Fix spelling for strings and comments (#1730)

doc/Tutorials/Bokeh_Elements.ipynb

@@ -304,7 +304,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Spread`` elements have the same data format as the ``ErrorBars`` element, namely x- and y-values with associated symmetric or assymetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show both the mean (as a curve) and the spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples. "
+    "``Spread`` elements have the same data format as the ``ErrorBars`` element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show both the mean (as a curve) and the spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples. "
    ]
   },
   {

doc/Tutorials/Columnar_Data.ipynb

@@ -365,7 +365,7 @@
    "source": [
     "## Tabularizing space containers\n",
     "\n",
-    "Even deeply nested objects can be deconstructed in this way, serializing them to make it easier to get your raw data out of a collection of specialized Element types. Let's say we want to make multiple observations of a noisy signal. We can collect the data into a HoloMap to visualize it and then call ``.table()`` to get a columnar object where we can perform operations or transform it to other Element types. Deconstructing nested data in this way only works if the data is homogenous. In practical terms, the requirement is that your data structure contains Elements (of any types) in these Container types: NdLayout, GridSpace, HoloMap, and NdOverlay, with all dimensions consistent throughout (so that they can all fit into the same set of columns).\n",
+    "Even deeply nested objects can be deconstructed in this way, serializing them to make it easier to get your raw data out of a collection of specialized Element types. Let's say we want to make multiple observations of a noisy signal. We can collect the data into a HoloMap to visualize it and then call ``.table()`` to get a columnar object where we can perform operations or transform it to other Element types. Deconstructing nested data in this way only works if the data is homogeneous. In practical terms, the requirement is that your data structure contains Elements (of any types) in these Container types: NdLayout, GridSpace, HoloMap, and NdOverlay, with all dimensions consistent throughout (so that they can all fit into the same set of columns).\n",
     "\n",
     "Let's now go back to the Image example. We will now collect a number of observations of some noisy data into a HoloMap and display it:"
    ]

doc/Tutorials/Composing_Data.ipynb

@@ -123,7 +123,7 @@
     "As shown in the diagram, there are three different types of container involved:\n",
     "\n",
     "- Basic Element: elementary HoloViews object containing raw data in an external format like Numpy or pandas.\n",
-    "- Homogenous container (UniformNdMapping): collections of Elements or other HoloViews components that are all the same type.  These are indexed using array-style key access with values sorted along some dimension(s), e.g. ``[0.50]`` or ``[\"a\",7.6]``.\n",
+    "- Homogeneous container (UniformNdMapping): collections of Elements or other HoloViews components that are all the same type.  These are indexed using array-style key access with values sorted along some dimension(s), e.g. ``[0.50]`` or ``[\"a\",7.6]``.\n",
     "- Heterogenous container (AttrTree): collections of data of different types, e.g. different types of Element.  These are accessed by categories using attributes, e.g. ``.Parameters.Sines``, which does not assume any ordering of a dimension.\n",
     "\n",
     "We will now go through each of the containers of these different types, at each level."

doc/Tutorials/Dynamic_Map.ipynb

@@ -207,7 +207,7 @@
     "* What you see in the cell above will not be exported in any HTML snapshot of the notebook\n",
     "\n",
     "\n",
-    "We will now explore how ``DynamicMaps`` relate to ``HoloMaps`` including conversion operations between the two types. As we will see, there are other ways to display a ``DynamicMap`` without using explict indexing or redim."
+    "We will now explore how ``DynamicMaps`` relate to ``HoloMaps`` including conversion operations between the two types. As we will see, there are other ways to display a ``DynamicMap`` without using explicit indexing or redim."
    ]
   },
   {

doc/Tutorials/Elements.ipynb

@@ -228,7 +228,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Spread`` elements have the same data format as the ``ErrorBars`` element, namely x- and y-values with associated symmetric or assymetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show both the mean (as a curve) and the spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples. "
+    "``Spread`` elements have the same data format as the ``ErrorBars`` element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show both the mean (as a curve) and the spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples. "
    ]
   },
   {

examples/reference/elements/bokeh/ErrorBars.ipynb

@@ -30,7 +30,7 @@
    "source": [
     "``ErrorBars`` provide a visual indicator for the variability of the plotted data on a graph. They are usually applied on top of other plots such as scatter, curve or bar plots to indicate the variability in each sample. \n",
     "\n",
-    "``ErrorBars`` may be used to represent symmetric error or assymetric error. An ``ErrorBars`` Element must have one key dimensions representing the samples along the x-axis and two or three value dimensions representing the value of the sample and positive and negative error values associated with that sample. See the [Columnar Data Tutorial](../Tutorials/Columnar_Data.ipynb) for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
+    "``ErrorBars`` may be used to represent symmetric error or asymmetric error. An ``ErrorBars`` Element must have one key dimensions representing the samples along the x-axis and two or three value dimensions representing the value of the sample and positive and negative error values associated with that sample. See the [Columnar Data Tutorial](../Tutorials/Columnar_Data.ipynb) for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
    ]
   },
   {

examples/reference/elements/bokeh/Spread.ipynb

@@ -28,7 +28,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or assymetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show an average trend along with a corresponding spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples. \n",
+    "``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show an average trend along with a corresponding spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples. \n",
     "\n",
     "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less that one, making it partially transparent. \n",
     "\n",

examples/reference/elements/matplotlib/ErrorBars.ipynb

@@ -30,7 +30,7 @@
    "source": [
     "``ErrorBars`` provide a visual indicator for the variability of the plotted data on a graph. They are usually applied on top of other plots such as scatter, curve or bar plots to indicate the variability in each sample. \n",
     "\n",
-    "``ErrorBars`` may be used to represent symmetric error or assymetric error. An ``ErrorBars`` Element must have one key dimensions representing the samples along the x-axis and two or three value dimensions representing the value of the sample and positive and negative error values associated with that sample. See the [Columnar Data Tutorial](../Tutorials/Columnar_Data.ipynb) for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
+    "``ErrorBars`` may be used to represent symmetric error or asymmetric error. An ``ErrorBars`` Element must have one key dimensions representing the samples along the x-axis and two or three value dimensions representing the value of the sample and positive and negative error values associated with that sample. See the [Columnar Data Tutorial](../Tutorials/Columnar_Data.ipynb) for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
    ]
   },
   {

examples/reference/elements/matplotlib/Spread.ipynb

@@ -28,7 +28,7 @@
    "cell_type": "markdown",
    "metadata": {},
    "source": [
-    "``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or assymetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show an average trend along with a corresponding spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples.\n",
+    "``Spread`` elements have the same data format as the [``ErrorBars``](ErrorBars.ipynb) element, namely x- and y-values with associated symmetric or asymmetric errors, but are interpreted as samples from a continuous distribution (just as ``Curve`` is the continuous version of ``Scatter``).  These are often paired with an overlaid ``Curve`` to show an average trend along with a corresponding spread of values; see the [Columnar Data tutorial](Columnar_Data.ipynb) for examples.\n",
     "\n",
     "Note that as the ``Spread`` element is used to add information to a plot (typically a ``Curve``) the default alpha value is less that one, making it partially transparent. \n",
     "\n",

examples/reference/elements/plotly/ErrorBars.ipynb

@@ -30,7 +30,7 @@
    "source": [
     "``ErrorBars`` provide a visual indicator for the variability of the plotted data on a graph. They are usually applied on top of other plots such as scatter, curve or bar plots to indicate the variability in each sample. \n",
     "\n",
-    "``ErrorBars`` may be used to represent symmetric error or assymetric error. An ``ErrorBars`` Element must have one key dimensions representing the samples along the x-axis and two or three value dimensions representing the value of the sample and positive and negative error values associated with that sample. See the [Columnar Data Tutorial](../Tutorials/Columnar_Data.ipynb) for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
+    "``ErrorBars`` may be used to represent symmetric error or asymmetric error. An ``ErrorBars`` Element must have one key dimensions representing the samples along the x-axis and two or three value dimensions representing the value of the sample and positive and negative error values associated with that sample. See the [Columnar Data Tutorial](../Tutorials/Columnar_Data.ipynb) for supported data formats, which include arrays, pandas dataframes and dictionaries of arrays."
    ]
   },
   {

examples/user_guide/05-Building_Composite_Objects.ipynb

@@ -127,7 +127,7 @@
     "As shown in the diagram, there are three different types of container involved:\n",
     "\n",
     "- Basic Element: elementary HoloViews object containing raw data in an external format like Numpy or pandas.\n",
-    "- Homogenous container (UniformNdMapping): collections of Elements or other HoloViews components that are all the same type.  These are indexed using array-style key access with values sorted along some dimension(s), e.g. ``[0.50]`` or ``[\"a\",7.6]``.\n",
+    "- Homogeneous container (UniformNdMapping): collections of Elements or other HoloViews components that are all the same type.  These are indexed using array-style key access with values sorted along some dimension(s), e.g. ``[0.50]`` or ``[\"a\",7.6]``.\n",
     "- Heterogenous container (AttrTree): collections of data of different types, e.g. different types of Element.  These are accessed by categories using attributes, e.g. ``.Parameters.Sines``, which does not assume any ordering of a dimension.\n",
     "\n",
     "We will now go through each of the containers of these different types, at each level."

examples/user_guide/06-Live_Data.ipynb

@@ -215,7 +215,7 @@
     "* What you see in the cell above will not be exported in any HTML snapshot of the notebook\n",
     "\n",
     "\n",
-    "We will now explore how ``DynamicMaps`` relate to ``HoloMaps`` including conversion operations between the two types. As we will see, there are other ways to display a ``DynamicMap`` without using explict indexing or redim."
+    "We will now explore how ``DynamicMaps`` relate to ``HoloMaps`` including conversion operations between the two types. As we will see, there are other ways to display a ``DynamicMap`` without using explicit indexing or redim."
    ]
   },
   {

examples/user_guide/07-Tabular_Datasets.ipynb

@@ -372,7 +372,7 @@
    "source": [
     "## Tabularizing space containers\n",
     "\n",
-    "Even deeply nested objects can be deconstructed in this way, serializing them to make it easier to get your raw data out of a collection of specialized ``Element`` types. Let's say we want to make multiple observations of a noisy signal. We can collect the data into a ``HoloMap`` to visualize it and then call ``.table()`` to get a columnar object where we can perform operations or transform it to other ``Element`` types. Deconstructing nested data in this way only works if the data is homogenous. In practical terms, the requirement is that your data structure contains Elements (of any types) in these Container types: ``NdLayout``, ``GridSpace``, ``HoloMap``, and ``NdOverlay``, with all dimensions consistent throughout (so that they can all fit into the same set of columns). To read more about these containers see the [Dimensioned Containers](./Dimensioned_Containers.ipynb) guide.\n",
+    "Even deeply nested objects can be deconstructed in this way, serializing them to make it easier to get your raw data out of a collection of specialized ``Element`` types. Let's say we want to make multiple observations of a noisy signal. We can collect the data into a ``HoloMap`` to visualize it and then call ``.table()`` to get a columnar object where we can perform operations or transform it to other ``Element`` types. Deconstructing nested data in this way only works if the data is homogeneous. In practical terms, the requirement is that your data structure contains Elements (of any types) in these Container types: ``NdLayout``, ``GridSpace``, ``HoloMap``, and ``NdOverlay``, with all dimensions consistent throughout (so that they can all fit into the same set of columns). To read more about these containers see the [Dimensioned Containers](./Dimensioned_Containers.ipynb) guide.\n",
     "\n",
     "Let's now go back to the ``Image`` example. We will now collect a number of observations of some noisy data into a ``HoloMap`` and display it:"
    ]

examples/user_guide/Plots_and_Renderers.ipynb

@@ -16,7 +16,7 @@
    "source": [
     "HoloViews ordinarily hides the plotting machinery from the user, this allows for very quickly iterating over different visualization and exploring a dataset, however often times it is important to customize the precise details of a plot. HoloViews makes it very easy to customize existing plots, or even create completely novel plots. This manual will provide a general overview of the plotting system.\n",
     "\n",
-    "The separation of the data from the precise details of the visualization is one of the core principles of the HoloViews. [``Elements``](http://holoviews.org/reference/index.html#elements) provide thin wrappers around chunks of actual data, while [containers](http://holoviews.org/reference/index.html#containers) allow composing these Elements into overlays, layouts, grids and animations/widgets. Each Element or container type has a corresponding plotting class, which renders a visual representation of the data for a particular backend. While the precise details of the implementation differ between backends to accomodate the vastly different APIs  plotting backends provide, many of the high-level details are shared across backends.\n",
+    "The separation of the data from the precise details of the visualization is one of the core principles of the HoloViews. [``Elements``](http://holoviews.org/reference/index.html#elements) provide thin wrappers around chunks of actual data, while [containers](http://holoviews.org/reference/index.html#containers) allow composing these Elements into overlays, layouts, grids and animations/widgets. Each Element or container type has a corresponding plotting class, which renders a visual representation of the data for a particular backend. While the precise details of the implementation differ between backends to accommodate the vastly different APIs  plotting backends provide, many of the high-level details are shared across backends.\n",
     "\n",
     "# The Store object\n",
     "\n",

holoviews/__init__.py

@@ -32,7 +32,7 @@
 from . import util # noqa (API import)
 from .util import extension, renderer, output, opts # noqa (API import)
 
-# Surpress warnings generated by NumPy in matplotlib
+# Suppress warnings generated by NumPy in matplotlib
 # Expected to be fixed in next matplotlib release
 import warnings
 warnings.filterwarnings("ignore",

holoviews/core/data/__init__.py

@@ -77,7 +77,7 @@ def __call__(self, new_type, kdims=None, vdims=None, groupby=None,
         optionally the key dimensions (kdims), value dimensions
         (vdims) and the dimensions.  to group over. Converted Columns
         can be automatically sorted via the sort option and kwargs can
-        bepassed through.
+        be passed through.
         """
         if 'mdims' in kwargs:
             if groupby:
@@ -412,7 +412,7 @@ def sample(self, samples=[], closest=True, **kwargs):
             samples = [tuple(sample)]
 
         # Note: Special handling sampling of gridded 2D data as Curve
-        # may be replaced wih more general handling
+        # may be replaced with more general handling
         # see https://github.com/ioam/holoviews/issues/1173
         from ...element import Table, Curve
         if len(samples) == 1:

holoviews/core/data/grid.py

@@ -26,7 +26,7 @@ class GridInterface(DictInterface):
     representing the values in that column.
 
     To use this compressed format, the key dimensions must be orthogonal
-    to one another with each key dimension specifiying an axis of the
+    to one another with each key dimension specifying an axis of the
     multidimensional space occupied by the value dimension data. For
     instance, given an temperature recordings sampled regularly across
     the earth surface, a list of N unique latitudes and M unique

holoviews/core/dimension.py

@@ -221,7 +221,7 @@ class Dimension(param.Parameterized):
     specifically the type of unit) will be part of the core dimension
     specification used to establish equality.
 
-    Until this feature is implemented, there are two auxillary
+    Until this feature is implemented, there are two auxiliary
     parameters that hold some partial information about the unit: the
     name of the unit and whether or not it is cyclic. The name of the
     unit is used as part of the pretty-printed representation and
@@ -463,7 +463,7 @@ class LabelledData(param.Parameterized):
     [group='Height', label='Children'] and another may use
     [group='Height', label='Adults'].
 
-    Note: Another level of specification is implict in the type (i.e
+    Note: Another level of specification is implicit in the type (i.e
     class) of the LabelledData object. A full specification of a
     LabelledData object is therefore given by the tuple
     (<type>, <group>, label>). This additional level of specification is