Removed Sensorium 2022 dependencies

notebooks/2dcores_starter.ipynb

@@ -110,137 +110,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "Before learning how to use the cores, let's load the data. \n",
-    "\n",
-    "DISCLAIMER! Here we are importing data used in **Sensorium 2022** competition (https://github.com/sinzlab/sensorium). \n",
-    "\n",
-    "In case you do not have **sensorium package** installed, feel free to skip to section\n",
-    "[Simulated batch of images](#another_cell)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Imports"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 2,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "# install sensorium from the repo\n",
-    "!pip install git+https://github.com/sinzlab/sensorium.git"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 3,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "import warnings\n",
-    "warnings.filterwarnings('ignore')\n",
-    "import numpy as np\n",
-    "import matplotlib.pyplot as plt\n",
-    "import seaborn as sns\n",
-    "import torch\n",
-    "from nnfabrik.builder import get_data\n",
-    "\n",
-    "device = \"cpu\"\n",
-    "random_seed = 42\n"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {
-    "pycharm": {
-     "name": "#%% md\n"
-    }
-   },
-   "source": [
-    "### Instantiate DataLoader"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 4,
-   "metadata": {
-    "pycharm": {
-     "name": "#%%\n"
-    }
-   },
-   "outputs": [],
-   "source": [
-    "# loading the SENSORIUM+ dataset\n",
-    "filenames = ['/project/data/static22846-10-16-GrayImageNet-94c6ff995dac583098847cfecd43e7b6.zip', ]\n",
-    "\n",
-    "dataset_fn = 'sensorium.datasets.static_loaders'\n",
-    "dataset_config = {'paths': filenames,\n",
-    "                 'normalize': True,\n",
-    "                 'include_behavior': False,\n",
-    "                 'include_eye_position': True,\n",
-    "                 'batch_size': 32,\n",
-    "                 'scale':1,\n",
-    "                 'cuda': False\n",
-    "                 }\n",
-    "\n",
-    "dataloaders = get_data(dataset_fn, dataset_config)"
-   ]
-  },
-  {
-   "attachments": {},
-   "cell_type": "markdown",
-   "metadata": {},
-   "source": [
-    "### Access the input images in a batch of train data"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 5,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "dl = dataloaders['train']['22846-10-16']"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 6,
-   "metadata": {},
-   "outputs": [],
-   "source": [
-    "batch = next(iter(dl))\n",
-    "images = batch.images"
-   ]
-  },
-  {
-   "cell_type": "code",
-   "execution_count": 7,
-   "metadata": {},
-   "outputs": [
-    {
-     "data": {
-      "text/plain": [
-       "torch.Size([32, 1, 144, 256])"
-      ]
-     },
-     "execution_count": 7,
-     "metadata": {},
-     "output_type": "execute_result"
-    }
-   ],
-   "source": [
-    "images.shape"
+    "Before learning how to use the cores, let's create a dummy data **images**. This data will be similar to a batch of images."
    ]
   },
   {