bump: version 1.0.1 → 1.1.0

CHANGELOG.md

@@ -1,3 +1,51 @@
+## v1.1.0 (2024-03-05)
+
+[Detailed release notes](https://github.com/cytomining/pycytominer/releases/tag/v1.1.0)
+
+### Fix
+
+- **build**: fix build versioning
+- simplify Spherize transform – epsilon to regularize instead of clip, add additional checks (see #320)
+
+### Refactor
+
+- **docs**: apply flake8-builtins checks
+- **dev**: apply pyflakes checks
+- **dev**: apply flake8-simplify checks
+
+### Test
+
+- add flake8-bandit ignores
+- add clarifying comments for cell_loc test
+- update cell_loc s3 paths and testing
+
+### Docs
+
+- update Readme with Citation section
+- **template**: PR template attribution to comment
+- **changelog**: add commitizen template
+- add description of ruff linting/formatting
+- reorganize style guide
+
+### CI
+
+- add versioned artifact build action
+- **integration-test**: add explicit artifact retention time
+- add pygrep-hooks and flake8-20202 checks
+
+### Style
+
+- swap out black for ruff-format
+- apply pyupgrade checks
+- **devcontainer**: add ruff extension
+- apply ruff native checks
+- apply pycodestyle checks
+- add flake8-comprehensions checks
+
+### Build
+
+- **poetry**: make dev dep group optional
+
 ## v1.0.1 (2023-11-07)
 
 [Detailed Release Notes](https://github.com/cytomining/pycytominer/releases/tag/v1.0.1)

CITATION.cff

@@ -85,7 +85,7 @@ authors:
     orcid: https://orcid.org/0000-0002-0503-9348
 title: "Reproducible image-based profiling with Pycytominer"
 # This version is updated using `cz bump` command
-version: "1.0.1"
+version: "1.1.0"
 license: BSD 3-Clause License
 repository-code: "https://github.com/cytomining/pycytominer"
 doi: 10.48550/arXiv.2311.13417

CONTRIBUTING.md

@@ -179,9 +179,9 @@ Creating a new release includes the following steps:
 1. Create a new branch from `main` for the release (e.g. `release-v1.0.0`)
 2. Review the [commit history](https://github.com/cytomining/pycytominer/compare) from the last release and check whether it includes commits that don't follow the [conventional commit standard](https://www.conventionalcommits.org/en/v1.0.0/#summary).
    If all changes follow conventional commits, skip to step 5.
-3. Run the command `cz bump --files-only` to update the version number in `CITATION.cff` and `pyproject.toml:tool.commitizen` and generate the draft changelog.
+3. Run the command `poetry run cz bump --files-only` to update the version number in `CITATION.cff` and `pyproject.toml:tool.commitizen` and generate the draft changelog.
 4. Review the changes to `CHANGELOG.md`. If necessary, add descriptions of missing changes and modify descriptions to match conventional commits standard.
-5. `git add` any manual changes and run `cz bump` to create the release commit.
+5. `git add` any manual changes and run `poetry run cz bump` to create the release commit.
    Push the changes to the release branch.
 6. Create a pull request for the release branch into `main`.
 7. Request a review from another maintainer.

dev_tools/commitizen/CHANGELOG.md.j2

@@ -4,7 +4,7 @@
 ## {{ entry.version }}{% if entry.date %} ({{ entry.date }}){% endif %}
 
 {# Add link to the detailed GitHub release notes #}
-[Detailed Release Notes](https://github.com/cytomining/pycytominer/releases/tag/{{ entry.version }})
+[Detailed release notes](https://github.com/cytomining/pycytominer/releases/tag/{{ entry.version }})
 
 {% for change_key, changes in entry.changes.items() %}
 

pyproject.toml

@@ -101,7 +101,7 @@ files = ["pycytominer/__about__.py"]
 
 [tool.commitizen]
 # This version is used for changelog tracking and is updated using `cz bump`
-version = "1.0.1"
+version = "1.1.0"
 name = "cz_conventional_commits"
 tag_format = "v$version"
 version_scheme = "pep440"

tests/test_cyto_utils/conftest.py

@@ -44,15 +44,15 @@ def fixture_metadata_input_file_s3() -> str:
     """
     Provide a metadata input file for cell_locations test data
     """
-    return "s3://cellpainting-gallery/test-cpg0016-jump/source_4/workspace/load_data_csv/2021_08_23_Batch12/BR00126114/test_BR00126114_load_data_with_illum.parquet"
+    return "s3://cellpainting-gallery/cpg0016-jump/source_4/workspace/load_data_csv/2021_08_23_Batch12/BR00126114/load_data_with_illum.parquet"
 
 
 @pytest.fixture(name="single_cell_input_file_s3")
 def fixture_single_cell_input_file_s3() -> str:
     """
     Provide a single cell input file for cell_locations test data
     """
-    return "s3://cellpainting-gallery/test-cpg0016-jump/source_4/workspace/backend/2021_08_23_Batch12/BR00126114/test_BR00126114.sqlite"
+    return "s3://cellpainting-gallery/cpg0016-jump/source_4/workspace/backend/2021_08_23_Batch12/BR00126114/BR00126114.sqlite"
 
 
 @pytest.fixture(name="metadata_input_dataframe")
@@ -113,27 +113,3 @@ def fixture_cell_loc_obj3(
         metadata_input=metadata_input_file_s3,
         single_cell_input=single_cell_input_file_s3,
     )
-
-
-@pytest.fixture(name="cell_loc1")
-def fixture_cell_loc1(cell_loc_obj1: CellLocation) -> pd.DataFrame:
-    """
-    Provide the output of running CellLocation.add_cell_location
-    """
-    return cell_loc_obj1.add_cell_location()
-
-
-@pytest.fixture(name="cell_loc2")
-def fixture_cell_loc2(cell_loc_obj2: CellLocation) -> pd.DataFrame:
-    """
-    Provide the output of running CellLocation.add_cell_location
-    """
-    return cell_loc_obj2.add_cell_location()
-
-
-@pytest.fixture(name="cell_loc3")
-def fixture_cell_loc3(cell_loc_obj3: CellLocation) -> pd.DataFrame:
-    """
-    Provide the output of running CellLocation.add_cell_location
-    """
-    return cell_loc_obj3.add_cell_location()

tests/test_cyto_utils/test_cell_locations.py

@@ -2,22 +2,50 @@
 
 import pandas as pd
 import pytest
-import sqlalchemy
-from typing import Type
+from typing import Type, List
+from pycytominer.cyto_utils.cell_locations import CellLocation
 from _pytest.fixtures import FixtureRequest
 
 
-@pytest.mark.parametrize("cell_loc", ["cell_loc1", "cell_loc2", "cell_loc3"])
+def get_metadata_input_dataframe(cell_loc: CellLocation) -> pd.DataFrame:
+    """
+    Gathers the metadata input dataframe given various conditions
+    from a CellLocation object.
+    """
+
+    return (
+        pd.read_parquet(
+            cell_loc.metadata_input,
+            # set storage options if we have an s3 path
+            storage_options={"anon": True}
+            if isinstance(cell_loc.metadata_input, str)
+            and cell_loc.metadata_input.startswith("s3://")
+            else None,
+        )
+        if isinstance(cell_loc.metadata_input, str)
+        else cell_loc.metadata_input
+    )
+
+
+@pytest.mark.parametrize(
+    "cell_loc_param",
+    [
+        "cell_loc_obj1",
+        "cell_loc_obj2",
+        "cell_loc_obj3",
+    ],
+)
 def test_output_shape_and_required_columns(
-    cell_loc: str,
-    metadata_input_dataframe: pd.DataFrame,
+    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
     """
 
-    cell_loc = request.getfixturevalue(cell_loc)
+    cls_cell_loc = request.getfixturevalue(cell_loc_param)
+    cell_loc = cls_cell_loc.add_cell_location()
+    metadata_input_dataframe = get_metadata_input_dataframe(cell_loc=cls_cell_loc)
 
     # check the shape of the data
     assert cell_loc.shape == (
@@ -31,17 +59,25 @@ def test_output_shape_and_required_columns(
     assert "Nuclei_Location_Center_Y" in cell_loc["CellCenters"][0][0]
 
 
-@pytest.mark.parametrize("cell_loc", ["cell_loc1", "cell_loc2", "cell_loc3"])
+@pytest.mark.parametrize(
+    "cell_loc_param",
+    [
+        "cell_loc_obj1",
+        "cell_loc_obj2",
+        "cell_loc_obj3",
+    ],
+)
 def test_output_value_correctness(
-    cell_loc: str,
-    metadata_input_dataframe: pd.DataFrame,
-    single_cell_input_file: str,
+    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
     """
-    cell_loc = request.getfixturevalue(cell_loc)
+
+    cls_cell_loc = request.getfixturevalue(cell_loc_param)
+    cell_loc = cls_cell_loc.add_cell_location()
+    metadata_input_dataframe = get_metadata_input_dataframe(cell_loc=cls_cell_loc)
 
     # if we restrict the columns of cell_loc to the ones in metadata_input_dataframe, we should get the same dataframe
     assert (
@@ -50,7 +86,8 @@ def test_output_value_correctness(
         .equals(metadata_input_dataframe.reset_index(drop=True))
     )
 
-    engine = sqlalchemy.create_engine(f"sqlite:///{single_cell_input_file}")
+    # gather an engine from the cell_loc class
+    _, engine = cls_cell_loc._get_single_cell_engine()
 
     nuclei_query = "SELECT ImageNumber, ObjectNumber, Nuclei_Location_Center_X, Nuclei_Location_Center_Y FROM Nuclei;"
 
@@ -59,7 +96,9 @@ def test_output_value_correctness(
     # get the values in the Nuclear_Location_Center_X and Nuclear_Location_Center_Y columns
     # for the rows in nuclei_df that have ImageNumber == 1
 
-    nuclei_df_row1 = nuclei_df[nuclei_df["ImageNumber"] == "1"]
+    # note: we cast to "int64" type to ensure all cell_loc_obj's are treated the same
+    # (some include ImageNumber's of type obj, others are int64)
+    nuclei_df_row1 = nuclei_df[nuclei_df["ImageNumber"].astype("int64") == 1]
 
     observed_x = [x["Nuclei_Location_Center_X"] for x in cell_loc.CellCenters[0]]
     observed_y = [x["Nuclei_Location_Center_Y"] for x in cell_loc.CellCenters[0]]