installed ruff and ran

Author: gsheni

Makefile

@@ -81,9 +81,6 @@ install-develop: clean-build clean-pyc ## install the package in editable mode a
 
 .PHONY: lint-deepecho
 lint-deepecho: ## check style with flake8 and isort
-	flake8 deepecho
-	isort -c --recursive deepecho
-	pylint deepecho --rcfile=setup.cfg
 
 .PHONY: lint-tests
 lint-tests: ## check style with flake8 and isort
@@ -92,17 +89,15 @@ lint-tests: ## check style with flake8 and isort
 
 .PHONY: lint
 lint:  ## Run all code style checks
-	invoke lint
+	ruff check .
+	ruff format .  --check
 
 .PHONY: fix-lint
-fix-lint: ## fix lint issues using autoflake, autopep8, and isort
-	find deepecho tests -name '*.py' | xargs autoflake --in-place --remove-all-unused-imports --remove-unused-variables
-	autopep8 --in-place --recursive --aggressive deepecho tests
-	isort --apply --atomic --recursive deepecho tests
-
+fix-lint: ## fix lint issues using ruff
+	ruff check --fix .
+	ruff format .
 
 # TEST TARGETS
-
 .PHONY: test-unit
 test-unit: ## run unit tests using pytest
 	invoke unit

deepecho/__init__.py

@@ -1,16 +1,16 @@
 """Top-level package for DeepEcho."""
 
-__author__ = 'DataCebo, Inc.'
-__email__ = '[email protected]'
-__version__ = '0.5.1.dev0'
-__path__ = __import__('pkgutil').extend_path(__path__, __name__)
+__author__ = "DataCebo, Inc."
+__email__ = "[email protected]"
+__version__ = "0.5.1.dev0"
+__path__ = __import__("pkgutil").extend_path(__path__, __name__)
 
 from deepecho.demo import load_demo
 from deepecho.models.basic_gan import BasicGANModel
 from deepecho.models.par import PARModel
 
 __all__ = [
-    'load_demo',
-    'BasicGANModel',
-    'PARModel',
+    "load_demo",
+    "BasicGANModel",
+    "PARModel",
 ]

deepecho/demo.py

@@ -4,9 +4,11 @@
 
 import pandas as pd
 
-_DATA_PATH = os.path.join(os.path.dirname(__file__), 'data')
+_DATA_PATH = os.path.join(os.path.dirname(__file__), "data")
 
 
 def load_demo():
     """Load the demo DataFrame."""
-    return pd.read_csv(os.path.join(_DATA_PATH, 'demo.csv'), parse_dates=['date'])
+    return pd.read_csv(
+        os.path.join(_DATA_PATH, "demo.csv"), parse_dates=["date"]
+    )

deepecho/models/__init__.py

@@ -3,4 +3,4 @@
 from deepecho.models.basic_gan import BasicGANModel
 from deepecho.models.par import PARModel
 
-__all__ = ['PARModel', 'BasicGANModel']
+__all__ = ["PARModel", "BasicGANModel"]

deepecho/models/base.py

@@ -6,7 +6,7 @@
 from deepecho.sequences import assemble_sequences
 
 
-class DeepEcho():
+class DeepEcho:
     """The base class for DeepEcho models."""
 
     _verbose = True
@@ -28,14 +28,20 @@ def _validate(sequences, context_types, data_types):
             data_types:
                 See `fit`.
         """
-        dtypes = set(['continuous', 'categorical', 'ordinal', 'count', 'datetime'])
+        dtypes = set([
+            "continuous",
+            "categorical",
+            "ordinal",
+            "count",
+            "datetime",
+        ])
         assert all(dtype in dtypes for dtype in context_types)
         assert all(dtype in dtypes for dtype in data_types)
 
         for sequence in sequences:
-            assert len(sequence['context']) == len(context_types)
-            assert len(sequence['data']) == len(data_types)
-            lengths = [len(x) for x in sequence['data']]
+            assert len(sequence["context"]) == len(context_types)
+            assert len(sequence["data"]) == len(data_types)
+            lengths = [len(x) for x in sequence["data"]]
             assert len(set(lengths)) == 1
 
     def fit_sequences(self, sequences, context_types, data_types):
@@ -87,20 +93,29 @@ def _get_data_types(data, data_types, columns):
             else:
                 dtype = data[column].dtype
                 kind = dtype.kind
-                if kind in 'fiud':
-                    dtypes_list.append('continuous')
-                elif kind in 'OSUb':
-                    dtypes_list.append('categorical')
-                elif kind == 'M':
-                    dtypes_list.append('datetime')
+                if kind in "fiud":
+                    dtypes_list.append("continuous")
+                elif kind in "OSUb":
+                    dtypes_list.append("categorical")
+                elif kind == "M":
+                    dtypes_list.append("datetime")
                 else:
-                    error = f'Unsupported data_type for column {column}: {dtype}'
+                    error = (
+                        f"Unsupported data_type for column {column}: {dtype}"
+                    )
                     raise ValueError(error)
 
         return dtypes_list
 
-    def fit(self, data, entity_columns=None, context_columns=None,
-            data_types=None, segment_size=None, sequence_index=None):
+    def fit(
+        self,
+        data,
+        entity_columns=None,
+        context_columns=None,
+        data_types=None,
+        segment_size=None,
+        sequence_index=None,
+    ):
         """Fit the model to a dataframe containing time series data.
 
         Args:
@@ -131,17 +146,19 @@ def fit(self, data, entity_columns=None, context_columns=None,
                 such as integer values or datetimes.
         """
         if not entity_columns and segment_size is None:
-            raise TypeError('If the data has no `entity_columns`, `segment_size` must be given.')
+            raise TypeError(
+                "If the data has no `entity_columns`, `segment_size` must be given."
+            )
         if segment_size is not None and not isinstance(segment_size, int):
             if sequence_index is None:
                 raise TypeError(
-                    '`segment_size` must be of type `int` if '
-                    'no `sequence_index` is given.'
+                    "`segment_size` must be of type `int` if "
+                    "no `sequence_index` is given."
                 )
-            if data[sequence_index].dtype.kind != 'M':
+            if data[sequence_index].dtype.kind != "M":
                 raise TypeError(
-                    '`segment_size` must be of type `int` if '
-                    '`sequence_index` is not a `datetime` column.'
+                    "`segment_size` must be of type `int` if "
+                    "`sequence_index` is not a `datetime` column."
                 )
 
             segment_size = pd.to_timedelta(segment_size)
@@ -159,9 +176,16 @@ def fit(self, data, entity_columns=None, context_columns=None,
             self._data_columns.remove(sequence_index)
 
         data_types = self._get_data_types(data, data_types, self._data_columns)
-        context_types = self._get_data_types(data, data_types, self._context_columns)
+        context_types = self._get_data_types(
+            data, data_types, self._context_columns
+        )
         sequences = assemble_sequences(
-            data, self._entity_columns, self._context_columns, segment_size, sequence_index)
+            data,
+            self._entity_columns,
+            self._context_columns,
+            segment_size,
+            sequence_index,
+        )
 
         # Validate and fit
         self._validate(sequences, context_types, data_types)
@@ -212,7 +236,9 @@ def sample(self, num_entities=None, context=None, sequence_length=None):
         """
         if context is None:
             if num_entities is None:
-                raise TypeError('Either context or num_entities must be not None')
+                raise TypeError(
+                    "Either context or num_entities must be not None"
+                )
 
             context = self._context_values.sample(num_entities, replace=True)
             context = context.reset_index(drop=True)
@@ -242,7 +268,7 @@ def sample(self, num_entities=None, context=None, sequence_length=None):
             # Reformat as a DataFrame
             group = pd.DataFrame(
                 dict(zip(self._data_columns, sequence)),
-                columns=self._data_columns
+                columns=self._data_columns,
             )
             group[self._entity_columns] = entity_values
             for column, value in zip(self._context_columns, context_values):