convert all the NaN to nan

Author: gpiantoni

CHANGES.rst

@@ -2,6 +2,7 @@ Change Log
 ==========
 Version 7
 ----------
+- **7.15**: change all the NaN to nan (followup of v7.12)
 - **7.14**: micromed uses latin-1, not utf-8
 - **7.13**: release to pypi
 - **7.12**: tukey from scipy.signal.windows and nan instead of NaN for numpy

tests/test_ioeeg_utils.py

@@ -4,7 +4,7 @@
                    cumsum,
                    empty,
                    isnan,
-                   NaN
+                   nan
                    )
 from numpy.testing import assert_array_equal
 from pytest import raises
@@ -59,10 +59,10 @@ def _assert_begsam_endsam(begsam, endsam):
     dat_on_disk, intervals = _generate_dat_on_disk()
 
     expected_dat = arange(begsam, endsam).astype('float')
-    expected_dat[(expected_dat < 0) & (expected_dat > intervals[-1])] = NaN
+    expected_dat[(expected_dat < 0) & (expected_dat > intervals[-1])] = nan
 
     dat = empty(endsam - begsam)
-    dat.fill(NaN)
+    dat.fill(nan)
 
     for i_dat, blk, i_blk in _select_blocks(BLOCKS, begsam, endsam):
         # print('{: 3d}-{: 3d} = {: 3d}: {: 3d}-{: 3d}'.format(i_dat[0], i_dat[1], blk, i_blk[0], i_blk[1]))

wonambi/VERSION

@@ -1 +1 @@
-7.14
+7.15

wonambi/source/linear.py

@@ -6,7 +6,7 @@
 from logging import getLogger
 from multiprocessing import Pool
 
-from numpy import (arange, asarray, atleast_2d, empty, exp, isnan, NaN,
+from numpy import (arange, asarray, atleast_2d, empty, exp, isnan, nan,
                    nansum)
 from numpy.linalg import norm
 try:
@@ -49,7 +49,7 @@ def __call__(self, data, parameter='chan'):
         for i, one_trl in enumerate(data):
             output.axis['surf'][i] = arange(self.inv.shape[0])
             output.data[i] = self.inv.dot(data.data[i])
-            output.data[i][exclude_vert, ] = NaN
+            output.data[i][exclude_vert, ] = nan
 
         return output
 
@@ -122,12 +122,12 @@ def calc_xyz2surf(surf, xyz, threshold=20, exponent=None, std=None):
         external_threshold_value = gauss(std, std) # this is around 0.607
     lg.debug('Values thresholded at ' + str(threshold_value))
 
-    xyz2surf[xyz2surf < threshold_value] = NaN
+    xyz2surf[xyz2surf < threshold_value] = nan
 
     # here we deal with vertices that are within the threshold value but far
     # from a single electrodes, so those remain empty
     sumval = nansum(xyz2surf, axis=1)
-    sumval[sumval < external_threshold_value] = NaN
+    sumval[sumval < external_threshold_value] = nan
 
     # normalize by the number of electrodes
     xyz2surf /= atleast_2d(sumval).T

wonambi/trans/frequency.py

@@ -6,7 +6,7 @@
 from multiprocessing import Pool
 
 from numpy import (arange, array, asarray, copy, empty, exp, isnan, log, max, mean,
-                   median, moveaxis, NaN, pi, real, reshape, sqrt, swapaxes, zeros)
+                   median, moveaxis, nan, pi, real, reshape, sqrt, swapaxes, zeros)
 from numpy.linalg import norm
 import numpy.fft as np_fft
 from scipy import fftpack
@@ -677,7 +677,7 @@ def _fft(x, s_freq, detrend='linear', taper=None, output='spectraldensity',
         x = detrend_func(x, axis=axis, type=detrend)
 
         if has_nan.any():
-            x[has_nan] = NaN
+            x[has_nan] = nan
 
     tapered = tapers * x[..., None, :]
 

wonambi/trans/select.py

@@ -12,8 +12,8 @@
 from collections.abc import Iterable
 from logging import getLogger
 
-from numpy import (arange, array, asarray, diff, empty, hstack, inf, 
-                   issubsctype, linspace, nan_to_num, ndarray, ones, ravel, 
+from numpy import (arange, array, asarray, diff, empty, hstack, inf,
+                   linspace, nan_to_num, ndarray, ones, ravel,
                    setdiff1d, floor)
 from numpy.lib.stride_tricks import as_strided
 from math import isclose
@@ -78,7 +78,7 @@ def read_data(self, chan=[], ref_chan=[], grp_name=None, concat_chan=False,
         concat_chan : bool
             if True, data from all channels will be concatenated
         average_channels : bool
-            if True, all channels will be averaged into a single virtual 
+            if True, all channels will be averaged into a single virtual
             channel with label 'avg_chan'
         max_s_freq: : int
             maximum sampling frequency
@@ -121,7 +121,7 @@ def read_data(self, chan=[], ref_chan=[], grp_name=None, concat_chan=False,
                                              t0, t1))
                 active_chan = chan if chan else [seg['chan'].split(' (')[0]]
                 if isinstance(active_chan, str):
-                    active_chan = [active_chan] 
+                    active_chan = [active_chan]
                 chan_to_read = active_chan + ref_chan
 
                 data = self.dataset.read_data(chan=chan_to_read, begtime=t0,
@@ -154,7 +154,7 @@ def read_data(self, chan=[], ref_chan=[], grp_name=None, concat_chan=False,
                             [x(chan=ch)[0] for x in subseg])
 
             if average_channels:
-                one_segment.data[0] = one_segment.data[0].mean(0, 
+                one_segment.data[0] = one_segment.data[0].mean(0,
                                                                keepdims=True)
                 one_segment.axis['chan'][0] = array(['avg_chan'], dtype='<U2')
                 active_chan = ['avg_chan']
@@ -248,7 +248,7 @@ def select(data, trial=None, invert=False, **axes_to_select):
                     selected_values = asarray(values_to_select, dtype='U')
 
                 else:
-                    if isinstance(values_to_select, ndarray) and issubsctype(values_to_select.dtype, bool):
+                    if isinstance(values_to_select, ndarray) and issubclass(values_to_select.dtype, bool):
                         bool_values = values_to_select
                     elif (values_to_select[0] is None and
                        values_to_select[1] is None):
@@ -325,7 +325,7 @@ def resample(data, s_freq, axis='time'):
     return output
 
 
-def smart_chan(dataset, simple_chan_name, test_chan=None):    
+def smart_chan(dataset, simple_chan_name, test_chan=None):
     """From a list of simple channel names, attempts to find the corresponding
     channel names in the dataset and returns a list (with same order).
     Parameters
@@ -334,19 +334,19 @@ def smart_chan(dataset, simple_chan_name, test_chan=None):
         info about record
     simple_chan_name : list of str
         simple names for channels, e.g. ['F3', 'Fp2', 'ECG']
-        
+
     Returns
     -------
     list
         corresponding channel labels as they appear in dataset
     """
     chan_key = {}
-    
+
     if test_chan is None:
         orig_chan_name = dataset.header['chan_name']
     else:
         orig_chan_name = test_chan
-    
+
     for s in simple_chan_name:
         # look for exact matches
         candidates = [x for x in orig_chan_name if s == x]
@@ -356,9 +356,9 @@ def smart_chan(dataset, simple_chan_name, test_chan=None):
         elif len(candidates) > 1:
             raise ValueError( f'The record contains {len(candidates)} '
                              f'duplicates of channel label {s}')
-        
+
         # look for s in first position
-        candidates = [x for x in orig_chan_name if s == x[:min(len(s), 
+        candidates = [x for x in orig_chan_name if s == x[:min(len(s),
                                                                len(x))]]
         if len(candidates) == 1:
             chan_key[s] = candidates[0]
@@ -367,7 +367,7 @@ def smart_chan(dataset, simple_chan_name, test_chan=None):
             # s appears in first position more than once
             raise ValueError(
                 f'Too many candidates corresponding to {s}: {candidates}')
-        
+
         # look for unique occurrences of s somewhere in chan label
         candidates = [x for x in orig_chan_name if s in x]
         if len(candidates) == 1:
@@ -383,7 +383,7 @@ def smart_chan(dataset, simple_chan_name, test_chan=None):
                 raise ValueError(
                     f'Too many candidates corresponding to {s}: {candidates}')
         raise ValueError(f'Unable to find channel containing {s}')
-    
+
     return [chan_key[x] for x in simple_chan_name]
 
 

wonambi/widgets/utils.py

@@ -3,7 +3,7 @@
 from ast import literal_eval
 from logging import getLogger
 from math import ceil, floor
-from numpy import arange, NaN
+from numpy import arange, nan
 from os.path import dirname, join, realpath
 
 from PyQt5.QtCore import QRectF, QSettings, Qt
@@ -302,7 +302,7 @@ def get_value(self, default=0):
         text = self.text()
 
         if text == 'N/A':
-            return NaN
+            return nan
 
         try:
             text = float(text)