diff --git a/.devcontainer/devcontainer.json b/.devcontainer/devcontainer.json index 6fc1e112..008288b6 100644 --- a/.devcontainer/devcontainer.json +++ b/.devcontainer/devcontainer.json @@ -25,7 +25,11 @@ }, // Add the IDs of extensions you want installed when the container is created. - "extensions": ["ms-python.python", "ms-python.vscode-pylance"] + "extensions": [ + "ms-python.python", + "ms-python.vscode-pylance", + "charliermarsh.ruff" + ] } } } diff --git a/.pre-commit-config.yaml b/.pre-commit-config.yaml index 210a5c30..ee168710 100644 --- a/.pre-commit-config.yaml +++ b/.pre-commit-config.yaml @@ -1,9 +1,10 @@ repos: - - repo: https://github.com/psf/black - rev: 23.1.0 + - repo: https://github.com/astral-sh/ruff-pre-commit + rev: "v0.1.6" hooks: - - id: black - language_version: python3.9 + - id: ruff + exclude: tutorials/nbconverted/ + - id: ruff-format exclude: tutorials/nbconverted/ - repo: https://github.com/pre-commit/mirrors-prettier rev: "v2.7.1" diff --git a/CONTRIBUTING.md b/CONTRIBUTING.md index e51262e8..d4af6316 100644 --- a/CONTRIBUTING.md +++ b/CONTRIBUTING.md @@ -23,8 +23,10 @@ If you are stuck, please feel free to ask any questions or ask for help. - [Dev environments](#dev-environments) - [Releases](#releases) -[Style guides](#style-guides) +[Code Quality](#code-quality) +- [Formatting](#formatting) +- [Linting](#linting) - [Git commit messages](#git-commit-messages) - [Python style guide](#python-style-guide) - [Documentation style guide](#documentation-style-guide) @@ -189,23 +191,33 @@ Creating a new release includes the following steps: 11. The release will be automatically published to [PyPI](https://pypi.org/project/pycytominer/) via Github Actions. 12. Manually create the release at [conda-forge](https://anaconda.org/conda-forge/pycytominer). -## Style guides +## Code Quality -Please follow all style guides to the best of your abilities. +Please follow the below quality guides to the best of your abilities. +If you have configured your [dev environment](#dev-environments) as described above, the formatting and linting rules will also be enforced automatically using the installed [pre-commit](https://pre-commit.com/) hooks. + +### Formatting + +We use [ruff](https://docs.astral.sh/ruff/) for formatting Python code, and [prettier](https://prettier.io/) for formatting markdown, json and yaml files. +Ruff includes a python code formatter similar to Black. +We include `ruff` in the poetry dev dependencies so it can be run manually using `ruff format` +Prettier (which is not python-based) is not included in the poetry dev dependencies, but can be installed and run manually. +Alternately, both `ruff format` and `prettier` will be run automatically at commit time with the pre-commit hooks installed. + +### Linting + +For python code linting, we also use [ruff](https://docs.astral.sh/ruff/), which can perform same linting checks as Flake8. +You can use the command `ruff check` to check for linting errors. +The list of linting rules and exceptions are defined in the `pyproject.toml` file under the `[tool.ruff.lint]` section. +We also include some commented-out rules in that section that we are working towards enabling in the future. +All linting checks will also be run automatically at commit time with the pre-commit hooks as described above. ### Git commit messages Pycytominer uses [Conventional Commits](https://www.conventionalcommits.org/en/v1.0.0/) standard for commit messages to aid in automatic changelog generation. We prepare commit messages that follow this standard using [commitizen](https://commitizen-tools.github.io/commitizen/), which comes with the poetry dev dependencies. -### Python style guide - -For python code style, we use [black](https://github.com/psf/black). -Please use black before committing any code. -We will not accept code contributions that do not use black. -Configuring your [dev environment](#dev-environments) as described above will ensure your code is formatted correctly automatically (using a tool called [pre-commit](https://pre-commit.com/)). - ### Documentation style guide We use the [numpy documentation style guide](https://numpydoc.readthedocs.io/en/latest/format.html). -We also use [prettier](https://prettier.io/) for automatic formatting of markdown, json and yaml files. +When writing markdown documentation, please also ensure that each sentence is on a new line. diff --git a/README.md b/README.md index 0f646c47..6b5d542e 100644 --- a/README.md +++ b/README.md @@ -4,7 +4,7 @@ [![Build Status](https://github.com/cytomining/pycytominer/actions/workflows/integration-test.yml/badge.svg?branch=main)](https://github.com/cytomining/pycytominer/actions/workflows/integration-test.yml?query=branch%3Amain) [![Coverage Status](https://codecov.io/gh/cytomining/pycytominer/branch/main/graph/badge.svg)](https://codecov.io/github/cytomining/pycytominer?branch=main) -[![Code style: black](https://img.shields.io/badge/code%20style-black-000000.svg)](https://github.com/psf/black) +[![Ruff](https://img.shields.io/endpoint?url=https://raw.githubusercontent.com/astral-sh/ruff/main/assets/badge/v2.json)](https://github.com/astral-sh/ruff) [![RTD](https://readthedocs.org/projects/pycytominer/badge/?version=latest&style=flat)](https://pycytominer.readthedocs.io/) [![DOI](https://img.shields.io/badge/DOI-10.48550/arXiv.2311.13417-blue)](https://doi.org/10.48550/arXiv.2311.13417) diff --git a/docs/conf.py b/docs/conf.py index 1dbda7ed..e70ac1eb 100644 --- a/docs/conf.py +++ b/docs/conf.py @@ -12,21 +12,19 @@ # import os import sys -import pathlib import dunamai from datetime import date sys.path.insert(0, os.path.abspath("..")) -import pycytominer +# Ignore rules regarding import order which is required for sphinx build process +import pycytominer # noqa: E402, RUF100 # -- Project information ----------------------------------------------------- project = pycytominer.__about__.__project__ author = pycytominer.__about__.__author__ -copyright = "Copyright 2019 - {date} {author}".format( - date=date.today().year, author=author -) +project_copyright = f"Copyright 2019 - {date.today().year} {author}" # Get the version from dunamai (the backend of poetry-dynamic-versioning) auto_version = dunamai.Version.from_git() diff --git a/poetry.lock b/poetry.lock index 902f8745..28501460 100644 --- a/poetry.lock +++ b/poetry.lock @@ -2,13 +2,13 @@ [[package]] name = "agate" -version = "1.9.0" +version = "1.9.1" 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a/pycytominer/aggregate.py +++ b/pycytominer/aggregate.py @@ -117,7 +117,7 @@ def aggregate( if col in population_df.columns: population_df = population_df.drop([col], axis="columns") - if output_file != None: + if output_file is not None: output( df=population_df, output_filename=output_file, diff --git a/pycytominer/annotate.py b/pycytominer/annotate.py index f593e865..2f616dc1 100644 --- a/pycytominer/annotate.py +++ b/pycytominer/annotate.py @@ -3,7 +3,6 @@ """ import os -import numpy as np import pandas as pd from pycytominer.cyto_utils import ( output, @@ -99,10 +98,10 @@ def annotate( annotated = cp_clean(annotated) if not isinstance(external_metadata, pd.DataFrame): - if external_metadata != None: + if external_metadata is not None: assert os.path.exists( external_metadata - ), "external metadata at {} does not exist".format(external_metadata) + ), f"external metadata at {external_metadata} does not exist" external_metadata = pd.read_csv(external_metadata) else: @@ -111,7 +110,7 @@ def annotate( if isinstance(external_metadata, pd.DataFrame): external_metadata.columns = [ - "Metadata_{}".format(x) if not x.startswith("Metadata_") else x + f"Metadata_{x}" if not x.startswith("Metadata_") else x for x in external_metadata.columns ] @@ -133,7 +132,7 @@ def annotate( annotated = annotated.loc[:, meta_cols + other_cols] - if output_file != None: + if output_file is not None: output( df=annotated, output_filename=output_file, diff --git a/pycytominer/consensus.py b/pycytominer/consensus.py index 44357c74..b1326abe 100644 --- a/pycytominer/consensus.py +++ b/pycytominer/consensus.py @@ -2,9 +2,6 @@ Acquire consensus signatures for input samples """ -import numpy as np -import pandas as pd - from pycytominer import aggregate from pycytominer.cyto_utils import ( output, @@ -120,7 +117,7 @@ def consensus( subset_data_df=None, ) - if output_file != None: + if output_file is not None: output( df=consensus_df, output_filename=output_file, diff --git a/pycytominer/cyto_utils/DeepProfiler_processing.py b/pycytominer/cyto_utils/DeepProfiler_processing.py index dd7ec332..ac8af064 100644 --- a/pycytominer/cyto_utils/DeepProfiler_processing.py +++ b/pycytominer/cyto_utils/DeepProfiler_processing.py @@ -1,9 +1,9 @@ """ Utility function to load and process the output files of a DeepProfiler run. """ + import os import pathlib -import numpy as np import pandas as pd import warnings @@ -280,7 +280,7 @@ def aggregate_deep(self): df = pd.concat([df, meta_df], axis=1) # save metalevel file - if self.output_file != None: + if self.output_file is not None: if not os.path.exists(self.output_file): os.mkdir(self.output_file) file_path = os.path.join( @@ -291,7 +291,7 @@ def aggregate_deep(self): # Concatenate all of the above created profiles self.aggregated_profiles = pd.concat( - [x for x in self.aggregated_profiles] + list(self.aggregated_profiles) ).reset_index(drop=True) # clean and reindex columns @@ -474,7 +474,7 @@ def normalize_deep_single_cells( normalized.insert(1, "Location_Center_Y", y_locations) # separate code because normalize() will not return if it has an output file specified - if output_file != None: + if output_file is not None: output( df=normalized, output_filename=output_file, diff --git a/pycytominer/cyto_utils/annotate_custom.py b/pycytominer/cyto_utils/annotate_custom.py index d9ef67b0..0e176174 100644 --- a/pycytominer/cyto_utils/annotate_custom.py +++ b/pycytominer/cyto_utils/annotate_custom.py @@ -1,6 +1,4 @@ -import os import numpy as np -import pandas as pd def annotate_cmap( @@ -84,14 +82,13 @@ def annotate_cmap( "Metadata_mg_per_ml", ] = 0 - if perturbation_mode == "genetic": - if "Metadata_pert_name" in annotated.columns: - annotated = annotated.assign( - Metadata_broad_sample_type=[ - "control" if x == "EMPTY" else "trt" - for x in annotated.Metadata_pert_name - ] - ) + if perturbation_mode == "genetic" and "Metadata_pert_name" in annotated.columns: + annotated = annotated.assign( + Metadata_broad_sample_type=[ + "control" if x == "EMPTY" else "trt" + for x in annotated.Metadata_pert_name + ] + ) if "Metadata_broad_sample_type" in annotated.columns: annotated = annotated.assign( diff --git a/pycytominer/cyto_utils/cell_locations.py b/pycytominer/cyto_utils/cell_locations.py index 9a86c5f9..1532e2e9 100644 --- a/pycytominer/cyto_utils/cell_locations.py +++ b/pycytominer/cyto_utils/cell_locations.py @@ -4,12 +4,13 @@ import pathlib import pandas as pd +import tempfile +from typing import Optional, Union + import boto3 import botocore -import tempfile import collections import sqlalchemy -from typing import Union class CellLocation: @@ -77,7 +78,7 @@ def __init__( self, metadata_input: Union[str, pd.DataFrame], single_cell_input: Union[str, sqlalchemy.engine.Engine], - augmented_metadata_output: str = None, + augmented_metadata_output: Optional[str] = None, overwrite: bool = False, image_column: str = "ImageNumber", object_column: str = "ObjectNumber", @@ -234,7 +235,7 @@ def _create_nested_df(self, df: pd.DataFrame): output_df_list = collections.defaultdict(list) # iterate over each group of cells in the merged DataFrame - group_cols = self.image_key + [self.image_column] + group_cols = [*self.image_key, self.image_column] for group_values, cell_df in df.groupby(group_cols): # add the image-level information to the output dictionary diff --git a/pycytominer/cyto_utils/cells.py b/pycytominer/cyto_utils/cells.py index f4954150..b9cb351b 100644 --- a/pycytominer/cyto_utils/cells.py +++ b/pycytominer/cyto_utils/cells.py @@ -24,7 +24,7 @@ default_linking_cols = get_default_linking_cols() -class SingleCells(object): +class SingleCells: """This is a class to interact with single cell morphological profiles. Interaction includes aggregation, normalization, and output. @@ -124,7 +124,7 @@ def __init__( # Check that the subsample_frac is between 0 and 1 assert ( - 0 < subsample_frac and 1 >= subsample_frac + subsample_frac > 0 and subsample_frac <= 1 ), "subsample_frac must be between 0 and 1" self.sql_file = sql_file @@ -331,7 +331,7 @@ def count_cells(self, compartment="cells", count_subset=False): ) else: query_cols = "TableNumber, ImageNumber, ObjectNumber" - query = "select {} from {}".format(query_cols, compartment) + query = f"select {query_cols} from {compartment}" count_df = self.image_df.merge( pd.read_sql(sql=query, con=self.conn), how="inner", on=self.merge_cols ) @@ -360,7 +360,7 @@ def subsample_profiles(self, df, rename_col=True): A subsampled pandas dataframe of single cell profiles. """ - if self.subsampling_random_state == None: + if self.subsampling_random_state is None: random_state = np.random.randint(0, 10000, size=1)[0] self.set_subsample_random_state(random_state) @@ -402,7 +402,7 @@ def get_subsample(self, df=None, compartment="cells", rename_col=True): check_compartments(compartment) query_cols = "TableNumber, ImageNumber, ObjectNumber" - query = "select {} from {}".format(query_cols, compartment) + query = f"select {query_cols} from {compartment}" # Load query and merge with image_df if df is None: @@ -679,7 +679,7 @@ def _compartment_df_generator( def merge_single_cells( self, compute_subsample: bool = False, - sc_output_file: str = None, + sc_output_file: Optional[str] = None, compression_options: Optional[str] = None, float_format: Optional[str] = None, single_cell_normalize: bool = False, @@ -726,8 +726,8 @@ def merge_single_cells( # Specify how to indicate merge suffixes merge_suffix = [ - "_{comp_l}".format(comp_l=left_compartment), - "_{comp_r}".format(comp_r=right_compartment), + f"_{left_compartment}", + f"_{right_compartment}", ] merge_suffix_rename += merge_suffix left_link_col = self.compartment_linking_cols[left_compartment][ @@ -754,8 +754,8 @@ def merge_single_cells( sc_df = sc_df.merge( self.load_compartment(compartment=right_compartment), - left_on=self.merge_cols + [left_link_col], - right_on=self.merge_cols + [right_link_col], + left_on=[*self.merge_cols, left_link_col], + right_on=[*self.merge_cols, right_link_col], suffixes=merge_suffix, ) @@ -766,14 +766,12 @@ def merge_single_cells( full_merge_suffix_original = [] for col_name in self.merge_cols + list(self.linking_col_rename.keys()): full_merge_suffix_original.append(col_name) - full_merge_suffix_rename.append("Metadata_{x}".format(x=col_name)) + full_merge_suffix_rename.append(f"Metadata_{col_name}") for col_name in self.merge_cols + list(self.linking_col_rename.keys()): for suffix in set(merge_suffix_rename): - full_merge_suffix_original.append("{x}{y}".format(x=col_name, y=suffix)) - full_merge_suffix_rename.append( - "Metadata_{x}{y}".format(x=col_name, y=suffix) - ) + full_merge_suffix_original.append(f"{col_name}{suffix}") + full_merge_suffix_rename.append(f"Metadata_{col_name}{suffix}") self.full_merge_suffix_rename = dict( zip(full_merge_suffix_original, full_merge_suffix_rename) @@ -787,9 +785,8 @@ def merge_single_cells( self.image_df.merge(sc_df, on=self.merge_cols, how="right") # pandas rename performance may be improved using copy=False, inplace=False # reference: https://ryanlstevens.github.io/2022-05-06-pandasColumnRenaming/ + .rename(self.linking_col_rename, axis="columns", copy=False, inplace=False) .rename( - self.linking_col_rename, axis="columns", copy=False, inplace=False - ).rename( self.full_merge_suffix_rename, axis="columns", copy=False, inplace=False ) ) @@ -798,9 +795,9 @@ def merge_single_cells( if normalize_args is None: normalize_args = {} features = infer_cp_features(sc_df, compartments=self.compartments) - elif "features" not in normalize_args: - features = infer_cp_features(sc_df, compartments=self.compartments) - elif normalize_args["features"] == "infer": + elif ("features" not in normalize_args) or ( + normalize_args["features"] == "infer" + ): features = infer_cp_features(sc_df, compartments=self.compartments) else: features = normalize_args["features"] @@ -816,7 +813,7 @@ def merge_single_cells( ) # if output argument is provided, call it using df_merged_sc and kwargs - if sc_output_file != None: + if sc_output_file is not None: return output( df=sc_df, output_filename=sc_output_file, @@ -860,11 +857,10 @@ def aggregate_profiles( else will write to file and return the filepath of the file """ - if output_file != None: + if output_file is not None: self.set_output_file(output_file) - compartment_idx = 0 - for compartment in self.compartments: + for compartment_idx, compartment in enumerate(self.compartments): if compartment_idx == 0: aggregated = self.aggregate_compartment( compartment=compartment, @@ -882,11 +878,10 @@ def aggregate_profiles( on=self.strata, how="inner", ) - compartment_idx += 1 self.is_aggregated = True - if self.output_file != None: + if self.output_file is not None: return output( df=aggregated, output_filename=self.output_file, diff --git a/pycytominer/cyto_utils/collate.py b/pycytominer/cyto_utils/collate.py index bc91347d..f9e26e2d 100644 --- a/pycytominer/cyto_utils/collate.py +++ b/pycytominer/cyto_utils/collate.py @@ -7,7 +7,7 @@ def run_check_errors(cmd): """Run a system command, and exit if an error occurred, otherwise continue""" - if type(cmd) == str: + if isinstance(cmd, str): cmd = cmd.split() output = subprocess.run(cmd, capture_output=True, text=True) if output.stderr != "": @@ -76,7 +76,7 @@ def collate( import cytominer_database.munge except ImportError: raise ImportError( - """Optional dependency cytominer-database is not installed. + """Optional dependency cytominer-database is not installed. Please install the `collate` optional dependency group: e.g. `pip install pycytominer[collate]` """ ) @@ -143,7 +143,7 @@ def collate( ) for eachcompartment in ["Cells", "Cytoplasm", "Nuclei"]: cursor.execute( - f"""CREATE INDEX IF NOT EXISTS table_image_object_{eachcompartment.lower()}_idx + f"""CREATE INDEX IF NOT EXISTS table_image_object_{eachcompartment.lower()}_idx ON {eachcompartment}(TableNumber, ImageNumber, ObjectNumber);""" ) cursor.execute( diff --git a/pycytominer/cyto_utils/collate_cmd.py b/pycytominer/cyto_utils/collate_cmd.py index 0ccfcb9c..4f9542a4 100644 --- a/pycytominer/cyto_utils/collate_cmd.py +++ b/pycytominer/cyto_utils/collate_cmd.py @@ -65,7 +65,7 @@ parser.add_argument( "--image-feature-categories", dest="image_feature_categories", - type=lambda s: [item for item in s.split(",")], + type=lambda s: list(s.split(",")), default="Granularity,Texture,ImageQuality,Count,Threshold", help="Which image feature categories should be added if adding image features to the aggregated profiles. Multiple values can be passed in if comma separated with no spaces between them", ) diff --git a/pycytominer/cyto_utils/cp_image_features.py b/pycytominer/cyto_utils/cp_image_features.py index 00b653df..1e9f29b2 100644 --- a/pycytominer/cyto_utils/cp_image_features.py +++ b/pycytominer/cyto_utils/cp_image_features.py @@ -2,7 +2,6 @@ Functions for counting the number of fields and aggregating other images features """ -import pandas as pd import numpy as np from pycytominer import aggregate @@ -32,7 +31,7 @@ def aggregate_fields_count(image_df, strata, fields_of_view_feature): fields_count_df.groupby(strata)[fields_of_view_feature] .count() .reset_index() - .rename(columns={f"{fields_of_view_feature}": f"Metadata_Site_Count"}) + .rename(columns={f"{fields_of_view_feature}": "Metadata_Site_Count"}) ) return fields_count_df @@ -127,7 +126,7 @@ def aggregate_image_features( ) # Aggregate other image features - if not len(np.setdiff1d(image_feature_categories, [count_prefix])) == 0: + if len(np.setdiff1d(image_feature_categories, [count_prefix])) != 0: image_features_df = image_features_df.drop( remove_cols, axis="columns", errors="ignore" ) diff --git a/pycytominer/cyto_utils/features.py b/pycytominer/cyto_utils/features.py index 11932b49..b88aba34 100644 --- a/pycytominer/cyto_utils/features.py +++ b/pycytominer/cyto_utils/features.py @@ -29,7 +29,7 @@ def get_blocklist_features(blocklist_file=blocklist_file, population_df=None): blocklist = pd.read_csv(blocklist_file) assert any( - [x == "blocklist" for x in blocklist.columns] + x == "blocklist" for x in blocklist.columns ), "one column must be named 'blocklist'" blocklist_features = blocklist.blocklist.to_list() @@ -63,12 +63,10 @@ def label_compartment(cp_features, compartment, metadata_cols): assert ( compartment in avail_compartments - ), "provide valid compartment. One of: {}".format(avail_compartments) + ), f"provide valid compartment. One of: {avail_compartments}" cp_features = [ - "Metadata_{}".format(x) - if x in metadata_cols - else "{}_{}".format(compartment, x) + f"Metadata_{x}" if x in metadata_cols else f"{compartment}_{x}" for x in cp_features ] @@ -104,11 +102,11 @@ def infer_cp_features( compartments = [x.title() for x in compartments] if image_features: - compartments = list(set(["Image"] + compartments)) + compartments = list({"Image", *compartments}) features = [] for col in population_df.columns.tolist(): - if any([col.startswith(x.title()) for x in compartments]): + if any(col.startswith(x.title()) for x in compartments): features.append(col) if metadata: diff --git a/pycytominer/cyto_utils/load.py b/pycytominer/cyto_utils/load.py index 27333d70..7abdd06d 100644 --- a/pycytominer/cyto_utils/load.py +++ b/pycytominer/cyto_utils/load.py @@ -59,7 +59,7 @@ def infer_delim(file: str): the delimiter used in the dataframe (typically either tab or commas) """ try: - with open(file, "r") as csvfile: + with open(file) as csvfile: line = csvfile.readline() except UnicodeDecodeError: with gzip.open(file, "r") as gzipfile: diff --git a/pycytominer/cyto_utils/modz.py b/pycytominer/cyto_utils/modz.py index 978f2677..7dd0b30f 100644 --- a/pycytominer/cyto_utils/modz.py +++ b/pycytominer/cyto_utils/modz.py @@ -1,10 +1,9 @@ import numpy as np -import pandas as pd from pycytominer.cyto_utils.util import ( get_pairwise_correlation, check_correlation_method, - infer_cp_features, ) +from pycytominer.cyto_utils.features import infer_cp_features def modz_base(population_df, method="spearman", min_weight=0.01, precision=4): @@ -106,9 +105,9 @@ def modz( Consensus signatures with metadata for all replicates in the given DataFrame """ population_features = population_df.columns.tolist() - assert_error = "{} not in input dataframe".format(replicate_columns) + assert_error = f"{replicate_columns} not in input dataframe" if isinstance(replicate_columns, list): - assert all([x in population_features for x in replicate_columns]), assert_error + assert all(x in population_features for x in replicate_columns), assert_error elif isinstance(replicate_columns, str): assert replicate_columns in population_features, assert_error replicate_columns = replicate_columns.split() diff --git a/pycytominer/cyto_utils/single_cell_ingest_utils.py b/pycytominer/cyto_utils/single_cell_ingest_utils.py index 26812ce5..1097ac02 100644 --- a/pycytominer/cyto_utils/single_cell_ingest_utils.py +++ b/pycytominer/cyto_utils/single_cell_ingest_utils.py @@ -1,5 +1,5 @@ from collections import Counter -from pycytominer.cyto_utils import check_compartments, get_default_compartments +from pycytominer.cyto_utils import get_default_compartments def get_default_linking_cols(): @@ -55,27 +55,23 @@ def assert_linking_cols_complete(linking_cols="default", compartments="default") unique_linking_cols = [] for x in linking_cols: unique_linking_cols.append(x) - assert x in compartments, "{com} {err}".format(com=x, err=comp_err) + assert x in compartments, f"{x} {comp_err}" for y in linking_cols[x]: unique_linking_cols.append(y) - assert y in compartments, "{com} {err}".format(com=y, err=comp_err) + assert y in compartments, f"{y} {comp_err}" linking_check.append("-".join(sorted([x, y]))) # Make sure that each combination has been specified exactly twice linking_counter = Counter(linking_check) for combo in linking_counter: - assert ( - linking_counter[combo] == 2 - ), "Missing column identifier between {combo}".format(combo=combo) + assert linking_counter[combo] == 2, f"Missing column identifier between {combo}" # Confirm that every compartment has been specified in the linking_cols - unique_linking_cols = sorted(list(set(unique_linking_cols))) + unique_linking_cols = sorted(set(unique_linking_cols)) diff_column = set(compartments).difference(unique_linking_cols) - assert unique_linking_cols == sorted( - compartments - ), "All compartments must be specified in the linking_cols, {miss} is missing".format( - miss=diff_column - ) + assert ( + unique_linking_cols == sorted(compartments) + ), f"All compartments must be specified in the linking_cols, {diff_column} is missing" def provide_linking_cols_feature_name_update(linking_cols="default"): @@ -103,7 +99,7 @@ def provide_linking_cols_feature_name_update(linking_cols="default"): update_name = dict( zip( metadata_update_cols, - ["Metadata_{x}".format(x=y) for y in metadata_update_cols], + [f"Metadata_{y}" for y in metadata_update_cols], ) ) return update_name diff --git a/pycytominer/cyto_utils/util.py b/pycytominer/cyto_utils/util.py index a7c5107c..a9c4d5fb 100644 --- a/pycytominer/cyto_utils/util.py +++ b/pycytominer/cyto_utils/util.py @@ -3,12 +3,10 @@ """ import os -import sys import warnings import numpy as np import pandas as pd from pycytominer.cyto_utils.features import ( - infer_cp_features, convert_compartment_format_to_list, ) @@ -134,7 +132,7 @@ def check_aggregate_operation(operation): avail_ops = ["mean", "median"] assert ( operation in avail_ops - ), "operation {} not supported, select one of {}".format(operation, avail_ops) + ), f"operation {operation} not supported, select one of {avail_ops}" return operation @@ -192,7 +190,7 @@ def check_fields_of_view_format(fields_of_view): return list(map(int, fields_of_view)) except ValueError: raise TypeError( - f"Variables of type int expected, however some of the input fields of view are not integers." + "Variables of type int expected, however some of the input fields of view are not integers." ) else: raise TypeError( @@ -245,7 +243,7 @@ def check_image_features(image_features, image_columns): Nothing is returned. """ - if "Image" in list(set(img_col.split("_")[0] for img_col in image_columns)): + if "Image" in list({img_col.split("_")[0] for img_col in image_columns}): # Image has already been prepended to most, but not all, columns level = 1 image_columns = [x for x in image_columns if "_" in x] @@ -254,7 +252,7 @@ def check_image_features(image_features, image_columns): try: assert all( - feature in list(set(img_col.split("_")[level] for img_col in image_columns)) + feature in list({img_col.split("_")[level] for img_col in image_columns}) for feature in image_features ) except AssertionError: diff --git a/pycytominer/cyto_utils/write_gct.py b/pycytominer/cyto_utils/write_gct.py index e2df71a2..a058da3d 100644 --- a/pycytominer/cyto_utils/write_gct.py +++ b/pycytominer/cyto_utils/write_gct.py @@ -63,7 +63,7 @@ def write_gct( # Step 2: Get the sample metadata portion of the output file metadata_part = metadata_df.transpose() - metadata_part.columns = ["SAMPLE_{}".format(x) for x in metadata_part.columns] + metadata_part.columns = [f"SAMPLE_{x}" for x in metadata_part.columns] metadata_part = ( metadata_part.transpose() .reset_index() diff --git a/pycytominer/feature_select.py b/pycytominer/feature_select.py index d7b9f3e4..b8588714 100644 --- a/pycytominer/feature_select.py +++ b/pycytominer/feature_select.py @@ -1,6 +1,7 @@ """ Select features to use in downstream analysis based on specified selection method """ + from pycytominer.operations import ( correlation_threshold, variance_threshold, @@ -112,12 +113,10 @@ def feature_select( # Make sure the user provides a supported operation if isinstance(operation, list): assert all( - [x in all_ops for x in operation] - ), "Some operation(s) {} not supported. Choose {}".format(operation, all_ops) + x in all_ops for x in operation + ), f"Some operation(s) {operation} not supported. Choose {all_ops}" elif isinstance(operation, str): - assert operation in all_ops, "{} not supported. Choose {}".format( - operation, all_ops - ) + assert operation in all_ops, f"{operation} not supported. Choose {all_ops}" operation = operation.split() else: return ValueError("Operation must be a list or string") @@ -182,7 +181,7 @@ def feature_select( selected_df = profiles.drop(excluded_features, axis="columns") - if output_file != None: + if output_file is not None: output( df=selected_df, output_filename=output_file, diff --git a/pycytominer/normalize.py b/pycytominer/normalize.py index 2fbe705c..66989835 100644 --- a/pycytominer/normalize.py +++ b/pycytominer/normalize.py @@ -5,7 +5,7 @@ import pandas as pd from sklearn.preprocessing import StandardScaler, RobustScaler -from pycytominer.cyto_utils import output, infer_cp_features, load_profiles, util +from pycytominer.cyto_utils import output, infer_cp_features, load_profiles from pycytominer.operations import Spherize, RobustMAD @@ -127,7 +127,7 @@ def normalize( method = method.lower() avail_methods = ["standardize", "robustize", "mad_robustize", "spherize"] - assert method in avail_methods, "operation must be one {}".format(avail_methods) + assert method in avail_methods, f"operation must be one {avail_methods}" if method == "standardize": scaler = StandardScaler() @@ -184,7 +184,7 @@ def normalize( context = f"the `{method}` method in `pycytominer.normalize`" raise ValueError(f"{error_detail}. This is likely a bug in {context}.") - if output_file != None: + if output_file is not None: output( df=normalized, output_filename=output_file, diff --git a/pycytominer/operations/correlation_threshold.py b/pycytominer/operations/correlation_threshold.py index d3620f50..0e03d217 100644 --- a/pycytominer/operations/correlation_threshold.py +++ b/pycytominer/operations/correlation_threshold.py @@ -3,8 +3,6 @@ specified threshold """ -import numpy as np -import pandas as pd from pycytominer.cyto_utils import ( infer_cp_features, get_pairwise_correlation, diff --git a/pycytominer/operations/get_na_columns.py b/pycytominer/operations/get_na_columns.py index ad9a4078..ad36c377 100644 --- a/pycytominer/operations/get_na_columns.py +++ b/pycytominer/operations/get_na_columns.py @@ -3,7 +3,6 @@ Note: This was called `drop_na_columns` in cytominer for R """ -import pandas as pd from pycytominer.cyto_utils.features import infer_cp_features diff --git a/pycytominer/operations/noise_removal.py b/pycytominer/operations/noise_removal.py index 917f463f..5bb89470 100644 --- a/pycytominer/operations/noise_removal.py +++ b/pycytominer/operations/noise_removal.py @@ -2,8 +2,6 @@ Remove noisy features, as defined by features with excessive standard deviation within the same perturbation group. """ -import numpy as np -import pandas as pd from pycytominer.cyto_utils import infer_cp_features diff --git a/pycytominer/operations/transform.py b/pycytominer/operations/transform.py index b912a527..90404e89 100644 --- a/pycytominer/operations/transform.py +++ b/pycytominer/operations/transform.py @@ -5,14 +5,11 @@ .. [1] Kessy et al. 2016 "Optimal Whitening and Decorrelation" arXiv: https://arxiv.org/abs/1512.00809 """ -import os import numpy as np import pandas as pd -from scipy.linalg import eigh from scipy.stats import median_abs_deviation from sklearn.base import BaseEstimator, TransformerMixin from sklearn.preprocessing import StandardScaler -from pycytominer.cyto_utils import util class Spherize(BaseEstimator, TransformerMixin): @@ -117,11 +114,9 @@ def fit(self, X, y=None): if not ((r == d) | (self.center & (r == n - 1)) | (not self.center & (r == n))): raise ValueError( - ( - "The data matrix X is not full rank: n = {n}, d = {d}, r = {r}." - "Perfect linear dependencies are unusual in data matrices so something seems amiss." - "Check for linear dependencies in the data and remove them." - ) + "The data matrix X is not full rank: n = {n}, d = {d}, r = {r}." + "Perfect linear dependencies are unusual in data matrices so something seems amiss." + "Check for linear dependencies in the data and remove them." ) # Get the eigenvalues and eigenvectors of the covariance matrix diff --git a/pycytominer/operations/variance_threshold.py b/pycytominer/operations/variance_threshold.py index c569ab6c..d901e339 100644 --- a/pycytominer/operations/variance_threshold.py +++ b/pycytominer/operations/variance_threshold.py @@ -4,7 +4,6 @@ """ import numpy as np -import pandas as pd from pycytominer.cyto_utils import infer_cp_features diff --git a/pyproject.toml b/pyproject.toml index de9e5801..d39c9cc1 100644 --- a/pyproject.toml +++ b/pyproject.toml @@ -62,12 +62,16 @@ cytominer-database = {version = "0.3.4", optional=true} cell-locations = ["fsspec", "s3fs", "boto3", "fire"] collate = ["cytominer-database"] +[tool.poetry.group.dev] +optional=true + [tool.poetry.group.dev.dependencies] pytest = ">=5.0.1" codecov = ">=2.1.12" pytest-cov = "^4.1.0" pre-commit = ">=3.3.2" commitizen = "^3.12.0" +ruff = "^0.2.1" [tool.poetry.group.docs] optional = true @@ -108,9 +112,59 @@ version_files = [ "CITATION.cff" ] -[tool.black] +[tool.ruff] +target-version = "py38" line-length = 88 -target-version = ['py39'] +fix = true + +[tool.ruff.lint] +select = [ + # flake8-2020 + "YTT", + # flake8-builtins + "A", + # flake8-comprehensions + "C4", + # flake8-debugger + "T10", + # flake8-simplify + "SIM", + # pycodestyle + "E", "W", + # pyflakes + "F", + # pygrep-hooks + "PGH", + # pyupgrade + "UP", + # ruff + "RUF", + # The following checks are disabled, but we are working towards enabling them in the future. + # flake8-bandit + # "S", + # flake8-bugbear + # "B", + # isort + # "I", + # mccabe + # "C90", + # tryceratops + # "TRY", +] +ignore = [ + # LineTooLong + "E501", + # DoNotAssignLambda + "E731", +] + +[tool.ruff.lint.per-file-ignores] +# Ignore `E402` and `F401` (unusued imports) in all `__init__.py` files +"__init__.py" = ["E402", "F401"] +"tests/*" = ["S101"] # Ignore assert statements in tests + +[tool.ruff.format] +preview = true [tool.pytest.ini_options] testpaths = "tests" diff --git a/tests/test_aggregate.py b/tests/test_aggregate.py index 4e2e3685..00c7805d 100644 --- a/tests/test_aggregate.py +++ b/tests/test_aggregate.py @@ -235,7 +235,7 @@ def test_aggregate_incorrect_object_feature(): incorrect_object_feature = "DOES NOT EXIST" with pytest.raises(KeyError) as err: - aggregate_result = aggregate( + aggregate( population_df=data_df, strata=["g"], features="infer", diff --git a/tests/test_consensus.py b/tests/test_consensus.py index f2c8324f..ccd1d3e0 100644 --- a/tests/test_consensus.py +++ b/tests/test_consensus.py @@ -82,9 +82,6 @@ def test_consensus_modz(): assert modz_df.shape == (2, 5) assert np.round(modz_df.loc[0, "Cells_x"], 5) == 3.7602 - mean_df = consensus( - data_df, replicate_columns="Metadata_treatment", operation="mean" - ) modz_df = consensus( data_df, replicate_columns="Metadata_treatment", diff --git a/tests/test_cyto_utils/test_DeepProfiler_processing.py b/tests/test_cyto_utils/test_DeepProfiler_processing.py index daffeb7c..d446b855 100644 --- a/tests/test_cyto_utils/test_DeepProfiler_processing.py +++ b/tests/test_cyto_utils/test_DeepProfiler_processing.py @@ -1,10 +1,10 @@ """ This tests the output from a DeepProfiler run (May 2021) """ + import os import random import pytest -import numpy as np import pandas as pd import pathlib import numpy.testing as npt diff --git a/tests/test_cyto_utils/test_annotate_custom.py b/tests/test_cyto_utils/test_annotate_custom.py index 8139cd2d..5c60acc1 100644 --- a/tests/test_cyto_utils/test_annotate_custom.py +++ b/tests/test_cyto_utils/test_annotate_custom.py @@ -4,7 +4,6 @@ import pytest import pandas as pd from pycytominer import annotate -from pycytominer.cyto_utils import cp_clean random.seed(123) @@ -56,7 +55,7 @@ def test_annotate_cmap_assert(): with pytest.raises(AssertionError) as nocmap: - anno_result = annotate( + annotate( profiles=data_df, platemap=platemap_df, join_on=["Metadata_well_position", "Metadata_Well"], @@ -209,10 +208,8 @@ def test_annotate_cp_clean(): ) assert all( - [ - x in anno_result.columns - for x in ["Image_Metadata_Well", "Image_Metadata_Plate"] - ] + x in anno_result.columns + for x in ["Image_Metadata_Well", "Image_Metadata_Plate"] ) anno_result = annotate( @@ -222,4 +219,4 @@ def test_annotate_cp_clean(): join_on=["Metadata_well_position", "Image_Metadata_Well"], ) - assert all([x in anno_result.columns for x in ["Metadata_Well", "Metadata_Plate"]]) + assert all(x in anno_result.columns for x in ["Metadata_Well", "Metadata_Plate"]) diff --git a/tests/test_cyto_utils/test_cell_locations.py b/tests/test_cyto_utils/test_cell_locations.py index f7102273..4426c801 100644 --- a/tests/test_cyto_utils/test_cell_locations.py +++ b/tests/test_cyto_utils/test_cell_locations.py @@ -25,9 +25,10 @@ def test_output_shape_and_required_columns( metadata_input_dataframe.shape[1] + 2, ) - # verify that the Nuclear_Location_Center_X and Nuclear_Location_Center_Y columns are present - assert "Nuclei_Location_Center_X" in cell_loc["CellCenters"][0][0].keys() - assert "Nuclei_Location_Center_Y" in cell_loc["CellCenters"][0][0].keys() + assert isinstance(cell_loc["CellCenters"][0][0], dict) + # verify that the Nuclear_Location_Center_X and Nuclear_Location_Center_Y dictionary keys are present + assert "Nuclei_Location_Center_X" in cell_loc["CellCenters"][0][0] + assert "Nuclei_Location_Center_Y" in cell_loc["CellCenters"][0][0] @pytest.mark.parametrize("cell_loc", ["cell_loc1", "cell_loc2", "cell_loc3"]) diff --git a/tests/test_cyto_utils/test_cells.py b/tests/test_cyto_utils/test_cells.py index 14ac3c94..21b17a42 100644 --- a/tests/test_cyto_utils/test_cells.py +++ b/tests/test_cyto_utils/test_cells.py @@ -32,9 +32,7 @@ def build_random_data( {"a": a_feature, "b": b_feature, "c": c_feature, "d": d_feature} ).reset_index(drop=True) - data_df.columns = [ - "{}_{}".format(compartment.capitalize(), x) for x in data_df.columns - ] + data_df.columns = [f"{compartment.capitalize()}_{x}" for x in data_df.columns] data_df = data_df.assign( ObjectNumber=list(range(1, 51)) * 2, @@ -224,11 +222,11 @@ def test_SingleCells_init(): assert AP_SUBSAMPLE.subsample_frac == 1 assert AP.subsample_n == "all" assert AP_SUBSAMPLE.subsample_n == 2 - assert AP.subset_data_df == None - assert AP.output_file == None + assert AP.subset_data_df is None + assert AP.output_file is None assert AP.aggregation_operation == "median" assert not AP.is_aggregated - assert AP.subsampling_random_state == None + assert AP.subsampling_random_state is None assert AP_SUBSAMPLE.subsampling_random_state == 123 assert AP.fields_of_view == "all" assert AP.fields_of_view_feature == "Metadata_Site" @@ -244,7 +242,7 @@ def test_SingleCells_reset_variables(): ap_switch = SingleCells(sql_file=TMP_SQLITE_FILE) assert ap_switch.subsample_frac == 1 assert ap_switch.subsample_n == "all" - assert ap_switch.subsampling_random_state == None + assert ap_switch.subsampling_random_state is None ap_switch.set_subsample_frac(0.8) assert ap_switch.subsample_frac == 0.8 ap_switch.set_subsample_frac(1) @@ -313,7 +311,7 @@ def test_load_compartment(): # note: pd.api.types.is_integer sometimes is unable to detect int64 or CELLS_DF[colname].dtype == "int64" # avoid recasting the metadata_types - and colname not in metadata_types.keys() + and colname not in metadata_types } # create deep copy of CELLS_DF with manually re-typed float columns as float32 diff --git a/tests/test_cyto_utils/test_cp_image_features.py b/tests/test_cyto_utils/test_cp_image_features.py index b0c3b1bd..929438f6 100644 --- a/tests/test_cyto_utils/test_cp_image_features.py +++ b/tests/test_cyto_utils/test_cp_image_features.py @@ -1,5 +1,3 @@ -import os -import pytest import pandas as pd from pycytominer.cyto_utils import ( aggregate_fields_count, diff --git a/tests/test_cyto_utils/test_feature_blocklist.py b/tests/test_cyto_utils/test_feature_blocklist.py index 41b6f84f..5cfbfdff 100644 --- a/tests/test_cyto_utils/test_feature_blocklist.py +++ b/tests/test_cyto_utils/test_feature_blocklist.py @@ -1,8 +1,3 @@ -import os -import random -import pytest -import tempfile -import warnings import pathlib import pandas as pd from pycytominer.cyto_utils.features import get_blocklist_features diff --git a/tests/test_cyto_utils/test_feature_drop_outlier.py b/tests/test_cyto_utils/test_feature_drop_outlier.py index 22d34932..42316733 100644 --- a/tests/test_cyto_utils/test_feature_drop_outlier.py +++ b/tests/test_cyto_utils/test_feature_drop_outlier.py @@ -1,8 +1,3 @@ -import os -import random -import pytest -import tempfile -import warnings import pandas as pd from pycytominer.cyto_utils.features import drop_outlier_features diff --git a/tests/test_cyto_utils/test_feature_infer.py b/tests/test_cyto_utils/test_feature_infer.py index f8d9e11f..9d20e187 100644 --- a/tests/test_cyto_utils/test_feature_infer.py +++ b/tests/test_cyto_utils/test_feature_infer.py @@ -1,5 +1,3 @@ -import os -import random import pytest import pandas as pd from pycytominer.cyto_utils.features import infer_cp_features @@ -46,7 +44,7 @@ def test_feature_infer(): def test_feature_infer_nocp(): with pytest.raises(AssertionError) as nocp: - features = infer_cp_features(population_df=non_cp_data_df) + infer_cp_features(population_df=non_cp_data_df) assert "No CP features found." in str(nocp.value) diff --git a/tests/test_cyto_utils/test_features_util.py b/tests/test_cyto_utils/test_features_util.py index 5191fcf7..b9a642fb 100644 --- a/tests/test_cyto_utils/test_features_util.py +++ b/tests/test_cyto_utils/test_features_util.py @@ -1,7 +1,3 @@ -import os -import random -import pytest -import pandas as pd from pycytominer.cyto_utils.features import convert_compartment_format_to_list diff --git a/tests/test_cyto_utils/test_load.py b/tests/test_cyto_utils/test_load.py index 81665f2f..b39847bd 100644 --- a/tests/test_cyto_utils/test_load.py +++ b/tests/test_cyto_utils/test_load.py @@ -22,10 +22,10 @@ output_data_file = os.path.join(tmpdir, "test_data.csv") output_data_comma_file = os.path.join(tmpdir, "test_data_comma.csv") output_data_parquet = os.path.join(tmpdir, "test_parquet.parquet") -output_data_gzip_file = "{}.gz".format(output_data_file) +output_data_gzip_file = f"{output_data_file}.gz" output_platemap_file = os.path.join(tmpdir, "test_platemap.csv") output_platemap_comma_file = os.path.join(tmpdir, "test_platemap_comma.csv") -output_platemap_file_gzip = "{}.gz".format(output_platemap_file) +output_platemap_file_gzip = f"{output_platemap_file}.gz" output_npz_file = os.path.join(tmpdir, "test_npz.npz") output_npz_with_model_file = os.path.join(tmpdir, "test_npz_withmodel.npz") output_npz_without_metadata_file = os.path.join(tmpdir, "test_npz_withoutmetadata.npz") @@ -95,9 +95,9 @@ platemap_df.to_csv(output_platemap_file_gzip, sep="\t", index=False, compression="gzip") # Write npz temp files -key_values = {k: npz_metadata_dict[k] for k in npz_metadata_dict.keys()} +key_values = {k: npz_metadata_dict[k] for k in npz_metadata_dict} npz_metadata_dict.update(npz_model_key) -key_with_model_values = {k: npz_metadata_dict[k] for k in npz_metadata_dict.keys()} +key_with_model_values = {k: npz_metadata_dict[k] for k in npz_metadata_dict} np.savez_compressed(output_npz_file, features=npz_feats, metadata=key_values) np.savez_compressed( @@ -161,16 +161,14 @@ def test_load_npz(): core_cols = ["Metadata_Plate", "Metadata_Well", "Metadata_Site"] assert npz_df.shape == (6, 5) - assert npz_df.columns.tolist() == core_cols + ["DP_0", "DP_1"] + assert npz_df.columns.tolist() == [*core_cols, "DP_0", "DP_1"] assert npz_custom_prefix_df.shape == (6, 5) - assert npz_custom_prefix_df.columns.tolist() == core_cols + [ - "test_0", - "test_1", - ] + assert npz_custom_prefix_df.columns.tolist() == [*core_cols, "test_0", "test_1"] assert npz_with_model_df.shape == (6, 6) - assert npz_with_model_df.columns.tolist() == core_cols + [ + assert npz_with_model_df.columns.tolist() == [ + *core_cols, "Metadata_Model", "cnn_0", "cnn_1", @@ -185,11 +183,11 @@ def test_load_npz(): # Check real data assert real_data_df.shape == (206, 54) - assert all([x in real_data_df.columns for x in core_cols + ["Metadata_Model"]]) + assert all(x in real_data_df.columns for x in [*core_cols, "Metadata_Model"]) assert len(real_data_df.Metadata_Model.unique()) == 1 assert real_data_df.Metadata_Model.unique()[0] == "cnn" assert real_data_df.drop( - core_cols + ["Metadata_Model"], axis="columns" + [*core_cols, "Metadata_Model"], axis="columns" ).columns.tolist() == [f"cnn_{x}" for x in range(0, 50)] # Check locations data diff --git a/tests/test_cyto_utils/test_modz.py b/tests/test_cyto_utils/test_modz.py index ca075ed8..08f03ad4 100644 --- a/tests/test_cyto_utils/test_modz.py +++ b/tests/test_cyto_utils/test_modz.py @@ -1,14 +1,10 @@ -import os -import random -import pytest -import numpy as np import pandas as pd from pycytominer.cyto_utils import modz from pycytominer.cyto_utils.modz import modz_base # No replicate information data_df = pd.DataFrame({"x": [1, 1, -1], "y": [5, 5, -5], "z": [2, 2, -2]}) -data_df.index = ["sample_{}".format(x) for x in data_df.index] +data_df.index = [f"sample_{x}" for x in data_df.index] # Include replicate information data_replicate_df = pd.concat( @@ -31,7 +27,7 @@ ), ] ).reset_index(drop=True) -data_replicate_df.index = ["sample_{}".format(x) for x in data_replicate_df.index] +data_replicate_df.index = [f"sample_{x}" for x in data_replicate_df.index] precision = 4 replicate_columns = "Metadata_g" @@ -164,7 +160,7 @@ def test_modz_multiple_columns_feature_specify(): ] ).reset_index(drop=True) data_replicate_feature_df.index = [ - "sample_{}".format(x) for x in data_replicate_feature_df.index + f"sample_{x}" for x in data_replicate_feature_df.index ] # The expected result is to completely remove influence of anticorrelated sample diff --git a/tests/test_cyto_utils/test_output.py b/tests/test_cyto_utils/test_output.py index 2d6a9ed7..c106e7d3 100644 --- a/tests/test_cyto_utils/test_output.py +++ b/tests/test_cyto_utils/test_output.py @@ -1,5 +1,4 @@ import io -import os import pathlib import random import tempfile diff --git a/tests/test_cyto_utils/test_util.py b/tests/test_cyto_utils/test_util.py index ef90683e..4727e3e3 100644 --- a/tests/test_cyto_utils/test_util.py +++ b/tests/test_cyto_utils/test_util.py @@ -45,27 +45,27 @@ def test_check_compartments_not_valid(): warnings.simplefilter("always") with warnings.catch_warnings(record=True) as w: not_valid = ["SOMETHING"] - output = check_compartments(not_valid) + check_compartments(not_valid) assert issubclass(w[-1].category, UserWarning) assert warn_expected_string in str(w[-1].message) with warnings.catch_warnings(record=True) as w: not_valid = "SOMETHING" # Also works with strings - output = check_compartments(not_valid) + check_compartments(not_valid) assert issubclass(w[-1].category, UserWarning) assert warn_expected_string in str(w[-1].message) with warnings.catch_warnings(record=True) as w: not_valid = ["CelLs", "CytopLasM", "SOMETHING"] - output = check_compartments(not_valid) + check_compartments(not_valid) assert issubclass(w[-1].category, UserWarning) assert warn_expected_string in str(w[-1].message) with warnings.catch_warnings(record=True) as w: not_valid = ["CelLs", "CytopLasM", "SOMETHING", "NOTHING"] - output = check_compartments(not_valid) + check_compartments(not_valid) assert issubclass(w[-1].category, UserWarning) - assert "{x}, nothing".format(x=warn_expected_string) in str(w[-1].message) + assert f"{warn_expected_string}, nothing" in str(w[-1].message) def test_get_default_compartments(): @@ -114,7 +114,7 @@ def test_check_aggregate_operation_method(): assert operation == expected_op with pytest.raises(AssertionError) as nomethod: - method = check_aggregate_operation(operation="DOES NOT EXIST") + check_aggregate_operation(operation="DOES NOT EXIST") assert "not supported, select one of" in str(nomethod.value) @@ -127,7 +127,7 @@ def test_check_consensus_operation_method(): assert operation == expected_op with pytest.raises(AssertionError) as nomethod: - method = check_consensus_operation(operation="DOES NOT EXIST") + check_consensus_operation(operation="DOES NOT EXIST") assert "not supported, select one of" in str(nomethod.value) diff --git a/tests/test_cyto_utils/test_write_gct.py b/tests/test_cyto_utils/test_write_gct.py index e07aae39..0c4634cd 100644 --- a/tests/test_cyto_utils/test_write_gct.py +++ b/tests/test_cyto_utils/test_write_gct.py @@ -55,7 +55,7 @@ def test_write_gct(): version="#1.3", ) gct_row_list = [] - with open(output_filename, "r") as gct_file: + with open(output_filename) as gct_file: gctreader = csv.reader(gct_file, delimiter="\t") for row in gctreader: gct_row_list.append(row) @@ -102,7 +102,7 @@ def test_write_gct_infer_features(): version="#1.3", ) gct_row_list = [] - with open(output_filename, "r") as gct_file: + with open(output_filename) as gct_file: gctreader = csv.reader(gct_file, delimiter="\t") for row in gctreader: gct_row_list.append(row) @@ -147,7 +147,7 @@ def test_write_gct_with_feature_metadata(): version="#1.3", ) gct_row_list = [] - with open(output_filename, "r") as gct_file: + with open(output_filename) as gct_file: gctreader = csv.reader(gct_file, delimiter="\t") for row in gctreader: gct_row_list.append(row) diff --git a/tests/test_data/cytominer_database_example_data/shrink_SQ00014613.sqlite.py b/tests/test_data/cytominer_database_example_data/shrink_SQ00014613.sqlite.py index 2f5baff1..dd413aaa 100644 --- a/tests/test_data/cytominer_database_example_data/shrink_SQ00014613.sqlite.py +++ b/tests/test_data/cytominer_database_example_data/shrink_SQ00014613.sqlite.py @@ -20,8 +20,8 @@ with sqlite3.connect(sqlite_target) as conn: conn.execute( """ - DELETE FROM Image - WHERE TableNumber NOT IN + DELETE FROM Image + WHERE TableNumber NOT IN ('dd77885d07028e67dc9bcaaba4df34c6', '1e5d8facac7508cfd4086f3e3e950182') """ @@ -29,7 +29,7 @@ for table in ["Cells", "Nuclei", "Cytoplasm"]: conn.execute( f""" - DELETE FROM {table} + DELETE FROM {table} WHERE TableNumber NOT IN (SELECT TableNumber FROM Image) OR ObjectNumber > 1 """ diff --git a/tests/test_feature_select.py b/tests/test_feature_select.py index 66072ab2..17f77d84 100644 --- a/tests/test_feature_select.py +++ b/tests/test_feature_select.py @@ -312,7 +312,7 @@ def test_feature_select_variance_threshold(): pd.testing.assert_frame_equal(result, expected_result) na_data_unique_test_df = data_unique_test_df.copy() - na_data_unique_test_df.iloc[[x for x in range(0, 50)], 1] = np.nan + na_data_unique_test_df.iloc[list(range(0, 50)), 1] = np.nan result = feature_select( na_data_unique_test_df, features=na_data_unique_test_df.columns.tolist(), @@ -324,7 +324,7 @@ def test_feature_select_variance_threshold(): pd.testing.assert_frame_equal(result, expected_result) na_data_unique_test_df = data_unique_test_df.copy() - na_data_unique_test_df.iloc[[x for x in range(0, 50)], 1] = np.nan + na_data_unique_test_df.iloc[list(range(0, 50)), 1] = np.nan result = feature_select( na_data_unique_test_df, @@ -363,7 +363,7 @@ def test_feature_select_correlation_threshold(): def test_feature_select_all(): data_all_test_df = data_unique_test_df.assign(zz=a_feature) data_all_test_df.iloc[1, 4] = 2 - data_all_test_df.iloc[[x for x in range(0, 50)], 1] = np.nan + data_all_test_df.iloc[list(range(0, 50)), 1] = np.nan result = feature_select( profiles=data_all_test_df, diff --git a/tests/test_operations/test_correlation_threshold.py b/tests/test_operations/test_correlation_threshold.py index 1e5cf8e7..f560f22e 100644 --- a/tests/test_operations/test_correlation_threshold.py +++ b/tests/test_operations/test_correlation_threshold.py @@ -90,7 +90,7 @@ def test_correlation_threshold_featureinfer(): assert "No CP features found." in str(nocp.value) data_cp_df = data_df.copy() - data_cp_df.columns = ["Cells_{}".format(x) for x in data_df.columns] + data_cp_df.columns = [f"Cells_{x}" for x in data_df.columns] correlation_threshold_result = correlation_threshold( population_df=data_cp_df, diff --git a/tests/test_operations/test_get_na_columns.py b/tests/test_operations/test_get_na_columns.py index eb0725f7..ba4640c6 100644 --- a/tests/test_operations/test_get_na_columns.py +++ b/tests/test_operations/test_get_na_columns.py @@ -69,7 +69,7 @@ def test_get_na_columns_sample(): def test_get_na_columns_featureinfer(): with pytest.raises(AssertionError) as nocp: - na_result = get_na_columns( + get_na_columns( population_df=data_df, samples="all", features="infer", cutoff=0.1 ) diff --git a/tests/test_operations/test_transform.py b/tests/test_operations/test_transform.py index 4ee08ddc..595e38a6 100644 --- a/tests/test_operations/test_transform.py +++ b/tests/test_operations/test_transform.py @@ -1,4 +1,3 @@ -import os import random import pytest import numpy as np diff --git a/tests/test_operations/test_variance_threshold.py b/tests/test_operations/test_variance_threshold.py index 9d697a43..38d9745e 100644 --- a/tests/test_operations/test_variance_threshold.py +++ b/tests/test_operations/test_variance_threshold.py @@ -112,7 +112,7 @@ def test_variance_threshold_featureinfer(): assert "No CP features found." in str(nocp.value) data_cp_df = data_unique_test_df.copy() - data_cp_df.columns = ["Cells_{}".format(x) for x in data_unique_test_df.columns] + data_cp_df.columns = [f"Cells_{x}" for x in data_unique_test_df.columns] excluded_features = variance_threshold( population_df=data_cp_df, features="infer", unique_cut=unique_cut diff --git a/walkthroughs/nbconverted/single_cell_usage.py b/walkthroughs/nbconverted/single_cell_usage.py index 227cd375..96e09139 100644 --- a/walkthroughs/nbconverted/single_cell_usage.py +++ b/walkthroughs/nbconverted/single_cell_usage.py @@ -1,5 +1,4 @@ #!/usr/bin/env python -# coding: utf-8 # # Single-cell Profiling Walkthrough # @@ -113,7 +112,7 @@ # setting up sqlite address -sqlite_address = f"sqlite:///{str(plate_data)}" +sqlite_address = f"sqlite:///{plate_data!s}" # loading single cell morphology data into pycyotminer's SingleCells Object single_cell_profile = SingleCells(