diff --git a/doc/Tutorials/Bokeh_Elements.ipynb b/doc/Tutorials/Bokeh_Elements.ipynb index 494915fe21..5539d4161e 100644 --- a/doc/Tutorials/Bokeh_Elements.ipynb +++ b/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. " ] }, { diff --git a/doc/Tutorials/Columnar_Data.ipynb b/doc/Tutorials/Columnar_Data.ipynb index df56ac2a9c..40012e9e29 100644 --- a/doc/Tutorials/Columnar_Data.ipynb +++ b/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:" ] diff --git a/doc/Tutorials/Composing_Data.ipynb b/doc/Tutorials/Composing_Data.ipynb index b041561cfd..6bc8478faf 100644 --- a/doc/Tutorials/Composing_Data.ipynb +++ b/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." diff --git a/doc/Tutorials/Dynamic_Map.ipynb b/doc/Tutorials/Dynamic_Map.ipynb index 0f770232df..d542efd9d8 100644 --- a/doc/Tutorials/Dynamic_Map.ipynb +++ b/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." ] }, { diff --git a/doc/Tutorials/Elements.ipynb b/doc/Tutorials/Elements.ipynb index 48c3a26a8f..d5f44b647e 100644 --- a/doc/Tutorials/Elements.ipynb +++ b/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. " ] }, { diff --git a/examples/reference/elements/bokeh/ErrorBars.ipynb b/examples/reference/elements/bokeh/ErrorBars.ipynb index ec11a520f9..192720eb25 100644 --- a/examples/reference/elements/bokeh/ErrorBars.ipynb +++ b/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." ] }, { diff --git a/examples/reference/elements/bokeh/Spread.ipynb b/examples/reference/elements/bokeh/Spread.ipynb index 54aedeced1..11f2892904 100644 --- a/examples/reference/elements/bokeh/Spread.ipynb +++ b/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", diff --git a/examples/reference/elements/matplotlib/ErrorBars.ipynb b/examples/reference/elements/matplotlib/ErrorBars.ipynb index ba11db8b7d..a947f6f433 100644 --- a/examples/reference/elements/matplotlib/ErrorBars.ipynb +++ b/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." ] }, { diff --git a/examples/reference/elements/matplotlib/Spread.ipynb b/examples/reference/elements/matplotlib/Spread.ipynb index f5c7544cf3..4a9ba93078 100644 --- a/examples/reference/elements/matplotlib/Spread.ipynb +++ b/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", diff --git a/examples/reference/elements/plotly/ErrorBars.ipynb b/examples/reference/elements/plotly/ErrorBars.ipynb index d41a9853ff..d3c7d2fea4 100644 --- a/examples/reference/elements/plotly/ErrorBars.ipynb +++ b/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." ] }, { diff --git a/examples/user_guide/05-Building_Composite_Objects.ipynb b/examples/user_guide/05-Building_Composite_Objects.ipynb index 8b2184ac14..4862114e7b 100644 --- a/examples/user_guide/05-Building_Composite_Objects.ipynb +++ b/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." diff --git a/examples/user_guide/06-Live_Data.ipynb b/examples/user_guide/06-Live_Data.ipynb index 175f14cbb1..f08b988699 100644 --- a/examples/user_guide/06-Live_Data.ipynb +++ b/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." ] }, { diff --git a/examples/user_guide/07-Tabular_Datasets.ipynb b/examples/user_guide/07-Tabular_Datasets.ipynb index f35818558c..3913f7045b 100644 --- a/examples/user_guide/07-Tabular_Datasets.ipynb +++ b/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:" ] diff --git a/examples/user_guide/Plots_and_Renderers.ipynb b/examples/user_guide/Plots_and_Renderers.ipynb index 9d4a38e0b6..17be008716 100644 --- a/examples/user_guide/Plots_and_Renderers.ipynb +++ b/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", diff --git a/holoviews/__init__.py b/holoviews/__init__.py index 027a6da9ae..333948c82a 100644 --- a/holoviews/__init__.py +++ b/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", diff --git a/holoviews/core/data/__init__.py b/holoviews/core/data/__init__.py index 4944bb4780..96bdbc17a1 100644 --- a/holoviews/core/data/__init__.py +++ b/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: diff --git a/holoviews/core/data/grid.py b/holoviews/core/data/grid.py index defb92dece..140358dd7d 100644 --- a/holoviews/core/data/grid.py +++ b/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 diff --git a/holoviews/core/dimension.py b/holoviews/core/dimension.py index e1c2ef9402..b0fcf458f8 100644 --- a/holoviews/core/dimension.py +++ b/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 (, , label>). This additional level of specification is diff --git a/holoviews/core/element.py b/holoviews/core/element.py index 1e424c5375..e035f23816 100644 --- a/holoviews/core/element.py +++ b/holoviews/core/element.py @@ -102,7 +102,7 @@ def closest(self, coords): def sample(self, samples=[], **sample_values): """ Base class signature to demonstrate API for sampling Elements. - To sample an Element supply either a list of sampels or keyword + To sample an Element supply either a list of samples or keyword arguments, where the key should match an existing key dimension on the Element. """ diff --git a/holoviews/core/io.py b/holoviews/core/io.py index 9748318547..34bb626fa4 100644 --- a/holoviews/core/io.py +++ b/holoviews/core/io.py @@ -765,7 +765,7 @@ def _unique_name(self, basename, ext, existing, force=False): either a list of (basename, ext) pairs or an absolute path to a directory. - By default, uniqueness is enforced dependning on the state of + By default, uniqueness is enforced depending on the state of the unique_name parameter (for export names). If force is True, this parameter is ignored and uniqueness is guaranteed. """ diff --git a/holoviews/core/layout.py b/holoviews/core/layout.py index 1ad9743e43..ae6bada6ec 100644 --- a/holoviews/core/layout.py +++ b/holoviews/core/layout.py @@ -1,7 +1,7 @@ """ Supplies Pane, Layout, NdLayout and AdjointLayout. Pane extends View to allow multiple Views to be presented side-by-side in a NdLayout. An -AdjointLayout allows one or two Views to be ajoined to a primary View +AdjointLayout allows one or two Views to be adjoined to a primary View to act as supplementary elements. """ diff --git a/holoviews/core/options.py b/holoviews/core/options.py index afe4eca827..654c78862b 100644 --- a/holoviews/core/options.py +++ b/holoviews/core/options.py @@ -713,7 +713,7 @@ def __repr__(self): groups = self.__dict__['groups'] # Tab and group entry separators tab, gsep = ' ', ',\n\n' - # Entry seperator and group specifications + # Entry separator and group specifications esep, gspecs = (",\n"+(tab*2)), [] for group in groups.keys(): diff --git a/holoviews/core/overlay.py b/holoviews/core/overlay.py index c5ae61119a..b99ba1357a 100644 --- a/holoviews/core/overlay.py +++ b/holoviews/core/overlay.py @@ -167,7 +167,7 @@ def collapse(self, function): types = [type(el) for el in elements] values = [el.group for el in elements] if not len(set(types)) == 1 and len(set(values)) == 1: - raise Exception("Overlay is not homogenous in type or group " + raise Exception("Overlay is not homogeneous in type or group " "and cannot be collapsed.") else: return elements[0].clone(types[0].collapse_data([el.data for el in elements], diff --git a/holoviews/core/sheetcoords.py b/holoviews/core/sheetcoords.py index bd460f0044..7571c04b0a 100644 --- a/holoviews/core/sheetcoords.py +++ b/holoviews/core/sheetcoords.py @@ -15,7 +15,7 @@ schemes. Where it is necessary to address a specific element of the matrix (as is often the case in calculations), we also have the usual matrix index coordinates (r_idx, c_idx). We refer to these as -matrixidx coordinates. SheetCoordinateSystem provies methods for converting +matrixidx coordinates. SheetCoordinateSystem proxies methods for converting between sheet and matrix coordinates, as well as sheet and matrixidx coordinates. @@ -283,7 +283,7 @@ def matrixidx2sheet(self,row,col): def closest_cell_center(self,x,y): """ - Given arbitary sheet coordinates, return the sheet coordinates + Given arbitrary sheet coordinates, return the sheet coordinates of the center of the closest unit. """ return self.matrixidx2sheet(*self.sheet2matrixidx(x,y)) @@ -432,7 +432,7 @@ def crop_to_sheet(self,sheet_coord_system): @staticmethod def _createoddslicespec(bounds,scs,min_matrix_radius): """ - Create the 'odd' Slice that best approximates the specifed + Create the 'odd' Slice that best approximates the specified sheet-coordinate bounds. The supplied bounds are translated to have a center at the diff --git a/holoviews/core/spaces.py b/holoviews/core/spaces.py index 0485bf965a..ebe56f6c7a 100644 --- a/holoviews/core/spaces.py +++ b/holoviews/core/spaces.py @@ -258,8 +258,8 @@ def collate(self, merge_type=None, drop=[], drop_constant=False): def collapse(self, dimensions=None, function=None, spreadfn=None, **kwargs): """ Allows collapsing one of any number of key dimensions - on the HoloMap. Homogenous Elements may be collapsed by - supplying a function, inhomogenous elements are merged. + on the HoloMap. Homogeneous Elements may be collapsed by + supplying a function, inhomogeneous elements are merged. """ if not dimensions: dimensions = self.kdims @@ -294,7 +294,7 @@ def sample(self, samples=[], bounds=None, **sample_values): requested using dimension-value pairs. Optionally, the bounds argument can be used to specify the bounding extent from which the coordinates are to regularly sampled. Regular sampling - assumes homogenous and regularly sampled data. + assumes homogeneous and regularly sampled data. For 1D sampling, the shape is simply as the desired number of samples (and not a tuple). The bounds format for 1D sampling @@ -689,7 +689,7 @@ def __init__(self, callback, initial_items=None, **params): if 'sampled' in params: self.warning('DynamicMap sampled parameter is deprecated ' - 'and no longer neededs to be specified.') + 'and no longer needs to be specified.') del params['sampled'] super(DynamicMap, self).__init__(initial_items, callback=callback, **params) diff --git a/holoviews/core/util.py b/holoviews/core/util.py index 4526e9a00a..ce9c6db065 100644 --- a/holoviews/core/util.py +++ b/holoviews/core/util.py @@ -424,12 +424,12 @@ class sanitize_identifier_fn(param.ParameterizedFunction): 'dollar sign' As these names are often very long, this parameterized function - allows filtered, substitions and transforms to help shorten these + allows filtered, substitutions and transforms to help shorten these names appropriately. """ version = param.ObjectSelector(sys.version_info.major, objects=[2,3], doc=""" - The sanitization version. If set to 2, more aggresive + The sanitization version. If set to 2, more aggressive sanitization appropriate for Python 2 is applied. Otherwise, if set to 3, more relaxed, Python 3 sanitization is used.""") @@ -549,7 +549,7 @@ def shortened_character_name(self_or_cls, c, eliminations=[], substitutions={}, # Filtering for elim in eliminations: name = name.replace(elim, '') - # Substitition + # Substitution for i,o in substitutions.items(): name = name.replace(i, o) for transform in transforms: @@ -711,7 +711,7 @@ def max_extents(extents, zrange=False): """ Computes the maximal extent in 2D and 3D space from list of 4-tuples or 6-tuples. If zrange is enabled - all extents are converted to 6-tuples to comput + all extents are converted to 6-tuples to compute x-, y- and z-limits. """ if zrange: @@ -984,7 +984,7 @@ def walk_depth_first(name): def is_cyclic(graph): """ Return True if the directed graph g has a cycle. The directed graph - should be represented as adictionary mapping of edges for each node. + should be represented as a dictionary mapping of edges for each node. """ path = set() @@ -1107,7 +1107,7 @@ def find_file(folder, filename): def is_dataframe(data): """ - Checks whether the supplied data is DatFrame type. + Checks whether the supplied data is DataFrame type. """ return((pd is not None and isinstance(data, pd.DataFrame)) or (dd is not None and isinstance(data, dd.DataFrame))) @@ -1262,7 +1262,7 @@ def wrap_tuple_streams(unwrapped, kdims, streams): def drop_streams(streams, kdims, keys): """ - Drop any dimensionsed streams from the keys and kdims. + Drop any dimensioned streams from the keys and kdims. """ stream_params = stream_parameters(streams) inds, dims = zip(*[(ind, kdim) for ind, kdim in enumerate(kdims) diff --git a/holoviews/element/chart.py b/holoviews/element/chart.py index 36265c760f..13befc9d6d 100644 --- a/holoviews/element/chart.py +++ b/holoviews/element/chart.py @@ -71,11 +71,11 @@ class ErrorBars(Chart): ErrorBars is a Chart Element type representing any number of errorbars situated in a 2D space. The errors must be supplied as an Nx3 or Nx4 array representing the x/y-positions and - either the symmetric error or assymetric errors respectively. + either the symmetric error or asymmetric errors respectively. """ group = param.String(default='ErrorBars', constant=True, doc=""" - A string describing the quantitity measured by the ErrorBars + A string describing the quantity measured by the ErrorBars object.""") kdims = param.List(default=[Dimension('x')], @@ -110,12 +110,12 @@ def range(self, dim, data_range=True): class Spread(ErrorBars): """ - Spread is a Chart Element type respresenting a spread of + Spread is a Chart Element type representing a spread of values as given by a mean and standard error or confidence intervals. Just like the ErrorBars Element type, mean and deviations from the mean should be supplied as either an Nx3 or Nx4 array representing the x-values, mean values - and symmetric or assymetric errors respective. Internally + and symmetric or asymmetric errors respective. Internally the data is always expanded to an Nx4 array. """ diff --git a/holoviews/element/comparison.py b/holoviews/element/comparison.py index 7fd560ad59..9ea1394f75 100644 --- a/holoviews/element/comparison.py +++ b/holoviews/element/comparison.py @@ -13,7 +13,7 @@ equal, including information regarding what exactly failed to match. Note that this functionality could not be provided using comparison -methods on all objects as comparison opertors only return Booleans and +methods on all objects as comparison operators only return Booleans and thus would not supply any information regarding *why* two elements are considered different. """ diff --git a/holoviews/element/path.py b/holoviews/element/path.py index c27aada2f5..4f3a0ad1a4 100644 --- a/holoviews/element/path.py +++ b/holoviews/element/path.py @@ -3,7 +3,7 @@ overlayed on top of other elements. Subclasses of Path are designed to generate certain common shapes -quickly and condeniently. For instance, the Box path is often useful +quickly and conveniently. For instance, the Box path is often useful for marking areas of a raster image. Contours is also a subclass of Path but in addition to simply @@ -222,12 +222,12 @@ class Ellipse(BaseShape): Ellipse(x,y, (width, height)) - There is also an apect parameter allowing you to generate an ellipse + There is also an aspect parameter allowing you to generate an ellipse by specifying a multiplicating factor that will be applied to the height only. Note that as a subclass of Path, internally an Ellipse is a - sequency of (x,y) sample positions. Ellipse could also be + sequence of (x,y) sample positions. Ellipse could also be implemented as an annotation that uses a dedicated ellipse artist. """ x = param.Number(default=0, doc="The x-position of the ellipse center.") diff --git a/holoviews/element/raster.py b/holoviews/element/raster.py index 6b29731c7a..ad3d25a7c0 100644 --- a/holoviews/element/raster.py +++ b/holoviews/element/raster.py @@ -19,7 +19,7 @@ class Raster(Element2D): Raster is a basic 2D element type for presenting either numpy or dask arrays as two dimensional raster images. - Arrays with a shape of (N,M) are valid inputs for Raster wheras + Arrays with a shape of (N,M) are valid inputs for Raster whereas subclasses of Raster (e.g. RGB) may also accept 3D arrays containing channel information. diff --git a/holoviews/element/tabular.py b/holoviews/element/tabular.py index f71bfcebf8..42b7a8f345 100644 --- a/holoviews/element/tabular.py +++ b/holoviews/element/tabular.py @@ -109,7 +109,7 @@ def pprint_cell(self, row, col): def hist(self, *args, **kwargs): - raise NotImplementedError("ItemTables are not homogenous and " + raise NotImplementedError("ItemTables are not homogeneous and " "don't support histograms.") diff --git a/holoviews/ipython/__init__.py b/holoviews/ipython/__init__.py index f9f8e0c489..8de447bd98 100644 --- a/holoviews/ipython/__init__.py +++ b/holoviews/ipython/__init__.py @@ -24,7 +24,7 @@ def show_traceback(): """ - Display the full traceback after an abbreviated traceback has occured. + Display the full traceback after an abbreviated traceback has occurred. """ from .display_hooks import FULL_TRACEBACK print(FULL_TRACEBACK) diff --git a/holoviews/ipython/archive.py b/holoviews/ipython/archive.py index 8ae500f705..0b63efaaa3 100644 --- a/holoviews/ipython/archive.py +++ b/holoviews/ipython/archive.py @@ -223,7 +223,7 @@ def _clear_notebook(self, node): # pragma: no cover def _export_with_html(self): # pragma: no cover - "Computes substitions before using nbconvert with preprocessors" + "Computes substitutions before using nbconvert with preprocessors" self.export_success = False try: tstamp = time.strftime(self.timestamp_format, self._timestamp) diff --git a/holoviews/ipython/widgets.py b/holoviews/ipython/widgets.py index 9c835a6edd..14b33aeb97 100644 --- a/holoviews/ipython/widgets.py +++ b/holoviews/ipython/widgets.py @@ -36,7 +36,7 @@ class ProgressBar(ProgressIndicator): a different process.""") width = param.Integer(default=70, doc=""" - The width of the progress bar as the number of chararacters""") + The width of the progress bar as the number of characters""") fill_char = param.String(default='#', doc=""" The character used to fill the progress bar.""") diff --git a/holoviews/operation/element.py b/holoviews/operation/element.py index 854c99db2e..d9d323f250 100644 --- a/holoviews/operation/element.py +++ b/holoviews/operation/element.py @@ -133,7 +133,7 @@ class transform(Operation): output Image. This operator is extremely versatile; for instance, you could - implement an alternative to the explict threshold operator with: + implement an alternative to the explicit threshold operator with: operator=lambda x: np.clip(x, 0, 0.5) diff --git a/holoviews/operation/normalization.py b/holoviews/operation/normalization.py index 4f4188f089..c7321c7f6f 100644 --- a/holoviews/operation/normalization.py +++ b/holoviews/operation/normalization.py @@ -26,7 +26,7 @@ class Normalization(Operation): This class standardizes how normalization is specified using the ranges and keys parameter. The ranges parameter is designed to be - very flexible, allowing a concise desciption for simple + very flexible, allowing a concise description for simple normalization while allowing complex key- and element- specific normalization to also be specified. """ @@ -57,7 +57,7 @@ class Normalization(Operation):