linting issues

Author: joocer

opteryx/__init__.py

@@ -183,7 +183,7 @@ def query_to_arrow(
     visibility_filters: Optional[Dict[str, Any]] = None,
     limit: int = None,
     **kwargs,
- ) -> "pyarrow.Table":
+) -> "pyarrow.Table":
     """
     Helper function to execute a query and return a pyarrow Table.
 

opteryx/__main__.py

@@ -205,6 +205,7 @@ def main():
 
 if __name__ == "__main__":
     import readline  # pragma: no cover
+
     try:
         main()
     except Exception as e:

opteryx/__version__.py

@@ -1,9 +1,9 @@
 # THIS FILE IS AUTOMATICALLY UPDATED DURING THE BUILD PROCESS
 # DO NOT EDIT THIS FILE DIRECTLY
 
-__build__ = 1688
+__build__ = 1690
 __author__ = "@joocer"
-__version__ = "0.26.0-beta.1688"
+__version__ = "0.26.0-beta.1690"
 
 # Store the version here so:
 # 1) we don't load dependencies by storing it in __init__.py

opteryx/compiled/list_ops/list_in_string.pyx

@@ -67,7 +67,7 @@ init_searcher()
 cdef inline int fast_memcmp_short(const char *a, const char *b, size_t n):
     cdef uint64_t aval = 0, bval = 0
     cdef uint64_t mask
-    
+
     if n == 0:
         return 0
     elif n <= 8:

opteryx/config.py

@@ -28,6 +28,7 @@ def memory_allocation_calculation(allocation: Union[float, int]) -> int:
     Returns:
         int: Memory size in bytes to be allocated.
     """
+
     # Import psutil lazily to avoid paying the import cost at module import time.
     # Use a small helper so tests or callers that need the value will trigger the
     # import only when this function is called.

opteryx/connectors/__init__.py

@@ -87,7 +87,6 @@ def get_dataset_schema(self, dataset):
 
 import os
 
-
 # Lazy imports - connectors are only loaded when actually needed
 # This significantly improves module import time from ~500ms to ~130ms
 
@@ -194,9 +193,10 @@ def register_store(prefix, connector, *, remove_prefix: bool = False, **kwargs):
 def register_df(name, frame):
     """register a orso, pandas or Polars dataframe"""
     # Lazy import ArrowConnector
-    from opteryx.connectors.arrow_connector import ArrowConnector
     import pyarrow
 
+    from opteryx.connectors.arrow_connector import ArrowConnector
+
     # polars (maybe others) - the polars to arrow API is a mess
     if hasattr(frame, "_df"):  # pragma: no cover
         frame = frame._df

opteryx/connectors/virtual_data.py

@@ -12,12 +12,12 @@
 """
 
 import datetime
+import importlib
 import typing
+from typing import Tuple
 
 from orso.schema import RelationSchema
 
-import importlib
-from typing import Tuple
 from opteryx.connectors.base.base_connector import BaseConnector
 from opteryx.connectors.base.base_connector import DatasetReader
 from opteryx.connectors.capabilities import Partitionable

opteryx/managers/cache/__init__.py

@@ -51,4 +51,11 @@ def __getattr__(name: str):
     raise AttributeError(f"module {__name__!r} has no attribute {name!r}")
 
 
-__all__ = ("CacheManager", "get_cache_manager", "MemcachedCache", "RedisCache", "ValkeyCache", "NullCache")
+__all__ = (
+    "CacheManager",
+    "get_cache_manager",
+    "MemcachedCache",
+    "RedisCache",
+    "ValkeyCache",
+    "NullCache",
+)

opteryx/managers/expression/ops.py

@@ -228,7 +228,11 @@ def _inner_filter_operations(arr, operator, value):
         matches = compute.match_like(arr, value).to_numpy(False).astype(dtype=numpy.bool_)
         return numpy.invert(matches)
     if operator == "ILike":
-        return compute.match_like(arr, value, ignore_case=True).to_numpy(False).astype(dtype=numpy.bool_)
+        return (
+            compute.match_like(arr, value, ignore_case=True)
+            .to_numpy(False)
+            .astype(dtype=numpy.bool_)
+        )
     if operator == "NotILike":
         matches = compute.match_like(arr, value, ignore_case=True)
         return numpy.invert(matches)

pyproject.toml

@@ -1,6 +1,6 @@
 [project]
 name = "opteryx"
-version = "0.26.0-beta.1688"
+version = "0.26.0-beta.1690"
 description = "Query your data, where it lives"
 requires-python = '>=3.11'
 readme = {file = "README.md", content-type = "text/markdown"}