diff --git a/eyeseg/io_utils/input_pipe.py b/eyeseg/io_utils/input_pipe.py
index 1b97db4..53d905e 100644
--- a/eyeseg/io_utils/input_pipe.py
+++ b/eyeseg/io_utils/input_pipe.py
@@ -18,6 +18,7 @@ def _parse_function(input_proto):
         other_features = {
             "volume": tf.io.FixedLenFeature([], tf.string),
             "bscan": tf.io.FixedLenFeature([], tf.string),
+            "layer_positions": tf.io.FixedLenFeature([], tf.string),
             "image": tf.io.FixedLenFeature([], tf.string),
             "group": tf.io.FixedLenFeature([], tf.string),
         }
@@ -32,7 +33,12 @@ def _parse_function(input_proto):
         data = tf.io.parse_single_example(input_proto, image_feature_description)
 
         image = tf.io.parse_tensor(data["image"], tf.uint8)
-        image = tf.reshape(image, input_shape + (1,))
+        image = tf.reshape(image, input_shape + (1,), name="reshape_1")
+
+        layer_positions = tf.io.parse_tensor(data["layer_positions"], tf.float32)
+        layer_positions = tf.reshape(
+            layer_positions, input_shape + (len(mapping),), name="reshape_1.5"
+        )
 
         # Sort mapping for guaranteed order
         layerout = tf.stack(
@@ -42,13 +48,16 @@ def _parse_function(input_proto):
             ],
             axis=-1,
         )
-        layerout = tf.reshape(layerout, (input_shape[1], len(mapping)))
+        layerout = tf.reshape(
+            layerout, (input_shape[1], len(mapping)), name="reshape_2"
+        )
 
         volume = data["volume"]
         bscan = data["bscan"]
         group = data["group"]
 
         return {
+            "layer_positions": layer_positions,
             "image": image,
             "layerout": layerout,
             "Volume": volume,
@@ -80,7 +89,11 @@ def _augment(in_data):
             lambda: image,
         )
 
-        return {"image": image, "layerout": layerout}
+        return {
+            "image": image,
+            "layerout": layerout,
+            "layer_positions": in_data["layer_positions"],
+        }
 
     return _augment
 
@@ -97,14 +110,41 @@ def _normalize(in_data):
     image = tf.cast(image, tf.float32)
     image = image - tf.math.reduce_mean(image)
     image = image / tf.math.reduce_std(image)
-    return {**in_data, **{"image": image, "layerout": layerout}}
+    return {
+        **in_data,
+        **{
+            "image": image,
+            "layerout": layerout,
+            "layer_positions": in_data["layer_positions"],
+        },
+    }
 
 
 @tf.function
 def _prepare_train(in_data):
-    image, layerout = in_data["image"], in_data["layerout"]
+    image, layerout, layer_positions = (
+        in_data["image"],
+        in_data["layerout"],
+        in_data["layer_positions"],
+    )
+
+    return image, {"layer_output": layerout, "columnwise_softmax": layer_positions}
+
+
+@tf.function
+def _prepare_test(in_data):
+    volume, bscan, group, image, layerout = (
+        in_data["Volume"],
+        in_data["Bscan"],
+        in_data["Group"],
+        in_data["image"],
+        in_data["layerout"],
+    )
     return image, {
         "layer_output": layerout,
+        "Volume": volume,
+        "Bscan": bscan,
+        "Group": group,
     }
 
 
@@ -119,6 +159,7 @@ def _prepare_train(in_data):
         )
         return image, {
             "layer_output": layerout,
+            "columnwise_softmax": in_data["layer_positions"],
         }
 
     return _prepare_train
@@ -228,13 +269,15 @@ def _transform(in_data):
                 tf.linalg.inv(combined_matrix)
             ),
             interpolation="bilinear",
+            output_shape=input_shape,
         )
-        # combined_matrix = tf.linalg.inv(combined_matrix)
 
         # Warp 1D data
         x_vals = (
             tf.tile(
-                tf.reshape(tf.range(0, width, dtype=tf.float32), (1, width)),
+                tf.reshape(
+                    tf.range(0, width, dtype=tf.float32), (1, width), name="reshape_3"
+                ),
                 [num_classes, 1],
             )
             + 0.5
@@ -326,13 +369,21 @@ def get_split(
             parsed_data.shuffle(
                 14000, seed, reshuffle_each_iteration=True
             )  # .map(_augment)
-            .map(_transform)
+            # .map(_transform)
             .map(_normalize)
             .batch(batch_size)
             .map(_prepare_train)
             .repeat(epochs)
             .prefetch(tf.data.experimental.AUTOTUNE)
         )
+    elif split == "test":
+        dataset = (
+            parsed_data.map(_normalize)
+            .batch(batch_size)
+            .map(_prepare_test)
+            .repeat(epochs)
+            .prefetch(tf.data.experimental.AUTOTUNE)
+        )
     else:
         dataset = (
             parsed_data.map(_normalize)
diff --git a/eyeseg/io_utils/losses.py b/eyeseg/io_utils/losses.py
index 79d1b18..3ac577a 100644
--- a/eyeseg/io_utils/losses.py
+++ b/eyeseg/io_utils/losses.py
@@ -1,6 +1,41 @@
 import tensorflow as tf
 
 
+def layer_ce(true, pred):
+    # true : batch, columns, channels
+
+    # pred: batch, rows, columns, channels
+    all = tf.math.log(tf.reduce_sum(true * pred, axis=1))
+    mask = tf.logical_not(tf.math.is_inf(all))
+    all_clean = tf.ragged.boolean_mask(all, mask)
+
+    return tf.reduce_sum(-tf.reduce_sum(all_clean, axis=1), axis=-1)
+
+    ctrue = tf.cast(tf.round(true), dtype=tf.int32)
+
+    cols = tf.range(1024, dtype=tf.int32)
+    layers = tf.range(9, dtype=tf.int32)
+
+    batch_losses = []
+    for batch in tf.range(1, dtype=tf.int32):
+        layer_losses = []
+        for layer in layers:
+            layer_results = []
+            for c in cols:
+                row = ctrue[batch, c, layer]
+                # if True: #not tf.math.is_nan(true[batch, c, layer]):
+                value = pred[batch, row, c, layer]
+                layer_results.append(value)
+            layer_losses.append(
+                -tf.reduce_sum(
+                    tf.math.log(tf.cast(tf.stack(layer_results), tf.float32))
+                )
+            )
+        batch_losses.append(tf.reduce_mean(layer_losses))
+
+    return tf.stack(batch_losses)
+
+
 class MovingMeanFocalSSE(tf.keras.losses.Loss):
     # initialize instance attributes
     def __init__(self, window_size, curv_weight=0):
diff --git a/eyeseg/io_utils/test.ipynb b/eyeseg/io_utils/test.ipynb
new file mode 100644
index 0000000..de56ea2
--- /dev/null
+++ b/eyeseg/io_utils/test.ipynb
@@ -0,0 +1,136 @@
+{
+ "cells": [
+  {
+   "cell_type": "code",
+   "execution_count": 58,
+   "metadata": {
+    "collapsed": true
+   },
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<tf.Tensor: shape=(2,), dtype=float32, numpy=array([7.9214387, 7.9214387], dtype=float32)>"
+     },
+     "execution_count": 58,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "import tensorflow as tf\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "\n",
+    "    #heights = tf.cast(tf.math.round(true), tf.int32)\n",
+    "\n",
+    "    #return tf.reduce_mean(-tf.reduce_sum(tf.gather(pred, indices,  )\n",
+    "\n",
+    "layer_ce(true, pred)"
+   ]
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 56,
+   "outputs": [],
+   "source": [
+    "import tensorflow as tf\n",
+    "true = tf.tile(tf.expand_dims(tf.tile(tf.expand_dims(tf.range(10), 1), multiples=[1, 5]), 0), [2, 1,1])\n",
+    "pred = tf.tile(tf.expand_dims(tf.range(10), 1), [1,10])\n",
+    "pred = tf.tile(tf.expand_dims(tf.tile(tf.expand_dims(pred, -1) , [1,1,5]), 0), [2,1,1,1]) / 10"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 42,
+   "outputs": [],
+   "source": [
+    "x_indices = tf.reshape(tf.range(true.shape[1]), (1,10,1))\n",
+    "x_indices = tf.tile(x_indices, (2,1,5))\n",
+    "\n",
+    "indices = tf.stack([true, x_indices], axis=1)"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 43,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<tf.Tensor: shape=(2, 2, 10, 5), dtype=int32, numpy=\narray([[[[0, 0, 0, 0, 0],\n         [1, 1, 1, 1, 1],\n         [2, 2, 2, 2, 2],\n         [3, 3, 3, 3, 3],\n         [4, 4, 4, 4, 4],\n         [5, 5, 5, 5, 5],\n         [6, 6, 6, 6, 6],\n         [7, 7, 7, 7, 7],\n         [8, 8, 8, 8, 8],\n         [9, 9, 9, 9, 9]],\n\n        [[0, 0, 0, 0, 0],\n         [1, 1, 1, 1, 1],\n         [2, 2, 2, 2, 2],\n         [3, 3, 3, 3, 3],\n         [4, 4, 4, 4, 4],\n         [5, 5, 5, 5, 5],\n         [6, 6, 6, 6, 6],\n         [7, 7, 7, 7, 7],\n         [8, 8, 8, 8, 8],\n         [9, 9, 9, 9, 9]]],\n\n\n       [[[0, 0, 0, 0, 0],\n         [1, 1, 1, 1, 1],\n         [2, 2, 2, 2, 2],\n         [3, 3, 3, 3, 3],\n         [4, 4, 4, 4, 4],\n         [5, 5, 5, 5, 5],\n         [6, 6, 6, 6, 6],\n         [7, 7, 7, 7, 7],\n         [8, 8, 8, 8, 8],\n         [9, 9, 9, 9, 9]],\n\n        [[0, 0, 0, 0, 0],\n         [1, 1, 1, 1, 1],\n         [2, 2, 2, 2, 2],\n         [3, 3, 3, 3, 3],\n         [4, 4, 4, 4, 4],\n         [5, 5, 5, 5, 5],\n         [6, 6, 6, 6, 6],\n         [7, 7, 7, 7, 7],\n         [8, 8, 8, 8, 8],\n         [9, 9, 9, 9, 9]]]], dtype=int32)>"
+     },
+     "execution_count": 43,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "indices"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  },
+  {
+   "cell_type": "code",
+   "execution_count": 47,
+   "outputs": [
+    {
+     "data": {
+      "text/plain": "<tf.Tensor: shape=(1, 1, 1), dtype=int32, numpy=array([[[5]]], dtype=int32)>"
+     },
+     "execution_count": 47,
+     "metadata": {},
+     "output_type": "execute_result"
+    }
+   ],
+   "source": [
+    "tf.gather_nd(pred, [[[[0,5,5,0]]]])"
+   ],
+   "metadata": {
+    "collapsed": false,
+    "pycharm": {
+     "name": "#%%\n"
+    }
+   }
+  }
+ ],
+ "metadata": {
+  "kernelspec": {
+   "display_name": "Python 3",
+   "language": "python",
+   "name": "python3"
+  },
+  "language_info": {
+   "codemirror_mode": {
+    "name": "ipython",
+    "version": 2
+   },
+   "file_extension": ".py",
+   "mimetype": "text/x-python",
+   "name": "python",
+   "nbconvert_exporter": "python",
+   "pygments_lexer": "ipython2",
+   "version": "2.7.6"
+  }
+ },
+ "nbformat": 4,
+ "nbformat_minor": 0
+}
diff --git a/eyeseg/io_utils/utils.py b/eyeseg/io_utils/utils.py
index 7ac3734..0ce7133 100644
--- a/eyeseg/io_utils/utils.py
+++ b/eyeseg/io_utils/utils.py
@@ -44,7 +44,7 @@ def preprocess_split(volume_paths, savepath, split, excluded=None):
         # Load volume
         data = ep.Oct.from_duke_mat(p)
         # Compute center of annotation
-        bm_annotation = (~np.isnan(data.layers["BM"])).astype(int)
+        bm_annotation = (~np.isnan(data.analyse["BM"])).astype(int)
         height_center, width_center = [
             int(c) for c in ndimage.measurements.center_of_mass(bm_annotation)
         ]
@@ -61,13 +61,13 @@ def preprocess_split(volume_paths, savepath, split, excluded=None):
                 image = bscan.scan[:, width_center - 256 : width_center + 256].astype(
                     np.uint8
                 )
-                bm = bscan.layers["BM"][width_center - 256 : width_center + 256].astype(
-                    np.float32
-                )
-                rpe = bscan.layers["RPE"][
+                bm = bscan.analyse["BM"][
+                    width_center - 256 : width_center + 256
+                ].astype(np.float32)
+                rpe = bscan.analyse["RPE"][
                     width_center - 256 : width_center + 256
                 ].astype(np.float32)
-                ilm = bscan.layers["ILM"][
+                ilm = bscan.analyse["ILM"][
                     width_center - 256 : width_center + 256
                 ].astype(np.float32)
 
diff --git a/eyeseg/models/feature_refinement_net.py b/eyeseg/models/feature_refinement_net.py
index bf55d30..68f089e 100644
--- a/eyeseg/models/feature_refinement_net.py
+++ b/eyeseg/models/feature_refinement_net.py
@@ -51,11 +51,7 @@ def clip_norm_func(input):
 
     # Receptive field 727
     dilations = [
-        [
-            3,
-            9,
-            3,
-        ],
+        [3, 9, 3],
         [9, 27, 9],
         [27, 243, 27],
     ]
@@ -124,14 +120,30 @@ def clip_norm_func(input):
         outputs = layers.Multiply()([output_features, attention_map])
     else:
         outputs = x
-    output = get_output(
-        outputs,
-        num_classes,
-        input_shape,
-        guaranteed_order=guaranteed_order,
-        soft=soft_layerhead,
-    )
 
-    output = layers.Reshape((input_shape[1], num_classes), name="layer_output")(output)
-    model = Model(inputs=[inputs], outputs=[output])
+    if soft_layerhead:
+        outputs, col_softmax = get_output(
+            outputs,
+            num_classes,
+            input_shape,
+            guaranteed_order=guaranteed_order,
+            soft=soft_layerhead,
+        )
+        output = layers.Reshape((input_shape[1], num_classes), name="layer_output")(
+            outputs
+        )
+        model = Model(inputs=[inputs], outputs=[output, col_softmax])
+    else:
+        outputs, _ = get_output(
+            outputs,
+            num_classes,
+            input_shape,
+            guaranteed_order=guaranteed_order,
+            soft=soft_layerhead,
+        )
+        output = layers.Reshape((input_shape[1], num_classes), name="layer_output")(
+            outputs
+        )
+        model = Model(inputs=[inputs], outputs=[output])
+
     return model
diff --git a/eyeseg/models/parts.py b/eyeseg/models/parts.py
index 48549c6..b3a7e7f 100644
--- a/eyeseg/models/parts.py
+++ b/eyeseg/models/parts.py
@@ -6,7 +6,7 @@
 import tensorflow as tf
 
 
-class FRNLayer(keras.layers.Layer):
+class FRNLayer(layers.Layer):
     def __init__(self):
         super(FRNLayer, self).__init__()
 
@@ -27,7 +27,7 @@ def call(self, x):
         return x * self.gamma + self.beta
 
 
-class TLU(keras.layers.Layer):
+class TLU(layers.Layer):
     def __init__(self):
         super(TLU, self).__init__()
 
@@ -42,7 +42,7 @@ def call(self, x):
         return K.maximum(x, self.tau)
 
 
-class TSwish(keras.layers.Layer):
+class TSwish(layers.Layer):
     def __init__(self):
         super(TSwish, self).__init__()
 
@@ -164,7 +164,9 @@ def get_output(input, num_classes, input_shape, guaranteed_order=True, soft=Fals
             num_classes, (1, 1), kernel_initializer="he_uniform"
         )(input)
 
-        col_softmax = activations.softmax(output_top_to_bottom, axis=1)
+        col_softmax = layers.Softmax(axis=1, name="columnwise_softmax")(
+            output_top_to_bottom
+        )
 
         col_softargmax = tf.reduce_sum(
             col_softmax
@@ -182,7 +184,13 @@ def get_output(input, num_classes, input_shape, guaranteed_order=True, soft=Fals
                         col_softargmax[..., i] - output_list_top_to_bottom[0]
                     ),
                 )
-            output_top_to_bottom = tf.stack(output_list_top_to_bottom, axis=-1)
+            output_top_to_bottom = tf.stack(
+                output_list_top_to_bottom, axis=-1, name="layer_output"
+            )
+
+        # Return the layer heights for L1 loss and the column-wise softmax for CE Loss as in He et al
+        return output_top_to_bottom, col_softmax
+
     else:
         output_top_to_bottom = layers.Conv2D(
             num_classes, (1, 1), activation="relu", kernel_initializer="he_uniform"
@@ -200,6 +208,8 @@ def get_output(input, num_classes, input_shape, guaranteed_order=True, soft=Fals
         output_top_to_bottom = layers.AveragePooling2D((input_shape[0], 1))(
             output_top_to_bottom
         )
-        output_top_to_bottom = tf.math.multiply(output_top_to_bottom, input_shape[0])
+        output_top_to_bottom = tf.math.multiply(
+            output_top_to_bottom, input_shape[0], name="layer_output"
+        )
 
-    return output_top_to_bottom
+        return output_top_to_bottom, False
diff --git a/eyeseg/scripts/commands/layers.py b/eyeseg/scripts/commands/analyse.py
similarity index 67%
rename from eyeseg/scripts/commands/layers.py
rename to eyeseg/scripts/commands/analyse.py
index 0fc2b61..8469cee 100644
--- a/eyeseg/scripts/commands/layers.py
+++ b/eyeseg/scripts/commands/analyse.py
@@ -3,6 +3,8 @@
 
 from importlib import resources
 
+import skimage.transform
+
 from eyeseg.models import weights as weights_resources
 from eyeseg.models.utils import load_model
 from eyeseg.scripts.utils import find_volumes
@@ -21,9 +23,16 @@
     default=False,
     help="Whether to overwrite existing layers. Default is --no-overwrite.",
 )
+@click.option(
+    "--drusen_threshold",
+    "-t",
+    type=click.INT,
+    default=2,
+    help="Minimum height for drusen to be included",
+)
 @click.argument("model_id", type=click.STRING, default="2c41ukad")
 @click.pass_context
-def layers(ctx: click.Context, model_id, overwrite):
+def analyse(ctx: click.Context, model_id, overwrite, drusen_threshold):
     """Predict OCT layers
 
     \b
@@ -69,27 +78,47 @@ def layers(ctx: click.Context, model_id, overwrite):
             # Predict layers
             data = get_layers(data, model_id)
             # Save predicted layers
-            output_dir = output_path / path.relative_to(input_path).parent / path.name
+            output_dir = output_path / path.relative_to(input_path).parent
             output_dir.mkdir(parents=True, exist_ok=True)
-            with open(output_dir / ("layers.pkl"), "wb") as myfile:
-                pickle.dump(
-                    {name: data.data for name, data in data.layers.items()}, myfile
+
+            if "RPE" in data.layers and "BM" in data.layers:
+                drusen = ep.drusen(
+                    data.layers["RPE"],
+                    data.layers["BM"],
+                    data.shape,
+                    minimum_height=drusen_threshold,
                 )
+                data.add_voxel_annotation(drusen, name="drusen")
+
+            data.save(output_dir / (path.name + ".eye"))
 
-    click.echo("\nPredicted OCT layers are saved. You can now use the 'drusen' command")
+    click.echo(
+        "\nComputed layers and drusen are saved. You can now use the 'quantify', 'plot-enface' and 'plot-bscans' commands"
+    )
 
 
 def get_layers(data, model_id):
-    layer_model, model_config = load_model(model_id, (512, data[0].shape[1], 1))
+    if data.meta["scale_x"] < 0.009:
+        factor = 2
+    else:
+        factor = 1
+
+    width = data[0].shape[1]
+    layer_model, model_config = load_model(model_id, (512, width // factor, 1))
     results = []
     for bscan in tqdm(data, desc=f"Predict '{data.meta['visit_date']}': "):
-        img = preprocess_standard(bscan.data, bscan.shape[1])
+        img = skimage.transform.rescale(bscan.data, (1, 1 / factor))
+        img = preprocess_standard(img, width // factor)
         prediction = layer_model.predict(img)[0]
         results.append(prediction)
 
     results = np.flip(np.stack(results, axis=0), axis=0)
     for index, name in model_config["layer_mapping"].items():
-        height_map = results[..., index]
+        if factor != 1:
+            height_map = skimage.transform.rescale(results[..., index], (1, factor))
+            # height_map = np.interp(np.arange(width), np.arange(width//factor) * factor, results[..., index])
+        else:
+            height_map = results[..., index]
         data.add_layer_annotation(height_map, name=name)
     return data
 
diff --git a/eyeseg/scripts/commands/drusen.py b/eyeseg/scripts/commands/drusen.py
deleted file mode 100644
index b9572c7..0000000
--- a/eyeseg/scripts/commands/drusen.py
+++ /dev/null
@@ -1,94 +0,0 @@
-import click
-from pathlib import Path
-import logging
-
-import eyepy as ep
-from tqdm import tqdm
-import pickle
-import numpy as np
-import pandas as pd
-
-from eyeseg.scripts.utils import find_volumes
-
-logger = logging.getLogger("eyeseg.drusen")
-
-
-@click.command()
-@click.option(
-    "--drusen_threshold",
-    "-t",
-    type=click.INT,
-    default=2,
-    help="Minimum height for drusen to be included",
-)
-@click.option(
-    "--overwrite/--no-overwrite",
-    default=False,
-    help="Whether to overwrite existing drusen. Default is --no-overwrite.",
-)
-@click.pass_context
-def drusen(ctx: click.Context, drusen_threshold, overwrite):
-    """Compute drusen from BM and RPE layer segmentation
-
-    \f
-    :param drusen_threshold:
-    :return:
-    """
-    input_path = ctx.obj["input_path"]
-    output_path = ctx.obj["output_path"]
-
-    volumes = find_volumes(input_path)
-
-    # Check for which volumes drusen need to be predicted
-    if overwrite is False and output_path.is_dir():
-        # Remove path from volumes if layers are found in the output location
-        precomputed_drusen = [
-            p.name for p in output_path.iterdir() if (p / "drusen.pkl").exists()
-        ]
-        for datatype in volumes.keys():
-            volumes[datatype] = [
-                v for v in volumes[datatype] if v.name not in precomputed_drusen
-            ]
-
-    data_readers = {"vol": ep.import_heyex_vol, "xml": ep.import_heyex_xml}
-    # Read data
-    no_layers_volumes = []
-    results = []
-    for datatype, volumes in volumes.items():
-        for path in tqdm(volumes):
-            # Load data
-            data = data_readers[datatype](path)
-            # Read layers
-            output_dir = output_path / path.relative_to(input_path).parent / path.name
-            layers_filepath = output_dir / "layers.pkl"
-            try:
-                with open(layers_filepath, "rb") as myfile:
-                    layers = pickle.load(myfile)
-            except FileNotFoundError:
-                logger.warning(f"No layers.pkl found for {path.name}")
-                no_layers_volumes.append(path)
-                continue
-
-            for name, layer in layers.items():
-                data.add_layer_annotation(layer, name=name)
-
-            # Compute drusen
-            drusen = ep.drusen(
-                data.layers["RPE"],
-                data.layers["BM"],
-                data.shape,
-                minimum_height=drusen_threshold,
-            )
-            output_dir = output_path / path.relative_to(input_path).parent / path.name
-            drusen_filepath = output_dir / "drusen.pkl"
-            with open(drusen_filepath, "wb") as myfile:
-                pickle.dump(drusen, myfile)
-
-    if len(no_layers_volumes) > 0:
-        click.echo(
-            f"No retinal layers found for {len(no_layers_volumes)} volumes. To predict layers run the 'layers' command."
-        )
-    else:
-        click.echo(
-            "\nComputed drusen are saved. You can now use the 'quantify', 'plot-enface' and 'plot-bscans' commands"
-        )
diff --git a/eyeseg/scripts/commands/evaluate.py b/eyeseg/scripts/commands/evaluate.py
index 9cbdef7..31be861 100644
--- a/eyeseg/scripts/commands/evaluate.py
+++ b/eyeseg/scripts/commands/evaluate.py
@@ -1,8 +1,9 @@
-import os
 import click
 import logging
 import yaml
-import wandb
+import numpy as np
+import pickle
+import matplotlib.pyplot as plt
 
 from eyeseg.models.feature_refinement_net import model
 from eyeseg.io_utils.losses import MovingMeanFocalSSE
@@ -27,12 +28,16 @@
     help="Shape of the data.",
 )
 @click.option("-b", "--batch_size", type=int, help="Batch size used during training")
+@click.option(
+    "-p", "--plot", type=bool, default=True, help="Plot test dataset with predictions"
+)
 @click.pass_context
 def evaluate(
     ctx: click.Context,
     run_path,
     input_shape,
     batch_size,
+    plot,
 ):
     """Evaluate a model on a test dataset"""
     input_path = ctx.obj["input_path"]
@@ -61,7 +66,7 @@ def evaluate(
         input_path,
         config["layer_mapping"],
         input_shape,
-        batch_size,
+        1,
         1,
         "test",
     )
@@ -84,5 +89,32 @@ def evaluate(
         sample_weight_mode="temporal",
     )
 
-    results = my_model.evaluate(test_data, batch_size=batch_size, return_dict=True)
-    print(results)
+    results = {}
+    for image, data in test_data:
+        prediction = my_model.predict(image)
+        layerout = data["layer_output"]
+        volume, bscan, group = (
+            bytes.decode(data["Volume"].numpy()[0]),
+            bytes.decode(data["Bscan"].numpy()[0]),
+            bytes.decode(data["Group"].numpy()[0]),
+        )
+
+        results[(volume, bscan, group)] = np.abs(prediction - layerout)
+
+        (output_path / "results").mkdir(parents=True, exist_ok=True)
+        if plot:
+            plt.imshow(image[0, ..., 0], cmap="gray")
+            for layer in range(9):
+                plt.plot(image.shape[1] - prediction[0, ..., layer])
+            plt.savefig(
+                output_path
+                / "results"
+                / f"{np.mean(results[(volume, bscan, group)]):.2f}_{volume}_{bscan}.jpeg"
+            )
+            plt.close()
+
+    with open(output_path / "results" / "test_results.pkl", "wb") as myfile:
+        pickle.dump(results, myfile)
+
+    # results = my_model.evaluate(test_data, batch_size=batch_size, return_dict=True)
+    # print(results)
diff --git a/eyeseg/scripts/commands/plot_bscans.py b/eyeseg/scripts/commands/plot_bscans.py
index 9e3c815..c41cc2d 100644
--- a/eyeseg/scripts/commands/plot_bscans.py
+++ b/eyeseg/scripts/commands/plot_bscans.py
@@ -25,16 +25,8 @@
     default=[],
     help="Layers predictions to overlay on the B-scan",
 )
-@click.option(
-    "--volumes",
-    "-v",
-    type=click.STRING,
-    multiple=True,
-    default=[],
-    help="Volumes to plot B-scans for. If not specified B-scans are plotted for all volumes.",
-)
 @click.pass_context
-def plot_bscans(ctx: click.Context, drusen, layers, volumes):
+def plot_bscans(ctx: click.Context, drusen, layers):
     """Plot B-scans
 
     \f
@@ -43,79 +35,30 @@ def plot_bscans(ctx: click.Context, drusen, layers, volumes):
     input_path = ctx.obj["input_path"]
     output_path = ctx.obj["output_path"]
 
-    available_volumes = find_volumes(input_path)
-    if len(volumes) != 0:
-        # Select volume path if any folder in the volumes path is in the folders specified by volumes
-        new_volumes = {
-            k: [
-                v
-                for v in volums
-                if not set(v.relative_to(input_path).parts).isdisjoint(set(volumes))
-            ]
-            for k, volums in available_volumes.items()
-        }
-        click.echo(
-            f"You specified {len(volumes)} volumes. {sum([len(v) for v in new_volumes.values()])} volumes were found."
-        )
+    volumes = [p for p in (input_path / "processed").iterdir() if p.suffix == ".eye"]
+    if len(volumes) == 0:
+        click.echo("\nNo volumes found.")
+        sys.exit()
 
-        if sum([len(v) for v in new_volumes.values()]) == 0:
-            sys.exit()
-        volumes = new_volumes
+    if drusen:
+        areas = ["drusen"]
     else:
-        volumes = available_volumes
-
-    data_readers = {"vol": ep.import_heyex_vol, "xml": ep.import_heyex_xml}
-    # Read data
-    for datatype, volumes in volumes.items():
-        for path in tqdm(volumes):
-            # Load data
-            data = data_readers[datatype](path)
-            # Load layers and drusen
-            output_dir = output_path / path.relative_to(input_path).parent / path.name
-            layers_filepath = output_dir / "layers.pkl"
-            drusen_filepath = output_dir / "drusen.pkl"
-
-            try:
-                with open(layers_filepath, "rb") as myfile:
-                    layer_data = pickle.load(myfile)
-            except FileNotFoundError:
-                logger.warning(f"No layers.pkl found for {path.name}")
-                continue
-
-            try:
-                with open(drusen_filepath, "rb") as myfile:
-                    drusen_data = pickle.load(myfile)
-            except FileNotFoundError:
-                logger.warning(f"No drusen.pkl found for {path.stem}")
-                continue
-
-            for name, layer in layer_data.items():
-                data.add_layer_annotation(layer, name=name)
-            data.add_voxel_annotation(drusen_data, name="drusen")
-
-            if "iRPE" in layers:
-                irpe = ep.quantification._drusen.ideal_rpe(
-                    data.layers["RPE"].data, data.layers["BM"].data, data.shape
-                )
-                data.add_layer_annotation(irpe, name="iRPE")
+        ares = []
 
-            save_path = (
-                output_path
-                / "plots"
-                / "bscans"
-                / path.relative_to(input_path).parent
-                / path.stem
+    for path in tqdm(volumes):
+        # Load data
+        data = ep.EyeVolume.load(path)
+        save_path = output_path / "plots" / "bscans" / path.stem
+        save_path.mkdir(parents=True, exist_ok=True)
+        for bscan in tqdm(data):
+            bscan.plot(areas=areas, layers=layers)
+            plt.axis("off")
+            plt.savefig(
+                save_path / f"{bscan.index}.jpeg",
+                bbox_inches="tight",
+                pad_inches=0,
+                dpi=200,
             )
-            save_path.mkdir(parents=True, exist_ok=True)
-            for bscan in tqdm(data):
-                bscan.plot(areas=["drusen"], layers=layers)
-                plt.axis("off")
-                plt.savefig(
-                    save_path / f"{bscan.index}.jpeg",
-                    bbox_inches="tight",
-                    pad_inches=0,
-                    dpi=200,
-                )
-                plt.close()
+            plt.close()
 
     click.echo("\nB-scan plots are saved.")
diff --git a/eyeseg/scripts/commands/plot_data.py b/eyeseg/scripts/commands/plot_data.py
index d275552..331def8 100644
--- a/eyeseg/scripts/commands/plot_data.py
+++ b/eyeseg/scripts/commands/plot_data.py
@@ -13,6 +13,7 @@
 @click.option(
     "-c",
     "--model-config",
+    default="./config.yaml",
     type=click.Path(exists=True),
     help="Path to to model configuration as yaml file. If not provided a new file is generated from the provided arguments.",
 )
diff --git a/eyeseg/scripts/commands/plot_enface.py b/eyeseg/scripts/commands/plot_enface.py
index f1b551d..9df40be 100644
--- a/eyeseg/scripts/commands/plot_enface.py
+++ b/eyeseg/scripts/commands/plot_enface.py
@@ -37,54 +37,23 @@ def plot_enface(ctx: click.Context, drusen, bscan_area, bscan_positions):
     input_path = ctx.obj["input_path"]
     output_path = ctx.obj["output_path"]
 
-    volumes = find_volumes(input_path)
-
-    data_readers = {"vol": ep.import_heyex_vol, "xml": ep.import_heyex_xml}
-    # Read data
-    for datatype, volumes in volumes.items():
-        for path in tqdm(volumes):
-            # Load data
-            data = data_readers[datatype](path)
-            # Load layers and drusen
-            output_dir = output_path / path.relative_to(input_path).parent / path.name
-            layers_filepath = output_dir / "layers.pkl"
-            drusen_filepath = output_dir / "drusen.pkl"
-
-            try:
-                with open(layers_filepath, "rb") as myfile:
-                    layer_data = pickle.load(myfile)
-            except FileNotFoundError:
-                logger.warning(f"No layers.pkl found for {path.name}")
-                continue
-
-            try:
-                with open(drusen_filepath, "rb") as myfile:
-                    drusen_data = pickle.load(myfile)
-            except FileNotFoundError:
-                logger.warning(f"No drusen.pkl found for {path.stem}")
-                continue
-
-            for name, layer in layer_data.items():
-                data.add_layer_annotation(layer, name=name)
-            data.add_voxel_annotation(drusen_data, name="drusen")
-
-            save_path = (
-                output_path
-                / "plots"
-                / "enface"
-                / path.relative_to(input_path).parent
-                / path.stem
-            )
-            save_path.mkdir(parents=True, exist_ok=True)
-
-            if not bscan_positions:
-                bscan_positions = None
-            data.plot(
-                projections=["drusen"],
-                bscan_region=bscan_area,
-                bscan_positions=bscan_positions,
-            )
-            plt.savefig(save_path / f"{path.stem}.jpeg", bbox_inches="tight", dpi=200)
-            plt.close()
+    volumes = [p for p in (input_path / "processed").iterdir() if p.suffix == ".eye"]
+
+    for path in tqdm(volumes):
+        # Load data
+        data = ep.EyeVolume.load(path)
+
+        save_path = output_path / "plots" / "enface"
+        save_path.mkdir(parents=True, exist_ok=True)
+
+        if not bscan_positions:
+            bscan_positions = None
+        data.plot(
+            projections=["drusen"],
+            bscan_region=bscan_area,
+            bscan_positions=bscan_positions,
+        )
+        plt.savefig(save_path / f"{path.stem}.jpeg", bbox_inches="tight", dpi=200)
+        plt.close()
 
     click.echo("\nDrusen enface plots are saved.")
diff --git a/eyeseg/scripts/commands/quantify.py b/eyeseg/scripts/commands/quantify.py
index 9cdfbc2..0ab3f46 100644
--- a/eyeseg/scripts/commands/quantify.py
+++ b/eyeseg/scripts/commands/quantify.py
@@ -1,17 +1,10 @@
 import click
-from pathlib import Path
 import logging
 
 import eyepy as ep
 from tqdm import tqdm
-import pickle
-import numpy as np
 import pandas as pd
 
-from eyeseg.scripts.utils import find_volumes
-
-# from eyeseg.grids import grid
-
 logger = logging.getLogger("eyeseg.quantify")
 
 
@@ -53,32 +46,23 @@ def quantify(ctx: click.Context, radii, sectors, offsets):
     input_path = ctx.obj["input_path"]
     output_path = ctx.obj["output_path"]
 
-    volumes = find_volumes(input_path)
+    volumes = [p for p in (input_path / "processed").iterdir() if p.suffix == ".eye"]
 
-    data_readers = {"vol": ep.import_heyex_vol, "xml": ep.import_heyex_xml}
     # Read data
-    no_drusen_volumes = []
     results = []
-    for datatype, volumes in volumes.items():
-        for path in tqdm(volumes):
-            # Load data
-            data = data_readers[datatype](path)
-            # Read layers
-            output_dir = output_path / path.relative_to(input_path).parent / path.name
-            layers_filepath = output_dir / "layers.pkl"
-            drusen_filepath = output_dir / "drusen.pkl"
 
-            try:
-                with open(drusen_filepath, "rb") as myfile:
-                    drusen = pickle.load(myfile)
-            except FileNotFoundError:
-                no_drusen_volumes.append(path)
-                continue
+    for path in tqdm(volumes):
+        # Load data
+        data = ep.EyeVolume.load(path)
 
-            data.add_voxel_annotation(
-                drusen, name="drusen", radii=radii, n_sectors=sectors, offsets=offsets
-            )
-            results.append(data.volume_maps["drusen"].quantification)
+        vm = data.volume_maps["drusen"]
+        vm.radii = radii
+        vm.n_sectors = sectors
+        vm.offsets = offsets
+
+        quant = vm.quantification
+        quant["Visit"] = path.stem
+        results.append(vm.quantification)
 
     # Save quantification results as csv
     if len(results) > 0:
@@ -88,8 +72,3 @@ def quantify(ctx: click.Context, radii, sectors, offsets):
         csv.to_csv(output_path / f"drusen_results.csv")
 
         click.echo(f"Drusen quantification saved for {len(csv)} volumes.")
-
-    if len(no_drusen_volumes) > 0:
-        click.echo(
-            f"No drusen found for {len(no_drusen_volumes)} volumes. To compute drusen run the 'drusen' command after having predicted layers with the 'layers' command."
-        )
diff --git a/eyeseg/scripts/commands/train.py b/eyeseg/scripts/commands/train.py
index bc83969..ca85067 100644
--- a/eyeseg/scripts/commands/train.py
+++ b/eyeseg/scripts/commands/train.py
@@ -14,7 +14,7 @@
     _normalize,
     _prepare_train,
 )
-from eyeseg.io_utils.losses import MovingMeanFocalSSE
+from eyeseg.io_utils.losses import MovingMeanFocalSSE, layer_ce
 from eyeseg.io_utils.input_pipe import get_split, count_samples
 from eyeseg.io_utils.utils import get_metrics
 
@@ -266,13 +266,17 @@ def get_metric(*args, **kwargs):
         window_size=config["training"]["boosting_window_size"],
         curv_weight=config["training"]["curv_weight"],
     )
+    if config["parameters"]["soft_layerhead"]:
+        losses = {"layer_output": loss_fn, "columnwise_softmax": layer_ce}
+    else:
+        losses = {"layer_output": loss_fn}
     metrics["layer_output"].append(internal_metric(loss_fn, "ema", name="EMA"))
 
     my_model.compile(
         optimizer=tf.keras.optimizers.Adam(
             learning_rate=config["training"]["lr"], clipnorm=1.0, clipvalue=0.5
         ),
-        loss={"layer_output": loss_fn},
+        loss=losses,
         metrics=metrics,
         sample_weight_mode="temporal",
     )
diff --git a/eyeseg/scripts/main.py b/eyeseg/scripts/main.py
index 9e3cb7d..9668821 100644
--- a/eyeseg/scripts/main.py
+++ b/eyeseg/scripts/main.py
@@ -2,8 +2,7 @@
 import os
 from pathlib import Path
 from eyeseg.scripts.commands.check import check
-from eyeseg.scripts.commands.layers import layers
-from eyeseg.scripts.commands.drusen import drusen
+from eyeseg.scripts.commands.analyse import analyse
 from eyeseg.scripts.commands.quantify import quantify
 from eyeseg.scripts.commands.plot_enface import plot_enface
 from eyeseg.scripts.commands.plot_bscans import plot_bscans
@@ -102,8 +101,7 @@ def main(ctx, input_path, output_path, log_level, gpu):
 
 
 main.add_command(check)
-main.add_command(layers)
-main.add_command(drusen)
+main.add_command(analyse)
 main.add_command(quantify)
 main.add_command(plot_enface)
 main.add_command(plot_bscans)
diff --git a/eyeseg/scripts/utils.py b/eyeseg/scripts/utils.py
index edfffc4..36577d7 100644
--- a/eyeseg/scripts/utils.py
+++ b/eyeseg/scripts/utils.py
@@ -2,16 +2,23 @@ def find_volumes(data_path):
     if data_path.is_dir():
         vol_volumes = data_path.glob("**/*.vol")
         xml_volumes = data_path.glob("**/*.xml")
+        eye_volumes = data_path.glob("**/*.eye")
         # We do not support multiple XML exports in the same folder.
         xml_volumes = [v.parent for v in xml_volumes]
     elif data_path.is_file():
         if ".vol" == data_path.suffix:
             vol_volumes = [data_path]
             xml_volumes = []
+            eye_volumes = []
         if ".xml" == data_path.suffix:
             xml_volumes = [data_path]
             vol_volumes = []
+            eye_volumes = []
+        if ".eye" == data_path.suffix:
+            eye_volumes = [data_path]
+            xml_volumes = []
+            vol_volumes = []
     else:
         raise ValueError("Data not found")
 
-    return {"vol": set(vol_volumes), "xml": set(xml_volumes)}
+    return {"vol": set(vol_volumes), "xml": set(xml_volumes), "eye": set(eye_volumes)}
diff --git a/poetry.lock b/poetry.lock
index 2db7fe3..70a38ca 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -6,6 +6,14 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "appnope"
+version = "0.1.3"
+description = "Disable App Nap on macOS >= 10.9"
+category = "main"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "argcomplete"
 version = "1.12.3"
@@ -17,6 +25,51 @@ python-versions = "*"
 [package.extras]
 test = ["coverage", "flake8", "pexpect", "wheel"]
 
+[[package]]
+name = "argon2-cffi"
+version = "21.3.0"
+description = "The secure Argon2 password hashing algorithm."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+argon2-cffi-bindings = "*"
+
+[package.extras]
+dev = ["pre-commit", "cogapp", "tomli", "coverage[toml] (>=5.0.2)", "hypothesis", "pytest", "sphinx", "sphinx-notfound-page", "furo"]
+docs = ["sphinx", "sphinx-notfound-page", "furo"]
+tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pytest"]
+
+[[package]]
+name = "argon2-cffi-bindings"
+version = "21.2.0"
+description = "Low-level CFFI bindings for Argon2"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+cffi = ">=1.0.1"
+
+[package.extras]
+dev = ["pytest", "cogapp", "pre-commit", "wheel"]
+tests = ["pytest"]
+
+[[package]]
+name = "asttokens"
+version = "2.0.8"
+description = "Annotate AST trees with source code positions"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+six = "*"
+
+[package.extras]
+test = ["astroid (<=2.5.3)", "pytest"]
+
 [[package]]
 name = "astunparse"
 version = "1.6.3"
@@ -40,7 +93,7 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 name = "attrs"
 version = "21.4.0"
 description = "Classes Without Boilerplate"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
@@ -50,6 +103,45 @@ docs = ["furo", "sphinx", "zope.interface", "sphinx-notfound-page"]
 tests = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "zope.interface", "cloudpickle"]
 tests_no_zope = ["coverage[toml] (>=5.0.2)", "hypothesis", "pympler", "pytest (>=4.3.0)", "six", "mypy", "pytest-mypy-plugins", "cloudpickle"]
 
+[[package]]
+name = "backcall"
+version = "0.2.0"
+description = "Specifications for callback functions passed in to an API"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "beautifulsoup4"
+version = "4.11.1"
+description = "Screen-scraping library"
+category = "main"
+optional = false
+python-versions = ">=3.6.0"
+
+[package.dependencies]
+soupsieve = ">1.2"
+
+[package.extras]
+html5lib = ["html5lib"]
+lxml = ["lxml"]
+
+[[package]]
+name = "bleach"
+version = "5.0.1"
+description = "An easy safelist-based HTML-sanitizing tool."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+six = ">=1.9.0"
+webencodings = "*"
+
+[package.extras]
+css = ["tinycss2 (>=1.1.0,<1.2)"]
+dev = ["build (==0.8.0)", "flake8 (==4.0.1)", "hashin (==0.17.0)", "pip-tools (==6.6.2)", "pytest (==7.1.2)", "Sphinx (==4.3.2)", "tox (==3.25.0)", "twine (==4.0.1)", "wheel (==0.37.1)", "black (==22.3.0)", "mypy (==0.961)"]
+
 [[package]]
 name = "cachetools"
 version = "5.2.0"
@@ -66,6 +158,17 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "cffi"
+version = "1.15.1"
+description = "Foreign Function Interface for Python calling C code."
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+pycparser = "*"
+
 [[package]]
 name = "cfgv"
 version = "3.3.1"
@@ -140,6 +243,14 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "debugpy"
+version = "1.6.3"
+description = "An implementation of the Debug Adapter Protocol for Python"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "decli"
 version = "0.5.2"
@@ -156,6 +267,14 @@ category = "main"
 optional = false
 python-versions = ">=3.5"
 
+[[package]]
+name = "defusedxml"
+version = "0.7.1"
+description = "XML bomb protection for Python stdlib modules"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
+
 [[package]]
 name = "distlib"
 version = "0.3.4"
@@ -183,6 +302,22 @@ python-versions = "*"
 [package.dependencies]
 six = ">=1.4.0"
 
+[[package]]
+name = "entrypoints"
+version = "0.4"
+description = "Discover and load entry points from installed packages."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "executing"
+version = "1.0.0"
+description = "Get the currently executing AST node of a frame, and other information"
+category = "main"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "eyepie"
 version = "0.6.7"
@@ -197,6 +332,17 @@ itk = ">=5.2.1,<6.0.0"
 matplotlib = ">=3.5.1,<4.0.0"
 scikit-image = ">=0.19.1,<0.20.0"
 
+[[package]]
+name = "fastjsonschema"
+version = "2.16.1"
+description = "Fastest Python implementation of JSON schema"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.extras]
+devel = ["colorama", "jsonschema", "json-spec", "pylint", "pytest", "pytest-benchmark", "pytest-cache", "validictory"]
+
 [[package]]
 name = "filelock"
 version = "3.7.1"
@@ -418,6 +564,21 @@ docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)"]
 perf = ["ipython"]
 testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.0.1)", "packaging", "pyfakefs", "flufl.flake8", "pytest-perf (>=0.9.2)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)", "importlib-resources (>=1.3)"]
 
+[[package]]
+name = "importlib-resources"
+version = "5.9.0"
+description = "Read resources from Python packages"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+zipp = {version = ">=3.1.0", markers = "python_version < \"3.10\""}
+
+[package.extras]
+docs = ["sphinx", "jaraco.packaging (>=9)", "rst.linker (>=1.9)", "jaraco.tidelift (>=1.4)"]
+testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-cov", "pytest-enabler (>=1.3)", "pytest-black (>=0.3.7)", "pytest-mypy (>=0.9.1)"]
+
 [[package]]
 name = "iniconfig"
 version = "1.1.1"
@@ -426,6 +587,91 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "ipykernel"
+version = "6.15.2"
+description = "IPython Kernel for Jupyter"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+appnope = {version = "*", markers = "platform_system == \"Darwin\""}
+debugpy = ">=1.0"
+ipython = ">=7.23.1"
+jupyter-client = ">=6.1.12"
+matplotlib-inline = ">=0.1"
+nest-asyncio = "*"
+packaging = "*"
+psutil = "*"
+pyzmq = ">=17"
+tornado = ">=6.1"
+traitlets = ">=5.1.0"
+
+[package.extras]
+test = ["flaky", "ipyparallel", "pre-commit", "pytest-cov", "pytest-timeout", "pytest (>=6.0)"]
+
+[[package]]
+name = "ipython"
+version = "8.4.0"
+description = "IPython: Productive Interactive Computing"
+category = "main"
+optional = false
+python-versions = ">=3.8"
+
+[package.dependencies]
+appnope = {version = "*", markers = "sys_platform == \"darwin\""}
+backcall = "*"
+colorama = {version = "*", markers = "sys_platform == \"win32\""}
+decorator = "*"
+jedi = ">=0.16"
+matplotlib-inline = "*"
+pexpect = {version = ">4.3", markers = "sys_platform != \"win32\""}
+pickleshare = "*"
+prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
+pygments = ">=2.4.0"
+stack-data = "*"
+traitlets = ">=5"
+
+[package.extras]
+all = ["black", "Sphinx (>=1.3)", "ipykernel", "nbconvert", "nbformat", "ipywidgets", "notebook", "ipyparallel", "qtconsole", "pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "numpy (>=1.19)", "pandas", "trio"]
+black = ["black"]
+doc = ["Sphinx (>=1.3)"]
+kernel = ["ipykernel"]
+nbconvert = ["nbconvert"]
+nbformat = ["nbformat"]
+notebook = ["ipywidgets", "notebook"]
+parallel = ["ipyparallel"]
+qtconsole = ["qtconsole"]
+test = ["pytest (<7.1)", "pytest-asyncio", "testpath"]
+test_extra = ["pytest (<7.1)", "pytest-asyncio", "testpath", "curio", "matplotlib (!=3.2.0)", "nbformat", "numpy (>=1.19)", "pandas", "trio"]
+
+[[package]]
+name = "ipython-genutils"
+version = "0.2.0"
+description = "Vestigial utilities from IPython"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "ipywidgets"
+version = "8.0.2"
+description = "Jupyter interactive widgets"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+ipykernel = ">=4.5.1"
+ipython = ">=6.1.0"
+jupyterlab-widgets = ">=3.0,<4.0"
+traitlets = ">=4.3.1"
+widgetsnbextension = ">=4.0,<5.0"
+
+[package.extras]
+test = ["jsonschema", "pytest (>=3.6.0)", "pytest-cov", "pytz"]
+
 [[package]]
 name = "itk"
 version = "5.2.1.post1"
@@ -509,11 +755,26 @@ python-versions = "*"
 [package.dependencies]
 itk-filtering = "5.2.1.post1"
 
+[[package]]
+name = "jedi"
+version = "0.18.1"
+description = "An autocompletion tool for Python that can be used for text editors."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+parso = ">=0.8.0,<0.9.0"
+
+[package.extras]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["Django (<3.1)", "colorama", "docopt", "pytest (<7.0.0)"]
+
 [[package]]
 name = "jinja2"
 version = "3.1.2"
 description = "A very fast and expressive template engine."
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 
@@ -523,6 +784,110 @@ MarkupSafe = ">=2.0"
 [package.extras]
 i18n = ["Babel (>=2.7)"]
 
+[[package]]
+name = "jsonschema"
+version = "4.15.0"
+description = "An implementation of JSON Schema validation for Python"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+attrs = ">=17.4.0"
+importlib-resources = {version = ">=1.4.0", markers = "python_version < \"3.9\""}
+pkgutil-resolve-name = {version = ">=1.3.10", markers = "python_version < \"3.9\""}
+pyrsistent = ">=0.14.0,<0.17.0 || >0.17.0,<0.17.1 || >0.17.1,<0.17.2 || >0.17.2"
+
+[package.extras]
+format = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3987", "uri-template", "webcolors (>=1.11)"]
+format-nongpl = ["fqdn", "idna", "isoduration", "jsonpointer (>1.13)", "rfc3339-validator", "rfc3986-validator (>0.1.0)", "uri-template", "webcolors (>=1.11)"]
+
+[[package]]
+name = "jupyter"
+version = "1.0.0"
+description = "Jupyter metapackage. Install all the Jupyter components in one go."
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+ipykernel = "*"
+ipywidgets = "*"
+jupyter-console = "*"
+nbconvert = "*"
+notebook = "*"
+qtconsole = "*"
+
+[[package]]
+name = "jupyter-client"
+version = "7.3.5"
+description = "Jupyter protocol implementation and client libraries"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+entrypoints = "*"
+jupyter-core = ">=4.9.2"
+nest-asyncio = ">=1.5.4"
+python-dateutil = ">=2.8.2"
+pyzmq = ">=23.0"
+tornado = ">=6.2"
+traitlets = "*"
+
+[package.extras]
+doc = ["ipykernel", "myst-parser", "sphinx-rtd-theme", "sphinx (>=1.3.6)", "sphinxcontrib-github-alt"]
+test = ["codecov", "coverage", "ipykernel (>=6.5)", "ipython", "mypy", "pre-commit", "pytest", "pytest-asyncio (>=0.18)", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyter-console"
+version = "6.4.4"
+description = "Jupyter terminal console"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+ipykernel = "*"
+ipython = "*"
+jupyter-client = ">=7.0.0"
+prompt-toolkit = ">=2.0.0,<3.0.0 || >3.0.0,<3.0.1 || >3.0.1,<3.1.0"
+pygments = "*"
+
+[package.extras]
+test = ["pexpect"]
+
+[[package]]
+name = "jupyter-core"
+version = "4.11.1"
+description = "Jupyter core package. A base package on which Jupyter projects rely."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+pywin32 = {version = ">=1.0", markers = "sys_platform == \"win32\" and platform_python_implementation != \"PyPy\""}
+traitlets = "*"
+
+[package.extras]
+test = ["ipykernel", "pre-commit", "pytest", "pytest-cov", "pytest-timeout"]
+
+[[package]]
+name = "jupyterlab-pygments"
+version = "0.2.2"
+description = "Pygments theme using JupyterLab CSS variables"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[[package]]
+name = "jupyterlab-widgets"
+version = "3.0.3"
+description = "Jupyter interactive widgets for JupyterLab"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "keras"
 version = "2.9.0"
@@ -564,6 +929,20 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "lxml"
+version = "4.9.1"
+description = "Powerful and Pythonic XML processing library combining libxml2/libxslt with the ElementTree API."
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, != 3.4.*"
+
+[package.extras]
+cssselect = ["cssselect (>=0.7)"]
+html5 = ["html5lib"]
+htmlsoup = ["beautifulsoup4"]
+source = ["Cython (>=0.29.7)"]
+
 [[package]]
 name = "markdown"
 version = "3.3.7"
@@ -582,7 +961,7 @@ testing = ["coverage", "pyyaml"]
 name = "markupsafe"
 version = "2.1.1"
 description = "Safely add untrusted strings to HTML/XML markup."
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.7"
 
@@ -605,6 +984,104 @@ pyparsing = ">=2.2.1"
 python-dateutil = ">=2.7"
 setuptools_scm = ">=4"
 
+[[package]]
+name = "matplotlib-inline"
+version = "0.1.6"
+description = "Inline Matplotlib backend for Jupyter"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
+[package.dependencies]
+traitlets = "*"
+
+[[package]]
+name = "mistune"
+version = "2.0.4"
+description = "A sane Markdown parser with useful plugins and renderers"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "nbclient"
+version = "0.6.7"
+description = "A client library for executing notebooks. Formerly nbconvert's ExecutePreprocessor."
+category = "main"
+optional = false
+python-versions = ">=3.7.0"
+
+[package.dependencies]
+jupyter-client = ">=6.1.5"
+nbformat = ">=5.0"
+nest-asyncio = "*"
+traitlets = ">=5.2.2"
+
+[package.extras]
+sphinx = ["autodoc-traits", "mock", "moto", "myst-parser", "Sphinx (>=1.7)", "sphinx-book-theme"]
+test = ["black", "check-manifest", "flake8", "ipykernel", "ipython", "ipywidgets", "mypy", "nbconvert", "pip (>=18.1)", "pre-commit", "pytest (>=4.1)", "pytest-asyncio", "pytest-cov (>=2.6.1)", "setuptools (>=60.0)", "testpath", "twine (>=1.11.0)", "xmltodict"]
+
+[[package]]
+name = "nbconvert"
+version = "7.0.0"
+description = "Converting Jupyter Notebooks"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+beautifulsoup4 = "*"
+bleach = "*"
+defusedxml = "*"
+importlib-metadata = {version = ">=3.6", markers = "python_version < \"3.10\""}
+jinja2 = ">=3.0"
+jupyter-core = ">=4.7"
+jupyterlab-pygments = "*"
+lxml = "*"
+markupsafe = ">=2.0"
+mistune = ">=2.0.3,<3"
+nbclient = ">=0.5.0"
+nbformat = ">=5.1"
+packaging = "*"
+pandocfilters = ">=1.4.1"
+pygments = ">=2.4.1"
+tinycss2 = "*"
+traitlets = ">=5.0"
+
+[package.extras]
+all = ["ipykernel", "ipython", "ipywidgets (>=7)", "nbsphinx (>=0.2.12)", "pre-commit", "pyppeteer (>=1,<1.1)", "pyqtwebengine (>=5.15)", "pytest", "pytest-cov", "pytest-dependency", "sphinx-rtd-theme", "sphinx (==5.0.2)", "tornado (>=6.1)"]
+docs = ["ipython", "nbsphinx (>=0.2.12)", "sphinx-rtd-theme", "sphinx (==5.0.2)"]
+qtpdf = ["pyqtwebengine (>=5.15)"]
+qtpng = ["pyqtwebengine (>=5.15)"]
+serve = ["tornado (>=6.1)"]
+test = ["ipykernel", "ipywidgets (>=7)", "pre-commit", "pyppeteer (>=1,<1.1)", "pytest", "pytest-cov", "pytest-dependency"]
+webpdf = ["pyppeteer (>=1,<1.1)"]
+
+[[package]]
+name = "nbformat"
+version = "5.4.0"
+description = "The Jupyter Notebook format"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+fastjsonschema = "*"
+jsonschema = ">=2.6"
+jupyter-core = "*"
+traitlets = ">=5.1"
+
+[package.extras]
+test = ["check-manifest", "testpath", "pytest", "pre-commit"]
+
+[[package]]
+name = "nest-asyncio"
+version = "1.5.5"
+description = "Patch asyncio to allow nested event loops"
+category = "main"
+optional = false
+python-versions = ">=3.5"
+
 [[package]]
 name = "networkx"
 version = "2.8.4"
@@ -628,6 +1105,36 @@ category = "dev"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "notebook"
+version = "6.4.12"
+description = "A web-based notebook environment for interactive computing"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+argon2-cffi = "*"
+ipykernel = "*"
+ipython-genutils = "*"
+jinja2 = "*"
+jupyter-client = ">=5.3.4"
+jupyter-core = ">=4.6.1"
+nbconvert = ">=5"
+nbformat = "*"
+nest-asyncio = ">=1.5"
+prometheus-client = "*"
+pyzmq = ">=17"
+Send2Trash = ">=1.8.0"
+terminado = ">=0.8.3"
+tornado = ">=6.1"
+traitlets = ">=4.2.1"
+
+[package.extras]
+docs = ["sphinx", "nbsphinx", "sphinxcontrib-github-alt", "sphinx-rtd-theme", "myst-parser"]
+json-logging = ["json-logging"]
+test = ["pytest", "coverage", "requests", "testpath", "nbval", "selenium", "pytest-cov", "requests-unixsocket"]
+
 [[package]]
 name = "numpy"
 version = "1.22.4"
@@ -696,6 +1203,26 @@ pytz = ">=2020.1"
 [package.extras]
 test = ["hypothesis (>=5.5.3)", "pytest (>=6.0)", "pytest-xdist (>=1.31)"]
 
+[[package]]
+name = "pandocfilters"
+version = "1.5.0"
+description = "Utilities for writing pandoc filters in python"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "parso"
+version = "0.8.3"
+description = "A Python Parser"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+qa = ["flake8 (==3.8.3)", "mypy (==0.782)"]
+testing = ["docopt", "pytest (<6.0.0)"]
+
 [[package]]
 name = "pathtools"
 version = "0.1.2"
@@ -704,6 +1231,25 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "pexpect"
+version = "4.8.0"
+description = "Pexpect allows easy control of interactive console applications."
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+ptyprocess = ">=0.5"
+
+[[package]]
+name = "pickleshare"
+version = "0.7.5"
+description = "Tiny 'shelve'-like database with concurrency support"
+category = "main"
+optional = false
+python-versions = "*"
+
 [[package]]
 name = "pillow"
 version = "9.1.1"
@@ -716,6 +1262,14 @@ python-versions = ">=3.7"
 docs = ["olefile", "sphinx (>=2.4)", "sphinx-copybutton", "sphinx-issues (>=3.0.1)", "sphinx-removed-in", "sphinx-rtd-theme (>=1.0)", "sphinxext-opengraph"]
 tests = ["check-manifest", "coverage", "defusedxml", "markdown2", "olefile", "packaging", "pyroma", "pytest", "pytest-cov", "pytest-timeout"]
 
+[[package]]
+name = "pkgutil-resolve-name"
+version = "1.3.10"
+description = "Resolve a name to an object."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
 [[package]]
 name = "platformdirs"
 version = "2.5.2"
@@ -756,6 +1310,17 @@ pyyaml = ">=5.1"
 toml = "*"
 virtualenv = ">=20.0.8"
 
+[[package]]
+name = "prometheus-client"
+version = "0.14.1"
+description = "Python client for the Prometheus monitoring system."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+twisted = ["twisted"]
+
 [[package]]
 name = "promise"
 version = "2.3"
@@ -774,7 +1339,7 @@ test = ["pytest (>=2.7.3)", "pytest-cov", "coveralls", "futures", "pytest-benchm
 name = "prompt-toolkit"
 version = "3.0.29"
 description = "Library for building powerful interactive command lines in Python"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=3.6.2"
 
@@ -800,11 +1365,30 @@ python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
 [package.extras]
 test = ["ipaddress", "mock", "enum34", "pywin32", "wmi"]
 
+[[package]]
+name = "ptyprocess"
+version = "0.7.0"
+description = "Run a subprocess in a pseudo terminal"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pure-eval"
+version = "0.2.2"
+description = "Safely evaluate AST nodes without side effects"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.extras]
+tests = ["pytest"]
+
 [[package]]
 name = "py"
 version = "1.11.0"
 description = "library with cross-python path, ini-parsing, io, code, log facilities"
-category = "dev"
+category = "main"
 optional = false
 python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*, !=3.4.*"
 
@@ -827,6 +1411,25 @@ python-versions = "*"
 [package.dependencies]
 pyasn1 = ">=0.4.6,<0.5.0"
 
+[[package]]
+name = "pycparser"
+version = "2.21"
+description = "C parser in Python"
+category = "main"
+optional = false
+python-versions = ">=2.7, !=3.0.*, !=3.1.*, !=3.2.*, !=3.3.*"
+
+[[package]]
+name = "pygments"
+version = "2.13.0"
+description = "Pygments is a syntax highlighting package written in Python."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.extras]
+plugins = ["importlib-metadata"]
+
 [[package]]
 name = "pyparsing"
 version = "3.0.9"
@@ -838,6 +1441,14 @@ python-versions = ">=3.6.8"
 [package.extras]
 diagrams = ["railroad-diagrams", "jinja2"]
 
+[[package]]
+name = "pyrsistent"
+version = "0.18.1"
+description = "Persistent/Functional/Immutable data structures"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "pytest"
 version = "6.2.5"
@@ -889,6 +1500,22 @@ python-versions = ">=3.7"
 [package.dependencies]
 numpy = ">=1.17.3"
 
+[[package]]
+name = "pywin32"
+version = "304"
+description = "Python for Window Extensions"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "pywinpty"
+version = "2.0.7"
+description = "Pseudo terminal support for Windows from Python."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "pyyaml"
 version = "6.0"
@@ -897,6 +1524,54 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "pyzmq"
+version = "23.2.1"
+description = "Python bindings for 0MQ"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+cffi = {version = "*", markers = "implementation_name == \"pypy\""}
+py = {version = "*", markers = "implementation_name == \"pypy\""}
+
+[[package]]
+name = "qtconsole"
+version = "5.3.2"
+description = "Jupyter Qt console"
+category = "main"
+optional = false
+python-versions = ">= 3.7"
+
+[package.dependencies]
+ipykernel = ">=4.1"
+ipython-genutils = "*"
+jupyter-client = ">=4.1"
+jupyter-core = "*"
+pygments = "*"
+pyzmq = ">=17.1"
+qtpy = ">=2.0.1"
+traitlets = "<5.2.1 || >5.2.1,<5.2.2 || >5.2.2"
+
+[package.extras]
+doc = ["Sphinx (>=1.3)"]
+test = ["flaky", "pytest", "pytest-qt"]
+
+[[package]]
+name = "qtpy"
+version = "2.2.0"
+description = "Provides an abstraction layer on top of the various Qt bindings (PyQt5/6 and PySide2/6)."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+packaging = "*"
+
+[package.extras]
+test = ["pytest-qt", "pytest-cov (>=3.0.0)", "pytest (>=6,!=7.0.0,!=7.0.1)"]
+
 [[package]]
 name = "questionary"
 version = "1.10.0"
@@ -990,6 +1665,19 @@ python-versions = ">=3.8,<3.11"
 [package.dependencies]
 numpy = ">=1.17.3,<1.25.0"
 
+[[package]]
+name = "send2trash"
+version = "1.8.0"
+description = "Send file to trash natively under Mac OS X, Windows and Linux."
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.extras]
+nativelib = ["pyobjc-framework-cocoa", "pywin32"]
+objc = ["pyobjc-framework-cocoa"]
+win32 = ["pywin32"]
+
 [[package]]
 name = "sentry-sdk"
 version = "1.5.12"
@@ -1071,6 +1759,30 @@ category = "main"
 optional = false
 python-versions = ">=3.6"
 
+[[package]]
+name = "soupsieve"
+version = "2.3.2.post1"
+description = "A modern CSS selector implementation for Beautiful Soup."
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[[package]]
+name = "stack-data"
+version = "0.5.0"
+description = "Extract data from python stack frames and tracebacks for informative displays"
+category = "main"
+optional = false
+python-versions = "*"
+
+[package.dependencies]
+asttokens = "*"
+executing = "*"
+pure-eval = "*"
+
+[package.extras]
+tests = ["cython", "littleutils", "pygments", "typeguard", "pytest"]
+
 [[package]]
 name = "tensorboard"
 version = "2.9.1"
@@ -1208,6 +1920,22 @@ category = "main"
 optional = false
 python-versions = "*"
 
+[[package]]
+name = "terminado"
+version = "0.15.0"
+description = "Tornado websocket backend for the Xterm.js Javascript terminal emulator library."
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.dependencies]
+ptyprocess = {version = "*", markers = "os_name != \"nt\""}
+pywinpty = {version = ">=1.1.0", markers = "os_name == \"nt\""}
+tornado = ">=6.1.0"
+
+[package.extras]
+test = ["pre-commit", "pytest-timeout", "pytest (>=6.0)"]
+
 [[package]]
 name = "tifffile"
 version = "2022.5.4"
@@ -1222,6 +1950,21 @@ numpy = ">=1.19.2"
 [package.extras]
 all = ["imagecodecs (>=2021.11.20)", "matplotlib (>=3.3)", "lxml"]
 
+[[package]]
+name = "tinycss2"
+version = "1.1.1"
+description = "A tiny CSS parser"
+category = "main"
+optional = false
+python-versions = ">=3.6"
+
+[package.dependencies]
+webencodings = ">=0.4"
+
+[package.extras]
+doc = ["sphinx", "sphinx-rtd-theme"]
+test = ["pytest", "pytest-cov", "pytest-flake8", "pytest-isort", "coverage"]
+
 [[package]]
 name = "toml"
 version = "0.10.2"
@@ -1246,6 +1989,14 @@ category = "dev"
 optional = false
 python-versions = ">=3.6,<4.0"
 
+[[package]]
+name = "tornado"
+version = "6.2"
+description = "Tornado is a Python web framework and asynchronous networking library, originally developed at FriendFeed."
+category = "main"
+optional = false
+python-versions = ">= 3.7"
+
 [[package]]
 name = "tqdm"
 version = "4.64.0"
@@ -1263,6 +2014,17 @@ notebook = ["ipywidgets (>=6)"]
 slack = ["slack-sdk"]
 telegram = ["requests"]
 
+[[package]]
+name = "traitlets"
+version = "5.3.0"
+description = ""
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
+[package.extras]
+test = ["pre-commit", "pytest"]
+
 [[package]]
 name = "typeguard"
 version = "2.13.3"
@@ -1351,7 +2113,15 @@ sweeps = ["sweeps (>=0.1.0)"]
 name = "wcwidth"
 version = "0.2.5"
 description = "Measures the displayed width of unicode strings in a terminal"
-category = "dev"
+category = "main"
+optional = false
+python-versions = "*"
+
+[[package]]
+name = "webencodings"
+version = "0.5.1"
+description = "Character encoding aliases for legacy web content"
+category = "main"
 optional = false
 python-versions = "*"
 
@@ -1366,6 +2136,14 @@ python-versions = ">=3.7"
 [package.extras]
 watchdog = ["watchdog"]
 
+[[package]]
+name = "widgetsnbextension"
+version = "4.0.3"
+description = "Jupyter interactive widgets for Jupyter Notebook"
+category = "main"
+optional = false
+python-versions = ">=3.7"
+
 [[package]]
 name = "wrapt"
 version = "1.14.1"
@@ -1389,17 +2167,49 @@ testing = ["pytest (>=6)", "pytest-checkdocs (>=2.4)", "pytest-flake8", "pytest-
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 lock-version = "1.1"
 python-versions = ">=3.8,<3.11"
-content-hash = "82724c57bf779b674c6337071c4f0dc8036ac47f5018c26f30042800ab346cf5"
+content-hash = "f5a3946b918fea863bf7f901f5fda2ffefb50cb59f05aab1b2261b89411c2cf3"
 
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+]
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 ]
+argon2-cffi = [
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+]
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+    {file = "argon2_cffi_bindings-21.2.0-pp38-pypy38_pp73-win_amd64.whl", hash = "sha256:5e00316dabdaea0b2dd82d141cc66889ced0cdcbfa599e8b471cf22c620c329a"},
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 astunparse = [
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@@ -1412,6 +2222,15 @@ attrs = [
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 ]
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+    {file = "backcall-0.2.0.tar.gz", hash = "sha256:5cbdbf27be5e7cfadb448baf0aa95508f91f2bbc6c6437cd9cd06e2a4c215e1e"},
+]
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+]
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 cachetools = [
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     {file = "cachetools-5.2.0.tar.gz", hash = "sha256:6a94c6402995a99c3970cc7e4884bb60b4a8639938157eeed436098bf9831757"},
@@ -1420,6 +2239,7 @@ certifi = [
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     {file = "certifi-2022.6.15.tar.gz", hash = "sha256:84c85a9078b11105f04f3036a9482ae10e4621616db313fe045dd24743a0820d"},
 ]
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 cfgv = [
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@@ -1448,6 +2268,7 @@ cycler = [
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 ]
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 decli = [
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@@ -1456,6 +2277,10 @@ decorator = [
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     {file = "decorator-5.1.1.tar.gz", hash = "sha256:637996211036b6385ef91435e4fae22989472f9d571faba8927ba8253acbc330"},
 ]
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+    {file = "defusedxml-0.7.1.tar.gz", hash = "sha256:1bb3032db185915b62d7c6209c5a8792be6a32ab2fedacc84e01b52c51aa3e69"},
+]
 distlib = [
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@@ -1488,10 +2313,16 @@ docker-pycreds = [
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 ]
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+]
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 eyepie = [
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 ]
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 filelock = [
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@@ -1647,10 +2478,18 @@ importlib-metadata = [
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 ]
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 ]
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+ipython = []
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+mistune = []
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+nbconvert = []
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@@ -2157,10 +3051,38 @@ pyasn1-modules = [
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@@ -2205,6 +3127,8 @@ pywavelets = [
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@@ -2240,6 +3164,9 @@ pyyaml = [
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@@ -2309,6 +3236,10 @@ scipy = [
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 ]
+send2trash = [
+    {file = "Send2Trash-1.8.0-py3-none-any.whl", hash = "sha256:f20eaadfdb517eaca5ce077640cb261c7d2698385a6a0f072a4a5447fd49fa08"},
+    {file = "Send2Trash-1.8.0.tar.gz", hash = "sha256:d2c24762fd3759860a0aff155e45871447ea58d2be6bdd39b5c8f966a0c99c2d"},
+]
 sentry-sdk = [
     {file = "sentry-sdk-1.5.12.tar.gz", hash = "sha256:259535ba66933eacf85ab46524188c84dcb4c39f40348455ce15e2c0aca68863"},
     {file = "sentry_sdk-1.5.12-py2.py3-none-any.whl", hash = "sha256:778b53f0a6c83b1ee43d3b7886318ba86d975e686cb2c7906ccc35b334360be1"},
@@ -2402,6 +3333,8 @@ smmap = [
     {file = "smmap-5.0.0-py3-none-any.whl", hash = "sha256:2aba19d6a040e78d8b09de5c57e96207b09ed71d8e55ce0959eeee6c8e190d94"},
     {file = "smmap-5.0.0.tar.gz", hash = "sha256:c840e62059cd3be204b0c9c9f74be2c09d5648eddd4580d9314c3ecde0b30936"},
 ]
+soupsieve = []
+stack-data = []
 tensorboard = [
     {file = "tensorboard-2.9.1-py3-none-any.whl", hash = "sha256:baa727f791776f9e5841d347127720ceed4bbd59c36b40604b95fb2ae6029276"},
 ]
@@ -2471,10 +3404,15 @@ tensorflow-probability = [
 termcolor = [
     {file = "termcolor-1.1.0.tar.gz", hash = "sha256:1d6d69ce66211143803fbc56652b41d73b4a400a2891d7bf7a1cdf4c02de613b"},
 ]
+terminado = []
 tifffile = [
     {file = "tifffile-2022.5.4-py3-none-any.whl", hash = "sha256:52b4c02040d00c1811e26c0f6abd41e77e2d57559b3657ff3e873955f74f5c57"},
     {file = "tifffile-2022.5.4.tar.gz", hash = "sha256:b03147a15862b7c1d90d47435197f149bef7a52c25ad67cf1f9b465faa71b8d2"},
 ]
+tinycss2 = [
+    {file = "tinycss2-1.1.1-py3-none-any.whl", hash = "sha256:fe794ceaadfe3cf3e686b22155d0da5780dd0e273471a51846d0a02bc204fec8"},
+    {file = "tinycss2-1.1.1.tar.gz", hash = "sha256:b2e44dd8883c360c35dd0d1b5aad0b610e5156c2cb3b33434634e539ead9d8bf"},
+]
 toml = [
     {file = "toml-0.10.2-py2.py3-none-any.whl", hash = "sha256:806143ae5bfb6a3c6e736a764057db0e6a0e05e338b5630894a5f779cabb4f9b"},
     {file = "toml-0.10.2.tar.gz", hash = "sha256:b3bda1d108d5dd99f4a20d24d9c348e91c4db7ab1b749200bded2f839ccbe68f"},
@@ -2487,10 +3425,12 @@ tomlkit = [
     {file = "tomlkit-0.11.0-py3-none-any.whl", hash = "sha256:0f4050db66fd445b885778900ce4dd9aea8c90c4721141fde0d6ade893820ef1"},
     {file = "tomlkit-0.11.0.tar.gz", hash = "sha256:71ceb10c0eefd8b8f11fe34e8a51ad07812cb1dc3de23247425fbc9ddc47b9dd"},
 ]
+tornado = []
 tqdm = [
     {file = "tqdm-4.64.0-py2.py3-none-any.whl", hash = "sha256:74a2cdefe14d11442cedf3ba4e21a3b84ff9a2dbdc6cfae2c34addb2a14a5ea6"},
     {file = "tqdm-4.64.0.tar.gz", hash = "sha256:40be55d30e200777a307a7585aee69e4eabb46b4ec6a4b4a5f2d9f11e7d5408d"},
 ]
+traitlets = []
 typeguard = [
     {file = "typeguard-2.13.3-py3-none-any.whl", hash = "sha256:5e3e3be01e887e7eafae5af63d1f36c849aaa94e3a0112097312aabfa16284f1"},
     {file = "typeguard-2.13.3.tar.gz", hash = "sha256:00edaa8da3a133674796cf5ea87d9f4b4c367d77476e185e80251cc13dfbb8c4"},
@@ -2515,10 +3455,15 @@ wcwidth = [
     {file = "wcwidth-0.2.5-py2.py3-none-any.whl", hash = "sha256:beb4802a9cebb9144e99086eff703a642a13d6a0052920003a230f3294bbe784"},
     {file = "wcwidth-0.2.5.tar.gz", hash = "sha256:c4d647b99872929fdb7bdcaa4fbe7f01413ed3d98077df798530e5b04f116c83"},
 ]
+webencodings = [
+    {file = "webencodings-0.5.1-py2.py3-none-any.whl", hash = "sha256:a0af1213f3c2226497a97e2b3aa01a7e4bee4f403f95be16fc9acd2947514a78"},
+    {file = "webencodings-0.5.1.tar.gz", hash = "sha256:b36a1c245f2d304965eb4e0a82848379241dc04b865afcc4aab16748587e1923"},
+]
 werkzeug = [
     {file = "Werkzeug-2.1.2-py3-none-any.whl", hash = "sha256:72a4b735692dd3135217911cbeaa1be5fa3f62bffb8745c5215420a03dc55255"},
     {file = "Werkzeug-2.1.2.tar.gz", hash = "sha256:1ce08e8093ed67d638d63879fd1ba3735817f7a80de3674d293f5984f25fb6e6"},
 ]
+widgetsnbextension = []
 wrapt = [
     {file = "wrapt-1.14.1-cp27-cp27m-macosx_10_9_x86_64.whl", hash = "sha256:1b376b3f4896e7930f1f772ac4b064ac12598d1c38d04907e696cc4d794b43d3"},
     {file = "wrapt-1.14.1-cp27-cp27m-manylinux1_i686.whl", hash = "sha256:903500616422a40a98a5a3c4ff4ed9d0066f3b4c951fa286018ecdf0750194ef"},
diff --git a/pyproject.toml b/pyproject.toml
index 68a1bb7..1e9e1bb 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -15,6 +15,7 @@ tensorflow-addons = "^0.17.1"
 wandb = "^0.12.18"
 pandas = "^1.4.2"
 tensorflow-probability = "^0.17.0"
+jupyter = "^1.0.0"
 
 [tool.poetry.dev-dependencies]
 pytest = "^6.2.5"