Merge pull request #158 from luzpaz/misc-typos

arokem · web-flow · commit 7b5254db55c1 · 2018-02-16T16:56:25.000-08:00
Misc. typos
diff --git a/doc/discussion/base_classes.rst b/doc/discussion/base_classes.rst
@@ -23,7 +23,7 @@ representation of financial time-series, which can be compared against each
 other, using the common reference and for which the concept of the work-week
 applies. 
 
-However, because most often the absolute calender time of the occurence of
+However, because most often the absolute calendar time of the occurrence of
 events in an experiment is of no importance, we can disregard it. Rather, the
 comparison of the time progression of data in different experiments conducted
 in different calendar times (different days, different times in the same day)
@@ -71,9 +71,9 @@ the essential differences between them.
 -------------------
 
 This class has the least restrictions on it: it will be a 1d array, which
-contains time-points that are not neccesarily ordered. It can also contain
+contains time-points that are not necessarily ordered. It can also contain
 several copies of the same time-point. This class will be used in order to
-represent sparsely occuring events, measured at some unspecified sampling rate
+represent sparsely occurring events, measured at some unspecified sampling rate
 and possibly collected from several different channels, where the data is
 sampled in order of channel and not in order of time. As in the case of the
 :class:`np.ndarray`, slicing into this kind of representation should allow a
diff --git a/doc/discussion/interval_object.rst b/doc/discussion/interval_object.rst
@@ -9,7 +9,7 @@ time-series. For example, a typical case, is when two time-series are recorded
 simultaneously. One is recorded from measurement of some continuous
 physilogical variable, such as fMRI BOLD (and is represented by a an object of
 type :ref:`UniformTimeSeries`). The other is a series of discrete events
-occuring concurrently (and can be represented by a :ref:`EventSeries` or by a
+occurring concurrently (and can be represented by a :ref:`EventSeries` or by a
 :ref:`NonUniformTimeSeries`). For example, button presses by the subject, or
 trials of different kinds. If we want to analyze the progression of the
 physiological time-series, locked to particular kinds of events in the
diff --git a/doc/discussion/time_series_access.rst b/doc/discussion/time_series_access.rst
@@ -52,7 +52,7 @@ for which $t_i$ is the maximal one, which still fulfills: $t_i<t$.
 
 Questions
 ~~~~~~~~~
-The follwing questions apply to all three cases: 
+The following questions apply to all three cases: 
 
 * what happens when the t is smaller than the smallest entry in the array
   return None?
diff --git a/doc/examples/event_related_fmri.py b/doc/examples/event_related_fmri.py
@@ -7,7 +7,7 @@
 ==================
 
 Extracting the average time-series from one signal, time-locked to the
-occurence of some type of event in another signal is a very typical operation
+occurrence of some type of event in another signal is a very typical operation
 in the analysis of time-series from neuroscience experiments. Therefore, we
 have an additional example of this kind of analysis in :ref:`grasshopper`
 
@@ -72,8 +72,8 @@
 :ref:`grasshopper` example), but here, instead of providing an :class:`Events`
 object as input, another :class:`TimeSeries` object is provided, containing an
 equivalent time-series with the same dimensions as the time-series on which the
-analysis is done, with '0' wherever no event of interest occured and an integer
-wherever an even of interest occured (sequential different integers for the
+analysis is done, with '0' wherever no event of interest occurred and an integer
+wherever an even of interest occurred (sequential different integers for the
 different kinds of events).
 
 """
diff --git a/doc/examples/grasshopper.py b/doc/examples/grasshopper.py
@@ -8,7 +8,7 @@
 =====================================
 
 Extracting the average time-series from one signal, time-locked to the
-occurence of some type of event in another signal is a very typical operation in
+occurrence of some type of event in another signal is a very typical operation in
 the analysis of time-series from neuroscience experiments. Therefore, we have
 an additional example of this kind of analysis in :ref:`et-fmri`
 
diff --git a/doc/examples/multi_taper_spectral_estimation.py b/doc/examples/multi_taper_spectral_estimation.py
@@ -216,7 +216,7 @@ def dB(x, out=None):
 .. image:: fig/multi_taper_spectral_estimation_03.png
 
 Next, we use Welch's periodogram, by applying :func:`tsa.get_spectra`. Note
-that we explicitely provide the function with a 'method' dict, which specifies
+that we explicitly provide the function with a 'method' dict, which specifies
 the method used in order to calculate the PSD, but the default method is 'welch'.
 
 """
diff --git a/doc/users/overview.rst b/doc/users/overview.rst
@@ -101,7 +101,7 @@ representation of financial time-series, which can be compared against each
 other, using the common reference and for which the concept of the work-week
 applies. 
 
-However, because most often the absolute calender time of the occurence of
+However, because most often the absolute calendar time of the occurrence of
 events in an experiment is of no importance, we can disregard it. Rather, the
 comparison of the time progression of data in different experiments conducted
 in different calendar times (different days, different times in the same day)
@@ -142,7 +142,7 @@ For the time being, there are two types of Time classes: :ref:`TimeArray` and :r
 :class:`TimeArray`
 -------------------
 
-This class has less restrictions on it: it is made of an 1-d array, which contains time-points that are not neccesarily ordered. It can also contain several copies of the same time-point. This class can be used in order to represent sparsely occuring events, measured at some unspecified sampling rate and possibly collected from several different channels, where the data is sampled in order of channel and not in order of time. As in the case of the :class:`np.ndarray`. This representation of time carries, in addition to the array itself an attribute :attr:`time_unit`, which is the unit in which we would like to present the time-points (recall that the underlying representation is always in the base-unit).
+This class has less restrictions on it: it is made of an 1-d array, which contains time-points that are not necessarily ordered. It can also contain several copies of the same time-point. This class can be used in order to represent sparsely occurring events, measured at some unspecified sampling rate and possibly collected from several different channels, where the data is sampled in order of channel and not in order of time. As in the case of the :class:`np.ndarray`. This representation of time carries, in addition to the array itself an attribute :attr:`time_unit`, which is the unit in which we would like to present the time-points (recall that the underlying representation is always in the base-unit).
 
 .. _UniformTime:
 
@@ -229,10 +229,10 @@ contains several attributes:
 ---------------
 
 This is an object which represents a collection of events. For example, this
-can represent discrete button presses occuring during an experiment. This
+can represent discrete button presses occurring during an experiment. This
 object contains a :ref:`TimeArray` as its representation of time. This means
 that the events recorded in the :attr:`data` array can be organized
-according to any organizing principle you would want, not neccesarily according
+according to any organizing principle you would want, not necessarily according
 to their organization or order in time. For example, if events are read from
 different devices, the order of the events in the data array can be arbitrarily
 chosen to be the order of the devices from which data is read.
diff --git a/nitime/_mpl_units.py b/nitime/_mpl_units.py
@@ -143,7 +143,7 @@ def default_units(x, axis):
     def convert(obj, unit, axis):
         """
         convert obj using unit for the specified axis.  If obj is a sequence,
-        return the converted sequence.  The ouput must be a sequence of scalars
+        return the converted sequence.  The output must be a sequence of scalars
         that can be used by the numpy array layer
         """
         return obj
diff --git a/nitime/algorithms/__init__.py b/nitime/algorithms/__init__.py
@@ -50,7 +50,7 @@
 :func:`entropy_cc`, :func:`transfer_entropy`
 
 The algorithms in this library are the functional form of the algorithms, which
-accept as inputs numpy array and produce numpy array outputs. Therfore, they
+accept as inputs numpy array and produce numpy array outputs. Therefore, they
 can be used on any type of data which can be represented in numpy arrays. See
 also :mod:`nitime.analysis` for simplified analysis interfaces, using the
 data containers implemented in :mod:`nitime.timeseries`
diff --git a/nitime/algorithms/autoregressive.py b/nitime/algorithms/autoregressive.py
@@ -148,7 +148,7 @@ def AR_est_LD(x, order, rxx=None):
     else:
         rxx_m = utils.autocorr(x)[:order + 1]
     w = np.zeros((order + 1, ), rxx_m.dtype)
-    # intialize the recursion with the R[0]w[1]=r[1] solution (p=1)
+    # initialize the recursion with the R[0]w[1]=r[1] solution (p=1)
     b = rxx_m[0].real
     w_k = rxx_m[1] / b
     w[1] = w_k
diff --git a/nitime/algorithms/cohere.py b/nitime/algorithms/cohere.py
@@ -17,7 +17,7 @@
 from .spectral import get_spectra, get_spectra_bi
 import nitime.utils as utils
 
-# To suppport older versions of numpy that don't have tril_indices:
+# To support older versions of numpy that don't have tril_indices:
 from nitime.index_utils import tril_indices
 
 
@@ -995,7 +995,7 @@ def cache_fft(time_series, ij, lb=0, ub=None,
     else:
         norm_val = (np.abs(window_vals) ** 2).sum() / 2
 
-    # cache the FFT of every windowed, detrended NFFT length segement
+    # cache the FFT of every windowed, detrended NFFT length segment
     # of every channel.  If prefer_speed_over_memory, cache the conjugate
     # as well
 
diff --git a/nitime/algorithms/event_related.py b/nitime/algorithms/event_related.py
@@ -29,7 +29,7 @@ def fir(timeseries, design):
 
           where A is a (number of TRs) x (length of HRF) matrix with a unity
           matrix placed with its top left corner placed in each TR in which
-          event of type A occured in the design. B is the equivalent for
+          event of type A occurred in the design. B is the equivalent for
           events of type B, etc.
 
     Returns
diff --git a/nitime/analysis/coherence.py b/nitime/analysis/coherence.py
@@ -8,7 +8,7 @@
 from nitime import utils as tsu
 from nitime import algorithms as tsa
 
-# To suppport older versions of numpy that don't have tril_indices:
+# To support older versions of numpy that don't have tril_indices:
 from nitime.index_utils import tril_indices, triu_indices
 
 from .base import BaseAnalyzer
@@ -119,7 +119,7 @@ def coherency(self):
     def spectrum(self):
         """
         The spectra of each of the channels and cross-spectra between
-        different channles  in the input TimeSeries object
+        different channels  in the input TimeSeries object
         """
         f, spectrum = tsa.get_spectra(self.input.data, method=self.method)
         return spectrum
diff --git a/nitime/analysis/correlation.py b/nitime/analysis/correlation.py
@@ -4,7 +4,7 @@
 from nitime import timeseries as ts
 from nitime import algorithms as tsa
 
-# To suppport older versions of numpy that don't have tril_indices:
+# To support older versions of numpy that don't have tril_indices:
 from nitime.index_utils import tril_indices
 
 from .base import BaseAnalyzer
diff --git a/nitime/analysis/event_related.py b/nitime/analysis/event_related.py
@@ -25,12 +25,12 @@ def __init__(self, time_series, events, len_et, zscore=False,
            A time-series with data on which the event-related analysis proceeds
 
         events_time_series : a TimeSeries object or an Events object
-            The events which occured in tandem with the time-series in the
+            The events which occurred in tandem with the time-series in the
             EventRelatedAnalyzer. This object's data has to have the same
             dimensions as the data in the EventRelatedAnalyzer object. In each
             sample in the time-series, there is an integer, which denotes the
-            kind of event which occured at that time. In time-bins in which no
-            event occured, a 0 should be entered. The data in this time series
+            kind of event which occurred at that time. In time-bins in which no
+            event occurred, a 0 should be entered. The data in this time series
             object needs to have the same dimensionality as the data in the
             data time-series
 
@@ -46,7 +46,7 @@ def __init__(self, time_series, events, len_et, zscore=False,
         point in the event-triggered average (where possible)
 
         offset : the offset of the beginning of the event-related time-series,
-        relative to the event occurence
+        relative to the event occurrence
         """
         #XXX Change so that the offset and length of the eta can be given in
         #units of time
@@ -226,20 +226,20 @@ def xcorr_eta(self):
 
     @desc.setattr_on_read
     def et_data(self):
-        """The event-triggered data (all occurences).
+        """The event-triggered data (all occurrences).
 
         This gets the time-series corresponding to the inidividual event
-        occurences. Returns a list of lists of time-series. The first dimension
+        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
 
         The time-series itself has the first diemnsion of the data being the
-        specific occurence, with time 0 locked to the that occurence
+        specific occurrence, with time 0 locked to the that occurrence
         of the event and the last dimension is time.e
 
         This complicated structure is so that it can deal with situations where
         each channel has different events and different events have different #
-        of occurences
+        of occurrences
         """
         #Make a list for the output
         h = [0] * self._len_h
diff --git a/nitime/analysis/granger.py b/nitime/analysis/granger.py
@@ -11,7 +11,7 @@
 
 from .base import BaseAnalyzer
 
-# To suppport older versions of numpy that don't have tril_indices:
+# To support older versions of numpy that don't have tril_indices:
 from nitime.index_utils import tril_indices_from
 
 def fit_model(x1, x2, order=None, max_order=10,
diff --git a/nitime/tests/test_timeseries.py b/nitime/tests/test_timeseries.py
@@ -491,7 +491,7 @@ def test_TimeSeries():
     t1 = ts.UniformTime(length=8, sampling_rate=2)
     #duration is the same, but we're downsampling to 1Hz
     tseries1 = ts.TimeSeries(data=[1, 2, 3, 4], time=t1, sampling_rate=1)
-    #If you didn't explicitely provide the rate you want to downsample to, that
+    #If you didn't explicitly provide the rate you want to downsample to, that
     #is an error:
     with pytest.raises(ValueError) as e_info:
         ts.TimeSeries(dict(data=[1, 2, 3, 4], time=t1))
@@ -534,7 +534,7 @@ def test_TimeSeries():
     with pytest.raises(ValueError) as e_info:
         ts.TimeSeries(dict(data=data, time=t))
 
-    # test basic arithmetics wiht TimeSeries
+    # test basic arithmetics 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/timeseries.py b/nitime/timeseries.py
@@ -1151,8 +1151,8 @@ class UniformTime. Note that you can still also provide a different
                 duration = time.duration
                 length = time.shape[-1]
                 #If changing the duration requires a change to the
-                #sampling_rate, make sure that this was explicitely required by
-                #the user - if the user did not explicitely set the
+                #sampling_rate, make sure that this was explicitly required by
+                #the user - if the user did not explicitly set the
                 #sampling_rate, or it is inconsistent, throw an error:
                 data_len = np.array(data).shape[-1]
 
@@ -1490,7 +1490,7 @@ def __init__(self, time, labels=None, indices=None,
         Parameters
         ----------
         time : array or TimeArray
-            The times at which events occured
+            The times at which events occurred
 
         labels : array, optional
 
diff --git a/nitime/utils.py b/nitime/utils.py
@@ -269,7 +269,7 @@ def expected_jk_variance(K):
     asymptotic expansion of the trigamma function derived in
     [Thompson_1994]
 
-    Paramters
+    Parameters
     ---------
 
     K : int
@@ -1016,7 +1016,7 @@ def get_freqs(Fs, n):
 
 def circle_to_hz(omega, Fsamp):
     """For a frequency grid spaced on the unit circle of an imaginary plane,
-    return the corresponding freqency grid in Hz.
+    return the corresponding frequency grid in Hz.
     """
     return Fsamp * omega / (2 * np.pi)
 
@@ -1893,9 +1893,9 @@ def fir_design_matrix(events, len_hrf):
     ----------
 
     events : 1-d int array
-       Integers denoting different kinds of events, occuring at the time
+       Integers denoting different kinds of events, occurring at the time
        corresponding to the bin represented by each slot in the array. In
-       time-bins in which no event occured, a 0 should be entered. If negative
+       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
        and 1 can be used for a simple contrast of two conditions)
diff --git a/nitime/viz.py b/nitime/viz.py
@@ -844,7 +844,7 @@ def mkgraph(cmat, threshold=0.0, threshold2=None):
     The values in the original matrix cmat can be thresholded out.  If only one
     threshold is given, all values below that are omitted when creating edges.
     If two thresholds are given, then values in the th2-th1 range are
-    ommitted.  This allows for the easy creation of weighted graphs with
+    omitted.  This allows for the easy creation of weighted graphs with
     positive and negative values where a range of weights around 0 is omitted.
 
     Parameters
diff --git a/tools/ex2rst b/tools/ex2rst
@@ -99,7 +99,7 @@ def exfile2rst(filename):
             proc_line = None
             # handle doc start
             if not indocs:
-                # guarenteed to start with """
+                # guaranteed to start with """
                 if len(cleanline) > 3 \
                   and (cleanline.endswith('"""') \
                        or cleanline.endswith("'''")):
diff --git a/tools/make_examples.py b/tools/make_examples.py
@@ -41,7 +41,7 @@
 
 
 """
-# Function defintions::
+# Function definitions::
 
 # These global variables let show() be called by the scripts in the usual
 # manner, but when generating examples, we override it to write the figures to
