feat: support numpy 2+

Makefile

@@ -1,5 +1,10 @@
 LINT_TARGET_DIRS := PyEMD doc example
 
+init:
+	python -m venv .venv
+	.venv/bin/pip install -r requirements.txt
+	@echo "Run 'source .venv/bin/activate' to activate the virtual environment"
+
 test:
 	python -m PyEMD.tests.test_all
 

PyEMD/EMD.py

@@ -13,7 +13,7 @@
 from scipy.interpolate import interp1d
 
 from PyEMD.splines import akima, cubic, cubic_hermite, cubic_spline_3pts, pchip
-from PyEMD.utils import get_timeline
+from PyEMD.utils import get_timeline, deduce_common_type
 
 FindExtremaOutput = Tuple[np.ndarray, np.ndarray, np.ndarray, np.ndarray, np.ndarray]
 
@@ -765,7 +765,7 @@ def check_imf(
     @staticmethod
     def _common_dtype(x: np.ndarray, y: np.ndarray) -> Tuple[np.ndarray, np.ndarray]:
         """Casts inputs (x, y) into a common numpy DTYPE."""
-        dtype = np.find_common_type([x.dtype, y.dtype], [])
+        dtype = deduce_common_type(x.dtype, y.dtype)
         if x.dtype != dtype:
             x = x.astype(dtype)
         if y.dtype != dtype:

PyEMD/tests/test_checks.py

@@ -77,13 +77,13 @@ def test_whitenoise_check_rescaling_imf(self):
 
     def test_whitenoise_check_nan_values(self):
         """whitenoise check with nan in IMF."""
-        S = np.array([np.full(100, np.NaN) for i in range(5, 0, -1)])
+        S = np.array([np.full(100, np.nan) for i in range(5, 0, -1)])
         res = whitenoise_check(S)
-        self.assertEqual(res, None, "Input NaN returns None")
+        self.assertEqual(res, None, "Input nan returns None")
 
     def test_invalid_alpha(self):
         """Test if invalid alpha return AssertionError."""
-        S = np.array([np.full(100, np.NaN) for i in range(5, 0, -1)])
+        S = np.array([np.full(100, np.nan) for i in range(5, 0, -1)])
         self.assertRaises(AssertionError, whitenoise_check, S, alpha=1)
         self.assertRaises(AssertionError, whitenoise_check, S, alpha=0)
         self.assertRaises(AssertionError, whitenoise_check, S, alpha=-10)
@@ -99,7 +99,7 @@ def test_invalid_test_name(self):
 
     def test_invalid_input_type(self):
         """Test if invalid input type return AssertionError."""
-        S = [np.full(100, np.NaN) for i in range(5, 0, -1)]
+        S = [np.full(100, np.nan) for i in range(5, 0, -1)]
         self.assertRaises(AssertionError, whitenoise_check, S)
         self.assertRaises(AssertionError, whitenoise_check, 1)
         self.assertRaises(AssertionError, whitenoise_check, 1.2)

PyEMD/tests/test_utils.py

@@ -2,7 +2,7 @@
 
 import numpy as np
 
-from PyEMD.utils import get_timeline
+from PyEMD.utils import get_timeline, deduce_common_type
 
 
 class MyTestCase(unittest.TestCase):
@@ -31,15 +31,11 @@ def test_get_timeline_does_not_overflow_int16(self):
         self.assertEqual(T[-1], len(S) - 1, "Range is kept")
         self.assertEqual(T.dtype, np.uint16, "UInt16 is the min type that matches requirements")
 
-    def test_get_timeline_does_not_overflow_float16(self):
-        S = np.random.random(int(np.finfo(np.float16).max) + 5).astype(dtype=np.float16)
-        T = get_timeline(len(S), dtype=S.dtype)
-
-        self.assertGreater(len(S), np.finfo(S.dtype).max, "Length of the signal is greater than its type max value")
-        self.assertEqual(len(T), len(S), "Lengths must be equal")
-        self.assertEqual(T[-1], len(S) - 1, "Range is kept")
-        self.assertEqual(T.dtype, np.float32, "Float32 is the min type that matches requirements")
-
+    def test_deduce_common_types(self):
+        self.assertEqual(deduce_common_type(np.int16, np.int32), np.int32)
+        self.assertEqual(deduce_common_type(np.int32, np.int16), np.int32)
+        self.assertEqual(deduce_common_type(np.int32, np.int32), np.int32)
+        self.assertEqual(deduce_common_type(np.float32, np.float64), np.float64)
 
 if __name__ == "__main__":
     unittest.main()

PyEMD/utils.py

@@ -1,4 +1,5 @@
 from typing import Optional
+from functools import cache
 
 import numpy as np
 
@@ -50,3 +51,14 @@ def smallest_inclusive_dtype(ref_dtype: np.dtype, ref_value) -> np.dtype:
         raise ValueError("Requested too large integer range. Exceeds max( float64 ) == '{}.".format(max_val))
 
     raise ValueError("Unsupported dtype '{}'. Only intX and floatX are supported.".format(ref_dtype))
+
+@cache
+def deduce_common_type(xtype: np.dtype, ytype: np.dtype) -> np.dtype:
+    if xtype == ytype:
+        return xtype
+    if np.version.version[0] == '1':
+        dtype = np.find_common_type([xtype, ytype], [])
+    else:
+        dtype = np.promote_types(xtype, ytype)
+    return dtype
+

requirements.txt

@@ -1,3 +1,4 @@
 numpy>=1.12
 scipy>=0.19
 pathos>=0.2.1
+tqdm>=4.64.0,<5.0