big.fit_transform(metadata_big)\n",
+    "\n",
+    "# Flatten each latent vector to combine the channels with spatial dimensions\n",
+    "latent_vectors_reshaped_big = latent_vectors_big.reshape(latent_vectors_big.shape[0], -1)\n",
+    "\n",
+    "\n",
+    "# Split data into training and testing sets\n",
+    "X_train_big, X_test_big, y_train_big, y_test_big = train_test_split(latent_vectors_reshaped_big, metadata_encoded_big, test_size=0.3, random_state=42)\n",
+    "\n",
+    "# Initialize and train the RandomForestClassifier\n",
+    "rf = RandomForestClassifier(n_estimators=100, random_state=42)\n",
+    "rf.fit(X_train_big, y_train_big)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 40,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import pickle\n",
+    "save_dir = \"/mnt/efs/dlmbl/G-et/data/neuromast/models/\"\n",
+    "# Define the full file path\n",
+    "file_path = os.path.join(save_dir, 'random_forest_model_big.pkl')\n",
+    "\n",
+    "# Save the model to a .pkl file\n",
+    "with open(file_path, 'wb') as f:\n",
+    "    pickle.dump(rf, f)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.8834570802776075\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "          SC       0.96      0.76      0.85      7732\n",
+      "          MC       0.76      0.99      0.86      7666\n",
+      "          HC       0.98      0.90      0.94      7840\n",
+      "\n",
+      "    accuracy                           0.88     23238\n",
+      "   macro avg       0.90      0.88      0.88     23238\n",
+      "weighted avg       0.90      0.88      0.88     23238\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "\n",
+    "y_pred_big = rf.predict(X_test_big)\n",
+    "print(\"Accuracy:\", balanced_accuracy_score(y_test_big, y_pred_big)) #The best value is 1 and the worst value is 0 when adjusted=False\n",
+    "print(\"\\nClassification Report:\\n\", classification_report(y_test_big, y_pred_big, target_names=[\"SC\", \"MC\", \"HC\"]))\n",
+    "\n",
+    "# Generate the confusion matrix\n",
+    "\n",
+    "conf_mat = confusion_matrix(y_test_big, y_pred_big)\n",
+    "# Calculate percentages\n",
+    "conf_mat_percent = conf_mat / conf_mat.sum(axis=1, keepdims=True) * 100\n",
+    "# Create a custom annotation format to add % symbol\n",
+    "labels = np.asarray([f\"{value:.2f}%\" for value in conf_mat_percent.flatten()]).reshape(conf_mat.shape)\n",
+    "# Plot the confusion matrix\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(conf_mat_percent, annot=True, fmt='', cmap='Greens', xticklabels=label_encoder_big.classes_, yticklabels=label_encoder_big.classes_)\n",
+    "plt.xlabel('Predicted Labels')\n",
+    "plt.ylabel('True Labels')\n",
+    "plt.title('Confusion_Matrix_Big')\n",
+    "plt.show()\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Accuracy: 0.7511992273032039\n",
+      "\n",
+      "Classification Report:\n",
+      "               precision    recall  f1-score   support\n",
+      "\n",
+      "          SC       0.87      0.60      0.71      1405\n",
+      "          MC       0.45      0.80      0.57       550\n",
+      "          HC       0.87      0.85      0.86       460\n",
+      "\n",
+      "    accuracy                           0.69      2415\n",
+      "   macro avg       0.73      0.75      0.71      2415\n",
+      "weighted avg       0.77      0.69      0.71      2415\n",
+      "\n"
+     ]
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 800x600 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "y_pred_test = rf.predict(latent_vectors_reshaped_test)\n",
+    "print(\"Accuracy:\", balanced_accuracy_score(metadata_encoded_test, y_pred_test)) #The best value is 1 and the worst value is 0 when adjusted=False\n",
+    "print(\"\\nClassification Report:\\n\", classification_report(metadata_encoded_test, y_pred_test, target_names=[\"SC\", \"MC\", \"HC\"]))\n",
+    "# Generate the confusion matrix\n",
+    "conf_mat = confusion_matrix(y_test, y_pred)\n",
+    "# Calculate percentages\n",
+    "conf_mat_percent = conf_mat / conf_mat.sum(axis=1, keepdims=True) * 100\n",
+    "\n",
+    "\n",
+    "# Plot the confusion matrix\n",
+    "plt.figure(figsize=(8, 6))\n",
+    "sns.heatmap(conf_mat_percent, annot=True, fmt='.2f', cmap='Greens', xticklabels=label_encoder_test.classes_, yticklabels=label_encoder_test.classes_)\n",
+    "plt.xlabel('Predicted Labels')\n",
+    "plt.ylabel('True Labels')\n",
+    "plt.title('Confusion_Matrix_Big')\n",
+    "plt.show()"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/dataset_neuromast.ipynb b/notebooks/dataset_neuromast.ipynb
new file mode 100644
index 0000000..bedbe92
--- /dev/null
+++ b/notebooks/dataset_neuromast.ipynb
@@ -0,0 +1,1287 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from iohub.ngff import open_ome_zarr\n",
+    "from natsort import natsorted\n",
+    "from glob import glob\n",
+    "from pathlib import Path \n",
+    "import torch\n",
+    "from torch.utils.data import Dataset\n",
+    "from scipy.ndimage import measurements\n",
+    "from scipy.ndimage import center_of_mass\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd\n",
+    "\n",
+    "zarr_dir = \"/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/\"\n",
+    "# defines input zarr file name with the zarr file structure\n",
+    "zarr_file = 'structured_celltype_classifier_data.zarr/*/*/*'\n",
+    "position_paths = natsorted(glob(zarr_dir + zarr_file))\n",
+    "# print(position_paths)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Number of positions:  1000\n",
+      "/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/structured_celltype_classifier_data.zarr/1/0/0\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(\"Number of positions: \", len(position_paths))\n",
+    "print(position_paths[500])\n",
+    "for path in position_paths:\n",
+    "    #print(path)\n",
+    "    # Extract neuromast ID and t from the paths\n",
+    "    string = Path(path).parts[-3:] \n",
+    "    # print(string)\n",
+    "    neuromast_id = int(string[-3]) # Assuming neuromast ID is in this position\n",
+    "    #print(neuromast_id)\n",
+    "    timepoint = int(string[-2])  \n",
+    "    #print(timepoint)\n",
+    "    "
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(1, 4, 73, 1024, 1024)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "3"
+      ]
+     },
+     "execution_count": 7,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset = open_ome_zarr(position_paths[0], mode=\"r\")\n",
+    "print(dataset.data.shape)\n",
+    "all_chan = dataset.channel_names\n",
+    "chan = 'celltypes'\n",
+    "all_chan.index(chan)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 51,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "class NeuromastDatasetTrain(Dataset):\n",
+    "    def __init__(self, file_path):\n",
+    "        self.file_path = file_path\n",
+    "        zarr_file = 'structured_celltype_classifier_data.zarr/*/*/*'\n",
+    "        position_paths = natsorted(glob(zarr_dir + zarr_file))\n",
+    "        self.position_paths = position_paths[:500]\n",
+    "        \n",
+    "        \n",
+    "\n",
+    "        self.metadata = pd.read_csv(\"/mnt/efs/dlmbl/G-et/data/neuromast/metadata_neuromast_test.csv\")\n",
+    "\n",
+    "        # Convert the list of data into a pandas DataFrame\n",
+    "        df = self.metadata\n",
+    "\n",
+    "        # Calculate the ranges for X, Y, and Z\n",
+    "        df['X_range'] = df['X_max'] - df['X_min']\n",
+    "        df['Y_range'] = df['Y_max'] - df['Y_min']\n",
+    "        df['Z_range'] = df['Z_max'] - df['Z_min']\n",
+    "\n",
+    "        # Find the maximum range across all dimensions\n",
+    "        max_x_range = df['X_range'].max()\n",
+    "        max_y_range = df['Y_range'].max()\n",
+    "        max_z_range = df['Z_range'].max()\n",
+    "\n",
+    "        self.crop_size = [max_z_range, max_y_range, max_x_range]\n",
+    "\n",
+    "        self.shape = (open_ome_zarr(self.position_paths[0], mode=\"r\")).data.shape  \n",
+    "\n",
+    "        \n",
+    "    def crop_image(self, idx):\n",
+    "        pad = 1\n",
+    "        row = self.metadata.iloc[idx]\n",
+    "        # Get centroid coordinates\n",
+    "        centroid_z = int(row['Centroid_Z'])\n",
+    "        centroid_y = int(row['Centroid_Y'])\n",
+    "        centroid_x = int(row['Centroid_X'])\n",
+    "        \n",
+    "        #get the label number\n",
+    "        label = row['Label']\n",
+    "\n",
+    "        # Compute the cropping box boundaries\n",
+    "        z_min = int(max((int(centroid_z - self.crop_size[0] // 2)), 0))\n",
+    "        z_max = int(min((int(centroid_z + self.crop_size[0] // 2)), self.shape[2]))\n",
+    "        y_min = int(max((int(centroid_y - self.crop_size[1] // 2)),0))\n",
+    "        y_max = int(min((int(centroid_y + self.crop_size[1] // 2)), self.shape[3]))\n",
+    "        x_min = int(max((int(centroid_x - self.crop_size[2] // 2)), 0))\n",
+    "        x_max = int(min((int(centroid_x + self.crop_size[2] // 2)), self.shape[4]))\n",
+    "\n",
+    "\n",
+    "        print(\"calculate cropsizes\", z_min, z_max, y_min, y_max, x_min, x_max)\n",
+    "        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])\n",
+    "        dataset = open_ome_zarr(self.position_paths[idx], mode=\"r\")\n",
+    "        image = dataset.data[0,0,:,:,:]\n",
+    "        segmented_data = dataset.data[0,2,:,:,:] #segmention masks\n",
+    "        # celltypes = dataset.data[0,3:,:,:,:]\n",
+    "        # Get a binary mask of the current segment\n",
+    "        segment_mask = segmented_data == label\n",
+    "        \n",
+    "\n",
+    "        # Find the unique label numbers in the celltypes image for this segment\n",
+    "        cell_type = int(row['Cell_Type'])\n",
+    "        masked_image_green=np.where(segment_mask, image, 0)\n",
+    "        print(\"shape\", masked_image_green.shape)\n",
+    "        \n",
+    "        # Crop the image\n",
+    "        cropped_image = masked_image_green[z_min:z_max+3, y_min:y_max, x_min:x_max]\n",
+    "        \n",
+    "        return cropped_image, cell_type\n",
+    "\n",
+    "\n",
+    "    def __len__(self):\n",
+    "        return len(self.position_paths)\n",
+    "\n",
+    "    def __getitem__(self, idx):\n",
+    "        dataset = open_ome_zarr(position_paths[idx], mode=\"r\")\n",
+    "        cell, cell_type = self.crop_image(idx)\n",
+    "        print(len(self.metadata))\n",
+    "        return cell, cell_type\n",
+    "\n",
+    "        \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 52,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "calculate cropsizes 0 40 221 341 508 642\n",
+      "shape (73, 1024, 1024)\n",
+      "48\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "((43, 120, 134), 1)"
+      ]
+     },
+     "execution_count": 52,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "\n",
+    "neuromast_cells = NeuromastDatasetTrain(zarr_dir)\n",
+    "random_cell = 2\n",
+    "cell, cell_type = neuromast_cells[random_cell]\n",
+    "cell.shape, cell_type\n",
+    "\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "z_max = cell.shape[0]\n",
+    "\n",
+    "# Function to display a specific slice\n",
+    "def show_slice(z_idx, cell):\n",
+    "    plt.figure(figsize=(3,3))\n",
+    "    plt.imshow(cell[z_idx], cmap=\"gray\")\n",
+    "    plt.title(f\"Slice {z_idx} of {z_max}\")\n",
+    "    plt.axis('off')\n",
+    "    plt.show()\n",
+    "\n",
+    "\n",
+    "\n",
+    "# Loop through all slices in the Z dimension and display them\n",
+    "for z_idx in range(0, 50):\n",
+    "    show_slice(z_idx, cell)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 38,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "Counts for cell types 1, 2, and 3:\n",
+      "Cell_Type\n",
+      "1    26\n",
+      "2    14\n",
+      "3     8\n",
+      "Name: count, dtype: int64\n",
+      "Minimum count among cell types 1, 2, and 3: 8\n"
+     ]
+    }
+   ],
+   "source": [
+    "metadata = pd.read_csv(\"/mnt/efs/dlmbl/G-et/data/neuromast/metadata_neuromast_test.csv\")\n",
+    "celltype_counts = metadata['Cell_Type'].value_counts()\n",
+    "\n",
+    "# Filter for counts of cell types 1, 2, and 3\n",
+    "filtered_counts = celltype_counts[celltype_counts.index.isin([1, 2, 3])]\n",
+    "\n",
+    "# Find the minimum count among the cell types 1, 2, and 3\n",
+    "min_count = filtered_counts.min()\n",
+    "\n",
+    "# Display the results\n",
+    "print(f\"Counts for cell types 1, 2, and 3:\\n{filtered_counts}\")\n",
+    "print(f\"Minimum count among cell types 1, 2, and 3: {min_count}\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "AttributeError",
+     "evalue": "'str' object has no attribute 'iloc'",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mAttributeError\u001b[0m                            Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[50], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m metadata\u001b[38;5;241m=\u001b[39m \u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast.csv\u001b[39m\u001b[38;5;124m\"\u001b[39m\n\u001b[0;32m----> 2\u001b[0m filtered_metadata \u001b[38;5;241m=\u001b[39m metadata[\u001b[43mmetadata\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43miloc\u001b[49m[:, \u001b[38;5;241m0\u001b[39m]  \u001b[38;5;241m==\u001b[39m \u001b[38;5;241m0\u001b[39m]\n\u001b[1;32m      4\u001b[0m \u001b[38;5;66;03m# Step 2: Initialize an empty list to store the balanced data\u001b[39;00m\n\u001b[1;32m      5\u001b[0m balanced_data \u001b[38;5;241m=\u001b[39m []\n",
+      "\u001b[0;31mAttributeError\u001b[0m: 'str' object has no attribute 'iloc'"
+     ]
+    }
+   ],
+   "source": [
+    "metadata= pd.read_csv(\"/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast.csv\")\n",
+    "filtered_metadata = metadata[metadata['Neuromast_ID'] == 0]\n",
+    "\n",
+    "# Step 2: Initialize an empty list to store the balanced data\n",
+    "balanced_data = []\n",
+    "\n",
+    "# Step 3: Group by 'timepoint' and process each group separately\n",
+    "for timepoint, group in filtered_metadata.groupby('T_value'):\n",
+    "    \n",
+    "    # Step 4: Find the counts for the specific cell types (e.g., 1, 2, 3)\n",
+    "    celltype_counts = group['Cell_Type'].value_counts()\n",
+    "    #print(celltype_counts)\n",
+    "    \n",
+    "    # Determine the minimum count among the three cell types\n",
+    "    min_count = celltype_counts.min()\n",
+    "    # print(min_count)\n",
+    "\n",
+    "    # Step 5: For each of the three cell types, sample `min_count` rows\n",
+    "    for cell_type in celltype_counts.index:\n",
+    "        sampled_rows = group[group['Cell_Type'] == cell_type].sample(n=min_count, random_state=42)\n",
+    "        balanced_data.append(sampled_rows)\n",
+    "        print(f\"Sampled {len(sampled_rows)} rows for cell type {cell_type} in timepoint {timepoint}\")\n",
+    "\n",
+    "# Step 6: Combine all sampled rows into a single DataFrame\n",
+    "metadata_balanced_train = pd.concat(balanced_data)\n",
+    "\n",
+    "# Step 7: Save the balanced DataFrame to a CSV file\n",
+    "metadata_balanced_train.to_csv(\"/mnt/efs/dlmbl/G-et/data/neuromast/metadata_balanced_train.csv\", index=False)\n",
+    "\n",
+    "print(\"Balanced dataset saved as metadata_balanced_train.csv\")\n",
+    "print(metadata_balanced_train.head())"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 66,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "KeyboardInterrupt",
+     "evalue": "",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m                         Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[66], line 6\u001b[0m\n\u001b[1;32m      4\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m i, paths \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28menumerate\u001b[39m(position_paths):\n\u001b[1;32m      5\u001b[0m     dataset \u001b[38;5;241m=\u001b[39m open_ome_zarr(paths, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124mr\u001b[39m\u001b[38;5;124m\"\u001b[39m)\n\u001b[0;32m----> 6\u001b[0m     image \u001b[38;5;241m=\u001b[39m \u001b[43mdataset\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdata\u001b[49m\u001b[43m[\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[38;5;241;43m0\u001b[39;49m\u001b[43m:\u001b[49m\u001b[38;5;241;43m2\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m,\u001b[49m\u001b[43m:\u001b[49m\u001b[43m]\u001b[49m\n\u001b[1;32m      7\u001b[0m     celltype \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m3\u001b[39m:\u001b[38;5;241m4\u001b[39m,:,:,:]\n\u001b[1;32m      8\u001b[0m     segmented_data \u001b[38;5;241m=\u001b[39m dataset\u001b[38;5;241m.\u001b[39mdata[\u001b[38;5;241m0\u001b[39m,\u001b[38;5;241m2\u001b[39m:\u001b[38;5;241m3\u001b[39m,:,:,:]\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/core.py:824\u001b[0m, in \u001b[0;36mArray.__getitem__\u001b[0;34m(self, selection)\u001b[0m\n\u001b[1;32m    822\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mvindex[selection]\n\u001b[1;32m    823\u001b[0m \u001b[38;5;28;01melif\u001b[39;00m is_pure_orthogonal_indexing(pure_selection, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mndim):\n\u001b[0;32m--> 824\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mget_orthogonal_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mpure_selection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    825\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m    826\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mget_basic_selection(pure_selection, fields\u001b[38;5;241m=\u001b[39mfields)\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/core.py:1106\u001b[0m, in \u001b[0;36mArray.get_orthogonal_selection\u001b[0;34m(self, selection, out, fields)\u001b[0m\n\u001b[1;32m   1103\u001b[0m \u001b[38;5;66;03m# setup indexer\u001b[39;00m\n\u001b[1;32m   1104\u001b[0m indexer \u001b[38;5;241m=\u001b[39m OrthogonalIndexer(selection, \u001b[38;5;28mself\u001b[39m)\n\u001b[0;32m-> 1106\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_get_selection\u001b[49m\u001b[43m(\u001b[49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/core.py:1284\u001b[0m, in \u001b[0;36mArray._get_selection\u001b[0;34m(self, indexer, out, fields)\u001b[0m\n\u001b[1;32m   1281\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m math\u001b[38;5;241m.\u001b[39mprod(out_shape) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[1;32m   1282\u001b[0m     \u001b[38;5;66;03m# allow storage to get multiple items at once\u001b[39;00m\n\u001b[1;32m   1283\u001b[0m     lchunk_coords, lchunk_selection, lout_selection \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mzip\u001b[39m(\u001b[38;5;241m*\u001b[39mindexer)\n\u001b[0;32m-> 1284\u001b[0m     \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_chunk_getitems\u001b[49m\u001b[43m(\u001b[49m\n\u001b[1;32m   1285\u001b[0m \u001b[43m        \u001b[49m\u001b[43mlchunk_coords\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlchunk_selection\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mout\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mlout_selection\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m   1286\u001b[0m \u001b[43m        \u001b[49m\u001b[43mdrop_axes\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mindexer\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mdrop_axes\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mfields\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mfields\u001b[49m\n\u001b[1;32m   1287\u001b[0m \u001b[43m    \u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1288\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m out\u001b[38;5;241m.\u001b[39mshape:\n\u001b[1;32m   1289\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m out\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/core.py:2028\u001b[0m, in \u001b[0;36mArray._chunk_getitems\u001b[0;34m(self, lchunk_coords, lchunk_selection, out, lout_selection, drop_axes, fields)\u001b[0m\n\u001b[1;32m   2026\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m \u001b[38;5;28misinstance\u001b[39m(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_meta_array, np\u001b[38;5;241m.\u001b[39mndarray):\n\u001b[1;32m   2027\u001b[0m         contexts \u001b[38;5;241m=\u001b[39m ConstantMap(ckeys, constant\u001b[38;5;241m=\u001b[39mContext(meta_array\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_meta_array))\n\u001b[0;32m-> 2028\u001b[0m     cdatas \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mchunk_store\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mgetitems\u001b[49m\u001b[43m(\u001b[49m\u001b[43mckeys\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mcontexts\u001b[49m\u001b[38;5;241;43m=\u001b[39;49m\u001b[43mcontexts\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   2030\u001b[0m \u001b[38;5;28;01mfor\u001b[39;00m ckey, chunk_select, out_select \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mzip\u001b[39m(ckeys, lchunk_selection, lout_selection):\n\u001b[1;32m   2031\u001b[0m     \u001b[38;5;28;01mif\u001b[39;00m ckey \u001b[38;5;129;01min\u001b[39;00m cdatas:\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/_storage/store.py:160\u001b[0m, in \u001b[0;36mBaseStore.getitems\u001b[0;34m(self, keys, contexts)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgetitems\u001b[39m(\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;28mself\u001b[39m, keys: Sequence[\u001b[38;5;28mstr\u001b[39m], \u001b[38;5;241m*\u001b[39m, contexts: Mapping[\u001b[38;5;28mstr\u001b[39m, Context]\n\u001b[1;32m    137\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Mapping[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[1;32m    138\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Retrieve data from multiple keys.\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \n\u001b[1;32m    140\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;124;03m    keys and/or to utilize the contexts.\u001b[39;00m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {k: \u001b[38;5;28mself\u001b[39m[k] \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m keys \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m}\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/_storage/store.py:160\u001b[0m, in \u001b[0;36m<dictcomp>\u001b[0;34m(.0)\u001b[0m\n\u001b[1;32m    135\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mgetitems\u001b[39m(\n\u001b[1;32m    136\u001b[0m     \u001b[38;5;28mself\u001b[39m, keys: Sequence[\u001b[38;5;28mstr\u001b[39m], \u001b[38;5;241m*\u001b[39m, contexts: Mapping[\u001b[38;5;28mstr\u001b[39m, Context]\n\u001b[1;32m    137\u001b[0m ) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Mapping[\u001b[38;5;28mstr\u001b[39m, Any]:\n\u001b[1;32m    138\u001b[0m \u001b[38;5;250m    \u001b[39m\u001b[38;5;124;03m\"\"\"Retrieve data from multiple keys.\u001b[39;00m\n\u001b[1;32m    139\u001b[0m \n\u001b[1;32m    140\u001b[0m \u001b[38;5;124;03m    Parameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m    158\u001b[0m \u001b[38;5;124;03m    keys and/or to utilize the contexts.\u001b[39;00m\n\u001b[1;32m    159\u001b[0m \u001b[38;5;124;03m    \"\"\"\u001b[39;00m\n\u001b[0;32m--> 160\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m {k: \u001b[38;5;28;43mself\u001b[39;49m\u001b[43m[\u001b[49m\u001b[43mk\u001b[49m\u001b[43m]\u001b[49m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m keys \u001b[38;5;28;01mif\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m \u001b[38;5;28mself\u001b[39m}\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/storage.py:1086\u001b[0m, in \u001b[0;36mDirectoryStore.__getitem__\u001b[0;34m(self, key)\u001b[0m\n\u001b[1;32m   1084\u001b[0m filepath \u001b[38;5;241m=\u001b[39m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpath, key)\n\u001b[1;32m   1085\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m os\u001b[38;5;241m.\u001b[39mpath\u001b[38;5;241m.\u001b[39misfile(filepath):\n\u001b[0;32m-> 1086\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_fromfile\u001b[49m\u001b[43m(\u001b[49m\u001b[43mfilepath\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1087\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[1;32m   1088\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mKeyError\u001b[39;00m(key)\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/zarr/storage.py:1061\u001b[0m, in \u001b[0;36mDirectoryStore._fromfile\u001b[0;34m(fn)\u001b[0m\n\u001b[1;32m   1048\u001b[0m \u001b[38;5;250m\u001b[39m\u001b[38;5;124;03m\"\"\" Read data from a file\u001b[39;00m\n\u001b[1;32m   1049\u001b[0m \n\u001b[1;32m   1050\u001b[0m \u001b[38;5;124;03mParameters\u001b[39;00m\n\u001b[0;32m   (...)\u001b[0m\n\u001b[1;32m   1058\u001b[0m \u001b[38;5;124;03mfile reading logic.\u001b[39;00m\n\u001b[1;32m   1059\u001b[0m \u001b[38;5;124;03m\"\"\"\u001b[39;00m\n\u001b[1;32m   1060\u001b[0m \u001b[38;5;28;01mwith\u001b[39;00m \u001b[38;5;28mopen\u001b[39m(fn, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mrb\u001b[39m\u001b[38;5;124m'\u001b[39m) \u001b[38;5;28;01mas\u001b[39;00m f:\n\u001b[0;32m-> 1061\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mf\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mread\u001b[49m\u001b[43m(\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mKeyboardInterrupt\u001b[0m: "
+     ]
+    }
+   ],
+   "source": [
+    "centroids = {}\n",
+    "bounding_boxes = {}\n",
+    "data = []\n",
+    "for i, paths in enumerate(position_paths):\n",
+    "    dataset = open_ome_zarr(paths, mode=\"r\")\n",
+    "    image = dataset.data[:,0:2,:,:,:]\n",
+    "    celltype = dataset.data[0,3:4,:,:,:]\n",
+    "    segmented_data = dataset.data[0,2:3,:,:,:]\n",
+    "    \n",
+    "    segment_labels = np.unique(segmented_data)\n",
+    "    segment_labels = segment_labels[segment_labels != 0]  # Exclude background\n",
+    "\n",
+    "\n",
+    "    # Calculate the centroid for each segment\n",
+    "    for label in segment_labels:\n",
+    "        # Get a binary mask of the current segment\n",
+    "        segment_mask = segmented_data == label\n",
+    "        # Find the indices where the segment is present\n",
+    "        t, z_indices, y_indices, x_indices = np.where(segment_mask)\n",
+    "        # Mask the nuclei image with the segment\n",
+    "        masked_image_green=np.where(segment_mask, image, 0)\n",
+    "\n",
+    "        # Calculate the bounding box (min and max in each dimension)\n",
+    "        z_min, z_max = z_indices.min(), z_indices.max()\n",
+    "        y_min, y_max = y_indices.min(), y_indices.max()\n",
+    "        x_min, x_max = x_indices.min(), x_indices.max()\n",
+    "        \n",
+    "        \n",
+    "        # # Crop the segment using the bounding box\n",
+    "        # cropped_image_green = masked_image_green[0,0,z_min-2:z_max+2, y_min-2:y_max+2, x_min-2:x_max+2]\n",
+    "        # # cropped_image_red = masked_image_red[0,1,z_min-2:z_max+2, y_min-2:y_max+2, x_min-2:x_max+2]\n",
+    "        \n",
+    "        # Compute the centroid\n",
+    "        coords = np.array(np.nonzero(segment_mask))\n",
+    "        centroid = np.mean(coords, axis=1)\n",
+    "        centroids[label] = centroid\n",
+    "\n",
+    "        \n",
+    "        # Extract neuromast ID and t from the paths\n",
+    "        neuromast_id = paths[-3]  # Assuming neuromast ID is in this position\n",
+    "        timepoint = paths[-2]       # Assuming t value is in this position\n",
+    "\n",
+    "        # Append the data to the list\n",
+    "        data.append({\n",
+    "            \"Neuromast ID\": neuromast_id,\n",
+    "            \"Label\": label,\n",
+    "            \"Z_min\": z_min,\n",
+    "            \"Z_max\": z_max,\n",
+    "            \"Y_min\": y_min,\n",
+    "            \"Y_max\": y_max,\n",
+    "            \"X_min\": x_min,\n",
+    "            \"X_max\": x_max,\n",
+    "            \"Centroid_Z\": centroid[0],\n",
+    "            \"Centroid_Y\": centroid[1],\n",
+    "            \"Centroid_X\": centroid[2],\n",
+    "            \"T_value\": timepoint\n",
+    "        })\n",
+    "        break\n",
+    "    if i == 0 :\n",
+    "        break\n",
+    "# # Convert the list of data into a pandas DataFrame\n",
+    "# df = pd.DataFrame(data)\n",
+    "\n",
+    "# # Calculate the ranges for X, Y, and Z\n",
+    "# df['X_range'] = df['X_max'] - df['X_min']\n",
+    "# df['Y_range'] = df['Y_max'] - df['Y_min']\n",
+    "# df['Z_range'] = df['Z_max'] - df['Z_min']\n",
+    "\n",
+    "# # Find the maximum range across all dimensions\n",
+    "# max_x_range = df['X_range'].max()\n",
+    "# max_y_range = df['Y_range'].max()\n",
+    "# max_z_range = df['Z_range'].max()\n",
+    "\n",
+    "# # Print the maximum ranges\n",
+    "# print(f\"Maximum X range: {max_x_range}\")\n",
+    "# print(f\"Maximum Y range: {max_y_range}\")\n",
+    "# print(f\"Maximum Z range: {max_z_range}\")\n",
+    "\n",
+    "# filepath = '/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast.csv'\n",
+    "# df.to_csv(filepath, index=False)\n",
+    "\n",
+    "# print(\"Data saved to segment_data.csv\")\n",
+    "    \n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "{np.float32(1.0): array([ 22.28359574, 364.65511098, 507.8500329 ]), np.float32(2.0): array([ 21.6632611 , 392.1020675 , 445.27676021]), np.float32(5.0): array([ 19.75494745, 281.31417602, 575.57808447]), np.float32(8.0): array([ 22.66114712, 231.68523837, 600.05448429]), np.float32(9.0): array([ 27.98172077, 318.63275896, 540.8995952 ]), np.float32(10.0): array([ 31.61848906, 343.12289724, 698.40874544]), np.float32(15.0): array([ 38.13752156, 384.69683539, 605.06497141]), np.float32(16.0): array([ 45.3853932 , 423.26591833, 553.61388177]), np.float32(17.0): array([ 20.64655241, 403.92765184, 531.90995972]), np.float32(18.0): array([ 25.13083591, 330.89338961, 465.71371071]), np.float32(19.0): array([ 27.67123045, 427.70689891, 420.58248928]), np.float32(24.0): array([ 22.70533772, 465.77846607, 714.9355733 ]), np.float32(25.0): array([ 27.1369425 , 472.76597422, 503.5036917 ]), np.float32(26.0): array([ 25.55168401, 545.42116907, 519.02048694]), np.float32(27.0): array([ 50.14989878, 488.9763509 , 502.16916688]), np.float32(28.0): array([ 58.58126287, 526.82718765, 580.77056684]), np.float32(29.0): array([ 34.68090586, 499.19236435, 592.21243549]), np.float32(30.0): array([ 27.51919522, 275.03066324, 651.28264198]), np.float32(31.0): array([ 23.61757492, 355.30451286, 640.18248815]), np.float32(34.0): array([ 26.84529137, 435.12606875, 642.38398132]), np.float32(35.0): array([ 31.45937318, 484.43641969, 442.93533939]), np.float32(36.0): array([ 32.78395985, 403.09770428, 694.99884398]), np.float32(37.0): array([ 36.26566671, 406.6467953 , 746.4481829 ]), np.float32(38.0): array([ 27.22035248, 571.46372718, 619.23564246]), np.float32(41.0): array([ 30.67192449, 309.32134905, 425.86525134]), np.float32(42.0): array([ 35.34676958, 320.87977058, 618.6016295 ]), np.float32(45.0): array([ 30.9420487 , 362.44471377, 377.4198563 ]), np.float32(46.0): array([ 32.65063464, 550.22035972, 455.0767972 ]), np.float32(47.0): array([ 25.02025505, 398.03607931, 312.19095225]), np.float32(48.0): array([ 34.09267169, 448.58188153, 372.3572412 ]), np.float32(49.0): array([ 34.99863885, 483.99651379, 751.67453263]), np.float32(50.0): array([ 33.15967332, 333.21356612, 751.41211854]), np.float32(51.0): array([ 32.40350058, 511.56948807, 672.27305657]), np.float32(52.0): array([ 35.56597188, 536.28644355, 410.34897413]), np.float32(53.0): array([ 33.55610006, 397.76106881, 800.78645534]), np.float32(54.0): array([ 44.48195489, 581.73951985, 540.52934969]), np.float32(57.0): array([ 38.27206124, 604.25908967, 501.22691361]), np.float32(60.0): array([ 40.348226  , 537.44238817, 698.74416838]), np.float32(61.0): array([ 33.73704823, 471.20623731, 811.47705366]), np.float32(62.0): array([ 49.09785776, 588.02460131, 678.98177419]), np.float32(63.0): array([ 46.01341522, 637.24130757, 648.9579734 ]), np.float32(64.0): array([ 45.02422465, 637.30866589, 577.73070608]), np.float32(65.0): array([ 39.31652079, 525.03543748, 791.94262223]), np.float32(66.0): array([ 49.46895324, 605.91486814, 612.18907944]), np.float32(67.0): array([ 42.47597083, 588.19830475, 729.98322492]), np.float32(68.0): array([ 45.81507857, 657.73231674, 538.85641304]), np.float32(70.0): array([ 54.1351836 , 670.43846057, 661.01570534]), np.float32(71.0): array([ 52.36743298, 483.82683676, 645.12194556])}\n"
+     ]
+    }
+   ],
+   "source": [
+    "print(centroids)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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flmXZYhwRmSwxWXNS1SlNZaUl6bmCz116vi8AB5EJ3MLTfyqLdwxUDp+HT/vKHw9RANgxPx8GpI6wpqYGq1evxvDwsL2Nto+MjGDr1q0m/i9CGMIrMGXKFNx55524/vrrbQLqCK8aRuMqPSctF+loqYrlpWVXWUqy1lIA5PvxYUPSGYAvJvpw8Bifx/l8qi5/aKempgarVq2yh/nI8peVlaGvrw/RaNQQvghR8oQvKytDU1MTqqqqbLI1NDRgypQpjhlvktwqVV3lnktrriK5SryTY/gSUgegNHLR+XYiO5+tR6B13hnwJZGeewn8AR7alzo28gpmzJiBtrY2R1nUaRjBb/xQ8qLdzJkz8eijj2LRokVIJpNIJBK2BSsvL0cqlbKH3lSEV4lsKjfejfxulh7IddMBONxvIhLF2WSFuWXmPw45VCjXqU6V2y/FwmAwaM9N6O7uxsDAgD3MRx1EMBjE73//ezzzzDOIRqPn4YqWLoxo5wK6YSdPnoy5c+di4cKFSCQSiMViyGQyiMViDpGOT3uldZXYlo+0MoandTlerxrC45ZbWnW/3+8ol1z0TCZjDylKwss6vIhsXAcgq0/DeHS+/H4/6urqMGPGDLsTCAQCKCsrQygUwunTpzFp0iTbQwC+6MiM4Hd+UZKE9/l8WLRoEVasWIFLLrkEjY2NSCaTtoXPZDJ2zJ5KpZBMJu1JNW5inEqY0xEdgJbYqjRap/bLNJUgKNV7PuYuwwUeGriFEtzaqzwLPuuPBD6aux8OhxEKhVBdXY1Vq1ZhaGjI8QBQKBRCIpHAli1bsGvXrrG63AYMJUv4q666CmvXrsWkSZOQSCRsYieTyRzCJxIJhyKvUtB1RFaRyC2u1/1428la8/L403eUT8bj0m1XeRvcK5FLwCnu8ZECLuhxNZ86ASJ8MBhEdXU1/uiP/gh+vx+RSMTuCCKRCAYHBxGNRg3hzxNKivCRSATNzc2YMmUKWlpaEA6HbQvo9uMkl4R3c8H5uu6/mzVVQbr2qpjcq1eQr9NRtY2Om3c8lmU5QgrqfILBYI6ukclkbGGPj9lT2OH3+zFz5kwsWLDA0Wlxwe/48eNIJpOezpeBEyUl2jU3N+Oxxx7D4sWLMWXKFFRXV8OyLAwNDSGVSiEej2NkZASZTAYjIyNIpVJIp9O2S88tvJwMoxK/5H+VG07/87n+EnzITCXgyTF3ADleAa9DjjpQ+1ReBkHWz//LsXn+WC5Zex7Xh0IhlJeXw7Is9PT0YHBw0N7On/Dbtm0bnnzySXR0dIzmFpjQ8ELlkrDwdNNVVlaitbUVixYtQjwetwU6OXtOPrHG1XlOeEAdY7tBkl233Qu4ay/T3Nqk62B4eMLdfJ3HwIf0+Aw+Oj/8P3kARHjgi/CA8lNHVV9fj1mzZtluPhE/EAigu7sbkydPRjgcVp4TulYGakx4wvt8PixevBgrVqxAQ0MD6uvrEY/HkUgkEI/HkU6n7Rg+kUjYoh099UZxPFfpZaxLUBHQDaoQgKfr9lFNuCECyjhetXSz8PniedVxyrie0ngnAMAR16fTafh8PvsJP3q0l6b2UryfTCYdgl9NTQ3uu+8+9Pf35ww5JhIJvPXWW9ixY4fna1BqKAnCX3311Vi3bh0qKipsJZ5ILwmfTCZtN56r9JZl5RAecCenaoKLCl48BLcOgZNfZ/F1sb70VNxGHXSWUzVLTzV2T4Tmc/OJ8MFg0H5OP51O2yq/JHxdXR2+/vWvO0YHyDMYHBxEX18fdu7cWZCnVEqYsISPRCKYNWsWqqqq0NTUZE8SIUut+ukejFERIN/QFW3zavHdYn9dfrL0XCyjY3Rri0q007n5KrJLwVCq+G7Hz70ZIjelcfef8vJn++k/vdxDIhAIYMaMGWhvb3eUQ3kHBwdx8uRJJBIJ13M7kTFhRbvm5mZ85zvfwaJFizBt2jTU1dXBsiyHG0+Ta0igI8ufyWTsJX/slT/9phOyCDp3Wq5LSDffi0YgZ9JxN1r1gksOlUuveoAn3zG7HS9vhxzKI+tOVpxm5pHAR7E7ufhEeD6jEIDjHX69vb2OOqmujz/+GE8//fSEFfxKRrSji8onllRWVmLevHm48sorHS66/FGcLkU7KdK5xbQqcAssx85l2yk/31dVnmobj+cJPK4nN58TUaUd6Cw7rz+fEKgaLpReAD8PKuGPrqNlWbbLzjsd6ow44ckz8PnOvluwvr7e8Qgw5evv78+Z4cfPSSnM8JsQhJ8+fTpuvfVWzJo1yybm9OnTUVNTY1tvmg/PLTxZcdXEG8rP581LdZ4vOVQkVt3kPJ2gIpmbuCfL4uId/6+qi5cpY3VVmqoNumNXHaOqA6SyuUdC5KOOl0hP8bzOwgNwCHny5R70hF9/f3/O0GUymcS2bdsmvOA3IQhPUzWvu+46B0Ety1ISnkhOoh11AnwaLX/OXfXmGa8CG0Hn8npx72mdk81NQFOJZ7IuHv9TeZLc50J41bGpOiROeOoEaNIOxe9c3KMpuCrRjocMPLShH83wA+CY0kvP8A8NDU14we+iJnxtbS0aGhowe/ZsVFZW2jcN3ahkITjh+ZNvqrF3OdbuNhbtBml9VaKcm6vP99G57CrQNp1yrqtDR3LVsbvVL9+Rl0/74WVRJ0DCKpVHLjz9p2OidMpD26VeIJ/w4x4Y36++vh5tbW3Kdo+MjEwIwe+iFe38fj/uuece3H///aiqqkJ9fb393jlOdCIzWW5y38mtpzQe06vG3PNZNol8saBUkPNZeklMr2KeqmydbqD7qRR6Ny9GdzyyE5Jk1D3my+fj88dy+fv6VDMPqQ3yXX7SOwiFQrAsC11dXUp3PxAIYM+ePfjJT36CEydOqC9SEWDCi3Y1NTVoa2tDJBJxiG+c8NzCy06A1Hc5JKeLY4EvXijhBi/CjxzKcoObG+/WEUkBLV8dqk5FpVm41ecmTErvg5fLt+nezcctPSczXRNOeF4Pv2ZE5Gw2q/yYSH19vaNDIMIPDw+joqLCIfjRvXMx4aImPBHY7/fb1ppbaR6P80dcybWXafztNqpXUvF6CV7JKtcB9Tg2B79hZRnSCsttXsHzynp05Y5GtOPpPGaX23XkpmtCBOdinMp9B9w9Cj6qQ5oAV/2lhzFt2jTcc8896Ovrs7d3dHRg69atF9Wnui5qwpNyC8BBZE54mUZTZ6lj0L3JhpZArqV0s5heSUNwE/NUU3d5uTohT+cJuKWp1lVpow1VpPWXk4NkfpWQR9afK/OS6CrBkqdx60/7JJPJnAePKNYnwk+dOhV33323XW8gEMBHH32E3bt3G8KfT0QiETQ2NqKyshINDQ0A4CArF9640i5JzfO5CXWA+5i613FbN3IVoo/kc9/zxdsq8TDfeiFj0yq3XcKLSMlJTuVydZ/W5TVRkVzlQVDZslORr/Sm+4BrB1ReRUUF5syZg0gkYqcPDw/j1KlTRSvuXXSi3axZs/Dwww9j/vz5qKqqwtSpU2FZFmKxmG3VuUBHXgARnq9zgY7iVf6cu4TuZpJws8QqUqluTL5O/1UiG2+ralacKvbWeQ664ygEqvMira88Tt12Ho9z910lwnEL7uY1qaw/eQzSnadYXn6Eg9JisRi6urrsZwD8fj/279+P9evXj4u4N6FEOzrRlZWVuOyyyzB//nz7ARjVG2S5Nedpbu67SqRyg7T2HG6Ek8o3nzsuic7rUUEXKrgR3YvlHw1kO/lx6EIZahcRVXUclE75yMpz0P46fUVHfum5SatPbeAdD6WVlZWhpaXFMYqQTCYxefJkhEKhopy5d1EQ3uc7+w66P/zDP7TH3iWp5ZRZIjeP16VLr/vKq3T7eDtUPzey8bha5W4DzrfIEFQxKc/jhaC6TsyN+Dq4eXj52kT1cbKoOkpdmmyz7ASkS09ton1UHShBN1GHrDqf8MOn+lJsz7UFen3XypUr0dXVhe3bt2P37t1j0pmOFS4Kwvv9flx11VX49re/jXA4nPNRCD6kJgmvG6qT1j8fOagdqlhRBelWq4a66L8kNL9Zpcsq63Cr2609sl35zr8MP9zAO0GVlecdhI7gMk2eF0lyXrZbuMXbyPNz0vOwgV7TxT0LIjel0TKbPTul++6777anbu/Zs8cQfjTgwyTcEvN1nqb6z3+c5DI/QV4o3Tiy7qZVkV269royAOcQlg46XUCXV7VPPvA26NrqVmch+d06UVV7uLBWCLiXwMnOOxQiNyGdTttWXWo9RHi6T0OhEOrr63HFFVdgaGioaIS8i4bwXG2XbrxU3uUUWdV/TnoudnmxvvR8NkESUjU059Wy6qaQclGK2kLlyzp53W4CnRe9gurRqe9eyEztJxXcC9w8Gt4R8mflvVp3WY8U/Hh5dM+QaEf3Eq1TfM8JT+288cYbsWDBAhw4cAAvvPBCUczSK2rCk2tF6qgXyy0tqBfLz/MSvMa3UijiyEd2Wbab6MS385tqNFC1QVVeIZb5XPYpZF8V6bm1LhQyXFBpM7yjpxhfZ+Fp3e8/+3RefX19UQl5RU34mpoa3HLLLWhubkZ7ezsAKC26FN1UhCbkuynyEV0Vm+Zz6QHnF2B1baOyuGWRQ09eOxivkCq1G+lUQ15u6/x4aH8v5co63MrXdYCjDT2kNkPCnRyf53XzUQP+PD9dq9raWtxzzz2IRqP46KOPsGvXrnGL64ua8NOnT8fq1auxZMkSeyqsnPfOh9x4DJ7PgntFvvjYzfVUufY83a08Ocwkb0Zdvfk8FipLxqsqgujCFjdCyv9ehDO3/XRlqYgn0+W6rg3y2KVl56GD7MD4e/o46cnVz2QyqK2txV133WXfw+Op3Bc14YEvxt/lW2jyufMSXsSg0UDWp4ulVQR0u+j8Js4n3Mm2uJFdB+nC6vKoLLFqH50IWQi8dhyA/hx58ehkOCDJL/PowjSy9Nzy86fzAKChoWFchbyiJzw92ipfGy2H4nSuvEp0I5dLWjm+9EJkVT43HUDG8G7gbqGcCKJzrXk9+QQ5KpcfmySVfPiEr3sRx1RzGOR6IeV7Ib2sNx9kR5fvmvH4nd9Dss381VuUNxAIYNmyZbjyyitx4MABrF+/Hp9//rnnto4FipLwZNWpV5SuOxfevCjNQO64Lb84/MLI/DqoYn0d+d10AZVwB+S6qXRzcQuiK8tNmZd1UbmyTt4Wr2RXtcktps/nFXjpVFQohPi6EID+S2+JawaqEEo1BMu9hEsuuQQzZsxAOp1GZWXlBRfyio7wPt/ZD0csXboUdXV1qKur8/R2Gt4JqNxlnSBDS+6CqYgre2u3pSpNR0JVXZz0PI2TUxVz68qWS537zofMdHG6jGFlGW7hgGqp6yRGS3YONxIV4gUQ+HXgBPf5fPaHNQA4hutkB0756+rq8JWvfAXRaBTbt2/Hxx9/fEHi+qIjvN/vx6JFi/Dtb38bZWVljrfRuCn0RHauhvMypegie2l+QVRws95uxM9HdJ3V5ze6ymLl0yTcyqf986XrXHCdq19o+eOJfF6AtPxyGx+Ck16iDMeALzpT2lZbW4t7770X8XgcqVQKO3fuVN67Y42iIzwAx3fGATgIne8H6NVv6dLLNFVeKk+1fSzBy9OJSHz4p5DydGGDLs3NurqR3SvJi4X0bij0POvuRS7k0X8+fTccDqOhoQFtbW0YGhpCR0cH4vH4+TkoFCnhyW0ny0w//kEI1TPsMn6SULn1UsCT02ZpP8oL6D/p5BX5QgHuOvKbxedzTvXUzbpzg+zMVFa8EKJ7Jb0K3OX1ml9irGNf2fHJ+4VDEpvuSX5PUT75HAbd236/H8uWLUN7ezsOHDiA5557DseOHRvTY+IoSsIDX7hNbuPrqnhd5WqrwN0weQNLonOSqOJgnlfmK+R45X9dOTLGl2q7DlLt5+WdK9kLtdoqcdBrflX6WBB/NJ2WvPfomsmpttIY8UlVtbW1uOSSS5BKpc77jLyiJrxKiNO59irxjspRKfC0lE+C8ZtXTrXkBJMdhewIaJlv6EvXgfBtqnPD6/Z6Y+gmqMjzQiiUlLp9R4tCQgFen9dhTypTdlx0X/ClTFeFhio9SMb1cn9+j9TU1GDlypW2kHc+3pFflIRXxeQqgquWbtafQxezys5AEpvg1bKoiK/yDtxibjeoiKtrB9WvG8cn6Iiu2kdad7dOQuUR6f7L9hRifd3IrxI+dYSnNJXwls/NJ7KTYk8xO99XCsdEeBKqd+/ePeZCXtEQvry8HE1NTaiqqkJjY6PWQvCTrIuBaanqNPJBZWlVbr6sV3cjSkLKToSXky8U0IlvXjq1sYDUFnjdXjsdeT5G42UUclw0zVVXhrTUKosuvQHeCai2EVThpvQAOOmpvfRo7eWXX46hoSF0dnaOmZBXNIRvaGjAX/3VX6G9vR1TpkxBOBzWzoLj4CdUxvz51H1ZJr8hKZ0TVI7R83xcvNF1SvIG0A3Z6Sy+DBd4O1RQ7afKI9Ol68/r0NVP6fmGu1Rt0JGQr6ussepY83lKqro4eaUF5mmyM1C1T4Lfh4DzvYN0vvx+v+09UDlLly7FvHnzcOjQIbz44os4fvy48nwWiqIhfHl5OebOnYsrr7wSyWTSfs88wa1X1/Wk8r/qBvBqXTlUN7/KdVfllcfiRiS345Wdylhbc7cRC6/1u7VLZ9llZ+uV8Lwct2vq1qnw+lQvytQRXrZH1Rm5GR3LsmzFns5ZTU0NqqurkclkxlTIKxrCA8g5qdTzcavr1pPKGF9afLfYvhCyS6unIoF01XT1cBePOgyVtZJtcPuvSh9Nh6C7uaTaL4efaF+3UQFaSkurIh7Vke8eoDao1t3q5jE6HyOXhJdW2O245DHxjl1FeO4dcUyfPh233347rrrqKuzatQu7d+8+J9IXDeH5SSy0Z1cRndZ5uszL00fTXrf/VJfcTi9AlKQeqyEYN5dZB50V1EE1HEjXiHeGKtLrllyY48RXrRfaeanCNynKSRdefltOZ+FV9RBUnQvPK8M92UbLsmzCk8e7d+/eiUF4IPfEuF1YGWPKddUkHNmzyrnxY9H+fNvoE8g6t1AVM6vK8UrssXD1dW2g8+f2NtpsNuuwjLSU6yrvjse2RDpen45Iunar2pCP8KrPWeUjvMoDVNWra5tlnf2wZXd3t31OU6kUent7z/leLUrCc1de9vJu8SJZbP76ITe3XrW/XNcJa7zNqnX5n7v4nNiq/Xjdbnl11lIF1XBUvvrlukyjcij+pLbSddPd1HQtqV3SjeYfhOAE9Eo8t7ariOf2IQq//4tPTLvVq3PVJfllp8aXtD2VSuG9997Da6+9Zg/rZbNZ9Pb2nvMw3bgTnt5EGw6Hc5Rdr9Zed1OqblyVK6+7weW6W++qchc5VOP5qhEAStN1bKr68oG7r6qlDm7E59aI3Heut+jarCKMzn3nZBsN4d2gIp7saPjHJXmHJOtV6UM6w8KvLb/2/Bolk0mcPn0a+/fvn1jj8H6/H9dddx1WrFiBhoYGNDU1AfB2I8r4PN9J54RXkV3mkfWp1smiSbKrCCUJQktuwbk14MclrbwsU3W+8o1fF0r+fNCNR+ssmhu5aZ0v3VzsQtsvOx5qu/xENCd9IYTnVplf23Q6jZ07d9qvuJJl0TKTyZyzOKfDuBN+6dKleOyxxxCJROyHYgBvN6IuPh8N2VVpMsZXdQQqy666iLwMTmDq6QG17sCH99zCCdkGuV1HcJlH1aHp6s8H3Ti2isCcXPTZJkl8STxpGeXxFOLSq9oiCS+Ph9chx9qJ+FJA3r17NzZs2JDXcvPOYixRFC59KBRCMBjUCm205JacoCK8TB9LuMXeEvk6LW4FyZ0HnCQnF99LfaqyVe2QN71uP7cyZfvzWXFOLlVs7vf7c77TTveGz+dzdAI6wns9PyoLryK8ZVmIRqPo7u52CIiquvi9KTUkWk8mk4hGo/bXj8YD4054N7FDuuwAHD2mSpCT7j0vD4CDQAQ366UrR0ISTOXaymOjevnHFORnjagjUFl9CWnteFt4urxhVZqDhGoGnSQMkQZQC2DcHSf3nROZyB0KhRyEJwvP3XsZV6uO3Q0qwvO2BoNBJBIJvP3223jllVdctQkOt2uUzWbR3d09bmQHioDwBJXIoSObaukWg48GhV4Urx2BapvOiqtuMOkJuJFVkl3lcXglivxijKpsaelVLr1KfeeWm5bUEXCXnq/LDsTN0qvCFG59eQfM57mn02mcOnUK+/btG1eSjiXGnfAqa13oT3oBKvCYi1tQSqMbgVvVfENy8kaSBNTFyTxdJ95RebrhRMuyHOPfqnpkndLbUHkihbj4chYcd7GlAEZWUwp0RGzuvlOIp7LwvBOQll6eXx2SySTeeecd7Nixw2G5pbWnV0+dj1h6vFB0hM/XAejedMPVUekpSGIDuVZVimmqdvKlW9mq7TpLzPfj7iC/CbnL7xZWyDp1N790Z2U7dR2VXPJ9vbjv3Jq7ET4cDucQnncCUsHnHQ2foKM617Tcvn07nn/+eUenrvIMzpd4Nl4Yd8IDuVNjpUVT/dwsnw7ygqosvSqfl/Z72Yfn4XVJK0NLbvl5O73UrbLwbpadu+Mqiy/TAaeFlzE1X7pZeCK0m/vOVXoV4QcHB3Hy5EnE43FHh6WCZVmIxWLo7Oy0X5lWShh3wpPVVn3rXfdFWJWVz2eBpRXnS7KgJI7J/fj+EpJw5NarXGMVCSXx5ToXG6XAx7erPBpJaNXwmG6d9pGdgPQO5L75ZsvJeF0n2nELr7P69H/Pnj146qmn0NHR4X6zsWt06tSpkiM7UASEB7wp9fk8ABXp3UjO86jce0Ct6I81VB2L3M7T5NAdoLbych83srp1AtI70CnyKuKr3HydS6/qBMia07HQcBf3zHw+H86cOYNPP/0UR48ePefrMdEx7oRXxexub6XVzY2nsiRUFp7ycnVWuvderK88DlX5KkjXntdNlpxPVZXly05L1SmpCMoFNfpPpJXxMLfWfF1uB5yEV01S0U1skYSXbn4wGEQoFEIymcTbb7+NvXv3Kuv97LPP0N/frzzXBk6MK+El2eV34nRfh1VZe16mJDdBRVZOUEn40br1uvbwsmT9ssMhckohT9XJqQRBGZsDsD/dxQU1PtmFk5EvJZG5tZblqQifz+q7KffBYBCxWAzvvPMOXnrppZzjpHOQTqeV2wycGBfCl5eXo7m5GVOmTMHMmTMdNzKgd/HlBBs36MgpCcfz+P1+x5RHunHlNEhd2bxMtzxeRT6qS3og/HzpylHF7yriSZJz4smYG9B7Airvwa0TkIQfHBzE6dOnkUqlctoyPDxsbzM4N4wL4RsbG7F27Vq0t7ejpqbGJhW36CqXHsg/801nTVVE5/tzMun25d6AhKos6TWQu87FQQ6pGcj8uv3k8dNSxuFuc8algq5KI+vLOwNenltcz9NkPr/fj927d+PZZ59FNBrN0S2y2Sw6Oztdj9vAG8aF8JFIBJdeeina2tq0Yh1fly5yPuhc+nz7SLIUWoaM82VbScFXKeqyLVReIWGJbL8kvU5B55acyA3Akc4nu8g57TKGB3JHBeh4ZNhE6f39/Thw4IBnpd1gdBgXwkejUbz44ot4++23sWTJElx77bXw+Zwzy3Tj7NLyulnxfOl8uxfL6YX80spLC89n4+k6Btlu2T7dOcinxvN1lVpOeeQDKz7f2WExKbKpLLxcUrvi8Tjeffdd7Nu3z3EMlO/IkSMYHBzMe34Nzg3jQviuri787Gc/QygUwl/8xV9g0aJFCIfD2uE2gpurLt1hDhVRVVaSx8o8zQ26kMLnc86Q4+RXPYgCONV2lWXnaW7kVhFPZ5HJmpMyLl163WQXGevLTkX+YrEYfve732HTpk1Kz4VCOYPzi3EhPKmqlvXFo4Re3HbdDc9voNG44vnqLbQM6drrdAUJ2Wmp8lInoSO3dKN1IhqtywdRpMvOfzrlXlp4euikr6/PbuPAwAC6urqM8DbOGPdxeED/1hmVG0s3FYlYdKPx/WS5qnpkutuYtmyDW9lynb/vjcqn/6qn3eR/Xq9KgNMp6CprrlLN842R039p4X0+55Ae73xisRhefvll/Pa3v7WPIZ1OG+GtCFAUhAfUAp0KKneRp8tOwu2/V7iFCypIay7jdsqjcttV0MXkRDbVXHWdoOZGfqncS7GOL4Hcr6hQOYlEAsePH8eePXsKOMsGFwJFQ3gVuDWnm5N/oSMQCNhCnyQU4HzqTFp3lV6g26Yiu8pdlyTON07OLb+M0WW6JLxKQXebrirJrQoB3Ky+FPcOHTqErVu3Ynh4OKfzjcVitjhnUFwoWsLLG55uPDkuzd1jWnIrriJrPtLrXH9VG3WTbLyQP5+3IvNwK8wtbygUAgCEw2GHyAY4xThJbt267BBUncCxY8fw05/+FNFoVHl+jQBXnChawuugIwi53dK15+64jLNVltxraKFql9f/OjLrSM+3uYlsMuaWbr6O8LIOEt46OjowMDCgdP2PHDmCWCxmRLiLDEVDeJW14y69tDLAWdKGQiGbqNx9dxPodC/c4O67jvgqIkudgJMWyH1eXDcBhhMQUL9RRj50Qhbe7/dr3wXnRnjZVqovFovh1VdfxTvvvOPYTsuBgQEMDAwUcIUNigFFQ3gVVJaQb3P76aCy8m7TdvON7evidGm5VcfDiagjo1t8rRoy4x0B9wC4leZekOwsKZ2Et08++cTbxTK4KFAUhM9HXnnzy+mbtA44iSqJKN13t6fvVGRXWUMVeJu4Zde525yUfB+V2y0Vef52GB7Dq2bLScLv37/fFt4k4vE4Dh48WOilNChyjDvh3cQqnsZvfGkdASfhSbUnAlNMStt1xOcP6eQbhuOut6q9lIfIqxvT5laadwwqa68KDTi5CyX8sWPH8OKLL6Krq0vZORrhbeJh3Anf2dmJHTt2oLKyEnV1dSgvL1eShyOfCy/jaklelQXP58Jz6IbTdC67tPA6kY1bcDclHUDORxvyhQhnzpxBZ2cn0um0XdZnn32GWCxmniUvIYwr4TOZDLZs2YK9e/dizpw5+Na3voXLL79ca93d3HwiAlfqZWegG4KT78WjvNQGgpxRJutws8j5XHC3b6ipfrwt0pqrwoa9e/fi2WefdbwZ5syZMxgaGjpv19eg+DDuFj4ajSIajSKdTmNkZCRvfCxJJq25Dqqxd/4fyP89L7fwws0F52PZktz0XzV11Y3wXM/goxb0jAI/br/fj56eHuzbtw89PT15z5XBxMW4E55DksTNvfZKcpXV5gKdKh+1haB7ykzltgP6YTT6T0NmMuaWhOdWnNqkCxuGh4fxu9/9DocPH87RNkigi8Viec+ZwcRGURFeNz6cz+p7yacaV5fWXdcmN6Vd9eSZ3Ccf4elljbpxczctg9qYTCbx5ptv4je/+Y1jG8E8fmoAFBHhaRgoGAxi6tSpqK6uVubTzYIrZEqsajt5FJwoKgFMparzH6B+i6ubSq9y6VUjEL29vejo6Mh5757P50Nvby96e3uNAGfgiqIhfGdnJ5555hlUVlZi5cqVWLVqleOd5EBh02HzzZPnoDoCgYA9CYWTnEjIZ7LRG2Aojbv5qgdWpHfAyyMLT+XJjoba+N577+HZZ5/NmeHm8/mQSqXM46cGeVE0hI/H4zh8+DACgQCWLFkCIP8EF4KO6HKbF+hGAIis3DLLsXTVG2DyPXkmH3ah8W85XRcA+vr6sH//fvT19RV0TAYGhKIhPIccr+aWDnC33iqyu1l22amoFHAZc4dCIds6y6E1SW7uKUjC8++qhUIhDA4O4u2338Znn31mnweOffv2IR6Pj+m5NigtFCXhyfpxxVoSXjUNVvXiSzcxjsrirrx046kdwWAQZWVlNjnLysrg9/vtND67TTVbTuoAAByEDwaDSCQSeOONN2zhTcIIbwbniqIjvGVZiEaj2LVrFyZPnoxp06ahrKwsR7nn+XXlqCA7D16maupqMBhEOBx2xNxk4XkM74XwVE8gEEAqlcKxY8cwODhok76rqws9PT3mkVOD84aiI3w2m8XWrVuxf/9+NDc34xvf+AZaW1sdb7qRbrhqrF1CEls+IcatryQ3fbk0EonYApu08OQFAFB6JlIbGBgYwEsvvYQPPvjAbiM9g25gcL5QdIQHzr7GuqurC/F4HAMDA/b35jiB/H7nJ6DcPhnNoYvZuVWXQ2ZcTaf/cp06CW7heX30NR2qKx6P4+jRo+a9bwYXFEVJeEJvby/+93//Fx9++CGuuOIKLFy4EIFAwB5r5jFtIPDF++74mDqfdgqop+JyVZ3cd7Lc4XAYkUgEgUAAkUjEduf5dp1KT/X19/dj8+bNOHjwoJ0+MDCAI0eOXJDzaGBAKHrCv/LKKwgGg/j617+ORYsWOVxn/vJKctP5ODoRn7ZJcMtOVp2sObnvnPDl5eWuhJdPuhESiQRef/11vPHGG3aaefzUYDxQ1IQHYI9Jd3Z24pNPPkFFRYUt5PEPTxJ5OMH5sBap9VyRB+CI2+XMN+oAyG2nWD4cDiMcDiOVStmfSFI93Ubo6OgwYpxBUaDoCQ+cJev//d//4ciRI2hqasKaNWvQ2tpqW1SKjcmlT6fTdhoN1xHRuWtPBOXvhCPLTda8rKwM5eXldhoRvqysDB0dHXjhhRfw/vvvK0cQCMlkEidOnLig58zAQIWLgvAA0NPTg56eHiSTSQwPD+e8iJKPq/PPNUsQ6VVz5MmqkzpPVluO89OnrOPxOD777DPs2rXrQp8OA4NR4aIhPKGvrw+//OUv8dFHHyGVSiGdTmPq1KlYsmQJGhoakEqlkEwmkc1mEQwGHd+cB5Bj4cmy01Ja808++QQffvghksmkLeiR+9/f32+EN4OLChcl4X/1q185XOeWlhZcfvnlmDNnDlKpFAKBs1+kSaVSNuH5O+2AXMKTAFdWVoZgMGgT/+DBg3j++eeVL3o0wpvBxYaLjvAAckg2MjKCo0ePIhwOI51OI5VK2STPZrOYOnUqpk+fbrv5lmWhv78fPT09sCzLYeHJipMy//nnnyORSBjBzWBCwGd5fJzM65Nr44FwOIz6+npMmjRJaXVvvvlmrFy50lb2s9ks3nrrLbz88stIJBIOxZ7PuqPnzE+fPl3QxyQNDMYDXqh8UVp4iWQyiePHjyu3+Xw+tLe35zxYQ/G3ee2TQSlhQhDeDZZlYd++fXjppZcQDAZtxf3TTz81brpByWFCuPT5wKfYEvhHJwwMJgK8ULkkCG9gUArwQmV/3hwGBgYTBobwBgYlBEN4A4MSgiG8gUEJwRDewKCEYAhvYFBCMIQ3MCghGMIbGJQQDOENDEoIhvAGBiUEQ3gDgxKCIbyBQQnBEN7AoIRgCG9gUEIwhDcwKCEYwhsYlBAM4Q0MSgiG8AYGJQRDeAODEoIhvIFBCcEQ3sCghGAIb2BQQjCENzAoIRjCGxiUEAzhDQxKCIbwBgYlBEN4A4MSgiG8gUEJwRDewKCEYAhvYFBCCHrN6PGr0gYGBkUMY+ENDEoIhvAGBiUEQ3gDgxKCIbyBQQnBEN7AoIRgCG9gUEIwhDcwKCEYwhsYlBAM4Q0MSgj/D7V6IOsPOZWKAAAAAElFTkSuQmCC",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import random\n",
+    "\n",
+    "# Function to display a specific slice\n",
+    "def show_slice(z_idx, cropped_image):\n",
+    "    plt.figure(figsize=(3,3))\n",
+    "    plt.imshow(cropped_image[z_idx, :, :], cmap=\"gray\")\n",
+    "    plt.title(f\"Slice {z_idx} of {z_max}\")\n",
+    "    plt.axis('off')\n",
+    "    plt.show()\n",
+    "\n",
+    "# Randomly select a number between 0 and 999\n",
+    "random_number = random.randint(0, 999)\n",
+    "img, cell_type = neuromast_cells[random_number]\n",
+    "metadata = pd.read_csv(\"/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata.csv\")\n",
+    "row = metadata.iloc[random_number]\n",
+    "z_min = row['Z_min']\n",
+    "z_max = row['Z_max']\n",
+    "\n",
+    "# Loop through all slices in the Z dimension and display them\n",
+    "for z_idx in range(z_min, z_max-1):\n",
+    "    show_slice(z_idx, img)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 57,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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N7xel58SmeqQA4Odp18vv8t6kgOGCpFQqlVk7hUIBhUIBk5OTiMfjZWa+C+7NTyxYwqfTabzsZS/zgnGRSATpdBrNzc3ehBXbeLgkBf9u0/JaJp0087WIvYQktSZobJqet0VCChoqm7sIJFQoSEj7m5ubsXTpUkxNTXnXjI2NYWBgwPn68wwLlvCpVApLly5FZ2dnmbaVaa6APcDFYYuQS2gJOFS2LeuO12GzNLiG50TX2iI1vWbma+fx+rll0NzcjNraWhhjUCgUUCwW0dvbi+HhYUf4eYYFQfhYLIb6+novAQYAGhoaPPNUkpCgmfIa6eWwl00waH605tf7BdV4Ci3VzU18TkiK4tsEUJgEH9l2HswjAQkcj+5TW8haampq8rQ+AOTzeWQyGWfqz2EsiEy7hoYGLF++HG1tbR6JE4kEGhoavIg8j4xrhDuV9+c3M07ToBzchCeCUUCtVCp532lOPI+029wMW/10jhx9kHUUi0Xvez6fR6lU8vL0KbBYKpUwPDyMnp4eF9ybJSz4TDt6gCkY19railgspkbJg7QrQRvmIgR1qGYtSIIB9mE5HkTj/rSWN68F8U52JRxZpwwUUvyBWxQ0h4AP6WWzWW9eAe8zp/HnDuY14dvb29HV1YW6ujo0NTV55js9cNKMDvLdNSEQRvP7XWPT9n5lc7Oez5gjsslhO5o+K9NxZXv82ikFCY93ACf6koQSjydQjkBTUxO6u7vL/Prx8XEcOXLE+fpzBPOW8JFIBO3t7Vi+fDkSiQQSiURFYE4Ljtnmoofx5TWf2Obb28qbDuFlkg1PvolEIt5v7nfLwF4QbPdJ2py3iROeWx4kABobG1FfXw/ghHtw5MgRF9ybQ5g3hE+n094Kr/RpaGhAPB4vm56qJbZomi2ML2+L3oclubYvSJDIRBup6YvFolXTUvm26L1Wn4Sm6bVgIe9rEjx0nOYDAMf/tzPOOAPZbBaTk5NlU3UdZh7zhvCdnZ1YunQp4vG4Z7pTmmw0Gi3T6kHmuy2AZctFD0NkDk1Q2L5LEFG4hieTnbQ87ZOBPJ4fzwN509H2cmiOt1cKFW7ua3WRFdbQ0IBsNosXXngBL774Yqi2OJwezHnCE4lra2vR0tKCWCxWRnKN4DZCBfnoNn/fz1oIk7wT5jdBkoq0JZHYRi6ZJ8DNfO0cW3s4mXlb+HGbya8JFlqdN5fLoaamxhpncJgZzGnCp1IpdHV1ob6+Hu3t7YjFYp6vTtH46Wh2Dn6uTHuV/r+ftvfz/4NIbnvoZQYdN9t50E5qdSpfan0+fKfB1k7qCy2zj8cT5NRdrQ9isRi6u7txxhln4OjRozhw4IAbvpsFzGnCp9NpLFmyBO3t7YjH455m59F4bTaa9FH9tBltqQxJfpsFESYO4Ge6S4Jo2ppH6CX5tXF6mkLLNTxNgOFDfJqgkZYO1/IyYMeH5/wEiYynLFmyBACwf/9+9PX1OcLPAuYk4dPpNGpqatDQ0OCN9/LxXSKiNOnDpMjaIEksx/Kp3iAf3+bTB61Uw0kufWRuwtvMaG5+029udlNwbTqkpy2Pxsv2avei9Sc/p76+3lvtd3R0FJlMpuJ6h9ODOZdpF4lEcPbZZ+Occ85BKpVCY2Mjkskk4vG4lzpLZr38BPnF/D64ZUDk5i4CCRO/SS9+ST18K9uimcg2M97vWmmqc21O3+U+OXynQVpIMg4g69XMeeky8bJzuRzGx8eRyWTwzDPPYPfu3da2OIRHGCrPWQ3f3NyMRCLhmfE2f1tLmSVIrUfw8+dt9QD2WXBaGTbYSC6/U3COIHPpuWCQpjeZ3VoZ1FZqNyezzULi5fD65H1pLg9dz3/X1taitrYW2WwWdXV1FavwOpw+zEnCc21LmpaPt9P0VplGq8Fm8kofnbsJUsPLwKCm2YNMe43cMhgnj3NSSfLyc/iyWNz8lkE2Muu5i6HFO2wWik1I0W+b9WMLqMZiMZx//vloa2vDwMAAXnjhBWSzWfV/dDg1mJOE51F4/p2CddLclqY2UEks+fBqAT++T9bN/Xqu4eUDTfsIknjcHOaajQ9XaYSX0DQ3D6TxOACRndrK03Ft0XUqzya4eL2yXzULiW8JqVQK559/PpYtW4YdO3bgwIEDjvCnGXOG8LTeXCKRQG1trTUwxwnHTXpNU/FAlxY0s2lmLT4gNb3m1wf57rQlAUREpPaRKa6ZzBI2oULlUsReBvmkUJT9wkmp3RNvl591wPvINjWYC7jGxkYsWrQIjY2NXh2ZTAYjIyNlr9pyeGmYM4SvqanBsmXL0NzcXJZBR+Y75ctHIpGyYTkb4QH7e9jofElwzaqgsX+qV8YOwhKeD6eRFuYaPmgCDMEmTPh4eKlU8qazRqNRz4wnknFLgPv+VL5mfmv9q7VN0+o2Dc/b3t3djaamJu+lGcYY7Nu3D+vXr8fo6Ghg3Q7hMOuEpwckmUyisbERLS0tZck13LzmwSab/8zBx695fdzftH2kdqfyNEHjRw4ius0Hl1lxtlgEtV1+5+Y7L5dreF6XFH4yGGjzw/3axdsj+4r+P1tfkQCsq6tDTU2NF28olUoYHx9HKpXy1tjj5zucHGad8K2trWhra0N9fT3q6+u94Bxpce6386BdWB9eWxYKsJvvXMNr9frFD/xMeSIbHwundnC/m/vbGmyRdKnhqVx+Hdfm2qIZmtDT7k1rmyS8dH+CNDz1DR82XLRoEVatWoXJyUmvDwcGBrBt2zY3dn+SmFXCRyIRtLa2YtmyZUgkEl6SDZ8gw01rHp2nh8e2vDRQvuyzjIzzh1k+nJz8vC0yrZdfq5FUBumIbHyqqJ/bIdvK75PXFyRUyKynczTCcw2vBd5k/fw/5Fu/QKi00IDyiUG8/aVSCWeddRba29vLjm3fvh379u1zhD9JzArhY7GY9xJGWhOea1KpPf2++/nNtrrDmPRBZn5QDj9QbjJz8mlBRA2SHDbTW1o2BLl6DbVXG1qT90l9Re0I0vS2vpJCUQoMLpBJw5NAku6OMQZNTU14+ctf7r0zz4ZMJoOhoSE3D19gVjLt6uvr8YpXvAJNTU1oaGjwSJ9MJr0gGQXK+Mw4OSzH/cMgDavdJl3DLQfS4vQ+OZ78Q+0jrW/T8FzjErH91ofjQTZtIorUkDbzmJv0RB6qg9dLW95WLjw0c1zeo7aVwoJ++/nwfia9nO5bKh1/G+7w8HDZG3FlTAIA9u3bh3Xr1mFkZMT/YVxACEPlWdPwDQ0NaG5u9tJmNc0e5C9LDW8TSqRhNX+Vf5cPpLQqpHXBz9EIT+DBODpPDpHZ4KdFeZncfaE28GE5fpxeXSXLl8LEtjagdh3t0ywxPw3PXRq6Dz5cybV+NBpFfX09amtrvWukQKfvk5OTZS/VCIJN2C40zArh6c9LJBJl5jwP2JGmlb681EA2c5rXJbUYfecPr02Y+Jn1mhYE9Lnh/GGSD6f8yGCb1m7NnZFDbFwAyHbJY2FMcU1I8u8a4QGUDWfa2ix9eJvWlxYB719e3llnnYWrr74aExMTqgsj6y0Wi3jhhRfQ09OjPkcLBbMWtOPk5t/5VhsXt5nRQdpSHpeanLb8QZV1aBpfJgYBlZNNgkxZvznrpKU14SNJxGMG8je9gZb7yLI/JNG1eEkYwtP/q5WnCWfZD5zccoUfeZ7sN7q2q6vLC/hp18hgYTabRTabxf79+xe0lp9RwtO0V77CrE1rcnNe06SaZufanEPul9cA9sSSMGa3rT7twaat1FZS+8i6bdpY9gEFBKWWlyvlcFNfK48HJm3WlOw7Tm7pw0tznn/nk3rot5YiLAWa7TzZPikYbMKio6MD3d3dakA1m81iaGgI+Xze55+f+5gxwkciEXR2dqK7u7vsHW98xdlEImEdCtOCP1Ru2Prlb/qE0UC2ejThYyO7DKjxwJRcoYaXKR9oWTcnDz+HCxFJGCkYNVPelvDEr6Gt1pf8f7MJDWonoC/uoWl9uU9qf+08zRLgAjCZTOKyyy7D+eefX9YmOt7T04N169ZhcHBQfQ7mC2Zcw1MmHSeyfEA0UzIoWiwfbBs0k1TutyGsqRfUBl6e7cPL4ftkPZqAkvdIWp6++5nzQfEJTdhKy8DPLZDuEr8v3k4Zj+BLavFAHpVLY/e8b+WWly2PRaNRtLa2orW1tay/STDQmnw8CCjjCPMBM0p46Z/zbDp6SLRhLykEAH1p5zDgD6ufUOEmr/S3pYbh5VF7+LV+w0ykoaTG4ffFo+0ELc7ACUImPL8P7sNLt0Ezwf00vBS6kty26cWy3RySnJpGlv691PJ8n03D21woXh9/towx6OzsxJVXXomxsTGv7KGhIWzZsgVjY2Ohnr25gBklPP3pWqCOf+c+vEZ4TaNJkvi1AdDf8mrT8vxhobq4xtEeVPnQ2sivPZRcG8s2yHuRHyI5na+VIQmvmeBan8v+0fpSCgvNh7f9j7ydtJXCk2t4Tm6e58CP861WnqxP9hV9zjzzTKxatQrGHM9tyOfz2L17N/bt2+cI7wfNZJcPltQs/APo/jj9kdxktT1Qfu6BTevQd82HlPXK8/g+/l3TKGFgM+HpO9f0/L7ow/uHk9amkcMSnn+3XcsDgdq98L6m8oDKSTNSYPG+p/M101xzk+R/rJVD7eTtCjvGP5cw4yY9jb3zQB1pdj72TiY9H8PVNIM0TbmG0477+Z1+dZDZGImcWDWGP8T8N11D2kYLzvlZBjbYSCyP8funJBsiMn94eRlcI2tZhNKl4HXJcqTQtgXtZD/7WWaahi8UCl6/0r3R/XJXSRvDl4LWRnouuKVlRPc1nzAjhLdFa7Vgjh8RbRqeruN/mvxz+H6/jwYp4ekh4P6x/A1URoI1bc/P1+oLA+1czQrgRJdug1/AVCO8FDDyPN6n2n+qWVU28ktNTfuA4ynLdD/0H/B+1srRNDYvW/7X9J0HFf2E4FzGaSd8TU2NN9mhq6vLS6XVEm/4sBz34f20L2A3l+k3oOd+24QMHdceFp6Sys07rV2ar65pcE3T2QjjJ5zkQ8y1Lo85yHqon/k0YKmltYdbsxT4+bZEqSDCB2l6rnGlK0XlyfvkbhidK58RshL4cRk4JSuC9jsNL1BTU4PzzjsP7e3tSCaTHqml+c4fOtvMOT8y0tYvEKM9aPRbkpbXxbVLNBr1THseQebt43VraaJ+xCdIAWfzpW3akWtxKk8GNHkdXMjSxCWZRWh7uDUBFSYOINuu/b9SiEmhy4fjuIaW+4isMq5i23JLgG+pD+ejdgdmgPD04GiLR/AHwmZO+gWNeB18q5Hddq5mYtrOpTI1IaAF3/yGgGSbeF1cU/lpRtlODZL48pjtf+CEp37S+lF+19w2WUaQhWLT+tyiksTTnhXaxzW7ZgH4YTrnzgfMGOEp2UbT8HKiDAkH+SAC/gSdTpu0bVB50o+kfTYtyDUKaXdeDzdJKYtQ9htpX81EloFC2z3Sdz6VGDixOjDvc25xyfTnMP3pR3Rehia0gywX6lMqn/x30uhcsNJ13CTn1oHNGpSmvE1YzlchMKNRek2b20x3TghNGweZmTZMl+QcMtCmmXnyHL8AnSQ9h+1ebYFLKWhsx2QfaH3tN3nGBtm+6Wh22aYggc6fB+lLyz7lgTbtGhtx/Y7RuL8U4vMBM6LheWDO9tHILx90TcvzP5Lq41vejjDQTG8t2i+JzMlO53FtofntNsFFv4OSWGzBQu1h1crnfS3/I2lpcQvEj4TcmggjmKUA1LZhIO/Zz2KgICavwyaopdbP5/PYunUrduzYgYGBgXmVdAPM4LCcnOYqs+v4g03HALtvZtNO0hogBJll3M+j37bgmhzKiUROLD6pwUZ6rhG5f0pbHlCzxTgkmWwPvfw/qAzqZ/mfUPBOEj6I7LaP1n+2uIb2v4XRpFK72whP98vz4Elg2Mx72k5NTWHr1q34+9//XpG/Px8w46m12oMQZOb57fcTAn51aA8d/em29FzNpJfftWum87BK7Ss1sZ9bI8luI5JmHWlanwskLkD9SC/NdxvhZfDThum4SEHC2W+/jexE6omJCfT29mJ0dBTDw8Nly4TNJ8wI4bWHmO8DUGa22ghsK5M0ELcSwmhBzWTTpLpGWu3B8xMSMmGE3wN9p37g2pffE9fIQT41j/QH/R+2uIocMg0SorZjPFhnS4GVfepnYmsTabR9tv9ZtkNeS1l6/HPo0CHcd9996Ovrw8jIyLwkOzBDPrztwZCk5cdPph5JdJ76aNMyUqNrefg2k1kiSMPY3ARJFE3Ta8LQ1g9ce0rt72d2h6lbEzRaW2wWAbei6DyZOKNpbUleLYsxTF9rZWkfMvcLhQLy+TxGR0dx8OBBHD58WO33+YLTTng/SasRjs6jLU9Zlcc1aKYwtyR4GSTNZSRX1sP9cxuRpnPvGjSLRQtm2kgXRCJZz3SJL/PqbQJRktwmaKnP+XvwZJ9qWl0mMsk5Cny2nO0aoHy1HH4+lUlEf+6557Bz504cOXJkQbzyalam+2hS2SatI5HK+eDyGptmkhNvqDyg8g8PE7DT0mrlfWnto/r4Md4W+i7bH2aKMO8DqieM2a1ZFLZ2aB9etk3j21wcLpxkn8jvmrkt/zciKABVCNB36h9p+muTcQqFArLZLDZt2oR169aVlTGfMaOE9zOh/CaaSNJrxLLB5neezD6uOTXNZTP9g9obxuWRlopGJjrOySQtkpcCm3DQztO+83ZI10NCM+E1bW7z4ekD2BfAlP76wMAARkZGPMLncrl5HaDTMCOEl50LnJDE/KEuFAplgSnAHnyifTy1VT7cGmEkuIak+jUrg87RhI40R/mDrGl7+fDwa+SQGZn3Nu0q2yoFo2ap+LkGGmS7tEDqdAUKXW9zpbim1bSvJLyfENCWupYLZWSzWTz22GN4/PHHy4TDfA7QaZgVDc/XJJNankjMHwh+Pd9ne6A5gkxb+m7MiVcr+12nQWp+m9bibZbt09rMy5MfWaa8F/ndDydD1pdynV/b/Nw8qTQ0ra+Z75rmz+fznhApFovIZDLo7+/Hnj17FhTBJWaE8LLDSZtryTXGmLL9XPNJbcCJxjU8D8Rp2lqDzfSncug+5FZq+qBz+Jb3D38wtbFs6cPza2lL/aEJIHmvsixZps29om0Q2cOY6Zp1wklLr8fiQTTS9pqW5v+BTcMXi0Xk83ls3rwZL7zwgreflqxayGQHZjBKL/8E/qCRKc1NZjpGW3qgOQm04TSa06x9gvxGXq9m2sv7kvfHy5XBM9kftrJ4G7jQCUN43q/UD9yfJ2jJO1r7NEEURAhpkdm+a+PgUotzwnONzInPSU39zgUHgArCT05O4tlnn8W6desqBPZCx6yY9Jyk9OHmPf3mGouup22QSWjTItIl8IOf6SrL4A8wL1cLoNnKCetP860sT5r0EhrZNV/cj6x+/ednzWiCRDPPtY9MhOH7NKExMTGBI0eOYGpqqiJQl81mMTg4OO9fKnEymDGTnv8ZQPnigJrpTufwYSmgPBJNWp7OpS09BNqED16fJhB42fw6OmYLMAEoi01Iv5KXq5XBtxx+2piO8z6WdfitY6flLHDwtfvk/2UTgtI81+5PancejOPavFgsYmpqytsSeeUbcXmZVO+ePXvwpz/9yXtxhNTkw8PDFe2vBszKOLx8GPgfD0DV7EEax/axmaI2U53XT98Be9qoMabsfNluHp+wEYWfq90jgZv4vC+18zRNbwsABlkwmpmvtdHvP9DOo/ZLU15qdb6f75uamvLMe/k5evQo9u3bh/7+frW/qxUzmkvPwTWh1ID8txxyk2Xw71JwUBYXaQHNX+XX8vKIVJysfkEu7jvy+AI/Js1lTkjNtNZMbamF+TEb0WV5WtaeLYjHTW8axaD/hQda6Vy5wAQnvCY4OOGlhi8UCp5WJw1P2j6Xy2Hz5s1eVF2W29fXh4mJiYq+qnbMSC49bf1MWflgcc0cJiKsaRUeyNOi1pqmonql6RuJ+K80w014AJ6QCSI8N5PD9CHf+p3P3STaR8JCexOMNrYu+5K7PTbNLd0bTmrNOuBBN665yVwnwudyuTLCj4+P4+mnn8Yjjzyi9gH//x1OYEY1PNcwNhOZX2Mri5epnUsPuqZltBRdvwfD5u9SWbLcUql8uSrSeLSPXABNu9uEiR947oDWdt4ffJ/t/5Ck16wuut6WhEQEloJX0/RSw9N3LgDy+Tz6+vowODjo/Z6cnMTIyIgnWB3CYUbXpde0pKZNCZLUWoaY9pBqviMXAjZLg/aXSiUvD5+3WZumyq+R7gkneSQS8R5ifo3088Pmq08HNkslEil/0YfN5eGRdC0ewcnLCa+RX7PqOMmNMZiamvJM+mw2i4mJCTzyyCPYsGFDmSVYrYG3l4IZX9NOPizaA+13XBMA/DppMmr+uQ2aMJDCKIiQpA1pK9eEJw0v69T6ZbraXvYB36edFzREx0H3oN0z3YcxxjqJBajMfKPrAJSZ79yHz+Vy3uITu3fvnlZfOFRiRnx4rr20lVdtOeMaybh25efZNL007f0eatsxSXqausr3E7mJ7FKTAyeWVaI2yaEuOke7r5Ppd2nKa+6JX9/JuIdtuFJqaiI+J3yQeX/gwAFs2LABY2NjZefn83lMTU1h79690+4Dh0rMmIaX7xqTpr3fYpac6Jr5azNJAf8EEI1I0vS3aXd6kSCPVHMC89wBHsjir0biQT7pPkjBZyN+UPxB/pYWhDbsyPsirGXETW0ZhOPanpOe/zd79uzB3Xffjb6+PrV8F4A7NTjthM/n8xgeHkYsFkNTUxOam5srtIq2wAMPjNmGkWwayvbw2oRBWA0q65FDU6TZedvlIpC2aDWVb7u/sAhDUHm+5gbwsjTiSwHKCQ+gYnZboVBAX18fjh49qt7//v37kclkXBDuNOO0E35sbAwbN25EKpXCihUrcMkll6jLU0ci5QEk+TIEbhX4vftMZuVxBJHB9uDz4zbXhMqPx+Nelhi1jZuxdFwGswiaq6OZ22HuSxNy/Jhm9fBj3OfWxtdlOTJox1ePGR8fx0MPPYQnn3xSbe/4+DiOHTtm6XmHU4XTTvhCoYDh4WFEo1EvEUKa51Krae80k1pP04Ayes+3YWAjiOYHy3bR9TIoR34vEZ3O0zLzeB1+sQlb2/1+h7lvLdDJzXBbpJ1rcy4gOOEnJiZw6NAh7NixI3S7HE49Zjy1VprttA46J7pNm2vBPc0F0Ex7G2H4fhux5fmaX08gMnAhQESgMXPpw/OAIq/DZqkAlUEvqZF5W3g7bMKDWynS39bGyAmZTAZPP/009u7dWxF95+3KZrMu8DYHMOPr0nNCayY7FwRyK81oGeSiOjSyBmlIP8HANS5BEzbcl5c5AED5ZBQSANJE1trC65Xl+n143bZMQ5lQQ+0kzc21tKbtjx07hn/961/WjDeOhbAm3HzHrEyeASo1pdwGBedsZr1NKwe1RZ7np+Ft5/hZCNReSXSec29rlwapQeU+aTXQcdpSvWR18Os54QuFAkZHR9Hb2+vNUuP1jY2NYWhoyAXb5glmlfDSZ/cz6W1j+NyUp3Kn479rJJWCRI4U2N6bRpqSWwOcRFK7EtE1LU/Q8tW1gJrcpw1jUbt53IDaI4cOjTHI5/MoFovYsmUL7rvvPhw9erSiTaVSCQMDA4H97DA3MGuE57BpRD8S2sx2G9n9yC+1sK09cqtZANw85sd50g19JMH5Ph4L8DPj5cQUrq1l2XzIUNbJE2b4hJVSqYShoSHs3r3bpbIuAMw44WVgCagkkfa6KM3cl9qVlyW/c2jCgJcjtbotF4ALI1t9QcQO6iuCJLi2JruMlAfdI5XLr+3p6cHGjRsxOTnp1XHgwAFkMpnA9jrMfczKElcy0wqwa3ROLmlK25JTpkt6roU1E96P9Px6TmRN+58sNPPdbyFHm0kv+1haBsYY7Nq1C/feey9GRkbK6neZbgsDM0r40dFRHDp0CPX19ejo6EBdXR2A4BROP40cdG4QNM3OSWHLCZB1yXr9oufSDJc+vJZ6yofL5NCZtia7RnoK0A0NDWF8fLyibGMMDh8+jGw264JwCxQzRvhSqYTdu3ejv78fHR0duOqqq5BOp8smnIQxQ/0+YSEJq2l2+R57uY+fpwkAjZR8JVZ+nM6X/rskIx8e08rjq7jKBBlq39jYGB566CFs3rxZ7ZuxsTFMTk6G7kuH+YUZ1fDj4+OeZslms9aHXEL6+Nqx6UIL9k33E6YNflpd7pP9wDW+1PBy+qncx4NwnPCU8eammlYnZi1KT9qKa04+8UKmq2oIQ3YtOMjHp3nAzU/Dk3kv8/9pKwlK2pevoU7aWHtVkrxHOWYuCUxDZlQHn6QCwHubyrZt28pecZzNZuf9K48dTh6z9vZYevgp2w44kYklM9DooZc559r3oHptkXQeCJQfLU9ABvQ0/1ou00Skl29QsQXENA0vCU9vZKE6eGbcsWPH8OSTT+J///tfWbkuAFe9mBXC5/N5HDlyBNFoFK2trUgkEgBOTCSRkXxNw2vklUNNYaG5DEGmvPyt+ewyNVXzuaVJr92nDNppPnyhUEB/fz/Gxsa8c0ZHR70FJRwcgFki/LFjx7Bhwwak02lcfPHFWLlyZVkAD9CnovJ0UMC+Tl3YcW5bINBvMQ6a7MOHCKktfJll+tD6bLlcztPG3ATn2pu3S94Hz4Tj2pwSZUZHR7F+/Xo8//zzXnuKxWLZ8JqDw6wQvlAoYHBwENFoFOeccw7y+TxisViZpi+VShXvieP7TtUYN8EvIBik3YHKoTOeFKNpZCKtpuFlGi6Vz/11SfhMJoO+vj7s37//lPaLw8LCrKbWGmOwf/9+PPbYY2hubsby5cvR0tLiaV5u4gP+k1wk/LS8n9muJdtIa4O3nzQ0aW4iJWl32sc1PKWsEnm5hre5FwDQ09OD5557zisLOBEgJMI7OPhh1gl/4MABHDp0CJ2dnejq6kJ9fb33sJNG56a8MUY1eW2mPdVDvzUtrgXtgnx3KpcPiWmEp5cn5HI5bz8nvLbqDW8Lv489e/bgwQcfxPj4uCrMXDDOIQizPnmGyMIXPOTvjqeAE702Cih/FzwfvtJID+iZenJ++3TaK5NhqN227/LDh+jkucPDw8jlcqqQ6e/v92ICDg4ng1knPMEYg1wu5yXkACgb65b7ONn5FFkbiaV5rA3Dab65bKMUUGSW0/vO6FVIpMnJ/M5ms57Wz2az3jVc0I2NjeFf//oX9u7dq7ZhfHwc2Wz2JfSyQ7VjzhCeAlxTU1NlY/M8kYS/0ICIqWWpBWn4oP0atPx0Py0uh+K4difBQGPzVA69cMEF3hxOF+YM4ScnJ/Hcc8/hwIEDWLx4Mc4991wkEgnPlwfK53TzXHH+Ygq/gJ70izUfnYO0OVkVfE15Hqwj8vKXHfK3n3K/vqenB5s2bSrT7iRMMpmM9z5zB4fTgTlD+ImJCWzdutWb0XXWWWd5mpwCW2TecwuAAnlERG3aKg/00f4gwstUWbkla0MOj/FxeB6lp989PT145JFH1FcZ+2XdOTicCswZwgMnUkiPHTuGF198EbW1tejo6EBNTQ1isZg3bs2XveIanyDNfS4UbODHeQScEmq4wOATVmQAjn9GRkZw+PBh5PN5z4c/cuSIp90dHGYac4rwwHGC7du3D4ODg2hpacHrX/96dHZ2euYvme/xeNwjHR+ui0ajnitAv6Xm57n6MrKv5a8T+D6qmyfCkIan+eRbtmzBAw88gMnJSe/a8fFxTE1NzXCvOjgcx5wjPHDcvJ+YmECxWMT4+Lg3TEWZeBTIi0aj3hAeaUyegqsl7wDlL3+QAT8uBOi3FAJyeSkZuKPx96GhIezbt0813x0cZgNzkvCEiYkJbNmyBQcPHqwI5JGGBU4My1EOPBFeW/GWtD0JDZnkApzIV6fz+egAn+nHNTz58GNjY3jqqae8OedOmzvMJcxpwk9OTmL79u0eQbu6upBOpwEAiURCJXw8Hi/T6jxDjxNeRvblkB7X6nKhCZ4hx4N2hUIBQ0NDWL9+PTZt2uRd5+AwVzCnCQ+c0KpjY2Po7e1FMplEKpVCLBZDS0sL2traPAJz0hLRpDkPVAbleIBPBvck4Xk2YC6XQ29vL8bHx719tF6cy4ZzmIuImJCTx08mDfVUoq6uzsuzp2DcihUr8NrXvhapVAqpVMoL2CUSibKhO5rOKqe78vN4th0HT5/ls9yI3H/605+8FySSxh8cHHTrwjnMOMJQec5reAIF8giRSARnn302crkcgBPmPE0t5em2fGYZUP6uN24JcFNfM+n5ijW5XA5jY2M4ePCgWx/OYd5g3hBewhiDgwcP4t///jcaGxvxqle9Ci0tLYjH42UvqOTr0/FAHp1H30m7k1VA0Hz3PXv2YMuWLRgZGUF/f/8s9YCDw/QxbwkPAIcOHcLhw4fR0dGB1tZW1NTUqES2rUtnO4+Da3gi/M6dO/Hggw964+sODvMF85rwFGTLZrMYGBhQNXs0GkVDQwOam5sr/Ho+9EbDc/F4HIVCAcPDw8hkMhVTYIvFosuWc5i3mDdBOz/E43E0NTUhmUyq88gvuOACXHrppUgmk2VBO67heZ7+yMgI/vnPf6Knp6cinx44/gadkZGRUEESB4eZwoIK2vmBIuYaIpEIzjzzTGQyGRSLxQpyyyh9NBr1Xom1Z8+eGb4TB4fTiwVBeD8YY9Db24snnnjCI7vMrpOLX0xOTloFiIPDfMaCMOmDIM38ILhpqg7zEVVj0gdBLgPt4FCtiAaf4uDgsFDgCO/gUEVwhHdwqCI4wjs4VBEc4R0cqgiO8A4OVQRHeAeHKoIjvINDFcER3sGhiuAI7+BQRXCEd3CoIjjCOzhUERzhHRyqCI7wDg5VBEd4B4cqQuj58G4+uYPD/IfT8A4OVQRHeAeHKoIjvINDFcER3sGhiuAI7+BQRXCEd3CoIjjCOzhUERzhHRyqCI7wDg5VhP8HOILJh9TeF6gAAAAASUVORK5CYII=",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    },
+    {
+     "data": {
+      "image/png": "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",
+      "text/plain": [
+       "<Figure size 300x300 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "# Determine the number of slices in the Z dimension\n",
+    "num_slices = cropped_image_green.shape[0]\n",
+    "\n",
+    "# Function to display a specific slice\n",
+    "def show_slice(z_idx, cropped_image):\n",
+    "    plt.figure(figsize=(3,3))\n",
+    "    plt.imshow(cropped_image[z_idx, :, :], cmap=\"gray\")\n",
+    "    plt.title(f\"Slice {z_idx} of {num_slices}\")\n",
+    "    plt.axis('off')\n",
+    "    plt.show()\n",
+    "# Loop through all slices in the Z dimension and display them\n",
+    "for z_idx in range(z_min, z_max-1):\n",
+    "    show_slice(z_idx, cropped_image_green)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/nontargeting_experiments/get_vgg_results.py b/notebooks/nontargeting_experiments/get_vgg_results.py
new file mode 100644
index 0000000..24cee56
--- /dev/null
+++ b/notebooks/nontargeting_experiments/get_vgg_results.py
@@ -0,0 +1,134 @@
+# %%  [markdown]
+# Loading the results of vgg experiments and showing their losses, accuracies, and confusion matrices.
+# 
+# %% 
+from pathlib import Path
+import matplotlib.pyplot as plt
+import pandas as pd
+import torch
+from sklearn.metrics import confusion_matrix
+from tqdm import tqdm
+import numpy as np
+from torch.utils.data import DataLoader
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from funlib.learn.torch.models import Vgg2D
+from embed_time.static_utils import read_config
+from torchvision import transforms as v2
+import seaborn as sns
+
+# %% Utilities
+def plot_metrics(metrics): 
+    metrics.plot(subplots=True, figsize=(10, 10))
+    plt.show()
+
+def load_best_checkpoint(directory, metrics):
+    # get epoch in metric with highest val_accuracy
+    best_index = metrics['val_accuracy'].idxmax()
+    best_epoch = metrics['epoch'][best_index]
+    checkpoint = directory / f"{best_epoch}.pth"
+    return checkpoint
+
+device = torch.device('cuda' if torch.cuda.is_available() else 'cpu')
+
+def get_confusion_matrix(model, val_dataloader, class_names, label_type, normalize='true'):
+    model.eval()
+    predictions = []
+    labels = []
+
+    for batch in tqdm(val_dataloader, desc="Validation", total=len(val_dataloader)):
+        images, batch_labels = batch['cell_image'], batch[label_type]
+        batch_labels = torch.tensor(
+            [class_names.index(label) for label in batch_labels]
+        )
+        images = images.to(device)
+        batch_labels = batch_labels.to(device)
+
+        output = model(images)
+        predictions.append(output.argmax(dim=1).cpu().numpy())
+        labels.append(batch_labels.cpu().numpy())
+
+    cm = confusion_matrix(np.concatenate(labels), np.concatenate(predictions), normalize=normalize)
+    return cm
+
+
+def create_dataloader(dataset, label_type, batch_size=16, num_workers=8, balance_dataset=True):
+    csv_file = f"/mnt/efs/dlmbl/G-et/csv/dataset_split_{dataset}.csv"
+    subdir = Path(f"/mnt/efs/dlmbl/G-et/da_testing/vgg2d_{dataset}/{label_type}_{balance_dataset}")
+    df = pd.read_csv(csv_file)
+    class_names = df[label_type].sort_values().unique().tolist()
+    num_classes = len(class_names)
+
+    metadata_keys = ['gene', 'barcode', 'stage']
+    images_keys = ['cell_image']
+    crop_size = 96
+    normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+    yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+    dataset = "benchmark"
+    dataset_mean, dataset_std = read_config(yaml_file_path)
+
+    val_dataset = ZarrCellDataset(
+        parent_dir = '/mnt/efs/dlmbl/S-md/',
+        csv_file = csv_file, 
+        split='val',
+        channels=[0, 1, 2, 3], 
+        mask='min', 
+        normalizations=normalizations,
+        interpolations=None, 
+        mean=dataset_mean, 
+        std=dataset_std
+    )
+
+    # Create a DataLoader for the validation dataset
+    val_dataloader = DataLoader(
+        val_dataset, 
+        batch_size=batch_size, 
+        shuffle=False, 
+        num_workers=num_workers,
+        collate_fn=collate_wrapper(metadata_keys, images_keys),
+        drop_last=False
+    )
+    return subdir, val_dataloader, class_names, num_classes
+
+# %% Setup happens here
+dataset = "benchmark_nontargeting_barcode"
+label_type = 'barcode'
+batch_size = 16
+num_workers = 8
+balance_dataset = True
+
+subdir, val_dataloader, class_names, num_classes = create_dataloader(dataset, label_type, batch_size, num_workers)
+
+metrics = pd.read_csv(subdir / "metrics.csv")
+plot_metrics(metrics)
+# %% Get the model to load the best checkpoint, create a confusion matrix
+checkpoint = load_best_checkpoint(subdir, metrics)
+model = Vgg2D(
+    input_size=(96, 96),
+    input_fmaps=4,
+    output_classes=num_classes,
+)
+model = model.to(device)
+model.load_state_dict(torch.load(checkpoint)["model_state_dict"])
+model.eval()
+
+cm = get_confusion_matrix(model, val_dataloader, class_names, label_type)
+
+# %% Validation loop for confusion matrix
+sns.heatmap(cm, annot=True, fmt='.2f', cmap='Blues')
+plt.xlabel('Predicted')
+plt.ylabel('True')
+# Set tick labels
+# plt.xticks(np.arange(num_classes) + 0.5, class_names)
+# plt.yticks(np.arange(num_classes) + 0.5, class_names)
+plt.show()
+
+# %%
+len(class_names)
+# %%
+df = pd.read_csv(f"/mnt/efs/dlmbl/G-et/csv/dataset_split_{dataset}_{balance_dataset}.csv")
+df = df[df.split == 'val']
+df.barcode.value_counts()
+# %%
+dataset
+# %%
diff --git a/notebooks/nontargeting_experiments/make_benchmark_dataset.py b/notebooks/nontargeting_experiments/make_benchmark_dataset.py
new file mode 100644
index 0000000..53e6f6c
--- /dev/null
+++ b/notebooks/nontargeting_experiments/make_benchmark_dataset.py
@@ -0,0 +1,29 @@
+
+# %% Make an intermediate dataset
+import pandas as pd
+
+location = "/mnt/efs/dlmbl/G-et/csv/dataset_split_1168.csv"
+
+metadata = pd.read_csv(location)
+
+# %%
+assert "nontargeting" in metadata['gene'].values
+assert "CCT2" in metadata['gene'].values
+# %% Keep only the nontargeting and CCT2 genes
+sample = metadata[metadata['gene'].isin(["nontargeting", "CCT2"])]
+
+# %%
+sample[sample.split=="train"].gene.value_counts()
+
+# %%
+# Sub-sample the non-targeting ones to have the same number of cells as CCT2
+sampled_nontargeting = sample[sample.gene=="nontargeting"].sample(n=len(sample[sample.gene=="CCT2"]), random_state=42)
+sampled_cct2 = sample[sample.gene=="CCT2"]
+
+# %%
+sampled = pd.concat([sampled_nontargeting, sampled_cct2])
+
+# %%
+sampled.to_csv("/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark.csv", index=False)
+
+# %%
diff --git a/notebooks/nontargeting_experiments/make_nontargeting_benchmark.py b/notebooks/nontargeting_experiments/make_nontargeting_benchmark.py
new file mode 100644
index 0000000..e94e06e
--- /dev/null
+++ b/notebooks/nontargeting_experiments/make_nontargeting_benchmark.py
@@ -0,0 +1,18 @@
+
+# %% Make an intermediate dataset
+import pandas as pd
+
+location = "/mnt/efs/dlmbl/G-et/csv/dataset_split_1168.csv"
+benchmark_location = "/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark.csv"
+
+metadata = pd.read_csv(location)
+benchmark_metadata = pd.read_csv(benchmark_location)
+
+# %% Randomly samply a subset of metadata that is the same size as the benchmark data
+sample = metadata[metadata['gene'] == "nontargeting"]
+sample = sample.sample(n=benchmark_metadata.shape[0])
+
+# %%
+sample.to_csv("/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark_nontargeting.csv", index=False)
+
+# %%
diff --git a/notebooks/nontargeting_experiments/make_nontargeting_benchmark_barcode.py b/notebooks/nontargeting_experiments/make_nontargeting_benchmark_barcode.py
new file mode 100644
index 0000000..f918c5d
--- /dev/null
+++ b/notebooks/nontargeting_experiments/make_nontargeting_benchmark_barcode.py
@@ -0,0 +1,33 @@
+
+# %% Make an intermediate dataset
+# This includes *only* a subset of barcodes that are nontargeting
+import pandas as pd
+import numpy as np
+
+# %%
+location = "/mnt/efs/dlmbl/G-et/csv/dataset_split_1168.csv"
+
+metadata = pd.read_csv(location)
+# %%
+sample = metadata[metadata['gene'] == "nontargeting"]
+np.random.seed(42)
+barcodes = np.random.choice(
+    sample["barcode"].sort_values().unique(), 
+    size=10, 
+    replace=False, 
+)
+# %% Randomly samply a subset of metadata that is the same size as the benchmark data
+sample = metadata[metadata['barcode'].isin(barcodes)]
+
+# %%
+sample["split"].value_counts()
+# %%
+# make sure each barcode is in each split
+for split in ["train", "val", "test"]:
+    assert set(barcodes) == set(sample[sample["split"] == split]["barcode"].unique())
+
+# %%
+sample.to_csv("/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark_nontargeting_barcode.csv", index=False)
+
+
+# %%
diff --git a/notebooks/nontargeting_experiments/make_nontargeting_benchmark_barcode_with_cct2.py b/notebooks/nontargeting_experiments/make_nontargeting_benchmark_barcode_with_cct2.py
new file mode 100644
index 0000000..45ee7cb
--- /dev/null
+++ b/notebooks/nontargeting_experiments/make_nontargeting_benchmark_barcode_with_cct2.py
@@ -0,0 +1,29 @@
+
+# %% Make an intermediate dataset
+# This includes *only* a subset of barcodes that are nontargeting and *all* barcodes that are CCT2
+import pandas as pd
+import numpy as np
+
+# %%
+location = "/mnt/efs/dlmbl/G-et/csv/dataset_split_1168.csv"
+nontargeting_location = "/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark_nontargeting_barcode.csv" 
+
+metadata = pd.read_csv(location)
+nontargeting_metadata = pd.read_csv(nontargeting_location)
+# %%
+cct2 = metadata[metadata['gene'] == "CCT2"]
+# %%
+sample = pd.concat([nontargeting_metadata, cct2])
+sample["split"].value_counts()
+# %%
+barcodes = sample["barcode"].sort_values().unique()
+genes = sample["gene"].sort_values().unique()
+# %%
+# make sure each barcode is in each split
+for split in ["train", "val", "test"]:
+    assert set(barcodes) == set(sample[sample["split"] == split]["barcode"].unique())
+
+# %%
+sample.to_csv("/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark_nontargeting_barcode_with_cct2.csv", index=False)
+
+# %%
diff --git a/notebooks/nontargeting_experiments/make_nontargeting_dataset.py b/notebooks/nontargeting_experiments/make_nontargeting_dataset.py
new file mode 100644
index 0000000..602b8af
--- /dev/null
+++ b/notebooks/nontargeting_experiments/make_nontargeting_dataset.py
@@ -0,0 +1,17 @@
+
+# %% Make an intermediate dataset
+import pandas as pd
+
+location = "/mnt/efs/dlmbl/G-et/csv/dataset_split_1168.csv"
+
+metadata = pd.read_csv(location)
+
+# %%
+assert "nontargeting" in metadata['gene'].values
+# %% Keep only the nontargeting and CCT2 genes
+sample = metadata[metadata['gene'] == "nontargeting"]
+
+# %%
+sample.to_csv("/mnt/efs/dlmbl/G-et/csv/dataset_split_nontargeting.csv", index=False)
+
+# %%
diff --git a/notebooks/nontargeting_experiments/visualize_latent.py b/notebooks/nontargeting_experiments/visualize_latent.py
new file mode 100644
index 0000000..98427a5
--- /dev/null
+++ b/notebooks/nontargeting_experiments/visualize_latent.py
@@ -0,0 +1,211 @@
+# %%
+# An attempt to create a reactive app to plot the latent space
+from dash import Dash, html, dcc, Output, Input, no_update
+import plotly.express as px
+import pandas as pd
+import numpy as np
+from embed_time.dataset_static import ZarrCellDataset
+from torchvision.transforms import v2
+from embed_time.static_utils import read_config
+import numpy as np
+from sklearn.decomposition import PCA
+import base64
+import io
+from PIL import Image
+
+# %% Load the dataset
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+csv_file = f"/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark.csv"
+label_type = 'gene'
+balance_classes = True
+
+latents_file = '/mnt/efs/dlmbl/G-et/example_latents/val_3_latent_vectors.csv'
+
+df = pd.read_csv(latents_file)
+ordered_df = df.sort_values(by=["gene", "barcode", "stage", "cell_idx",])
+labels = ordered_df[label_type].tolist()
+class_names = ordered_df[label_type].sort_values().unique().tolist()
+num_classes = len(class_names)
+
+data_df = pd.read_csv(csv_file)
+data_df = data_df[data_df['split'] == 'val'].reset_index()
+
+# %% Run pca
+pca = PCA(n_components=2)
+latent_columns = [c for c in ordered_df.columns if 'latent' in c]
+
+data = pca.fit_transform(ordered_df[latent_columns])
+
+metadata_options = ['gene', 'barcode', 'stage'] 
+
+# %% Load the training dataset
+# Create the dataset
+dataset_mean, dataset_std = read_config(yaml_file_path)
+dataset = ZarrCellDataset(
+    parent_dir = '/mnt/efs/dlmbl/S-md/',
+    csv_file = csv_file, 
+    split='val',
+    channels=[0, 1, 2, 3], 
+    mask='min', 
+    normalizations=normalizations,
+    interpolations=None, 
+    mean=dataset_mean, 
+    std=dataset_std
+)
+
+def renorm(image):
+    """
+    Turns 4 channel, channel-first tensor from the dataset into a single channel, channel-last numpy array
+    """
+    im = image.cpu().numpy() 
+    return (im - im.min()) / (im.max() - im.min())
+
+def encode_image(image_array):
+    """
+    Encodes a numpy array as a base64 string
+    """
+    image = Image.fromarray((renorm(image_array)*255).clip(0, 255).astype(np.uint8))  # Normalize from [-1, 1] to [0, 255]
+    buffered = io.BytesIO()
+    image.save(buffered, format="PNG")
+    return base64.b64encode(buffered.getvalue()).decode()
+
+
+df = pd.DataFrame(data, columns=['pc0', 'pc1'])
+for opt in metadata_options:
+    df[opt] = ordered_df[opt].tolist()
+
+app = Dash()
+app.layout = html.Div([
+    html.H1(children='Latent Space Visualization', style={'textAlign': 'center'}),
+    html.Div([
+        # html.Div([
+        #     dcc.Dropdown(
+        #         id='channel-dropdown',
+        #         options=[{'label': f'Channel {i}', 'value': i} for i in range(4)],
+        #         value=0,
+        #         clearable=False,
+        #         style={'width': '150px'}
+        #     ),
+        #     dcc.Graph(
+        #         id='image',
+        #         figure=px.imshow(
+        #             renorm(dataset[0]["cell_image"][0]),
+        #             color_continuous_scale='gray'
+        #         ).update_layout(coloraxis_showscale=False, xaxis_visible=False, yaxis_visible=False),
+        #         style={'width': '400px', 'height': '400px'}
+        #     ),
+        # ], style={'display': 'flex', 'flexDirection': 'column', 'alignItems': 'center'}),
+        html.Div([
+                dcc.Dropdown(
+                    id='color-dropdown',
+                    options=[{'label': f'{label}', 'value': label} for label in metadata_options],
+                    value="gene",
+                    clearable=False,
+                    style={'width': '150px'}
+                ),
+                dcc.Graph(
+                    id='latent-space',
+                    figure=px.scatter(df, x='pc0', y='pc1', color='gene'),
+                    style={'width': '1200px', 'height': '600px'}
+                ), 
+                dcc.Tooltip(id='latent-space-tooltip')
+            ]
+        )
+    ], style={'display': 'flex', 'flexDirection': 'row'})
+])
+
+# @app.callback(
+#     Output('image', 'figure'),
+#     [Input('latent-space', 'clickData'), Input('channel-dropdown', 'value')]
+# )
+# def update_image(clickData, channel_index):
+#     if clickData is None:
+#         return no_update
+#         # return px.imshow(
+#         #     turn_into_rgb(dataset[0]["cell_image"], channel_index),
+#         #     color_continuous_scale='gray'
+#         # ).update_layout(coloraxis_showscale=False, xaxis_visible=False, yaxis_visible=False)
+
+#     print(clickData) 
+#     point_index = clickData['points'][0]['pointIndex']
+#     # Get the sample in the ordered_df
+#     row = ordered_df.iloc[point_index]
+#     # Find the corresponding row in the data_df
+#     other_row = data_df[
+#         (data_df['gene'] == row['gene']) &
+#         (data_df['barcode'] == row['barcode']) &
+#         (data_df['stage'] == row['stage']) &
+#         (data_df['cell_idx'] == row['cell_idx'])
+#     ]
+#     point_index = other_row.index[0]
+#     return px.imshow(
+#         renorm(dataset[point_index]["cell_image"][channel_index]),
+#         color_continuous_scale='gray'
+#     ).update_layout(coloraxis_showscale=False, xaxis_visible=False, yaxis_visible=False)
+
+@app.callback(
+    Output("latent-space-tooltip", "show"),
+    Output("latent-space-tooltip", "bbox"),
+    Output("latent-space-tooltip", "children"),
+    Input("latent-space", "hoverData"),
+)
+def display_hover(hoverData):
+    if hoverData is None or not hoverData["points"]:
+        return False, no_update, no_update
+
+    # demo only shows the first point, but other points may also be available
+    hover_data = hoverData["points"][0]
+    bbox = hover_data["bbox"]
+    point_index = hover_data["pointNumber"]
+
+    # point_index = clickData['points'][0]['pointIndex']
+    # Get the sample in the ordered_df
+    row = ordered_df.iloc[point_index]
+    # Find the corresponding row in the data_df
+    other_row = data_df[
+        (data_df['gene'] == row['gene']) &
+        (data_df['barcode'] == row['barcode']) &
+        (data_df['stage'] == row['stage']) &
+        (data_df['cell_idx'] == row['cell_idx'])
+    ]
+    
+    channel_index = 0
+    point_index = other_row.index[0]
+
+    images = [dataset[point_index]["cell_image"][i] for i in range(4)]
+    encoded_images = [encode_image(image) for image in images]
+
+    children = [
+        html.Div([
+            html.Div([
+                html.Img(src=f'data:image/png;base64,{encoded_images[i]}', style={'width': '100px', 'height': '100px'}),
+                html.P(f'Channel {i}')
+            ], style={'display': 'flex', 'flexDirection': 'column', 'alignItems': 'center'}) for i in range(4)
+        ], style={'display': 'flex', 'flexDirection': 'row', 'justifyContent': 'center'}),
+        html.P(f'Gene: {row["gene"]}'),
+        html.P(f'Barcode: {row["barcode"]}'),
+        html.P(f'Stage: {row["stage"]}'),
+        html.P(f'Cell Index: {row["cell_idx"]}')
+    ]
+
+    return True, bbox, children
+
+# Callback to change what we color the latent space points by
+@app.callback(
+    Output('latent-space', 'figure'),
+    [Input('color-dropdown', 'value')]
+)
+def update_latent_space(value):
+    return px.scatter(df, x='pc0', y='pc1', color=value)
+
+
+
+if __name__ == '__main__':
+    app.run(debug=True)
+
+# %%
diff --git a/notebooks/restructure.py b/notebooks/restructure.py
new file mode 100644
index 0000000..f809be2
--- /dev/null
+++ b/notebooks/restructure.py
@@ -0,0 +1,104 @@
+
+from iohub.ngff import open_ome_zarr
+from iohub.ngff_meta import TransformationMeta
+import numpy as np
+from natsort import natsorted
+from glob import glob
+import click
+from pathlib import Path
+from tqdm import tqdm
+
+
+
+sample_dir = '/hpc/projects/jacobo_group/iSim_processed_files/hair_cell_classification/training_data_DL_MBL/'
+# defines input zarr file name with the zarr file structure
+sample_zarr_file = 'celltype_classifier_data_pyramid.zarr/*/*/*'
+# generates a list of paths to the zarr files that match the specified zarr file structure
+position_paths = natsorted(glob(sample_dir + sample_zarr_file))
+output_zarr_file = 'structured_celltype_classifier_data_pyramid.zarr'
+# constructs the full path for the output zarr file
+output_path = sample_dir + output_zarr_file
+output_path = Path(output_path)
+
+"""Create an empty zarr store mirroring another store"""
+DTYPE = np.float32
+MAX_CHUNK_SIZE = 500e6  # in bytes
+bytes_per_pixel = np.dtype(DTYPE).itemsize
+
+# Load the first position to infer dataset information
+input_dataset = open_ome_zarr(position_paths[0], mode="r")
+T, C, Z, Y, X = input_dataset.data.shape
+output_zyx_shape = (Z, Y, X)
+voxel_size = tuple(input_dataset.scale[-3:])
+click.echo("Creating empty array...")
+
+"""Create an empty zarr store mirroring another store"""
+
+
+# Handle transforms and metadata
+transform = TransformationMeta(
+    type="scale",
+    scale=2 * (1,) + voxel_size,
+)
+
+channel_names = input_dataset.channel_names
+
+# Output shape needed for the datloader
+output_shape = (1, len(channel_names)) + output_zyx_shape
+click.echo(f"Number of positions: {len(position_paths)}")
+click.echo(f"Output shape: {output_shape}")
+
+# Create output dataset
+output_dataset = open_ome_zarr(
+    output_path, layout="hcs", mode="w", channel_names=channel_names
+)
+
+chunk_zyx_shape = list(output_zyx_shape)
+    # chunk_zyx_shape[-3] > 1 ensures while loop will not stall if single
+    # XY image is larger than MAX_CHUNK_SIZE
+while (
+    chunk_zyx_shape[-3] > 1
+    and np.prod(chunk_zyx_shape) * bytes_per_pixel > MAX_CHUNK_SIZE
+):
+    chunk_zyx_shape[-3] = np.ceil(chunk_zyx_shape[-3] / 2).astype(int)
+chunk_zyx_shape = tuple(chunk_zyx_shape)
+
+chunk_size = 2 * (1,) + chunk_zyx_shape
+click.echo(f"Chunk size: {chunk_size}")
+# This takes care of the logic for single position or multiple position by wildcards
+
+for path in position_paths:
+    path_strings = Path(path).parts[-3:]
+    dataset = open_ome_zarr(path)
+    for t_idx in range(T):
+        pos = output_dataset.create_position(str(path_strings[1]), str(t_idx), "0")
+        output_array = pos.create_zeros(
+            name="0",
+            shape=output_shape,
+            chunks=chunk_size,
+            dtype=DTYPE,
+            transform=[transform],
+        )
+input_dataset.close()
+output_dataset.close()
+
+
+total_iterations = len(position_paths) * T
+progress_bar = tqdm(total=total_iterations, desc="Processing")
+
+# Copy data from input to output in the dataloader format
+for path in position_paths:
+
+    path_strings = Path(path).parts[-3:]
+    dataset = open_ome_zarr(path)
+    
+    for t_idx in range(T):
+        output_dataset = open_ome_zarr(
+            output_path / str(path_strings[1]) / str(t_idx) / "0", mode="r+"
+        )
+        output_dataset.data[0,:,:,:,:] = dataset.data[t_idx,:,:,:,:]
+        progress_bar.update(1)  # Update the progress bar
+        output_dataset.close()
+    dataset.close()
+
+
diff --git a/notebooks/simclr_example.ipynb b/notebooks/simclr_example.ipynb
new file mode 100644
index 0000000..154d4b5
--- /dev/null
+++ b/notebooks/simclr_example.ipynb
@@ -0,0 +1,48 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "id": "6b572c20-a9ea-4f68-a14c-360f4ae96be6",
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import numpy as np\n",
+    "import pandas as pd\n",
+    "import shutil, time, os, requests, random, copy\n",
+    "\n",
+    "import torch\n",
+    "import torch.nn as nn\n",
+    "import torch.optim as optim\n",
+    "from torch.utils.data import Dataset, DataLoader\n",
+    "from torchvision import datasets, transforms, models\n",
+    "\n",
+    "import matplotlib.pyplot as plt\n",
+    "%matplotlib inline\n",
+    "\n",
+    "from sklearn.manifold import TSNE"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python [conda env:embed_time]",
+   "language": "python",
+   "name": "conda-env-embed_time-py"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 3
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython3",
+   "version": "3.11.9"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 5
+}
diff --git a/notebooks/test_neuromast_3D.ipynb b/notebooks/test_neuromast_3D.ipynb
new file mode 100644
index 0000000..d914914
--- /dev/null
+++ b/notebooks/test_neuromast_3D.ipynb
@@ -0,0 +1,347 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Calculated padded input size per channel: (2 x 2). Kernel size: (3 x 3). Kernel size can't be greater than actual input size",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[4], line 75\u001b[0m\n\u001b[1;32m     72\u001b[0m input_tensor \u001b[38;5;241m=\u001b[39m input_tensor\u001b[38;5;241m.\u001b[39munsqueeze(\u001b[38;5;241m0\u001b[39m)\n\u001b[1;32m     74\u001b[0m \u001b[38;5;66;03m# Pass the sample input through the model\u001b[39;00m\n\u001b[0;32m---> 75\u001b[0m output, mu, logvar \u001b[38;5;241m=\u001b[39m \u001b[43mmodel\u001b[49m\u001b[43m(\u001b[49m\u001b[43minput_tensor\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m     77\u001b[0m graph \u001b[38;5;241m=\u001b[39m draw_graph(model, input_tensor)\n\u001b[1;32m     78\u001b[0m graph\u001b[38;5;241m.\u001b[39mvisual_graph\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/embed_time/src/embed_time/model_VAE_resnet18.py:147\u001b[0m, in \u001b[0;36mVAEResNet18.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m    146\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m--> 147\u001b[0m     mu, log_var \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mencoder\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m    148\u001b[0m     z \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mreparameterize(mu, log_var)\n\u001b[1;32m    149\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdecoder(z)\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/embed_time/src/embed_time/model_VAE_resnet18.py:97\u001b[0m, in \u001b[0;36mResNet18Enc.forward\u001b[0;34m(self, x)\u001b[0m\n\u001b[1;32m     96\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, x):\n\u001b[0;32m---> 97\u001b[0m     x \u001b[38;5;241m=\u001b[39m torch\u001b[38;5;241m.\u001b[39mrelu(\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mbn1(\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv1\u001b[49m\u001b[43m(\u001b[49m\u001b[43mx\u001b[49m\u001b[43m)\u001b[49m))\n\u001b[1;32m     98\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer1(x)\n\u001b[1;32m     99\u001b[0m     x \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mlayer2(x)\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/module.py:1553\u001b[0m, in \u001b[0;36mModule._wrapped_call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1551\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_compiled_call_impl(\u001b[38;5;241m*\u001b[39margs, \u001b[38;5;241m*\u001b[39m\u001b[38;5;241m*\u001b[39mkwargs)  \u001b[38;5;66;03m# type: ignore[misc]\u001b[39;00m\n\u001b[1;32m   1552\u001b[0m \u001b[38;5;28;01melse\u001b[39;00m:\n\u001b[0;32m-> 1553\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_call_impl\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/module.py:1562\u001b[0m, in \u001b[0;36mModule._call_impl\u001b[0;34m(self, *args, **kwargs)\u001b[0m\n\u001b[1;32m   1557\u001b[0m \u001b[38;5;66;03m# If we don't have any hooks, we want to skip the rest of the logic in\u001b[39;00m\n\u001b[1;32m   1558\u001b[0m \u001b[38;5;66;03m# this function, and just call forward.\u001b[39;00m\n\u001b[1;32m   1559\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;129;01mnot\u001b[39;00m (\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_forward_pre_hooks\n\u001b[1;32m   1560\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_backward_pre_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_backward_hooks\n\u001b[1;32m   1561\u001b[0m         \u001b[38;5;129;01mor\u001b[39;00m _global_forward_hooks \u001b[38;5;129;01mor\u001b[39;00m _global_forward_pre_hooks):\n\u001b[0;32m-> 1562\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mforward_call\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;241;43m*\u001b[39;49m\u001b[43margs\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[43mkwargs\u001b[49m\u001b[43m)\u001b[49m\n\u001b[1;32m   1564\u001b[0m \u001b[38;5;28;01mtry\u001b[39;00m:\n\u001b[1;32m   1565\u001b[0m     result \u001b[38;5;241m=\u001b[39m \u001b[38;5;28;01mNone\u001b[39;00m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/conv.py:458\u001b[0m, in \u001b[0;36mConv2d.forward\u001b[0;34m(self, input)\u001b[0m\n\u001b[1;32m    457\u001b[0m \u001b[38;5;28;01mdef\u001b[39;00m \u001b[38;5;21mforward\u001b[39m(\u001b[38;5;28mself\u001b[39m, \u001b[38;5;28minput\u001b[39m: Tensor) \u001b[38;5;241m-\u001b[39m\u001b[38;5;241m>\u001b[39m Tensor:\n\u001b[0;32m--> 458\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43m_conv_forward\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;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[43mweight\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[43mbias\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/conv.py:454\u001b[0m, in \u001b[0;36mConv2d._conv_forward\u001b[0;34m(self, input, weight, bias)\u001b[0m\n\u001b[1;32m    450\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode \u001b[38;5;241m!=\u001b[39m \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mzeros\u001b[39m\u001b[38;5;124m'\u001b[39m:\n\u001b[1;32m    451\u001b[0m     \u001b[38;5;28;01mreturn\u001b[39;00m F\u001b[38;5;241m.\u001b[39mconv2d(F\u001b[38;5;241m.\u001b[39mpad(\u001b[38;5;28minput\u001b[39m, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m_reversed_padding_repeated_twice, mode\u001b[38;5;241m=\u001b[39m\u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mpadding_mode),\n\u001b[1;32m    452\u001b[0m                     weight, bias, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mstride,\n\u001b[1;32m    453\u001b[0m                     _pair(\u001b[38;5;241m0\u001b[39m), \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mdilation, \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39mgroups)\n\u001b[0;32m--> 454\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m \u001b[43mF\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mconv2d\u001b[49m\u001b[43m(\u001b[49m\u001b[38;5;28;43minput\u001b[39;49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mweight\u001b[49m\u001b[43m,\u001b[49m\u001b[43m \u001b[49m\u001b[43mbias\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[43mstride\u001b[49m\u001b[43m,\u001b[49m\n\u001b[1;32m    455\u001b[0m \u001b[43m                \u001b[49m\u001b[38;5;28;43mself\u001b[39;49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mpadding\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[43mdilation\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[43mgroups\u001b[49m\u001b[43m)\u001b[49m\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Calculated padded input size per channel: (2 x 2). Kernel size: (3 x 3). Kernel size can't be greater than actual input size"
+     ]
+    }
+   ],
+   "source": [
+    "import os\n",
+    "\n",
+    "from embed_time.model_VAE_resnet18 import VAEResNet18\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import functional as F\n",
+    "from torch.nn import utils as U\n",
+    "from torch import optim\n",
+    "from torchvision.transforms import v2\n",
+    "import matplotlib.pyplot as plt\n",
+    "import subprocess\n",
+    "import pandas as pd\n",
+    "import numpy as np\n",
+    "from torch.utils.tensorboard import SummaryWriter\n",
+    "from datetime import datetime\n",
+    "import yaml\n",
+    "from datasets.neuromast import NeuromastDatasetTrain, NeuromastDatasetTest, NeuromastDatasetTrain_T10\n",
+    "from torchview import draw_graph\n",
+    "#%%\n",
+    "\n",
+    "\n",
+    "beta = 1e-4\n",
+    "lr = 1e-3\n",
+    "z_dim = 22\n",
+    "model_name = \"neuromast_resnet18_vae_conv2D\"\n",
+    "run_name= \"z_dim-\"+str(z_dim)+\"_lr-\"+str(lr)+\"_beta-\"+str(beta)\n",
+    "metadata = pd.read_csv(\"/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_balanced_train.csv\")\n",
+    "\n",
+    "if torch.cuda.is_available():\n",
+    "    device = torch.device(\"cuda\")\n",
+    "else:\n",
+    "    device = torch.device(\"cpu\")\n",
+    "\n",
+    "#launch tensorboard\n",
+    "\n",
+    "\n",
+    "\n",
+    "#%% Generate Dataset\n",
+    "dataset = NeuromastDatasetTrain_T10()\n",
+    "\n",
+    "#dataloader\n",
+    "train_loader = DataLoader(dataset, batch_size=1, shuffle=True, num_workers=8)\n",
+    "\n",
+    "# Initiate VAE-ResNet18 model\n",
+    "vae = VAEResNet18(nc = 1, z_dim = z_dim ).to(device)\n",
+    "\n",
+    "#%% Define Optimizar\n",
+    "optimizer = torch.optim.AdamW(vae.parameters(), lr=lr)\n",
+    "\n",
+    "#%% Define loss function\n",
+    "def loss_function(recon_x, x, mu, logvar):\n",
+    "    MSE = F.mse_loss(recon_x, x, reduction='mean')\n",
+    "    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())\n",
+    "    return MSE, KLD   \n",
+    "import torch\n",
+    "from torchviz import make_dot\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "# Assuming your VAEResNet18_3D model is defined as `VAEResNet18_3D`\n",
+    "# Initialize the model\n",
+    "model = VAEResNet18(nc=1, z_dim=10)\n",
+    "\n",
+    "#import torch\n",
+    "from torchviz import make_dot\n",
+    "import torch.nn.functional as F\n",
+    "\n",
+    "# Assuming your VAEResNet18_3D model is defined as `VAEResNet18_3D`\n",
+    "# Initialize the model\n",
+    "model = VAEResNet18(nc=1, z_dim=22)\n",
+    "\n",
+    "# Create a sample input tensor with shape (1, 1, 64, 256, 256)\n",
+    "input_tensor,label = dataset[11] # Adjust nc=1 for single channel input\n",
+    "input_tensor = torch.from_numpy(input_tensor)\n",
+    "input_tensor = input_tensor.unsqueeze(0)\n",
+    "\n",
+    "# Pass the sample input through the model\n",
+    "output, mu, logvar = model(input_tensor)\n",
+    "\n",
+    "graph = draw_graph(model, input_tensor)\n",
+    "graph.visual_graph"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 6,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from iohub.ngff import open_ome_zarr\n",
+    "from natsort import natsorted\n",
+    "from glob import glob\n",
+    "from pathlib import Path \n",
+    "import torch\n",
+    "from torch.utils.data import Dataset\n",
+    "from scipy.ndimage import measurements\n",
+    "from scipy.ndimage import center_of_mass\n",
+    "import numpy as np\n",
+    "import matplotlib.pyplot as plt\n",
+    "import pandas as pd"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 7,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/structured_celltype_classifier_data.zarr/0/0/0\n",
+      "(1, 4, 73, 1024, 1024)\n"
+     ]
+    }
+   ],
+   "source": [
+    "file_path = \"/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/\"\n",
+    "zarr_file = 'structured_celltype_classifier_data.zarr/*/*/*'\n",
+    "position_paths = natsorted(glob(file_path + zarr_file))\n",
+    "position_paths = position_paths[:500]\n",
+    "cell_count = 40 # number of cells to sample from each timepoint\n",
+    "print(position_paths[0])\n",
+    "print(open_ome_zarr(position_paths[0]).data. shape)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 8,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "1024\n"
+     ]
+    }
+   ],
+   "source": [
+    "shape = open_ome_zarr(position_paths[0]).data.shape\n",
+    "print(shape[3])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 9,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "crop_size:  [48, 256, 256]\n",
+      "centroid_z:  27 centroid_y:  427 centroid_x:  420\n",
+      "label:  19\n",
+      "timepoint:  0\n",
+      "mid_z:  28\n",
+      "y_min:  299 y_max:  555\n",
+      "x_min:  292 x_max:  548\n",
+      "(1, 256, 256)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Find the maximum range across all dimensions\n",
+    "max_x_range = 256\n",
+    "max_y_range = 256\n",
+    "max_z_range = 48 # not used for cropping\n",
+    "\n",
+    "crop_size = [max_z_range, max_y_range, max_x_range]\n",
+    "print(\"crop_size: \", crop_size)\n",
+    "\n",
+    "row = metadata.iloc[0]\n",
+    "# Get centroid coordinates\n",
+    "centroid_z = int(row['Centroid_Z'])\n",
+    "centroid_y = int(row['Centroid_Y'])\n",
+    "centroid_x = int(row['Centroid_X'])\n",
+    "print(\"centroid_z: \", centroid_z, \"centroid_y: \", centroid_y, \"centroid_x: \", centroid_x)\n",
+    "\n",
+    "#get the label number\n",
+    "label = int(row['Label'])\n",
+    "print(\"label: \", label)\n",
+    "timepoint = int(row['T_value'])\n",
+    "print(\"timepoint: \", timepoint)\n",
+    "\n",
+    "# Compute the cropping box boundaries\n",
+    "z_min = int(row['Z_min'])\n",
+    "z_max = int(row['Z_max'])\n",
+    "y_min = int(max((int(centroid_y - crop_size[1] // 2)),0))\n",
+    "y_max = int(min((int(centroid_y + crop_size[1] // 2)), shape[3]-1))\n",
+    "x_min = int(max((int(centroid_x - crop_size[2] // 2)), 0))\n",
+    "x_max = int(min((int(centroid_x + crop_size[2] // 2)), shape[4]-1))\n",
+    "\n",
+    "mid_z = (z_min + z_max) // 2\n",
+    "print(\"mid_z: \", mid_z)\n",
+    "print(\"y_min: \", y_min, \"y_max: \", y_max)\n",
+    "print(\"x_min: \", x_min, \"x_max: \", x_max)\n",
+    "\n",
+    "# Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])\n",
+    "dataset = open_ome_zarr(position_paths[timepoint], mode=\"r\")\n",
+    "image = dataset.data[0,0:1,mid_z,y_min:y_max, x_min:x_max]\n",
+    "print(image.shape)\n",
+    " segmented_data = dataset.data[0,2:3,mid_z,y_min:y_max, x_min:x_max] #segmention masks\n",
+    "        # celltypes = dataset.data[0,3:,:,:,:]\n",
+    "        # Get a binary mask of the current segment\n",
+    "        segment_mask = segmented_data == label\n",
+    "        \n",
+    "\n",
+    "        # Find the unique label numbers in the celltypes image for this segment\n",
+    "        cell_type = int(row['Cell_Type'])\n",
+    "        cropped_image=np.where(segment_mask, image, 0)\n",
+    "        \n",
+    "        \n",
+    "        return cropped_image, cell_type"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "TensorBoard started at http://localhost:39227. \n",
+      "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL.\n"
+     ]
+    },
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "TensorFlow installation not found - running with reduced feature set.\n",
+      "\n",
+      "NOTE: Using experimental fast data loading logic. To disable, pass\n",
+      "    \"--load_fast=false\" and report issues on GitHub. More details:\n",
+      "    https://github.com/tensorflow/tensorboard/issues/4784\n",
+      "\n",
+      "Serving TensorBoard on localhost; to expose to the network, use a proxy or pass --bind_all\n",
+      "TensorBoard 2.17.1 at http://localhost:39227/ (Press CTRL+C to quit)\n"
+     ]
+    }
+   ],
+   "source": [
+    "# Function to find an available port\n",
+    "def find_free_port():\n",
+    "    import socket\n",
+    "\n",
+    "    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:\n",
+    "        s.bind((\"\", 0))\n",
+    "        return s.getsockname()[1]\n",
+    "\n",
+    "\n",
+    "# Launch TensorBoard on the browser\n",
+    "def launch_tensorboard(log_dir):\n",
+    "    port = find_free_port()\n",
+    "    tensorboard_cmd = f\"tensorboard --logdir={log_dir} --port={port}\"\n",
+    "    process = subprocess.Popen(tensorboard_cmd, shell=True)\n",
+    "    print(\n",
+    "        f\"TensorBoard started at http://localhost:{port}. \\n\"\n",
+    "        \"If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL.\"\n",
+    "    )\n",
+    "    return process\n",
+    "\n",
+    "# Launch tensorboard and click on the link to view the logs.\n",
+    "\n",
+    "tensorboard_process = launch_tensorboard(\"embed_time_static_runs\")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 13,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "153"
+      ]
+     },
+     "execution_count": 13,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataset_train = NeuromastDatasetTrain_T10()\n",
+    "\n",
+    "dataloader_train = DataLoader(dataset_train, batch_size=2, shuffle=True, num_workers=8)\n",
+    "len(dataloader_train.dataset)\n",
+    "len(dataloader_train)"
+   ]
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/test_resnet.ipynb b/notebooks/test_resnet.ipynb
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diff --git a/notebooks/time_series_subgroup/exploring_resnet18_as_encoder.ipynb b/notebooks/time_series_subgroup/exploring_resnet18_as_encoder.ipynb
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diff --git a/notebooks/time_series_subgroup/investigate_model.ipynb b/notebooks/time_series_subgroup/investigate_model.ipynb
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+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "/mnt/efs/dlmbl/G-et/checkpoints/time-series/2024-08-31_UNEt_encdec_checkpoints\n",
+      "2\n",
+      "(576, 576)\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "VAE(\n",
+       "  (encoder): UNetEncoder(\n",
+       "    (downsample): MaxPool2d(kernel_size=2, stride=2, padding=0, dilation=1, ceil_mode=False)\n",
+       "    (convs): ModuleList(\n",
+       "      (0): ConvBlock(\n",
+       "        (conv_pass): Sequential(\n",
+       "          (0): Conv2d(2, 8, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (1): ReLU()\n",
+       "          (2): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (3): ReLU()\n",
+       "        )\n",
+       "      )\n",
+       "      (1): ConvBlock(\n",
+       "        (conv_pass): Sequential(\n",
+       "          (0): Conv2d(8, 16, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (1): ReLU()\n",
+       "          (2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (3): ReLU()\n",
+       "        )\n",
+       "      )\n",
+       "      (2): ConvBlock(\n",
+       "        (conv_pass): Sequential(\n",
+       "          (0): Conv2d(16, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (1): ReLU()\n",
+       "          (2): Conv2d(32, 32, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (3): ReLU()\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (fc1): Linear(in_features=663552, out_features=20, bias=True)\n",
+       "    (fc2): Linear(in_features=663552, out_features=20, bias=True)\n",
+       "  )\n",
+       "  (decoder): UNetDecoder(\n",
+       "    (upsample): Upsample(scale_factor=2.0, mode='nearest')\n",
+       "    (convs): ModuleList(\n",
+       "      (0): ConvBlock(\n",
+       "        (conv_pass): Sequential(\n",
+       "          (0): Conv2d(8, 2, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (1): ReLU()\n",
+       "          (2): Conv2d(2, 2, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (3): ReLU()\n",
+       "        )\n",
+       "      )\n",
+       "      (1): ConvBlock(\n",
+       "        (conv_pass): Sequential(\n",
+       "          (0): Conv2d(16, 8, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (1): ReLU()\n",
+       "          (2): Conv2d(8, 8, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (3): ReLU()\n",
+       "        )\n",
+       "      )\n",
+       "      (2): ConvBlock(\n",
+       "        (conv_pass): Sequential(\n",
+       "          (0): Conv2d(32, 16, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (1): ReLU()\n",
+       "          (2): Conv2d(16, 16, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "          (3): ReLU()\n",
+       "        )\n",
+       "      )\n",
+       "    )\n",
+       "    (fc1): Linear(in_features=20, out_features=663552, bias=True)\n",
+       "    (final_conv): Sequential(\n",
+       "      (0): Conv2d(8, 2, kernel_size=(3, 3), stride=(1, 1), padding=same)\n",
+       "      (1): Sigmoid()\n",
+       "    )\n",
+       "  )\n",
+       ")"
+      ]
+     },
+     "execution_count": 1,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torch\n",
+    "import matplotlib.pyplot as plt\n",
+    "from embed_time.dataloader_rs import LiveTLSDataset\n",
+    "from embed_time.model import VAE\n",
+    "from embed_time.UNet_based_encoder_decoder import UNetDecoder, UNetEncoder\n",
+    "import torch\n",
+    "from torch.utils.data import DataLoader\n",
+    "from torch.nn import functional as F\n",
+    "from tqdm import tqdm\n",
+    "from pathlib import Path\n",
+    "import os\n",
+    "import skimage.io as io\n",
+    "import torchvision.transforms as trans\n",
+    "from torchvision.transforms import v2\n",
+    "from embed_time.transforms import CustomToTensor, SelectRandomTPNumpy, CustomCropCentroid\n",
+    "from embed_time.dataloader_rs import LiveTLSDataset\n",
+    "from datetime import datetime\n",
+    "\n",
+    "base_dir = \"/mnt/efs/dlmbl/G-et/checkpoints/time-series\"\n",
+    "checkpoint_dir = Path(base_dir) / f\"{datetime.today().strftime('%Y-%m-%d')}_UNEt_encdec_checkpoints\"\n",
+    "print(checkpoint_dir)\n",
+    "\n",
+    "checkpoint_dir.mkdir(exist_ok=True)\n",
+    "data_location = \"/mnt/efs/dlmbl/G-et/data/live-TLS\"\n",
+    "folder_imgs = data_location +\"/\"+'Control_Dataset_4TP_Normalized'\n",
+    "metadata = data_location + \"/\" +'Control_Dataset_4TP_Ground_Truth'\n",
+    "\n",
+    "loading_transforms_test = trans.Compose([\n",
+    "    SelectRandomTPNumpy(0),\n",
+    "    CustomCropCentroid(0,0,598),\n",
+    "    CustomToTensor(),\n",
+    "    v2.Resize((576,576)),\n",
+    "    #v2.RandomAffine(\n",
+    "    #    degrees=90,\n",
+    "    #    translate=[0.1,0.1],\n",
+    "    #),\n",
+    "    #v2.RandomHorizontalFlip(),\n",
+    "    #v2.RandomVerticalFlip(),\n",
+    "    #v2.GaussianNoise(0,0.05)\n",
+    "])\n",
+    "\n",
+    "dataset_w_t = LiveTLSDataset(\n",
+    "    metadata,\n",
+    "    folder_imgs,\n",
+    "    metadata_columns=[\"Run\",\"Plate\",\"ID\"],\n",
+    "    return_metadata=False,\n",
+    "    transform = loading_transforms_test,\n",
+    ")\n",
+    "\n",
+    "sample, label = dataset_w_t[0]\n",
+    "in_channels, y, x = sample.shape\n",
+    "print(in_channels)\n",
+    "print((y,x))\n",
+    "\n",
+    "NUM_EPOCHS = 50\n",
+    "encoder = UNetEncoder(\n",
+    "    in_channels = in_channels,\n",
+    "    n_fmaps = 8,\n",
+    "    depth = 3,\n",
+    "    in_spatial_shape = (y,x),\n",
+    "    z_dim = 20,\n",
+    ")\n",
+    "\n",
+    "decoder = UNetDecoder(\n",
+    "    in_channels = in_channels,\n",
+    "    n_fmaps = 8,\n",
+    "    depth = 3,\n",
+    "    in_spatial_shape = (y,x),\n",
+    "    z_dim = 20,\n",
+    "    upsample_mode=\"nearest\"\n",
+    ")\n",
+    "\n",
+    "model = VAE(encoder, decoder)\n",
+    "dataloader = DataLoader(dataset_w_t, batch_size=4, shuffle=True, pin_memory=True)\n",
+    "device = torch.device(\"cuda\" if torch.cuda.is_available() else \"cpu\")\n",
+    "model.to(device)\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['checkpoint_25.pth',\n",
+       " 'checkpoint_7.pth',\n",
+       " 'checkpoint_43.pth',\n",
+       " 'checkpoint_34.pth',\n",
+       " 'checkpoint_16.pth',\n",
+       " 'checkpoint_21.pth',\n",
+       " 'checkpoint_3.pth',\n",
+       " 'checkpoint_30.pth',\n",
+       " 'checkpoint_12.pth',\n",
+       " 'checkpoint_29.pth',\n",
+       " 'checkpoint_47.pth',\n",
+       " 'checkpoint_38.pth',\n",
+       " 'checkpoint_1.pth',\n",
+       " 'checkpoint_10.pth',\n",
+       " 'checkpoint_27.pth',\n",
+       " 'checkpoint_9.pth',\n",
+       " 'checkpoint_45.pth',\n",
+       " 'checkpoint_36.pth',\n",
+       " 'checkpoint_18.pth',\n",
+       " 'checkpoint_23.pth',\n",
+       " 'checkpoint_5.pth',\n",
+       " 'checkpoint_41.pth',\n",
+       " 'checkpoint_32.pth',\n",
+       " 'checkpoint_14.pth',\n",
+       " 'checkpoint_49.pth',\n",
+       " 'checkpoint_0.pth',\n",
+       " 'checkpoint_8.pth',\n",
+       " 'checkpoint_26.pth',\n",
+       " 'checkpoint_44.pth',\n",
+       " 'checkpoint_35.pth',\n",
+       " 'checkpoint_17.pth',\n",
+       " 'checkpoint_4.pth',\n",
+       " 'checkpoint_22.pth',\n",
+       " 'checkpoint_40.pth',\n",
+       " 'checkpoint_31.pth',\n",
+       " 'checkpoint_13.pth',\n",
+       " 'checkpoint_48.pth',\n",
+       " 'checkpoint_39.pth',\n",
+       " 'checkpoint_2.pth',\n",
+       " 'checkpoint_20.pth',\n",
+       " 'checkpoint_11.pth',\n",
+       " 'checkpoint_28.pth',\n",
+       " 'checkpoint_46.pth',\n",
+       " 'checkpoint_37.pth',\n",
+       " 'checkpoint_19.pth',\n",
+       " 'checkpoint_6.pth',\n",
+       " 'checkpoint_24.pth',\n",
+       " 'checkpoint_42.pth',\n",
+       " 'checkpoint_33.pth',\n",
+       " 'checkpoint_15.pth']"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "os.listdir(checkpoint_dir)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_106523/158696822.py:1: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  dict = torch.load(checkpoint_dir/'checkpoint_49.pth')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['model', 'optimizer', 'epoch'])"
+      ]
+     },
+     "execution_count": 4,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dict = torch.load(checkpoint_dir/'checkpoint_49.pth')\n",
+    "dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 5,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<All keys matched successfully>"
+      ]
+     },
+     "execution_count": 5,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model_params = dict['model']\n",
+    "model.load_state_dict(model_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 576, 576])"
+      ]
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "dataloader = DataLoader(dataset_w_t, batch_size=1, shuffle=False, pin_memory=True)\n",
+    "\n",
+    "for i,first in enumerate(dataloader):\n",
+    "    if i == 50:\n",
+    "        test_image = first[0]\n",
+    "        break\n",
+    "\n",
+    "test_image.squeeze(0).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 44,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x725a273f19f0>"
+      ]
+     },
+     "execution_count": 44,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 2 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "plot_size = 5\n",
+    "plot_images = 2\n",
+    "fig, ax = plt.subplots(1,plot_images,figsize=(plot_images*plot_size,plot_size))\n",
+    "\n",
+    "ax[0].imshow(test_image.squeeze(0).numpy()[0])\n",
+    "ax[1].imshow(test_image.squeeze(0).numpy()[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 45,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([1, 2, 576, 576])"
+      ]
+     },
+     "execution_count": 45,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "model.eval()\n",
+    "result = model(test_image.to(device))[0]\n",
+    "result.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 46,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 576, 576)"
+      ]
+     },
+     "execution_count": 46,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "result = result.detach().cpu().squeeze().numpy()\n",
+    "result.shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x725a25f9bbe0>"
+      ]
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x1000 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig, ax = plt.subplots(2,2,figsize=(2*plot_size,2*plot_size))\n",
+    "\n",
+    "ax[0,0].imshow(test_image.squeeze(0).numpy()[0])\n",
+    "ax[0,1].imshow(test_image.squeeze(0).numpy()[1])\n",
+    "ax[1,0].imshow(result[0])\n",
+    "ax[1,1].imshow(result[1])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 49,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_106523/647816853.py:2: FutureWarning: You are using `torch.load` with `weights_only=False` (the current default value), which uses the default pickle module implicitly. It is possible to construct malicious pickle data which will execute arbitrary code during unpickling (See https://github.com/pytorch/pytorch/blob/main/SECURITY.md#untrusted-models for more details). In a future release, the default value for `weights_only` will be flipped to `True`. This limits the functions that could be executed during unpickling. Arbitrary objects will no longer be allowed to be loaded via this mode unless they are explicitly allowlisted by the user via `torch.serialization.add_safe_globals`. We recommend you start setting `weights_only=True` for any use case where you don't have full control of the loaded file. Please open an issue on GitHub for any issues related to this experimental feature.\n",
+      "  dict = torch.load(Path(checkpoints_ian)/'checkpoint_49.pth')\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "dict_keys(['model', 'optimizer', 'epoch'])"
+      ]
+     },
+     "execution_count": 49,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "checkpoints_ian = '/mnt/efs/dlmbl/G-et/checkpoints/time-series/2024-08-31_UNEt_encdec_02_checkpoints'\n",
+    "dict = torch.load(Path(checkpoints_ian)/'checkpoint_49.pth')\n",
+    "dict.keys()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 50,
+   "metadata": {},
+   "outputs": [
+    {
+     "ename": "RuntimeError",
+     "evalue": "Error(s) in loading state_dict for VAE:\n\tUnexpected key(s) in state_dict: \"encoder.convs.3.conv_pass.0.weight\", \"encoder.convs.3.conv_pass.0.bias\", \"encoder.convs.3.conv_pass.2.weight\", \"encoder.convs.3.conv_pass.2.bias\", \"decoder.convs.3.conv_pass.0.weight\", \"decoder.convs.3.conv_pass.0.bias\", \"decoder.convs.3.conv_pass.2.weight\", \"decoder.convs.3.conv_pass.2.bias\". \n\tsize mismatch for encoder.convs.0.conv_pass.0.weight: copying a param with shape torch.Size([10, 2, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 2, 3, 3]).\n\tsize mismatch for encoder.convs.0.conv_pass.0.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for encoder.convs.0.conv_pass.2.weight: copying a param with shape torch.Size([10, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for encoder.convs.0.conv_pass.2.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for encoder.convs.1.conv_pass.0.weight: copying a param with shape torch.Size([20, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 8, 3, 3]).\n\tsize mismatch for encoder.convs.1.conv_pass.0.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for encoder.convs.1.conv_pass.2.weight: copying a param with shape torch.Size([20, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for encoder.convs.1.conv_pass.2.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for encoder.convs.2.conv_pass.0.weight: copying a param with shape torch.Size([40, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 16, 3, 3]).\n\tsize mismatch for encoder.convs.2.conv_pass.0.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for encoder.convs.2.conv_pass.2.weight: copying a param with shape torch.Size([40, 40, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for encoder.convs.2.conv_pass.2.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for encoder.fc1.weight: copying a param with shape torch.Size([25, 414720]) from checkpoint, the shape in current model is torch.Size([20, 663552]).\n\tsize mismatch for encoder.fc1.bias: copying a param with shape torch.Size([25]) from checkpoint, the shape in current model is torch.Size([20]).\n\tsize mismatch for encoder.fc2.weight: copying a param with shape torch.Size([25, 414720]) from checkpoint, the shape in current model is torch.Size([20, 663552]).\n\tsize mismatch for encoder.fc2.bias: copying a param with shape torch.Size([25]) from checkpoint, the shape in current model is torch.Size([20]).\n\tsize mismatch for decoder.convs.0.conv_pass.0.weight: copying a param with shape torch.Size([2, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([2, 8, 3, 3]).\n\tsize mismatch for decoder.convs.1.conv_pass.0.weight: copying a param with shape torch.Size([10, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 16, 3, 3]).\n\tsize mismatch for decoder.convs.1.conv_pass.0.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for decoder.convs.1.conv_pass.2.weight: copying a param with shape torch.Size([10, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for decoder.convs.1.conv_pass.2.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for decoder.convs.2.conv_pass.0.weight: copying a param with shape torch.Size([20, 40, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 32, 3, 3]).\n\tsize mismatch for decoder.convs.2.conv_pass.0.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for decoder.convs.2.conv_pass.2.weight: copying a param with shape torch.Size([20, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for decoder.convs.2.conv_pass.2.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for decoder.fc1.weight: copying a param with shape torch.Size([414720, 25]) from checkpoint, the shape in current model is torch.Size([663552, 20]).\n\tsize mismatch for decoder.fc1.bias: copying a param with shape torch.Size([414720]) from checkpoint, the shape in current model is torch.Size([663552]).\n\tsize mismatch for decoder.final_conv.0.weight: copying a param with shape torch.Size([2, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([2, 8, 3, 3]).",
+     "output_type": "error",
+     "traceback": [
+      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
+      "\u001b[0;31mRuntimeError\u001b[0m                              Traceback (most recent call last)",
+      "Cell \u001b[0;32mIn[50], line 2\u001b[0m\n\u001b[1;32m      1\u001b[0m model_params \u001b[38;5;241m=\u001b[39m \u001b[38;5;28mdict\u001b[39m[\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mmodel\u001b[39m\u001b[38;5;124m'\u001b[39m]\n\u001b[0;32m----> 2\u001b[0m \u001b[43mmodel\u001b[49m\u001b[38;5;241;43m.\u001b[39;49m\u001b[43mload_state_dict\u001b[49m\u001b[43m(\u001b[49m\u001b[43mmodel_params\u001b[49m\u001b[43m)\u001b[49m\n",
+      "File \u001b[0;32m~/miniforge3/envs/embed_time/lib/python3.10/site-packages/torch/nn/modules/module.py:2215\u001b[0m, in \u001b[0;36mModule.load_state_dict\u001b[0;34m(self, state_dict, strict, assign)\u001b[0m\n\u001b[1;32m   2210\u001b[0m         error_msgs\u001b[38;5;241m.\u001b[39minsert(\n\u001b[1;32m   2211\u001b[0m             \u001b[38;5;241m0\u001b[39m, \u001b[38;5;124m'\u001b[39m\u001b[38;5;124mMissing key(s) in state_dict: \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(\n\u001b[1;32m   2212\u001b[0m                 \u001b[38;5;124m'\u001b[39m\u001b[38;5;124m, \u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(\u001b[38;5;124mf\u001b[39m\u001b[38;5;124m'\u001b[39m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;132;01m{\u001b[39;00mk\u001b[38;5;132;01m}\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;124m'\u001b[39m \u001b[38;5;28;01mfor\u001b[39;00m k \u001b[38;5;129;01min\u001b[39;00m missing_keys)))\n\u001b[1;32m   2214\u001b[0m \u001b[38;5;28;01mif\u001b[39;00m \u001b[38;5;28mlen\u001b[39m(error_msgs) \u001b[38;5;241m>\u001b[39m \u001b[38;5;241m0\u001b[39m:\n\u001b[0;32m-> 2215\u001b[0m     \u001b[38;5;28;01mraise\u001b[39;00m \u001b[38;5;167;01mRuntimeError\u001b[39;00m(\u001b[38;5;124m'\u001b[39m\u001b[38;5;124mError(s) in loading state_dict for \u001b[39m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m:\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;132;01m{}\u001b[39;00m\u001b[38;5;124m'\u001b[39m\u001b[38;5;241m.\u001b[39mformat(\n\u001b[1;32m   2216\u001b[0m                        \u001b[38;5;28mself\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__class__\u001b[39m\u001b[38;5;241m.\u001b[39m\u001b[38;5;18m__name__\u001b[39m, \u001b[38;5;124m\"\u001b[39m\u001b[38;5;130;01m\\n\u001b[39;00m\u001b[38;5;130;01m\\t\u001b[39;00m\u001b[38;5;124m\"\u001b[39m\u001b[38;5;241m.\u001b[39mjoin(error_msgs)))\n\u001b[1;32m   2217\u001b[0m \u001b[38;5;28;01mreturn\u001b[39;00m _IncompatibleKeys(missing_keys, unexpected_keys)\n",
+      "\u001b[0;31mRuntimeError\u001b[0m: Error(s) in loading state_dict for VAE:\n\tUnexpected key(s) in state_dict: \"encoder.convs.3.conv_pass.0.weight\", \"encoder.convs.3.conv_pass.0.bias\", \"encoder.convs.3.conv_pass.2.weight\", \"encoder.convs.3.conv_pass.2.bias\", \"decoder.convs.3.conv_pass.0.weight\", \"decoder.convs.3.conv_pass.0.bias\", \"decoder.convs.3.conv_pass.2.weight\", \"decoder.convs.3.conv_pass.2.bias\". \n\tsize mismatch for encoder.convs.0.conv_pass.0.weight: copying a param with shape torch.Size([10, 2, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 2, 3, 3]).\n\tsize mismatch for encoder.convs.0.conv_pass.0.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for encoder.convs.0.conv_pass.2.weight: copying a param with shape torch.Size([10, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for encoder.convs.0.conv_pass.2.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for encoder.convs.1.conv_pass.0.weight: copying a param with shape torch.Size([20, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 8, 3, 3]).\n\tsize mismatch for encoder.convs.1.conv_pass.0.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for encoder.convs.1.conv_pass.2.weight: copying a param with shape torch.Size([20, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for encoder.convs.1.conv_pass.2.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for encoder.convs.2.conv_pass.0.weight: copying a param with shape torch.Size([40, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 16, 3, 3]).\n\tsize mismatch for encoder.convs.2.conv_pass.0.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for encoder.convs.2.conv_pass.2.weight: copying a param with shape torch.Size([40, 40, 3, 3]) from checkpoint, the shape in current model is torch.Size([32, 32, 3, 3]).\n\tsize mismatch for encoder.convs.2.conv_pass.2.bias: copying a param with shape torch.Size([40]) from checkpoint, the shape in current model is torch.Size([32]).\n\tsize mismatch for encoder.fc1.weight: copying a param with shape torch.Size([25, 414720]) from checkpoint, the shape in current model is torch.Size([20, 663552]).\n\tsize mismatch for encoder.fc1.bias: copying a param with shape torch.Size([25]) from checkpoint, the shape in current model is torch.Size([20]).\n\tsize mismatch for encoder.fc2.weight: copying a param with shape torch.Size([25, 414720]) from checkpoint, the shape in current model is torch.Size([20, 663552]).\n\tsize mismatch for encoder.fc2.bias: copying a param with shape torch.Size([25]) from checkpoint, the shape in current model is torch.Size([20]).\n\tsize mismatch for decoder.convs.0.conv_pass.0.weight: copying a param with shape torch.Size([2, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([2, 8, 3, 3]).\n\tsize mismatch for decoder.convs.1.conv_pass.0.weight: copying a param with shape torch.Size([10, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 16, 3, 3]).\n\tsize mismatch for decoder.convs.1.conv_pass.0.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for decoder.convs.1.conv_pass.2.weight: copying a param with shape torch.Size([10, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([8, 8, 3, 3]).\n\tsize mismatch for decoder.convs.1.conv_pass.2.bias: copying a param with shape torch.Size([10]) from checkpoint, the shape in current model is torch.Size([8]).\n\tsize mismatch for decoder.convs.2.conv_pass.0.weight: copying a param with shape torch.Size([20, 40, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 32, 3, 3]).\n\tsize mismatch for decoder.convs.2.conv_pass.0.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for decoder.convs.2.conv_pass.2.weight: copying a param with shape torch.Size([20, 20, 3, 3]) from checkpoint, the shape in current model is torch.Size([16, 16, 3, 3]).\n\tsize mismatch for decoder.convs.2.conv_pass.2.bias: copying a param with shape torch.Size([20]) from checkpoint, the shape in current model is torch.Size([16]).\n\tsize mismatch for decoder.fc1.weight: copying a param with shape torch.Size([414720, 25]) from checkpoint, the shape in current model is torch.Size([663552, 20]).\n\tsize mismatch for decoder.fc1.bias: copying a param with shape torch.Size([414720]) from checkpoint, the shape in current model is torch.Size([663552]).\n\tsize mismatch for decoder.final_conv.0.weight: copying a param with shape torch.Size([2, 10, 3, 3]) from checkpoint, the shape in current model is torch.Size([2, 8, 3, 3])."
+     ]
+    }
+   ],
+   "source": [
+    "model_params = dict['model']\n",
+    "model.load_state_dict(model_params)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/time_series_subgroup/normalize entire dataset.ipynb b/notebooks/time_series_subgroup/normalize entire dataset.ipynb
new file mode 100644
index 0000000..103cd0e
--- /dev/null
+++ b/notebooks/time_series_subgroup/normalize entire dataset.ipynb	
@@ -0,0 +1,531 @@
+{
+ "cells": [
+  {
+   "cell_type": "markdown",
+   "metadata": {},
+   "source": [
+    "# Normalization Notebook\n",
+    "In this notebook I normalize the whole dataset so that we do not need to do it on the fly when training the model\n",
+    "Below I also test some of the transforms that were generated"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 14,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "['CTRLD_TR_PLATE_2_ID_G9.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A2.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H6.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H3.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_F2.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_D4.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H10.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_B7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_F12.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B6.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_F3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B9.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_D5.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_G11.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_B6.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F5.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C12.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G1.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E3.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C3.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E12.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C6.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C5.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_E6.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_D11.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E1.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E4.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C8.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F11.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A5.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_A8.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_E12.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_E3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G4.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D12.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_D10.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_B3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B2.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B5.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F1.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_A12.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B11.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_C10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C1.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C4.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A12.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_H4.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_F4.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F6.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H4.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_D9.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H2.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E7.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G5.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_G12.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_G5.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G8.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_C9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D10.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B3.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_A11.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C11.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A4.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_A10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C2.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_B6.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H5.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_D6.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D9.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_F2.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H2.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F4.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H3.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D6.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_B8.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C5.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_E8.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H11.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_A9.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E12.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E6.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A9.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G12.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G6.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_H11.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A7.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_B2.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H10.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_B5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B4.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D5.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_F10.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F12.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_D3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E11.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_B4.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D2.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G11.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C1.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E10.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A8.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C4.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C10.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_A5.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_E4.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D11.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_C12.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A5.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C6.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E2.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C12.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_B11.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_E1.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_D11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_A6.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H9.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_F8.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_F9.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_D8.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F8.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H6.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H7.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C9.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E9.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G7.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_G7.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_E9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B11.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C10.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B2.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_F9.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_H9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A3.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H7.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_F1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H4.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_D5.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_B8.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F6.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H2.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_B7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_G12.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F3.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_D3.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C6.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_G3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_E7.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H10.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E11.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G2.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E4.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C7.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G5.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A6.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_D12.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E2.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E5.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C9.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G11.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D4.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B3.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_D11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_B4.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_B1.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F2.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G10.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E10.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_D2.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_A11.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C2.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_C11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C3.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_A5.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_D10.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_B12.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C11.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A4.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_F5.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_F8.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_H5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_F7.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H3.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D9.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_G7.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E8.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G6.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G9.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_G6.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E6.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D2.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B4.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_E1.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A5.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_A11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_A3.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H9.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A11.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C3.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A2.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B9.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_F3.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_B7.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D7.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H4.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_F8.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_B9.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H1.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H3.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C8.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_G5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H12.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_G2.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C9.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_G4.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E6.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_F12.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_H12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G7.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_C8.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H10.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E7.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_E4.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_F11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_F2.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_D6.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_B5.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_B6.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_H2.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_F1.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_G10.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G12.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_B5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D3.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_D1.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_D4.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_B8.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_E12.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_D12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_E5.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_C4.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_E11.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_F10.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C2.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A9.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_C5.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_C11.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_E11.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_A7.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_D12.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_G3.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_E2.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_C7.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F10.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_A6.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_D10.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A1.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_F9.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_H5.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_H8.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_F9.tif',\n",
+       " 'CTRLD_RR_PLATE_4_ID_H7.tif',\n",
+       " 'CTRLD_RR_PLATE_6_ID_G6.tif',\n",
+       " 'CTRLD_TR_PLATE_1_ID_G9.tif',\n",
+       " 'CTRLD_RR_PLATE_1_ID_A10.tif',\n",
+       " 'CTRLD_TR_PLATE_2_ID_G8.tif']"
+      ]
+     },
+     "execution_count": 14,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from embed_time.transforms import complex_normalisation\n",
+    "import os\n",
+    "import skimage.io as io\n",
+    "\n",
+    "data_location = \"/mnt/efs/dlmbl/G-et/data/live-TLS\"\n",
+    "\n",
+    "folder_imgs = data_location +\"/\"+'Control_Dataset_4TP'\n",
+    "metadata = data_location + \"/\" +'Control_Dataset_4TP_Ground_Truth'\n",
+    "out_normalised = data_location + \"/\" +'Control_Dataset_4TP_Normalized'\n",
+    "if not os.path.isdir(out_normalised):\n",
+    "    os.mkdir(out_normalised)\n",
+    "\n",
+    "img_list = [path for path in os.listdir(folder_imgs) if path.endswith(\".tif\")]\n",
+    "img_list\n"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 4,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stderr",
+     "output_type": "stream",
+     "text": [
+      "/tmp/ipykernel_73419/1519346741.py:5: DeprecationWarning: <tifffile.imsave> is deprecated. Use tifffile.imwrite\n",
+      "  imsave(out_normalised+\"/\"+pth,norm)\n"
+     ]
+    }
+   ],
+   "source": [
+    "from tifffile import imsave\n",
+    "for pth in img_list:\n",
+    "    img = io.imread(folder_imgs+\"/\"+pth)\n",
+    "    norm = complex_normalisation(img,bf_quant=[0.001,0.999],bra_quant=[0.001,0.999])\n",
+    "    imsave(out_normalised+\"/\"+pth,norm)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 15,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from embed_time.dataloader import LiveTLSDataset\n",
+    "\n",
+    "dataset = LiveTLSDataset(metadata,out_normalised,metadata_columns=[\"Run\",\"Plate\",\"ID\"],return_metadata=True)\n",
+    "img, l, m = dataset[0]"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 16,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "import matplotlib.pyplot as plt\n",
+    "\n",
+    "fig,axs = plt.subplots(1,4,figsize=(10,5))\n",
+    "for i,ax in enumerate(axs):\n",
+    "    ax.imshow(img[i][0])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 17,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 1000x500 with 4 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "fig,axs = plt.subplots(1,4,figsize=(10,5))\n",
+    "for i,ax in enumerate(axs):\n",
+    "    ax.imshow(img[i][1])\n",
+    "plt.tight_layout()"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 18,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([4, 2, 598, 712])"
+      ]
+     },
+     "execution_count": 18,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import torchvision.transforms as trans\n",
+    "from torchvision.transforms import v2\n",
+    "from embed_time.transforms import CustomToTensor\n",
+    "\n",
+    "\n",
+    "loading_transforms = trans.Compose([\n",
+    "    CustomToTensor()\n",
+    "])\n",
+    "\n",
+    "dataset_w_t = LiveTLSDataset(\n",
+    "    metadata,\n",
+    "    out_normalised,\n",
+    "    metadata_columns=[\"Run\",\"Plate\",\"ID\"],\n",
+    "    return_metadata=True,\n",
+    "    transform = loading_transforms,\n",
+    ")\n",
+    "\n",
+    "tensor, l, m = dataset_w_t[0]\n",
+    "tensor.shape\n",
+    "\n",
+    "# Doesn't work need to make our own to tensor"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 19,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "torch.Size([2, 598, 712])"
+      ]
+     },
+     "execution_count": 19,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "from embed_time.transforms import SelectRandomTimepoint\n",
+    "\n",
+    "sel_tp = SelectRandomTimepoint(time_dimension=0)\n",
+    "\n",
+    "sel_tp(tensor).shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/notebooks/time_series_subgroup/split_train_test.ipynb b/notebooks/time_series_subgroup/split_train_test.ipynb
new file mode 100644
index 0000000..e69de29
diff --git a/notebooks/time_series_subgroup/testing_augmentations.ipynb b/notebooks/time_series_subgroup/testing_augmentations.ipynb
new file mode 100644
index 0000000..c57951c
--- /dev/null
+++ b/notebooks/time_series_subgroup/testing_augmentations.ipynb
@@ -0,0 +1,197 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 1,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "import os\n",
+    "import skimage.io as io\n",
+    "import torchvision.transforms as trans\n",
+    "from torchvision.transforms import v2\n",
+    "from embed_time.transforms import CustomToTensor, SelectRandomTimepoint\n",
+    "from embed_time.dataloader_rs import LiveTLSDataset\n",
+    "\n",
+    "data_location = \"/mnt/efs/dlmbl/G-et/data/live-TLS\"\n",
+    "\n",
+    "folder_imgs = data_location +\"/\"+'Control_Dataset_4TP_Normalized'\n",
+    "metadata = data_location + \"/\" +'Control_Dataset_4TP_Ground_Truth'\n",
+    "\n",
+    "loading_transforms = trans.Compose([\n",
+    "    CustomToTensor(),\n",
+    "    SelectRandomTimepoint(0),\n",
+    "    v2.RandomAffine(\n",
+    "        degrees=90,\n",
+    "        translate=[0.1,0.1],\n",
+    "    ),\n",
+    "    v2.RandomHorizontalFlip(),\n",
+    "    v2.RandomVerticalFlip(),\n",
+    "    v2.GaussianNoise(0,0.05)\n",
+    "])\n",
+    "\n",
+    "dataset_w_t = LiveTLSDataset(\n",
+    "    metadata,\n",
+    "    folder_imgs,\n",
+    "    metadata_columns=[\"Run\",\"Plate\",\"ID\"],\n",
+    "    return_metadata=True,\n",
+    "    transform = loading_transforms,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 2,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7cb8bd35a950>"
+      ]
+     },
+     "execution_count": 2,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tensor, l, m = dataset_w_t[0]\n",
+    "tensor.shape\n",
+    "\n",
+    "io.imshow(tensor.numpy()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 3,
+   "metadata": {},
+   "outputs": [
+    {
+     "data": {
+      "text/plain": [
+       "(2, 598, 712)"
+      ]
+     },
+     "execution_count": 3,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tensor.numpy().shape"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 11,
+   "metadata": {},
+   "outputs": [],
+   "source": [
+    "from embed_time.transforms import CustomCropCentroid, SelectRandomTPNumpy\n",
+    "\n",
+    "loading_transforms_wcrop = trans.Compose([\n",
+    "    \n",
+    "    SelectRandomTPNumpy(0),\n",
+    "    CustomCropCentroid(0,0,598),\n",
+    "    CustomToTensor(),\n",
+    "    v2.Resize((576,576)),\n",
+    "    v2.RandomAffine(\n",
+    "        degrees=90,\n",
+    "        translate=[0.1,0.1],\n",
+    "    ),\n",
+    "    v2.RandomHorizontalFlip(),\n",
+    "    v2.RandomVerticalFlip(),\n",
+    "    v2.GaussianNoise(0,0.05)\n",
+    "])\n",
+    "\n",
+    "dataset_w_t = LiveTLSDataset(\n",
+    "    metadata,\n",
+    "    folder_imgs,\n",
+    "    metadata_columns=[\"Run\",\"Plate\",\"ID\"],\n",
+    "    return_metadata=True,\n",
+    "    transform = loading_transforms_wcrop,\n",
+    ")"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 21,
+   "metadata": {},
+   "outputs": [
+    {
+     "name": "stdout",
+     "output_type": "stream",
+     "text": [
+      "(2, 598, 712)\n",
+      "torch.Size([2, 576, 576])\n"
+     ]
+    },
+    {
+     "data": {
+      "text/plain": [
+       "<matplotlib.image.AxesImage at 0x7cb8b5961120>"
+      ]
+     },
+     "execution_count": 21,
+     "metadata": {},
+     "output_type": "execute_result"
+    },
+    {
+     "data": {
+      "image/png": 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",
+      "text/plain": [
+       "<Figure size 640x480 with 1 Axes>"
+      ]
+     },
+     "metadata": {},
+     "output_type": "display_data"
+    }
+   ],
+   "source": [
+    "tensor, l, m = dataset_w_t[55]\n",
+    "print(tensor.shape)\n",
+    "\n",
+    "io.imshow(tensor.numpy()[0])"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": null,
+   "metadata": {},
+   "outputs": [],
+   "source": []
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "embed_time",
+   "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": "3.10.14"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 2
+}
diff --git a/pyproject.toml b/pyproject.toml
index 69065e6..286f438 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -17,5 +17,23 @@ authors = [
 ]
 dynamic = ["version"]
 dependencies = [
-    # Add your requirements here
+    "pandas",
+    "tifffile",
+    "torch",
+    "torchvision",
+    "scikit-image",
+    "matplotlib",
+    "tqdm",
+    "pathlib",
+    "zarr",
+    "numpy",
+    "json",
+]
+
+[project.optional-dependencies]
+dev = [
+    "pytest",
+    "torchview",
+    "graphviz",
+    "tensorboard",
 ]
\ No newline at end of file
diff --git a/scripts/20240901_ab_training_loop_resnet18.py b/scripts/20240901_ab_training_loop_resnet18.py
new file mode 100644
index 0000000..3471da9
--- /dev/null
+++ b/scripts/20240901_ab_training_loop_resnet18.py
@@ -0,0 +1,302 @@
+#%%
+import os
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18 import VAEResNet18
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torch.nn import utils as U
+from torch import optim
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import yaml
+
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+#%% Generate Dataset
+
+# Usage example:
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+output_path = '/mnt/efs/dlmbl/G-et/training_logs/'
+output_file = csv_file = output_path + 'example_split.csv'
+beta = 1e-4
+lr = 1e-3
+z_dim = 20
+model_name = "resnet18_vae_conv2D"
+run_name= "z_dim-"+str(z_dim)+"_lr-"+str(lr)+"_beta-"+str(beta)
+train_ratio = 0.7
+val_ratio = 0.15
+num_workers = 8
+#change to false if you already have tensorboard running
+find_port = True
+
+#%%read config
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
+#%% Define the logger for tensorboard
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+
+logger = SummaryWriter(f"embed_time_static_runs/{run_name}")
+
+# Create the dataset split CSV file
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_2.csv'
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = "masks"
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+#%% Generate Dataloader
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=16, 
+    shuffle=True, 
+    collate_fn=collate_wrapper(metadata_keys, images_keys),
+    num_workers=num_workers
+)
+
+
+#%% Create the model
+
+# Initiate VAE-ResNet18 model
+vae = VAEResNet18(nc = 4, z_dim = z_dim ).to(device)
+
+#%% Define Optimizar
+optimizer = torch.optim.Adam(vae.parameters(), lr=lr)
+
+#%% Define loss function
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+
+
+
+
+#%% Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    beta=beta,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + beta*KLD  
+        
+        loss.backward()
+        train_loss += loss.item()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+        optimizer.step()
+        
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                epoch,
+                batch_idx * len(data),
+                len(loader.dataset),
+                100.0 * batch_idx / len(loader),
+                loss.item(),
+            )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'mu': mu,
+                'logvar': logvar,
+                'loss': loss.item() / len(batch['cell_image']),
+                'MSE': MSE.item() / len(batch['cell_image']),
+                'KLD': KLD.item() / len(batch['cell_image'])
+            }
+            training_log.append(row)
+
+
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="MSE_loss", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="KLD_loss", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_image(
+                    tag="input_0", img_tensor=input_image[0:1,0,...], global_step=step
+                )
+                tb_logger.add_image(
+                    tag= "reconstruction_0", img_tensor=predicted_image[0:1,0,...], global_step=step
+                )
+                
+                tb_logger.add_image(
+                    tag="input_1", img_tensor=input_image[0:1,1,...], global_step=step
+                )
+                tb_logger.add_image(
+                    tag="reconstruction_1", img_tensor=predicted_image[0:1,1,...], global_step=step
+                )
+
+                tb_logger.add_image(
+                    tag="input_2", img_tensor=input_image[0:1,2,...], global_step=step
+                )
+                tb_logger.add_image(
+                    tag="reconstruction_2", img_tensor=predicted_image[0:1,2,...], global_step=step
+                )
+
+                tb_logger.add_image(
+                    tag="input_3", img_tensor=input_image[0:1,3,...], global_step=step
+                )
+                tb_logger.add_image(
+                    tag="reconstruction_3", img_tensor=predicted_image[0:1,3,...], global_step=step
+                )
+
+                
+                metadata = [list(item) for item in zip(*[batch[key] for key in metadata_keys])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header = metadata_keys
+                )
+               
+        
+            
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+        
+    
+    # save the DF
+
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+    return train_loss/len(dataloader.dataset)
+#%% Training loop
+
+#define the folder path for saving checkpoints and logs
+folder_suffix = datetime.now().strftime("%Y%m%d") + run_name
+checkpoint_path = '/mnt/efs/dlmbl/G-et/checkpoints/static/Akila/' + folder_suffix + "/"
+os.makedirs(checkpoint_path, exist_ok=True)
+log_path = '/mnt/efs/dlmbl/G-et/logs/static/Akila/'+ folder_suffix + "/"
+os.makedirs(log_path, exist_ok=True)
+
+#training loop
+for epoch in range(0, 100):
+    train_loss =train(epoch, beta = beta, log_interval=100, log_image_interval=20, tb_logger=logger)
+
+    
+
+    train_path = log_path + "_epoch_"+str(epoch)+"/"
+    os.makedirs(train_path, exist_ok=True)
+
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(train_path+"epoch_log.csv", index=False)
+
+    
+    
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(train_path+"epoch_summary_log.csv", index=False)
+
+  
+    
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': train_loss 
+    }
+    torch.save(checkpoint, checkpoint_path+"epoch_"+str(epoch)+"_checkpoint.pth")
diff --git a/scripts/20240902_ab_evaluation.py b/scripts/20240902_ab_evaluation.py
new file mode 100644
index 0000000..6f43624
--- /dev/null
+++ b/scripts/20240902_ab_evaluation.py
@@ -0,0 +1,318 @@
+#%%
+import os
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torchvision.transforms import v2
+import pandas as pd
+import matplotlib.pyplot as plt
+from sklearn.decomposition import PCA
+from sklearn.preprocessing import StandardScaler
+from sklearn.manifold import TSNE
+from matplotlib.colors import ListedColormap
+import umap
+from embed_time.model_VAE_resnet18 import VAEResNet18
+from embed_time.neuromast import NeuromastDatasetTest, NeuromastDatasetTrain_T10
+
+
+
+def load_checkpoint(checkpoint_path, model, device):
+    checkpoint = torch.load(checkpoint_path, map_location=device)
+    model.load_state_dict(checkpoint['model_state_dict'])
+    return model, checkpoint['epoch']
+#%%
+# Model Evaluation Function
+def evaluate_model(model, dataloader, device):
+    model.eval()
+    total_loss = total_mse = total_kld = 0
+    all_latent_vectors = []
+    all_metadata = []
+    
+    with torch.no_grad():
+        for idx, (batch, label) in enumerate(dataloader):
+            data = batch.to(device)
+            metadata = label
+            
+            recon_batch, mu, logvar = model(data)
+            mse = F.mse_loss(recon_batch, data, reduction='sum')
+            kld = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+            loss = mse + kld * 1e-7
+            
+            total_loss += loss.item()
+            total_mse += mse.item()
+            total_kld += kld.item()
+            
+            mu_flattened = mu.view(mu.size(0), -1)
+            all_latent_vectors.append(mu_flattened.cpu())
+            all_metadata.extend(metadata.tolist())
+    
+    avg_loss = total_loss / len(dataloader.dataset)
+    avg_mse = total_mse / len(dataloader.dataset)
+    avg_kld = total_kld / len(dataloader.dataset)
+    latent_vectors = torch.cat(all_latent_vectors, dim=0)
+    
+    return avg_loss, avg_mse, avg_kld, latent_vectors, all_metadata
+#%%
+# Visualization Functions
+def plot_reconstructions(model, dataloader, device):
+    model.eval()
+    with torch.no_grad():
+        batch, label = next(iter(dataloader))
+        data = batch.to(device)
+        recon_batch, _, _ = model(data)
+        
+        image_idx = np.random.randint(data.shape[0])
+        original = data[image_idx].cpu().numpy()
+        reconstruction = recon_batch[image_idx].cpu().numpy()
+        
+        fig, axes = plt.subplots(1,2, figsize=(20, 10))
+        
+        
+        axes[0].imshow(original[0], cmap='Greens')
+        axes[0].set_title(f'Input_image {label[image_idx]}', fontsize=15)
+        axes[0].axis('off')
+        axes[1].imshow(reconstruction[0], cmap='Greens')
+        axes[1].set_title(f'Reconstructed_image', fontsize=15)
+        axes[1].axis('off')
+        
+        plt.tight_layout()
+        plt.show()
+        
+        print(f"Image shape: {original.shape}")
+        print(f"Reconstruction shape: {reconstruction.shape}")
+        print(f"Original image min/max values: {original.min():.4f}/{original.max():.4f}")
+        print(f"Reconstructed image min/max values: {reconstruction.min():.4f}/{reconstruction.max():.4f}")
+#%%
+# Visualization Functions
+def plot_image(model, dataloader, device):
+    
+    with torch.no_grad():
+        batch, label = next(iter(dataloader))
+        data = batch.to(device)
+        
+        
+        image_idx = np.random.randint(data.shape[0])
+        original = data[image_idx].cpu().numpy()
+        
+        
+        fig, axes = plt.subplots(1,1, figsize=(20, 10))
+        
+        
+        axes.imshow(original[0], cmap='Greens')
+        axes.set_title(f'Input_image {label[image_idx]}', fontsize=15)
+        axes.axis('off')
+       
+        
+        plt.tight_layout()
+
+        plt.show()
+        
+#%%
+def create_pca_plots(train_latents, val_latents, train_df, val_df):
+   # Step 1: Scale the features
+    scaler = StandardScaler()
+    train_latents_scaled = scaler.fit_transform(train_latents)
+    val_latents_scaled = scaler.transform(val_latents)
+    
+    # Step 2: Perform PCA
+    pca = PCA(n_components=2)
+    train_latents_pca = pca.fit_transform(train_latents_scaled)
+    val_latents_pca = pca.transform(val_latents_scaled)
+    
+    # Step 3: Prepare the plot
+    fig, axes = plt.subplots(1,2, figsize=(25, 10))
+    
+    # Helper function to create a color map
+    def create_color_map(n):
+        return ListedColormap(plt.cm.viridis(np.linspace(0, 1, n)))
+    # Assuming you have 3 unique labels
+    
+    # Step 3: Plot PCA for the training set
+    ax = axes[0]
+    scatter = ax.scatter(train_latents_pca[:, 0], train_latents_pca[:, 1], c=train_df['Labels'], cmap=create_color_map(len(np.unique(train_df['Labels']))),s=100)
+    ax.set_title('PCA of Training Latents', fontsize=40)
+    ax.set_xlabel('PCA Component 1', fontsize=40)
+    ax.set_ylabel('PCA Component 2', fontsize=40)
+    # Create a color bar with specific ticks and labels
+    num_labels = len(np.unique(train_df['Labels']))
+    cbar = fig.colorbar(scatter, ax=ax)
+    cbar.set_ticks([1, 2, 3])
+    cbar.set_ticklabels(['1-SC', '2-MC', '3-HC'], fontsize=40)
+    
+
+    # Step 4: Plot PCA for the validation set
+    ax = axes[1]
+    scatter = ax.scatter(val_latents_pca[:, 0], val_latents_pca[:, 1], c=val_df['Labels'], cmap=create_color_map(len(np.unique(val_df['Labels']))),s=100)
+    ax.set_title('PCA of Validation Latents', fontsize=40)
+    ax.set_xlabel('PCA Component 1', fontsize=40)
+    ax.set_ylabel('PCA Component 2', fontsize=40)
+    num_labels = len(np.unique(val_df['Labels']))
+    cbar = fig.colorbar(scatter, ax=ax)
+    cbar.set_ticks([1, 2, 3])
+    cbar.set_ticklabels(['1-SC', '2-MC', '3-HC'], fontsize=40)
+    
+
+
+    # Optional: You can add more plots or subplots as required
+
+    # Debugging: Print shapes and check if the data is non-empty
+    print(f"Train Latents PCA shape: {train_latents_pca.shape}")
+    print(f"Val Latents PCA shape: {val_latents_pca.shape}")
+    print(f"Unique labels in training set: {np.unique(train_df['Labels'])}")
+    print(f"Unique labels in validation set: {np.unique(val_df['Labels'])}")
+    
+    # Adjust layout to prevent overlap
+    plt.tight_layout()
+    
+    # Step 5: Show the plot
+    plt.show()
+#%%
+def create_umap_plots(train_latents, val_latents, train_df, val_df):
+    
+
+    # Initialize UMAP
+    umap_reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, random_state=42)
+
+    # Fit and transform the training data
+    train_latents_umap = umap_reducer.fit_transform(train_latents)
+    # Transform the validation data using the same UMAP model
+    val_latents_umap = umap_reducer.transform(val_latents)
+
+    fig, axes = plt.subplots(1,2, figsize=(25, 10))
+    
+    def create_color_map(n):
+        return ListedColormap(plt.cm.viridis(np.linspace(0, 1, n)))
+
+    
+    # Step 5: Plot UMAP for the training set
+    ax = axes[0]
+    scatter = ax.scatter(train_latents_umap[:, 0], train_latents_umap[:, 1], c=train_df['Labels'], cmap=create_color_map(len(np.unique(train_df['Labels']))),s=100)
+    ax.set_title('UMAP of Training Latents', fontsize=40)
+    ax.set_xlabel('UMAP Component 1', fontsize=40)
+    ax.set_ylabel('UMAP Component 2', fontsize=40)
+    # Create a color bar with specific ticks and labels
+    num_labels = len(np.unique(train_df['Labels']))
+    cbar = fig.colorbar(scatter, ax=ax)
+    cbar.set_ticks([1, 2, 3])
+    cbar.set_ticklabels(['1-SC', '2-MC', '3-HC'], fontsize=40)
+    
+
+    # Step 6: Plot UMAP for the validation set
+    ax = axes[1]
+    scatter = ax.scatter(val_latents_umap[:, 0], val_latents_umap[:, 1], c=val_df['Labels'], cmap=create_color_map(len(np.unique(val_df['Labels']))),s=100)
+    ax.set_title('UMAP of Validation Latents', fontsize=40)
+    ax.set_xlabel('UMAP Component 1', fontsize=40)
+    ax.set_ylabel('UMAP Component 2', fontsize=40)
+    num_labels = len(np.unique(val_df['Labels']))
+    cbar = fig.colorbar(scatter, ax=ax)
+    cbar.set_ticks([1, 2, 3])
+    cbar.set_ticklabels(['1-SC', '2-MC', '3-HC'], fontsize=40)
+#%% 
+def create_tsne_plots(train_latents, val_latents, train_df, val_df):
+    # Step 1: Scale the features
+    scaler = StandardScaler()
+    train_latents_scaled = scaler.fit_transform(train_latents)
+    val_latents_scaled = scaler.transform(val_latents)
+    
+    # Step 2: Perform t-SNE
+    tsne = TSNE(n_components=2, random_state=42)
+    train_latents_tsne = tsne.fit_transform(train_latents_scaled)
+    val_latents_tsne = tsne.transform(val_latents_scaled)
+    
+    # Step 3: Prepare the plot
+    fig, axes = plt.subplots(1, 2, figsize=(25, 10))
+    
+    # Helper function to create a color map
+    def create_color_map(n):
+        return ListedColormap(plt.cm.viridis(np.linspace(0, 1, n)))
+    
+    # Step 4: Plot t-SNE for the training set
+    ax = axes[0]
+    scatter = ax.scatter(train_latents_tsne[:, 0], train_latents_tsne[:, 1], c=train_df['Labels'], cmap=create_color_map(len(np.unique(train_df['Labels']))), s=100)
+    ax.set_title('t-SNE of Training Latents', fontsize=40)
+    ax.set_xlabel('t-SNE Component 1', fontsize=40)
+    ax.set_ylabel('t-SNE Component 2', fontsize=40)
+    
+    # Create a color bar with specific ticks and labels
+    cbar = fig.colorbar(scatter, ax=ax)
+    cbar.set_ticks([1, 2, 3])
+    cbar.set_ticklabels(['1-SC', '2-MC', '3-HC'], fontsize=40)
+    
+    # Step 5: Plot t-SNE for the validation set
+    ax = axes[1]
+    scatter = ax.scatter(val_latents_tsne[:, 0], val_latents_tsne[:, 1], c=val_df['Labels'], cmap=create_color_map(len(np.unique(val_df['Labels']))), s=100)
+    ax.set_title('t-SNE of Validation Latents', fontsize=40)
+    ax.set_xlabel('t-SNE Component 1', fontsize=40)
+    ax.set_ylabel('t-SNE Component 2', fontsize=40)
+    
+    # Create a color bar with specific ticks and labels
+    cbar = fig.colorbar(scatter, ax=ax)
+    cbar.set_ticks([1, 2, 3])
+    cbar.set_ticklabels(['1-SC', '2-MC', '3-HC'], fontsize=40)
+    
+    plt.tight_layout()
+    plt.show()
+
+# Example usage (assuming you have train_latents, val_latents, train_df, val_df defined)
+# create_tsne_plots(train_latents, val_latents, train_df, val_df)
+
+#%%
+# Main Execution
+if __name__ == "__main__":
+    # Setup
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    
+    # Model initialization and loading
+    model = VAEResNet18(nc = 1, z_dim = 22 ).to(device)
+    checkpoint_dir = "/mnt/efs/dlmbl/G-et/checkpoints/static/Akila/20240903z_dim-22_lr-0.0001_beta-1e-07/_epoch_6/"
+    
+    checkpoint_path = os.path.join(checkpoint_dir, "checkpoint.pth")
+    model, epoch = load_checkpoint(checkpoint_path, model, device)
+    model = model.to(device)
+
+    
+
+    
+    dataset_train = NeuromastDatasetTrain_T10()
+    dataset_val = NeuromastDatasetTest()
+    
+
+    dataloader_train = DataLoader(dataset_train, batch_size=2, shuffle=True, num_workers=8)
+    dataloader_val = DataLoader(dataset_val, batch_size=2, shuffle=True, num_workers=8)
+
+    # Model evaluation
+    print("Evaluating on training data...")
+    train_loss, train_mse, train_kld, train_latents, train_metadata = evaluate_model(model, dataloader_train, device)
+    print(f"Training - Loss: {train_loss:.4f}, MSE: {train_mse:.4f}, KLD: {train_kld:.4f}")
+
+    print("Evaluating on validation data...")
+    val_loss, val_mse, val_kld, val_latents, val_metadata = evaluate_model(model, dataloader_val, device)
+    print(f"Validation - Loss: {val_loss:.4f}, MSE: {val_mse:.4f}, KLD: {val_kld:.4f}")
+
+    # Create DataFrames
+    train_df = pd.DataFrame(train_metadata, columns=['Labels'])
+    train_df = pd.concat([train_df, pd.DataFrame(train_latents.numpy())], axis=1)
+
+    val_df = pd.DataFrame(val_metadata, columns=['Labels'])
+    val_df = pd.concat([val_df, pd.DataFrame(val_latents.numpy())], axis=1)
+#%%
+# Visualizations
+plot_image(model, dataloader_train, device)
+# plot_reconstructions(model, dataloader_train, device)
+
+#%%
+plot_reconstructions(model, dataloader_val, device)
+
+
+#%%
+create_pca_plots(train_latents.numpy(), val_latents.numpy(), train_df, val_df)
+#%%
+create_umap_plots(train_latents.numpy(), val_latents.numpy(), train_df, val_df)
+# %%
+#save the dataframes
+checkpoint_dir = "/mnt/efs/dlmbl/G-et/checkpoints/static/Akila/20240903z_dim-22_lr-0.0001_beta-1e-07/_epoch_6/"
+train_df.to_csv(checkpoint_dir+"Train10_latentvectors_mu_df.csv", index=False)
+val_df.to_csv(checkpoint_dir+"Test10_latentvectors_mu_df.csv", index=False)
+# %%
+create_tsne_plots(train_latents.numpy(), val_latents.numpy(), train_df, val_df)
diff --git a/scripts/data_reader_static.py b/scripts/data_reader_static.py
new file mode 100644
index 0000000..e30cb96
--- /dev/null
+++ b/scripts/data_reader_static.py
@@ -0,0 +1,125 @@
+import argparse
+import matplotlib.pyplot as plt
+from torch.utils.data import DataLoader
+from torchvision.transforms import v2
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset, ZarrCellDataset_specific
+from embed_time.dataloader_static import collate_wrapper
+from datetime import datetime
+
+time = datetime.now().strftime("%Y%m%d_%H%M%S")
+
+def plot_cell_data(dataset_image):
+    sample = dataset_image
+    images = [sample['original_image'], sample['cell_mask'], sample['nuclei_mask'], sample['cell_image'], sample['nuclei_image']]
+    titles = ['Original', 'Cell Mask', 'Nuclei Mask', 'Cell Image', 'Nuclei Image']
+    
+    for i in range(2):  # Ensure cell and nuclei masks are 3D
+        if images[i+1].ndim == 2:
+            images[i+1] = images[i+1][None]
+
+    num_channels = images[0].shape[0]
+    fig, axes = plt.subplots(5, num_channels, figsize=(4*num_channels, 20))
+    if num_channels == 1:
+        axes = axes.reshape(-1, 1)
+    
+    for row, (image, title) in enumerate(zip(images, titles)):
+        for channel in range(num_channels):
+            im = axes[row, channel].imshow(image[channel], cmap='gray', vmin=-1 if row > 2 else None, vmax=1 if row > 2 else None)
+            axes[row, channel].set_title(f'{title} - Channel {channel}')
+            plt.colorbar(im, ax=axes[row, channel])
+    
+    for ax in axes.flatten():
+        ax.axis('off')
+    
+    plt.tight_layout()
+    plt.show()
+
+def print_cell_data_shapes(dataset_image):
+    for key, value in dataset_image.items():
+        print(f"{key}: {value.shape}")
+
+def main(args):
+    if args.generate_split and args.full:
+        DatasetSplitter(args.parent_dir, args.output_dir, args.train_ratio, args.val_ratio, args.num_workers).generate_split()
+
+    normalizations = v2.Compose([v2.CenterCrop(args.crop_size)])
+
+    if args.full:
+        dataset_class = ZarrCellDataset
+        dataset_args = [args.parent_dir, args.csv_file, args.split, args.channels, args.mask, normalizations, None]
+    else:
+        dataset_class = ZarrCellDataset_specific
+        dataset_args = [args.parent_dir, args.gene_name, args.barcode_name, args.channels, args.cell_cycle_stages, args.mask, normalizations, None]
+
+    dataset = dataset_class(*dataset_args)
+
+    print(f"The dataset contains {len(dataset)} images.")
+    print(f"Dataset mean: {dataset.mean}")
+    print(f"Dataset std: {dataset.std}")
+
+    if args.plot_sample:
+        plot_cell_data(dataset[args.sample_index])
+        print_cell_data_shapes(dataset[args.sample_index])
+
+    # save the dataset parameters and returned mean into a yaml file based on the datetime
+    with open(f"/mnt/efs/dlmbl/G-et/yaml/dataset_info_{time}.yaml", "w") as file:
+        file.write(f"Dataset mean: {dataset.mean}\n")
+        file.write(f"Dataset std: {dataset.std}\n")
+        file.write(f"Dataset length: {len(dataset)}\n")
+        file.write(f"Dataset image shape: {dataset[0]['original_image'].shape}\n")
+        file.write(f"Dataset nuclei shape: {dataset[0]['nuclei_image'].shape}\n")
+        file.write(f"Dataset cell shape: {dataset[0]['cell_image'].shape}\n")
+        file.write(f"Dataset cell mask shape: {dataset[0]['cell_mask'].shape}\n")
+        file.write(f"Dataset nuclei mask shape: {dataset[0]['nuclei_mask'].shape}\n")
+        file.write(f"Parent directory: {args.parent_dir}\n")
+        if args.full:
+            file.write(f"CSV file: {args.csv_file}\n")
+            file.write(f"Split: {args.split}\n")
+        else:
+            file.write(f"Gene name: {args.gene_name}\n")
+            file.write(f"Barcode name: {args.barcode_name}\n")
+            file.write(f"Cell cycle stages: {args.cell_cycle_stages}\n")
+        
+    dataloader = DataLoader(
+        dataset,
+        batch_size=args.batch_size,
+        shuffle=True,
+        collate_fn=collate_wrapper(args.metadata_keys, args.images_keys)
+    )
+
+    # Print first batch info
+    for batch in dataloader:
+        print("First batch:")
+        for key in args.metadata_keys + args.images_keys:
+            if key in args.metadata_keys:
+                print(f"{key}: {batch[key]}")
+            else:
+                print(f"{key} shape: {batch[key].shape}")
+        break
+
+if __name__ == "__main__":
+    parser = argparse.ArgumentParser(description="VAE architecture for optical pooled screening data")
+    parser.add_argument("--parent_dir", type=str, default="/mnt/efs/dlmbl/S-md/", help="Parent directory for dataset")
+    parser.add_argument("--output_dir", type=str, default="/mnt/efs/dlmbl/G-et/csv/", help="Output file for dataset split")
+    parser.add_argument("--generate_split", action="store_true", default=True, help="Generate dataset split")
+    parser.add_argument("--train_ratio", type=float, default=0.7, help="Train ratio for dataset split")
+    parser.add_argument("--val_ratio", type=float, default=0.15, help="Validation ratio for dataset split")
+    parser.add_argument("--num_workers", type=int, default=-1, help="Number of workers for dataset split")
+    parser.add_argument("--full", action="store_true", default=True, help="Use full dataset (default: True)")
+    parser.add_argument("--gene_name", type=str, default="AAAS", help="Gene name for specific dataset")
+    parser.add_argument("--barcode_name", type=str, default="ATATGAGCACAATAACGAGC", help="Barcode name for specific dataset")
+    parser.add_argument("--channels", nargs="+", type=int, default=[0, 1, 2, 3], help="Channels to use")
+    parser.add_argument("--cell_cycle_stages", type=str, default="interphase", help="Cell cycle stages")
+    parser.add_argument("--mask", type=str, default="min", help="Mask type")
+    parser.add_argument("--crop_size", type=int, default=100, help="Size for center crop")
+    parser.add_argument("--csv_file", type=str, default="/home/S-md/embed_time/notebooks/splits/split_804.csv", help="CSV file for dataset")
+    parser.add_argument("--split", type=str, default="train", help="Dataset split to use")
+    parser.add_argument("--plot_sample", action="store_true", help="Plot a sample from the dataset")
+    parser.add_argument("--sample_index", type=int, default=10, help="Index of sample to plot")
+    parser.add_argument("--batch_size", type=int, default=2, help="Batch size for dataloader")
+    parser.add_argument("--metadata_keys", nargs="+", default=['gene', 'barcode', 'stage'], help="Metadata keys for collate function")
+    parser.add_argument("--images_keys", nargs="+", default=['cell_image'], help="Image keys for collate function")
+
+    args = parser.parse_args()
+    main(args)
diff --git a/scripts/evaluate_md.py b/scripts/evaluate_md.py
new file mode 100644
index 0000000..65bf720
--- /dev/null
+++ b/scripts/evaluate_md.py
@@ -0,0 +1,83 @@
+import re
+import os
+import re
+from embed_time.evaluate_static import ModelEvaluator
+
+def get_checkpoint_dirs():
+    parent_dir = '/mnt/efs/dlmbl/G-et/checkpoints/static/Matteo/'
+    checkpoint_dirs = os.listdir(parent_dir)
+    checkpoint_dirs = [os.path.join(parent_dir, d) for d in checkpoint_dirs]
+    checkpoint_dirs = [d for d in checkpoint_dirs if os.path.isdir(d)]
+    
+    def get_timestamp(checkpoint_dir):
+        filename = checkpoint_dir.split('/')[-1]
+        match = re.search(r'(\d{8}_\d{4})', filename)
+        if match:
+            return match.group(1)
+        return ''
+    
+    checkpoint_dirs = sorted(checkpoint_dirs, key=lambda x: get_timestamp(x))
+    checkpoint_dirs = [d for d in checkpoint_dirs if get_timestamp(d) > '20240903_2100']
+    print("number of checkpoints:", len(checkpoint_dirs))
+
+    return checkpoint_dirs
+
+def parse_checkpoint_dir(checkpoint_dir):
+    filename = checkpoint_dir.split('/')[-1]
+    print(filename)
+    params = ['model', 'crop_size', 'nc', 'z_dim', 'lr', 'beta', 'transform', 'loss']
+    result = {}
+    model_match = re.search(r'_(VAE_ResNet18)_', filename)
+    if model_match:
+        result['model'] = model_match.group(1)
+    
+    for param in params:
+        if param == 'model':
+            continue
+        match = re.search(rf'{param}_([^_]+)', filename)
+        if match:
+            value = match.group(1)
+            try:
+                value = int(value)
+            except ValueError:
+                try:
+                    value = float(value)
+                except ValueError:
+                    pass
+            result[param] = value
+    
+    if 'benchmark' in filename:
+        result['csv_file'] = 'dataset_split_benchmark.csv'
+    
+    return result
+
+def generate_config(checkpoint_dir):
+    config = parse_checkpoint_dir(checkpoint_dir)
+    
+    # Add invariant parameters
+    config.update({
+        'checkpoint_dir': checkpoint_dir,
+        'parent_dir': '/mnt/efs/dlmbl/S-md/',
+        'channels': [0, 1, 2, 3],
+        'yaml_file_path': '/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml',
+        'output_dir': os.path.join('/home/S-md/embed_time/scripts/latent', checkpoint_dir.split('/')[-1]),
+        'sampling_number': 3,
+        'csv_file': '/mnt/efs/dlmbl/G-et/csv/' + config['csv_file'],
+        'batch_size': 16,
+        'num_workers': 8,
+        'metadata_keys': ['gene', 'barcode', 'stage', 'cell_idx'],
+        'images_keys': ['cell_image']
+    })
+    
+    return config
+
+def run_evaluator(checkpoint_dir):
+    config = generate_config(checkpoint_dir)
+    return ModelEvaluator(config)
+
+# Example usage
+if __name__ == "__main__":
+    # checkpoint_dir = '/mnt/efs/dlmbl/G-et/checkpoints/static/Matteo/20240903_2130_VAE_ResNet18_crop_size_64_nc_4_z_dim_30_lr_0.0001_beta_1e-05_transform_min_loss_L1_benchmark'
+    checkpoint_dirs = get_checkpoint_dirs()
+    for checkpoint_dir in checkpoint_dirs:
+        run_evaluator(checkpoint_dir)
\ No newline at end of file
diff --git a/scripts/grid_search.py b/scripts/grid_search.py
new file mode 100644
index 0000000..4f18e6c
--- /dev/null
+++ b/scripts/grid_search.py
@@ -0,0 +1,40 @@
+import itertools
+from tqdm import tqdm
+import subprocess
+import os
+from datetime import datetime
+
+# Define the parameter grid
+param_grid = {
+    'z_dim': [30, 10],
+    'loss_type': ['L1', 'MSE', 'SSIM'],
+    'crop_size': [64, 96],
+    'beta': [1e-5, 1e-6],
+    'transform': ['min', 'mask']
+}
+
+# Generate all combinations of parameters
+param_combinations = list(itertools.product(*param_grid.values()))
+
+# Main loop for grid search
+for params in tqdm(param_combinations, desc="Grid Search Progress"):
+    z_dim, loss_type, crop_size, beta, transform = params
+        
+    # Create command to run the main script with current parameters
+    command = [
+        "python", "training_loop_resnet18_md_grid.py",
+        "--z_dim", str(z_dim),
+        "--loss_type", loss_type,
+        "--crop_size", str(crop_size),
+        "--beta", str(beta),
+        "--transform", transform,
+    ]
+    
+    # Run the command
+    try:
+        subprocess.run(command, check=True)
+    except subprocess.CalledProcessError as e:
+        print(f"Error occurred with parameters: {params}")
+        print(f"Error details: {e}")
+
+print("Grid search completed!")
\ No newline at end of file
diff --git a/scripts/metadata_collect_neuromast.py b/scripts/metadata_collect_neuromast.py
new file mode 100644
index 0000000..de135e2
--- /dev/null
+++ b/scripts/metadata_collect_neuromast.py
@@ -0,0 +1,105 @@
+from iohub.ngff import open_ome_zarr
+from natsort import natsorted
+from glob import glob
+from pathlib import Path 
+import torch
+from torch.utils.data import Dataset
+from scipy.ndimage import measurements
+from scipy.ndimage import center_of_mass
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+zarr_dir = "/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/"
+# defines input zarr file name with the zarr file structure
+zarr_file = 'structured_celltype_classifier_data.zarr/*/*/*'
+position_paths = natsorted(glob(zarr_dir + zarr_file))
+# print(position_paths)
+
+
+
+centroids = {}
+bounding_boxes = {}
+data = []
+for i, paths in enumerate(position_paths):
+    dataset = open_ome_zarr(paths, mode="r")
+    image = dataset.data[:,0:2,:,:,:]
+    celltype = dataset.data[0,3:4,:,:,:]
+    segmented_data = dataset.data[0,2:3,:,:,:]
+    
+    segment_labels = np.unique(segmented_data)
+    segment_labels = segment_labels[segment_labels != 0]  # Exclude background
+
+
+    # Calculate the centroid for each segment
+    for label in segment_labels:
+        # Get a binary mask of the current segment
+        segment_mask = segmented_data == label
+        
+        # Find the indices where the segment is present
+        t, z_indices, y_indices, x_indices = np.where(segment_mask)
+        # Mask the nuclei image with the segment
+        masked_image_green=np.where(segment_mask, image, 0)
+
+        # Calculate the bounding box (min and max in each dimension)
+        z_min, z_max = z_indices.min(), z_indices.max()
+        y_min, y_max = y_indices.min(), y_indices.max()
+        x_min, x_max = x_indices.min(), x_indices.max()
+        
+        
+        # # Crop the segment using the bounding box
+        # cropped_image_green = masked_image_green[0,0,z_min-2:z_max+2, y_min-2:y_max+2, x_min-2:x_max+2]
+        # # cropped_image_red = masked_image_red[0,1,z_min-2:z_max+2, y_min-2:y_max+2, x_min-2:x_max+2]
+        
+        # Compute the centroid
+        coords = np.array(np.nonzero(segment_mask))
+        centroid = np.mean(coords, axis=1)
+        string = Path(paths).parts[-3:]
+        # Extract neuromast ID and t from the paths
+       
+        neuromast_id = int(string[-3]) # Assuming neuromast ID is in this position
+        timepoint = int(string[-2])      # Assuming t value is in this position
+        celltypes_segment = celltype[segment_mask]
+        cell_type = int(np.unique(celltypes_segment))
+
+
+        # Append the data to the list
+        data.append({
+            "Neuromast_ID": neuromast_id,
+            "Label": label,
+            "Cell_Type": cell_type,
+            "Z_min": z_min,
+            "Z_max": z_max,
+            "Y_min": y_min,
+            "Y_max": y_max,
+            "X_min": x_min,
+            "X_max": x_max,
+            "Centroid_Z": centroid[-3],
+            "Centroid_Y": centroid[-2],
+            "Centroid_X": centroid[-1],
+            "T_value": timepoint
+        })
+        print(f'collected info from celltype {cell_type},timepoint {timepoint} and neuromast {neuromast_id}')
+    
+# Convert the list of data into a pandas DataFrame
+df = pd.DataFrame(data)
+
+# Calculate the ranges for X, Y, and Z
+df['X_range'] = df['X_max'] - df['X_min']
+df['Y_range'] = df['Y_max'] - df['Y_min']
+df['Z_range'] = df['Z_max'] - df['Z_min']
+
+# Find the maximum range across all dimensions
+max_x_range = df['X_range'].max()
+max_y_range = df['Y_range'].max()
+max_z_range = df['Z_range'].max()
+
+# Print the maximum ranges
+print(f"Maximum X range: {max_x_range}")
+print(f"Maximum Y range: {max_y_range}")
+print(f"Maximum Z range: {max_z_range}")
+
+filepath = '/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast.csv'
+df.to_csv(filepath, index=False)
+
+print("Data saved to segment_data.csv")
\ No newline at end of file
diff --git a/scripts/metadata_neuromast_balance.py b/scripts/metadata_neuromast_balance.py
new file mode 100644
index 0000000..f54352a
--- /dev/null
+++ b/scripts/metadata_neuromast_balance.py
@@ -0,0 +1,34 @@
+import pandas as pd
+import numpy as np
+
+
+metadata= pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast.csv")
+filtered_metadata = metadata[metadata['Neuromast_ID'] == 0]
+
+# Step 2: Initialize an empty list to store the balanced data
+balanced_data = []
+
+# Step 3: Group by 'timepoint' and process each group separately
+for timepoint, group in filtered_metadata.groupby('T_value'):
+    
+    # Step 4: Find the counts for the specific cell types (e.g., 1, 2, 3)
+    celltype_counts = group['Cell_Type'].value_counts()
+    #print(celltype_counts)
+    
+    # Determine the minimum count among the three cell types
+    min_count = celltype_counts.min()
+    print(min_count)
+
+    # Step 5: For each of the three cell types, sample `min_count` rows
+    for cell_type in celltype_counts.index:
+        sampled_rows = group[group['Cell_Type'] == cell_type].sample(n=min_count, random_state=42)
+        balanced_data.append(sampled_rows)
+        print(f"Sampled {len(sampled_rows)} rows for cell type {cell_type} in timepoint {timepoint}")
+
+# Step 6: Combine all sampled rows into a single DataFrame
+metadata_balanced_train = pd.concat(balanced_data)
+
+# Step 7: Save the balanced DataFrame to a CSV file
+metadata_balanced_train.to_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_balanced_train.csv", index=False)
+
+print("Balanced dataset saved as metadata_balanced_train.csv")
diff --git a/scripts/metadata_test_10timepoints.py b/scripts/metadata_test_10timepoints.py
new file mode 100644
index 0000000..cef545e
--- /dev/null
+++ b/scripts/metadata_test_10timepoints.py
@@ -0,0 +1,29 @@
+import pandas as pd
+import numpy as np
+
+
+metadata= pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast.csv")
+filtered_metadata = metadata[metadata['Neuromast_ID'] == 1]
+
+# Step 2: Initialize an empty list to store the balanced data
+test_data = []
+# Step 3: Define the specific T_values you want to filter by
+target_t_values = [5, 50, 100, 150, 200, 250, 300, 350, 400, 450]
+
+# Step 4: Filter the filtered_metadata DataFrame for the desired T_values
+filtered_metadata = filtered_metadata[filtered_metadata['T_value'].isin(target_t_values)]
+
+# Step 5: Group by 'timepoint' and process each group separately
+for timepoint, group in filtered_metadata.groupby('T_value'):
+    
+    # Step 6: Append all the cell types (e.g., 1, 2, 3)
+    test_data.append(group)  
+
+
+# Step 6: Combine all sampled rows into a single DataFrame
+metadata_test = pd.concat(test_data)
+
+# Step 7: Save the balanced DataFrame to a CSV file
+metadata_test.to_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_test_T10.csv", index=False)
+
+print("Balanced dataset saved as metadata_balanced_train.csv")
\ No newline at end of file
diff --git a/scripts/metadata_train_10timepoints.py b/scripts/metadata_train_10timepoints.py
new file mode 100644
index 0000000..64e34c7
--- /dev/null
+++ b/scripts/metadata_train_10timepoints.py
@@ -0,0 +1,29 @@
+import pandas as pd
+import numpy as np
+
+
+metadata= pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_balanced_train.csv")
+filtered_metadata = metadata[metadata['Neuromast_ID'] == 0]
+
+# Step 2: Initialize an empty list to store the balanced data
+test_data = []
+# Step 3: Define the specific T_values you want to filter by
+target_t_values = [5, 50, 100, 150, 200, 250, 300, 350, 400, 450]
+
+# Step 4: Filter the filtered_metadata DataFrame for the desired T_values
+filtered_metadata = filtered_metadata[filtered_metadata['T_value'].isin(target_t_values)]
+
+# Step 5: Group by 'timepoint' and process each group separately
+for timepoint, group in filtered_metadata.groupby('T_value'):
+    
+    # Step 6: Append all the cell types (e.g., 1, 2, 3)
+    test_data.append(group)  
+
+
+# Step 6: Combine all sampled rows into a single DataFrame
+metadata_test = pd.concat(test_data)
+
+# Step 7: Save the balanced DataFrame to a CSV file
+metadata_test.to_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_train_T10.csv", index=False)
+
+print("Balanced dataset saved as metadata_balanced_train.csv")
\ No newline at end of file
diff --git a/scripts/metadata_train_unbalanced.py b/scripts/metadata_train_unbalanced.py
new file mode 100644
index 0000000..dcdfd9c
--- /dev/null
+++ b/scripts/metadata_train_unbalanced.py
@@ -0,0 +1,36 @@
+import pandas as pd
+import numpy as np
+
+
+metadata= pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast.csv")
+filtered_metadata = metadata[metadata['Neuromast_ID'] == 0]
+
+
+# Step 7: Save the balanced DataFrame to a CSV file
+filtered_metadata.to_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_unbalanced_train.csv", index=False)
+
+print("Balanced dataset saved as metadata_unbalanced_tain.csv")
+
+t50 = pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_unbalanced_train.csv")
+# Step 2: Initialize an empty list to store the balanced data
+test_data = []
+# Step 3: Define the specific T_values you want to filter by
+target_t_values =np.linspace(0, 499, 10)
+
+# Step 4: Filter the filtered_metadata DataFrame for the desired T_values
+filtered_metadata = filtered_metadata[filtered_metadata['T_value'].isin(target_t_values)]
+
+# Step 5: Group by 'timepoint' and process each group separately
+for timepoint, group in filtered_metadata.groupby('T_value'):
+    
+    # Step 6: Append all the cell types (e.g., 1, 2, 3)
+    test_data.append(group)  
+
+
+# Step 6: Combine all sampled rows into a single DataFrame
+metadata_test = pd.concat(test_data)
+
+# Step 7: Save the balanced DataFrame to a CSV file
+metadata_test.to_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_unbalanced_train_T10.csv", index=False)
+
+print("Balanced dataset saved as metadata_unbalanced_train_T10.csv")
\ No newline at end of file
diff --git a/scripts/navigate_worms.py b/scripts/navigate_worms.py
new file mode 100644
index 0000000..5b77398
--- /dev/null
+++ b/scripts/navigate_worms.py
@@ -0,0 +1,33 @@
+import torch
+from torch.utils.data import Dataset
+from torchvision.transforms import ToTensor
+from torchvision.datasets import ImageFolder
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+
+# Transforms
+data_transform_train = v2.Compose([
+                                        v2.RandomRotation(30),
+                                        v2.RandomHorizontalFlip(),
+                                        v2.ToTensor(),
+                                        v2.Normalize(mean=[0.485, 0.456, 0.406], std=[0.229, 0.224, 0.225])])
+
+
+# Bring the dataset
+dataset = ImageFolder(root='/nfs/research/uhlmann/afoix/datasets/image_datasets/bbbc010/BBBC010_v1_foreground_eachworm/', transform=data_transform_train)
+
+# Split datatset
+train, val, test = torch.utils.data.random_split(dataset, [0.6, 0.2, 0.2])
+
+# Create data datatloader
+batch_size = 8
+num_workers = 4
+trainLoader = torch.utils.data.DataLoader(train, batch_size=batch_size, 
+                                           num_workers=num_workers, drop_last=True, shuffle=True)
+valLoader = torch.utils.data.DataLoader(val, batch_size=batch_size, 
+                                          num_workers=num_workers, drop_last=True)
+testLoader = torch.utils.data.DataLoader(test, batch_size=batch_size, 
+                                          num_workers=num_workers, drop_last=True)
+
+
+print(trainLoader)
diff --git a/scripts/nontargeting_experiments/20240902_da_static_benchmark_nontargeting.py b/scripts/nontargeting_experiments/20240902_da_static_benchmark_nontargeting.py
new file mode 100644
index 0000000..b568d4d
--- /dev/null
+++ b/scripts/nontargeting_experiments/20240902_da_static_benchmark_nontargeting.py
@@ -0,0 +1,265 @@
+# Imports
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18_linear import VAEResNet18_Linear
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torchvision.transforms import v2
+import subprocess
+import pandas as pd
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+from pathlib import Path
+from tqdm import tqdm
+import yaml
+from embed_time.static_utils import read_config
+
+# All settings
+# Hyperparameters
+beta = 1e-4
+nc = 4
+z_dim = 320
+num_workers = 8
+lr = 1e-4
+batch_size = 16
+num_epochs = 30
+transform = "min"
+crop_size = 96
+channels = [0, 1, 2, 3]
+# Basic values for logging 
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+output_dir = '/mnt/efs/dlmbl/G-et/da_testing/'
+output_path = output_dir + 'training_logs/'
+model_name = f"static_resnet_linear_vae_da_benchmark_{beta}_{z_dim}_{lr}"
+run_name= "da_testing"
+find_port = True
+
+# Define variables for the dataset read in
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark_nontargeting.csv'
+split = 'train'
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard(output_path)
+logger = SummaryWriter(f"{output_path}/{model_name}")
+
+# Create the dataset
+dataset_mean, dataset_std = read_config(yaml_file_path)
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=batch_size, 
+    shuffle=True, 
+    num_workers=num_workers,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Create the model
+vae = VAEResNet18_Linear(nc=nc, z_dim=z_dim, input_spatial_dim=[crop_size,crop_size])
+
+vae = vae.to(device)
+
+# Define the optimizer
+optimizer = torch.optim.Adam(vae.parameters(), lr=lr)
+
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+
+
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    beta=1e-3
+    ):
+    pbar = tqdm(enumerate(loader), total=len(loader), desc=f"Epoch {epoch}")
+    model.train()
+    log_losses = {
+        "train_loss": 0, 
+        "train_MSE": 0,
+        "train_KLD": 0
+    }
+    train_loss = 0
+    for batch_idx, batch in pbar:
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, z, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + KLD * beta
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+
+        log_losses["train_loss"] += loss.item()
+        log_losses["train_MSE"] += MSE.item()
+        log_losses["train_KLD"] += KLD.item()
+        
+        if batch_idx % log_interval == 0:
+            pbar.set_postfix({'loss': log_losses["train_loss"] / log_interval })
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': log_losses["train_loss"] / log_interval,
+                'MSE': log_losses["train_MSE"] / log_interval,
+                'KLD': log_losses["train_KLD"] / log_interval 
+            }
+            training_log.append(row)
+            log_losses = {
+                "train_loss": 0, 
+                "train_MSE": 0,
+                "train_KLD": 0
+            }
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_MSE", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_KLD", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header=metadata_keys
+                )
+                           
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader)))
+
+# Training loop
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+checkpoint_path = output_path + "checkpoints/static/" + folder_suffix + "/"
+log_path = output_path + "logs/static/"+ folder_suffix + "/"
+
+# Create the directories
+Path(checkpoint_path).mkdir(parents=True, exist_ok=True)
+Path(log_path).mkdir(parents=True, exist_ok=True)
+
+print(
+    f"Saving checkpoints to {checkpoint_path} and logs to {log_path}",
+    f"Model: {model_name}",
+    f"Run: {run_name}",
+    sep="\n",
+)
+
+for epoch in range(0, num_epochs):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger, beta=beta)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch / len(dataloader)
+    }
+    torch.save(checkpoint, output_path + filename_suffix + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/nontargeting_experiments/20240902_da_static_benchmark_nontargeting_nonlinear.py b/scripts/nontargeting_experiments/20240902_da_static_benchmark_nontargeting_nonlinear.py
new file mode 100644
index 0000000..2acc6a4
--- /dev/null
+++ b/scripts/nontargeting_experiments/20240902_da_static_benchmark_nontargeting_nonlinear.py
@@ -0,0 +1,265 @@
+# Imports
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18 import VAEResNet18
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torchvision.transforms import v2
+import subprocess
+import pandas as pd
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+from pathlib import Path
+from tqdm import tqdm
+import yaml
+from embed_time.static_utils import read_config
+
+# All settings
+# Hyperparameters
+beta = 1e-5
+nc = 4
+z_dim = 10
+num_workers = 8
+lr = 1e-4
+batch_size = 16
+num_epochs = 30
+transform = "min"
+crop_size = 96
+channels = [0, 1, 2, 3]
+# Basic values for logging 
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+output_dir = '/mnt/efs/dlmbl/G-et/da_testing/'
+output_path = output_dir + 'training_logs/'
+model_name = f"static_resnet_linear_vae_da_benchmark_nonlinear_{beta}_{z_dim}_{lr}"
+run_name= "da_testing"
+find_port = True
+
+# Define variables for the dataset read in
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark_nontargeting.csv'
+split = 'train'
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard(output_path)
+logger = SummaryWriter(f"{output_path}/{model_name}")
+
+# Create the dataset
+dataset_mean, dataset_std = read_config(yaml_file_path)
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=batch_size, 
+    shuffle=True, 
+    num_workers=num_workers,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Create the model
+vae = VAEResNet18(nc=nc, z_dim=z_dim) # , input_spatial_dim=[crop_size,crop_size])
+
+vae = vae.to(device)
+
+# Define the optimizer
+optimizer = torch.optim.Adam(vae.parameters(), lr=lr)
+
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+
+
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    beta=1e-3
+    ):
+    pbar = tqdm(enumerate(loader), total=len(loader), desc=f"Epoch {epoch}")
+    model.train()
+    log_losses = {
+        "train_loss": 0, 
+        "train_MSE": 0,
+        "train_KLD": 0
+    }
+    train_loss = 0
+    for batch_idx, batch in pbar:
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + KLD * beta
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+
+        log_losses["train_loss"] += loss.item()
+        log_losses["train_MSE"] += MSE.item()
+        log_losses["train_KLD"] += KLD.item()
+        
+        if batch_idx % log_interval == 0:
+            pbar.set_postfix({'loss': log_losses["train_loss"] / log_interval })
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': log_losses["train_loss"] / log_interval,
+                'MSE': log_losses["train_MSE"] / log_interval,
+                'KLD': log_losses["train_KLD"] / log_interval 
+            }
+            training_log.append(row)
+            log_losses = {
+                "train_loss": 0, 
+                "train_MSE": 0,
+                "train_KLD": 0
+            }
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_MSE", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_KLD", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header=metadata_keys
+                )
+                           
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader)))
+
+# Training loop
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+checkpoint_path = output_path + "checkpoints/static/" + folder_suffix + "/"
+log_path = output_path + "logs/static/"+ folder_suffix + "/"
+
+# Create the directories
+Path(checkpoint_path).mkdir(parents=True, exist_ok=True)
+Path(log_path).mkdir(parents=True, exist_ok=True)
+
+print(
+    f"Saving checkpoints to {checkpoint_path} and logs to {log_path}",
+    f"Model: {model_name}",
+    f"Run: {run_name}",
+    sep="\n",
+)
+
+for epoch in range(0, num_epochs):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger, beta=beta)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch / len(dataloader)
+    }
+    torch.save(checkpoint, checkpoint_path + filename_suffix + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/nontargeting_experiments/20240902_da_static_training_loop.py b/scripts/nontargeting_experiments/20240902_da_static_training_loop.py
new file mode 100644
index 0000000..05ec762
--- /dev/null
+++ b/scripts/nontargeting_experiments/20240902_da_static_training_loop.py
@@ -0,0 +1,255 @@
+# Imports
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18_linear import VAEResNet18_Linear
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torchvision.transforms import v2
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+from pathlib import Path
+from tqdm import tqdm
+import torchview
+import yaml
+import sys
+from embed_time.static_utils import read_config
+
+# All settings
+# Basic values for logging 
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+output_dir = '/mnt/efs/dlmbl/G-et/da_testing/'
+output_path = output_dir + 'training_logs/'
+logger_dir = output_path + 'tb_logs/'
+model_name = "static_resnet_linear_vae_da_10"
+run_name= "da_testing"
+find_port = True
+
+# Define variables for the dataset read in
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_17_sampled.csv'
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = "masks"
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+# Hyperparameters
+beta = 1e-5
+nc = 4
+z_dim = 32
+num_workers = 8
+lr = 1e-5
+batch_size = 16
+
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard(logger_dir)
+logger = SummaryWriter(f"{logger_dir}/{model_name}")
+
+# Create the dataset
+dataset_mean, dataset_std = read_config(yaml_file_path)
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=batch_size, 
+    shuffle=True, 
+    num_workers=num_workers,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Create the model
+vae = VAEResNet18_Linear(nc=nc, z_dim=z_dim, input_spatial_dim=[crop_size,crop_size])
+
+vae = vae.to(device)
+
+# Define the optimizer
+optimizer = torch.optim.Adam(vae.parameters(), lr=lr)
+
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    beta=1e-3
+    ):
+    pbar = tqdm(enumerate(loader), total=len(loader), desc=f"Epoch {epoch}")
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in pbar: # enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, z, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + KLD * beta
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        loss_per_epoch = train_loss / len(dataloader.dataset)
+        
+        # log to tqdm in the console
+        if batch_idx % 100 == 0:
+            pbar.set_postfix({'loss': loss_per_epoch})
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch['cell_image']),  # TODO fix
+                'MSE': MSE.item() / len(batch['cell_image']),  # TODO fix
+                'KLD': KLD.item() / len(batch['cell_image'])   # TODO fix
+            }
+            training_log.append(row)
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_MSE", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_KLD", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header=metadata_keys
+                )
+                           
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+
+# Training loop
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+checkpoint_path = output_path + "checkpoints/static/" + folder_suffix + "/"
+log_path = output_path + "logs/static/"+ folder_suffix + "/"
+
+# Create the directories
+Path(checkpoint_path).mkdir(parents=True, exist_ok=True)
+Path(log_path).mkdir(parents=True, exist_ok=True)
+
+print(
+    f"Saving checkpoints to {checkpoint_path} and logs to {log_path}",
+    f"Model: {model_name}",
+    f"Run: {run_name}",
+    sep="\n",
+)
+
+for epoch in range(1, 100):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger, beta=beta)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch 
+    }
+    torch.save(checkpoint, output_path + filename_suffix + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/nontargeting_experiments/train_vgg.py b/scripts/nontargeting_experiments/train_vgg.py
new file mode 100644
index 0000000..72eaa75
--- /dev/null
+++ b/scripts/nontargeting_experiments/train_vgg.py
@@ -0,0 +1,195 @@
+# %%
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from funlib.learn.torch.models import Vgg2D
+from torchvision.transforms import v2
+from embed_time.static_utils import read_config
+from torch.utils.data import DataLoader
+import torch
+from tqdm import tqdm
+import numpy as np
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+from pathlib import Path
+# %% Load the dataset
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset = "benchmark_nontargeting_barcode"
+csv_file = f"/mnt/efs/dlmbl/G-et/csv/dataset_split_{dataset}.csv"
+label_type = 'barcode'
+balance_classes = True
+
+save_dir = Path(f"/mnt/efs/dlmbl/G-et/da_testing/vgg2d_{dataset}/{label_type}_{balance_classes}")
+save_dir.mkdir(exist_ok=True, parents=True)
+
+df = pd.read_csv(csv_file)
+class_names = df[label_type].sort_values().unique().tolist()
+num_classes = len(class_names)
+
+print(f"Class names: {class_names}")
+
+# Hyperparameters
+batch_size = 16
+num_workers = 16
+epochs = 30
+
+# %% Load the training dataset
+# Create the dataset
+dataset_mean, dataset_std = read_config(yaml_file_path)
+dataset = ZarrCellDataset(
+    parent_dir = '/mnt/efs/dlmbl/S-md/',
+    csv_file = csv_file, 
+    split='train',
+    channels=[0, 1, 2, 3], 
+    mask='min', 
+    normalizations=normalizations,
+    interpolations=None, 
+    mean=dataset_mean, 
+    std=dataset_std
+)
+
+if balance_classes:
+    df = pd.read_csv(csv_file)
+    df = df[df['split'] == 'train']
+    all_labels = df[label_type].tolist()
+    weights = [1 / all_labels.count(label) for label in all_labels]
+    print(f"Weighting classes: {np.unique(weights)}")
+    balanced_sampler = torch.utils.data.WeightedRandomSampler(
+        weights=weights,
+        num_samples=len(dataset),
+        replacement=True
+    )
+    dataloader = DataLoader(
+        dataset, 
+        batch_size=batch_size, 
+        num_workers=num_workers,
+        sampler=balanced_sampler,
+        collate_fn=collate_wrapper(metadata_keys, images_keys),
+        drop_last=True
+    )
+else:
+    # Create a DataLoader for the dataset
+    dataloader = DataLoader(
+        dataset, 
+        batch_size=batch_size, 
+        shuffle=True, 
+        num_workers=num_workers,
+        collate_fn=collate_wrapper(metadata_keys, images_keys)
+    )
+
+# %% Load the validation dataset
+val_dataset = ZarrCellDataset(
+    parent_dir = '/mnt/efs/dlmbl/S-md/',
+    csv_file = csv_file, 
+    split='val',
+    channels=[0, 1, 2, 3], 
+    mask='min', 
+    normalizations=normalizations,
+    interpolations=None, 
+    mean=dataset_mean, 
+    std=dataset_std
+)
+
+# Create a DataLoader for the validation dataset
+val_dataloader = DataLoader(
+    val_dataset, 
+    batch_size=batch_size, 
+    shuffle=True, 
+    num_workers=num_workers,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+# %%
+# print the length of both datasets
+len(dataset), len(val_dataset)
+
+# %% Define the model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = Vgg2D(
+    input_size=(96, 96),
+    input_fmaps=4,
+    output_classes=num_classes,
+)
+model = model.to(device)
+
+# %% Define the loss function
+loss_function = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
+
+# %% Training loop
+losses = []
+val_losses = []
+val_accuracies = []
+for epoch in range(epochs):
+    model.train()
+    epoch_loss = 0
+    for batch in tqdm(dataloader, desc=f"Epoch {epoch}", total=len(dataloader)):
+        images, labels = batch['cell_image'], batch[label_type]
+        labels = torch.tensor(
+            [class_names.index(label) for label in labels]
+        )
+        images = images.to(device)
+        labels = labels.to(device)
+
+        optimizer.zero_grad()
+        output = model(images)
+        loss = loss_function(output, labels)
+        loss.backward()
+        optimizer.step()
+        epoch_loss += loss.item()
+    print(f"Epoch {epoch}, loss: {epoch_loss / len(dataloader)}")
+    losses.append(epoch_loss / len(dataloader))
+
+    model.eval()
+    epoch_val_loss = 0
+    correct = 0
+    with torch.inference_mode():
+        for batch in tqdm(val_dataloader, desc=f"Validation", total=len(val_dataloader)):
+            images, labels = batch['cell_image'], batch[label_type]
+            labels = torch.tensor(
+                [class_names.index(label) for label in labels]
+            )
+            images = images.to(device)
+            labels = labels.to(device)
+
+            output = model(images)
+            loss = loss_function(output, labels)
+            epoch_val_loss += loss.item()
+
+            correct += (output.argmax(dim=1) == labels).sum().item()
+    print(f"Validation loss: {epoch_val_loss / len(val_dataloader)}")
+    val_losses.append(epoch_val_loss / len(val_dataloader))
+    print(f"Validation accuracy: {correct / len(val_dataset)}")
+    val_accuracies.append(correct / len(val_dataset))
+
+    # Save the model
+    state_dict = {
+        'epoch': epoch,
+        'model_state_dict': model.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'epoch_loss': epoch_loss / len(dataloader),
+        'epoch_val_loss': epoch_val_loss / len(val_dataloader),
+        'val_accuracy': correct / len(val_dataset)
+    }
+    torch.save(state_dict, save_dir / f"{epoch}.pth")
+
+
+# %% Plot the loss
+plt.plot(losses, label="Train")
+plt.plot(val_losses, label="Validation")
+plt.legend()
+plt.show()
+plt.plot(val_accuracies, label="Validation accuracy")
+plt.legend()
+plt.show()
+
+# %% Save the losses and accuracies
+with open(save_dir / "metrics.csv", "w") as f:
+    f.write("epoch,loss,val_loss,val_accuracy\n")
+    for i in range(epochs):
+        f.write(f"{i},{losses[i]},{val_losses[i]},{val_accuracies[i]}\n")
diff --git a/scripts/print_model_ac.py b/scripts/print_model_ac.py
new file mode 100644
index 0000000..332f73d
--- /dev/null
+++ b/scripts/print_model_ac.py
@@ -0,0 +1,39 @@
+import torch
+import torch.nn as nn
+import torchview as tv
+import matplotlib.pyplot as plt
+from embed_time.model_VAE_resnet18 import VAEResNet18
+from embed_time.model_VAE_resnet18_linear_ac import VAEResNet18_linear
+
+output_path = '/mnt/efs/dlmbl/G-et/logs/'
+filename = "VAEResNet18_zdim10"
+
+# Example model
+# class SimpleModel(nn.Module):
+#     def __init__(self):
+#         super(SimpleModel, self).__init__()
+#         self.conv1 = nn.Conv2d(1, 10, kernel_size=5)
+#         self.conv2 = nn.Conv2d(10, 20, kernel_size=5)
+#         self.fc1 = nn.Linear(320, 50)
+#         self.fc2 = nn.Linear(50, 10)
+
+#     def forward(self, x):
+#         x = torch.relu(self.conv1(x))
+#         x = torch.relu(self.conv2(x))
+#         x = x.view(-1, 320)
+#         x = torch.relu(self.fc1(x))
+#         x = self.fc2(x)
+#         return x
+    
+
+
+# Instantiate the model and create a dummy input
+model = VAEResNet18(nc=4, z_dim=10)
+dummy_input = torch.randn(1, 4, 128, 128)
+
+# Draw the model graph
+graph = tv.draw_graph(model, input_data=dummy_input, 
+                      save_graph=True, filename=filename,
+                      directory=output_path)
+
+
diff --git a/scripts/time-series/train_first_vanilla_model.py b/scripts/time-series/train_first_vanilla_model.py
new file mode 100644
index 0000000..032ae93
--- /dev/null
+++ b/scripts/time-series/train_first_vanilla_model.py
@@ -0,0 +1,125 @@
+"""
+This script was used to train the pre-trained model weights that were given as an option during the exercise.
+"""
+
+from embed_time.dataloader_rs import LiveTLSDataset
+from embed_time.model import Encoder, Decoder, VAE
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from tqdm import tqdm
+from pathlib import Path
+import os
+import skimage.io as io
+import torchvision.transforms as trans
+from torchvision.transforms import v2
+from embed_time.transforms import CustomToTensor, SelectRandomTimepoint
+from embed_time.dataloader_rs import LiveTLSDataset
+
+
+# return reconstruction error + KL divergence losses
+def loss_function(recon_x, x, mu, log_var):
+    MSE = F.mse_loss(recon_x,x,reduction='mean')
+    KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
+    return MSE + KLD
+
+def train(epoch, model, loss_fn, optimizer, train_loader,checkpoint_dir):
+    model.train()
+    train_loss = 0
+    losses = []
+    for batch_idx, (data, _) in enumerate(train_loader):
+        data = data.cuda()
+        optimizer.zero_grad()
+        
+        recon_batch, mu, log_var = model(data)
+        loss = loss_fn(recon_batch, data, mu, log_var)
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        
+        if batch_idx % 10 == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item() / len(data)))
+        losses.append(loss.item())
+    
+    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))
+
+    PATH = os.path.join(checkpoint_dir, f'chkpnt_e{epoch}.pth')
+
+    torch.save(
+        {
+            "model": model.state_dict(),
+            "optimizer": optimizer.state_dict(),
+            "epoch": epoch,
+        },
+        checkpoint_dir / f"checkpoint_{epoch}.pth",
+    )
+from datetime import datetime
+
+if __name__ == "__main__":
+    base_dir = "/mnt/efs/dlmbl/G-et/checkpoints/time-series"
+    checkpoint_dir = Path(base_dir) / f"{datetime.today().strftime('%Y-%m-%d')}_checkpoints"
+    print(checkpoint_dir)
+
+    checkpoint_dir.mkdir(exist_ok=True)
+    data_location = "/mnt/efs/dlmbl/G-et/data/live-TLS"
+    folder_imgs = data_location +"/"+'Control_Dataset_4TP_Normalized'
+    metadata = data_location + "/" +'Control_Dataset_4TP_Ground_Truth'
+
+    loading_transforms = trans.Compose([
+        CustomToTensor(),
+        SelectRandomTimepoint(0),
+        v2.RandomAffine(
+            degrees=90,
+            translate=[0.1,0.1],
+        ),
+        v2.RandomHorizontalFlip(),
+        v2.RandomVerticalFlip(),
+        v2.GaussianNoise(0,0.05)
+    ])
+
+    dataset_w_t = LiveTLSDataset(
+        metadata,
+        folder_imgs,
+        metadata_columns=["Run","Plate","ID"],
+        return_metadata=False,
+        transform = loading_transforms,
+    )
+
+    sample, label = dataset_w_t[0]
+    in_channels, y, x = sample.shape
+    print(in_channels)
+    print((y,x))
+
+    NUM_EPOCHS = 50
+    encoder = Encoder(input_shape=(y,x),
+                      x_dim=in_channels,
+                      h_dim1=8,
+                      h_dim2=16,
+                      z_dim=10)
+    decoder = Decoder(z_dim=10,
+                      h_dim1=16,
+                      h_dim2=8,
+                      x_dim=2,
+                      output_shape=(y,x))
+    model = VAE(encoder, decoder)
+    dataloader = DataLoader(dataset_w_t, batch_size=4, shuffle=True, pin_memory=True)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    print(device)
+    for epoch in range(NUM_EPOCHS):
+        train(
+            epoch,
+            model,
+            loss_function,
+            optimizer,
+            dataloader,
+            checkpoint_dir=checkpoint_dir)
+        # test()
+    
+
+   
\ No newline at end of file
diff --git a/scripts/time-series/train_second_model_unet_encdec.py b/scripts/time-series/train_second_model_unet_encdec.py
new file mode 100755
index 0000000..5b60078
--- /dev/null
+++ b/scripts/time-series/train_second_model_unet_encdec.py
@@ -0,0 +1,141 @@
+"""
+This script was used to train the pre-trained model weights that were given as an option during the exercise.
+"""
+
+from embed_time.dataloader_rs import LiveTLSDataset
+from embed_time.model import VAE
+from embed_time.UNet_based_encoder_decoder import UNetDecoder, UNetEncoder
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from tqdm import tqdm
+from pathlib import Path
+import os
+import skimage.io as io
+import torchvision.transforms as trans
+from torchvision.transforms import v2
+from embed_time.transforms import CustomToTensor, SelectRandomTPNumpy, CustomCropCentroid
+from embed_time.dataloader_rs import LiveTLSDataset
+
+
+# return reconstruction error + KL divergence losses
+def loss_function(recon_x, x, mu, log_var):
+    MSE = F.mse_loss(recon_x,x,reduction='mean')
+    KLD = -0.5 * torch.sum(1 + log_var - mu.pow(2) - log_var.exp())
+    return MSE + KLD
+
+def train(epoch, model, loss_fn, optimizer, train_loader,checkpoint_dir, metadata=None):
+    model.train()
+    train_loss = 0
+    losses = []
+    for batch_idx, (data, _) in enumerate(train_loader):
+        data = data.cuda()
+        optimizer.zero_grad()
+        
+        recon_batch, mu, log_var = model(data)
+        loss = loss_fn(recon_batch, data, mu, log_var)
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        
+        if batch_idx % 10 == 0:
+            print('Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}'.format(
+                epoch, batch_idx * len(data), len(train_loader.dataset),
+                100. * batch_idx / len(train_loader), loss.item() / len(data)))
+        losses.append(loss.item())
+    
+    print('====> Epoch: {} Average loss: {:.4f}'.format(epoch, train_loss / len(train_loader.dataset)))
+
+    PATH = os.path.join(checkpoint_dir, f'chkpnt_e{epoch}.pth')
+
+    torch.save(
+        {
+            "model": model.state_dict(),
+            "optimizer": optimizer.state_dict(),
+            "epoch": epoch,
+            "metadata": metadata
+        },
+        checkpoint_dir / f"checkpoint_{epoch}.pth",
+    )
+from datetime import datetime
+
+if __name__ == "__main__":
+    base_dir = "/mnt/efs/dlmbl/G-et/checkpoints/time-series"
+    checkpoint_dir = Path(base_dir) / f"{datetime.today().strftime('%Y-%m-%d')}_UNEt_encdec_02_checkpoints"
+    print(checkpoint_dir)
+
+    checkpoint_dir.mkdir(exist_ok=True)
+    data_location = "/mnt/efs/dlmbl/G-et/data/live-TLS"
+    folder_imgs = data_location +"/"+'Control_Dataset_4TP_Normalized'
+    metadata = data_location + "/" +'Control_Dataset_4TP_Ground_Truth'
+
+    loading_transforms_wcrop = trans.Compose([
+        SelectRandomTPNumpy(0),
+        CustomCropCentroid(0,0,598),
+        CustomToTensor(),
+        v2.Resize((576,576)),
+        v2.RandomAffine(
+            degrees=90,
+            translate=[0.1,0.1],
+        ),
+        v2.RandomHorizontalFlip(),
+        v2.RandomVerticalFlip(),
+        v2.GaussianBlur(kernel_size=3, sigma=(0.1,1.0)),
+    ])
+
+    dataset_w_t = LiveTLSDataset(
+        metadata,
+        folder_imgs,
+        metadata_columns=["Run","Plate","ID"],
+        return_metadata=False,
+        transform = loading_transforms_wcrop,
+    )
+
+    sample, label = dataset_w_t[0]
+    in_channels, y, x = sample.shape
+    print(in_channels)
+    print((y,x))
+
+    NUM_EPOCHS = 50
+    n_fmaps = 10
+    depth = 4
+    z_dim = 25
+    model_dict = {'num_epochs': NUM_EPOCHS,
+                  'n_fmaps': n_fmaps,
+                  'depth': depth,
+                  'z_dim': z_dim}
+    encoder = UNetEncoder(
+        in_channels = in_channels,
+        n_fmaps = n_fmaps,
+        depth = depth,
+        in_spatial_shape = (y,x),
+        z_dim = z_dim,
+    )
+
+    decoder = UNetDecoder(
+        in_channels = in_channels,
+        n_fmaps = n_fmaps,
+        depth = depth,
+        in_spatial_shape = (y,x),
+        z_dim = z_dim,
+        upsample_mode="bicubic"
+    )
+
+    model = VAE(encoder, decoder)
+    dataloader = DataLoader(dataset_w_t, batch_size=4, shuffle=True, pin_memory=True)
+
+    optimizer = torch.optim.Adam(model.parameters(), lr=1e-4)
+    device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+    model.to(device)
+    print(device)
+    for epoch in range(NUM_EPOCHS):
+        train(
+            epoch,
+            model,
+            loss_function,
+            optimizer,
+            dataloader,
+            checkpoint_dir=checkpoint_dir,
+            metadata=model_dict)
+        # test()
\ No newline at end of file
diff --git a/scripts/train_vgg_md.py b/scripts/train_vgg_md.py
new file mode 100644
index 0000000..896b6fe
--- /dev/null
+++ b/scripts/train_vgg_md.py
@@ -0,0 +1,205 @@
+# %%
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from funlib.learn.torch.models import Vgg2D
+from torchvision.transforms import v2
+from embed_time.static_utils import read_config
+from torch.utils.data import DataLoader
+import torch
+from tqdm import tqdm
+import numpy as np
+from sklearn.metrics import confusion_matrix
+import seaborn as sns
+import matplotlib.pyplot as plt
+import pandas as pd
+from pathlib import Path
+from datetime import datetime
+
+# %% Load the dataset
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+crop_size = 96
+channels = [0, 1, 2, 3]
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset = "benchmark"
+csv_file = f"/mnt/efs/dlmbl/G-et/csv/dataset_split_{dataset}.csv"
+label_type = 'barcode'
+balance_classes = True
+output_dir = "/mnt/efs/dlmbl/G-et/"
+find_port = True
+
+# Hyperparameters
+batch_size = 16
+num_workers = 8
+epochs = 30
+model_name = "Vgg2D"
+transform = "min"
+
+run_name = f"{model_name}_transform_{transform}_{dataset}"
+
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+log_path = output_dir + "logs/static/Matteo/"+ folder_suffix + "/"
+checkpoint_path = output_dir + "checkpoints/static/Matteo/" + folder_suffix + "/"
+
+df = pd.read_csv(csv_file)
+class_names = df[label_type].sort_values().unique().tolist()
+num_classes = len(class_names)
+print(f"Class names: {class_names}")
+
+# %% Load the training dataset
+# Create the dataset
+dataset_mean, dataset_std = read_config(yaml_file_path)
+dataset = ZarrCellDataset(
+    parent_dir = parent_dir,
+    csv_file = csv_file, 
+    split='train',
+    channels=[0, 1, 2, 3], 
+    mask=transform, 
+    normalizations=normalizations,
+    interpolations=None, 
+    mean=dataset_mean, 
+    std=dataset_std
+)
+
+if balance_classes:
+    df = pd.read_csv(csv_file)
+    df = df[df['split'] == 'train']
+    all_labels = df[label_type].tolist()
+    weights = [1 / all_labels.count(label) for label in all_labels]
+    print(f"Weighting classes: {np.unique(weights)}")
+    balanced_sampler = torch.utils.data.WeightedRandomSampler(
+        weights=weights,
+        num_samples=len(dataset),
+        replacement=True
+    )
+    dataloader = DataLoader(
+        dataset, 
+        batch_size=batch_size, 
+        num_workers=num_workers,
+        sampler=balanced_sampler,
+        collate_fn=collate_wrapper(metadata_keys, images_keys),
+        drop_last=True
+    )
+else:
+    # Create a DataLoader for the dataset
+    dataloader = DataLoader(
+        dataset, 
+        batch_size=batch_size, 
+        shuffle=True, 
+        num_workers=num_workers,
+        collate_fn=collate_wrapper(metadata_keys, images_keys)
+    )
+
+# %% Load the validation dataset
+val_dataset = ZarrCellDataset(
+    parent_dir = '/mnt/efs/dlmbl/S-md/',
+    csv_file = csv_file, 
+    split='val',
+    channels=[0, 1, 2, 3], 
+    mask='min', 
+    normalizations=normalizations,
+    interpolations=None, 
+    mean=dataset_mean, 
+    std=dataset_std
+)
+
+# Create a DataLoader for the validation dataset
+val_dataloader = DataLoader(
+    val_dataset, 
+    batch_size=batch_size, 
+    shuffle=True, 
+    num_workers=num_workers,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+# %%
+# print the length of both datasets
+print(len(dataset), len(val_dataset))
+
+# %% Define the model
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model = Vgg2D(
+    input_size=(96, 96),
+    input_fmaps=4,
+    output_classes=num_classes,
+)
+model = model.to(device)
+
+# %% Define the loss function
+loss_function = torch.nn.CrossEntropyLoss()
+optimizer = torch.optim.AdamW(model.parameters(), lr=1e-4)
+
+# %% Training loop
+losses = []
+val_losses = []
+val_accuracies = []
+for epoch in range(epochs):
+    model.train()
+    epoch_loss = 0
+    for batch in tqdm(dataloader, desc=f"Epoch {epoch}", total=len(dataloader)):
+        images, labels = batch['cell_image'], batch[label_type]
+        labels = torch.tensor(
+            [class_names.index(label) for label in labels]
+        )
+        images = images.to(device)
+        labels = labels.to(device)
+
+        optimizer.zero_grad()
+        output = model(images)
+        loss = loss_function(output, labels)
+        loss.backward()
+        optimizer.step()
+        epoch_loss += loss.item()
+    print(f"Epoch {epoch}, loss: {epoch_loss / len(dataloader)}")
+    losses.append(epoch_loss / len(dataloader))
+
+    model.eval()
+    epoch_val_loss = 0
+    correct = 0
+    with torch.inference_mode():
+        for batch in tqdm(val_dataloader, desc=f"Validation", total=len(val_dataloader)):
+            images, labels = batch['cell_image'], batch[label_type]
+            labels = torch.tensor(
+                [class_names.index(label) for label in labels]
+            )
+            images = images.to(device)
+            labels = labels.to(device)
+
+            output = model(images)
+            loss = loss_function(output, labels)
+            epoch_val_loss += loss.item()
+
+            correct += (output.argmax(dim=1) == labels).sum().item()
+    print(f"Validation loss: {epoch_val_loss / len(val_dataloader)}")
+    val_losses.append(epoch_val_loss / len(val_dataloader))
+    print(f"Validation accuracy: {correct / len(val_dataset)}")
+    val_accuracies.append(correct / len(val_dataset))
+
+    # Save the model
+    state_dict = {
+        'epoch': epoch,
+        'model_state_dict': model.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'epoch_loss': epoch_loss / len(dataloader),
+        'epoch_val_loss': epoch_val_loss / len(val_dataloader),
+        'val_accuracy': correct / len(val_dataset)
+    }
+    torch.save(state_dict, checkpoint_path + f"epoch_{epoch}.pt")
+
+
+# %% Plot the loss
+plt.plot(losses, label="Train")
+plt.plot(val_losses, label="Validation")
+plt.legend()
+plt.show()
+plt.plot(val_accuracies, label="Validation accuracy")
+plt.legend()
+plt.show()
+
+# %% Save the losses and accuracies
+with open(log_path / "metrics.csv", "w") as f:
+    f.write("epoch,loss,val_loss,val_accuracy\n")
+    for i in range(epochs):
+        f.write(f"{i},{losses[i]},{val_losses[i]},{val_accuracies[i]}\n")
diff --git a/scripts/training_loop.py b/scripts/training_loop.py
new file mode 100644
index 0000000..ac7d56c
--- /dev/null
+++ b/scripts/training_loop.py
@@ -0,0 +1,286 @@
+#%%
+import os
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model import Encoder, Decoder, VAE
+import torch
+from torch.utils.data import DataLoader
+
+from torchvision.transforms import v2
+from torch.nn import functional as F
+from torch import optim
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import yaml
+
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+#%% Generate Dataset
+
+# Usage example:
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+output_path = '/mnt/efs/dlmbl/G-et/training_logs/'
+# output_file = csv_file = output_path + 'example_split.csv'
+model_name = "static_vanilla_vae"
+run_name= "initial_params"
+train_ratio = 0.7
+val_ratio = 0.15
+num_workers = -1
+#change to false if you already have tensorboard running
+find_port = True
+#%% Define the logger for tensorboard
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+
+logger = SummaryWriter(f"embed_time_static_runs/{model_name}")
+
+# Create the dataset split CSV file
+# DatasetSplitter(parent_dir, output_file, train_ratio, val_ratio, num_workers).generate_split()
+
+#already generated split csv
+csv_file = '/mnt/efs/dlmbl/G-et/csv/split_804.csv'
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = "masks"
+crop_size = 100
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+#%% Generate Dataloader
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=16, 
+    shuffle=True, 
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+
+#%% Create the model
+
+encoder = Encoder(input_shape=(100, 100),
+                  x_dim=4,
+                  h_dim1=16,
+                  h_dim2=8,
+                  z_dim=4)
+decoder = Decoder(z_dim=4,
+                  h_dim1=8,
+                  h_dim2=16,
+                  x_dim=4,
+                  output_shape=(100, 100))
+
+# Initiate VAE
+vae = VAE(encoder, decoder).to(device)
+
+#%% Define Optimizar
+optimizer = torch.optim.Adam(vae.parameters(), lr=1e-4)
+
+def loss_function(recon_x, x, mu, logvar):
+    BCE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return BCE, KLD   
+
+
+
+
+#%% Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = vae(data)
+        BCE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = BCE + KLD  
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        loss_per_epoch = train_loss / len(dataloader.dataset)
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(loader.dataset),
+                    100.0 * batch_idx / len(loader),
+                    loss.item(),
+                )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch['cell_image']),
+                'BCE': BCE.item() / len(batch['cell_image']),
+                'KLD': KLD.item() / len(batch['cell_image'])
+            }
+            training_log.append(row)
+
+
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+
+                
+                metadata = list(zip(batch['gene'], batch['barcode'], batch['stage']))
+                tb_logger.add_embedding(
+                    mu, metadata=metadata, label_img = input_image[:,2:3,...],global_step=step
+                )
+               
+                # TODO saving model
+            
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+
+#%% Training loop
+
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+checkpoint_path = output_path + "checkpoints/static/" + folder_suffix + "/"
+os.makedirs(checkpoint_path, exist_ok=True)
+log_path = output_path + "logs/static/"+ folder_suffix + "/"
+os.makedirs(log_path, exist_ok=True)
+for epoch in range(1, 10):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch 
+    }
+    torch.save(checkpoint, checkpoint_path + filename_suffix + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/training_loop_VaeResnet18_ac.py b/scripts/training_loop_VaeResnet18_ac.py
new file mode 100644
index 0000000..0c1df7c
--- /dev/null
+++ b/scripts/training_loop_VaeResnet18_ac.py
@@ -0,0 +1,292 @@
+#%%
+import os
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model import Encoder, Decoder, VAE
+from embed_time.model_VAE_resnet18 import VAEResNet18
+from embed_time.model_VAE_resnet18_linear_ac import VAEResNet18_linear
+
+import torch
+from torchvision.transforms import v2
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torch import optim
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import yaml
+
+# Parameters
+model_name = "test_linear_ac_latent_128_b5e-6"
+run_name= "Linear_dataset_split_17_latent_128_b5e-6"
+latent_space_dim = 128
+beta = 5e-6
+n_epochs = 15
+find_port = False #change to false if you already have tensorboard running
+
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_17_sampled.csv' 
+# csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_804.csv'
+
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+#%% Generate Dataset
+
+
+
+# Usage example:
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+output_path = '/mnt/efs/dlmbl/G-et/logs/'
+train_ratio = 0.7
+val_ratio = 0.15
+
+#%% Define the logger for tensorboard
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+
+logger = SummaryWriter(f"embed_time_static_runs/{model_name}")
+
+
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = "masks"
+crop_size = 128
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+#%% Generate Dataloader
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=16, 
+    shuffle=True, 
+    num_workers=8,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+
+#%% Create the model
+# Initiate VAE
+model = VAEResNet18_linear(nc=4, z_dim=latent_space_dim).to(device)
+
+#%% Define Optimizar
+optimizer = torch.optim.Adam(model.parameters(), lr=1e-5)
+
+def loss_function(recon_x, x, mu, logvar):
+    BCE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return BCE, KLD   
+
+
+
+
+#%% Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = model,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    beta=1,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = model(data)
+        BCE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = BCE + beta*KLD  
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        loss_per_epoch = train_loss / len(dataloader.dataset)
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(loader.dataset),
+                    100.0 * batch_idx / len(loader),
+                    loss.item(),
+                )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch['cell_image']),
+                'BCE': BCE.item() / len(batch['cell_image']),
+                'KLD': KLD.item() / len(batch['cell_image'])
+            }
+            training_log.append(row)
+
+
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="BCE_loss", scalar_value=BCE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="KLD_loss", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="Channel_0_input", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "Channel_0_reconstruction", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="Channel_1_input", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="Channel_1_reconstruction", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="Channel_2_input", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="Channel_2_reconstruction", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="Channel_3_input", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="Channel_3_reconstruction", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+
+                
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, 
+                    label_img = input_image[:,2:3,...], global_step=step,
+                    metadata_header = metadata_keys
+
+                )
+               
+            
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+
+#%% Training loop
+
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+checkpoint_path = output_path + "checkpoints/static/" + folder_suffix + "/"
+
+os.makedirs(checkpoint_path, exist_ok=True)
+log_path = output_path + "logs/static/"+ folder_suffix + "/"
+os.makedirs(log_path, exist_ok=True)
+# training
+for epoch in range(1, n_epochs):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger, beta=beta)
+    
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': model.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch 
+    }
+    torch.save(checkpoint, checkpoint_path + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/training_loop_basic_md.py b/scripts/training_loop_basic_md.py
new file mode 100644
index 0000000..c7c79bb
--- /dev/null
+++ b/scripts/training_loop_basic_md.py
@@ -0,0 +1,284 @@
+# Imports
+import os
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model import Encoder, Decoder, VAE
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torch import optim
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import torchview
+import yaml
+
+# Yaml file reader
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+# Basic values for logging 
+model_name = "static_basic_vae_md"
+find_port = True
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+logger = SummaryWriter(f"embed_time_static_runs/{model_name}")
+
+# Define variables for the dataset read in
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_2.csv'
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = "masks"
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=16, 
+    shuffle=True, 
+    num_workers=8,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Create the model
+encoder = Encoder(input_shape=(96, 96),
+                  x_dim=4,
+                  h_dim1=16,
+                  h_dim2=8,
+                  z_dim=4)
+decoder = Decoder(z_dim=4,
+                  h_dim1=8,
+                  h_dim2=16,
+                  x_dim=4,
+                  output_shape=(96, 96))
+
+# Initiate VAE
+vae = VAE(encoder, decoder)
+
+torchview.draw_graph(
+    vae,
+    dataset[0]['cell_image'].unsqueeze(dim=0),
+    roll=True,
+    depth=3,  # adjust depth to zoom in.
+    device="cpu",
+    save_graph=True,
+    filename="graphs/" + model_name
+)
+
+vae = vae.to(device)
+
+# Define the optimizer
+optimizer = torch.optim.Adam(vae.parameters(), lr=1e-4)
+
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + KLD * 1e-5
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        loss_per_epoch = train_loss / len(dataloader.dataset)
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(loader.dataset),
+                    100.0 * batch_idx / len(loader),
+                    loss.item(),
+                )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch['cell_image']),
+                'MSE': MSE.item() / len(batch['cell_image']),
+                'KLD': KLD.item() / len(batch['cell_image'])
+            }
+            training_log.append(row)
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_MSE", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_KLD", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.rand_like(mu), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header=metadata_keys
+                )
+                           
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+
+# Training loop
+output_dir = '/mnt/efs/dlmbl/G-et/'
+run_name= "basic_test"
+
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+log_path = output_dir + "logs/static/Matteo/"+ folder_suffix + "/"
+checkpoint_path = output_dir + "checkpoints/static/Matteo/" + folder_suffix + "/"
+
+if not os.path.exists(log_path):
+    os.makedirs(log_path)
+if not os.path.exists(checkpoint_path):
+    os.makedirs(checkpoint_path)
+
+for epoch in range(1, 100):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch 
+    }
+    torch.save(checkpoint, checkpoint_path + filename_suffix + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/training_loop_cond_test_md.py b/scripts/training_loop_cond_test_md.py
new file mode 100644
index 0000000..d1ffcb0
--- /dev/null
+++ b/scripts/training_loop_cond_test_md.py
@@ -0,0 +1,276 @@
+#%%
+# Imports
+import torch
+from torch.utils.data import DataLoader
+from torch.optim import Adam
+from torch.utils.tensorboard import SummaryWriter
+from torchvision.utils import make_grid
+from torch.nn import functional as F
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+from datetime import datetime
+import subprocess
+import os
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import yaml
+
+# Import your custom modules
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.models_contrastive import VAEmodel, Encoder, Decoder
+
+# Set device
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+
+# Yaml file reader
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+# Basic values for logging 
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+output_path = parent_dir + 'training_logs/'
+model_name = "static_vanilla_vae_md_10"
+run_name= "initial_params"
+find_port = True
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# # Launch TensorBoard on the browser
+# def launch_tensorboard(log_dir):
+#     port = find_free_port()
+#     tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+#     process = subprocess.Popen(tensorboard_cmd, shell=True)
+#     print(
+#         f"TensorBoard started at http://localhost:{port}. \n"
+#         "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+#     )
+#     return process
+
+# # Launch tensorboard and click on the link to view the logs.
+# if find_port:
+#     tensorboard_process = launch_tensorboard("embed_time_static_runs")
+# logger = SummaryWriter(f"embed_time_static_runs/{model_name}")
+#%%
+# Define variables for the dataset read in
+csv_file = '/mnt/efs/dlmbl/G-et/csv/split_804.csv'
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = "masks"
+crop_size = 100
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+#%%
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['nuclei_image']
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=16, 
+    shuffle=True, 
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Hyperparameters
+batch_size = 16
+learning_rate = 1e-4
+num_epochs = 100
+latent_dim = 32
+base_channel_size = 32
+step_size = 1000  # for cyclic KL annealing
+#%%
+
+# Model setup
+from embed_time.models_contrastive import VAEmodel, Encoder, Decoder
+from embed_time.model_VAE_resnet18 import VAEResNet18
+
+model = VAEmodel(
+    model_name="static_vanilla_vae_md_10",
+    optimizer_param={"optimizer": "Adam", "lr": learning_rate},
+    latent_dim=latent_dim,
+    base_channel_size=base_channel_size,
+    num_input_channels=4,
+    image_size=96,
+    step_size=step_size,
+    encoder_class=Encoder,
+    decoder_class=Decoder
+)
+model = model.to(device)
+
+#%%
+dataset[0]['nuclei_image'].unsqueeze(dim=0).shape
+#%%
+# use torchview to visualize the model and save the image
+# import torchview
+# torchview.draw_graph(
+#     model,
+#     dataset[0]['nuclei_image'].unsqueeze(dim=0),
+#     roll=True,
+#     depth=3,  # adjust depth to zoom in.
+#     device="cpu",
+#     save_graph=True,
+#     filename="graphs/cond_test_md_96"
+# )
+
+vae = VAEResNet18(nc = 4, z_dim = 10 ).to(device)
+
+torchview.draw_graph(
+    vae,
+    dataset[0]['cell_image'].unsqueeze(dim=0),
+    roll=True,
+    depth=3,  # adjust depth to zoom in.
+    device="cpu",
+    save_graph=True,
+    filename="graphs/vae_100_md"
+)
+#%%
+# Optimizer
+optimizer = model.configure_optimizer()
+
+# TensorBoard setup
+log_dir = f"embed_time_static_runs/gt_vanilla_vae_md_10/initial_params_{datetime.now().strftime('%Y%m%d-%H%M%S')}"
+writer = SummaryWriter(log_dir)
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+
+def train(
+        epoch, 
+        model, 
+        loader, 
+        optimizer, 
+        log_interval=100, 
+        log_image_interval=20, 
+        tb_logger=None,  # Changed from writer to None as default
+        device=device,
+        early_stop=False,
+        training_log=training_log,
+        epoch_log=epoch_log,
+        loss_per_epoch=loss_per_epoch):
+    
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(loader):
+        data = batch['nuclei_image'].to(device)
+        optimizer.zero_grad()
+        
+        # Use the model's train_step method
+        loss, metrics = model.train_step(data)
+
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+
+        # Log to console
+        if batch_idx % 5 == 0:
+            print(f"Train Epoch: {epoch} [{batch_idx * len(data)}/{len(loader.dataset)} "
+                  f"({100. * batch_idx / len(loader):.0f}%)]\tLoss: {loss.item():.6f}")
+
+        # Log to training_log
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(data),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(data),
+                **{k: v / len(data) for k, v in metrics.items()}
+            }
+            training_log.append(row)
+        
+        # Log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar("train/loss", loss.item(), step)
+            for key, value in metrics.items():
+                tb_logger.add_scalar(f"train/{key}", value, step)
+            
+            # Log images
+            if step % log_image_interval == 0:
+                with torch.no_grad():
+                    x_hat, _, _ = model(data)
+                    for i in range(model.num_input_channels):
+                        tb_logger.add_images(f"input_{i}", data[:, i:i+1, ...], step)
+                        tb_logger.add_images(f"reconstruction_{i}", x_hat[:, i:i+1, ...], step)
+                
+                # Add embedding (adjust as necessary)
+                metadata = list(zip(batch.get('gene', []), batch.get('barcode', []), batch.get('stage', [])))
+                embeddings = model.get_image_embedding(data)
+                tb_logger.add_embedding(embeddings, metadata=metadata, label_img=data[:, 2:3, ...], global_step=step)
+        
+        if early_stop and batch_idx > 5:
+            print("Stopping training early!")
+            break
+    
+    # Log epoch summary
+    avg_loss = train_loss / len(loader.dataset)
+    print(f'====> Epoch: {epoch} Average loss: {avg_loss:.4f}')
+    epoch_log.append({'epoch': epoch, 'Average Loss': avg_loss})
+    if tb_logger is not None:
+        tb_logger.add_scalar('train/epoch_loss', avg_loss, epoch)
+    
+    return avg_loss  # Return the average loss for the epoch
+
+# Training loop
+num_epochs = 2  # Adjust as needed
+
+# You can uncomment and adjust these paths when you're ready to save checkpoints and logs
+# output_path = "path/to/your/output/"
+# folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+# checkpoint_path = os.path.join(output_path, "checkpoints", "static", folder_suffix)
+# log_path = os.path.join(output_path, "logs", "static", folder_suffix)
+# os.makedirs(checkpoint_path, exist_ok=True)
+# os.makedirs(log_path, exist_ok=True)
+
+for epoch in range(1, num_epochs + 1):
+    avg_loss = train(epoch, model, dataloader, optimizer)
+    loss_per_epoch = avg_loss
+    
+    # You can uncomment this section when you're ready to save checkpoints
+    # checkpoint = {
+    #     'epoch': epoch,
+    #     'model_state_dict': model.state_dict(),
+    #     'optimizer_state_dict': optimizer.state_dict(),
+    #     'loss': loss_per_epoch 
+    # }
+    # torch.save(checkpoint, os.path.join(checkpoint_path, f"epoch_{epoch}_checkpoint.pth"))
+
+print("Training completed!")
\ No newline at end of file
diff --git a/scripts/training_loop_neuromast.py b/scripts/training_loop_neuromast.py
new file mode 100644
index 0000000..8de6147
--- /dev/null
+++ b/scripts/training_loop_neuromast.py
@@ -0,0 +1,209 @@
+import os
+
+from embed_time.model_VAE_resnet18 import VAEResNet18
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torch.nn import utils as U
+from torch import optim
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import yaml
+from datasets.neuromast import NeuromastDatasetTrain
+from torchview import draw_graph
+
+beta = 1e-7
+lr = 1e-4
+z_dim = 22
+model_name = "neuromast_resnet18_vae_conv2D"
+run_name= "z_dim-"+str(z_dim)+"_lr-"+str(lr)+"_beta-"+str(beta)
+metadata = pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_balanced_train.csv")
+find_port = True
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+
+#launch tensorboard
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+
+logger = SummaryWriter(f"embed_time_static_runs/{run_name}")
+
+#%% Generate Dataset
+Train_dataset = NeuromastDatasetTrain()
+
+#dataloader
+train_loader = DataLoader(Train_dataset, batch_size=2, shuffle=True, num_workers=8)
+
+# Initiate VAE-ResNet18 model
+vae = VAEResNet18(nc = 1, z_dim = z_dim ).to(device)
+
+#%% Define Optimizar
+optimizer = torch.optim.AdamW(vae.parameters(), lr=lr)
+
+#%% Define loss function
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+import torch
+from torchviz import make_dot
+import torch.nn.functional as F
+
+
+
+#%% Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader =train_loader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    beta=beta,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, (batch,label) in enumerate(train_loader):
+        data = batch.to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + beta*KLD  
+        
+        loss.backward()
+        train_loss += loss.item()
+        torch.nn.utils.clip_grad_norm_(model.parameters(), max_norm=1.0)
+        optimizer.step()
+        
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                epoch,
+                batch_idx * len(data),
+                len(loader.dataset),
+                100.0 * batch_idx / len(loader),
+                loss.item(),
+            )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch),
+                'MSE': MSE.item() / len(batch),
+                'KLD': KLD.item() / len(batch)
+            }
+            training_log.append(row)
+
+
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="MSE_loss", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="KLD_loss", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_image(
+                    tag="input_0", img_tensor=input_image[0:1,0,...], global_step=step
+                )
+                tb_logger.add_image(
+                    tag= "reconstruction_0", img_tensor=predicted_image[0:1,0,...], global_step=step
+                )
+                
+                # tb_logger.add_embedding(
+                #     torch.flatten(mu, start_dim=1), metadata=label[0:1], label_img = input_image[0:1,...], global_step=step
+                # )
+               
+        
+
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(train_loader.dataset)))
+    return train_loss/len(train_loader.dataset)
+#%% Training loop
+
+#define the folder path for saving checkpoints and logs
+folder_suffix = datetime.now().strftime("%Y%m%d") + run_name
+checkpoint_path = '/mnt/efs/dlmbl/G-et/checkpoints/static/Akila/' + folder_suffix + "/"
+os.makedirs(checkpoint_path, exist_ok=True)
+log_path = '/mnt/efs/dlmbl/G-et/logs/static/Akila/'+ folder_suffix + "/"
+os.makedirs(log_path, exist_ok=True)
+
+#training loop
+for epoch in range(0, 100):
+    train_loss =train(epoch, beta = beta, log_interval=100, log_image_interval=20, tb_logger=logger)
+
+    train_path = log_path + "_epoch_"+str(epoch)+"/"
+    os.makedirs(train_path, exist_ok=True)
+
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(train_path+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': train_loss 
+    }
+    save_path = checkpoint_path + "_epoch_"+str(epoch)+"/"
+    os.makedirs(save_path, exist_ok=True)
+
+    torch.save(checkpoint, save_path+"checkpoint.pth")
+
diff --git a/scripts/training_loop_resnet18_linear_md.py b/scripts/training_loop_resnet18_linear_md.py
new file mode 100644
index 0000000..7bf4821
--- /dev/null
+++ b/scripts/training_loop_resnet18_linear_md.py
@@ -0,0 +1,272 @@
+# Imports
+import os
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18_linear import VAEResNet18_Linear
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torch import optim
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import torchview
+import yaml
+
+# Yaml file reader
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+# Basic values for logging 
+model_name = "static_resnet_linear_vae_md_nomask"
+find_port = True
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+logger = SummaryWriter(f"embed_time_static_runs/{model_name}")
+
+# Define variables for the dataset read in
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_17_sampled.csv'
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = None
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=16, 
+    shuffle=True, 
+    num_workers=8,
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Create the model
+vae = VAEResNet18_Linear(nc = 4, z_dim = 32, input_spatial_dim = [96,96])
+
+torchview.draw_graph(
+    vae,
+    dataset[0]['cell_image'].unsqueeze(dim=0),
+    roll=True,
+    depth=3,  # adjust depth to zoom in.
+    device="cpu",
+    save_graph=True,
+    filename="graphs/" + model_name
+)
+
+vae = vae.to(device)
+
+# Define the optimizer
+optimizer = torch.optim.Adam(vae.parameters(), lr=1e-3)
+
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, z, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + KLD * 1e-4
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        loss_per_epoch = train_loss / len(dataloader.dataset)
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(loader.dataset),
+                    100.0 * batch_idx / len(loader),
+                    loss.item(),
+                )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch['cell_image']),
+                'MSE': MSE.item() / len(batch['cell_image']),
+                'KLD': KLD.item() / len(batch['cell_image'])
+            }
+            training_log.append(row)
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_MSE", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_KLD", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header=metadata_keys
+                )
+                           
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+
+# Training loop
+output_dir = '/mnt/efs/dlmbl/G-et/'
+run_name= "resnet_linear_17_32dim_nomask"
+
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+log_path = output_dir + "logs/static/Matteo/"+ folder_suffix + "/"
+checkpoint_path = output_dir + "checkpoints/static/Matteo/" + folder_suffix + "/"
+
+if not os.path.exists(log_path):
+    os.makedirs(log_path)
+if not os.path.exists(checkpoint_path):
+    os.makedirs(checkpoint_path)
+
+for epoch in range(1, 100):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch 
+    }
+    torch.save(checkpoint, checkpoint_path + filename_suffix + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/training_loop_resnet18_md.py b/scripts/training_loop_resnet18_md.py
new file mode 100644
index 0000000..c27b72d
--- /dev/null
+++ b/scripts/training_loop_resnet18_md.py
@@ -0,0 +1,273 @@
+# Imports
+import os
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18 import VAEResNet18
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torch import optim
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import torchview
+import yaml
+
+# Yaml file reader
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+# Basic values for logging 
+model_name = "benchmark_static_resnet_vae_min_mask_360_1e-5"
+find_port = True
+
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+logger = SummaryWriter(f"embed_time_static_runs/{model_name}")
+
+# Define variables for the dataset read in
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+csv_file = '/mnt/efs/dlmbl/G-et/csv/dataset_split_benchmark.csv'
+split = 'train'
+channels = [0, 1, 2, 3]
+transform = "min"
+crop_size = 96
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+# Define the metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=16, 
+    shuffle=True, 
+    num_workers=8,    
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Create the model
+vae = VAEResNet18(nc = 4, z_dim = 10)
+
+
+torchview.draw_graph(
+    vae,
+    dataset[0]['cell_image'].unsqueeze(dim=0),
+    roll=True,
+    depth=3,  # adjust depth to zoom in.
+    device="cpu",
+    save_graph=True,
+    filename="graphs/" + model_name
+)
+
+vae = vae.to(device)
+
+# Define the optimizer
+optimizer = torch.optim.Adam(vae.parameters(), lr=1e-4)
+
+def loss_function(recon_x, x, mu, logvar):
+    MSE = F.mse_loss(recon_x, x, reduction='mean')
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return MSE, KLD   
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = vae(data)
+        MSE, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = MSE + KLD * 1e-8
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        loss_per_epoch = train_loss / len(dataloader.dataset)
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(loader.dataset),
+                    100.0 * batch_idx / len(loader),
+                    loss.item(),
+                )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch['cell_image']),
+                'MSE': MSE.item() / len(batch['cell_image']),
+                'KLD': KLD.item() / len(batch['cell_image'])
+            }
+            training_log.append(row)
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_MSE", scalar_value=MSE.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_KLD", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[:,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[:,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[:,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[:,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[:,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[:,2:3,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[:,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[:,3:4,...], global_step=step
+                )
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header=metadata_keys
+                )
+                           
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+
+# Training loop
+output_dir = '/mnt/efs/dlmbl/G-et/'
+run_name= model_name
+
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+log_path = output_dir + "logs/static/Matteo/"+ folder_suffix + "/"
+checkpoint_path = output_dir + "checkpoints/static/Matteo/" + folder_suffix + "/"
+
+if not os.path.exists(log_path):
+    os.makedirs(log_path)
+if not os.path.exists(checkpoint_path):
+    os.makedirs(checkpoint_path)
+
+for epoch in range(1, 30):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch 
+    }
+    torch.save(checkpoint, checkpoint_path + filename_suffix + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/scripts/training_loop_resnet18_md_grid.py b/scripts/training_loop_resnet18_md_grid.py
new file mode 100644
index 0000000..7c2bdc9
--- /dev/null
+++ b/scripts/training_loop_resnet18_md_grid.py
@@ -0,0 +1,281 @@
+# Imports
+import os
+from embed_time.splitter_static import DatasetSplitter
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18 import VAEResNet18
+from embed_time.static_utils import read_config
+import piq
+from ignite.metrics import SSIM
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torch import optim
+from torchvision.transforms import v2
+import matplotlib.pyplot as plt
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+from datetime import datetime
+import torchview
+import yaml
+import argparse
+
+loss_ssim = piq.SSIMLoss()
+
+parser = argparse.ArgumentParser(description='VAE Training')
+parser.add_argument('--z_dim', type=int, default=30, help='Dimension of latent space')
+parser.add_argument('--loss_type', type=str, default='MSE', choices=['L1', 'MSE', 'SSIM'], help='Type of reconstruction loss')
+parser.add_argument('--crop_size', type=int, default=64, help='Size of image crop')
+parser.add_argument('--beta', type=float, default=1e-5, help='Weight of KL divergence in loss')
+parser.add_argument('--transform', type=str, default='min', help='Masking type')
+args = parser.parse_args()
+
+# Define metadata keys
+metadata_keys = ['gene', 'barcode', 'stage']
+images_keys = ['cell_image']
+crop_size = args.crop_size
+channels = [0, 1, 2, 3]
+split = 'train'
+parent_dir = '/mnt/efs/dlmbl/S-md/'
+normalizations = v2.Compose([v2.CenterCrop(crop_size)])
+yaml_file_path = "/mnt/efs/dlmbl/G-et/yaml/dataset_info_20240901_155625.yaml"
+dataset_mean, dataset_std = read_config(yaml_file_path)
+dataset = "benchmark"
+csv_file = f"/mnt/efs/dlmbl/G-et/csv/dataset_split_{dataset}.csv"
+output_dir = "/mnt/efs/dlmbl/G-et/"
+find_port = True
+
+# Hyperparameters
+batch_size = 16
+num_workers = 8
+epochs = 20
+nc = 4
+z_dim = args.z_dim
+lr = 1e-4
+beta = args.beta
+alpha = 0.5
+loss_type = args.loss_type
+transform = args.transform
+model_name = "VAE_ResNet18"
+
+# run name concatenates all hyperparameters
+run_name = f"{model_name}_crop_size_{crop_size}_nc_{nc}_z_dim_{z_dim}_lr_{lr}_beta_{beta}_transform_{transform}_loss_{loss_type}_{dataset}"
+
+folder_suffix = datetime.now().strftime("%Y%m%d_%H%M_") + run_name
+log_path = output_dir + "logs/static/Matteo/"+ folder_suffix + "/"
+checkpoint_path = output_dir + "checkpoints/static/Matteo/" + folder_suffix + "/"
+
+# Check and create necessary directories
+for path in [log_path, checkpoint_path]:
+    if not os.path.exists(path):
+        os.makedirs(path)
+
+if torch.cuda.is_available():
+    device = torch.device("cuda")
+else:
+    device = torch.device("cpu")
+    
+# Function to find an available port
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(f"TensorBoard started at http://localhost:{port}.")
+    print("If using VSCode remote session, forward the port using the PORTS tab next to TERMINAL.")
+    return process
+
+# Launch tensorboard and click on the link to view the logs.
+if find_port:
+    tensorboard_process = launch_tensorboard("embed_time_static_runs")
+logger = SummaryWriter(f"embed_time_static_runs/{run_name}")
+
+# Create the dataset
+dataset = ZarrCellDataset(parent_dir, csv_file, split, channels, transform, normalizations, None, dataset_mean, dataset_std)
+
+# Create a DataLoader for the dataset
+dataloader = DataLoader(
+    dataset, 
+    batch_size=batch_size, 
+    shuffle=True, 
+    num_workers=num_workers,    
+    collate_fn=collate_wrapper(metadata_keys, images_keys)
+)
+
+# Create the model
+vae = VAEResNet18(nc = nc, z_dim = z_dim)
+
+torchview.draw_graph(
+    vae,
+    dataset[0]['cell_image'].unsqueeze(dim=0),
+    roll=True,
+    depth=3,  # adjust depth to zoom in.
+    device="cpu",
+    save_graph=True,
+    filename="graphs/" + run_name,
+)
+
+vae = vae.to(device)
+
+# Define the optimizer
+optimizer = torch.optim.Adam(vae.parameters(), lr=lr)
+
+def loss_function(recon_x, x, mu, logvar, loss_type=loss_type): 
+    if loss_type == "MSE":
+        RECON = F.mse_loss(recon_x, x, reduction='mean')
+    elif loss_type == "L1":
+        RECON = F.l1_loss(recon_x, x, reduction='mean')
+    elif loss_type == "SSIM":
+        # normalize x for ssim (remember shape is BxCxHxW)
+        x_norm = (x - x.min()) / (x.max() - x.min())
+        recon_x_norm = (recon_x - recon_x.min()) / (recon_x.max() - recon_x.min())
+        ssim = loss_ssim(recon_x_norm, x_norm)
+        RECON = F.l1_loss(recon_x, x, reduction='mean') + ssim * alpha
+    KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+    return RECON, KLD   
+
+# Define training function
+training_log = []
+epoch_log = []
+loss_per_epoch = 0
+def train(
+    epoch,
+    model = vae,
+    loader = dataloader,
+    optimizer = optimizer,
+    loss_function = loss_function,
+    log_interval=100,
+    log_image_interval=20,
+    tb_logger=None,
+    device=device,
+    early_stop=False,
+    training_log = training_log,
+    epoch_log = epoch_log,
+    loss_per_epoch = loss_per_epoch
+    ):
+    model.train()
+    train_loss = 0
+    for batch_idx, batch in enumerate(dataloader):
+        data = batch['cell_image'].to(device)
+        optimizer.zero_grad()
+        
+        recon_batch, mu, logvar = vae(data)
+        RECON, KLD  = loss_function(recon_batch, data, mu, logvar)
+        loss = RECON + KLD * beta
+        
+        loss.backward()
+        train_loss += loss.item()
+        optimizer.step()
+        loss_per_epoch = train_loss / len(dataloader.dataset)
+        
+        # log to console
+        if batch_idx % 5 == 0:
+            print(
+                "Train Epoch: {} [{}/{} ({:.0f}%)]\tLoss: {:.6f}".format(
+                    epoch,
+                    batch_idx * len(data),
+                    len(loader.dataset),
+                    100.0 * batch_idx / len(loader),
+                    loss.item(),
+                )
+            )
+            
+        if batch_idx % log_interval == 0:
+            row = {
+                'epoch': epoch,
+                'batch_idx': batch_idx,
+                'len_data': len(batch['cell_image']),
+                'len_dataset': len(loader.dataset),
+                'loss': loss.item() / len(batch['cell_image']),
+                'RECON': RECON.item() / len(batch['cell_image']),
+                'KLD': KLD.item() / len(batch['cell_image'])
+            }
+            training_log.append(row)
+
+        # log to tensorboard
+        if tb_logger is not None:
+            step = epoch * len(loader) + batch_idx
+            tb_logger.add_scalar(
+                tag="train_loss", scalar_value=loss.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_RECON", scalar_value=RECON.item(), global_step=step
+            )
+            tb_logger.add_scalar(
+                tag="train_KLD", scalar_value=KLD.item(), global_step=step
+            )
+            # check if we log images in this iteration
+            if step % log_image_interval == 0:
+                input_image = data.to("cpu").detach()       
+                predicted_image = recon_batch.to("cpu").detach()
+
+                tb_logger.add_images(
+                    tag="input_channel_0", img_tensor=input_image[0:3,0:1,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag= "reconstruction_0", img_tensor=predicted_image[0:3,0:1,...], global_step=step
+                )
+                
+                tb_logger.add_images(
+                    tag="input_1", img_tensor=input_image[0:3,1:2,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_1", img_tensor=predicted_image[0:3,1:2,...], global_step=step
+                )
+
+                tb_logger.add_images(
+                    tag="input_2", img_tensor=input_image[0:3,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_2", img_tensor=predicted_image[0:3,2:3,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="input_3", img_tensor=input_image[0:3,3:4,...], global_step=step
+                )
+                tb_logger.add_images(
+                    tag="reconstruction_3", img_tensor=predicted_image[0:3,3:4,...], global_step=step
+                )
+                metadata = [list(item) for item in zip(batch['gene'], batch['barcode'], batch['stage'])]
+                tb_logger.add_embedding(
+                    torch.flatten(mu, start_dim=1), metadata=metadata, label_img = input_image[:,2:3,...], global_step=step, metadata_header=metadata_keys
+                )
+                           
+        # early stopping
+        if early_stop and batch_idx > 5:
+            print("Stopping test early!")
+            break
+    
+    # save the DF
+    epoch_raw = {
+                'epoch': epoch,
+                'Average Loss': train_loss / len(dataloader.dataset)}
+    epoch_log.append(epoch_raw)
+
+    print('====> Epoch: {} Average loss: {:.4f}'.format(
+            epoch, train_loss / len(dataloader.dataset)))
+
+for epoch in range(epochs):
+    train(epoch, log_interval=100, log_image_interval=20, tb_logger=logger)
+    filename_suffix = datetime.now().strftime("%Y%m%d_%H%M%S_") + "epoch_"+str(epoch) + "_"
+    training_logDF = pd.DataFrame(training_log)
+    training_logDF.to_csv(log_path + filename_suffix+"training_log.csv", index=False)
+
+    epoch_logDF = pd.DataFrame(epoch_log)
+    epoch_logDF.to_csv(log_path + filename_suffix+"epoch_log.csv", index=False)
+
+    checkpoint = {
+        'epoch': epoch,
+        'model_state_dict': vae.state_dict(),
+        'optimizer_state_dict': optimizer.state_dict(),
+        'loss': loss_per_epoch 
+    }
+    torch.save(checkpoint, checkpoint_path + filename_suffix + str(epoch) + "checkpoint.pth")
\ No newline at end of file
diff --git a/src/datasets b/src/datasets
new file mode 160000
index 0000000..bce9aa9
--- /dev/null
+++ b/src/datasets
@@ -0,0 +1 @@
+Subproject commit bce9aa9b5db5495ff431b1114a9e2dda30d27af0
diff --git a/src/embed_time/UNet_based_encoder_decoder.py b/src/embed_time/UNet_based_encoder_decoder.py
new file mode 100644
index 0000000..b29ca6d
--- /dev/null
+++ b/src/embed_time/UNet_based_encoder_decoder.py
@@ -0,0 +1,281 @@
+import torch.nn as nn
+import torch
+from torch.nn import functional as F
+import numpy as np
+
+class ConvBlock(torch.nn.Module):
+    def __init__(
+        self,
+        in_channels: int,
+        out_channels: int,
+        kernel_size: int,
+        padding: str = "same",
+    ):
+        """A convolution block for a U-Net. Contains two convolutions, each followed by a ReLU.
+
+        Args:
+            in_channels (int): The number of input channels for this conv block. Depends on
+                the layer and side of the U-Net and the hyperparameters.
+            out_channels (int): The number of output channels for this conv block. Depends on
+                the layer and side of the U-Net and the hyperparameters.
+            kernel_size (int): The size of the kernel. A kernel size of N signifies an
+                NxN square kernel.
+            padding (str): The type of convolution padding to use. Either "same" or "valid".
+                Defaults to "same".
+        """
+        super().__init__()
+
+        if kernel_size % 2 == 0:
+            msg = "Only allowing odd kernel sizes."
+            raise ValueError(msg)
+
+        # SOLUTION 3.1: Initialize your modules and define layers.
+        self.conv_pass = torch.nn.Sequential(
+            torch.nn.Conv2d(
+                in_channels, out_channels, kernel_size=kernel_size, padding=padding
+            ),
+            torch.nn.ReLU(),
+            torch.nn.Conv2d(
+                out_channels, out_channels, kernel_size=kernel_size, padding=padding
+            ),
+            torch.nn.ReLU(),
+        )
+
+        for _name, layer in self.named_modules():
+            if isinstance(layer, torch.nn.Conv2d):
+                torch.nn.init.kaiming_normal_(layer.weight, nonlinearity="relu")
+
+    def forward(self, x):
+        # SOLUTION 3.2: Apply the modules you defined to the input x
+        return self.conv_pass(x)
+
+
+class UNetEncoder(nn.Module):
+    def __init__(
+            self, 
+            in_channels: int, 
+            n_fmaps: int,
+            depth: int, 
+            in_spatial_shape:tuple, 
+            z_dim: int,
+            fmap_inc_factor=2,
+            padding: str = "same",
+            downsample_factor: int = 2,
+            kernel_size: int = 3,
+            n_convs: int = 2
+        ):
+        self.depth = depth
+        self.num_fmaps = n_fmaps
+        self.in_channels = in_channels
+        self.in_spatial_shape = in_spatial_shape
+        self.kernel_size = kernel_size
+        self.downsample_factor = downsample_factor
+        self.fmap_inc_factor = fmap_inc_factor
+        self.padding = padding
+        self.n_convs = n_convs
+        super(UNetEncoder, self).__init__()
+        self.downsample = nn.MaxPool2d(self.downsample_factor,self.downsample_factor)
+        self.convs = nn.ModuleList()
+        # SOLUTION 6.2A: Initialize list here
+        for level in range(self.depth):
+            fmaps_in, fmaps_out = self.compute_fmaps_encoder(level)
+            self.convs.append(
+                ConvBlock(fmaps_in, fmaps_out, self.kernel_size, self.padding)
+            )
+        self.fc_layer_len = self.compute_final_layers()
+        self.fc1 = nn.Linear(in_features=self.fc_layer_len,out_features=z_dim)
+        self.fc2 = nn.Linear(in_features=self.fc_layer_len,out_features=z_dim)
+
+    def compute_fmaps_encoder(self, level: int) -> tuple[int, int]:
+        """Compute the number of input and output feature maps for
+        a conv block at a given level of the UNet encoder (left side).
+
+        Args:
+            level (int): The level of the U-Net which we are computing
+            the feature maps for. Level 0 is the input level, level 1 is
+            the first downsampled layer, and level=depth - 1 is the bottom layer.
+
+        Output (tuple[int, int]): The number of input and output feature maps
+            of the encoder convolutional pass in the given level.
+        """
+        # SOLUTION 6.1A: Implement this function
+        if level == 0:
+            fmaps_in = self.in_channels
+        else:
+            fmaps_in = self.num_fmaps * self.fmap_inc_factor ** (level - 1)
+
+        fmaps_out = self.num_fmaps * self.fmap_inc_factor**level
+        return fmaps_in, fmaps_out
+    
+    def compute_spatial_shape(self, level: int) -> tuple[int, int]:
+        # TODO Add warning when shape is odd before maxpool
+        spatial_shape = np.array(self.in_spatial_shape)
+        if level == 0:
+            if self.padding == "same":
+                return spatial_shape
+            
+            # 2 convolutions and 2 sizes 
+            spatial_shape = spatial_shape - self.n_convs * (2 * (self.kernel_size //2))
+            return spatial_shape
+
+        if self.padding == "same":
+            spatial_shape = (np.array(self.compute_spatial_shape(level-1))//(self.downsample_factor)) 
+        else:
+            spatial_shape = self.compute_spatial_shape(level-1)
+            spatial_shape = spatial_shape // self.downsample_factor
+            spatial_shape = spatial_shape - self.n_convs * (2 * (self.kernel_size //2))
+        return spatial_shape
+    
+    def compute_final_layers(self):
+        spatial_dims_final = self.compute_spatial_shape(self.depth-1)
+        num_fmaps_final = self.compute_fmaps_encoder(self.depth-1)
+        return num_fmaps_final[1] * np.prod(spatial_dims_final)
+
+    
+    def forward(self, x):
+        for level in range(self.depth -1):
+            x = self.convs[level](x)
+            x = self.downsample(x)
+        x = self.convs[-1](x)
+        # print("shape after convs encoder", x.shape)
+        x = x.view(-1,self.fc_layer_len)
+        return self.fc1(x), self.fc2(x)
+
+class UNetDecoder(nn.Module):
+    def __init__(
+            self, 
+            in_channels: int, 
+            n_fmaps: int,
+            depth: int, 
+            in_spatial_shape:tuple, 
+            z_dim: int,
+            fmap_inc_factor=2,
+            padding: str = "same",
+            downsample_factor: int = 2,
+            kernel_size: int = 3,
+            n_convs: int = 2,
+            upsample_mode = 'bilinear',
+            final_activation = nn.Sigmoid
+        ):
+        self.depth = depth
+        self.num_fmaps = n_fmaps
+        self.in_channels = in_channels
+        self.in_spatial_shape = in_spatial_shape
+        self.kernel_size = kernel_size
+        self.downsample_factor = downsample_factor
+        self.fmap_inc_factor = fmap_inc_factor
+        self.padding = padding
+        self.n_convs = n_convs
+        super(UNetDecoder, self).__init__()
+
+        self.upsample  = torch.nn.Upsample(
+            scale_factor=self.downsample_factor,
+            mode=upsample_mode,
+        )
+        self.convs = nn.ModuleList()
+
+        for level in range(self.depth):
+            fmaps_in, fmaps_out = self.compute_fmaps_encoder(level)
+            self.convs.append(
+                ConvBlock(fmaps_out,fmaps_in, self.kernel_size, self.padding)
+            )
+        
+        fc_layer_len = self.compute_final_layers()
+ 
+        self.shape_first_img = (self.compute_fmaps_encoder(depth-1)[1], *self.compute_spatial_shape(depth-1))
+        self.fc1 = nn.Linear(in_features=z_dim,out_features=fc_layer_len)
+        self.final_conv = nn.Sequential(
+            nn.Conv2d(in_channels=n_fmaps,out_channels=in_channels,kernel_size=kernel_size,padding=padding),
+            final_activation()
+        )
+
+    def compute_fmaps_encoder(self, level: int) -> tuple[int, int]:
+        """Compute the number of input and output feature maps for
+        a conv block at a given level of the UNet encoder (left side).
+
+        Args:
+            level (int): The level of the U-Net which we are computing
+            the feature maps for. Level 0 is the input level, level 1 is
+            the first downsampled layer, and level=depth - 1 is the bottom layer.
+
+        Output (tuple[int, int]): The number of input and output feature maps
+            of the encoder convolutional pass in the given level.
+        """
+        # SOLUTION 6.1A: Implement this function
+        if level == 0:
+            fmaps_in = self.in_channels
+        else:
+            fmaps_in = self.num_fmaps * self.fmap_inc_factor ** (level - 1)
+
+        fmaps_out = self.num_fmaps * self.fmap_inc_factor**level
+        return fmaps_in, fmaps_out
+    
+    def compute_spatial_shape(self, level: int) -> tuple[int, int]:
+        spatial_shape = np.array(self.in_spatial_shape)
+        if level == 0:
+            if self.padding == "same":
+                return spatial_shape
+            
+            # 2 convolutions and 2 sizes 
+            spatial_shape = spatial_shape - self.n_convs * (2 * (self.kernel_size //2))
+            return spatial_shape
+
+        if self.padding == "same":
+            spatial_shape = (np.array(self.compute_spatial_shape(level-1))//(self.downsample_factor)) 
+        else:
+            spatial_shape = self.compute_spatial_shape(level-1)
+            spatial_shape = spatial_shape // self.downsample_factor
+            spatial_shape = spatial_shape - self.n_convs * (2 * (self.kernel_size //2))
+        return spatial_shape
+    
+    def compute_final_layers(self):
+        spatial_dims_final = self.compute_spatial_shape(self.depth-1)
+        num_fmaps_final = self.compute_fmaps_encoder(self.depth-1)
+        return num_fmaps_final[1] * np.prod(spatial_dims_final)
+
+    
+    def forward(self, z):
+        z = F.relu(self.fc1(z))
+        #print(self.shape_first_img)
+        x = z.view(-1, *self.shape_first_img)
+        # print("after unflattening",x.shape)
+        for level in range(self.depth-1,0,-1):
+            # print("did upsample and conv")
+            #print(x.shape)
+            x = self.upsample(x)
+            #print("aft",x.shape)
+            x = self.convs[level](x)
+        # final conv
+        x = self.final_conv(x)
+        return x
+
+
+if __name__ == "__main__":
+    shape = (512,512)
+    depth = 4
+    in_channels = 10
+    encoder = UNetEncoder(
+        in_channels=in_channels,
+        in_spatial_shape=shape,
+        kernel_size=3,
+        n_fmaps=8,
+        padding="same",
+        depth =depth,
+        z_dim=5,
+    )
+
+    decoder = UNetDecoder(
+        in_channels=in_channels,
+        in_spatial_shape=shape,
+        kernel_size=3,
+        n_fmaps=8,
+        padding="same",
+        depth =depth,
+        z_dim=5,
+    )
+    example_tensor = torch.zeros(2,in_channels,shape[0],shape[1])
+    mu,smth= encoder(example_tensor)
+    # print(encoder.compute_fmaps_encoder(depth-1)[1],*encoder.compute_spatial_shape(depth-1))
+    decode = decoder(mu)
+    # print(decode.shape)
+
diff --git a/src/embed_time/dataloader_ij.py b/src/embed_time/dataloader_ij.py
new file mode 100644
index 0000000..f4698a3
--- /dev/null
+++ b/src/embed_time/dataloader_ij.py
@@ -0,0 +1,36 @@
+import os
+import pandas as pd
+# from torchvision.io import read_image
+from torch.utils.data import Dataset
+import tifffile as tiff
+
+class LiveGastruloidDataset(Dataset):
+    def __init__(
+        self, 
+        img_dir,
+        transform=None, 
+        target_transform=None,
+    ):
+        self.img_dir = img_dir
+        self.transform = transform
+        self.target_transform = target_transform
+        self.img_folders = os.listdir(img_dir)
+
+    def __len__(self):
+        return len(self.img_folders)
+
+    def __getitem__(self, idx):
+        img_path = os.path.join(
+            self.img_dir,
+            self.img_names[idx]
+        )
+
+        image = tiff.imread(img_path)
+        
+        if self.transform:
+            image = self.transform(image)
+            
+        if self.target_transform:
+            label = self.target_transform(label)
+
+        return image
\ No newline at end of file
diff --git a/src/embed_time/dataloader_rs.py b/src/embed_time/dataloader_rs.py
new file mode 100644
index 0000000..7dbb61e
--- /dev/null
+++ b/src/embed_time/dataloader_rs.py
@@ -0,0 +1,50 @@
+import os
+import pandas as pd
+from torchvision.io import read_image
+from torch.utils.data import Dataset
+import tifffile as tiff
+
+class LiveTLSDataset(Dataset):
+    def __init__(
+        self, 
+        annotations_file, 
+        img_dir, 
+        file_name_column = "Image Name", 
+        label_column ="Morph",
+        metadata_columns = ["Plate","ID",], 
+        transform=None, 
+        target_transform=None,
+        return_metadata =False,
+    ):
+        self.annotations = pd.read_csv(annotations_file)
+        self.img_dir = img_dir
+        self.transform = transform
+        self.target_transform = target_transform
+        self.metadata_columns = metadata_columns
+        self.label_column = label_column
+        self.file_name_column = file_name_column
+        self.return_metadata = return_metadata
+
+    def __len__(self):
+        return len(self.annotations)
+
+    def __getitem__(self, idx):
+        img_path = os.path.join(
+            self.img_dir,
+            self.annotations.iloc[idx][self.file_name_column]
+        )
+
+        image = tiff.imread(img_path)
+        label = self.annotations.iloc[idx][self.label_column]
+        
+
+        if self.transform:
+            image = self.transform(image)
+            
+        if self.target_transform:
+            label = self.target_transform(label)
+
+        if self.return_metadata:
+            metadata = self.annotations[self.metadata_columns].iloc[idx].to_numpy()
+            return image, label, metadata
+        return image, label
\ No newline at end of file
diff --git a/src/embed_time/dataloader_static.py b/src/embed_time/dataloader_static.py
new file mode 100644
index 0000000..abed02e
--- /dev/null
+++ b/src/embed_time/dataloader_static.py
@@ -0,0 +1,48 @@
+import torch
+from collections import defaultdict
+
+class CustomBatch:
+    def __init__(self, data, metadata_keys, images_keys):
+        self.metadata = defaultdict(list)
+        self.images = defaultdict(list)
+        
+        for item in data:
+            for key in images_keys:
+                # convert to float and then to tensor
+                self.images[key].append(torch.tensor(item[key], dtype=torch.float32))
+            for key in metadata_keys:
+                self.metadata[key].append(item[key])
+        
+        # Convert lists to tensors
+        for key in self.images:
+            self.images[key] = torch.stack(self.images[key], 0)
+        
+        # Convert metadata to tensors where possible
+        for key in self.metadata:
+            if all(isinstance(item, (int, float)) for item in self.metadata[key]):
+                self.metadata[key] = torch.tensor(self.metadata[key])
+            else:
+                self.metadata[key] = tuple(self.metadata[key])
+    
+    def __getitem__(self, key):
+        if key in self.images:
+            return self.images[key]
+        elif key in self.metadata:
+            return self.metadata[key]
+        else:
+            raise KeyError(f"Key '{key}' not found in batch")
+        
+    def pin_memory(self):
+        for key in self.images:
+            self.images[key] = self.images[key].pin_memory()
+        return self
+
+    def to(self, device):
+        for key in self.images:
+            self.images[key] = self.images[key].to(device)
+        return self
+
+def collate_wrapper(metadata_keys, images_keys):
+    def collate_fn(batch):
+        return CustomBatch(batch, metadata_keys, images_keys)
+    return collate_fn
\ No newline at end of file
diff --git a/src/embed_time/dataset_static.py b/src/embed_time/dataset_static.py
new file mode 100644
index 0000000..b6be198
--- /dev/null
+++ b/src/embed_time/dataset_static.py
@@ -0,0 +1,305 @@
+import os
+import numpy as np
+import zarr
+import json
+from pathlib import Path
+import torch
+from torch.utils.data import Dataset
+import pandas as pd
+
+class ZarrCellDataset(Dataset):
+    def __init__(self, parent_dir, csv_file, split="train", channels=[0, 1, 2, 3], 
+                 mask="masks", normalizations=None, interpolations=None, mean=None, std=None):
+        self.parent_dir = Path(parent_dir)
+        self.channels = channels
+        self.mask = mask
+        self.normalizations = normalizations
+        self.interpolations = interpolations
+
+        self.data_info = pd.read_csv(csv_file)
+        self.data_info = self.data_info[self.data_info['split'] == split]
+        self.grouped_data = self.data_info.groupby(['gene', 'barcode', 'stage'])
+        self.zarr_data = self._load_all_zarr_data()
+
+        self._mean = mean
+        self._std = std
+
+    def __len__(self):
+        return len(self.data_info)
+
+    def __getitem__(self, idx):
+        row = self.data_info.iloc[idx]
+        gene = row['gene']
+        barcode = row['barcode']
+        stage = row['stage']
+        cell_idx = row['cell_idx']
+
+        # Get the zarr data for this gene, barcode, and stage
+        zarr_group = self.zarr_data[(gene, barcode, stage)]
+
+        # Load images and masks
+        original_image = zarr_group['images'][cell_idx]
+        original_image = original_image[self.channels]  # Select specified channels
+        cell_mask = zarr_group['cells'][cell_idx]
+        nuclei_mask = zarr_group['nuclei'][cell_idx]
+
+        # Apply mask and normalization
+        cell_image, nuclei_image = self._apply_mask_normalization(original_image, cell_mask, nuclei_mask)
+
+        # Apply interpolations
+        cell_image, nuclei_image = self._apply_interpolation(cell_image, nuclei_image)
+
+        sample = {
+            'gene': gene,
+            'barcode': barcode,
+            'stage': stage,
+            'cell_idx': cell_idx,
+            'split': row['split'],
+            'original_image': original_image,
+            'cell_mask': cell_mask,
+            'nuclei_mask': nuclei_mask,
+            'cell_image': cell_image,
+            'nuclei_image': nuclei_image
+        }
+
+        return sample
+    
+    @property
+    def mean(self):
+        if self._mean is None:
+            self._mean = self._compute_mean()
+        return self._mean
+    
+    @property
+    def std(self):
+        if self._std is None:
+            self._std = self._compute_std()
+        return self._std
+
+    def _load_all_zarr_data(self):
+        zarr_data = {}
+        for (gene, barcode, stage), group in self.grouped_data:
+            zarr_file = self.parent_dir / f"{gene}.zarr" / barcode / stage
+            if not zarr_file.is_dir():
+                raise ValueError(f"Zarr file not found: {zarr_file}")
+            zarr_data[(gene, barcode, stage)] = zarr.open(zarr_file, mode='r')
+        return zarr_data
+    
+    def _compute_mean(self):
+        total_sum = np.zeros(len(self.channels))
+        total_count = 0
+        for batch in self:
+            image = batch['original_image']
+            total_sum += image.sum(axis=(1, 2))
+            total_count += image.shape[1] * image.shape[2]
+        mean = total_sum / total_count
+        return mean
+    
+    def _compute_std(self):
+        sum_squared_diff = np.zeros(len(self.channels))
+        total_count = 0
+        for batch in self:
+            image = batch['original_image']
+            sum_squared_diff += ((image - self.mean[:, None, None]) ** 2).sum(
+                axis=(1, 2)
+            )
+            total_count += image.shape[1] * image.shape[2]
+
+        variance = sum_squared_diff / total_count
+        std = np.sqrt(variance)
+        return std
+      
+    def _apply_mask_normalization(self, original_image, cell_mask, nuclei_mask):
+        if self.mask == "masks":
+            fill = self._mean[:, None, None] if self._mean is not None else 0
+            cell_image = np.where(cell_mask, original_image, fill)
+            nuclei_image = np.where(nuclei_mask, original_image, fill)
+        elif self.mask == "min":
+            fill = original_image.min(axis=(1, 2))[:, None, None]
+            cell_image = np.where(cell_mask, original_image, fill)
+            nuclei_image = np.where(nuclei_mask, original_image, fill)
+        else:
+            cell_image = original_image
+            nuclei_image = original_image
+
+        if self._mean is not None and self._std is not None:
+            cell_image = (cell_image - self._mean[:, None, None]) / self._std[:, None, None]
+            nuclei_image = (nuclei_image - self._mean[:, None, None]) / self._std[:, None, None]
+        cell_image = torch.from_numpy(cell_image).float()
+        nuclei_image = torch.from_numpy(nuclei_image).float()
+
+        if self.normalizations:
+            if isinstance(self.normalizations, list):
+                for normalization in self.normalizations:
+                    cell_image = normalization(cell_image)
+                    nuclei_image = normalization(nuclei_image)
+            else:
+                cell_image = self.normalizations(cell_image)
+                nuclei_image = self.normalizations(nuclei_image)
+
+        return cell_image, nuclei_image
+
+    def _apply_interpolation(self, cell_image, nuclei_image):
+        if self.interpolations:
+            if isinstance(self.interpolations, list):
+                for interpolation in self.interpolations:
+                    cell_image, nuclei_image = interpolation(cell_image, nuclei_image)
+            else:
+                cell_image, nuclei_image = self.interpolations(cell_image, nuclei_image)
+        return cell_image, nuclei_image
+
+class ZarrCellDataset_specific(Dataset):
+    def __init__(self, parent_dir, gene_name, barcode_name, channels=[0, 1, 2, 3], cell_cycle_stages="interphase", 
+                 mask="masks", normalizations=None, interpolations=None, mean=None, std=None):
+        self.parent_dir = parent_dir
+        self.gene_name = gene_name
+        self.barcode_name = barcode_name
+        self.channels = channels
+        self.cell_cycle_stages = cell_cycle_stages
+        self.mask = mask
+        self.normalizations = normalizations
+        self.interpolations = interpolations
+        self._mean = mean
+        self._std = std
+
+        self.zarr_data = self._load_zarr_data()
+        self.original_images, self.cell_masks, self.nuclei_masks = self._load_images_and_masks()
+
+    def __len__(self):
+        return len(self.original_images)
+
+    def __getitem__(self, idx):
+        original_image = self.original_images[idx]
+        cell_mask = self.cell_masks[idx]
+        nuclei_mask = self.nuclei_masks[idx]
+        
+        cell_image, nuclei_image = self._apply_mask_normalization(original_image, cell_mask, nuclei_mask)
+        cell_image, nuclei_image = self._apply_interpolation(cell_image, nuclei_image)
+
+        sample = {
+            'gene': self.gene_name,
+            'barcode': self.barcode_name,
+            'stage': self.cell_cycle_stages,
+            'original_image': original_image,
+            'cell_mask': cell_mask,
+            'nuclei_mask': nuclei_mask,
+            'cell_image': cell_image,
+            'nuclei_image': nuclei_image
+        }
+        return sample
+    
+    @property
+    def mean(self):
+        if self._mean is None:
+            self._mean = self._compute_mean()
+        return self._mean
+    
+    @property
+    def std(self):
+        if self._std is None:
+            self._std = self._compute_std()
+        return self._std
+
+    def _load_zarr_data(self):
+        zarr_file_gene = os.path.join(self.parent_dir, f"{self.gene_name}.zarr")
+        if not os.path.isdir(zarr_file_gene):
+            raise ValueError(f"Gene {zarr_file_gene} does not exist")
+        
+        zarr_file_barcode = os.path.join(zarr_file_gene, self.barcode_name)
+        if not os.path.isdir(zarr_file_barcode):
+            raise ValueError(f"Barcode {zarr_file_barcode} does not exist")
+
+        zarr_file_stage = os.path.join(zarr_file_barcode, self.cell_cycle_stages)
+        if not os.path.isdir(zarr_file_stage):
+            raise ValueError(f"Stage {zarr_file_stage} does not exist")
+
+        self._read_zattrs(zarr_file_stage)  # You might want to do something with zattrs
+        
+        return zarr.open(zarr_file_gene, mode='r')
+
+    def _load_images_and_masks(self):
+        original_images = self.zarr_data[self.barcode_name][self.cell_cycle_stages]['images'][:, self.channels, :, :]
+        cell_masks = self.zarr_data[self.barcode_name][self.cell_cycle_stages]['cells']
+        nuclei_masks = self.zarr_data[self.barcode_name][self.cell_cycle_stages]['nuclei']
+
+        if len(original_images) != len(cell_masks) or len(original_images) != len(nuclei_masks):
+            raise ValueError("Number of images, cells, and nuclei are not the same")
+
+        cell_masks = np.expand_dims(cell_masks, 1)
+        nuclei_masks = np.expand_dims(nuclei_masks, 1)
+
+        return original_images, cell_masks, nuclei_masks
+    
+    def _compute_mean(self):
+        total_sum = np.zeros(len(self.channels)) # (1,4,250,250)
+        total_count = 0
+        for batch in self:
+            image = batch['original_image'] # (1,4,250,250)
+            total_sum += image.sum(axis=(1, 2))
+            total_count += image.shape[1] * image.shape[2]
+        mean = total_sum / total_count
+        return mean
+    
+    def _compute_std(self):
+        sum_squared_diff = np.zeros(len(self.channels))
+        total_count = 0
+        for batch in self:
+            image = batch['original_image']
+            sum_squared_diff += ((image - self.mean[:, None, None]) ** 2).sum(
+                axis=(1, 2)
+            )
+            total_count += image.shape[1] * image.shape[2]
+
+        variance = sum_squared_diff / total_count
+        std = np.sqrt(variance)
+        return std
+    
+    def _apply_mask_normalization(self, original_image, cell_mask, nuclei_mask):
+
+        if self.mask == "masks":
+            fill = self._mean[:, None, None] if self._mean is not None else 0
+            cell_image = np.where(cell_mask, original_image, fill)
+            nuclei_image = np.where(nuclei_mask, original_image, fill)
+        elif self.mask == "min":
+            fill = original_image.min(axis=(1, 2))[:, None, None]
+            cell_image = np.where(cell_mask, original_image, fill)
+            nuclei_image = np.where(nuclei_mask, original_image, fill)
+        else:
+            cell_image = original_image
+            nuclei_image = original_image
+
+
+        if self._mean is not None and self._std is not None:
+            cell_image = (cell_image - self._mean[:, None, None]) / self._std[:, None, None]
+            nuclei_image = (nuclei_image - self._mean[:, None, None]) / self._std[:, None, None]
+
+        cell_image = torch.from_numpy(cell_image).float()
+        nuclei_image = torch.from_numpy(nuclei_image).float()
+
+        if self.normalizations:
+            if isinstance(self.normalizations, list):
+                for normalization in self.normalizations:
+                    cell_image = normalization(cell_image)
+                    nuclei_image = normalization(nuclei_image)
+            else:
+                cell_image = self.normalizations(cell_image)
+                nuclei_image = self.normalizations(nuclei_image)
+
+        return cell_image, nuclei_image
+
+    def _apply_interpolation(self, cell_image, nuclei_image):
+        if self.interpolations:
+            if isinstance(self.interpolations, list):
+                for interpolation in self.interpolations:
+                    cell_image, nuclei_image = interpolation(cell_image, nuclei_image)
+            else:
+                cell_image, nuclei_image = self.interpolations(cell_image, nuclei_image)
+        return cell_image, nuclei_image
+
+    def _read_zattrs(self, path):
+        zattrs = {}
+        zattrs_path = os.path.join(path, ".zattrs")
+        if os.path.exists(zattrs_path):
+            with open(zattrs_path, "r") as f:
+                zattrs = json.load(f)
+        return zattrs
\ No newline at end of file
diff --git a/src/embed_time/evaluate_static.py b/src/embed_time/evaluate_static.py
new file mode 100644
index 0000000..088323a
--- /dev/null
+++ b/src/embed_time/evaluate_static.py
@@ -0,0 +1,406 @@
+import os
+import numpy as np
+import torch
+from torch.utils.data import DataLoader
+from torch.nn import functional as F
+from torchvision.transforms import v2
+from torchvision.utils import save_image
+import matplotlib.pyplot as plt
+import pandas as pd
+import yaml
+import argparse
+import piq
+from sklearn.decomposition import PCA
+from matplotlib.colors import ListedColormap
+import umap
+from sklearn.preprocessing import StandardScaler
+import seaborn as sns
+
+loss_ssim = piq.SSIMLoss()
+
+from embed_time.dataset_static import ZarrCellDataset
+from embed_time.dataloader_static import collate_wrapper
+from embed_time.model_VAE_resnet18_linear import VAEResNet18_Linear
+from embed_time.model_VAE_resnet18 import VAEResNet18
+from embed_time.model import VAE, Encoder, Decoder
+
+class ModelEvaluator():
+    def __init__(self, config):
+        self.config = config
+        self.device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+        self.model = self._init_model()
+        self.dataset_mean, self.dataset_std = self._read_config()
+        self.output_dir = self._create_output_dir()
+        self.train_df, train_loss, train_mse, train_kld = self._evaluate('train')
+        self.val_df, val_loss, val_mse, val_kld = self._evaluate('val')
+        self.create_pca_plots(self.train_df, self.val_df)
+        self.create_umap_plots(self.train_df, self.val_df)
+        accuracy = self.classifier(self.train_df, self.val_df)
+        # create a csv file with the results
+        results = pd.DataFrame({
+            'train_loss': [train_loss],
+            'train_mse': [train_mse],
+            'train_kld': [train_kld],
+            'val_loss': [val_loss],
+            'val_mse': [val_mse],
+            'val_kld': [val_kld],
+            'classification_accuracy': [accuracy]
+        })
+        results.to_csv(os.path.join(self.config['output_dir'], 'results.csv'), index=False)
+
+    def _init_model(self):
+        model = None  # Initialize model to None
+        if self.config['model'] == 'VAE_ResNet18':
+            model = VAEResNet18(nc=self.config['nc'], z_dim=self.config['z_dim'])
+        elif self.config['model'] == 'VAE_ResNet18_Linear':
+            model = VAEResNet18_Linear(nc=self.config['nc'], z_dim=self.config['z_dim'], input_spatial_dim=self.config['input_spatial_dim'])
+        elif self.config['model'] == 'VAE':
+            encoder = Encoder(self.config['nc'], self.config['z_dim'])
+            decoder = Decoder(self.config['z_dim'], self.config['h_dim1'], self.config['h_dim2'], self.config['nc'], self.config['output_shape'])
+            model = VAE(encoder, decoder)
+        else:
+            raise ValueError(f"Model {self.config['model']} not supported.")
+        checkpoints = sorted(os.listdir(self.config['checkpoint_dir']), key=lambda x: os.path.getmtime(os.path.join(self.config['checkpoint_dir'], x)))
+        checkpoint_path = os.path.join(self.config['checkpoint_dir'], checkpoints[-1])
+        model, _ = self._load_checkpoint(checkpoint_path, model)
+        return model.to(self.device)
+
+    def _read_config(self):
+        with open(self.config['yaml_file_path'], 'r') as file:
+            yaml_config = yaml.safe_load(file)
+        mean = [float(i) for i in yaml_config['Dataset mean'][0].split()]
+        std = [float(i) for i in yaml_config['Dataset std'][0].split()]
+        return np.array(mean), np.array(std)
+
+    def _load_checkpoint(self, checkpoint_path, model):
+        print(f"Loading checkpoint from {checkpoint_path}...")
+        checkpoint = torch.load(checkpoint_path, map_location=self.device)
+        print(f"Loading checkpoint from epoch {checkpoint['epoch']}...")
+        model.load_state_dict(checkpoint['model_state_dict'])
+        return model, checkpoint['epoch']
+
+    def _create_dataloader(self, split, drop_last=True):
+        dataset = ZarrCellDataset(
+            self.config['parent_dir'], 
+            self.config['csv_file'], 
+            split, 
+            self.config['channels'], 
+            self.config['transform'], 
+            v2.Compose([v2.CenterCrop(self.config['crop_size'])]), 
+            None, 
+            self.dataset_mean, 
+            self.dataset_std
+        )
+        return DataLoader(
+            dataset, 
+            batch_size=self.config['batch_size'], 
+            shuffle=False, 
+            num_workers=self.config['num_workers'], 
+            drop_last=drop_last,
+            collate_fn=collate_wrapper(self.config['metadata_keys'], self.config['images_keys'])
+        )
+    
+    def _create_output_dir(self):
+        output_dir = os.makedirs(self.config['output_dir'], exist_ok=True)
+        return output_dir
+    
+    def _evaluate_model(self, dataloader):
+        self.model.eval()
+        total_loss = total_mse = total_kld = 0
+        all_latent_vectors = []
+        all_metadata = []
+        
+        with torch.no_grad():
+            for batch_idx, batch in enumerate(dataloader):
+                data = batch['cell_image'].to(self.device)
+                metadata = [batch[key] for key in self.config['metadata_keys']]
+
+                if self.config['model'] == 'VAE_ResNet18_Linear':
+                    recon_batch, _, mu, logvar = self.model(data)
+                elif self.config['model'] == 'VAE_ResNet18':
+                    recon_batch, mu, logvar = self.model(data)
+
+                if self.config['loss'] == "MSE":
+                    RECON = F.mse_loss(recon_batch, data, reduction='mean')
+                elif self.config['loss'] == "L1":
+                    RECON = F.l1_loss(recon_batch, data, reduction='mean')
+                elif self.config['loss'] == "SSIM":
+                    # normalize x for ssim (remember shape is BxCxHxW)
+                    x_norm = (data - data.min()) / (data.max() - data.min())
+                    recon_x_norm = (recon_batch - recon_batch.min()) / (recon_batch.max() - recon_batch.min())
+                    ssim = loss_ssim(recon_x_norm, x_norm)
+                    RECON = F.l1_loss(recon_batch, data, reduction='mean') + ssim * 0.5
+                KLD = -0.5 * torch.sum(1 + logvar - mu.pow(2) - logvar.exp())
+                loss = RECON + KLD * self.config['beta']
+                
+                total_loss += loss.item()
+                total_mse += RECON.item()
+                total_kld += KLD.item()
+                
+                if batch_idx == 0:
+                    self._save_image(data, recon_batch, self.config['output_dir'])
+
+                if self.config['sampling_number'] > 1:
+                    print('Sampling {} times...'.format(self.config['sampling_number']))
+                    for i in range(self.config['sampling_number']):
+                        # Sample from the latent space
+                        z = self.model.reparameterize(mu, logvar)
+                        # save zs and metadata into additional latent representations
+                        all_latent_vectors.append(z.cpu())
+                        all_metadata.extend(zip(*metadata))
+                else:
+                    all_latent_vectors.append(mu.cpu())
+                    all_metadata.extend(zip(*metadata))
+        
+        avg_loss = total_loss / len(dataloader)
+        avg_mse = total_mse / len(dataloader)
+        avg_kld = total_kld / len(dataloader.dataset)
+        latent_vectors = torch.cat(all_latent_vectors, dim=0)
+        
+        return avg_loss, avg_mse, avg_kld, latent_vectors, all_metadata
+
+    def _evaluate(self, split):
+        if split == 'val':
+            drop_last = False
+        else:
+            drop_last = True
+        dataloader = self._create_dataloader(split, drop_last)
+        print(f"Evaluating on {split} data...")
+        loss, mse, kld, latents, metadata = self._evaluate_model(dataloader)
+        print(f"{split.capitalize()} - Loss: {loss:.4f}, MSE: {mse:.4f}, KLD: {kld:.4f}")
+        
+        if self.config['model'] == 'VAE_ResNet18_Linear':
+            print(f"Reconstruction shape: {latents.shape}")
+        elif self.config['model'] == 'VAE_ResNet18':
+            # flatten the latent vectors
+            latents = latents.view(latents.shape[0], -1)
+            print(f"Latent shape: {latents.shape}")
+        # Create DataFrame
+        df = pd.DataFrame(metadata, columns=self.config['metadata_keys'])
+        latent_df = pd.DataFrame(latents.numpy(), columns=[f'latent_{i}' for i in range(latents.shape[1])])
+        df = pd.concat([df, latent_df], axis=1)
+        # Save the latent vectors
+        df.to_csv(os.path.join(self.config['output_dir'], f"{split}_{self.config['sampling_number']}_latent_vectors.csv"), index=False)
+                               
+        return df, loss, mse, kld
+    
+    def _save_image(self, data, recon, output_dir):
+        image_idx = np.random.randint(data.shape[0])
+        original = data[image_idx].cpu().numpy()
+        reconstruction = recon[image_idx].cpu().numpy()
+        
+        fig, axes = plt.subplots(2, 4, figsize=(20, 10))
+        
+        channel_names = ['dapi', 'gh2ax', 'tubulin', 'actin']  # Adjust these names as needed
+        
+        for i in range(4):
+            # Original image
+            im = axes[0, i].imshow(original[i], cmap='viridis')
+            axes[0, i].set_title(f'Original {channel_names[i]}', fontsize=12)
+            axes[0, i].axis('off')
+            fig.colorbar(im, ax=axes[0, i], fraction=0.046, pad=0.04)
+            
+            # Reconstructed image
+            im = axes[1, i].imshow(reconstruction[i], cmap='viridis')
+            axes[1, i].set_title(f'Reconstructed {channel_names[i]}', fontsize=12)
+            axes[1, i].axis('off')
+            fig.colorbar(im, ax=axes[1, i], fraction=0.046, pad=0.04)
+
+        plt.tight_layout()
+        
+        # Create filename
+        filename = f"{self.config['model']}_sample_image.png"
+        
+        # save the image
+        plt.savefig(os.path.join(output_dir, filename), dpi=300, bbox_inches='tight')
+        plt.close(fig)  # Close the figure to free up memory
+
+    # add pca and umap
+    def create_pca_plots(self, train_latents, val_latents):
+
+        # Step 0: split the datasets into label data and latent data
+        train_df = train_latents[['gene', 'barcode', 'stage', 'cell_idx']]
+        val_df = val_latents[['gene', 'barcode', 'stage', 'cell_idx']]
+        train_latents = train_latents.drop(columns=['gene', 'barcode', 'stage', 'cell_idx'])
+        val_latents = val_latents.drop(columns=['gene', 'barcode', 'stage', 'cell_idx'])
+
+        # Step 1: Perform PCA
+        pca = PCA(n_components=2)
+        train_latents_pca = pca.fit_transform(train_latents)
+        val_latents_pca = pca.transform(val_latents)
+        
+        # Step 2: Prepare the plot
+        fig, axes = plt.subplots(1,2, figsize=(25, 10))
+        
+        # Helper function to create a color map
+        def create_color_map(n):
+            return ListedColormap(plt.cm.viridis(np.linspace(0, 1, n)))
+        # Assuming you have 3 unique labels
+        
+    # Convert 'gene' to categorical and get codes
+        train_df['gene'] = pd.Categorical(train_df['gene'])
+        val_df['gene'] = pd.Categorical(val_df['gene'])
+        train_gene_codes = train_df['gene'].cat.codes
+        val_gene_codes = val_df['gene'].cat.codes
+
+        # Step 3: Plot PCA for the training set
+        ax = axes[0]
+        scatter = ax.scatter(train_latents_pca[:, 0], train_latents_pca[:, 1], 
+                            c=train_gene_codes, 
+                            cmap=create_color_map(len(train_df['gene'].cat.categories)), 
+                            s=25, alpha=0.5)    
+        ax.set_title('PCA of Training Latents', fontsize=40)
+        ax.set_xlabel('PCA Component 1', fontsize=20)
+        ax.set_ylabel('PCA Component 2', fontsize=20)
+        cbar = fig.colorbar(scatter, ax=ax)
+        cbar.set_ticks(range(len(train_df['gene'].cat.categories)))
+        cbar.set_ticklabels(train_df['gene'].cat.categories, fontsize=20)
+        
+        # Step 4: Plot PCA for the validation set
+        ax = axes[1]
+        scatter = ax.scatter(val_latents_pca[:, 0], val_latents_pca[:, 1], 
+                            c=val_gene_codes, 
+                            cmap=create_color_map(len(val_df['gene'].cat.categories)),
+                            s=25, alpha=0.5)
+        ax.set_title('PCA of Validation Latents', fontsize=40)
+        ax.set_xlabel('PCA Component 1', fontsize=20)
+        ax.set_ylabel('PCA Component 2', fontsize=20)
+        cbar = fig.colorbar(scatter, ax=ax)
+        cbar.set_ticks(range(len(val_df['gene'].cat.categories)))
+        cbar.set_ticklabels(val_df['gene'].cat.categories, fontsize=20)
+        
+        print(f"Unique labels in training set: {np.unique(train_df['gene'])}")
+        print(f"Unique labels in validation set: {np.unique(val_df['gene'])}")
+        
+        # Adjust layout to prevent overlap
+        plt.tight_layout()
+        
+        # Step 5: Save the plot in the output directory
+        plt.savefig(os.path.join(self.config['output_dir'], 'pca_plot.png'))
+        plt.close(fig)  # Close the figure to free up memory
+
+    def create_umap_plots(self, train_latents, val_latents):
+        
+        # Step 0: split the datasets into label data and latent data
+        train_df = train_latents[['gene', 'barcode', 'stage', 'cell_idx']]
+        val_df = val_latents[['gene', 'barcode', 'stage', 'cell_idx']]
+        train_latents = train_latents.drop(columns=['gene', 'barcode', 'stage', 'cell_idx'])
+        val_latents = val_latents.drop(columns=['gene', 'barcode', 'stage', 'cell_idx'])
+
+        # Scale the data
+        Scaler = StandardScaler()
+        train_latents = Scaler.fit_transform(train_latents)
+        val_latents = Scaler.transform(val_latents)
+        
+        # Initialize UMAP
+        umap_reducer = umap.UMAP(n_neighbors=15, min_dist=0.1, n_components=2, random_state=42)
+
+        # Fit and transform the training data
+        train_latents_umap = umap_reducer.fit_transform(train_latents)
+        # Transform the validation data using the same UMAP model
+        val_latents_umap = umap_reducer.transform(val_latents)
+
+        fig, axes = plt.subplots(1,2, figsize=(25, 10))
+        
+        def create_color_map(n):
+            return ListedColormap(plt.cm.viridis(np.linspace(0, 1, n)))
+
+        # Convert 'gene' to categorical and get codes
+        train_df['gene'] = pd.Categorical(train_df['gene'])
+        val_df['gene'] = pd.Categorical(val_df['gene'])
+        train_gene_codes = train_df['gene'].cat.codes
+        val_gene_codes = val_df['gene'].cat.codes
+
+        # Step 5: Plot UMAP for the training set
+        ax = axes[0]
+        scatter = ax.scatter(train_latents_umap[:, 0], train_latents_umap[:, 1], 
+                            c=train_gene_codes, 
+                            cmap=create_color_map(len(train_df['gene'].cat.categories)), 
+                            s=25, alpha=0.5)
+        ax.set_title('UMAP of Training Latents', fontsize=40)
+        ax.set_xlabel('UMAP Component 1', fontsize=20)
+        ax.set_ylabel('UMAP Component 2', fontsize=20)
+        cbar = fig.colorbar(scatter, ax=ax)
+        cbar.set_ticks(range(len(train_df['gene'].cat.categories)))
+        cbar.set_ticklabels(train_df['gene'].cat.categories, fontsize=20)
+
+        # Step 6: Plot UMAP for the validation set
+        ax = axes[1]
+        scatter = ax.scatter(val_latents_umap[:, 0], val_latents_umap[:, 1], 
+                            c=val_gene_codes, 
+                            cmap=create_color_map(len(val_df['gene'].cat.categories)), 
+                            s=25, alpha=0.5)
+        ax.set_title('UMAP of Validation Latents', fontsize=40)
+        ax.set_xlabel('UMAP Component 1', fontsize=20)
+        ax.set_ylabel('UMAP Component 2', fontsize=20)
+        cbar = fig.colorbar(scatter, ax=ax)
+        cbar.set_ticks(range(len(val_df['gene'].cat.categories)))
+        cbar.set_ticklabels(val_df['gene'].cat.categories, fontsize=20)
+
+        # Adjust layout to prevent overlap
+        plt.tight_layout()
+            
+        # Step 5: Save the plot in the output directory
+        plt.savefig(os.path.join(self.config['output_dir'], 'umap_plot.png'))
+        plt.close(fig)  # Close the figure to free up memory
+
+    # write a function for random forest classifier
+    def classifier(self, train_latents, val_latents):
+        from sklearn.ensemble import RandomForestClassifier
+        from sklearn.metrics import accuracy_score, confusion_matrix
+        # Step 0: split the datasets into label data and latent data
+        train_df = train_latents[['gene', 'barcode', 'stage', 'cell_idx']]
+        val_df = val_latents[['gene', 'barcode', 'stage', 'cell_idx']]
+        train_latents = train_latents.drop(columns=['gene', 'barcode', 'stage', 'cell_idx'])
+        val_latents = val_latents.drop(columns=['gene', 'barcode', 'stage', 'cell_idx'])
+
+        # Scale the data
+        Scaler = StandardScaler()
+        train_latents = Scaler.fit_transform(train_latents)
+        val_latents = Scaler.transform(val_latents)
+        
+        # Initialize the Random Forest Classifier
+        clf = RandomForestClassifier(n_estimators=100, random_state=42)
+
+        # Fit the model on the training data
+        clf.fit(train_latents, train_df['gene'])
+
+        # Predict the labels for the validation data
+        val_predictions = clf.predict(val_latents)
+
+        # Calculate the accuracy of the model
+        accuracy = accuracy_score(val_df['gene'], val_predictions)
+
+        # Make a confusion matrix
+        cm = confusion_matrix(val_df['gene'], val_predictions)
+
+        # Convert 'gene' to categorical and get codes
+        train_df['gene'] = pd.Categorical(train_df['gene'])
+        val_df['gene'] = pd.Categorical(val_df['gene'])
+
+        # Calculate percentages for cm
+        cm_percentage = (cm.astype('float') / cm.sum(axis=1)[:, np.newaxis]) * 100
+
+        # Print the accuracy and confusion matrix
+        plt.figure()
+        sns.heatmap(cm_percentage, annot=True, fmt='.2f', cmap='Blues',
+                    xticklabels=val_df['gene'].cat.categories,
+                    yticklabels=val_df['gene'].cat.categories)
+        plt.title('Confusion Matrix', fontsize=20)
+        plt.xlabel('Predicted Labels', fontsize=15)
+        plt.ylabel('True Labels', fontsize=15)
+        plt.tight_layout()
+        plt.savefig(os.path.join(self.config['output_dir'], 'rf_confusion_matrix.png'))
+        plt.close()
+
+        return accuracy
+        
+
+def parse_args():
+    parser = argparse.ArgumentParser(description="Model Evaluation Script")
+    parser.add_argument("--config", type=str, required=True, help="Path to the configuration YAML file")
+    return parser.parse_args()
+
+def load_config(config_path):
+    with open(config_path, 'r') as file:
+        return yaml.safe_load(file)
diff --git a/src/embed_time/launch_tensorboard_ac.py b/src/embed_time/launch_tensorboard_ac.py
new file mode 100644
index 0000000..3656f54
--- /dev/null
+++ b/src/embed_time/launch_tensorboard_ac.py
@@ -0,0 +1,25 @@
+import os
+import subprocess
+import pandas as pd
+import numpy as np
+from torch.utils.tensorboard import SummaryWriter
+
+def find_free_port():
+    import socket
+
+    with socket.socket(socket.AF_INET, socket.SOCK_STREAM) as s:
+        s.bind(("", 0))
+        return s.getsockname()[1]
+
+# Launch TensorBoard on the browser
+def launch_tensorboard(log_dir):
+    port = find_free_port()
+    tensorboard_cmd = f"tensorboard --logdir={log_dir} --port={port}"
+    process = subprocess.Popen(tensorboard_cmd, shell=True)
+    print(
+        f"TensorBoard started at http://localhost:{port}. \n"
+        "If you are using VSCode remote session, forward the port using the PORTS tab next to TERMINAL."
+    )
+    return process
+
+tensorboard_process = launch_tensorboard("embed_time_static_runs")
\ No newline at end of file
diff --git a/src/embed_time/model.py b/src/embed_time/model.py
new file mode 100644
index 0000000..08866e2
--- /dev/null
+++ b/src/embed_time/model.py
@@ -0,0 +1,127 @@
+import math
+import torch
+from torch import nn
+import torch.nn.functional as F
+
+
+class Encoder(nn.Module):
+    def __init__(self, input_shape, x_dim, h_dim1, h_dim2, z_dim):
+        """
+        Basic encoding model.
+
+        Parameters
+        ----------
+        input_shape: tuple
+            shape of the input data in spatial dimensions (not channels)
+        x_dim: int
+            input channels in the input data
+        h_dim1: int
+            number of features in the first hidden layer
+        h_dim2: int
+            number of features in the second hidden layer
+        z_dim: int
+            number of latent features
+        """
+        super().__init__()
+        # encoder part
+        self.conv1 = nn.Conv2d(x_dim, h_dim1, kernel_size=3, stride=1, padding=1)
+        # o = [(i + 2*p - k) / s] + 1
+        output_shape = [(s + 2 * 1 - 3) + 1 for s in input_shape]
+        self.conv2 = nn.Conv2d(h_dim1, h_dim2, kernel_size=3, stride=1, padding=1)
+        self.output_shape = [(s + 2 * 1 - 3) + 1 for s in output_shape]
+        # Computing the shape of the data at this point
+        linear_h_dim = h_dim2 * math.prod(output_shape)
+        self.fc31 = nn.Linear(linear_h_dim, z_dim)
+        self.fc32 = nn.Linear(linear_h_dim, z_dim)
+
+    def forward(self, x):
+        """
+        x: torch.Tensor
+            input tensor
+
+        Returns
+        -------
+        mu: torch.Tensor
+            mean tensor
+        log_var: torch.Tensor
+            log variance tensor
+        """
+        h = F.relu(self.conv1(x))
+        h = F.relu(self.conv2(h))
+        batch_size = h.size(0)
+        h = h.view(batch_size, -1)
+        return self.fc31(h), self.fc32(h)  # mu, log_var
+
+
+class Decoder(nn.Module):
+    def __init__(self, z_dim, h_dim1, h_dim2, x_dim, output_shape):
+        """
+        Basic decoding model
+
+        Parameters
+        ----------
+        z_dim: int
+            number of latent features
+        h_dim1: int
+            number of features in the first hidden layer
+        h_dim2: int
+            number of features in the second hidden layer
+        x_dim: int
+            number of output channels
+        output_shape: tuple
+            shape of the output data in the spatial dimensions
+        """
+        super().__init__()
+        # decoder part
+        self.z_spatial_shape = (h_dim1, *output_shape)
+        spatial_shape = math.prod(self.z_spatial_shape)
+        # "Upsample" the data back to the amount we need for the output shape
+        self.fc = nn.Linear(z_dim, spatial_shape)
+        # Here there will be a reshape
+        self.conv1 = nn.Conv2d(h_dim1, h_dim2, kernel_size=3, padding="same")
+        self.conv2 = nn.Conv2d(h_dim2, x_dim, kernel_size=3, padding="same")
+
+    def forward(self, z):
+        z = F.relu(self.fc(z))
+        h = z.view(-1, *self.z_spatial_shape)
+        h = F.relu(self.conv1(h))
+        return F.sigmoid(self.conv2(h))
+
+
+class VAE(nn.Module):
+    def __init__(self, encoder, decoder):
+        super(VAE, self).__init__()
+        self.encoder = encoder
+        self.decoder = decoder
+
+    def check_shapes(self, data_shape, z_dim):
+        with torch.no_grad():
+            try:
+                output, mu, var = self.forward(torch.zeros(data_shape))
+                input_shape = data_shape
+                assert (
+                    output.shape == input_shape
+                ), f"Output shape {output.shape} is not the same as input shape {input_shape}"
+                assert (
+                    mu.shape[-1] == z_dim
+                ), f"Mu shape {mu.shape} is not the same as latent shape {z_dim}"
+                assert (
+                    var.shape[-1] == z_dim
+                ), f"Var shape {var.shape} is not the same as latent shape {z_dim}"
+                print("Model shapes are correct")
+            except AssertionError as e:
+                raise (e)
+            except Exception as e:
+                print("Error in checking shapes")
+                raise (e)
+
+    def reparametrize(self, mu, log_var):
+        std = torch.exp(0.5 * log_var)
+        eps = torch.randn_like(std)
+        z = eps.mul(std).add_(mu)
+        return z  # return z sample
+
+    def forward(self, x):
+        mu, log_var = self.encoder(x)
+        z = self.reparametrize(mu, log_var)
+        return self.decoder(z), mu, log_var
diff --git a/src/embed_time/model_VAE_resnet18.py b/src/embed_time/model_VAE_resnet18.py
new file mode 100644
index 0000000..ea06801
--- /dev/null
+++ b/src/embed_time/model_VAE_resnet18.py
@@ -0,0 +1,157 @@
+import torch
+from torch import nn, optim
+import torch.nn.functional as F
+
+class ResizeConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, mode='nearest'):
+        super().__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size//2)
+    
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+        x = self.conv(x)
+        return x
+
+class BasicBlockEnc(nn.Module):
+
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+
+        planes = in_planes*stride
+
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+
+        if stride == 1:
+            self.shortcut = nn.Identity()
+        else:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes)
+            )
+
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class BasicBlockDec(nn.Module):
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+        planes = int(in_planes/stride)
+
+        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(in_planes)
+        # self.bn1 could have been placed here, 
+        # but that messes up the order of the layers when printing the class
+
+        if stride == 1:
+            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential()
+        else:
+            self.conv1 = ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential(
+                ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride),
+                nn.BatchNorm2d(planes)
+            )
+    
+    def forward(self, x):
+        out = torch.relu(self.bn2(self.conv2(x)))
+        out = self.bn1(self.conv1(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+
+class ResNet18Enc(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 64
+        self.z_dim = z_dim
+        self.conv1 = nn.Conv2d(nc, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(BasicBlockEnc, 64, num_Blocks[0], stride=1)
+        self.layer2 = self._make_layer(BasicBlockEnc, 128, num_Blocks[1], stride=2)
+        self.layer3 = self._make_layer(BasicBlockEnc, 256, num_Blocks[2], stride=2)
+        self.layer4 = self._make_layer(BasicBlockEnc, 512, num_Blocks[3], stride=2)
+        self.linear = nn.Conv2d(512, 2 * z_dim, kernel_size=1)
+
+    def _make_layer(self, BasicBlockEnc, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in strides:
+            layers += [BasicBlockEnc(self.in_planes, stride)]
+            self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = torch.relu(self.bn1(self.conv1(x)))
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.linear(x)
+        mu, logvar = torch.chunk(x, 2, dim=1)
+        return mu, logvar
+
+class ResNet18Dec(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 512
+        self.nc = nc
+
+        self.linear = nn.Conv2d(z_dim, 512, kernel_size=1)
+
+        self.layer4 = self._make_layer(BasicBlockDec, 256, num_Blocks[3], stride=2)
+        self.layer3 = self._make_layer(BasicBlockDec, 128, num_Blocks[2], stride=2)
+        self.layer2 = self._make_layer(BasicBlockDec, 64, num_Blocks[1], stride=2)
+        self.layer1 = self._make_layer(BasicBlockDec, 64, num_Blocks[0], stride=1)
+        self.conv1 = ResizeConv2d(64, nc, kernel_size=3, scale_factor=2)
+
+    def _make_layer(self, BasicBlockDec, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in reversed(strides):
+            layers += [BasicBlockDec(self.in_planes, stride)]
+        self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, z):
+        x = self.linear(z)
+        x = self.layer4(x)
+        x = self.layer3(x)
+        x = self.layer2(x)
+        x = self.layer1(x)
+        x = torch.sigmoid(self.conv1(x))
+        return x
+    
+    
+class VAEResNet18(nn.Module):
+    
+    def __init__(self, nc, z_dim):
+        super().__init__()
+        self.encoder = ResNet18Enc(nc=nc, z_dim=z_dim)
+        self.decoder = ResNet18Dec(nc=nc, z_dim=z_dim)
+
+    def forward(self, x):
+        mu, log_var = self.encoder(x)
+        z = self.reparameterize(mu, log_var)
+        x = self.decoder(z)
+        # return x, z
+        return x, mu, log_var
+    
+    @staticmethod
+    def reparameterize(mean, logvar):
+        std = torch.exp(logvar / 2) # in log-space, squareroot is divide by two
+        epsilon = torch.randn_like(std)
+        return epsilon * std + mean
diff --git a/src/embed_time/model_VAE_resnet18_3D.py b/src/embed_time/model_VAE_resnet18_3D.py
new file mode 100644
index 0000000..b2ce840
--- /dev/null
+++ b/src/embed_time/model_VAE_resnet18_3D.py
@@ -0,0 +1,157 @@
+import torch
+from torch import nn, optim
+import torch.nn.functional as F
+
+class ResizeConv3d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, mode='nearest'):
+        super().__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.conv = nn.Conv3d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size//2)
+    
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+        x = self.conv(x)
+        return x
+
+class BasicBlockEnc(nn.Module):
+
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+
+        planes = in_planes*stride
+
+        self.conv1 = nn.Conv3d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm3d(planes)
+        self.conv2 = nn.Conv3d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm3d(planes)
+
+        if stride == 1:
+            self.shortcut = nn.Identity()
+        else:
+            self.shortcut = nn.Sequential(
+                nn.Conv3d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm3d(planes)
+            )
+
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class BasicBlockDec(nn.Module):
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+        planes = int(in_planes/stride)
+
+        self.conv2 = nn.Conv3d(in_planes, in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm3d(in_planes)
+        # self.bn1 could have been placed here, 
+        # but that messes up the order of the layers when printing the class
+
+        if stride == 1:
+            self.conv1 = nn.Conv3d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+            self.bn1 = nn.BatchNorm3d(planes)
+            self.shortcut = nn.Sequential()
+        else:
+            self.conv1 = ResizeConv3d(in_planes, planes, kernel_size=3, scale_factor=stride)
+            self.bn1 = nn.BatchNorm3d(planes)
+            self.shortcut = nn.Sequential(
+                ResizeConv3d(in_planes, planes, kernel_size=3, scale_factor=stride),
+                nn.BatchNorm3d(planes)
+            )
+    
+    def forward(self, x):
+        out = torch.relu(self.bn2(self.conv2(x)))
+        out = self.bn1(self.conv1(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+
+class ResNet18Enc(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 64
+        self.z_dim = z_dim
+        self.conv1 = nn.Conv3d(nc, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm3d(64)
+        self.layer1 = self._make_layer(BasicBlockEnc, 64, num_Blocks[0], stride=1)
+        self.layer2 = self._make_layer(BasicBlockEnc, 128, num_Blocks[1], stride=2)
+        self.layer3 = self._make_layer(BasicBlockEnc, 256, num_Blocks[2], stride=2)
+        self.layer4 = self._make_layer(BasicBlockEnc, 512, num_Blocks[3], stride=2)
+        self.linear = nn.Conv3d(512, 2 * z_dim, kernel_size=1)
+
+    def _make_layer(self, BasicBlockEnc, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in strides:
+            layers += [BasicBlockEnc(self.in_planes, stride)]
+            self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = torch.relu(self.bn1(self.conv1(x)))
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.linear(x)
+        mu, logvar = torch.chunk(x, 2, dim=1)
+        return mu, logvar
+
+class ResNet18Dec(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 512
+        self.nc = nc
+
+        self.linear = nn.Conv3d(z_dim, 512, kernel_size=1)
+
+        self.layer4 = self._make_layer(BasicBlockDec, 256, num_Blocks[3], stride=2)
+        self.layer3 = self._make_layer(BasicBlockDec, 128, num_Blocks[2], stride=2)
+        self.layer2 = self._make_layer(BasicBlockDec, 64, num_Blocks[1], stride=2)
+        self.layer1 = self._make_layer(BasicBlockDec, 64, num_Blocks[0], stride=1)
+        self.conv1 = ResizeConv3d(64, nc, kernel_size=3, scale_factor=2)
+
+    def _make_layer(self, BasicBlockDec, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in reversed(strides):
+            layers += [BasicBlockDec(self.in_planes, stride)]
+        self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, z):
+        x = self.linear(z)
+        x = self.layer4(x)
+        x = self.layer3(x)
+        x = self.layer2(x)
+        x = self.layer1(x)
+        x = torch.sigmoid(self.conv1(x))
+        return x
+    
+    
+class VAEResNet18_3D(nn.Module):
+    
+    def __init__(self, nc, z_dim):
+        super().__init__()
+        self.encoder = ResNet18Enc(nc=nc, z_dim=z_dim)
+        self.decoder = ResNet18Dec(nc=nc, z_dim=z_dim)
+
+    def forward(self, x):
+        mu, log_var = self.encoder(x)
+        z = self.reparameterize(mu, log_var)
+        x = self.decoder(z)
+        # return x, z
+        return x, mu, log_var
+    
+    @staticmethod
+    def reparameterize(mean, logvar):
+        std = torch.exp(logvar / 2) # in log-space, squareroot is divide by two
+        epsilon = torch.randn_like(std)
+        return epsilon * std + mean
diff --git a/src/embed_time/model_VAE_resnet18_linear.py b/src/embed_time/model_VAE_resnet18_linear.py
new file mode 100644
index 0000000..e184598
--- /dev/null
+++ b/src/embed_time/model_VAE_resnet18_linear.py
@@ -0,0 +1,209 @@
+import torch
+from torch import nn, optim
+import torch.nn.functional as F
+import numpy as np
+
+class ResizeConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, mode='nearest'):
+        super().__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size//2)
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+        x = self.conv(x)
+        return x
+
+class ResizeArbitrary(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, out_size, mode='nearest'):
+        super().__init__()
+        self.out_size = out_size
+        self.mode = mode
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size//2)
+
+    def forward(self, x):
+        x = F.interpolate(x, size=self.out_size, mode=self.mode)
+        x = torch.relu(self.conv(x))
+        return x
+
+class BasicBlockEnc(nn.Module):
+
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+
+        planes = in_planes*stride
+
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+
+        if stride == 1:
+            self.shortcut = nn.Identity()
+        else:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes)
+            )
+
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class BasicBlockDec(nn.Module):
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+        planes = int(in_planes/stride)
+
+        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(in_planes)
+        # self.bn1 could have been placed here, 
+        # but that messes up the order of the layers when printing the class
+
+        if stride == 1:
+            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential()
+        else:
+            self.conv1 = ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential(
+                ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride),
+                nn.BatchNorm2d(planes)
+            )
+    def forward(self, x):
+        out = torch.relu(self.bn2(self.conv2(x)))
+        out = self.bn1(self.conv1(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+
+class ResNet18Enc(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3, linear_downsample_factor = 8):
+        super().__init__()
+        self.in_planes = 64
+        self.z_dim = z_dim
+        self.conv1 = nn.Conv2d(nc, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(BasicBlockEnc, 64, num_Blocks[0], stride=1)
+        self.layer2 = self._make_layer(BasicBlockEnc, 128, num_Blocks[1], stride=2)
+        self.layer3 = self._make_layer(BasicBlockEnc, 256, num_Blocks[2], stride=2)
+        self.layer4 = self._make_layer(BasicBlockEnc, 512, num_Blocks[3], stride=2)
+        self.avg_pool = nn.AdaptiveAvgPool2d(output_size=2)
+        self.fc_layer_len = 512 * 2 * 2
+        self.linear_block = nn.Sequential(
+            nn.Linear(
+                self.fc_layer_len,
+                self.fc_layer_len
+            ), 
+            nn.ReLU(),
+            nn.Linear(
+                self.fc_layer_len,
+                z_dim * 2
+            )
+        )
+    def _make_layer(self, BasicBlockEnc, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in strides:
+            layers += [BasicBlockEnc(self.in_planes, stride)]
+            self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = torch.relu(self.bn1(self.conv1(x)))
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = self.avg_pool(x)
+        x = x.view(-1,self.fc_layer_len)
+        x = self.linear_block(x)
+        mu, logvar = torch.chunk(x, 2, dim=1)
+        return mu, logvar
+
+class ResNet18Dec(nn.Module):
+
+    def __init__(self, spatial_dim_bottle, num_Blocks=[2,2,2,2], z_dim=10, nc=3, linear_downsample_factor =8):
+        super().__init__()
+        self.in_planes = 512
+        self.nc = nc
+        self.shape_first_img = (512,spatial_dim_bottle[0],spatial_dim_bottle[1])
+        self.fc_layer_len = 512 * 2 * 2
+
+        self.linear_block = nn.Sequential(
+            nn.Linear(
+                z_dim,
+                self.fc_layer_len,
+            ),
+            nn.ReLU(),
+            nn.Linear(
+                self.fc_layer_len,
+                self.fc_layer_len,
+            ), 
+        )
+        self.upscale = ResizeArbitrary(512,512,3,spatial_dim_bottle,mode='bicubic')
+        self.layer4 = self._make_layer(BasicBlockDec, 256, num_Blocks[3], stride=2)
+        self.layer3 = self._make_layer(BasicBlockDec, 128, num_Blocks[2], stride=2)
+        self.layer2 = self._make_layer(BasicBlockDec, 64, num_Blocks[1], stride=2)
+        self.layer1 = self._make_layer(BasicBlockDec, 64, num_Blocks[0], stride=1)
+        self.conv1 = ResizeConv2d(64, nc, kernel_size=3, scale_factor=2)
+
+    def _make_layer(self, BasicBlockDec, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in reversed(strides):
+            layers += [BasicBlockDec(self.in_planes, stride)]
+        self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, z):
+        x = self.linear_block(z)
+        x = x.view(-1, 512, 2, 2)
+        x = self.upscale(x)
+        x = self.layer4(x)
+        x = self.layer3(x)
+        x = self.layer2(x)
+        x = self.layer1(x)
+        x = torch.sigmoid(self.conv1(x))
+        return x
+
+
+class VAEResNet18_Linear(nn.Module):
+    def __init__(self, nc, z_dim, input_spatial_dim):
+        super().__init__()
+        self.in_spatial_shape = input_spatial_dim
+        self.spat_shape_bottle = self.compute_spatial_shape(4)
+        self.spat_shape_bottle = (self.spat_shape_bottle[0],self.spat_shape_bottle[1])
+        self.encoder = ResNet18Enc(nc=nc, z_dim=z_dim)
+        self.decoder = ResNet18Dec(nc=nc, z_dim=z_dim, spatial_dim_bottle=self.spat_shape_bottle)
+        self.enc_linear = nn.Sequential(
+
+        )
+
+    def forward(self, x):
+        mean, logvar = self.encoder(x)
+        z = self.reparameterize(mean, logvar)
+        x = self.decoder(z)
+        return x, z, mean, logvar
+
+    @staticmethod
+    def reparameterize(mean, logvar):
+        std = torch.exp(logvar / 2) # in log-space, squareroot is divide by two
+        epsilon = torch.randn_like(std)
+        return epsilon * std + mean
+
+    def compute_spatial_shape(self, level: int) -> tuple[int, int]:
+        # TODO Add warning when shape is odd before maxpool
+        spatial_shape = np.array(self.in_spatial_shape)
+        if level == 0:
+            return spatial_shape
+        spatial_shape = np.array(self.compute_spatial_shape(level-1)) // 2 
+        if any([s%2 != 0 for s in spatial_shape]):
+            raise ValueError("Can't Decode Because Input Dimension is Lost during Downsampling")
+        return spatial_shape
diff --git a/src/embed_time/model_VAE_resnet18_linear_ac.py b/src/embed_time/model_VAE_resnet18_linear_ac.py
new file mode 100644
index 0000000..5d8495c
--- /dev/null
+++ b/src/embed_time/model_VAE_resnet18_linear_ac.py
@@ -0,0 +1,166 @@
+import torch
+from torch import nn, optim
+import torch.nn.functional as F
+
+class ResizeConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, mode='nearest'):
+        super().__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=kernel_size//2)
+    
+    def forward(self, x):
+        x = F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+        x = self.conv(x)
+        return x
+
+class BasicBlockEnc(nn.Module):
+
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+
+        planes = in_planes*stride
+
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+
+        if stride == 1:
+            self.shortcut = nn.Identity()
+        else:
+            self.shortcut = nn.Sequential(
+                nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
+                nn.BatchNorm2d(planes)
+            )
+
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class BasicBlockDec(nn.Module):
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+        planes = int(in_planes/stride)
+
+        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(in_planes)
+        # self.bn1 could have been placed here, 
+        # but that messes up the order of the layers when printing the class
+
+        if stride == 1:
+            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential()
+        else:
+            self.conv1 = ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential(
+                ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride),
+                nn.BatchNorm2d(planes)
+            )
+    
+    def forward(self, x):
+        out = torch.relu(self.bn2(self.conv2(x)))
+        out = self.bn1(self.conv1(out))
+        out = out + self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+
+class ResNet18Enc(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 64
+        self.z_dim = z_dim
+        self.conv1 = nn.Conv2d(nc, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._make_layer(BasicBlockEnc, 64, num_Blocks[0], stride=1)
+        self.layer2 = self._make_layer(BasicBlockEnc, 128, num_Blocks[1], stride=2)
+        self.layer3 = self._make_layer(BasicBlockEnc, 256, num_Blocks[2], stride=2)
+        self.layer4 = self._make_layer(BasicBlockEnc, 512, num_Blocks[3], stride=2)
+        self.linear = nn.Linear(int(512*(128/2**len(num_Blocks))**2), 2 * z_dim, bias = False)
+
+    def _make_layer(self, BasicBlockEnc, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in strides:
+            layers += [BasicBlockEnc(self.in_planes, stride)]
+            self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, x):
+        x = torch.relu(self.bn1(self.conv1(x)))
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = torch.flatten(x, start_dim=1, end_dim=-1).unsqueeze(1)
+        x = torch.relu(self.linear(x))
+        mu, logvar = torch.chunk(x, 2, dim=2)
+        return mu, logvar
+
+class ResNet18Dec(nn.Module):
+
+    def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 512
+        self.nc = nc
+        self.z_dim = z_dim
+
+        
+        self.linear = nn.Linear(z_dim, 256)
+        self.firstconv = nn.Conv2d(1, 512, kernel_size=1)
+        self.firstnorm = nn.BatchNorm2d(512)
+
+
+        self.layer4 = self._make_layer(BasicBlockDec, 256, num_Blocks[3], stride=2)
+        self.layer3 = self._make_layer(BasicBlockDec, 128, num_Blocks[2], stride=2)
+        self.layer2 = self._make_layer(BasicBlockDec, 64, num_Blocks[1], stride=2)
+        self.layer1 = self._make_layer(BasicBlockDec, 64, num_Blocks[0], stride=1)
+        self.conv1 = ResizeConv2d(64, nc, kernel_size=3, scale_factor=1)
+
+    def _make_layer(self, BasicBlockDec, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in reversed(strides):
+            layers += [BasicBlockDec(self.in_planes, stride)]
+        self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, z):
+        x = torch.relu(self.linear(z))
+        x= x.view(-1, 1, 16,16)
+        x = self.firstnorm(self.firstconv(x))
+        x = torch.relu(x)
+        x = self.layer4(x)
+        x = self.layer3(x)
+        x = self.layer2(x)
+        x = self.layer1(x)
+        x = torch.sigmoid(self.conv1(x))
+        return x
+    
+    
+class VAEResNet18_linear(nn.Module):
+    
+    def __init__(self, nc, z_dim):
+        super().__init__()
+        self.encoder = ResNet18Enc(nc=nc, z_dim=z_dim)
+        self.decoder = ResNet18Dec(nc=nc, z_dim=z_dim)
+
+    def forward(self, x):
+        mu, log_var = self.encoder(x)
+        z = self.reparameterize(mu, log_var)
+        x = self.decoder(z)
+        # return x, z
+        return x, mu, log_var
+    
+    @staticmethod
+    def reparameterize(mean, logvar):
+        std = torch.exp(logvar / 2) # in log-space, squareroot is divide by two
+        epsilon = torch.randn_like(std)
+        return epsilon * std + mean
diff --git a/src/embed_time/models_contrastive.py b/src/embed_time/models_contrastive.py
new file mode 100644
index 0000000..3f064f2
--- /dev/null
+++ b/src/embed_time/models_contrastive.py
@@ -0,0 +1,994 @@
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from torch.optim import Adam, SGD
+from torch.distributions import Normal
+from torch.distributions.kl import kl_divergence
+
+def apply_scaled_init(model):
+    for m in model.modules():
+        if isinstance(m, nn.Conv2d):
+            nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.Linear):
+            nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+class CyclicWeightScheduler:
+    def __init__(self, step_size, base_weight=0, max_weight=1):
+        self.base_weight = base_weight
+        self.max_weight = max_weight
+        self.step_size = step_size
+        self.cycle = 0
+        self.step_count = 0
+
+    def step(self):
+        # Compute the current position in the cycle
+        cycle_position = self.step_count / self.step_size
+
+        if cycle_position <= 1:
+            weight = self.base_weight + (self.max_weight - self.base_weight) * cycle_position
+        else:
+            weight = self.max_weight
+            # weight = self.max_weight - (self.max_weight - self.base_weight) * (cycle_position - 1)
+
+        self.step_count = (self.step_count + 1) % (self.step_size * 2)
+
+        return weight
+
+class Encoder(nn.Module):
+    def __init__(self, 
+                 latent_dim: int, 
+                 num_input_channels: int, 
+                 base_channel_size: int, 
+                 variational: bool = False,
+                 act_fn: object = nn.GELU,
+                 model: str = None,
+                 width: int = 64,
+                 height: int = 64):
+        """
+        Encoder network for VAE.
+
+        Args:
+            latent_dim (int): Dimensionality of the latent space.
+            num_input_channels (int): Number of input channels in the image.
+            base_channel_size (int): Number of channels in the first conv layer.
+            variational (bool): If True, encoder outputs mean and log variance.
+            act_fn (nn.Module): Activation function to use.
+            model (str): Specific model architecture to use ('uhler', 'test', or None).
+            width (int): Width of the input image.
+            height (int): Height of the input image.
+        """
+        super().__init__()
+        self.variational = variational
+        c_hid = base_channel_size
+
+        # Define the network architecture based on the 'model' parameter
+        if model == 'uhler':
+            print('using uhler encoder')
+            self.net = nn.Sequential(
+                nn.Conv2d(num_input_channels, c_hid, 4, 2, 1, bias=False),  # NxN => N/2 x N/2
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid, c_hid * 2, 4, 2, 1, bias=False),  # N/2 x N/2 => N/4 x N/4
+                nn.BatchNorm2d(c_hid * 2),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid * 2, c_hid * 4, 4, 2, 1, bias=False),  # N/4 x N/4 => N/8 x N/8
+                nn.BatchNorm2d(c_hid * 4),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid * 4, c_hid * 8, 4, 2, 1, bias=False),  # N/8 x N/8 => N/16 x N/16
+                nn.BatchNorm2d(c_hid * 8),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid * 8, c_hid * 8, 4, 2, 1, bias=False),  # N/16 x N/16 => N/32 x N/32
+                nn.BatchNorm2d(c_hid * 8),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Flatten(),
+            )
+        elif model == 'test':
+            print('using test encoder')
+            self.net = nn.Sequential(
+                nn.Conv2d(num_input_channels, c_hid, kernel_size=4, stride=2, padding=1, bias=False),  # 96x96 => 48x48
+                nn.LayerNorm([c_hid, 48, 48]),
+                nn.GELU(),
+                nn.Conv2d(c_hid, c_hid * 2, kernel_size=4, stride=2, padding=1, bias=False),  # 48x48 => 24x24
+                nn.LayerNorm([c_hid * 2, 24, 24]),
+                nn.GELU(),
+                nn.Conv2d(c_hid * 2, c_hid * 4, kernel_size=3, stride=2, padding=1, bias=False),  # 24x24 => 12x12
+                nn.LayerNorm([c_hid * 4, 12, 12]),
+                nn.GELU(),
+                nn.Conv2d(c_hid * 4, c_hid * 8, kernel_size=3, stride=2, padding=1, bias=False),  # 12x12 => 6x6
+                nn.LayerNorm([c_hid * 8, 6, 6]),
+                nn.GELU(),
+                nn.Conv2d(c_hid * 8, c_hid * 8, kernel_size=3, stride=2, padding=1, bias=False),  # 6x6 => 3x3
+                nn.LayerNorm([c_hid * 8, 3, 3]),
+                nn.GELU(),
+                nn.Flatten()
+            )
+        else:
+            if width == 96:
+                print('using width 96 encoder')
+                self.net = nn.Sequential(
+                    nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2),  # 96x96 => 48x48
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),  # 48x48 => 48x48
+                    act_fn(),
+                    nn.Conv2d(c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 48x48 => 24x24
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),  # 24x24 => 24x24
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 24x24 => 12x12
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 12x12 => 6x6
+                    act_fn(),
+                    nn.Flatten(),
+                )
+            elif width == 64:
+                print('using width 64 encoder')
+                self.net = nn.Sequential(
+                    nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2),  # 64x64 => 32x32
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),  # 32x32 => 32x32
+                    act_fn(),
+                    nn.Conv2d(c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 32x32 => 16x16
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),  # 16x16 => 16x16
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 16x16 => 8x8
+                    act_fn(),
+                    nn.Flatten(),
+                )
+        # Apply initialization
+        apply_scaled_init(self.net)
+        
+        # Set up the final linear layers for variational case
+        if self.variational:
+            if model is not None:
+                input_size = c_hid * 8 * 3 * 3
+            else:
+                input_size = 2 * (6 * 6 if width == 96 else 8 * 8) * c_hid
+            
+            self.fc_mu = nn.Linear(input_size, latent_dim)
+            self.fc_log_var = nn.Linear(input_size, latent_dim)
+            apply_scaled_init(self.fc_mu)
+            apply_scaled_init(self.fc_log_var)
+        else:
+            self.net.add_module('output', nn.Linear(input_size, latent_dim))
+
+    def forward(self, x):
+        """
+        Forward pass of the encoder.
+
+        Args:
+            x (torch.Tensor): Input tensor.
+
+        Returns:
+            If variational:
+                tuple: (mu, log_var) for the latent space distribution.
+            Else:
+                torch.Tensor: Encoded representation.
+        """
+        x = self.net(x)
+        if self.variational:
+            mu = self.fc_mu(x)
+            log_var = self.fc_log_var(x)
+            return mu, log_var
+        else:
+            return x
+
+def apply_scaled_init(model):
+    for m in model.modules():
+        if isinstance(m, (nn.Conv2d, nn.ConvTranspose2d, nn.Linear)):
+            nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+
+class Decoder(nn.Module):
+    def __init__(self, 
+                 latent_dim: int, 
+                 num_input_channels: int, 
+                 base_channel_size: int, 
+                 batch_latent_dim: int = 0,
+                 act_fn: object = nn.GELU,
+                 model: str = None,
+                 width: int = 64):
+        """
+        Decoder network for VAE.
+
+        Args:
+            latent_dim (int): Dimensionality of the latent space.
+            num_input_channels (int): Number of channels in the output image.
+            base_channel_size (int): Number of channels in the last conv layer.
+            batch_latent_dim (int): Additional latent dimensions for batch processing.
+            act_fn (nn.Module): Activation function to use.
+            model (str): Specific model architecture to use ('uhler', 'test', or None).
+            width (int): Width of the output image.
+        """
+        super().__init__()
+        c_hid = base_channel_size
+        print(width)
+        print(model)
+        # Define the network architecture based on the 'model' parameter
+        if model == 'uhler':
+            print('using uhler decoder')
+            self.linear = nn.Sequential(
+                nn.Linear(latent_dim + batch_latent_dim, 2 * 6 * 6 * c_hid), 
+                act_fn(),
+                nn.Unflatten(1, (2 * c_hid, 6, 6)),  # Reshape to 6x6
+            )
+            self.net = nn.Sequential(
+                nn.ConvTranspose2d(c_hid * 8, c_hid * 8, 4, 2, 1, bias=False),  # 6x6 => 12x12
+                nn.BatchNorm2d(c_hid * 8),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid * 8, c_hid * 4, 4, 2, 1, bias=False),  # 12x12 => 24x24
+                nn.BatchNorm2d(c_hid * 4),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid * 4, c_hid * 2, 4, 2, 1, bias=False),  # 24x24 => 48x48
+                nn.BatchNorm2d(c_hid * 2),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid * 2, c_hid, 4, 2, 1, bias=False),  # 48x48 => 96x96
+                nn.BatchNorm2d(c_hid),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid, num_input_channels, 4, 2, 1, bias=False),  # 96x96 => 192x192
+                nn.Tanh(),
+            )
+        elif model == 'test':
+            print('using test decoder')
+            self.linear = nn.Sequential(
+                nn.Linear(latent_dim + batch_latent_dim, 8 * 3 * 3 * c_hid),
+                nn.LayerNorm(8 * 3 * 3 * c_hid),
+                act_fn(),
+                nn.Unflatten(1, (8 * c_hid, 3, 3)), 
+            )
+            self.net = nn.Sequential(
+                nn.ConvTranspose2d(8 * c_hid, 4 * c_hid, kernel_size=4, padding=1, stride=2), # 3x3 => 6x6
+                nn.LayerNorm([4 * c_hid, 6, 6]),
+                act_fn(),
+                nn.ConvTranspose2d(4 * c_hid, 2 * c_hid, kernel_size=4, padding=1, stride=2), # 6x6 => 12x12
+                nn.LayerNorm([2 * c_hid, 12, 12]),
+                act_fn(),
+                nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), # 12x12 => 24x24
+                nn.LayerNorm([c_hid, 24, 24]),
+                act_fn(),
+                nn.ConvTranspose2d(c_hid, c_hid // 2, kernel_size=5, output_padding=1, padding=2, stride=2), # 24x24 => 48x48
+                nn.LayerNorm([c_hid // 2, 48, 48]),
+                act_fn(),
+                nn.ConvTranspose2d(c_hid // 2, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2), # 48x48 => 96x96
+                nn.Tanh(),
+            )
+        else:
+            if width == 96:
+                print('using width 96 decoder')
+                self.linear = nn.Sequential(
+                    nn.Linear(latent_dim + batch_latent_dim, 2 * 6 * 6 * c_hid), 
+                    act_fn(),
+                    nn.Unflatten(1, (2 * c_hid, 6, 6)),
+                )
+                self.net = nn.Sequential(
+                    nn.ConvTranspose2d(2 * c_hid, 2 * c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 6x6 => 12x12
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 12x12 => 24x24
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(c_hid, c_hid // 2, kernel_size=3, output_padding=1, padding=1, stride=2),  # 24x24 => 48x48
+                    act_fn(),
+                    nn.ConvTranspose2d(c_hid // 2, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2),  # 48x48 => 96x96
+                    nn.Tanh(),
+                )
+            elif width == 64:
+                print('using width 64 decoder')
+                self.linear = nn.Sequential(
+                    nn.Linear(latent_dim + batch_latent_dim, 2 * 8 * 8 * c_hid), 
+                    act_fn(),
+                    nn.Unflatten(1, (-1, 8, 8)),
+                )
+                self.net = nn.Sequential(
+                    nn.ConvTranspose2d(2 * c_hid, 2 * c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 8x8 => 16x16
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 16x16 => 32x32
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(c_hid, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2),  # 32x32 => 64x64
+                    nn.Tanh(),
+                )
+        
+        # Apply initialization
+        apply_scaled_init(self.linear)
+        apply_scaled_init(self.net)
+
+    def forward(self, x):
+        """
+        Forward pass of the decoder.
+
+        Args:
+            x (torch.Tensor): Input tensor from the latent space.
+
+        Returns:
+            torch.Tensor: Reconstructed image.
+        """
+        x = self.linear(x)
+        x = self.net(x)
+        return x
+        
+class BaseModel(nn.Module):
+    def __init__(
+        self,
+        model_name: str,
+        optimizer_param: dict,
+        latent_dim: int = 32,
+        base_channel_size: int = 32,
+        num_input_channels: int = 4,
+        image_size: int = 64,
+        act_fn = nn.GELU,
+        *args,
+        **kwargs,
+    ):
+        super().__init__()
+        
+        self.model_name = model_name
+        self.num_input_channels = num_input_channels
+        self.width = image_size
+        self.height = image_size
+        self.base_channel_size = base_channel_size
+        self.latent_dim = latent_dim
+        self.network_param = {
+            'latent_dim': latent_dim, 
+            'num_input_channels': num_input_channels,
+            'base_channel_size': base_channel_size, 
+            'act_fn': act_fn
+        }
+
+        self.optimizer_param = optimizer_param
+        
+        # Placeholder for encoder and decoder, to be defined in subclasses
+        self.encoder = None
+        self.decoder = None
+
+    def forward(self, x):
+        z = self.encoder(x)
+        x_hat = self.decoder(z)
+        return x_hat
+    
+    def _get_loss(self, x):
+        x_hat = self.forward(x)
+        loss = F.mse_loss(x, x_hat, reduction="none")
+        loss = loss.sum(dim=[1, 2, 3]).mean(dim=[0])
+        return loss
+    
+    def configure_optimizer(self):
+        lr = self.optimizer_param['lr']
+        if self.optimizer_param['optimizer'] == 'Adam':
+            optimizer = Adam(self.parameters(), lr=lr)
+        elif self.optimizer_param['optimizer'] == 'SGD':
+            momentum = self.optimizer_param['momentum']
+            nesterov = self.optimizer_param['nesterov']
+            optimizer = SGD(self.parameters(), lr=lr, momentum=momentum, nesterov=nesterov)
+        return optimizer
+
+    def train_step(self, batch):
+        self.train()
+        loss = self._get_loss(batch)
+        return loss
+
+    def val_step(self, batch):
+        self.eval()
+        with torch.no_grad():
+            loss = self._get_loss(batch)
+        return loss
+
+    def test_step(self, batch):
+        return self.val_step(batch)
+
+    def log_gpu_memory(self):
+        if torch.cuda.is_available():
+            current_memory_allocated = torch.cuda.memory_allocated() / (1024.0 ** 3)  # Convert bytes to GB
+            max_memory_allocated = torch.cuda.max_memory_allocated() / (1024.0 ** 3)  # Convert bytes to GB
+            return {
+                'Current GPU Memory (GB)': current_memory_allocated, 
+                'Max GPU Memory (GB)': max_memory_allocated
+            }
+        return {}
+
+class AEmodel(BaseModel):
+    def __init__(self, 
+                 encoder_class: object = Encoder,
+                 decoder_class: object = Decoder,
+                 *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.encoder = encoder_class(variational=False, **self.network_param)
+        self.decoder = decoder_class(**self.network_param)
+        self.example_input_array = torch.zeros(2, self.num_input_channels, self.width, self.height)
+
+    def get_image_embedding(self, x):
+        return self.encoder(x)
+
+class VAEmodel(BaseModel):
+    def __init__(self, 
+                 step_size: int,
+                 latent_dim: int = 64,
+                 encoder_class: object = Encoder,
+                 decoder_class: object = Decoder,
+                 *args, **kwargs):
+        super().__init__(latent_dim=latent_dim, *args, **kwargs)
+        
+        # Initialize the cyclic weight scheduler
+        self.kl_weight_scheduler = CyclicWeightScheduler(step_size=step_size)
+
+        # for the gaussian likelihood
+        self.log_scale = nn.Parameter(torch.Tensor([0.0]))
+        self.encoder = encoder_class(variational=True, **self.network_param)
+        self.decoder = decoder_class(**self.network_param)
+        self.example_input_array = torch.zeros(2, self.num_input_channels, self.width, self.height)
+    
+    def forward(self, x):
+        # encode x to get the mu and variance parameters
+        mu, log_var = self.encoder(x)
+        # sample z 
+        log_var = torch.maximum(log_var, torch.tensor(-20)) #clipping to prevent going to -inf
+        std = torch.exp(log_var / 2)
+        z = self.sampling(mu, std)
+        # decoded 
+        x_hat = self.decoder(z)
+        return x_hat, mu, std
+    
+    def get_image_embedding(self, x):
+        mu, _ = self.encoder(x)
+        return mu
+    
+    @staticmethod
+    def sampling(mu, std):
+        eps = torch.randn_like(std)
+        return mu + eps * std
+
+    @staticmethod
+    def reconstruction_loss(sample: torch.Tensor,
+                            mean: torch.Tensor, 
+                            logscale: torch.Tensor):
+        scale = torch.exp(logscale)
+        dist = Normal(mean, scale)
+        log_pxz = dist.log_prob(sample)
+        return -log_pxz.sum(dim=(1, 2, 3))
+
+    def _generic_loss(self, x, x_hat, mu, std):
+        # reconstruction probability
+        recon_loss = self.reconstruction_loss(x, x_hat, self.log_scale)
+
+        # kl
+        kl = self.latent_kl_divergence(mu, std)
+        kl_term_weight = self.kl_weight_scheduler.step()
+
+        # elbo
+        elbo = (kl_term_weight * kl + recon_loss)
+        elbo = elbo.mean()
+        return elbo, {
+            'elbo': elbo.item(),
+            'kl': kl.mean().item(),
+            'recon_loss': recon_loss.mean().item(), 
+            'kl_term_weight': kl_term_weight,
+        }
+
+    @staticmethod
+    def latent_kl_divergence(variational_mean, 
+                             variational_std, 
+                             prior_mean=None,
+                             prior_std=None) -> torch.Tensor:
+        if prior_mean is None:
+            prior_mean = torch.zeros_like(variational_mean)
+        prior_std = torch.ones_like(variational_std)
+        return kl_divergence(
+            Normal(variational_mean, variational_std),
+            Normal(prior_mean, prior_std)
+        ).sum(dim=-1)
+    
+    def _get_loss(self, x):
+        # get reconstruction, mu and std
+        x_hat, mu, std = self.forward(x)
+        elbo, metrics = self._generic_loss(x, x_hat, mu, std)
+        return elbo, metrics
+
+    def train_step(self, batch):
+        self.train()
+        loss, metrics = self._get_loss(batch)
+        return loss, metrics
+
+    def val_step(self, batch):
+        self.eval()
+        with torch.no_grad():
+            loss, metrics = self._get_loss(batch)
+        return loss, metrics
+
+# class ContrastiveVAEmodel(BaseModel):
+#     """
+#     Args:
+#     ----
+#         n_z_latent: Dimensionality of the background latent space.
+#         n_s_latent: Dimensionality of the salient latent space.
+#         wasserstein_penalty: Weight of the Wasserstein distance loss that further
+#             discourages shared variations from leaking into the salient latent space.
+#     """
+        
+#     def __init__(self, 
+#                  n_z_latent: int = 32, 
+#                  n_s_latent: int = 32, 
+#                  encoder_class: object = Encoder,
+#                  decoder_class: object = Decoder,
+#                  step_size: float=2000,
+#                  ngene: int=None,
+#                  adjust_prior_s: bool=False,
+#                  adjust_prior_z: bool=False,
+#                  classify_s: bool=False,
+#                  classify_z: bool=False,
+#                  wasserstein_penalty: float = 0,
+#                  BatchNorm = None,
+#                  n_unique_batch: int = 34,
+#                  model = None,
+#                  batch_size: int=1024,
+#                  tc_penalty: float=1,
+#                  classification_weight: float=1,
+#                  scale_factor: float=0.1,
+#                  max_kl_weight: float=1,
+#                  batch_latent_dim: int=32,
+#                  reg_type: str=None,
+#                  total_steps: int=3000,
+#                  klscheduler: str='cyclic',
+#                  *args, 
+#                  **kwargs):
+#         super().__init__(*args, **kwargs)
+#         self.step_size = step_size
+#         self.ngene = ngene
+#         self.adjust_prior_s = adjust_prior_s
+#         self.adjust_prior_z = adjust_prior_z
+#         self.n_s_latent = n_s_latent
+#         self.n_z_latent = n_z_latent
+#         self.wasserstein_penalty = wasserstein_penalty
+#         self.BatchNorm = BatchNorm
+#         self.n_unique_batch = n_unique_batch
+#         self.model = model
+#         self.batch_size = batch_size
+#         self.tc_penalty = tc_penalty
+#         self.classify_s = classify_s
+#         self.classify_z = classify_z
+#         self.classification_weight = classification_weight
+#         self.scale_factor = scale_factor
+#         self.batch_latent_dim = batch_latent_dim
+#         self.reg_type = reg_type
+#         self.total_steps = total_steps
+#         self.klscheduler = klscheduler
+
+#         if n_s_latent != n_z_latent:
+#             warnings.warn('Target latent dim does not equal background latent dim')
+
+#         # Initialize the weight scheduler
+#         if self.klscheduler == 'cyclic':
+#             self.kl_weight_scheduler = CyclicWeightScheduler(step_size=self.step_size, max_weight=max_kl_weight)
+#         elif self.klscheduler == 'ramp':
+#             self.kl_weight_scheduler = KLRampScheduler(total_steps=self.total_steps, max_weight=max_kl_weight)
+
+#         # Background encoder
+#         self.coder_param = {'num_input_channels': self.num_input_channels, "scale_factor": self.scale_factor, 
+#                          'base_channel_size': self.base_channel_size, 'variational': True, 'width': self.width, 'height':self.height,
+#                           'BatchNorm': self.BatchNorm, 'n_unique_batch': self.n_unique_batch, 'model': self.model,}
+#         self.z_encoder = encoder_class(
+#                                        latent_dim=self.n_z_latent,
+#                                        **self.coder_param,
+#                                        )
+#         # Salient encoder 
+#         self.s_encoder = encoder_class(
+#                                        latent_dim=self.n_s_latent,
+#                                        **self.coder_param,
+#                                        )
+        
+#         # Decoder from latent variable to distribution parameters in data space.
+#         self.n_total_latent = self.n_z_latent + self.n_s_latent        
+#         self.decoder = decoder_class(
+#                                     latent_dim=self.n_total_latent,
+#                                     batch_latent_dim=self.batch_latent_dim,
+#                                     **self.coder_param,
+#                                     )
+        
+#         if self.adjust_prior_z:
+#             self.zprior_embedding = nn.Embedding(self.ngene, self.n_z_latent)
+#         if self.adjust_prior_s:
+#             self.sprior_embedding = nn.Embedding(self.ngene, self.n_s_latent)
+    
+#         # for the gaussian likelihood
+#         self.log_scale = nn.Parameter(torch.Tensor([0.0]))
+
+#         # Example input array needed for visualizing the graph of the network
+#         self.example_input_array = {'background': torch.zeros(2, self.num_input_channels, self.width, self.height),
+#                                         'target': torch.zeros(2, self.num_input_channels, self.width, self.height)}
+#         if self.adjust_prior_s or self.adjust_prior_z:
+#             self.example_input_array['background_label'] = torch.zeros(2, dtype=torch.int32)
+#             self.example_input_array['target_label'] = torch.zeros(2, dtype=torch.int32)
+#         if self.batch_latent_dim > 0:
+#             self.batch_embedding = nn.Embedding(self.n_unique_batch, self.batch_latent_dim)
+#             self.example_input_array.update({'background_batch': torch.zeros(2, dtype=torch.int32),
+#                                             'target_batch': torch.zeros(2, dtype=torch.int32)})
+#         # Saving hyperparameters of autoencoder
+#         self.save_hyperparameters()
+
+#     def forward(self, background, target, **kwargs):
+#         background_label = kwargs.get('background_label')
+#         target_label = kwargs.get('target_label')
+#         prior_mu_background = {'zprior_m': None,  'sprior_m': None}
+#         prior_mu_target = {'zprior_m': None, 'sprior_m': None}
+#         # zlabel_embedding = None
+#         # slabel_embedding = None
+#         if self.adjust_prior_s:
+#             prior_mu_background['sprior_m'] = self.sprior_embedding(background_label.int())
+#             prior_mu_target['sprior_m'] = self.sprior_embedding(target_label.int())
+#             # slabel_embedding = torch.cat([prior_mu_background['sprior_m'], 
+#             #                               prior_mu_target['sprior_m']], dim=0)
+#         if self.adjust_prior_z:
+#             prior_mu_background['zprior_m'] = self.zprior_embedding(background_label.int())
+#             prior_mu_target['zprior_m'] = self.zprior_embedding(target_label.int())
+#             # zlabel_embedding = torch.cat([prior_mu_background['zprior_m'], 
+#             #                               prior_mu_target['zprior_m']], dim=0)
+#         inference_outputs = self.inference(background=background, 
+#                                            target=target)
+#         background_batch = kwargs.get('background_batch')
+#         target_batch = kwargs.get('target_batch')
+#         generative_outputs = self.generative(inference_outputs['background'], 
+#                                              inference_outputs['target'],
+#                                              background_batch=background_batch,
+#                                              target_batch=target_batch)
+#         recon = {'bg':generative_outputs['background']["px_m"], 
+#                  "tg":generative_outputs['target']["px_m"]}
+#         inference_outputs['background'].update(prior_mu_background)
+#         inference_outputs['target'].update(prior_mu_target)
+
+#         return recon, inference_outputs, generative_outputs
+    
+#     def get_image_embedding(self, img, label=None):
+#         qz_m, _ = self.z_encoder(img)
+#         qs_m, _ = self.s_encoder(img)
+#         return torch.cat((qs_m, qz_m), dim=1)
+
+#     def _generic_inference(self,
+#                              x: torch.Tensor,
+#                              ):
+#         qz_m, qz_lv = self.z_encoder(x)
+#         qs_m, qs_lv = self.s_encoder(x)
+        
+#         # sample from latent distribution
+#         qz_lv = torch.maximum(qz_lv, torch.tensor(-20)) #clipping to prevent going to -inf
+#         qs_lv = torch.maximum(qs_lv, torch.tensor(-20)) #clipping to prevent going to -inf
+#         qz_s = torch.exp(qz_lv / 2)
+#         qs_s = torch.exp(qs_lv / 2)
+#         qz = Normal(qz_m, qz_s)
+#         qs = Normal(qs_m, qs_s)
+#         z = qz.rsample()
+#         s = qs.rsample()
+
+#         outputs = dict(
+#             qz_m=qz_m,
+#             qz_s=qz_s,
+#             z=z,
+#             qs_m=qs_m,
+#             qs_s=qs_s,
+#             s=s,)
+#         return outputs
+    
+#     def inference(
+#             self,
+#             background: torch.Tensor,
+#             target: torch.Tensor,
+#         ) -> Dict[str, Dict[str, torch.Tensor]]:
+#             background_batch_size = background.shape[0]
+#             target_batch_size = target.shape[0]
+#             inference_input = torch.cat([background, target], dim=0)
+#             outputs = self._generic_inference(x=inference_input)
+#             background_outputs, target_outputs = {}, {}
+#             for key in outputs.keys():
+#                 if outputs[key] is not None:
+#                     background_tensor, target_tensor = torch.split(
+#                         outputs[key],
+#                         [background_batch_size, target_batch_size],
+#                         dim=0,
+#                     )
+#                 else:
+#                     background_tensor, target_tensor = None, None
+#                 background_outputs[key] = background_tensor
+#                 target_outputs[key] = target_tensor
+#             background_outputs["s"] = torch.zeros_like(background_outputs["s"])
+#             return dict(background=background_outputs, target=target_outputs)
+    
+#     def _generic_generative(self, 
+#                               z: torch.Tensor, 
+#                               s: torch.Tensor,
+#                               batch_embedding: torch.Tensor=None,):
+#         latent = torch.cat([z, s], dim=-1)
+#         if batch_embedding is not None:
+#             latent = torch.cat([latent, batch_embedding], dim=-1)
+#         px_m = self.decoder(latent)
+#         return dict(px_m=px_m, px_s=self.log_scale)
+
+#     def generative(
+#         self,
+#         background: Dict[str, torch.Tensor],
+#         target: Dict[str, torch.Tensor],
+#         **kwargs,
+#         ) -> Dict[str, Dict[str, torch.Tensor]]:
+#         latent_z_shape = background["z"].shape
+#         batch_size_dim = 0 if len(latent_z_shape) == 2 else 1
+#         background_batch_size = background["z"].shape[batch_size_dim]
+#         target_batch_size = target["z"].shape[batch_size_dim]
+#         generative_input = {}
+#         for key in ["z", "s"]:
+#             generative_input[key] = torch.cat(
+#                 [background[key], target[key]], dim=batch_size_dim
+#             )
+#         background_batch = kwargs.get("background_batch")
+#         target_batch = kwargs.get("target_batch")
+#         if background_batch is not None and target_batch is not None:
+#             generative_input["batch_embedding"] = torch.cat(
+#                 [self.batch_embedding(background_batch), 
+#                  self.batch_embedding(target_batch)], dim=batch_size_dim
+#                 )
+#         outputs = self._generic_generative(**generative_input)
+#         background_outputs, target_outputs = {}, {}
+#         if outputs["px_m"] is not None:
+#             background_tensor, target_tensor = torch.split(
+#                 outputs["px_m"],
+#                 [background_batch_size, target_batch_size],
+#                 dim=batch_size_dim,
+#             )
+#         else:
+#             background_tensor, target_tensor = None, None
+#         background_outputs["px_m"] = background_tensor
+#         target_outputs["px_m"] = target_tensor
+#         background_outputs["px_s"] = outputs["px_s"]
+#         target_outputs["px_s"] = outputs["px_s"]
+#         return dict(background=background_outputs, target=target_outputs)
+
+#     def _generic_loss(self, 
+#                       tensors: torch.Tensor,
+#                       inference_outputs: Dict[str, torch.Tensor], 
+#                       generative_outputs: Dict[str, torch.Tensor],
+#                     )-> Dict[str, torch.Tensor]:
+        
+#         qz_m = inference_outputs["qz_m"]
+#         qz_s = inference_outputs["qz_s"]
+#         qs_m = inference_outputs["qs_m"]
+#         qs_s = inference_outputs["qs_s"]
+#         zprior_m = inference_outputs["zprior_m"]
+#         sprior_m = inference_outputs["sprior_m"]
+#         px_m = generative_outputs["px_m"]
+#         px_s = generative_outputs["px_s"]
+
+#         recon_loss = VAEmodel.reconstruction_loss(tensors, px_m, px_s)
+#         kl_z = VAEmodel.latent_kl_divergence(qz_m, qz_s, prior_mean=zprior_m)
+#         kl_s = VAEmodel.latent_kl_divergence(qs_m, qs_s, prior_mean=sprior_m)
+#         return dict(recon_loss=recon_loss, kl_z=kl_z, kl_s=kl_s)
+    
+#     def compute_independent_loss(self, zb, zc):
+#         reg_type = self.reg_type
+#         if reg_type == "TC":
+#             return self.compute_tc(zb, zc)
+#         elif reg_type == "HSIC":
+#             return self.compute_HSIC(zb, zc)
+#         else:
+#             raise ValueError("reg_type should be TC or HSIC")
+    
+#     @staticmethod
+#     def rbf_kernel(X, sigma=1.0):
+#         # Compute the pairwise squared Euclidean distances
+#         pairwise_dists = torch.cdist(X, X, p=2) ** 2
+#         # Apply the RBF kernel function
+#         values = torch.div(-pairwise_dists, (2 * sigma**2))
+#         return values.exp()
+    
+#     @staticmethod
+#     def compute_HSIC(Z_b, Z_c):
+#         n = Z_b.shape[0]
+#         # Compute kernel matrices
+#         K = ContrastiveVAEmodel.rbf_kernel(Z_b)
+#         L = ContrastiveVAEmodel.rbf_kernel(Z_c)
+#         # print(K.shape, L.shape)
+#         # Implement the HSIC formula
+#         term1 = (1 / (n**2)) * torch.sum(K * L)
+#         term2 = (1 / (n**4)) * torch.sum(K) * torch.sum(L)
+#         term3 = (2 / (n**3)) * torch.sum(K @ L)
+#         HSIC_n = term1 + term2 - term3
+#         return HSIC_n * n
+    
+#     @staticmethod
+#     def compute_tc(zb, zc):
+#         # Calculate the empirical means
+#         mean_zb = torch.mean(zb, dim=0)
+#         mean_zc = torch.mean(zc, dim=0)
+#         # Calculate the centered variables
+#         centered_zb = zb - mean_zb
+#         centered_zc = zc - mean_zc
+#         # Calculate the covariance matrix of the concatenated latent variables
+#         z_concat = torch.cat([centered_zb, centered_zc], dim=1)
+#         cov_matrix = torch.matmul(z_concat.T, z_concat) / z_concat.shape[0]
+#         # Calculate the covariance matrices for zb and zc individually
+#         cov_zb = torch.matmul(centered_zb.T, centered_zb) / centered_zb.shape[0]
+#         cov_zc = torch.matmul(centered_zc.T, centered_zc) / centered_zc.shape[0]
+#         # Calculate total correlation loss
+#         tc_loss = torch.logdet(cov_matrix) - (torch.logdet(cov_zb) + torch.logdet(cov_zc))
+#         # Multiply by the weighting factor
+#         return -tc_loss
+
+#     def _get_loss(self, 
+#              concat_tensors: Dict[str, Tuple[Dict[str, torch.Tensor], int]],
+#              ):  
+#         _, inference_outputs, generative_outputs = self.forward(**concat_tensors)            
+
+#         background_losses = self._generic_loss(
+#             concat_tensors["background"],
+#             inference_outputs["background"],
+#             generative_outputs["background"],
+#         )
+#         target_losses = self._generic_loss(
+#             concat_tensors["target"],
+#             inference_outputs["target"],
+#             generative_outputs["target"],
+#         )
+#         recon_loss = background_losses["recon_loss"] + target_losses["recon_loss"]
+#         kl_divergence_z = background_losses["kl_z"] + target_losses["kl_z"]
+#         kl_divergence_s = target_losses["kl_s"]
+
+#         wasserstein_loss = (
+#             torch.norm(inference_outputs["background"]["qs_m"], dim=-1)**2
+#             + torch.sum(inference_outputs["background"]["qs_s"]**2, dim=-1)
+#         )
+
+#         if self.reg_type is not None:
+#             zb = torch.concat([inference_outputs["target"]["qz_m"], inference_outputs["background"]["qz_m"]], axis=0)
+#             zs = torch.concat([inference_outputs["target"]["qs_m"], inference_outputs["background"]["qs_m"]], axis=0)
+#             tc_loss = self.compute_independent_loss(zb, zs)
+#         else:
+#             tc_loss = torch.zeros(1, device=self.device)
+
+#         kl_term_weight = self.kl_weight_scheduler.step()
+        
+#         elbo = torch.mean(recon_loss + 
+#                           kl_term_weight * (kl_divergence_s + kl_divergence_z + 
+#                                             self.wasserstein_penalty * wasserstein_loss +
+#                                             self.tc_penalty * tc_loss))
+
+#         self.log_dict({
+#             'kl_divergence_z': kl_divergence_z.mean().detach(),
+#             'kl_divergence_s': kl_divergence_s.mean().detach(),
+#             'total_recon_loss': recon_loss.mean().detach(),
+#             'wasserstein_loss': wasserstein_loss.mean().detach(),
+#             'tc_loss': tc_loss.mean().detach(),
+#             # 'background_recon_loss': background_losses["recon_loss"].mean().detach(),
+#             # 'target_recon_loss': target_losses["recon_loss"].mean().detach(),
+#             'kl_term_weight': kl_term_weight,
+#         })
+#         return elbo
+    
+ 
+# class CyclicWeightScheduler:
+#     def __init__(self, step_size, base_weight=0, max_weight=1):
+#         self.base_weight = base_weight
+#         self.max_weight = max_weight
+#         self.step_size = step_size
+#         self.cycle = 0
+#         self.step_count = 0
+
+#     def step(self):
+#         # Compute the current position in the cycle
+#         cycle_position = self.step_count / self.step_size
+
+#         if cycle_position <= 1:
+#             weight = self.base_weight + (self.max_weight - self.base_weight) * cycle_position
+#         else:
+#             weight = self.max_weight
+#             # weight = self.max_weight - (self.max_weight - self.base_weight) * (cycle_position - 1)
+
+#         self.step_count = (self.step_count + 1) % (self.step_size * 2)
+
+#         return weight
+    
+# class KLRampScheduler:
+#     def __init__(self, start_weight=0, max_weight=1, total_steps=3000):
+#         self.start_weight = start_weight
+#         self.max_weight = max_weight
+#         self.total_steps = total_steps
+#         self.current_step = 0
+#         self.current_weight = start_weight
+
+#     def step(self):
+#         self.current_step += 1
+#         progress = self.current_step / self.total_steps
+#         self.current_weight = self.start_weight + (self.max_weight - self.start_weight) * progress
+#         # Clip the weight to be within the specified range
+#         self.current_weight = min(max(self.current_weight, self.start_weight), self.max_weight)
+#         return self.current_weight
+
+#     def get_weight(self):
+#         return self.current_weight
+    
+# class LinearDiscriminator(nn.Module):
+#     def __init__(self, input_features):
+#         super(LinearDiscriminator, self).__init__()
+#         self.linear = nn.Linear(input_features, 1)
+#         self.sigmoid = nn.Sigmoid()
+
+#     def forward(self, x):
+#         x = self.linear(x)
+#         x = self.sigmoid(x)
+#         return x
+
+# class ConditionalBatchNorm1d(nn.Module):
+#     def __init__(self, num_features, num_classes):
+#         super().__init__()
+#         self.num_features = num_features
+#         self.bn = nn.BatchNorm1d(num_features, affine=False)
+#         self.embed = nn.Embedding(num_classes, num_features * 2)
+#         self.embed.weight.data[:, :num_features].normal_(1, 0.02)  # Initialise scale at N(1, 0.02)
+#         self.embed.weight.data[:, num_features:].zero_()  # Initialise bias at 0
+
+#     def forward(self, x, y):
+#         # y is the condition, i.e. batch number
+#         out = self.bn(x)
+#         gamma, beta = self.embed(y).chunk(2, 1)
+#         gamma = gamma.expand_as(out)
+#         beta = beta.expand_as(out)
+#         return gamma * out + beta
+    
+# class ConditionalBatchNorm2d(nn.Module):
+#     def __init__(self, num_features, num_classes):
+#         super().__init__()
+#         self.num_features = num_features
+#         self.bn = nn.BatchNorm2d(num_features, affine=False)
+#         self.embed = nn.Embedding(num_classes, num_features * 2)
+#         self.embed.weight.data[:, :num_features].normal_(1, 0.02)  # Initialise scale at N(1, 0.02)
+#         self.embed.weight.data[:, num_features:].zero_()  # Initialise bias at 0
+
+#     def forward(self, x, y):
+#         # y is the condition, i.e. batch number
+#         out = self.bn(x)
+#         gamma, beta = self.embed(y).chunk(2, 1)
+#         gamma = gamma.unsqueeze(2).unsqueeze(3).expand_as(out)
+#         beta = beta.unsqueeze(2).unsqueeze(3).expand_as(out)
+#         return gamma * out + beta
+    
+# def add_encoder_batch_norm(model, 
+#                            BatchNorm, 
+#                            n_conditions=None
+#                            ):
+#     new_layers = []
+#     for layer in model:
+#         new_layers.append(layer)
+#         if isinstance(layer, nn.Conv2d):
+#             if BatchNorm == ConditionalBatchNorm2d:
+#                 new_layers.append(BatchNorm(layer.out_channels, n_conditions))
+#             else:
+#                 new_layers.append(BatchNorm(layer.out_channels))
+#     return nn.Sequential(*new_layers)
+
+# def add_decoder_batch_norm(linear, 
+#                            net, 
+#                            BatchNorm1d=nn.BatchNorm1d, 
+#                            BatchNorm2d=nn.BatchNorm2d, 
+#                            n_conditions=None):
+#     new_linear = []
+#     for layer in linear:
+#         new_linear.append(layer)
+#         if isinstance(layer, nn.Linear):
+#             if BatchNorm1d == ConditionalBatchNorm1d:
+#                 new_linear.append(BatchNorm1d(layer.out_features, n_conditions))
+#             else:
+#                 new_linear.append(BatchNorm1d(layer.out_features))
+    
+#     new_net = []
+#     for i, layer in enumerate(net):
+#         new_net.append(layer)
+#         if isinstance(layer, (nn.Conv2d, nn.ConvTranspose2d)) and i != len(net):
+#             if BatchNorm2d == ConditionalBatchNorm2d:
+#                 new_net.append(BatchNorm2d(layer.out_channels, n_conditions))
+#             else:
+#                 new_net.append(BatchNorm2d(layer.out_channels))
+
+#     return nn.Sequential(*new_linear), nn.Sequential(*new_net)
diff --git a/src/embed_time/models_contrastive_pl.py b/src/embed_time/models_contrastive_pl.py
new file mode 100644
index 0000000..15575d8
--- /dev/null
+++ b/src/embed_time/models_contrastive_pl.py
@@ -0,0 +1,933 @@
+import torch
+from torch import nn
+from torch.nn import functional as F
+import lightning as L
+
+from typing import Dict, Tuple
+import warnings
+from torch.distributions import Normal
+from torch.distributions import kl_divergence as kl
+
+def apply_scaled_init(model):
+    for m in model.modules():
+        if isinstance(m, nn.Conv2d):
+            nn.init.kaiming_normal_(m.weight, mode='fan_out', nonlinearity='relu')
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+        elif isinstance(m, nn.Linear):
+            nn.init.kaiming_normal_(m.weight, mode='fan_in', nonlinearity='relu')
+            if m.bias is not None:
+                nn.init.constant_(m.bias, 0)
+    
+class Encoder(nn.Module):
+    def __init__(self, 
+                 latent_dim: int, 
+                 num_input_channels: int, 
+                 base_channel_size: int, 
+                 variational: bool=False,
+                 label_latent_dim: int=0,
+                 BatchNorm = None,
+                 act_fn: object = nn.GELU,
+                 model=None,
+                 width=64,
+                 height=64,
+                 scale_factor=0.1,
+                 *args,
+                 **kwargs):
+        """
+        Args:
+           num_input_channels : Number of input channels of the image.
+           base_channel_size : Number of channels we use in the first convolutional layers. 
+           latent_dim : Dimensionality of latent representation z
+           act_fn : Activation function used throughout the encoder network
+        """
+        super().__init__()
+        self.variational = variational
+        c_hid = base_channel_size
+        if model == 'uhler':
+            self.net = nn.Sequential(
+                nn.Conv2d(num_input_channels, c_hid, 4, 2, 1, bias=False),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid, c_hid * 2, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid * 2),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid * 2, c_hid * 4, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid * 4),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid * 4, c_hid * 8, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid * 8),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Conv2d(c_hid * 8, c_hid * 8, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid * 8),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.Flatten(),  # Image grid to single feature vector
+            )
+        elif model == 'test':
+            self.net = nn.Sequential(
+                nn.Conv2d(num_input_channels, c_hid, kernel_size=4, stride=2, padding=1, bias=False),  # 96x96 => 48x48
+                nn.LayerNorm([c_hid, 48, 48]),
+                nn.GELU(),
+                nn.Conv2d(c_hid, c_hid * 2, kernel_size=4, stride=2, padding=1, bias=False),  # 48x48 => 24x24
+                nn.LayerNorm([c_hid * 2, 24, 24]),
+                nn.GELU(),
+                nn.Conv2d(c_hid * 2, c_hid * 4, kernel_size=3, stride=2, padding=1, bias=False),  # 24x24 => 12x12
+                nn.LayerNorm([c_hid * 4, 12, 12]),
+                nn.GELU(),
+                nn.Conv2d(c_hid * 4, c_hid * 8, kernel_size=3, stride=2, padding=1, bias=False),  # 12x12 => 6x6
+                nn.LayerNorm([c_hid * 8, 6, 6]),
+                nn.GELU(),
+                nn.Conv2d(c_hid * 8, c_hid * 8, kernel_size=3, stride=2, padding=1, bias=False),  # 6x6 => 3x3
+                nn.LayerNorm([c_hid * 8, 3, 3]),
+                nn.GELU(),
+                nn.Flatten()  # Image grid to single feature vector
+            )
+        else:
+            if width == 96:
+                self.net = nn.Sequential(
+                    nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2),  # 96x96 => 48x48
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.Conv2d(c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 48x48 => 24x24
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 24x24 => 12x12
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 12x12 => 6x6
+                    act_fn(),
+                    nn.Flatten(),  # Image grid to single feature vector
+                )
+            elif width == 64:
+                self.net = nn.Sequential(
+                    nn.Conv2d(num_input_channels, c_hid, kernel_size=3, padding=1, stride=2),  # 64x64 => 32x32
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.Conv2d(c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 32x32 => 16x16
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1, stride=2),  # 16x16 => 8x8
+                    act_fn(),
+                    nn.Flatten(),  # Image grid to single feature vector
+                )
+        apply_scaled_init(self.net)
+        
+        if self.variational:
+            if model is not None:
+                input_size = c_hid * 8 * 3 * 3
+                self.fc_mu = nn.Linear(input_size, latent_dim)
+                self.fc_log_var = nn.Linear(input_size, latent_dim)
+            else:
+                if width == 96:
+                    self.fc_mu = nn.Linear(2 * 6 * 6 * c_hid, latent_dim)
+                    self.fc_log_var = nn.Linear(2 * 6 * 6 * c_hid, latent_dim)
+                elif width == 64:
+                    self.fc_mu = nn.Linear(2 * 8 * 8 * c_hid, latent_dim)
+                    self.fc_log_var = nn.Linear(2 * 8 * 8 * c_hid, latent_dim)
+        else:
+            self.net = nn.Sequential(self.net, nn.Linear(input_size, latent_dim))
+        apply_scaled_init(self.fc_mu)
+        apply_scaled_init(self.fc_log_var)
+
+    def forward(self, x, **kwargs):
+        if self.variational:
+            x = self.net(x)
+            mu = self.fc_mu(x)
+            log_var = self.fc_log_var(x)
+            return mu, log_var
+        else:
+            x = self.net(x)
+            return x
+
+class Decoder(nn.Module):
+    def __init__(self, 
+                 latent_dim: int, 
+                 num_input_channels: int, 
+                 base_channel_size: int, 
+                 batch_latent_dim: int=0,
+                 BatchNorm = None,
+                 act_fn: object = nn.GELU,
+                 model=None,
+                 width=64,
+                 height=64,
+                 *args,
+                 **kwargs):
+        """
+        Args:
+           num_input_channels : Number of channels of the image to reconstruct.
+           base_channel_size : Number of channels we use in the last convolutional layers. Early layers might use a duplicate of it.
+           latent_dim : Dimensionality of latent representation z
+           act_fn : Activation function used throughout the decoder network
+        """
+        super().__init__()
+        c_hid = base_channel_size
+        
+        if model == 'uhler':
+            print('using uhler decoder')
+            self.linear = nn.Sequential(
+                nn.Linear(latent_dim + batch_latent_dim, 2 * 6 * 6 * c_hid), 
+                act_fn(),
+                nn.Unflatten(1, (2 * c_hid, 6, 6)),
+            )
+            self.net = nn.Sequential(
+                nn.ConvTranspose2d(c_hid * 8, c_hid * 8, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid * 8),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid * 8, c_hid * 4, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid * 4),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid * 4, c_hid * 2, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid * 2),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid * 2, c_hid, 4, 2, 1, bias=False),
+                nn.BatchNorm2d(c_hid),
+                nn.LeakyReLU(0.2, inplace=True),
+                nn.ConvTranspose2d(c_hid, num_input_channels, 4, 2, 1, bias=False),
+                nn.Tanh(),
+        )
+        elif model == 'test':
+            print('using test decoder')
+            self.linear = nn.Sequential(
+                nn.Linear(latent_dim + batch_latent_dim, 8 * 3 * 3 * c_hid),
+                nn.LayerNorm(8 * 3 * 3 * c_hid),
+                act_fn(),
+                nn.Unflatten(1, (8 * c_hid, 3, 3)), 
+            )
+
+            self.net = nn.Sequential(
+                nn.ConvTranspose2d(8 * c_hid, 4 * c_hid, kernel_size=4, padding=1, stride=2), # 3x3 => 6x6
+                nn.LayerNorm([4 * c_hid, 6, 6]),
+                act_fn(),
+                nn.ConvTranspose2d(4 * c_hid, 2 * c_hid, kernel_size=4, padding=1, stride=2), # 6x6 => 12x12
+                nn.LayerNorm([2 * c_hid, 12, 12]),
+                act_fn(),
+                nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2), # 12x12 => 24x24
+                nn.LayerNorm([c_hid, 24, 24]),
+                act_fn(),
+                nn.ConvTranspose2d(c_hid, c_hid // 2, kernel_size=5, output_padding=1, padding=2, stride=2), # 24x24 => 48x48, using a larger kernel
+                nn.LayerNorm([c_hid // 2, 48, 48]),
+                act_fn(),
+                nn.ConvTranspose2d(c_hid // 2, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2), # 48x48 => 96x96
+                nn.Tanh(),
+            )
+        else:
+            if width == 96:
+                print('using width 96 decoder')
+                self.linear = nn.Sequential(
+                    nn.Linear(latent_dim + batch_latent_dim, 2 * 6 * 6 * c_hid), 
+                    act_fn(),
+                    nn.Unflatten(1, (2 * c_hid, 6, 6)),
+                )
+                self.net = nn.Sequential(
+                    nn.ConvTranspose2d(2 * c_hid, 2 * c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 8x8 => 16x16
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 16x16 => 32x32
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(c_hid, c_hid // 2, kernel_size=3, output_padding=1, padding=1, stride=2),  # 32x32 => 64x64
+                    act_fn(),
+                    nn.ConvTranspose2d(c_hid // 2, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2),  # 64x64 => 96x96
+                    nn.Tanh(),
+                )
+            elif width == 64:
+                print('using width 64 decoder')
+                self.linear = nn.Sequential(
+                    nn.Linear(latent_dim + batch_latent_dim, 2 * 8 * 8 * c_hid), 
+                    act_fn(),
+                    nn.Unflatten(1, (-1, 8, 8)),
+                    )
+                self.net = nn.Sequential(
+                    nn.ConvTranspose2d(2 * c_hid, 2 * c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 8x8 => 16x16
+                    act_fn(),
+                    nn.Conv2d(2 * c_hid, 2 * c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(2 * c_hid, c_hid, kernel_size=3, output_padding=1, padding=1, stride=2),  # 16x16 => 32x32
+                    act_fn(),
+                    nn.Conv2d(c_hid, c_hid, kernel_size=3, padding=1),
+                    act_fn(),
+                    nn.ConvTranspose2d(c_hid, num_input_channels, kernel_size=3, output_padding=1, padding=1, stride=2),  # 32x32 => 64x64
+                    nn.Tanh(),
+                )
+        apply_scaled_init(self.linear)
+        apply_scaled_init(self.net)
+
+
+    def forward(self, x, **kwargs):
+        x = self.linear(x)
+        x = self.net(x)
+        return x
+        
+class BaseModel(L.LightningModule):
+    def __init__(
+        self,
+        model_name: str,
+        optimizer_param: dict,
+        latent_dim: int=32,
+        base_channel_size: int=32,
+        num_input_channels: int = 4,
+        image_size: int = 64,
+        act_fn=nn.GELU,
+        *args,
+        **kwargs,
+        ):
+        super().__init__()
+        
+        self.model_name = model_name
+        self.num_input_channels = num_input_channels
+        self.width = image_size
+        self.height = image_size
+        self.base_channel_size = base_channel_size
+        self.latent_dim = latent_dim
+        self.network_param = {'latent_dim': latent_dim, 'num_input_channels':num_input_channels,
+                              'base_channel_size':base_channel_size, 'act_fn':act_fn}
+
+        # Example input array needed for visualizing the graph of the network
+        self.optimizer_param = optimizer_param
+        
+
+    def forward(self, x):
+        z = self.encoder(x)
+        x_hat = self.decoder(z)
+        return x_hat
+    
+    def _get_loss(self, x):
+        x_hat = self.forward(x)
+        loss = F.mse_loss(x, x_hat, reduction="none")
+        loss = loss.sum(dim=[1, 2, 3]).mean(dim=[0])
+        return loss
+    
+    def configure_optimizers(self):
+        lr = self.optimizer_param['lr']
+        if self.optimizer_param['optimizer'] == 'Adam':
+            optimizer = torch.optim.Adam(self.parameters(), lr=lr)
+        elif self.optimizer_param['optimizer'] == 'SGD':
+            momentum = self.optimizer_param['momentum']
+            nesterov = self.optimizer_param['nesterov']
+            optimizer = torch.optim.SGD(self.parameters(), lr=lr, momentum=momentum, nesterov=nesterov)
+        # scheduler = torch.optim.lr_scheduler.ReduceLROnPlateau(optimizer, mode="min", factor=0.2, patience=20, min_lr=5e-5)
+        return {"optimizer": optimizer, "monitor": "train_loss"}
+
+    def training_step(self, batch, batch_idx):
+        loss = self._get_loss(batch)
+        self.log("train_loss", loss.detach())
+        while True:  # Inside your training loop
+            current_memory_allocated = torch.cuda.memory_allocated() / (1024.0 ** 3)  # Convert bytes to GB
+            max_memory_allocated = torch.cuda.max_memory_allocated() / (1024.0 ** 3)  # Convert bytes to GB
+            self.log_dict({'Current GPU Memory (GB)': current_memory_allocated, 'Max GPU Memory (GB)': max_memory_allocated})
+            return loss
+
+    def validation_step(self, batch, batch_idx):
+        loss = self._get_loss(batch)
+        self.log("val_loss", loss.detach())
+
+    def test_step(self, batch, batch_idx):
+        loss = self._get_loss(batch)
+        self.log("test_loss", loss.detach())
+
+class AEmodel(BaseModel):
+    def __init__(self, 
+                 encoder_class: object = Encoder,
+                 decoder_class: object = Decoder,
+                 *args, **kwargs):
+        super().__init__(*args, **kwargs)
+        self.encoder = encoder_class(variational=False, **self.network_param)
+        self.decoder = decoder_class(**self.network_param)
+        self.example_input_array = torch.zeros(2, self.num_input_channels, self.width, self.height)
+
+    def get_image_embedding(self, x):
+        return self.encoder(x)
+
+class VAEmodel(BaseModel):
+    def __init__(self, 
+                 step_size: int,
+                 latent_dim = 64,
+                 encoder_class: object = Encoder,
+                 decoder_class: object = Decoder,
+                 *args, **kwargs):
+        super().__init__(latent_dim=latent_dim, *args, **kwargs)
+        
+        # Initialize the cyclic weight scheduler
+        self.kl_weight_scheduler = CyclicWeightScheduler(step_size=step_size)
+
+        # for the gaussian likelihood
+        self.log_scale = nn.Parameter(torch.Tensor([0.0]))
+        self.encoder = encoder_class(variational=True, **self.network_param)
+        self.decoder = decoder_class(**self.network_param)
+        self.example_input_array = torch.zeros(2, self.num_input_channels, self.width, self.height)
+        # Saving hyperparameters of autoencoder
+        self.save_hyperparameters()
+    
+    def forward(self, x):
+        # encode x to get the mu and variance parameters
+        mu, log_var = self.encoder(x)
+        # sample z 
+        log_var = torch.maximum(log_var, torch.tensor(-20)) #clipping to prevent going to -inf
+        std = torch.exp(log_var / 2)
+        z = self.sampling(mu, std)
+        # decoded 
+        x_hat = self.decoder(z)
+        return x_hat, mu, std
+    
+    def get_image_embedding(self, x):
+        mu, _ = self.encoder(x)
+        return mu
+    
+    @staticmethod
+    def sampling(mu, std):
+        q = Normal(mu, std)
+        return q.rsample()
+
+    @staticmethod
+    def reconstruction_loss(sample: torch.Tensor,
+                            mean: torch.Tensor, 
+                            logscale: torch.Tensor, 
+                            ):
+        scale = torch.exp(logscale)
+        dist = Normal(mean, scale)
+        log_pxz = dist.log_prob(sample)
+        return -log_pxz.sum(dim=(1, 2, 3))
+
+    def _generic_loss(self, x, x_hat, mu, std):
+        # reconstruction probability
+        recon_loss = self.reconstruction_loss(x, x_hat, self.log_scale)
+
+        # kl
+        kl = self.latent_kl_divergence(mu, std)
+        kl_term_weight = self.kl_weight_scheduler.step()
+
+        # elbo
+        elbo = (kl_term_weight*kl + recon_loss)
+        elbo = elbo.mean()
+        self.log_dict({
+            'elbo': elbo.detach(),
+            'kl': kl.mean().detach(),
+            'recon_loss': recon_loss.mean().detach(), 
+            'kl_term_weight': kl_term_weight,
+        })
+        return elbo
+
+    @staticmethod
+    def latent_kl_divergence(variational_mean, 
+                             variational_std, 
+                             prior_mean=None,
+                             prior_std=None) -> torch.Tensor:
+        """
+        Compute KL divergence between a variational posterior and standard Gaussian prior.
+        Args:
+        ----
+            variational_mean: Mean of the variational posterior Gaussian.
+            variational_var: Variance of the variational posterior Gaussian.
+        Returns
+        -------
+            KL divergence for each data point. If number of latent samples == 1,
+            the tensor has shape `(batch_size, )`. If number of latent
+            samples > 1, the tensor has shape `(n_samples, batch_size)`.
+        """
+        if prior_mean is None:
+            prior_mean = torch.zeros_like(variational_mean)
+        prior_std = torch.ones_like(variational_std)
+        return kl(
+            Normal(variational_mean, variational_std),
+            Normal(prior_mean, prior_std),
+        ).sum(dim=-1)
+    
+    def _get_loss(self, x):
+        # get reconstruction, mu and std
+        x_hat, mu, std = self.forward(x)
+        elbo = self._generic_loss(x, x_hat, mu, std)
+        return elbo
+
+class ContrastiveVAEmodel(BaseModel):
+    """
+    Args:
+    ----
+        n_z_latent: Dimensionality of the background latent space.
+        n_s_latent: Dimensionality of the salient latent space.
+        wasserstein_penalty: Weight of the Wasserstein distance loss that further
+            discourages shared variations from leaking into the salient latent space.
+    """
+        
+    def __init__(self, 
+                 n_z_latent: int = 32, 
+                 n_s_latent: int = 32, 
+                 encoder_class: object = Encoder,
+                 decoder_class: object = Decoder,
+                 step_size: float=2000,
+                 ngene: int=None,
+                 adjust_prior_s: bool=False,
+                 adjust_prior_z: bool=False,
+                 classify_s: bool=False,
+                 classify_z: bool=False,
+                 wasserstein_penalty: float = 0,
+                 BatchNorm = None,
+                 n_unique_batch: int = 34,
+                 model = None,
+                 batch_size: int=1024,
+                 tc_penalty: float=1,
+                 classification_weight: float=1,
+                 scale_factor: float=0.1,
+                 max_kl_weight: float=1,
+                 batch_latent_dim: int=32,
+                 reg_type: str=None,
+                 total_steps: int=3000,
+                 klscheduler: str='cyclic',
+                 *args, 
+                 **kwargs):
+        super().__init__(*args, **kwargs)
+        self.step_size = step_size
+        self.ngene = ngene
+        self.adjust_prior_s = adjust_prior_s
+        self.adjust_prior_z = adjust_prior_z
+        self.n_s_latent = n_s_latent
+        self.n_z_latent = n_z_latent
+        self.wasserstein_penalty = wasserstein_penalty
+        self.BatchNorm = BatchNorm
+        self.n_unique_batch = n_unique_batch
+        self.model = model
+        self.batch_size = batch_size
+        self.tc_penalty = tc_penalty
+        self.classify_s = classify_s
+        self.classify_z = classify_z
+        self.classification_weight = classification_weight
+        self.scale_factor = scale_factor
+        self.batch_latent_dim = batch_latent_dim
+        self.reg_type = reg_type
+        self.total_steps = total_steps
+        self.klscheduler = klscheduler
+
+        if n_s_latent != n_z_latent:
+            warnings.warn('Target latent dim does not equal background latent dim')
+
+        # Initialize the weight scheduler
+        if self.klscheduler == 'cyclic':
+            self.kl_weight_scheduler = CyclicWeightScheduler(step_size=self.step_size, max_weight=max_kl_weight)
+        elif self.klscheduler == 'ramp':
+            self.kl_weight_scheduler = KLRampScheduler(total_steps=self.total_steps, max_weight=max_kl_weight)
+
+        # Background encoder
+        self.coder_param = {'num_input_channels': self.num_input_channels, "scale_factor": self.scale_factor, 
+                         'base_channel_size': self.base_channel_size, 'variational': True, 'width': self.width, 'height':self.height,
+                          'BatchNorm': self.BatchNorm, 'n_unique_batch': self.n_unique_batch, 'model': self.model,}
+        self.z_encoder = encoder_class(
+                                       latent_dim=self.n_z_latent,
+                                       **self.coder_param,
+                                       )
+        # Salient encoder 
+        self.s_encoder = encoder_class(
+                                       latent_dim=self.n_s_latent,
+                                       **self.coder_param,
+                                       )
+        
+        # Decoder from latent variable to distribution parameters in data space.
+        self.n_total_latent = self.n_z_latent + self.n_s_latent        
+        self.decoder = decoder_class(
+                                    latent_dim=self.n_total_latent,
+                                    batch_latent_dim=self.batch_latent_dim,
+                                    **self.coder_param,
+                                    )
+        
+        if self.adjust_prior_z:
+            self.zprior_embedding = nn.Embedding(self.ngene, self.n_z_latent)
+        if self.adjust_prior_s:
+            self.sprior_embedding = nn.Embedding(self.ngene, self.n_s_latent)
+    
+        # for the gaussian likelihood
+        self.log_scale = nn.Parameter(torch.Tensor([0.0]))
+
+        # Example input array needed for visualizing the graph of the network
+        self.example_input_array = {'background': torch.zeros(2, self.num_input_channels, self.width, self.height),
+                                        'target': torch.zeros(2, self.num_input_channels, self.width, self.height)}
+        if self.adjust_prior_s or self.adjust_prior_z:
+            self.example_input_array['background_label'] = torch.zeros(2, dtype=torch.int32)
+            self.example_input_array['target_label'] = torch.zeros(2, dtype=torch.int32)
+        if self.batch_latent_dim > 0:
+            self.batch_embedding = nn.Embedding(self.n_unique_batch, self.batch_latent_dim)
+            self.example_input_array.update({'background_batch': torch.zeros(2, dtype=torch.int32),
+                                            'target_batch': torch.zeros(2, dtype=torch.int32)})
+        # Saving hyperparameters of autoencoder
+        self.save_hyperparameters()
+
+    def forward(self, background, target, **kwargs):
+        background_label = kwargs.get('background_label')
+        target_label = kwargs.get('target_label')
+        prior_mu_background = {'zprior_m': None,  'sprior_m': None}
+        prior_mu_target = {'zprior_m': None, 'sprior_m': None}
+        # zlabel_embedding = None
+        # slabel_embedding = None
+        if self.adjust_prior_s:
+            prior_mu_background['sprior_m'] = self.sprior_embedding(background_label.int())
+            prior_mu_target['sprior_m'] = self.sprior_embedding(target_label.int())
+            # slabel_embedding = torch.cat([prior_mu_background['sprior_m'], 
+            #                               prior_mu_target['sprior_m']], dim=0)
+        if self.adjust_prior_z:
+            prior_mu_background['zprior_m'] = self.zprior_embedding(background_label.int())
+            prior_mu_target['zprior_m'] = self.zprior_embedding(target_label.int())
+            # zlabel_embedding = torch.cat([prior_mu_background['zprior_m'], 
+            #                               prior_mu_target['zprior_m']], dim=0)
+        inference_outputs = self.inference(background=background, 
+                                           target=target)
+        background_batch = kwargs.get('background_batch')
+        target_batch = kwargs.get('target_batch')
+        generative_outputs = self.generative(inference_outputs['background'], 
+                                             inference_outputs['target'],
+                                             background_batch=background_batch,
+                                             target_batch=target_batch)
+        recon = {'bg':generative_outputs['background']["px_m"], 
+                 "tg":generative_outputs['target']["px_m"]}
+        inference_outputs['background'].update(prior_mu_background)
+        inference_outputs['target'].update(prior_mu_target)
+
+        return recon, inference_outputs, generative_outputs
+    
+    def get_image_embedding(self, img, label=None):
+        qz_m, _ = self.z_encoder(img)
+        qs_m, _ = self.s_encoder(img)
+        return torch.cat((qs_m, qz_m), dim=1)
+
+    def _generic_inference(self,
+                             x: torch.Tensor,
+                             ):
+        qz_m, qz_lv = self.z_encoder(x)
+        qs_m, qs_lv = self.s_encoder(x)
+        
+        # sample from latent distribution
+        qz_lv = torch.maximum(qz_lv, torch.tensor(-20)) #clipping to prevent going to -inf
+        qs_lv = torch.maximum(qs_lv, torch.tensor(-20)) #clipping to prevent going to -inf
+        qz_s = torch.exp(qz_lv / 2)
+        qs_s = torch.exp(qs_lv / 2)
+        qz = Normal(qz_m, qz_s)
+        qs = Normal(qs_m, qs_s)
+        z = qz.rsample()
+        s = qs.rsample()
+
+        outputs = dict(
+            qz_m=qz_m,
+            qz_s=qz_s,
+            z=z,
+            qs_m=qs_m,
+            qs_s=qs_s,
+            s=s,)
+        return outputs
+    
+    def inference(
+            self,
+            background: torch.Tensor,
+            target: torch.Tensor,
+        ) -> Dict[str, Dict[str, torch.Tensor]]:
+            background_batch_size = background.shape[0]
+            target_batch_size = target.shape[0]
+            inference_input = torch.cat([background, target], dim=0)
+            outputs = self._generic_inference(x=inference_input)
+            background_outputs, target_outputs = {}, {}
+            for key in outputs.keys():
+                if outputs[key] is not None:
+                    background_tensor, target_tensor = torch.split(
+                        outputs[key],
+                        [background_batch_size, target_batch_size],
+                        dim=0,
+                    )
+                else:
+                    background_tensor, target_tensor = None, None
+                background_outputs[key] = background_tensor
+                target_outputs[key] = target_tensor
+            background_outputs["s"] = torch.zeros_like(background_outputs["s"])
+            return dict(background=background_outputs, target=target_outputs)
+    
+    def _generic_generative(self, 
+                              z: torch.Tensor, 
+                              s: torch.Tensor,
+                              batch_embedding: torch.Tensor=None,):
+        latent = torch.cat([z, s], dim=-1)
+        if batch_embedding is not None:
+            latent = torch.cat([latent, batch_embedding], dim=-1)
+        px_m = self.decoder(latent)
+        return dict(px_m=px_m, px_s=self.log_scale)
+
+    def generative(
+        self,
+        background: Dict[str, torch.Tensor],
+        target: Dict[str, torch.Tensor],
+        **kwargs,
+        ) -> Dict[str, Dict[str, torch.Tensor]]:
+        latent_z_shape = background["z"].shape
+        batch_size_dim = 0 if len(latent_z_shape) == 2 else 1
+        background_batch_size = background["z"].shape[batch_size_dim]
+        target_batch_size = target["z"].shape[batch_size_dim]
+        generative_input = {}
+        for key in ["z", "s"]:
+            generative_input[key] = torch.cat(
+                [background[key], target[key]], dim=batch_size_dim
+            )
+        background_batch = kwargs.get("background_batch")
+        target_batch = kwargs.get("target_batch")
+        if background_batch is not None and target_batch is not None:
+            generative_input["batch_embedding"] = torch.cat(
+                [self.batch_embedding(background_batch), 
+                 self.batch_embedding(target_batch)], dim=batch_size_dim
+                )
+        outputs = self._generic_generative(**generative_input)
+        background_outputs, target_outputs = {}, {}
+        if outputs["px_m"] is not None:
+            background_tensor, target_tensor = torch.split(
+                outputs["px_m"],
+                [background_batch_size, target_batch_size],
+                dim=batch_size_dim,
+            )
+        else:
+            background_tensor, target_tensor = None, None
+        background_outputs["px_m"] = background_tensor
+        target_outputs["px_m"] = target_tensor
+        background_outputs["px_s"] = outputs["px_s"]
+        target_outputs["px_s"] = outputs["px_s"]
+        return dict(background=background_outputs, target=target_outputs)
+
+    def _generic_loss(self, 
+                      tensors: torch.Tensor,
+                      inference_outputs: Dict[str, torch.Tensor], 
+                      generative_outputs: Dict[str, torch.Tensor],
+                    )-> Dict[str, torch.Tensor]:
+        
+        qz_m = inference_outputs["qz_m"]
+        qz_s = inference_outputs["qz_s"]
+        qs_m = inference_outputs["qs_m"]
+        qs_s = inference_outputs["qs_s"]
+        zprior_m = inference_outputs["zprior_m"]
+        sprior_m = inference_outputs["sprior_m"]
+        px_m = generative_outputs["px_m"]
+        px_s = generative_outputs["px_s"]
+
+        recon_loss = VAEmodel.reconstruction_loss(tensors, px_m, px_s)
+        kl_z = VAEmodel.latent_kl_divergence(qz_m, qz_s, prior_mean=zprior_m)
+        kl_s = VAEmodel.latent_kl_divergence(qs_m, qs_s, prior_mean=sprior_m)
+        return dict(recon_loss=recon_loss, kl_z=kl_z, kl_s=kl_s)
+    
+    def compute_independent_loss(self, zb, zc):
+        reg_type = self.reg_type
+        if reg_type == "TC":
+            return self.compute_tc(zb, zc)
+        elif reg_type == "HSIC":
+            return self.compute_HSIC(zb, zc)
+        else:
+            raise ValueError("reg_type should be TC or HSIC")
+    
+    @staticmethod
+    def rbf_kernel(X, sigma=1.0):
+        # Compute the pairwise squared Euclidean distances
+        pairwise_dists = torch.cdist(X, X, p=2) ** 2
+        # Apply the RBF kernel function
+        values = torch.div(-pairwise_dists, (2 * sigma**2))
+        return values.exp()
+    
+    @staticmethod
+    def compute_HSIC(Z_b, Z_c):
+        n = Z_b.shape[0]
+        # Compute kernel matrices
+        K = ContrastiveVAEmodel.rbf_kernel(Z_b)
+        L = ContrastiveVAEmodel.rbf_kernel(Z_c)
+        # print(K.shape, L.shape)
+        # Implement the HSIC formula
+        term1 = (1 / (n**2)) * torch.sum(K * L)
+        term2 = (1 / (n**4)) * torch.sum(K) * torch.sum(L)
+        term3 = (2 / (n**3)) * torch.sum(K @ L)
+        HSIC_n = term1 + term2 - term3
+        return HSIC_n * n
+    
+    @staticmethod
+    def compute_tc(zb, zc):
+        # Calculate the empirical means
+        mean_zb = torch.mean(zb, dim=0)
+        mean_zc = torch.mean(zc, dim=0)
+        # Calculate the centered variables
+        centered_zb = zb - mean_zb
+        centered_zc = zc - mean_zc
+        # Calculate the covariance matrix of the concatenated latent variables
+        z_concat = torch.cat([centered_zb, centered_zc], dim=1)
+        cov_matrix = torch.matmul(z_concat.T, z_concat) / z_concat.shape[0]
+        # Calculate the covariance matrices for zb and zc individually
+        cov_zb = torch.matmul(centered_zb.T, centered_zb) / centered_zb.shape[0]
+        cov_zc = torch.matmul(centered_zc.T, centered_zc) / centered_zc.shape[0]
+        # Calculate total correlation loss
+        tc_loss = torch.logdet(cov_matrix) - (torch.logdet(cov_zb) + torch.logdet(cov_zc))
+        # Multiply by the weighting factor
+        return -tc_loss
+
+    def _get_loss(self, 
+             concat_tensors: Dict[str, Tuple[Dict[str, torch.Tensor], int]],
+             ):  
+        _, inference_outputs, generative_outputs = self.forward(**concat_tensors)            
+
+        background_losses = self._generic_loss(
+            concat_tensors["background"],
+            inference_outputs["background"],
+            generative_outputs["background"],
+        )
+        target_losses = self._generic_loss(
+            concat_tensors["target"],
+            inference_outputs["target"],
+            generative_outputs["target"],
+        )
+        recon_loss = background_losses["recon_loss"] + target_losses["recon_loss"]
+        kl_divergence_z = background_losses["kl_z"] + target_losses["kl_z"]
+        kl_divergence_s = target_losses["kl_s"]
+
+        wasserstein_loss = (
+            torch.norm(inference_outputs["background"]["qs_m"], dim=-1)**2
+            + torch.sum(inference_outputs["background"]["qs_s"]**2, dim=-1)
+        )
+
+        if self.reg_type is not None:
+            zb = torch.concat([inference_outputs["target"]["qz_m"], inference_outputs["background"]["qz_m"]], axis=0)
+            zs = torch.concat([inference_outputs["target"]["qs_m"], inference_outputs["background"]["qs_m"]], axis=0)
+            tc_loss = self.compute_independent_loss(zb, zs)
+        else:
+            tc_loss = torch.zeros(1, device=self.device)
+
+        kl_term_weight = self.kl_weight_scheduler.step()
+        
+        elbo = torch.mean(recon_loss + 
+                          kl_term_weight * (kl_divergence_s + kl_divergence_z + 
+                                            self.wasserstein_penalty * wasserstein_loss +
+                                            self.tc_penalty * tc_loss))
+
+        self.log_dict({
+            'kl_divergence_z': kl_divergence_z.mean().detach(),
+            'kl_divergence_s': kl_divergence_s.mean().detach(),
+            'total_recon_loss': recon_loss.mean().detach(),
+            'wasserstein_loss': wasserstein_loss.mean().detach(),
+            'tc_loss': tc_loss.mean().detach(),
+            # 'background_recon_loss': background_losses["recon_loss"].mean().detach(),
+            # 'target_recon_loss': target_losses["recon_loss"].mean().detach(),
+            'kl_term_weight': kl_term_weight,
+        })
+        return elbo
+    
+ 
+class CyclicWeightScheduler:
+    def __init__(self, step_size, base_weight=0, max_weight=1):
+        self.base_weight = base_weight
+        self.max_weight = max_weight
+        self.step_size = step_size
+        self.cycle = 0
+        self.step_count = 0
+
+    def step(self):
+        # Compute the current position in the cycle
+        cycle_position = self.step_count / self.step_size
+
+        if cycle_position <= 1:
+            weight = self.base_weight + (self.max_weight - self.base_weight) * cycle_position
+        else:
+            weight = self.max_weight
+            # weight = self.max_weight - (self.max_weight - self.base_weight) * (cycle_position - 1)
+
+        self.step_count = (self.step_count + 1) % (self.step_size * 2)
+
+        return weight
+    
+class KLRampScheduler:
+    def __init__(self, start_weight=0, max_weight=1, total_steps=3000):
+        self.start_weight = start_weight
+        self.max_weight = max_weight
+        self.total_steps = total_steps
+        self.current_step = 0
+        self.current_weight = start_weight
+
+    def step(self):
+        self.current_step += 1
+        progress = self.current_step / self.total_steps
+        self.current_weight = self.start_weight + (self.max_weight - self.start_weight) * progress
+        # Clip the weight to be within the specified range
+        self.current_weight = min(max(self.current_weight, self.start_weight), self.max_weight)
+        return self.current_weight
+
+    def get_weight(self):
+        return self.current_weight
+    
+class LinearDiscriminator(nn.Module):
+    def __init__(self, input_features):
+        super(LinearDiscriminator, self).__init__()
+        self.linear = nn.Linear(input_features, 1)
+        self.sigmoid = nn.Sigmoid()
+
+    def forward(self, x):
+        x = self.linear(x)
+        x = self.sigmoid(x)
+        return x
+
+class ConditionalBatchNorm1d(nn.Module):
+    def __init__(self, num_features, num_classes):
+        super().__init__()
+        self.num_features = num_features
+        self.bn = nn.BatchNorm1d(num_features, affine=False)
+        self.embed = nn.Embedding(num_classes, num_features * 2)
+        self.embed.weight.data[:, :num_features].normal_(1, 0.02)  # Initialise scale at N(1, 0.02)
+        self.embed.weight.data[:, num_features:].zero_()  # Initialise bias at 0
+
+    def forward(self, x, y):
+        # y is the condition, i.e. batch number
+        out = self.bn(x)
+        gamma, beta = self.embed(y).chunk(2, 1)
+        gamma = gamma.expand_as(out)
+        beta = beta.expand_as(out)
+        return gamma * out + beta
+    
+class ConditionalBatchNorm2d(nn.Module):
+    def __init__(self, num_features, num_classes):
+        super().__init__()
+        self.num_features = num_features
+        self.bn = nn.BatchNorm2d(num_features, affine=False)
+        self.embed = nn.Embedding(num_classes, num_features * 2)
+        self.embed.weight.data[:, :num_features].normal_(1, 0.02)  # Initialise scale at N(1, 0.02)
+        self.embed.weight.data[:, num_features:].zero_()  # Initialise bias at 0
+
+    def forward(self, x, y):
+        # y is the condition, i.e. batch number
+        out = self.bn(x)
+        gamma, beta = self.embed(y).chunk(2, 1)
+        gamma = gamma.unsqueeze(2).unsqueeze(3).expand_as(out)
+        beta = beta.unsqueeze(2).unsqueeze(3).expand_as(out)
+        return gamma * out + beta
+    
+def add_encoder_batch_norm(model, 
+                           BatchNorm, 
+                           n_conditions=None
+                           ):
+    new_layers = []
+    for layer in model:
+        new_layers.append(layer)
+        if isinstance(layer, nn.Conv2d):
+            if BatchNorm == ConditionalBatchNorm2d:
+                new_layers.append(BatchNorm(layer.out_channels, n_conditions))
+            else:
+                new_layers.append(BatchNorm(layer.out_channels))
+    return nn.Sequential(*new_layers)
+
+def add_decoder_batch_norm(linear, 
+                           net, 
+                           BatchNorm1d=nn.BatchNorm1d, 
+                           BatchNorm2d=nn.BatchNorm2d, 
+                           n_conditions=None):
+    new_linear = []
+    for layer in linear:
+        new_linear.append(layer)
+        if isinstance(layer, nn.Linear):
+            if BatchNorm1d == ConditionalBatchNorm1d:
+                new_linear.append(BatchNorm1d(layer.out_features, n_conditions))
+            else:
+                new_linear.append(BatchNorm1d(layer.out_features))
+    
+    new_net = []
+    for i, layer in enumerate(net):
+        new_net.append(layer)
+        if isinstance(layer, (nn.Conv2d, nn.ConvTranspose2d)) and i != len(net):
+            if BatchNorm2d == ConditionalBatchNorm2d:
+                new_net.append(BatchNorm2d(layer.out_channels, n_conditions))
+            else:
+                new_net.append(BatchNorm2d(layer.out_channels))
+
+    return nn.Sequential(*new_linear), nn.Sequential(*new_net)
diff --git a/src/embed_time/neuromast.py b/src/embed_time/neuromast.py
new file mode 100644
index 0000000..ba756c4
--- /dev/null
+++ b/src/embed_time/neuromast.py
@@ -0,0 +1,236 @@
+
+from iohub.ngff import open_ome_zarr
+from natsort import natsorted
+from glob import glob
+from pathlib import Path 
+import torch
+from torch.utils.data import Dataset
+from scipy.ndimage import measurements
+from scipy.ndimage import center_of_mass
+import numpy as np
+import matplotlib.pyplot as plt
+import pandas as pd
+
+class NeuromastDatasetTrain(Dataset):
+    def __init__(self):
+        file_path = "/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/"
+        zarr_file = 'structured_celltype_classifier_data.zarr/*/*/*'
+        position_paths = natsorted(glob(file_path + zarr_file))
+        self.position_paths = position_paths[:500]
+        
+
+        self.metadata = pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_balanced_train.csv")
+
+
+        # Find the maximum range across all dimensions
+        max_x_range = 256
+        max_y_range = 256
+        max_z_range = 48 # not used for cropping
+
+        self.crop_size = [max_z_range, max_y_range, max_x_range]
+
+        self.shape = (open_ome_zarr(self.position_paths[0], mode="r")).data.shape  
+
+        
+    def crop_image(self, idx):
+        
+        row = self.metadata.iloc[idx]
+        # Get centroid coordinates
+        centroid_z = int(row['Centroid_Z'])
+        centroid_y = int(row['Centroid_Y'])
+        centroid_x = int(row['Centroid_X'])
+        
+        #get the label number
+        label = int(row['Label'])
+
+        timepoint = int(row['T_value'])
+
+        # Compute the cropping box boundaries
+        z_min = int(row['Z_min'])
+        z_max = int(row['Z_max'])
+        y_min = int(max((int(centroid_y - self.crop_size[1] // 2)),0))
+        y_max = int(min((int(centroid_y + self.crop_size[1] // 2)), self.shape[3]-1))
+        x_min = int(max((int(centroid_x - self.crop_size[2] // 2)), 0))
+        x_max = int(min((int(centroid_x + self.crop_size[2] // 2)), self.shape[4]-1))
+
+        mid_z = (z_min + z_max) // 2
+
+
+        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])
+        dataset = open_ome_zarr(self.position_paths[timepoint], mode="r")
+        image = dataset.data[0,0:1,mid_z,y_min:y_max, x_min:x_max]
+        segmented_data = dataset.data[0,2:3,mid_z,y_min:y_max, x_min:x_max] #segmention masks
+        # celltypes = dataset.data[0,3:,:,:,:]
+        # Get a binary mask of the current segment
+        segment_mask = segmented_data == label
+        
+
+        # Find the unique label numbers in the celltypes image for this segment
+        cell_type = int(row['Cell_Type'])
+        cropped_image=np.where(segment_mask, image, 0)
+        
+        # if z_max - z_min != 64 & z_max == self.shape[2]-1:
+        #     z_min = z_max - 64
+
+        # if z_max - z_min != 64 & z_min == 0:
+        #     z_max = z_min + 64
+        # Crop the image
+        
+        
+        return cropped_image, cell_type
+
+
+    def __len__(self):
+        return len(self.metadata)
+
+    def __getitem__(self, idx):
+        cell, cell_type = self.crop_image(idx)
+        return cell, cell_type
+    
+class NeuromastDatasetTrain_T10(Dataset):
+    def __init__(self):
+        file_path = "/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/"
+        zarr_file = 'structured_celltype_classifier_data.zarr/*/*/*'
+        position_paths = natsorted(glob(file_path + zarr_file))
+        self.position_paths = position_paths[:500]
+        self.cell_count = 40 # number of cells to sample from each timepoint
+       
+
+        self.metadata = pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_train_T10.csv")
+
+        # Find the maximum range across all dimensions
+        max_x_range = 256
+        max_y_range = 256
+        max_z_range = 48 # not used for cropping
+
+        self.crop_size = [max_z_range, max_y_range, max_x_range]
+
+        self.shape = (open_ome_zarr(self.position_paths[0], mode="r")).data.shape  
+
+        
+    def crop_image(self, idx):
+        
+        row = self.metadata.iloc[idx]
+        # Get centroid coordinates
+        centroid_z = int(row['Centroid_Z'])
+        centroid_y = int(row['Centroid_Y'])
+        centroid_x = int(row['Centroid_X'])
+        
+        #get the label number
+        label = int(row['Label'])
+
+        timepoint = int(row['T_value'])
+
+        # Compute the cropping box boundaries
+        z_min = int(row['Z_min'])
+        z_max = int(row['Z_max'])
+        y_min = int(max((int(centroid_y - self.crop_size[1] // 2)),0))
+        y_max = int(min((int(centroid_y + self.crop_size[1] // 2)), self.shape[3]-1))
+        x_min = int(max((int(centroid_x - self.crop_size[2] // 2)), 0))
+        x_max = int(min((int(centroid_x + self.crop_size[2] // 2)), self.shape[4]-1))
+
+        mid_z = (z_min + z_max) // 2
+        
+        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])
+        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])
+        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])
+        dataset = open_ome_zarr(self.position_paths[timepoint], mode="r")
+        image = dataset.data[0,0:1,mid_z,y_min:y_max, x_min:x_max]
+        segmented_data = dataset.data[0,2:3,mid_z,y_min:y_max, x_min:x_max] #segmention masks
+        # celltypes = dataset.data[0,3:,:,:,:]
+        # Get a binary mask of the current segment
+        segment_mask = segmented_data == label
+        
+
+        # Find the unique label numbers in the celltypes image for this segment
+        cell_type = int(row['Cell_Type'])
+        cropped_image=np.where(segment_mask, image, 0)
+        
+        # if z_max - z_min != 64 & z_max == self.shape[2]-1:
+        #     z_min = z_max - 64
+
+        # if z_max - z_min != 64 & z_min == 0:
+        #     z_max = z_min + 64
+        # Crop the image
+        
+        
+        return cropped_image, cell_type
+
+    def __len__(self):
+        
+        return len(self.metadata)
+
+    def __getitem__(self, idx):
+        cell, cell_type = self.crop_image(idx)
+        return cell, cell_type
+    
+
+class NeuromastDatasetTest(Dataset):
+    def __init__(self):
+        file_path = "/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/"
+        zarr_file = 'structured_celltype_classifier_data.zarr/*/*/*'
+        position_paths = natsorted(glob(file_path + zarr_file))
+        self.position_paths = position_paths[500:]
+        self.cell_count = 40 # number of cells to sample from each timepoint
+        
+
+        self.metadata = pd.read_csv("/mnt/efs/dlmbl/G-et/data/neuromast/Dataset/metadata_neuromast_test_T10.csv")
+
+        # Find the maximum range across all dimensions
+        max_x_range = 256
+        max_y_range = 256
+        max_z_range = 48 # not used for cropping
+
+        self.crop_size = [max_z_range, max_y_range, max_x_range]
+
+        self.shape = (open_ome_zarr(self.position_paths[0], mode="r")).data.shape  
+
+        
+    def crop_image(self, idx):
+        
+        row = self.metadata.iloc[idx]
+        # Get centroid coordinates
+        centroid_z = int(row['Centroid_Z'])
+        centroid_y = int(row['Centroid_Y'])
+        centroid_x = int(row['Centroid_X'])
+        
+        #get the label number
+        label = int(row['Label'])
+
+        timepoint = int(row['T_value'])
+
+        # Compute the cropping box boundaries
+        z_min = int(row['Z_min'])
+        z_max = int(row['Z_max'])
+        y_min = int(max((int(centroid_y - self.crop_size[1] // 2)),0))
+        y_max = int(min((int(centroid_y + self.crop_size[1] // 2)), self.shape[3]-1))
+        x_min = int(max((int(centroid_x - self.crop_size[2] // 2)), 0))
+        x_max = int(min((int(centroid_x + self.crop_size[2] // 2)), self.shape[4]-1))
+
+        mid_z = (z_min + z_max) // 2
+        
+        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])
+        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])
+        # Load the corresponding image from the dataset (assuming 5D dataset [T, C, Z, Y, X])
+        dataset = open_ome_zarr(self.position_paths[timepoint], mode="r")
+        image = dataset.data[0,0:1,mid_z,y_min:y_max, x_min:x_max]
+        segmented_data = dataset.data[0,2:3,mid_z,y_min:y_max, x_min:x_max] #segmention masks
+        # celltypes = dataset.data[0,3:,:,:,:]
+        # Get a binary mask of the current segment
+        segment_mask = segmented_data == label
+        
+
+        # Find the unique label numbers in the celltypes image for this segment
+        cell_type = int(row['Cell_Type'])
+        cropped_image=np.where(segment_mask, image, 0)
+        
+        
+        return cropped_image, cell_type
+
+
+    def __len__(self):
+        return len(self.metadata)
+
+    def __getitem__(self, idx):
+        cell, cell_type = self.crop_image(idx)
+        return cell, cell_type
\ No newline at end of file
diff --git a/src/embed_time/splitter_static.py b/src/embed_time/splitter_static.py
new file mode 100644
index 0000000..c2c6922
--- /dev/null
+++ b/src/embed_time/splitter_static.py
@@ -0,0 +1,97 @@
+import os
+import numpy as np
+import zarr
+import json
+from pathlib import Path
+import torch
+from torch.utils.data import Dataset
+import pandas as pd
+from joblib import Parallel, delayed
+import argparse
+
+
+class DatasetSplitter:
+    def __init__(self, parent_dir, output_dir, train_ratio=0.7, val_ratio=0.15, num_workers=-1):
+        self.parent_dir = Path(parent_dir)
+        self.output_dir = Path(output_dir)
+        self.train_ratio = train_ratio
+        self.val_ratio = val_ratio
+        self.num_workers = num_workers
+
+    def generate_cells_from_gene(self, gene_path):
+        gene_path = Path(gene_path)
+        gene_name = gene_path.stem
+        cell_data = []
+        
+        def filter_dirs(path):
+            return [item for item in path.iterdir() if item.is_dir() and item.name not in ['.zarr', '.DS_Store']]
+        
+        barcodes = filter_dirs(gene_path)
+
+        if not barcodes:
+            print(f"Warning: No barcodes found in {gene_path}. Skipping this gene.")
+            return cell_data
+        
+        for barcode in barcodes:
+            barcode_name = barcode.name 
+            stages = filter_dirs(barcode)
+            
+            if not stages:
+                print(f"Warning: No stages found in {barcode}. Skipping this barcode.")
+                continue
+            
+            for stage in stages:
+                stage_name = stage.name
+                try:
+                    cells_zarr = zarr.open(stage / "images")
+                    num_cells = cells_zarr.shape[0]
+                    
+                    if num_cells == 0:
+                        print(f"Warning: No cells found in {stage}. Skipping this stage.")
+                        continue
+                
+                    # Use torch to create a random permutation
+                    indices = torch.randperm(num_cells)
+                    
+                    # Calculate split sizes
+                    train_size = int(num_cells * self.train_ratio)
+                    val_size = int(num_cells * self.val_ratio)
+                    
+                    # Split indices
+                    train_indices = indices[:train_size]
+                    val_indices = indices[train_size:train_size+val_size]
+                    test_indices = indices[train_size+val_size:]
+                    
+                    # Create cell data
+                    for split, split_indices in [("train", train_indices), ("val", val_indices), ("test", test_indices)]:
+                        for cell_idx in split_indices.tolist():
+                            cell_data.append([gene_name, barcode_name, stage_name, cell_idx, split])
+                
+                except Exception as e:
+                    print(f"Error processing {stage}: {str(e)}. Skipping this stage.")
+        
+        return cell_data
+
+    def generate_split(self):
+        self.output_dir.mkdir(exist_ok=True)
+        
+        genes = list(self.parent_dir.glob("*.zarr"))
+        genes = [gene for gene in genes if any(gene.iterdir())]
+        # genes = ["/mnt/efs/dlmbl/S-md/AAAS.zarr", "/mnt/efs/dlmbl/S-md/AAGAB.zarr"]  # Uncomment this line to process only specific genes
+        
+        print(f"Processing {len(genes)} genes...")
+
+        # Use joblib.Parallel for parallelization
+        results = Parallel(n_jobs=self.num_workers, verbose=1)(
+            delayed(self.generate_cells_from_gene)(gene) for gene in genes
+        )
+        
+        print("Combining results...")
+        # Flatten the list of lists
+        all_cell_data = [item for sublist in results for item in sublist]
+
+        df = pd.DataFrame(all_cell_data, columns=["gene", "barcode", "stage", "cell_idx", "split"])
+        output_file = self.output_dir / f"dataset_split_{len(genes)}.csv"
+        df.to_csv(output_file, index=False)
+        print(f"Dataset split CSV saved to {output_file}")
+
diff --git a/src/embed_time/static_utils.py b/src/embed_time/static_utils.py
new file mode 100644
index 0000000..3f8f841
--- /dev/null
+++ b/src/embed_time/static_utils.py
@@ -0,0 +1,22 @@
+import yaml 
+import numpy as np
+
+
+# Yaml file reader
+def read_config(yaml_path):
+    with open(yaml_path, 'r') as file:
+        config = yaml.safe_load(file)
+    
+    # Extract 'Dataset mean' and 'Dataset std' from the config
+    mean = config['Dataset mean'][0]  # Access the first (and only) element of the list
+    std = config['Dataset std'][0]
+
+    # Split the strings and convert to floats
+    mean = [float(i) for i in mean.split()]
+    std = [float(i) for i in std.split()]
+    
+    # Convert to ndarrays
+    mean = np.array(mean)
+    std = np.array(std)
+
+    return mean, std
diff --git a/src/embed_time/transforms.py b/src/embed_time/transforms.py
new file mode 100644
index 0000000..b4fbb77
--- /dev/null
+++ b/src/embed_time/transforms.py
@@ -0,0 +1,156 @@
+import numpy as np
+from skimage.exposure import rescale_intensity
+from torch import from_numpy
+from skimage.measure import centroid
+
+
+def rescale_bf(img,quantiles = [0.01,0.99]):
+    min_max = np.quantile(img,quantiles)
+    rescaled = (
+        rescale_intensity(
+            img,
+            in_range=(min_max[0],min_max[1]),
+            out_range=(0,1)) -1
+    ) * -1
+    rescaled = np.clip(rescaled,0,1)
+    return rescaled
+
+def rescale_bra(bra_tl,quantiles = [0.001,0.999]):
+    min_max = np.quantile(bra_tl,quantiles)
+    rescaled = rescale_intensity(
+        bra_tl,
+        in_range=(min_max[0],min_max[1]),
+        out_range=(0,1)
+    )
+    return rescaled
+
+def complex_normalisation(
+        input_series, 
+        bf_quant, 
+        bra_quant
+    ):
+    """
+    input_series: np.ndarray 
+        dimensions = time, channel, y, x
+    bf_quant: list
+        lower and upper quantiles for rescaling brightfield images (channel 0)
+        Performed for each image individually
+    bra_quant: list
+        lower and upper quantiles for rescaling brachyury images (channel 1)
+        rescaled across the timelapse
+    """
+    bf_tl = input_series[:,0,:,:]
+    bra_tl = input_series[:,1,:,:]
+    out_bf = np.expand_dims(np.array([rescale_bf(img,bf_quant) for img in bf_tl]),1)
+    out_bra = np.expand_dims(rescale_bra(bra_tl,bra_quant),1)
+    return np.concatenate((out_bf,out_bra),axis=1)
+
+
+
+class NormalizeCustom(object):
+    """Normalise live TLS data with dimesnions t, c, y, x
+
+    Args:
+        bf_quantiles: list
+            lower and upper quantiles for rescaling brightfield images (channel 0)
+            Performed for each image individually
+        bra_quantiles: list
+            lower and upper quantiles for rescaling brachyury images (channel 1)
+            rescaled across the timelapse
+    """
+
+    def __init__(self, bf_quantiles, bra_quantiles):
+        self.bf_quantiles = bf_quantiles
+        self.bra_quantiles = bra_quantiles
+
+    def __call__(self, sample):
+        return complex_normalisation(sample,self.bf_quantiles,self.bra_quantiles)
+
+class SelectRandomTimepoint(object):
+    """select a random timepoint form the time series
+
+    time_dimension: int
+        dimension index of time 
+    """
+
+    def __init__(self, time_dimension):
+        self.td = time_dimension
+
+    def __call__(self, sample):
+        shape = sample.shape
+        random_tp = np.random.randint(0,shape[self.td])
+        
+        slice_objects = [
+            random_tp if i == self.td else slice(0,shape[i]) for i in range(len(shape))
+        ]
+        return sample[slice_objects]
+
+class SelectRandomTPNumpy(object):
+    """select a random timepoint form the time series
+
+    time_dimension: int
+        dimension index of time 
+    """
+
+    def __init__(self, time_dimension):
+        self.td = time_dimension
+
+    def __call__(self, sample):
+        shape = sample.shape
+        random_tp = np.random.randint(0,shape[self.td])
+        
+        out = np.take(sample,[random_tp],axis=self.td).squeeze(self.td)
+        # print(out.shape)
+        return out
+
+class CustomToTensor(object):
+    """Custom ToTensor: works with any shape and does not normalisation
+    """
+
+    def __init__(self):
+        pass
+
+    def __call__(self, sample):
+        return from_numpy(sample)
+
+class CustomCropCentroid(object):
+    def __init__(self,intensity_channel, channel_dim,crop_size):
+        self.intensity_channel = intensity_channel
+        self.channel_dim = channel_dim
+        self.crop_size = crop_size
+
+    def __call__(self, sample):
+        #shape = sample.shape
+        intensity_image = np.take(sample,[self.intensity_channel],axis=self.channel_dim).squeeze(self.channel_dim)
+        cent = centroid(intensity_image)[-2:]
+
+        cropped = crop_around_centroid_2D(sample,cent,self.crop_size,self.crop_size)
+
+        return cropped
+
+def crop_around_centroid_2D(image, centroid, crop_height = 800, crop_width = 800):
+    half_wid = int(crop_width//2)
+    half_hgt = int(crop_height//2)
+    c_0, c_1 = [int(c) for c in centroid]
+    
+    if c_0-half_wid < 0:
+        x_borders = np.amax(np.array([
+            [c_0-half_wid,0],
+            [c_0+half_wid,crop_width]]),axis = 1)
+    else:
+        x_borders = np.amin(np.array([
+            [c_0-half_wid,image.shape[0]-crop_width],
+            [c_0+half_wid,image.shape[0]]]),axis = 1)
+    if c_1-half_wid < 0:
+        y_borders = np.amax(np.array([
+            [c_1-half_hgt,0],
+            [c_1+half_hgt,crop_height]]),axis = 1)
+    else:
+        y_borders = np.amin(np.array([
+            [c_1-half_hgt,image.shape[1]-crop_height],
+            [c_1+half_hgt,image.shape[1]]]),axis = 1)
+
+    cropped_img = np.take(image,np.arange(y_borders[0],y_borders[1],1),axis=-2)
+    cropped_img = np.take(cropped_img,np.arange(x_borders[0],x_borders[1],1),axis=-1)
+    return cropped_img
+
diff --git a/src/embed_time/zarr_dataloader_ac.py b/src/embed_time/zarr_dataloader_ac.py
new file mode 100644
index 0000000..2bda071
--- /dev/null
+++ b/src/embed_time/zarr_dataloader_ac.py
@@ -0,0 +1,90 @@
+#%%
+import zarr
+from typing import Union, Optional, Callable, Dict
+from torch.utils.data import get_worker_info, Dataset
+from pathlib import Path
+import numpy as np
+from iohub import open_ome_zarr
+import scipy
+import matplotlib.pyplot as plt
+
+class ZarrDataset(Dataset):
+    """Dataset to extract patches from a zarr storage."""
+    def __init__(
+        self,
+        data_path: Union[str, Path],
+        image_transform: Optional[Callable] = None,
+        image_transform_params: Optional[Dict] = None,
+        ) -> None:
+        self.data_path = Path(data_path)
+        self.image_transform = image_transform
+        self.patch_transform_params = image_transform_params
+
+        self.data = open_ome_zarr(data_path)
+        self.indices = list(self.data.positions())[:4]
+        self.mean = self.calculate_mean()
+        self.std = self.calculate_std()
+
+    def calculate_mean(self):  
+        total_sum = np.zeros(2)  #(1, 2, 32, 2048, 2048)
+        total_count = 0
+        for name, pos in self.indices:
+            image = pos[0].numpy()
+            total_sum += image.sum(axis=(0, 2, 3, 4))
+            total_count += image.shape[2] * image.shape[3] * image.shape[3]
+        mean = total_sum / total_count
+        return mean
+    
+    def calculate_std(self):
+        sum_squared_diff = np.zeros(2)
+        total_count = 0
+        for name, pos in self.indices:
+            image = pos[0].numpy()
+            sum_squared_diff += ((image - self.mean[None, :, None, None, None]) ** 2).sum(
+                axis=(0, 2, 3, 4)
+            )
+            total_count += image.shape[2] * image.shape[3] * image.shape[3]
+        variance = sum_squared_diff / total_count
+        std = np.sqrt(variance)
+        return std
+    
+            
+    def __len__(self):
+        return len(self.indices)
+
+    def __getitem__(self, idx):
+        """
+        Iterate over data source and yield single patch.
+
+        Yields
+        ------
+        np.ndarray
+        """
+        name, pos = self.indices[idx]
+        array = pos[0].numpy() # (t,c,z,y,x)
+        print(array.shape)
+        patient = name.split("/")[0]
+
+        # transformation
+        transform_array = np.max(array, axis=2).squeeze(0)
+        # print(transform_array.shape)
+        # transform_array = scipy.ndimage.zoom(transform_array, zoom=(1, 0.5, 0.5))
+        # print(transform_array.shape)
+        # flip_prob = np.random.rand(1)
+        
+        # if flip_prob >0.5:
+        #     transform_array = np.flip(transform_array, axis=(1,2)) #(2,2024,2024) 
+        #     print(transform_array.shape)
+        # transform_array = np.rot90(transform_array, k=np.random.randint(4), axes=(1,2))
+        # print(transform_array.shape)
+        return transform_array
+    
+dataset = ZarrDataset("/home/S-ac/embed_time/zarrdata/mitochondria.zarr")
+
+for batch in dataset:
+    _, ax = plt.subplots(2)
+    ax[0].imshow(batch[0])
+    ax[1].imshow(batch[1])
+
+
+# %%
diff --git a/src/model_VAE_resnet18.py b/src/model_VAE_resnet18.py
new file mode 100644
index 0000000..d56dea6
--- /dev/null
+++ b/src/model_VAE_resnet18.py
@@ -0,0 +1,157 @@
+import torch
+from torch import nn, optim
+import torch.nn.functional as F
+
+class ResizeConv2d(nn.Module):
+    def __init__(self, in_channels, out_channels, kernel_size, scale_factor, mode='nearest'):
+        super.__init__()
+        self.scale_factor = scale_factor
+        self.mode = mode
+        self.conv = nn.Conv2d(in_channels, out_channels, kernel_size, stride=1, padding=1)
+    
+    def forward(self, x):
+        F.interpolate(x, scale_factor=self.scale_factor, mode=self.mode)
+        x = self.conv(x)
+        return x
+
+class BasicBlockEnc(nn.Module):
+    def __init__(self, in_planes, stride=1):
+        planes = in_planes * stride
+        self.conv1 = nn.Conv2d(in_planes, planes, kernel=3, strides=stride, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(planes)
+        self.conv2 = nn.Conv2d(planes, planes, kernel=3, strides=stride, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(planes)
+    
+    if strides == 1:
+        self.shortcut = nn.Sequential()
+    else: 
+        self.shortcut = nn.Sequential(
+            nn.Conv2d(in_planes, planes, kernel_size=1, stride=stride, bias=False),
+            nn.BatchNorm2d(planes)
+        )
+    
+    def forward(self, x):
+        out = torch.relu(self.bn1(self.conv1(x)))
+        out = self.bn2(self.conv2(out))
+        out += self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+class BasicBlockDec(nn.Module):
+    def __init__(self, in_planes, stride=1):
+        super().__init__()
+        planes = int(in_planes/stride)
+
+        self.conv2 = nn.Conv2d(in_planes, in_planes, kernel_size=3, stride=1, padding=1, bias=False)
+        self.bn2 = nn.BatchNorm2d(in_planes)
+        # self.bn1 could have been placed here, 
+        # but that messes up the order of the layers when printing the class
+
+        if stride == 1:
+            self.conv1 = nn.Conv2d(in_planes, planes, kernel_size=3, stride=1, padding=1, bias=False)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential()
+        else:
+            self.conv1 = ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride)
+            self.bn1 = nn.BatchNorm2d(planes)
+            self.shortcut = nn.Sequential(
+                ResizeConv2d(in_planes, planes, kernel_size=3, scale_factor=stride),
+                nn.BatchNorm2d(planes)
+            )
+    
+    def foward(self, x):
+        out = torch.relu(self.bn2(self.conv2(x)))
+        out = self.bn1(self.conv1(out))
+        out += self.shortcut(x)
+        out = torch.relu(out)
+        return out
+
+
+class Resnet18Enc(nn.Module):
+
+    def __init__(self, num_Block=[2, 2, 2, 2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 64
+        self.z_dim = z_dim
+        self.conv1 = nn.Conv2d(nc, 64, kernel_size=3, stride=2, padding=1, bias=False)
+        self.bn1 = nn.BatchNorm2d(64)
+        self.layer1 = self._makelayer(BasicBlockEnc, 64, num_Block[0], stride=1)
+        self.layer2 = self._makelayer(BasicBlockEnc, 128, num_Block[1], stride=2)
+        self.layer3 = self._makelayer(BasicBlockEnc, 256, num_Block[2], stride=2)
+        self.layer4 = self._makelayer(BasicBlockEnc, 512, num_Block[3], stride=2)
+        self.linear = nn.Linear(512, 2 * z_dim)
+
+    def _make_layer(self, BasicBlockEnc, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in strides:
+            layers += [BasicBlockEnc(self.in_planes, stride)]
+            self.in_planes = planes
+        return nn.Sequential(*layers)
+    
+    def forward(self, x):
+        x = torch.relu(self.bn1(self.conv1(x)))
+        x = self.layer1(x)
+        x = self.layer2(x)
+        x = self.layer3(x)
+        x = self.layer4(x)
+        x = F.adaptive_avg_pool2d(x, 1)
+        x = x.view(x.size(0), -1)
+        x = self.linear(x)
+        mu = x[:, :self.z_dim]
+        logvar = x[:, self.z_dim:]
+        return mu, logvar
+
+class Resnet18Dec(nn.Module):
+    
+     def __init__(self, num_Blocks=[2,2,2,2], z_dim=10, nc=3):
+        super().__init__()
+        self.in_planes = 512
+
+        self.linear = nn.Linear(z_dim, 512)
+
+        self.layer4 = self._make_layer(BasicBlockDec, 256, num_Blocks[3], stride=2)
+        self.layer3 = self._make_layer(BasicBlockDec, 128, num_Blocks[2], stride=2)
+        self.layer2 = self._make_layer(BasicBlockDec, 64, num_Blocks[1], stride=2)
+        self.layer1 = self._make_layer(BasicBlockDec, 64, num_Blocks[0], stride=1)
+        self.conv1 = ResizeConv2d(64, nc, kernel_size=3, scale_factor=2)
+
+    def _make_layer(self, BasicBlockDec, planes, num_Blocks, stride):
+        strides = [stride] + [1]*(num_Blocks-1)
+        layers = []
+        for stride in reversed(strides):
+            layers += [BasicBlockDec(self.in_planes, stride)]
+        self.in_planes = planes
+        return nn.Sequential(*layers)
+
+    def forward(self, z):
+        x = self.linear(z)
+        x = x.view(z.size(0), 512, 1, 1)
+        x = F.interpolate(x, scale_factor=4)
+        x = self.layer4(x)
+        x = self.layer3(x)
+        x = self.layer2(x)
+        x = self.layer1(x)
+        x = torch.sigmoid(self.conv1(x))
+        x = x.view(x.size(0), 3, 64, 64)
+        return x
+    
+    
+class VAE(nn.Module):
+    
+    def __init__(self, z_dim):
+        super().__init__()
+        self.encoder = Resnet18Enc(z_dim=z_dim)
+        self.decoder = Resnet18Dec(z_dim=z_dim)
+
+    def foward(self, x):
+        mean, logvar = self.encoder(x)
+        z = self.reparameterize(mean, logvar)
+        x = self.decoder(z)
+        return x, mean, logvar
+    
+    @staticmethod
+    def reparameterize(mean, logvar):
+        std = torch.exp(logvar / 2) # in log-space, squareroot is divide by two
+        epsilon = torch.rand_like(std)
+        return epsilon * std + mean