MAINT: add input validation to scales argument to cwt (#703)

Co-authored-by: Ralf Gommers <ralf.gommers@gmail.com>

pywt/_cwt.py

@@ -118,8 +118,11 @@ def cwt(data, scales, wavelet, sampling_period=1., method='conv', axis=-1):
     dt_cplx = np.result_type(dt, np.complex64)
     if not isinstance(wavelet, (ContinuousWavelet, Wavelet)):
         wavelet = DiscreteContinuousWavelet(wavelet)
-    if np.isscalar(scales):
-        scales = np.array([scales])
+
+    scales = np.atleast_1d(scales)
+    if np.any(scales <= 0):
+        raise ValueError("`scales` must only include positive values")
+
     if not np.isscalar(axis):
         raise np.AxisError("axis must be a scalar.")
 

pywt/tests/test_cwt_wavelets.py

@@ -441,6 +441,20 @@ def test_cwt_small_scales():
     # extremely short scale factors raise a ValueError
     assert_raises(ValueError, pywt.cwt, data, scales=0.01, wavelet='mexh')
 
+def test_cwt_zero_scale():
+    data = np.zeros(32)
+    scales = np.arange(0, 4)
+
+    # scale that includes 0 throws ValueError to prevent IndexError
+    assert_raises(ValueError, pywt.cwt, data, scales=scales, wavelet='morl')
+
+def test_cwt_negative_scale():
+    data = np.zeros(32)
+    scales = np.asarray([-1, -2, -3])
+
+    # scale that includes negative values throws ValueError to prevent IndexError
+    assert_raises(ValueError, pywt.cwt, data, scales=scales, wavelet='morl')
+
 
 def test_cwt_method_fft():
     rstate = np.random.RandomState(1)