Removing large files

Removing unneccessary files

Fixing pre-commit

[pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

Fixing PR issues

:technologist: pre-commit autoupdate (TissueImageAnalytics#910)

* 🧑‍💻 pre-commit autoupdate

updates:
- [github.com/executablebooks/mdformat: 0.7.21 → 0.7.22](hukkin/mdformat@0.7.21...0.7.22)
- [github.com/astral-sh/ruff-pre-commit: v0.8.6 → v0.9.4](astral-sh/ruff-pre-commit@v0.8.6...v0.9.4)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* 📌 Update `ruff` dependency

* 🔥 TIAToolbox does not support Python > 3.12 yet
- There is no need for this check as this will be tested while upgrading to Python 3.13

* ♻️ Refactor `typing` to `type_hints`.

* 🐛 Fix `mypy` workflow

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Shan E Ahmed Raza <[email protected]>

📝 Update Documentation Structure (TissueImageAnalytics#909)

- Use `Python 3.12` for docs build
- Update `copyright` year to `2025`
- Landing page now shows text from README
- Update documentation structure
- Update `readthedocs` Build
- Remove `usage.rst`
- Rename Jupyter Notebooks to Usage Examples
- Show README for Usage Examples instead of TOC
- Reduce TOC depth for basic functionalities and pipelines
- Improve `README` quality.

🐛 Fix in `test_arch_mapde` and `test_arch_sccnn` (TissueImageAnalytics#911)

- If cuda is available model should be moved to cuda otherwise tests will fail as test data is moved to cuda.

[skip ci] 📝 Improve Documentation (TissueImageAnalytics#913)

- Update CONTRIBUTING.rst
- Bug fix in conf.py to fix notebook links
- Update `examples/README.md`
- Update `docs/installation.rst`
- Update `docs/visualization.rst`

---------

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

:bug: Fix `MapDe` `dist_filter` Shape  (TissueImageAnalytics#914)

- Fix `dist_filter` in `MapDe` model for multi-class output.

Explanation:
Previously, if we set `num_class` to more than 1, the model would still output 1 channel. This was because the `dist_filter` always had size of 1 in its first dimension, however the first dimension determines the number of output channels in the tensor produced by `torch.functional.F.conv2d`.
This PR changes this by repeating the filters the match the number of output classes.

:technologist: pre-commit autoupdate (TissueImageAnalytics#916)

* 🧑‍💻 pre-commit autoupdate

updates:
- [github.com/astral-sh/ruff-pre-commit: v0.9.4 → v0.9.9](astral-sh/ruff-pre-commit@v0.9.4...v0.9.9)

* [pre-commit.ci] auto fixes from pre-commit.com hooks

for more information, see https://pre-commit.ci

* 🔨 Update `ruff` version

* 🔨 Update noqa for Unused static method argument

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Shan E Ahmed Raza <[email protected]>

Add FsspecJsonWSIReader class. (TissueImageAnalytics#897)

The `FsspecJsonWSIReader` reads fsspec json file which represents SVS or TIFF whole slide image. The images are accessible by HTTP range requests, eg:

`https://api.gdc.cancer.gov/data/73c69d24-6f9e-44e2-bfe5-a608d4cf5c27`

The whole image can be downloaded like:

`curl -C - -o TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B394-F3E0E2FF7DDA.svs   https://api.gdc.cancer.gov/data/73c69d24-6f9e-44e2-bfe5-a608d4cf5c27`

The `FsspecJsonWSIReader` class has a `_zarr_store` field which is created by reading json file using `fsspec`:

```
mapper = fsspec.get_mapper(
            "reference://", fo=str(input_img), target_protocol="file"
        )
self._zarr_array = zarr.open(mapper, mode="r")
self._zarr_store = self._zarr_array.store

self._zarr_lru_cache = zarr.LRUStoreCache(self._zarr_store, max_size=cache_size)
self._zarr_group = zarr.open(self._zarr_lru_cache)
```

This is equivalent to `TIFFWSIReader` code:

```
self._zarr_store = tifffile.imread(
            self.input_path,
            series=self.series_n,
            aszarr=True,
        )
        self._zarr_lru_cache = zarr.LRUStoreCache(self._zarr_store, max_size=cache_size)
        self._zarr_group = zarr.open(self._zarr_lru_cache)
```

Both FsspecJsonWSIReader and TIFFWSIReader forward calls to `read_bounds` and  `read_rect` methods of the`TIFFWSIReaderDelegate` delegate instance.

The method `_info` of the`TIFFWSIReaderDelegate` reads SVS metadata which is stored in the root group metadata like:
```
{
  ".zattrs": {
    "multiscales": [
      {
        "metadata": {
          "objective_power": 40,
          "vendor": "Aperio",
          "mpp": [0.2525, 0.2525]
        }
      }
    ]
  }
}
```

To test, execute from the root dir:
```
pip install -r requirements/requirements_dev.txt
mkdir -p samples/slides
mkdir -p samples/fsspec
cd samples/slides
curl -C - -o TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B394-F3E0E2FF7DDA.svs   https://api.gdc.cancer.gov/data/73c69d24-6f9e-44e2-bfe5-a608d4cf5c27
cd ../../
cp tiatoolbox/utils/tiff_to_fsspec.py .
python tiff_to_fsspec.py "samples/slides/TCGA-22-1017-01Z-00-DX1.9562FE79-A261-42D3-B394-F3E0E2FF7DDA.svs"  "samples/fsspec/73c69d24-6f9e-44e2-bfe5-a608d4cf5c27_fsspec.json" "https://api.gdc.cancer.gov/data/73c69d24-6f9e-44e2-bfe5-a608d4cf5c27"
```

Create `tileserver.py` inside of the project root:

```
from flask_cors import CORS

from tiatoolbox.visualization import TileServer
from tiatoolbox.wsicore.wsireader import FsspecJsonWSIReader

wsi = FsspecJsonWSIReader.open(
    "./samples/fsspec/73c69d24-6f9e-44e2-bfe5-a608d4cf5c27_fsspec.json"
)

tile_server = TileServer(
    title="Tiatoolbox TileServer",
    layers={"layer": wsi},
)
CORS(tile_server, send_wildcard=True)

tile_server.run(host="127.0.0.1", port=5000)
```

Open `http://127.0.0.1:5000/` and verify that it works.
---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Shan E Ahmed Raza <[email protected]>

✨ Support for Additional Foundation Models (TissueImageAnalytics#906)

- Add support for additional foundation models as feature extractors using the TimmBackbone.
- Added models include: UNI2, Virchow, Virchow2, kaiko and H-optimus-1.
- Add more information to docstrings.
- Allow foundation models with additional parameters.

---------

Co-authored-by: pre-commit-ci[bot] <66853113+pre-commit-ci[bot]@users.noreply.github.com>
Co-authored-by: Shan E Ahmed Raza <[email protected]>

Fixing PR issues