[FIX] replace the NumPy aliases removed in NumPy 2

pytta/_h5utils.py

@@ -32,7 +32,7 @@ def int_parser(attr):
                          np.int32,
                          np.int64,
                          int,
-                         np.int0)):
+                         np.intp)):
         return int(attr)
     else:
         return attr

pytta/classes/_base.py

@@ -490,8 +490,8 @@ def CF(self):
 
     @CF.setter
     def CF(self, newCF):
-        if isinstance(newCF, float) or isinstance(newCF, int):
-            self._CF = newCF
+        if isinstance(newCF, (int, float, np.integer, np.floating)):
+            self._CF = float(newCF)
         else:
             raise TypeError('Channel correction factor must be a number.')
         return

pytta/classes/signal.py

@@ -941,14 +941,14 @@ def __truediv__(self, other):
                         raise ValueError("Both signal-like objects must have the \
                                         same number of channels.")
                     result_freqSignal = np.zeros(self.freqSignal.shape,
-                                                dtype=np.complex_)
+                                                dtype=np.complex128)
                     for channel in range(other.numChannels):
                         result_freqSignal[:, channel] = \
                             currentFreqSignal[:, channel] \
                             / otherFreqSignal[:, channel]
                 else:
                     result_freqSignal = np.zeros(self.freqSignal.shape,
-                                                dtype=np.complex_)
+                                                dtype=np.complex128)
                     for channel in range(self.numChannels):
                         result_freqSignal[:, channel] = \
                             currentFreqSignal[:, channel] \
@@ -998,14 +998,14 @@ def __mul__(self, other):
                         raise ValueError("Both signal-like objects must have the \
                                         same number of channels.")
                     result_freqSignal = np.zeros(self.freqSignal.shape,
-                                                dtype=np.complex_)
+                                                dtype=np.complex128)
                     for channel in range(other.numChannels):
                         result_freqSignal[:, channel] = \
                             currentFreqSignal[:, channel] \
                             * otherFreqSignal[:, channel]
                 else:
                     result_freqSignal = np.zeros(self.freqSignal.shape,
-                                                dtype=np.complex_)
+                                                dtype=np.complex128)
                     for channel in range(self.numChannels):
                         result_freqSignal[:, channel] = \
                             currentFreqSignal[:, channel] \
@@ -1144,7 +1144,7 @@ def _ifft(self):
         """
         # spectrum denormalization
         if self.signalType == 'power':
-            adjustedFreqSignal = np.zeros(self._freqSignal.shape, dtype=np.complex_)
+            adjustedFreqSignal = np.zeros(self._freqSignal.shape, dtype=np.complex128)
             # * N/2 for AC
             adjustedFreqSignal[1:, :] = \
                 self._freqSignal * len(self._freqSignal)
@@ -1762,8 +1762,8 @@ def _crossfade_spectruns(self, a, b, freqLims, freqVector):
                                        leftWin,
                                        np.ones(totalSamples-len(leftWin)-f0idx)))
 
-        aFreqSignal = np.zeros(a.shape, dtype=np.complex_)
-        bFreqSignal = np.zeros(b.shape, dtype=np.complex_)
+        aFreqSignal = np.zeros(a.shape, dtype=np.complex128)
+        bFreqSignal = np.zeros(b.shape, dtype=np.complex128)
 
         for chIndex in range(a.shape[1]):
             aFreqSignal[:,chIndex] = a[:,chIndex] * fullRightWin
@@ -1791,14 +1791,14 @@ def _calc_csd_tf(self, sig1, sig2, samplingRate, windowName,
 
 
 def _make_rms_spectra(freqSignal):
-    newFreqSignal = np.zeros(freqSignal.shape, dtype=np.complex_)
+    newFreqSignal = np.zeros(freqSignal.shape, dtype=np.complex128)
     newFreqSignal[1:, :] = freqSignal[1:, :] / 2**(1/2)
     newFreqSignal[0, :] = freqSignal[0, :]
     return newFreqSignal
 
 
 def _make_pk_spectra(freqSignal):
-    newFreqSignal = np.zeros(freqSignal.shape, dtype=np.complex_)
+    newFreqSignal = np.zeros(freqSignal.shape, dtype=np.complex128)
     newFreqSignal[1:, :] = freqSignal[1:, :] * 2**(1/2)
     newFreqSignal[0, :] = freqSignal[0, :]
     return newFreqSignal

pytta/functions.py

@@ -251,7 +251,7 @@ def resample(signal, newSamplingRate):
     Resample the timeSignal of the input SignalObj to the
     given sample rate using the scipy.signal.resample() function
     """
-    newSignalSize = np.int(signal.timeLength*newSamplingRate)
+    newSignalSize = int(signal.timeLength*newSamplingRate)
     resampled = ss.resample(signal.timeSignal[:], newSignalSize)
     newSignal = SignalObj(resampled, "time", newSamplingRate)
     return newSignal

tests/test_calibration.py

@@ -0,0 +1,31 @@
+"""
+Test for SignalObj.calib_pressure: the correction factor is the reference
+rms over the rms of the calibrator recording. On NumPy 2 that ratio is a
+NumPy float32, which the channel must accept.
+"""
+import os
+import unittest
+
+os.environ.setdefault('MPLBACKEND', 'Agg')
+
+import numpy as np
+
+import pytta
+
+FS = 48000
+
+
+class TestCalibration(unittest.TestCase):
+
+    def test_correction_factor_from_a_calibrator(self):
+        tt = np.arange(FS) / FS
+        calibrator = pytta.SignalObj(
+            0.1 * np.sqrt(2) * np.sin(2 * np.pi * 1000 * tt), 'time', FS)
+        sig = pytta.SignalObj(np.ones(FS), 'time', FS)
+        sig.calib_pressure(0, calibrator, refPrms=1.0)
+        self.assertAlmostEqual(sig.channels[1].CF, 10, places=5)
+        self.assertTrue(np.allclose(sig.timeSignal, 10, rtol=1e-5))
+
+
+if __name__ == '__main__':
+    unittest.main()