Add scripts to link NeuroVault collections to PMIDs, download images, and create NiMARE dset

gen_nimare_dset.py

@@ -0,0 +1,105 @@
+import argparse
+import os.path as op
+
+import pandas as pd
+from nimare.dataset import Dataset
+from nimare.io import DEFAULT_MAP_TYPE_CONVERSION
+from nimare.transforms import ImageTransformer
+
+
+def _get_parser():
+    parser = argparse.ArgumentParser(description="Generate NiMARE dataset from NeuroVault data")
+    parser.add_argument(
+        "--project_dir",
+        dest="project_dir",
+        required=True,
+        help="Path to project directory",
+    )
+    return parser
+
+
+def convert_to_nimare_dataset(images_df, text_df, img_dir, suffix=""):
+    suffix = f"-{suffix}" if suffix else ""
+
+    images_df["pmid"] = images_df["pmid"].astype(int).astype(str)
+
+    dataset_dict = {}
+    for _, image in images_df.iterrows():
+        id_ = image["pmid"]
+        image_id = image["image_id"]
+        collection_id = image["collection_id"]
+        map_type = f"{image['map_type']} map"
+        new_contrast_name = f"{collection_id}-{image_id}" + suffix
+
+        if id_ not in dataset_dict:
+            dataset_dict[id_] = {}
+
+        if "contrasts" not in dataset_dict[id_]:
+            dataset_dict[id_]["contrasts"] = {}
+
+        text_df_row = text_df[text_df["pmid"] == int(id_)]
+        if text_df_row.shape[0] == 0:
+            title, keywords, abstract, body = None, None, None, None
+        else:
+            title = text_df_row["title"].values[0]
+            keywords = text_df_row["keywords"].values[0]
+            abstract = text_df_row["abstract"].values[0]
+            body = text_df_row["body"].values[0]
+
+        dataset_dict[id_]["contrasts"][new_contrast_name] = {
+            "metadata": {
+                "sample_sizes": [image["number_of_subjects"]],
+                "pmid": image["pmid"],
+                "pmcid": image["pmcid"],
+                "collection_id": collection_id,
+                "image_id": image_id,
+                "map_type": image["map_type"],
+                "cognitive_paradigm_cogatlas_id": image["cognitive_paradigm_cogatlas_id"],
+                "cognitive_contrast_cogatlas_id": image["cognitive_contrast_cogatlas_id"],
+                "contrast_definition": image["contrast_definition"],
+                "cognitive_paradigm_cogatlas_name": image["cognitive_paradigm_cogatlas_name"],
+                "cognitive_contrast_cogatlas_name": image["cognitive_contrast_cogatlas_name"],
+                "image_name": image["image_name"],
+                "image_file": image["image_file"],
+            },
+            "text": {
+                "title": title,
+                "keywords": keywords,
+                "abstract": abstract,
+                "body": body,
+            },
+            "images": {
+                DEFAULT_MAP_TYPE_CONVERSION[map_type]: op.join(img_dir, image["image_path"])
+            },
+        }
+
+    return Dataset(dataset_dict)
+
+
+def main(project_dir):
+    data_dir = op.join(project_dir, "data")
+    image_dir = op.join(data_dir, "neurovault", "images")
+
+    nv_collections_images_df = pd.read_csv(op.join(data_dir, "nv_all_collections_images.csv"))
+    nv_text_df = pd.read_csv(op.join(data_dir, "pmid_text.csv"))
+    dset_nv_fn = op.join(data_dir, "neurovault_all_dataset.pkl")
+
+    print(f"Creating full dataset {nv_collections_images_df.shape[0]}", flush=True)
+    dset_nv = convert_to_nimare_dataset(
+        nv_collections_images_df,
+        nv_text_df,
+        image_dir,
+    )
+    dset_nv = ImageTransformer("z").transform(dset_nv)
+    dset_nv = ImageTransformer("t").transform(dset_nv)
+    dset_nv.save(dset_nv_fn)
+
+
+def _main(argv=None):
+    option = _get_parser().parse_args(argv)
+    kwargs = vars(option)
+    main(**kwargs)
+
+
+if __name__ == "__main__":
+    _main()