Merge pull request #4 from alexisthual/feature/toggle_cache

Allow disabling cache

README.md

@@ -9,7 +9,8 @@
 - large surface fMRI datasets projected on surface meshes
 - alignments computed between brains, such as those computed with [fugw](https://alexisthual.github.io/fugw/index.html)
 
-You can try our [online demo](https://brain-cockpit.athual.fr/)!
+🚀 You can try our [online demo](https://brain-cockpit.tc.humanbrainproject.eu/) displaying the [Individual Brain Charting dataset](https://individual-brain-charting.github.io/docs/), a vast collection of fMRI protocols collected in a small number of human participants.
+This project was deployed with [EBRAINS](https://www.ebrains.eu/).
 
 ![Screenshot features dataset view](https://raw.githubusercontent.com/alexisthual/brain-cockpit/main/doc/images/main_screenshot.png)
 

api/src/brain_cockpit/endpoints/alignments_explorer.py

@@ -1,23 +1,20 @@
+"""Util functions to create Alignments Explorer endpoints."""
+
 import os
 import pickle
-
 from pathlib import Path
 
 import nibabel as nib
 import numpy as np
 import pandas as pd
+from flask import jsonify, request, send_from_directory
 
 from brain_cockpit.scripts.gifti_to_gltf import create_dataset_glft_files
 from brain_cockpit.utils import console, load_dataset_description
-from flask import jsonify, request, send_from_directory
 
 
 def create_endpoints_one_alignment_dataset(bc, id, dataset):
-    """
-    For a given alignment dataset, generate endpoints
-    serving dataset meshes and alignment transforms.
-    """
-
+    """Create all API endpoints for exploring a given Alignments dataset."""
     df, dataset_path = load_dataset_description(
         config_path=bc.config_path, dataset_path=dataset["path"]
     )
@@ -126,8 +123,7 @@ def align_single_voxel():
 
 
 def create_all_endpoints(bc):
-    """Create endpoints for all available alignments datasets."""
-
+    """Create endpoints for all available Alignments datasets."""
     if "alignments" in bc.config and "datasets" in bc.config["alignments"]:
         # Iterate through each alignment dataset
         for dataset_id, dataset in bc.config["alignments"]["datasets"].items():

api/src/brain_cockpit/endpoints/features_explorer.py

@@ -1,22 +1,24 @@
+"""Util functions to create Features Explorer endpoints."""
+
 import json
 import os
-
 from pathlib import Path
 
 import nibabel as nib
 import numpy as np
 import pandas as pd
+from flask import jsonify, request, send_from_directory
 
 from brain_cockpit import utils
 from brain_cockpit.scripts.gifti_to_gltf import create_dataset_glft_files
 from brain_cockpit.utils import console, load_dataset_description
-from flask import jsonify, request, send_from_directory
 
 # UTIL FUNCTIONS
 # These functions are useful for loading data
 
 
 def hemi_to_side(hemi):
+    """Transform hemisphere label from nilearn to HCP."""
     if hemi == "left":
         return "lh"
     elif hemi == "right":
@@ -25,6 +27,7 @@ def hemi_to_side(hemi):
 
 
 def side_to_hemi(hemi):
+    """Transform hemisphere label from HCP to nilearn."""
     if hemi == "lh":
         return "left"
     elif hemi == "rh":
@@ -33,6 +36,7 @@ def side_to_hemi(hemi):
 
 
 def multiindex_to_nested_dict(df):
+    """Transform DataFrame with multiple indices to python dict."""
     if isinstance(df.index, pd.core.indexes.multi.MultiIndex):
         return dict(
             (k, multiindex_to_nested_dict(df.loc[k]))
@@ -46,7 +50,8 @@ def multiindex_to_nested_dict(df):
 
 
 def parse_metadata(df):
-    """
+    """Parse metadata Dataframe.
+
     Parameters
     ----------
     df: pandas DataFrame
@@ -84,11 +89,12 @@ def parse_metadata(df):
 
 
 def create_endpoints_one_features_dataset(bc, id, dataset):
-    memory = utils.get_memory(bc)
+    """Create all API endpoints for exploring a given Features dataset."""
 
-    @memory.cache
+    @utils.bc_cache(bc)
     def load_data(df, config_path=None, dataset_path=None):
-        """
+        """Load data used in endpoints.
+
         Parameters
         ----------
         dataset_path: str
@@ -507,8 +513,7 @@ def get_contrast_mean():
 
 
 def create_all_endpoints(bc):
-    """Create endpoints for all available surface datasets."""
-
+    """Create endpoints for all available Features datasets."""
     if "features" in bc.config and "datasets" in bc.config["features"]:
         # Iterate through each surface dataset
         for dataset_id, dataset in bc.config["features"]["datasets"].items():

api/src/brain_cockpit/utils.py

@@ -1,10 +1,11 @@
-import os
+"""Util functions used throughout brain-cockpit."""
 
+import functools
+import os
 from pathlib import Path
 
 import pandas as pd
 import yaml
-
 from joblib import Memory
 from rich.console import Console
 from rich.progress import (
@@ -24,6 +25,7 @@
 
 # `rich` progress bar used throughout the codebase
 def get_progress(**kwargs):
+    """Return rich progress bar."""
     return Progress(
         SpinnerColumn(),
         TaskProgressColumn(),
@@ -38,10 +40,35 @@ def get_progress(**kwargs):
 
 
 def get_memory(bc):
+    """Return joblib memory."""
     return Memory(bc.config["cache_folder"], verbose=0)
 
 
+def bc_cache(bc):
+    """Cache functions with brain-cockpit cache."""
+
+    def _inner_decorator(func):
+        @functools.wraps(func)
+        def wrapped(*args, **kwargs):
+            # Use joblib cache if and only if cache_folder is defined
+            if (
+                bc.config["cache_folder"] is not None
+                and bc.config["cache_folder"] != ""
+            ):
+                console.log(f"Using cache {bc.config['cache_folder']}")
+                mem = get_memory(bc)
+                return mem.cache(func)(*args, **kwargs)
+            else:
+                console.log("Not using cache for dataset")
+                return func(*args, **kwargs)
+
+        return wrapped
+
+    return _inner_decorator
+
+
 def load_config(config_path=None, verbose=False):
+    """Load brain-cockpit yaml config from path."""
     config = None
 
     if config_path is not None and os.path.exists(config_path):
@@ -60,8 +87,7 @@ def load_config(config_path=None, verbose=False):
 
 
 def load_dataset_description(config_path=None, dataset_path=None):
-    """
-    Load dataset CSV file.
+    """Load dataset CSV file.
 
     Returns
     -------