Merge pull request #232 from effigies/deprecations

MNT: Address deprecations, update dependencies, apply numpy>1.21 style to doctests

Author: larsoner

.gitignore

@@ -14,3 +14,10 @@ dist/
 
 # setuptools_scm
 nitime/_version.py
+
+# coverage
+.coverage
+coverage.xml
+
+# tox
+.tox

min-requirements.txt

@@ -1,8 +1,8 @@
 # Auto-generated by tools/update_requirements.py
 --only-binary numpy,scipy
 --extra-index-url https://pypi.anaconda.org/scientific-python-nightly-wheels/simple
-matplotlib==3.5
-numpy==1.22
-scipy==1.8
-networkx==2.7
-nibabel==4.0
+matplotlib==3.7
+numpy==1.24
+scipy==1.10
+networkx==3.0
+nibabel==5.0

nitime/_compat.py

@@ -0,0 +1,5 @@
+# np.trapezoid was introduced and np.trapz deprecated in numpy 2.0
+try:  # NP2
+    from numpy import trapezoid
+except ImportError:  # NP1
+    from numpy import trapz as trapezoid

nitime/_mpl_units.py

@@ -3,6 +3,7 @@
 
 import nitime.algorithms as tsa
 import nitime.utils as utils
+from nitime._compat import trapezoid
 
 # Set the random seed:
 np.random.seed(1)
@@ -46,14 +47,14 @@ def test_AR_YW():
 
     # evaluate this integral numerically from 0 to pi
     dw = np.pi / len(psd)
-    avg_pwr_est = np.trapz(psd, dx=dw) / (2 * np.pi)
+    avg_pwr_est = trapezoid(psd, dx=dw) / (2 * np.pi)
     # consistency on the order of 10**0 is pretty good for this test
     npt.assert_almost_equal(avg_pwr, avg_pwr_est, decimal=0)
 
     # Test for providing the autocovariance as an input:
     ak, sigma_v = tsa.AR_est_YW(arsig, order, utils.autocov(arsig))
     w, psd = tsa.AR_psd(ak, sigma_v)
-    avg_pwr_est = np.trapz(psd, dx=dw) / (2 * np.pi)
+    avg_pwr_est = trapezoid(psd, dx=dw) / (2 * np.pi)
     npt.assert_almost_equal(avg_pwr, avg_pwr_est, decimal=0)
 
 
@@ -76,13 +77,13 @@ def test_AR_LD():
 
     # evaluate this integral numerically from 0 to pi
     dw = np.pi / len(psd)
-    avg_pwr_est = np.trapz(psd, dx=dw) / (2 * np.pi)
+    avg_pwr_est = trapezoid(psd, dx=dw) / (2 * np.pi)
     npt.assert_almost_equal(avg_pwr, avg_pwr_est, decimal=0)
 
     # Test for providing the autocovariance as an input:
     ak, sigma_v = tsa.AR_est_LD(arsig, order, utils.autocov(arsig))
     w, psd = tsa.AR_psd(ak, sigma_v)
-    avg_pwr_est = np.trapz(psd, dx=dw) / (2 * np.pi)
+    avg_pwr_est = trapezoid(psd, dx=dw) / (2 * np.pi)
     npt.assert_almost_equal(avg_pwr, avg_pwr_est, decimal=0)
 
 

nitime/algorithms/tests/test_autoregressive.py

@@ -39,7 +39,6 @@ def __init__(self, input=None, method=None, unwrap_phases=False):
         Examples
         --------
         >>> import nitime.timeseries as ts
-        >>> np.set_printoptions(precision=4)  # for doctesting
         >>> t1 = ts.TimeSeries(data = np.arange(0,1024,1).reshape(2,512),
         ...                                 sampling_rate=np.pi)
         >>> c1 = CoherenceAnalyzer(t1)
@@ -48,11 +47,11 @@ def __init__(self, input=None, method=None, unwrap_phases=False):
         >>> c1.method['this_method']
         'welch'
         >>> c1.coherence[0,1]
-        array([ 0.9024,  0.9027,  0.9652,  0.9433,  0.9297,  0.9213,  0.9161,
-                0.9126,  0.9102,  0.9085,  0.9072,  0.9063,  0.9055,  0.905 ,
-                0.9045,  0.9041,  0.9038,  0.9036,  0.9034,  0.9032,  0.9031,
-                0.9029,  0.9028,  0.9027,  0.9027,  0.9026,  0.9026,  0.9025,
-                0.9025,  0.9025,  0.9025,  0.9026,  1.    ])
+        array([0.9024, 0.9027, 0.9652, 0.9433, 0.9297, 0.9213, 0.9161, 0.9126,
+               0.9102, 0.9085, 0.9072, 0.9063, 0.9055, 0.905 , 0.9045, 0.9041,
+               0.9038, 0.9036, 0.9034, 0.9032, 0.9031, 0.9029, 0.9028, 0.9027,
+               0.9027, 0.9026, 0.9026, 0.9025, 0.9025, 0.9025, 0.9025, 0.9026,
+               1.    ])
         >>> c1.phase[0,1]
         array([ 0.    , -0.035 , -0.4839, -0.4073, -0.3373, -0.2828, -0.241 ,
                -0.2085, -0.1826, -0.1615, -0.144 , -0.1292, -0.1164, -0.1054,

nitime/analysis/coherence.py

@@ -24,7 +24,6 @@ def __init__(self, input=None):
 
         Examples
         --------
-        >>> np.set_printoptions(precision=4)  # for doctesting
         >>> t1 = ts.TimeSeries(data = np.sin(np.arange(0,
         ...                    10*np.pi,10*np.pi/100)).reshape(2,50),
         ...                                      sampling_rate=np.pi)

nitime/analysis/correlation.py

@@ -43,7 +43,6 @@ def __init__(self, input=None, method=None, BW=None, adaptive=False,
 
         Examples
         --------
-        >>> np.set_printoptions(precision=4)  # for doctesting
         >>> t1 = ts.TimeSeries(data = np.arange(0,1024,1).reshape(2,512),
         ... sampling_rate=np.pi)
         >>> s1 = SpectralAnalyzer(t1)
@@ -53,13 +52,13 @@ def __init__(self, input=None, method=None, BW=None, adaptive=False,
         3.1415926535... Hz
         >>> f,s = s1.psd
         >>> f
-        array([ 0.    ,  0.0491,  0.0982,  0.1473,  0.1963,  0.2454,  0.2945,
-                0.3436,  0.3927,  0.4418,  0.4909,  0.54  ,  0.589 ,  0.6381,
-                0.6872,  0.7363,  0.7854,  0.8345,  0.8836,  0.9327,  0.9817,
-                1.0308,  1.0799,  1.129 ,  1.1781,  1.2272,  1.2763,  1.3254,
-                1.3744,  1.4235,  1.4726,  1.5217,  1.5708])
+        array([0.    , 0.0491, 0.0982, 0.1473, 0.1963, 0.2454, 0.2945, 0.3436,
+               0.3927, 0.4418, 0.4909, 0.54  , 0.589 , 0.6381, 0.6872, 0.7363,
+               0.7854, 0.8345, 0.8836, 0.9327, 0.9817, 1.0308, 1.0799, 1.129 ,
+               1.1781, 1.2272, 1.2763, 1.3254, 1.3744, 1.4235, 1.4726, 1.5217,
+               1.5708])
         >>> s[0,0]   # doctest: +ELLIPSIS
-        1128276.92538360...
+        1128276.9253836009
         """
         BaseAnalyzer.__init__(self, input)
 

nitime/analysis/spectral.py

@@ -0,0 +1,7 @@
+import numpy as np
+import pytest
+
+
+@pytest.fixture(scope='session', autouse=True)
+def legacy_printoptions():
+    np.set_printoptions(legacy='1.21', precision=4)

nitime/conftest.py

@@ -44,9 +44,9 @@ def tri(N, M=None, k=0, dtype=float):
            [1, 1, 1, 1, 1]])
 
     >>> np.tri(3, 5, -1)
-    array([[ 0.,  0.,  0.,  0.,  0.],
-           [ 1.,  0.,  0.,  0.,  0.],
-           [ 1.,  1.,  0.,  0.,  0.]])
+    array([[0., 0., 0., 0., 0.],
+           [1., 0., 0., 0., 0.],
+           [1., 1., 0., 0., 0.]])
 
     """
     if M is None: M = N