[DATALAD RUNCMD] run codespell throughout but ignore fail

yarikoptic · yarikoptic · commit adf30c7918ce · 2023-12-14T22:10:28.000-05:00
=== Do not change lines below ===
{
 "chain": [],
 "cmd": "codespell -w || :",
 "exit": 0,
 "extra_inputs": [],
 "inputs": [],
 "outputs": [],
 "pwd": "."
}
^^^ Do not change lines above ^^^
diff --git a/doc/discussion/interval_object.rst b/doc/discussion/interval_object.rst
@@ -49,7 +49,7 @@ consistent, in the same manner that is already implemented in
   represent a time offset, relative to the attributes :attr:`t_start` and
   :attr:`t_stop`. That is, it can tell us where relative to these two
   time-points some interesting even, which this interval surrounds, or this
-  interval is close to, occurs. This can be used in order to interpert how
+  interval is close to, occurs. This can be used in order to interpret how
   time-series access is done using the :class:`TimeInterval` object. See
   :ref:`time_series_access`. This attribute can be implemented as an optional
   input on initialization, such that it defaults to be equal to
diff --git a/doc/discussion/multitaper_jackknife.rst b/doc/discussion/multitaper_jackknife.rst
@@ -16,7 +16,7 @@ General JN definitions
 | **pseudovalues**
 | :math:`\hat{\theta}_i = n\hat{\theta} - (n-1)\hat{\theta}_{-i}`
 
-Now the jackknifed esimator is computed as
+Now the jackknifed estimator is computed as
 
 :math:`\tilde{\theta} = \dfrac{1}{n}\sum_i \hat{\theta}_i = n\hat{\theta} - \dfrac{n-1}{n}\sum_i \hat{\theta}_{-i}`
 
diff --git a/doc/examples/filtering_fmri.py b/doc/examples/filtering_fmri.py
@@ -242,7 +242,7 @@
 We can do that by initializng a SpectralAnalyzer for each one of the filtered
 time-series resulting from the above operation and plotting their spectra. For
 ease of compariso, we only plot the spectra using the multi-taper spectral
-estimation. At the level of granularity provided by this method, the diferences
+estimation. At the level of granularity provided by this method, the differences
 between the methods are emphasized:
 
 """
diff --git a/doc/examples/multi_taper_spectral_estimation.py b/doc/examples/multi_taper_spectral_estimation.py
@@ -117,7 +117,7 @@
 .. image:: fig/multi_taper_spectral_estimation_02.png
 
 As before, the left figure displays the windowing function in the temporal
-domain and the figure on the left displays the attentuation of spectral leakage
+domain and the figure on the left displays the attenuation of spectral leakage
 in the other frequency bands in the spectrum. Notice that though different
 windowing functions have different spectral attenuation profiles, trading off
 attenuation of leakage from frequency bands near the frequency of interest
@@ -302,7 +302,7 @@ def dB(x, out=None):
 
 .. image:: fig/multi_taper_spectral_estimation_06.png
 
-As metioned above, in addition to estimating the spectrum itself, an estimate
+As mentioned above, in addition to estimating the spectrum itself, an estimate
 of the confidence interval of the spectrum can be generated using a
 jack-knifing procedure [Thomson2007]_.
 
diff --git a/doc/users/overview.rst b/doc/users/overview.rst
@@ -67,7 +67,7 @@ object does not trigger any intensive computations. Instead the computation of
 the attributes of analyzer objects is delayed until the moment the user calls
 these attributes. In addition, once a computation is triggered it is stored as
 an attribute of the object, which assures that accessing the results of an
-analysis will trigger the computation only on the first time the analysis resut
+analysis will trigger the computation only on the first time the analysis result
 is required. Thereafter, the result of the analysis is stored for further use
 of this result.
 
diff --git a/nitime/algorithms/autoregressive.py b/nitime/algorithms/autoregressive.py
@@ -289,7 +289,7 @@ def AR_psd(ak, sigma_v, n_freqs=1024, sides='onesided'):
     Returns
     -------
     (w, ar_psd)
-    w : Array of normalized frequences from [-.5, .5) or [0,.5]
+    w : Array of normalized frequencies from [-.5, .5) or [0,.5]
     ar_psd : A PSD estimate computed by sigma_v / |1-a(f)|**2 , where
              a(f) = DTFT(ak)
 
diff --git a/nitime/algorithms/event_related.py b/nitime/algorithms/event_related.py
@@ -84,7 +84,7 @@ def freq_domain_xcorr(tseries, events, t_before, t_after, Fs=1):
     -------
     xcorr: float array
         The correlation function between the tseries and the events. Can be
-        interperted as a linear filter from events to responses (the
+        interpreted as a linear filter from events to responses (the
         time-series) of an LTI.
 
     """
@@ -125,9 +125,9 @@ def freq_domain_xcorr_zscored(tseries, events, t_before, t_after, Fs=1):
     -------
     xcorr: float array
         The correlation function between the tseries and the events. Can be
-        interperted as a linear filter from events to responses (the
+        interpreted as a linear filter from events to responses (the
         time-series) of an LTI. Because it is normalized to its own mean and
-        variance, it can be interperted as measuring statistical significance
+        variance, it can be interpreted as measuring statistical significance
         relative to all time-shifted versions of the events.
 
     """
diff --git a/nitime/algorithms/tests/test_spectral.py b/nitime/algorithms/tests/test_spectral.py
@@ -94,7 +94,7 @@ def test_get_spectra_complex():
 
 def test_get_spectra_unknown_method():
     """
-    Test that providing an unknown method to get_spectra rasies a ValueError
+    Test that providing an unknown method to get_spectra raises a ValueError
 
     """
     tseries = np.array([[1, 2, 3], [4, 5, 6]])
@@ -179,7 +179,7 @@ def test_dpss_properties():
     N = 2000
     NW = 200
     d, lam = tsa.dpss_windows(N, NW, 2*NW-2)
-    # 2NW-2 lamdas should be all > 0.9
+    # 2NW-2 lambdas should be all > 0.9
     npt.assert_(
         (lam > 0.9).all(), 'Eigenvectors show poor spectral concentration'
         )
diff --git a/nitime/analysis/coherence.py b/nitime/analysis/coherence.py
@@ -28,7 +28,7 @@ def __init__(self, input=None, method=None, unwrap_phases=False):
 
         method : dict, optional,
             This is the method used for spectral analysis of the signal for the
-            coherence caclulation. See :func:`algorithms.get_spectra`
+            coherence calculation. See :func:`algorithms.get_spectra`
             documentation for details.
 
         unwrap_phases : bool, optional
@@ -167,7 +167,7 @@ def phase(self):
         """ The frequency-dependent phase relationship between all the pairwise
         combinations of time-series in the data"""
 
-        #XXX calcluate this from the standard output, instead of recalculating:
+        #XXX calculate this from the standard output, instead of recalculating:
 
         tseries_length = self.input.data.shape[0]
         spectrum_length = self.spectrum.shape[-1]
@@ -693,7 +693,7 @@ def coherency(self):
                     cache['FFT_conj_slices'][-1] = \
                                             seed_cache['FFT_conj_slices'][0]
 
-                #This performs the caclulation for this seed:
+                #This performs the calculation for this seed:
                 Cxy[seed_idx] = tsa.cache_to_coherency(cache, ij)
 
         #In the case where there is only one channel in the seed time-series:
diff --git a/nitime/analysis/event_related.py b/nitime/analysis/event_related.py
@@ -228,7 +228,7 @@ def xcorr_eta(self):
     def et_data(self):
         """The event-triggered data (all occurrences).
 
-        This gets the time-series corresponding to the inidividual event
+        This gets the time-series corresponding to the individual event
         occurrences. Returns a list of lists of time-series. The first dimension
         is the different channels in the original time-series data and the
         second dimension is each type of event in the event time series
diff --git a/nitime/tests/test_timeseries.py b/nitime/tests/test_timeseries.py
@@ -207,7 +207,7 @@ def test_TimeArray_convert_unit():
 
 def test_TimeArray_div():
 
-    #divide singelton by singleton:
+    #divide singleton by singleton:
     a = 2.0
     b = 6.0
     time1 = ts.TimeArray(a, time_unit='s')
@@ -217,7 +217,7 @@ def test_TimeArray_div():
     div2 = time1 / time2
     npt.assert_equal(div1, div2)
 
-    #Divide a TimeArray by a singelton:
+    #Divide a TimeArray by a singleton:
     a = np.array([1, 2, 3])
     b = 6.0
     time1 = ts.TimeArray(a, time_unit='s')
@@ -500,7 +500,7 @@ def test_TimeSeries():
     tseries3 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_rate=1000,
                                     time_unit='ms')
     #you can specify the sampling_rate or the sampling_interval, to the same
-    #effect, where specificying the sampling_interval is in the units of that
+    #effect, where specifying the sampling_interval is in the units of that
     #time-series:
     tseries4 = ts.TimeSeries(data=[1, 2, 3, 4], sampling_interval=1,
                                         time_unit='ms')
@@ -534,7 +534,7 @@ def test_TimeSeries():
     with pytest.raises(ValueError) as e_info:
         ts.TimeSeries(dict(data=data, time=t))
 
-    # test basic arithmetics with TimeSeries
+    # test basic arithmetic with TimeSeries
     tseries1 = ts.TimeSeries([1, 2, 3, 4, 5, 6, 7, 8, 9, 10], sampling_rate=1)
     tseries2 = tseries1 + 1
     npt.assert_equal(tseries1.data + 1, tseries2.data)
diff --git a/nitime/utils.py b/nitime/utils.py
@@ -188,11 +188,11 @@ def circularize(x, bottom=0, top=2 * np.pi, deg=False):
 
     bottom : float, optional (defaults to 0).
         If you want to set the bottom of the interval into which you
-        modulu to something else than 0.
+        modulo to something else than 0.
 
     top : float, optional (defaults to 2*pi).
         If you want to set the top of the interval into which you
-        modulu to something else than 2*pi
+        modulo to something else than 2*pi
 
     Returns
     -------
@@ -307,7 +307,7 @@ def jackknifed_sdf_variance(yk, eigvals, sides='onesided', adaptive=True):
     sides : str, optional
        Compute the jackknife pseudovalues over as one-sided or
        two-sided spectra
-    adpative : bool, optional
+    adaptive : bool, optional
        Compute the adaptive weighting for each jackknife pseudovalue
 
     Returns
@@ -1538,7 +1538,7 @@ def tril_indices(n, k=0):
 
     Examples
     --------
-    Commpute two different sets of indices to access 4x4 arrays, one for the
+    Compute two different sets of indices to access 4x4 arrays, one for the
     lower triangular part starting at the main diagonal, and one starting two
     diagonals further right:
 
@@ -1613,7 +1613,7 @@ def triu_indices(n, k=0):
 
     Examples
     --------
-    Commpute two different sets of indices to access 4x4 arrays, one for the
+    Compute two different sets of indices to access 4x4 arrays, one for the
     upper triangular part starting at the main diagonal, and one starting two
     diagonals further right:
 
@@ -2094,7 +2094,7 @@ def fir_design_matrix(events, len_hrf):
        corresponding to the bin represented by each slot in the array. In
        time-bins in which no event occurred, a 0 should be entered. If negative
        event values are entered, they will be used as "negative" events, as in
-       events that should be contrasted with the postitive events (typically -1
+       events that should be contrasted with the positive events (typically -1
        and 1 can be used for a simple contrast of two conditions)
 
     len_hrf : int
diff --git a/nitime/viz.py b/nitime/viz.py
@@ -276,7 +276,7 @@ def channel_formatter(x, pos=None):
     # diagonal values:
     idx_null = tril_indices(m.shape[0])
     m[idx_null] = np.nan
-    # tranpose the upper triangle to lower
+    # transpose the upper triangle to lower
     m = m.T
 
     # Extract the minimum and maximum values for scaling of the
@@ -709,7 +709,7 @@ def draw_graph(G,
     # Set default edge colormap
     if edge_cmap is None:
         # Make an object with the colormap API, that maps all input values to
-        # the default color (with proper alhpa)
+        # the default color (with proper alpha)
         edge_cmap = (lambda val, alpha:
                      colors.colorConverter.to_rgba(default_edge_color, alpha))
 
