Move to ruff (issue #1014)

.github/workflows/ibllib_ci.yml

@@ -35,12 +35,12 @@ jobs:
     - name: Install deps
       run: |
         python -m pip install --upgrade pip
-        python -m pip install flake8 pytest flake8-docstrings
+        python -m pip install flake8 ruff flake8-docstrings
         pip install -r requirements.txt
         pip install -e .
-    - name: Flake8
+    - name: Flake8 & ruff
       run: |
-        python -m flake8
+        python -m ruff check .
         python -m flake8 --select D --ignore E ibllib/qc/camera.py ibllib/qc/task_metrics.py
     - name: Brainbox tests
       run: |

brainbox/metrics/single_units.py

@@ -105,11 +105,11 @@ def unit_stability(units_b, units=None, feat_names=['amps'], dist='norm', test='
     """
 
     # Get units.
-    if not (units is None):  # we're using a subset of all units
+    if units is not None:  # we're using a subset of all units
         unit_list = list(units_b[feat_names[0]].keys())
         # for each `feat` and unit in `unit_list`, remove unit from `units_b` if not in `units`
         for feat in feat_names:
-            [units_b[feat].pop(unit) for unit in unit_list if not (int(unit) in units)]
+            [units_b[feat].pop(unit) for unit in unit_list if int(unit) not in units]
     unit_list = list(units_b[feat_names[0]].keys())  # get new `unit_list` after removing units
 
     # Initialize `p_vals` and `variances`.

brainbox/plot.py

@@ -77,10 +77,10 @@ def feat_vars(units_b, units=None, feat_name='amps', dist='norm', test='ks', cma
     '''
 
     # Get units.
-    if not (units is None):  # we're using a subset of all units
+    if units is not None:  # we're using a subset of all units
         unit_list = list(units_b['depths'].keys())
         # For each unit in `unit_list`, remove unit from `units_b` if not in `units`.
-        [units_b['depths'].pop(unit) for unit in unit_list if not (int(unit) in units)]
+        [units_b['depths'].pop(unit) for unit in unit_list if int(unit) not in units]
     unit_list = list(units_b['depths'].keys())  # get new `unit_list` after removing unit
 
     # Calculate coefficients of variation for all units

ibllib/__init__.py

@@ -1,18 +1,14 @@
 """Library implementing the International Brain Laboratory data pipeline."""
 import logging
 import warnings
+import os
 
 __version__ = '3.4.1'
 warnings.filterwarnings('always', category=DeprecationWarning, module='ibllib')
 
 # if this becomes a full-blown library we should let the logging configuration to the discretion of the dev
 # who uses the library. However since it can also be provided as an app, the end-users should be provided
 # with a useful default logging in standard output without messing with the complex python logging system
-USE_LOGGING = True
-#%(asctime)s,%(msecs)d
-if USE_LOGGING:
+if os.environ.get('IBLLIB_USE_LOGGING', '1').casefold() in ('1', 'true', 'yes'):
     from iblutil.util import setup_logger
     setup_logger(name='ibllib', level=logging.INFO)
-else:
-    # deactivate all log calls for use as a library
-    logging.getLogger('ibllib').addHandler(logging.NullHandler())

ibllib/io/extractors/camera.py

@@ -683,13 +683,13 @@ def groom_pin_state(gpio, ttl, ts, tolerance=2., display=False, take='first', mi
                 _logger.warning('Some onsets but not offsets (or vice versa) were not assigned; '
                                 'this may be a sign of faulty wiring or clock drift')
                 # Find indices of GPIO upticks where only the downtick was marked for removal
-                orphaned_onsets, =  np.where(~to_remove.reshape(-1, 2)[:, 0] & orphaned)
+                orphaned_onsets, = np.where(~to_remove.reshape(-1, 2)[:, 0] & orphaned)
                 # The onsets_ array already has the other TTLs removed (same size as to_remove ==
                 # False) so subtract the number of removed elements from index.
                 for i, v in enumerate(orphaned_onsets):
                     orphaned_onsets[i] -= to_remove.reshape(-1, 2)[:v, 0].sum()
                 # Same for offsets...
-                orphaned_offsets, =  np.where(~to_remove.reshape(-1, 2)[:, 1] & orphaned)
+                orphaned_offsets, = np.where(~to_remove.reshape(-1, 2)[:, 1] & orphaned)
                 for i, v in enumerate(orphaned_offsets):
                     orphaned_offsets[i] -= to_remove.reshape(-1, 2)[:v, 1].sum()
                 # Remove orphaned ttl onsets and offsets