refactor pytest fixtures to conftest.py. add adocstrings.

tests/test_integration/conftest.py

@@ -0,0 +1,194 @@
+import json
+from pathlib import Path
+
+import pooch
+import pytest
+
+from brainglobe_workflows.cellfinder.cellfinder_main import (
+    CellfinderConfig,
+)
+
+
+def make_config_dict_fetch_from_GIN(cellfinder_cache_dir):
+    """Generate a config dictionary with the required parameters
+    for the workflow
+
+    The input data is fetched from GIN and downloaded to cellfinder_cache_dir.
+    The results are also saved in a timestamped output subdirectory under
+    cellfinder_cache_dir
+
+    Parameters
+    ----------
+    cellfinder_cache_dir : Path
+        Path to the directory where the downloaded input data will be unzipped,
+        and the output will be saved
+
+    Returns
+    -------
+    dict
+        dictionary with the required parameters for the workflow
+    """
+    return {
+        "install_path": cellfinder_cache_dir,
+        "data_url": "https://gin.g-node.org/BrainGlobe/test-data/raw/master/cellfinder/cellfinder-test-data.zip",
+        "data_hash": (
+            "b0ef53b1530e4fa3128fcc0a752d0751909eab129d701f384fc0ea5f138c5914"
+        ),
+        "extract_relative_dir": "cellfinder_test_data",  # relative path
+        "signal_parent_dir": str(
+            cellfinder_cache_dir / "cellfinder_test_data" / "signal"
+        ),
+        "background_parent_dir": str(
+            cellfinder_cache_dir / "cellfinder_test_data" / "background"
+        ),
+        "output_path_basename": cellfinder_cache_dir / "cellfinder_output_",
+        "detected_cells_filename": "detected_cells.xml",
+        "voxel_sizes": [5, 2, 2],  # microns
+        "start_plane": 0,
+        "end_plane": -1,
+        "trained_model": None,  # if None, it will use a default model
+        "model_weights": None,
+        "model": "resnet50_tv",
+        "batch_size": 32,
+        "n_free_cpus": 2,
+        "network_voxel_sizes": [5, 1, 1],
+        "soma_diameter": 16,
+        "ball_xy_size": 6,
+        "ball_z_size": 15,
+        "ball_overlap_fraction": 0.6,
+        "log_sigma_size": 0.2,
+        "n_sds_above_mean_thresh": 10,
+        "soma_spread_factor": 1.4,
+        "max_cluster_size": 100000,
+        "cube_width": 50,
+        "cube_height": 50,
+        "cube_depth": 20,
+        "network_depth": "50",
+    }
+
+
+def prep_json(obj):
+    """
+    Returns a JSON encodable version of the object.
+
+    It uses the JSON default encoder for all objects
+    except those of type Path.
+
+    Parameters
+    ----------
+    obj : _type_
+        _description_
+
+    Returns
+    -------
+    _type_
+        JSON serializable version of input object
+    """
+    if isinstance(obj, Path):
+        return str(obj)
+    else:
+        return json.JSONEncoder.default(obj)
+
+
+@pytest.fixture(autouse=True)
+def cellfinder_cache_dir(tmp_path):
+    """Create a .cellfinder_workflows directory
+    under a temporary pytest directory and return
+    its path.
+
+    The temporary directory is available via pytest's tmp_path
+    fixture. A new temporary directory is created every function call
+
+    Parameters
+    ----------
+    tmp_path : Path
+        path to pytest-generated temporary directory
+
+    Returns
+    -------
+    Path
+        path to the created cellfinder_workflows cache directory
+    """
+
+    return Path(tmp_path) / ".cellfinder_workflows"
+
+
+@pytest.fixture()
+def path_to_config_fetch_GIN(tmp_path, cellfinder_cache_dir):
+    """Create an input config that fetches data from GIN and
+    return its path
+
+    Parameters
+    ----------
+    tmp_path : Path
+        path to a fresh pytest-generated temporary directory. The
+        generated config is saved here.
+
+    cellfinder_cache_dir : Path
+        path to the cellfinder cache directory, where the paths
+        in the config should point to.
+
+    Returns
+    -------
+    input_config_path : Path
+        path to config file that fetches data from GIN
+    """
+    # create config dict
+    config_dict = make_config_dict_fetch_from_GIN(cellfinder_cache_dir)
+
+    # create a temp json file to dump config data
+    input_config_path = (
+        tmp_path / "input_config.json"
+    )  # save it in a temp dir separate from cellfinder_cache_dir
+
+    # save config data to json file
+    with open(input_config_path, "w") as js:
+        json.dump(config_dict, js, default=prep_json)
+
+    # check json file exists
+    assert Path(input_config_path).is_file()
+
+    return input_config_path
+
+
+@pytest.fixture()
+def path_to_config_fetch_local(path_to_config_fetch_GIN):
+    """Create an input config that points to local data and
+    return its path.
+
+    The process is analogous to creating a config that
+    fetches data from GIN, except that here we download the data
+    from GIN prior to running the workflow.
+
+    Parameters
+    ----------
+    path_to_config_fetch_GIN : Path
+        path to a config file that fetches data from GIN
+
+    Returns
+    -------
+    path_to_config_fetch_GIN : Path
+        path to a config file that fetches data from GIN
+    """
+    # create config that fetches data from GIN
+    # path_to_config_fetch_GIN
+
+    # read into config class
+    with open(path_to_config_fetch_GIN) as cfg:
+        config_dict = json.load(cfg)
+    config = CellfinderConfig(**config_dict)
+
+    # Download GIN data
+    pooch.retrieve(
+        url=config.data_url,
+        known_hash=config.data_hash,
+        path=config.install_path,  # path to download zip to
+        progressbar=True,
+        processor=pooch.Unzip(
+            extract_dir=config.extract_relative_dir
+            # path to unzipped dir, *relative*  to 'path'
+        ),
+    )
+
+    # return path to config json
+    return path_to_config_fetch_GIN