refactor: update merge training (#702)

* refactor: update merge training

* feat: generate neg exs, site sampling

* rm exaspim dataloader

* refactor: image loader working for merge detection

* refactor: image config

* refactor: configs

* refactor: class rebalancing, ds creation

* refactor: improved swc and graph loader

* bug: config subclassing

* refactor: random site generation

---------

Co-authored-by: anna-grim <anna.grim@alleninstitute.org>

Author: anna-grim

src/neuron_proofreader/config.py

@@ -0,0 +1,128 @@
+"""
+Created on Frid Sept 15 16:00:00 2024
+
+@author: Anna Grim
+@email: anna.grim@alleninstitute.org
+
+Connfiguration classes used for setting storing parameters used in neuron
+proofreading pipelines.
+
+"""
+
+from abc import ABC
+from dataclasses import asdict, dataclass
+from typing import Tuple
+
+import os
+
+from neuron_proofreader.utils import util
+
+
+class Config(ABC):
+
+    def to_dict(self):
+        """
+        Converts configuration attributes to a dictionary.
+
+        Returns
+        -------
+        attrs : dict
+            Dictionary containing configuration attributes.
+        """
+        attrs = asdict(self)
+        for k, v in attrs.items():
+            if isinstance(v, tuple):
+                attrs[k] = list(v)
+        return attrs
+
+    def save(self, output_dir):
+        """
+        Saves configuration attributes to a JSON file.
+
+        dir_path : str
+            Path to directory to save JSON file.
+        """
+        path = os.path.join(output_dir, f"{self.name}.json")
+        util.write_json(path, self.to_dict())
+
+
+@dataclass
+class GraphConfig(Config):
+    """
+    Configuration class for skeleton graph parameters.
+
+    Attributes
+    ----------
+    anisotropy : Tuple[float]
+        Scaling factors used to transform physical to image coordinates.
+    min_cable_length : float
+        Minimum path length (in microns) of SWC files loaded into graph.
+    node_spacing : float
+        Physcial spacing (in microns) between nodes.
+    prune_depth : int
+        ...
+    remove_doubles : bool
+        Indication of whether to remove fragments that are likely a double of
+        another.
+    verbose : bool
+        Indication of whether to display a progress bar.
+    """
+
+    anisotropy: Tuple[float, float, float] = (1.0, 1.0, 1.0)
+    min_cable_length: float = 0.0
+    min_swc_pts: int = 1
+    name: str = "graph_config"
+    node_spacing: float = 1.0
+    prune_depth: float = 20.0
+    remove_doubles: bool = True
+    use_anisotropy: bool = True
+    verbose: bool = False
+
+
+@dataclass
+class ImageConfig(Config):
+    """
+    Configuration class for image processing parameters.
+
+    Attributes
+    ----------
+    brightness_clip : int
+        Intensity value that voxel brightness is clipped to.
+    percentiles : Tuple[float], optional
+        Percentiles used to normalize patches.
+    patch_shape : Tuple[int]
+        Shape of patch to be read from image.
+    transform : bool
+        Indication of whether to use image augmentation.
+    """
+
+    brightness_clip: int = 400
+    name: str = "image_config"
+    percentiles: Tuple[float, float] = (1, 99.5)
+    patch_shape: Tuple[int, int, int] = (128, 128, 128)
+    transform: bool = False
+
+    def set_train_mode(self):
+        self.transform = True
+
+
+@dataclass
+class ProposalsConfig(Config):
+    """
+    Configuration class for skeleton graph parameters.
+
+    Attributes
+    ----------
+    allow_nonleaf_proposals : bool
+        Indication of whether to generate proposals between leaf and nodes
+        with degree 2.
+    proposals_per_leaf : int
+        Maximum number of proposals generated at leaf nodes.
+    trim_endpoints_bool : bool
+        Indication of whether trim endpoints of isolated leaf-to-leaf
+        proposals.
+    """
+
+    allow_nonleaf_proposals: bool = False
+    proposals_per_leaf: int = 3
+    trim_endpoints_bool: bool = True