Bump python min version to 3.9

.github/workflows/tests.yml

@@ -36,7 +36,7 @@ jobs:
     strategy:
       matrix:
         os: [ubuntu-latest, macos-latest]
-        python-version: [3.7, 3.8, 3.9]
+        python-version: ['3.9', '3.10', '3.11']
 
     steps:
       - name: Clone repo

.pre-commit-config.yaml

@@ -1,32 +1,36 @@
 repos:
+    -   repo: https://github.com/asottile/pyupgrade
+        rev: v3.15.2
+        hooks:
+        -   id: pyupgrade
+            args: [--py39-plus]
+
     -  repo: https://github.com/pycqa/isort
-       rev: 5.10.1
+       rev: 5.13.2
        hooks:
        - id: isort
 
     -   repo: https://github.com/psf/black
-        rev: 22.10.0
+        rev: 24.4.0
         hooks:
         - id: black
 
     -   repo: https://github.com/pycqa/flake8
-        rev: 6.0.0
+        rev: 7.0.0
         hooks:
         - id: flake8
 
     -   repo: https://github.com/pre-commit/mirrors-mypy
-        rev: v0.991
+        rev: v1.9.0
         hooks:
         -   id: mypy
             args: [--strict, --ignore-missing-imports, --show-error-codes]
             additional_dependencies: [types-termcolor]
             exclude: (docs|tests)/
 
     -   repo: https://github.com/python-poetry/poetry
-        rev: 1.2.2
+        rev: 1.8.0
         hooks:
         - id: poetry-check
-        # Commented out due to lock files being generated by different versions of poetry
-        # Refer python-poetry/poetry#4978. Uncomment once poetry 1.2 is released. 
-        # - id: poetry-lock
-        #   args: [--no-update]
+        - id: poetry-lock
+          args: [--no-update]

changedet/algos/catalog.py

@@ -1,11 +1,11 @@
 from collections import UserDict
-from typing import TYPE_CHECKING, Callable, List, Type
+from typing import TYPE_CHECKING, Callable
 
 from changedet.algos.base import MetaAlgo
 
 # Refer https://github.com/python/typing/issues/60
 if TYPE_CHECKING:
-    _Base = UserDict[str, Type[MetaAlgo]]
+    _Base = UserDict[str, type[MetaAlgo]]  # pragma: no cover
 else:
     _Base = UserDict
 
@@ -30,16 +30,16 @@ class AlgoCatalog_(_Base):
 
     """
 
-    def register(self, name: str) -> Callable[[Type[MetaAlgo]], Type[MetaAlgo]]:
-        def inner_wrapper(wrapped_class: Type[MetaAlgo]) -> Type[MetaAlgo]:
+    def register(self, name: str) -> Callable[[type[MetaAlgo]], type[MetaAlgo]]:
+        def inner_wrapper(wrapped_class: type[MetaAlgo]) -> type[MetaAlgo]:
             if name in self.keys():
                 raise AssertionError(f"Algorithm {name} already exists.")
             self[name] = wrapped_class
             return wrapped_class
 
         return inner_wrapper
 
-    def get(self, name: str) -> Type[MetaAlgo]:  # type: ignore[override]
+    def get(self, name: str) -> type[MetaAlgo]:  # type: ignore[override]
         try:
             f = self[name]
         except KeyError as e:
@@ -52,7 +52,7 @@ def get(self, name: str) -> Type[MetaAlgo]:  # type: ignore[override]
                 f = None
         return f
 
-    def list(self) -> List[str]:
+    def list(self) -> list[str]:
         """List all registered algorithms.
 
         Returns:

changedet/algos/cva.py

@@ -1,4 +1,4 @@
-from typing import Any, Tuple
+from typing import Any
 
 import numpy as np
 
@@ -8,7 +8,7 @@
 
 def calc_cvs(
     im1: np.ndarray, im2: np.ndarray, distance: str = "euclidean"
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     diffmap = im2 - im1
     if distance == "manhattan":
         # Manhattan distance/L1 norm

changedet/pipeline.py

@@ -1,5 +1,5 @@
 from pathlib import Path
-from typing import Any, Tuple
+from typing import Any
 
 import numpy as np
 import rasterio as rio
@@ -35,7 +35,7 @@ def __init__(self, algo: str):
         self.logger = init_logger("changedet")
 
     # Image loading and sanity checks should be done here
-    def read(self, im1: str, im2: str, band: int) -> Tuple[np.ndarray, np.ndarray]:
+    def read(self, im1: str, im2: str, band: int) -> tuple[np.ndarray, np.ndarray]:
         """Read and prepare images
 
         Args:
@@ -75,7 +75,7 @@ def read(self, im1: str, im2: str, band: int) -> Tuple[np.ndarray, np.ndarray]:
 
         return arr1, arr2
 
-    def _read(self, im: str, band: int) -> Tuple[np.ndarray, CRS, Profile]:
+    def _read(self, im: str, band: int) -> tuple[np.ndarray, CRS, Profile]:
         with rio.open(im) as raster:
             profile = raster.profile
             crs = raster.crs

changedet/utils.py

@@ -1,7 +1,7 @@
 import logging
 import sys
 from pathlib import Path
-from typing import List, Optional, Tuple
+from typing import Optional
 
 import matplotlib.pyplot as plt
 import numpy as np
@@ -136,7 +136,7 @@ def prepare(self, im1: np.ndarray, im2: np.ndarray, plot: bool = True) -> np.nda
         return icm
 
     @staticmethod
-    def roots(mean: np.ndarray, var: np.ndarray, pi: np.ndarray) -> Tuple[float, float]:
+    def roots(mean: np.ndarray, var: np.ndarray, pi: np.ndarray) -> tuple[float, float]:
         """Compute the threshold between the no-change and change distributions
         from the mean, variance and mixture weight (pi) of no change, change and
         ambigous distributions.
@@ -183,7 +183,7 @@ def __init__(
 
     def init_cluster_params(
         self, X: np.ndarray
-    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Initialse cluster parameters
 
         Shape notation:
@@ -308,7 +308,7 @@ def e_step(
         cov: np.ndarray,
         pi: np.ndarray,
         sample_inds: ArrayLike,
-    ) -> Tuple[np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray]:
         """Expectation step
 
         Shape notation:
@@ -344,7 +344,7 @@ def e_step(
 
     def m_step(
         self, X: np.ndarray, resp: np.ndarray
-    ) -> Tuple[np.ndarray, np.ndarray, np.ndarray]:
+    ) -> tuple[np.ndarray, np.ndarray, np.ndarray]:
         """Maximisation step
 
         Shape notation:
@@ -455,7 +455,7 @@ def update(self, X: np.ndarray, weights: Optional[np.ndarray] = None) -> None:
 
 def np_weight_stats(
     x: np.ndarray, ws: Optional[np.ndarray] = None
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     """Calculate weighted mean and sample covariance.
 
     Args:
@@ -481,7 +481,7 @@ def contrast_stretch(
     *,
     target_type: str = "uint8",
     stretch_type: str = "minmax",
-    percentile: Tuple[int, int] = (2, 98),
+    percentile: tuple[int, int] = (2, 98),
 ) -> np.ndarray:
     """Change image distribution to cover full range of target_type.
 
@@ -518,7 +518,7 @@ def contrast_stretch(
 
 def histogram_equalisation(
     im: np.ndarray, nbr_bins: int = 256
-) -> Tuple[np.ndarray, np.ndarray]:
+) -> tuple[np.ndarray, np.ndarray]:
     # Refer http://www.janeriksolem.net/histogram-equalization-with-python-and.html
     # get image histogram
     imhist, bins = np.histogram(im.flatten(), nbr_bins)
@@ -560,7 +560,7 @@ def format(self, record: logging.LogRecord) -> str:
             # Python3 compatibility
             self._style._fmt = fmt
         self._fmt = fmt
-        return super(_ColorFormatter, self).format(record)
+        return super().format(record)
 
 
 def init_logger(name: str = "logger", output: Optional[str] = None) -> logging.Logger:
@@ -597,7 +597,7 @@ def init_logger(name: str = "logger", output: Optional[str] = None) -> logging.L
     return logger
 
 
-def histplot(xlist: ArrayLike, xlabel: List[str], bins: Optional[int] = 50) -> Figure:
+def histplot(xlist: ArrayLike, xlabel: list[str], bins: Optional[int] = 50) -> Figure:
     """Plot multiple histograms in the same figure
 
     Args: