feat: python3 support

CHANGELOG.rst

@@ -1,6 +1,10 @@
 Changelog
 =========
 
+:Version: 1.0.1 of 2019-01-07
+
+Add Python 3 support.
+
 :Version: 1.0.0 of 2017-09-29
 
 Some minor documentation and metadata changes. This version is included with 

scorr/__init__.py

@@ -1,8 +1,9 @@
-from corr2 import *
-from corr3 import *
+from .corr2 import *
+from .corr3 import *
+import imp
 
 def __reload_submodules__():
-    import helpers
-    reload(helpers)
-    reload(corr2)
-    reload(corr3)
+    from . import helpers
+    imp.reload(helpers)
+    imp.reload(corr2)
+    imp.reload(corr3)

scorr/corr2.py

@@ -10,7 +10,7 @@
 except ImportError:
     from logging import getLogger
 
-from helpers import is_number_like, is_string_like, get_nfft
+from .helpers import is_number_like, is_string_like, get_nfft
 
 # Helpers
 # ===========================================================================
@@ -41,15 +41,15 @@ def corr_mat(x, maxlag=None):
 def xcorrshift(x, maxlag=None, as_pandas=False):
     """Return shifted (cross- / auto) correlation to center lag zero."""
     if not maxlag:
-        maxlag = len(x) / 2
+        maxlag = len(x) // 2
     # force pandas output?
     if as_pandas and not hasattr(x, 'iloc'):
         if len(np.shape(x)) > 1:
             x = pd.DataFrame(x)
         else:
             x = pd.Series(x)
     # slice
-    ix = np.arange(-maxlag,maxlag+1)
+    ix = np.arange(-maxlag, maxlag+1, dtype=int)
     if hasattr(x, 'iloc'):
         xs = x.iloc[ix]
         xs.index = ix
@@ -112,7 +112,7 @@ def xcorr(
         Time series to analyse.
     norm: [optional]
         How to normalise the result
-            "corr": Return correlation, i.e. r \in [-1, 1] (default).
+            "corr": Return correlation, i.e. r \\in [-1, 1] (default).
             "cov":  Return covariance. E.g. the peak of an autocorrelation 
                     will have the height var(x) = var(y)
             int, float:
@@ -302,7 +302,7 @@ def xcorr_grouped_df(
 
     # determine fft segment size        
     nfft, events_required = get_nfft(nfft, g)
-    maxlag = int(min(nfft/2, events_required))
+    maxlag = int(min(nfft//2, events_required))
 
     # allocate
     acd = np.zeros((2*maxlag, len(g)))
@@ -385,7 +385,7 @@ def xcorr_grouped_df(
 
     # done
     if return_df:
-        lag = pd.Index(range(-maxlag,maxlag+1), name='lag')
+        lag = pd.Index(list(range(-maxlag,maxlag+1)), name='lag')
         return pd.DataFrame({
                 'xcorr':     xcorrshift(acdm, maxlag),
                 'xcorr_std': xcorrshift(acde, maxlag),
@@ -461,7 +461,7 @@ def acorr_grouped_df(
 
     # determine fft segment size
     nfft, events_required = get_nfft(nfft, g)
-    maxlag = int(min(nfft/2, events_required))
+    maxlag = int(min(nfft//2, events_required))
 
     # allocate
     acd = np.zeros((maxlag + 1, len(g)))
@@ -532,7 +532,7 @@ def acorr_grouped_df(
 
     # done
     if return_df:
-        lag = pd.Index(range(maxlag+1), name='lag')
+        lag = pd.Index(list(range(maxlag+1)), name='lag')
         return pd.DataFrame({
                 'acorr':     acdm,
                 'acorr_std': acde,

scorr/corr3.py

@@ -10,7 +10,7 @@
 except ImportError:
     from logging import getLogger
 
-from helpers import is_number_like, is_string_like, get_nfft
+from .helpers import is_number_like, is_string_like, get_nfft
 
 def fft2x(x, y, z, nfft=None):
     """Return Bi-cross-spectrum."""
@@ -49,11 +49,11 @@ def fft2x(x, y, z, nfft=None):
     ## create indices aligned with fftpack's fft2 quadrants
     lm = nfft / 2.
     i0 = np.roll(
-        np.arange(int(np.ceil(lm-1)), int(-lm-1), -1), 
+        np.arange(int(np.ceil(lm-1)), int(-lm-1), -1, dtype=int), 
         int(np.floor(lm)) + 1
     )
     i = i0 * np.ones((nfft, nfft), dtype=int)
-    j0 = np.arange(0, -nfft, -1)
+    j0 = np.arange(0, -nfft, -1, dtype=int)
     j = hankel(j0, np.roll(j0,1))
 
     # B
@@ -79,7 +79,7 @@ def padded_x3corr_norm(nfft, pad=0, segments=1, debias=True):
         with two-point xcorr defaults.
     """
     ndat = nfft - pad
-    nmid = min(ndat, nfft/2) # symmetry axis
+    nmid = min(ndat, nfft//2) # symmetry axis
     nemp = -min(0, ndat-pad) # completely empty
     w3 = np.ones((nfft,nfft))
 

scorr/helpers.py

@@ -72,7 +72,7 @@ def get_nfft(nfft, g):
             # largest power of two < n
             nfft = int(2**np.floor(np.log2(n)))
         else:
-            raise ValueError, "Can't understand nfft='%s'" % nfft
+            raise ValueError("Can't understand nfft='%s'" % nfft)
     else:
         nfft = int(nfft)
         events_required = int(np.ceil(nfft / 2.))

setup.py

@@ -2,7 +2,7 @@
 
 setup(
     name='scorr',
-    version='1.0.0',
+    version='1.0.1',
     description=(
         'Fast and flexible two- and three-point correlation analysis '
         'for time series using spectral methods.'
@@ -22,8 +22,8 @@
         'License :: OSI Approved :: MIT License',
         'Programming Language :: Python :: 2 :: Only',
         'Programming Language :: Python :: 2.7',
-        #'Programming Language :: Python :: 3',
-        #'Programming Language :: Python :: 3.6',
+        'Programming Language :: Python :: 3',
+        'Programming Language :: Python :: 3.6',
         'Topic :: Scientific/Engineering',
         'Topic :: Software Development :: Libraries',
         'Topic :: Software Development :: Libraries :: Python Modules'
@@ -34,7 +34,7 @@
     ],
     url='http://github.com/felixpatzelt/scorr',
     download_url=(
-      'https://github.com/felixpatzelt/scorr/archive/1.0.0.tar.gz'
+      'https://github.com/felixpatzelt/scorr/archive/1.0.1.tar.gz'
     ),
     author='Felix Patzelt',
     author_email='[email protected]',

tests/test_corr2.py

@@ -112,8 +112,8 @@ def test_acorr_grouped_df(self):
        rdf = corr2.acorr_grouped_df(df, nfft='auto pad')
        self.assertTrue(
            (
-               abs(r[:self.dlen/2+1] - rdf['acorr'].loc[0:self.dlen/2]) 
-               < 3*rdf['acorr_std'].loc[0:self.dlen/2]
+               abs(r[:self.dlen//2+1] - rdf['acorr'].loc[0:self.dlen//2]) 
+               < 3*rdf['acorr_std'].loc[0:self.dlen//2]
            ).all()
        )
 
@@ -123,14 +123,14 @@ def test_xcorr_grouped_df(self):
        # positive lags
        self.assertTrue(
            (
-               abs(r[:self.dlen/2+1] - rdf['xcorr'].loc[0:self.dlen/2]) 
-               < 3*rdf['xcorr_std'].loc[0:self.dlen/2]
+               abs(r[:self.dlen//2+1] - rdf['xcorr'].loc[0:self.dlen//2]) 
+               < 3*rdf['xcorr_std'].loc[0:self.dlen//2]
            ).all()
        )
        # negative lags
        self.assertTrue(
            (
-               abs(r[-self.dlen/2-1:] - rdf['xcorr'].loc[-self.dlen/2:0]) 
-               < 3*rdf['xcorr_std'].loc[-self.dlen/2:0]
+               abs(r[-self.dlen//2-1:] - rdf['xcorr'].loc[-self.dlen//2:0]) 
+               < 3*rdf['xcorr_std'].loc[-self.dlen//2:0]
            ).all()
        )