Noise models for paper notebooks (#285)

### Description

Small refac for submission notebooks. Training code works if called
explicitly. Training inside careamics needs further engineering.
NM code needs refactoring

### Changes Made

- **Added**: 
- Basic training
- plotting function


**Please ensure your PR meets the following requirements:**

- [x ] Code builds and passes tests locally, including doctests
- [ ] New tests have been added (for bug fixes/features)
- [x ] Pre-commit passes
- [ ] PR to the documentation exists (for bug fixes / features)

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: federico-carrara <[email protected]>
Co-authored-by: Joran Deschamps <[email protected]>

src/careamics/config/nm_model.py

@@ -11,9 +11,8 @@
     Field,
     PlainSerializer,
     PlainValidator,
-    model_validator,
 )
-from typing_extensions import Annotated, Self
+from typing_extensions import Annotated
 
 from careamics.utils.serializers import _array_to_json, _to_numpy
 
@@ -90,28 +89,29 @@ class GaussianMixtureNMConfig(BaseModel):
     tol: float = Field(default=1e-10)
     """Tolerance used in the computation of the noise model likelihood."""
 
-    @model_validator(mode="after")
-    def validate_path_to_pretrained_vs_training_data(self: Self) -> Self:
-        """Validate paths provided in the config.
-
-        Returns
-        -------
-        Self
-            Returns itself.
-        """
-        if self.path and (self.signal is not None or self.observation is not None):
-            raise ValueError(
-                "Either only 'path' to pre-trained noise model should be"
-                "provided or only signal and observation in form of paths"
-                "or numpy arrays."
-            )
-        if not self.path and (self.signal is None or self.observation is None):
-            raise ValueError(
-                "Either only 'path' to pre-trained noise model should be"
-                "provided or only signal and observation in form of paths"
-                "or numpy arrays."
-            )
-        return self
+    # @model_validator(mode="after")
+    # def validate_path_to_pretrained_vs_training_data(self: Self) -> Self:
+    #     """Validate paths provided in the config.
+
+    #     Returns
+    #     -------
+    #     Self
+    #         Returns itself.
+    #     """
+    #     if self.path and (self.signal is not None or self.observation is not None):
+    #         raise ValueError(
+    #             "Either only 'path' to pre-trained noise model should be"
+    #             "provided or only signal and observation in form of paths"
+    #             "or numpy arrays."
+    #         )
+    #     if not self.path and (self.signal is None or self.observation is None):
+    #         raise ValueError(
+    #             "Either only 'path' to pre-trained noise model should be"
+    #             "provided or only signal and observation in form of paths"
+    #             "or numpy arrays."
+    #         )
+    #     return self
+    # TODO revisit validation
 
 
 # The noise model is given by a set of GMMs, one for each target

src/careamics/config/vae_algorithm_model.py

@@ -86,9 +86,10 @@ def algorithm_cross_validation(self: Self) -> Self:
                     "Algorithm `denoisplit` with loss `denoisplit` only supports "
                     "`predict_logvar` as `None`."
                 )
+
             if self.noise_model is None:
                 raise ValueError("Algorithm `denoisplit` requires a noise model.")
-        # TODO: what if algorithm is not musplit or denoisplit (HDN?)
+        # TODO: what if algorithm is not musplit or denoisplit
         return self
 
     @model_validator(mode="after")

src/careamics/lvae_training/dataset/multifile_dataset.py

@@ -1,12 +1,13 @@
-from typing import Union, Callable, Sequence
+from collections.abc import Sequence
+from typing import Callable, Union
 
 import numpy as np
 from numpy.typing import NDArray
 
 from .config import DatasetConfig
+from .lc_dataset import LCMultiChDloader
 from .multich_dataset import MultiChDloader
 from .types import DataSplitType
-from .lc_dataset import LCMultiChDloader
 
 
 class TwoChannelData(Sequence):