Merge pull request #3144 from ericpre/drop_python3.7

Drop python 3.7

.github/workflows/release.yml

@@ -31,7 +31,7 @@ jobs:
       fail-fast: false
       matrix:
         os: [windows-latest, macos-latest]
-        python-version: ['3.7', '3.8', '3.9', '3.10', '3.11']
+        python-version: ['3.8', '3.9', '3.10', '3.11']
 
     steps:
       - uses: actions/checkout@v3
@@ -102,7 +102,7 @@ jobs:
       - name: Build manylinux Python wheels
         uses: RalfG/python-wheels-manylinux-build@ff8504699f7a33a08d3ff85b3c6d4e8f0e70462b
         with:
-          python-versions: 'cp37-cp37m cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311'
+          python-versions: 'cp38-cp38 cp39-cp39 cp310-cp310 cp311-cp311'
           build-requirements: 'cython'
 
       - name: Display content dist folder

.github/workflows/tests.yml

@@ -15,37 +15,31 @@ jobs:
       fail-fast: false
       matrix:
         os: [ubuntu, windows, macos]
-        PYTHON_VERSION: ['3.8', '3.9']
+        PYTHON_VERSION: ['3.9', '3.10']
         PIP_SELECTOR: ['[all, tests, coverage]']
         include:
-          # test oldest supported version of main dependencies on python 3.7
+          # test oldest supported version of main dependencies on python 3.8
           - os: ubuntu
-            PYTHON_VERSION: '3.7'
+            PYTHON_VERSION: '3.8'
             PIP_SELECTOR: '[all, tests, coverage]'
             OLDEST_SUPPORTED_VERSION: true
-            DEPENDENCIES: matplotlib==3.1.3 numpy==1.19.0 scipy==1.4 imagecodecs==2020.1.31 tifffile==2020.2.16 dask==2.11.0 distributed==2.11.0 scikit-image==0.15 numba==0.52 scikit-learn==1.0.1
+            DEPENDENCIES: dask[array]==2021.3.1 matplotlib==3.1.3 numba==0.52 numpy==1.20.0 scipy==1.5 scikit-image==0.18 scikit-learn==1.0.1
             LABEL: -oldest
           # test minimum requirement
           - os: ubuntu
             PYTHON_VERSION: '3.8'
             PIP_SELECTOR: '[tests, coverage]'
             LABEL: -minimum
           # Run coverage
-          - os: ubuntu
-            PYTHON_VERSION: '3.7'
-            PIP_SELECTOR: '[all, tests, coverage]'
           - os: ubuntu
             PYTHON_VERSION: '3.8'
             PIP_SELECTOR: '[all, tests, coverage]'
-          - os: ubuntu
-            PYTHON_VERSION: '3.10'
-            PIP_SELECTOR: '[all, tests, coverage]'
           - os: ubuntu
             PYTHON_VERSION: '3.11'
             PIP_SELECTOR: '[all, tests, coverage]'
 
     steps:
-      - uses: actions/checkout@v3.3.0
+      - uses: actions/checkout@v3
 
       - uses: actions/setup-python@v4
         name: Install Python
@@ -57,17 +51,10 @@ jobs:
           python --version
           pip --version
 
-      - name: Install numba rc
-        if: contains(matrix.PYTHON_VERSION, '3.11')
-        # Require for python 3.11 support, remove when numba 0.57 is release
-        shell: bash
-        run: |
-          pip install numba --pre
-
       - name: Install oldest supported version
         if: ${{ matrix.OLDEST_SUPPORTED_VERSION }}
         run: |
-          pip install ${{ matrix.DEPENDENCIES }}
+          pip install ${{ matrix.DEPENDENCIES }} -v
 
       - name: Install
         shell: bash

azure-pipelines.yml

@@ -25,31 +25,29 @@ resources:
 
 strategy:
   matrix:
-    # Needs to update matplotlib reference image to add python3.9 build
-    # matplotlib 3.1 doesn't have a python 3.9 build
-    Linux_Python38:
+    Linux_Python39:
       vmImage: 'ubuntu-latest'
-      PYTHON_VERSION: '3.8'
+      PYTHON_VERSION: '3.9'
       MAMBAFORGE_PATH: $(Agent.BuildDirectory)/mambaforge
-    Linux_Python37:
+    Linux_Python310:
       vmImage: 'ubuntu-latest'
-      PYTHON_VERSION: '3.7'
+      PYTHON_VERSION: '3.10'
       MAMBAFORGE_PATH: $(Agent.BuildDirectory)/mambaforge
-    MacOS_Python38:
+    MacOS_Python39:
       vmImage: 'macOS-latest'
-      PYTHON_VERSION: '3.8'
+      PYTHON_VERSION: '3.9'
       MAMBAFORGE_PATH: $(Agent.BuildDirectory)/mambaforge
-    MacOS_Python37:
+    MacOS_Python310:
       vmImage: 'macOS-latest'
-      PYTHON_VERSION: '3.7'
+      PYTHON_VERSION: '3.10'
       MAMBAFORGE_PATH: $(Agent.BuildDirectory)/mambaforge
-    Windows_Python38:
+    Windows_Python39:
       vmImage: 'windows-latest'
-      PYTHON_VERSION: '3.8'
+      PYTHON_VERSION: '3.9'
       MAMBAFORGE_PATH: $(Agent.BuildDirectory)\mambaforge
-    Windows_Python37:
+    Windows_Python310:
       vmImage: 'windows-latest'
-      PYTHON_VERSION: '3.7'
+      PYTHON_VERSION: '3.10'
       MAMBAFORGE_PATH: $(Agent.BuildDirectory)\mambaforge
 
 pool:

conda_environment.yml

@@ -2,28 +2,26 @@ name: test_env
 channels:
 - conda-forge
 dependencies:
-- dask-core >=2.11
+- dask-core >=2021.3.1
 - dill
-# dask-core < 2021.3.1 doesn't have fsspec as dependency
-- fsspec
 - importlib-metadata
 - ipyparallel
 - ipython !=8.0.*
 - jinja2
-- matplotlib-base
+- matplotlib-base >=3.1.3
 - natsort
-- numba
+- numba >=0.52
 - numexpr
-- numpy
+- numpy >=1.20
 - packaging
-- pint
+- pint >=0.10
 - prettytable
-- python-dateutil
+- python-dateutil >=2.5.0
 - pyyaml
 - requests
-- scikit-image
-- scikit-learn
-- scipy
-- sympy
-- tqdm
-- traits
+- scikit-image >=0.18
+- scikit-learn >=1.0.1
+- scipy >=1.5.0
+- sympy >=1.6.0
+- tqdm >=4.9.0
+- traits >=4.5.0

hyperspy/_components/error_function.py

@@ -17,8 +17,6 @@
 # along with HyperSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
 
 from hyperspy._components.expression import Expression
-from packaging.version import Version
-import sympy
 
 
 class Erf(Expression):
@@ -54,9 +52,6 @@ class Erf(Expression):
 
     def __init__(self, A=1., sigma=1., origin=0., module=["numpy", "scipy"],
                  **kwargs):
-        if Version(sympy.__version__) < Version("1.3"):
-            raise ImportError("The `ErrorFunction` component requires "
-                              "SymPy >= 1.3")
         super().__init__(
             expression="A * erf((x - origin) / sqrt(2) / sigma) / 2",
             name="Erf",

hyperspy/_components/power_law.py

@@ -22,8 +22,6 @@
 
 from hyperspy._components.expression import Expression
 
-from hyperspy.misc.utils import get_numpy_kwargs
-
 _logger = logging.getLogger(__name__)
 
 
@@ -138,9 +136,8 @@ def estimate_parameters(self, signal, x1, x2, only_current=False,
         else:
             from hyperspy.signal import BaseSignal
             shape = s.data.shape[:-1]
-            kw = get_numpy_kwargs(s.data)
-            I1_s = BaseSignal(np.empty(shape, dtype='float', **kw))
-            I2_s = BaseSignal(np.empty(shape, dtype='float', **kw))
+            I1_s = BaseSignal(np.empty(shape, dtype='float', like=s.data))
+            I2_s = BaseSignal(np.empty(shape, dtype='float', like=s.data))
             # Use the `out` parameters to avoid doing the deepcopy
             s.isig[i1:i3].integrate1D(2j, out=I1_s)
             s.isig[i3:i2].integrate1D(2j, out=I2_s)

hyperspy/_components/skew_normal.py

@@ -18,8 +18,6 @@
 
 import dask.array as da
 import numpy as np
-from packaging.version import Version
-import sympy
 
 from hyperspy.component import _get_scaling_factor
 from hyperspy._components.expression import Expression
@@ -142,9 +140,6 @@ class SkewNormal(Expression):
 
     def __init__(self, x0=0., A=1., scale=1., shape=0.,
                  module=['numpy', 'scipy'], **kwargs):
-        if Version(sympy.__version__) < Version("1.3"):
-            raise ImportError("The `SkewNormal` component requires "
-                              "SymPy >= 1.3")
         # We use `_shape` internally because `shape` is already taken in sympy
         # https://github.com/sympy/sympy/pull/20791
         super().__init__(

hyperspy/_components/voigt.py

@@ -17,8 +17,6 @@
 # along with HyperSpy. If not, see <https://www.gnu.org/licenses/#GPL>.
 
 import math
-from packaging.version import Version
-import sympy
 
 from hyperspy.component import _get_scaling_factor
 from hyperspy._components.expression import Expression
@@ -91,9 +89,6 @@ def __init__(self, centre=10., area=1., gamma=0.2, sigma=0.1,
         # Not to break scripts once we remove the legacy Voigt
         if "legacy" in kwargs:
             del kwargs["legacy"]
-        if Version(sympy.__version__) < Version("1.3"):
-            raise ImportError("The `Voigt` component requires "
-                              "SymPy >= 1.3")
         # We use `gamma_` internally to workaround the use of the `gamma`
         # function in sympy
         super().__init__(

hyperspy/_signals/lazy.py

@@ -161,7 +161,7 @@ class LazySignal(BaseSignal):
 
     """Lazy general signal class."""
 
-    _lazy = True    
+    _lazy = True
     __doc__ += LAZYSIGNAL_DOC.replace("__BASECLASS__", "BaseSignal")
 
     def __init__(self, *args, **kwargs):
@@ -176,7 +176,7 @@ def __init__(self, *args, **kwargs):
         self._cache_dask_chunk_slice = None
         if not self._clear_cache_dask_data in self.events.data_changed.connected:
             self.events.data_changed.connect(self._clear_cache_dask_data)
-        
+
     __init__.__doc__ = BaseSignal.__init__.__doc__.replace(
         ":py:class:`numpy.ndarray`", ":py:class:`dask.array.Array`"
         )
@@ -1253,12 +1253,9 @@ def compute_navigator(self, index=None, chunks_number=None,
             # Needs to reverse the chunks list to match dask chunking order
             signal_chunks = list(signal_chunks)[::-1]
             navigation_chunks = ['auto'] * len(self.axes_manager.navigation_shape)
-            if Version(dask.__version__ ) >= Version("2.30.0"):
-                kwargs = {'balance':True}
-            else:
-                kwargs = {}
-            chunks = self.data.rechunk([*navigation_chunks, *signal_chunks],
-                                       **kwargs).chunks
+            chunks = self.data.rechunk(
+                [*navigation_chunks, *signal_chunks], balance=True,
+                ).chunks
 
         # Get the slice of the corresponding chunk
         signal_size = len(signal_shape)

hyperspy/learn/mva.py

@@ -39,7 +39,6 @@
     stack,
     is_hyperspy_signal,
     is_cupy_array,
-    get_numpy_kwargs,
     )
 from hyperspy.external.progressbar import progressbar
 
@@ -2595,8 +2594,7 @@ def estimate_elbow_position(self, explained_variance_ratio=None,log=True,
             y1 = curve_values_adj[0]
             y2 = curve_values_adj[max_points]
 
-        kw = get_numpy_kwargs(self.data)
-        xs = np.arange(max_points, **kw)
+        xs = np.arange(max_points, like=self.data)
         if log:
             ys = np.log(curve_values_adj[:max_points])
         else:

hyperspy/learn/svd_pca.py

@@ -150,9 +150,6 @@ def svd_solve(
             )
         U, S, V = randomized_svd(data, n_components=output_dimension, **kwargs)
     elif svd_solver == "arpack":
-        if Version(scipy.__version__) < Version("1.4.0"):  # pragma: no cover
-            raise ValueError('`svd_solver="arpack"` requires scipy >= 1.4.0')
-
         if output_dimension >= min(m, n):
             raise ValueError(
                 "svd_solver='arpack' requires output_dimension "

hyperspy/misc/utils.py

@@ -1362,8 +1362,7 @@ def process_function_blockwise(data,
     chunk_nav_shape = tuple([data.shape[i] for i in sorted(nav_indexes)])
     output_shape = chunk_nav_shape + tuple(output_signal_size)
     # Pre-allocating the output array
-    kw = get_numpy_kwargs(data)
-    output_array = np.empty(output_shape, dtype=dtype, **kw)
+    output_array = np.empty(output_shape, dtype=dtype, like=data)
     if len(args) == 0:
         # There aren't any BaseSignals for iterating
         for nav_index in np.ndindex(chunk_nav_shape):
@@ -1647,21 +1646,3 @@ def get_array_module(array):
         pass
 
     return module
-
-
-def get_numpy_kwargs(array):
-    """
-    Convenience funtion to return a dictionary containing the `like` keyword
-    if numpy>=1.20.
-
-    Note
-    ----
-    `like` keyword is an experimental feature introduced in numpy 1.20 and is
-    pending on acceptance of NEP 35
-
-    """
-    kw = {}
-    if Version(np.__version__) >= Version("1.20"):
-         kw['like'] = array
-
-    return kw

hyperspy/model.py

@@ -23,15 +23,12 @@
 import tempfile
 import warnings
 from contextlib import contextmanager
-from packaging.version import Version
 from functools import partial
 
 import dill
 import numpy as np
-import dask
 import dask.array as da
 from dask.diagnostics import ProgressBar
-import scipy
 import scipy.odr as odr
 from IPython.display import display, display_pretty
 from scipy.linalg import svd
@@ -1079,12 +1076,6 @@ def _linear_fit(self, optimizer="lstsq", calculate_errors=False,
                 **kw)
             coefficient_array = result.T
 
-            if self.signal._lazy and not only_current and (
-                    Version(dask.__version__) < Version("2020.12.0")):
-                # Dask pre 2020.12 didn't support residuals on 2D input,
-                # we calculate them later.
-                residual = None  # pragma: no cover
-
         elif optimizer == "ridge_regression":
             if self.signal._lazy:
                 raise ValueError(
@@ -1305,9 +1296,6 @@ def fit(
         }
 
         if optimizer in ["Dual Annealing", "SHGO"]:
-            if Version(scipy.__version__) < Version("1.2.0"):
-                raise ValueError(f"`optimizer='{optimizer}'` requires scipy >= 1.2.0")
-
             from scipy.optimize import dual_annealing, shgo
 
             _supported_global.update({"Dual Annealing": dual_annealing, "SHGO": shgo})

hyperspy/signal.py

@@ -3675,11 +3675,7 @@ def _make_sure_data_is_contiguous(self):
             _logger.info("{0!r} data is replaced by its optimized copy, see "
                          "optimize parameter of ``Basesignal.transpose`` "
                          "for more information.".format(self))
-            # `like` keyword is necessary to support cupy array (NEP-35)
-            kw = {}
-            if Version(np.__version__) >= Version("1.20"):
-                 kw['like'] = self.data
-            self.data = np.ascontiguousarray(self.data, **kw)
+            self.data = np.ascontiguousarray(self.data, like=self.data)
 
     def _iterate_signal(self, iterpath=None):
         """Iterates over the signal data. It is faster than using the signal