Merge pull request #464 from d33bs/bump-minimum-python

build(python): bump minimum Python version to 3.9

.github/workflows/integration-test.yml

@@ -87,7 +87,7 @@ jobs:
         os:
           - macos-13
           - ubuntu-22.04
-        python-version: ["3.8", "3.9", "3.10", "3.11"]
+        python-version: ["3.9", "3.10", "3.11"]
     runs-on: ${{ matrix.os }}
     env:
       OS: ${{ matrix.os }}

pycytominer/aggregate.py

@@ -2,7 +2,7 @@
 Aggregate profiles based on given grouping variables.
 """
 
-from typing import Any, Dict, List, Optional, Union
+from typing import Any, Optional, Union
 
 import numpy as np
 import pandas as pd
@@ -16,15 +16,15 @@
 
 def aggregate(
     population_df: pd.DataFrame,
-    strata: List[str] = ["Metadata_Plate", "Metadata_Well"],
-    features: Union[List[str], str] = "infer",
+    strata: list[str] = ["Metadata_Plate", "Metadata_Well"],
+    features: Union[list[str], str] = "infer",
     operation: str = "median",
     output_file: Optional[str] = None,
     output_type: Optional[str] = "csv",
     compute_object_count: bool = False,
     object_feature: str = "Metadata_ObjectNumber",
     subset_data_df: Optional[pd.DataFrame] = None,
-    compression_options: Optional[Union[str, Dict[str, Any]]] = None,
+    compression_options: Optional[Union[str, dict[str, Any]]] = None,
     float_format: Optional[str] = None,
 ) -> Optional[pd.DataFrame]:
     """Combine population dataframe variables by strata groups using given operation.

pycytominer/cyto_utils/cells.py

@@ -1,4 +1,4 @@
-from typing import Dict, Union, Optional
+from typing import Union, Optional
 
 import numpy as np
 import pandas as pd
@@ -40,7 +40,7 @@ class SingleCells:
     output_file : str, default None
         If specified, the location to write the file.
     compartments : list of str, default ["cells", "cytoplasm", "nuclei"]
-        List of compartments to process.
+        list of compartments to process.
     compartment_linking_cols : dict, default noted below
         Dictionary identifying how to merge columns across tables.
     merge_cols : list of str, default ["TableNumber", "ImageNumber"]
@@ -50,9 +50,9 @@ class SingleCells:
     add_image_features: bool, default False
         Whether to add image features to the profiles.
     image_feature_categories : list of str, optional
-        List of categories of features from the image table to add to the profiles.
+        list of categories of features from the image table to add to the profiles.
     features: str or list of str, default "infer"
-        List of features that should be loaded or aggregated.
+        list of features that should be loaded or aggregated.
     load_image_data : bool, default True
         Whether or not the image data should be loaded into memory.
     image_table_name : str, default "image"
@@ -64,7 +64,7 @@ class SingleCells:
     subsampling_random_state : str or int, default None
         The random state to init subsample.
     fields_of_view : list of int, str, default "all"
-        List of fields of view to aggregate.
+        list of fields of view to aggregate.
     fields_of_view_feature : str, default "Metadata_Site"
         Name of the fields of view feature.
     object_feature : str, default "Metadata_ObjectNumber"
@@ -683,7 +683,7 @@ def merge_single_cells(
         compression_options: Optional[str] = None,
         float_format: Optional[str] = None,
         single_cell_normalize: bool = False,
-        normalize_args: Optional[Dict] = None,
+        normalize_args: Optional[dict] = None,
         platemap: Optional[Union[str, pd.DataFrame]] = None,
         **kwargs,
     ):
@@ -907,7 +907,7 @@ def _sqlite_strata_conditions(df, dtypes, n=1):
     df : pandas.core.frame.DataFrame
         A dataframe where columns are merge_cols and rows represent
         unique aggregation strata of the compartment table
-    dtypes : Dict[str, str]
+    dtypes : dict[str, str]
         Dictionary to look up SQLite datatype based on column name
     n : int
         Number of rows of the input df to combine in each output
@@ -918,7 +918,7 @@ def _sqlite_strata_conditions(df, dtypes, n=1):
 
     Returns
     -------
-    grouped_conditions : List[str]
+    grouped_conditions : list[str]
         A list of strings, each string being a valid SQLite conditional
 
     Examples

pycytominer/cyto_utils/features.py

@@ -4,7 +4,7 @@
 
 import os
 import pandas as pd
-from typing import Union, List
+from typing import Union
 
 blocklist_file = os.path.join(
     os.path.dirname(__file__), "..", "data", "blocklist_features.txt"
@@ -194,8 +194,8 @@ def drop_outlier_features(
 
 
 def convert_compartment_format_to_list(
-    compartments: Union[List[str], str],
-) -> List[str]:
+    compartments: Union[list[str], str],
+) -> list[str]:
     """Converts compartment to a list.
 
     Parameters

pycytominer/cyto_utils/output.py

@@ -2,7 +2,7 @@
 Utility function to compress output data
 """
 
-from typing import Dict, Union, Optional, Any
+from typing import Union, Optional, Any
 
 import pandas as pd
 
@@ -15,7 +15,7 @@ def output(
     output_type: Optional[str] = "csv",
     sep: str = ",",
     float_format: Optional[str] = None,
-    compression_options: Optional[Union[str, Dict[str, Any]]] = {
+    compression_options: Optional[Union[str, dict[str, Any]]] = {
         "method": "gzip",
         "mtime": 1,
     },
@@ -105,7 +105,7 @@ def output(
     return output_filename
 
 
-def set_compression_method(compression: Optional[Union[str, Dict]]) -> Dict[str, Any]:
+def set_compression_method(compression: Optional[Union[str, dict]]) -> dict[str, Any]:
     """Set the compression options
 
     Parameters

pyproject.toml

@@ -46,7 +46,7 @@ repository = "https://github.com/cytomining/pycytominer"
 include = [ { path = "poetry.lock", format = "sdist" } ]
 
 [tool.poetry.dependencies]
-python = ">=3.8,<3.12"
+python = ">=3.9,<3.12"
 numpy = ">=1.16.5"
 scipy = ">=1.5"
 pandas = ">=1.2.0"
@@ -117,7 +117,7 @@ files = [ "pycytominer/__about__.py" ]
 root = "."
 
 [tool.ruff]
-target-version = "py38"
+target-version = "py39"
 line-length = 88
 fix = true
 

tests/test_cyto_utils/test_cell_locations.py

@@ -1,7 +1,5 @@
 """This tests the output from CellLocation class"""
 
-from typing import List, Type
-
 import pandas as pd
 import pytest
 from _pytest.fixtures import FixtureRequest
@@ -38,8 +36,8 @@ def get_metadata_input_dataframe(cell_loc: CellLocation) -> pd.DataFrame:
     ],
 )
 def test_output_shape_and_required_columns(
-    cell_loc_param: List[str],
-    request: Type[FixtureRequest],
+    cell_loc_param: list[str],
+    request: type[FixtureRequest],
 ):
     """
     This tests the shape of the output from CellLocation class and verifies that the required columns are present
@@ -71,8 +69,8 @@ def test_output_shape_and_required_columns(
     ],
 )
 def test_output_value_correctness(
-    cell_loc_param: List[str],
-    request: Type[FixtureRequest],
+    cell_loc_param: list[str],
+    request: type[FixtureRequest],
 ):
     """
     This tests the correctness of the values in the output from CellLocation class by comparing the values in the output to the values in the input