From c85b6e626d051ef69295a618957446b5be92e145 Mon Sep 17 00:00:00 2001
From: Gabriel Borges Gutierrez <gabriel.bgs00@gmail.com>
Date: Sun, 12 May 2024 18:13:37 -0300
Subject: [PATCH] removing f3 datamodule since its a aplication data module,
 moving to minerva sismic

---
 minerva/data/data_modules/__init__.py         |   0
 .../data/data_modules/f3_reconstruction.py    | 129 ------------------
 2 files changed, 129 deletions(-)
 delete mode 100644 minerva/data/data_modules/__init__.py
 delete mode 100644 minerva/data/data_modules/f3_reconstruction.py

diff --git a/minerva/data/data_modules/__init__.py b/minerva/data/data_modules/__init__.py
deleted file mode 100644
index e69de29..0000000
diff --git a/minerva/data/data_modules/f3_reconstruction.py b/minerva/data/data_modules/f3_reconstruction.py
deleted file mode 100644
index 7793bb7..0000000
--- a/minerva/data/data_modules/f3_reconstruction.py
+++ /dev/null
@@ -1,129 +0,0 @@
-import os
-from pathlib import Path
-from typing import List, Tuple
-from minerva.data.readers.zarr_reader import PatchedZarrReader
-from minerva.data.datasets.supervised_dataset import (
-    SupervisedReconstructionDataset,
-)
-import lightning as L
-from minerva.utils.typing import PathLike
-from minerva.transforms.transform import _Transform
-from torch.utils.data import DataLoader
-
-
-class F3ReconstructionDataModule(L.LightningDataModule):
-    def __init__(
-        self,
-        # SupervisedReconstructionDataset args
-        input_path: PathLike,
-        target_path: PathLike,
-        data_shape: Tuple[int, ...] = (1, 500, 500),
-        stride: Tuple[int, ...] = None,
-        input_transform: _Transform = None,
-        target_transform: _Transform = None,
-        # DataLoader args
-        batch_size: int = 1,
-        num_workers: int = None,
-    ):
-        """Create a data module for the F3 reconstruction task, such as seismic
-        attribute regression. This  data module assumes that the input data and
-        the target data are stored in Zarr arrays. These arrays are volumetric
-        data, and thisdata module will generate patches from the input and
-        target data and provide them to the model during training.
-
-        Parameters
-        ----------
-        input_path : PathLike
-            Location of the input data Zarr array (e.g., seismic data)
-        target_path : PathLike
-            Location of the target data Zarr array (e.g., seismic attribute)
-        data_shape : Tuple[int, ...]
-            Shape of the patches to be extracted from the data arrays. Usually,
-            this is a 3D shape (channels, height, width), by default
-            (1, 500, 500)
-        stride : Tuple[int, ...], optional
-            The stride between consecutive patches. If `None`, the stide will
-            be the same as `data_shape`. By default None
-        input_transform : _Transform, optional
-            Transform to be applied to the input data, by default None
-        target_transforms : _Transform, optional
-            Transforms to be applied to the target data, by default None
-        batch_size : int, optional
-            The batch size to be used in the DataLoader, by default 1
-        num_workers : int, optional
-            The number of workers to be used in the DataLoader, by default None
-        """
-        super().__init__()
-
-        # SupervisedReconstructionDataset args
-        self.input_path = input_path
-        self.target_path = target_path
-        self.data_shape = data_shape
-        self.stride = stride
-        self.input_transform = input_transform
-        self.target_transform = target_transform
-
-        # DataLoader args
-        self.batch_size = batch_size
-        self.num_workers = (
-            num_workers if num_workers is not None else os.cpu_count()
-        )
-
-        # Private attributes
-        self._dataset = None
-
-    def _get_dataset(
-        self,
-        input_path,
-        target_path,
-        data_shape,
-        stride,
-        input_transform,
-        target_transform,
-    ):
-        data_reader = PatchedZarrReader(
-            path=input_path,
-            data_shape=data_shape,
-            stride=stride,
-        )
-        target_reader = PatchedZarrReader(
-            path=target_path,
-            data_shape=data_shape,
-            stride=stride,
-        )
-        return SupervisedReconstructionDataset(
-            readers=[data_reader, target_reader],
-            transforms=[input_transform, target_transform],
-        )
-
-    def setup(self, stage: str):
-        # TODO Here we should add balancing methods and to split the data
-
-        # For now, we are using the same dataset for train, test, and predict
-        if stage == "fit" or stage == "test" or stage == "predict":
-            self._dataset = self._get_dataset(
-                input_path=self.input_path,
-                target_path=self.target_path,
-                data_shape=self.data_shape,
-                stride=self.stride,
-                input_transform=self.input_transform,
-                target_transform=self.target_transform,
-            )
-
-        else:
-            raise ValueError(f"Stage {stage} is not valid")
-
-    def train_dataloader(self):
-        return DataLoader(
-            self._dataset, batch_size=self.batch_size, shuffle=True
-        )
-
-    def test_data_loader(self):
-        return DataLoader(
-            self._dataset, batch_size=self.batch_size, shuffle=False
-        )
-
-    def predict_dataloader(self):
-        return DataLoader(
-            self._dataset, batch_size=self.batch_size, shuffle=False
-        )