# Histogram2 Properties

Bivariate histogram appearance and behavior

`Histogram2` properties control the appearance and behavior of the histogram. By changing property values, you can modify aspects of the histogram. Use dot notation to refer to a particular object and property:

```h = histogram2(randn(10,1),randn(10,1)); c = h.NumBins; h.NumBins = [4 7];```

## Bins

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Number of bins in each dimension, specified as a two-element vector of positive integers. The first element gives the number of bins in the x-dimension, and the second element gives the number of bins in the y-dimension.

If you do not specify `NumBins`, then `histogram2` automatically calculates how many bins to use based on the values in `X` and `Y`.

If you specify `NumBins` with `BinMethod` or `BinWidth`, `histogram2` only honors the last parameter.

Example: ```histogram2(X,Y,'NumBins',[10 20])```

Width of bins in each dimension, specified as a two-element vector of positive values. The first element gives the width of the bins in the x-dimension, and the second element gives the width of the bins in the y-dimension.

If you specify `BinWidth`, then `histogram2` can use a maximum of 1024 bins (210) along each dimension. If instead the specified bin width requires more bins, then `histogram2` uses a larger bin width corresponding to the maximum number of bins.

If you specify `BinWidth` with `BinMethod` or `NumBins`, `histogram2` only honors the last parameter.

Example: ```histogram2(X,Y,'BinWidth',[5 10])``` uses bins with size `5` in the `x`-dimension and size `10` in the y-dimension.

Bin edges in x-dimension, specified as a vector. `Xedges(1)` is the first edge of the first bin in the x-dimension, and `Xedges(end)` is the outer edge of the last bin.

The value `[X(k),Y(k)]` is in the `(i,j)`th bin if `Xedges(i)``X(k)` < `Xedges(i+1)` and `Yedges(j)``Y(k)` < `Yedges(j+1)`. The last bins in each dimension also include the last (outer) edge. For example, `[X(k),Y(k)]` falls into the `i`th bin in the last row if `Xedges(end-1)``X(k)``Xedges(end)` and `Yedges(i)``Y(k)` < `Yedges(i+1)`.

If you specify `XBinEdges` and `YBinEdges` with `BinMethod`, `BinWidth`, or `NumBins`, `histogram2` only honors the bin edges and the bin edges must be specified last. If you specify `XBinEdges` with `XBinLimits`, `histogram2` only honors the `XBinEdges` and the `XBinEdges` must be specified last.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64` | `logical`

Bin edges in y-dimension, specified as a vector. `Yedges(1)` is the first edge of the first bin in the y-dimension, and `Yedges(end)` is the outer edge of the last bin.

The value `[X(k),Y(k)]` is in the `(i,j)`th bin if `Xedges(i)``X(k)` < `Xedges(i+1)` and `Yedges(j)``Y(k)` < `Yedges(j+1)`. The last bins in each dimension also include the last (outer) edge. For example, `[X(k),Y(k)]` falls into the `i`th bin in the last row if `Xedges(end-1)``X(k)``Xedges(end)` and `Yedges(i)``Y(k)` < `Yedges(i+1)`.

If you specify `XBinEdges` and `YBinEdges` with `BinMethod`, `BinWidth`, or `NumBins`, `histogram2` only honors the bin edges and the bin edges must be specified last. If you specify `YBinEdges` with `YBinLimits`, `histogram2` only honors the `YBinEdges` and the `YBinEdges` must be specified last.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64` | `logical`

Bin limits in x-dimension, specified as a two-element vector, `[xbmin,xbmax]`. The first element indicates the first bin edge in the x-dimension. The second element indicates the last bin edge in the x-dimension.

This option only bins data that falls within the bin limits inclusively, `X>=xbmin & X<=xbmax`.

Selection mode for bin limits in `x`-dimension, specified as `'auto'` or `'manual'`. The default value is `'auto'`, so that the bin limits automatically adjust to the data along the x-axis.

If you explicitly specify either `XBinLimits` or `XBinEdges`, then `XBinLimitsMode` is set automatically to `'manual'`. In that case, specify `XBinLimitsMode` as `'auto'` to rescale the bin limits to the data.

Bin limits in y-dimension, specified as a two-element vector, `[ybmin,ybmax]`. The first element indicates the first bin edge in the y-dimension. The second element indicates the last bin edge in the y-dimension.

This option only bins data that falls within the bin limits inclusively, `Y>=ybmin & Y<=ybmax`.

Selection mode for bin limits in y-dimension, specified as `'auto'` or `'manual'`. The default value is `'auto'`, so that the bin limits automatically adjust to the data along the y-axis.

If you explicitly specify either `YBinLimits` or `YBinEdges`, then `YBinLimitsMode` is set automatically to `'manual'`. In that case, specify `YBinLimitsMode` as `'auto'` to rescale the bin limits to the data.

Binning algorithm, specified as one of the values in this table.

ValueDescription
`'auto'`

The default `'auto'` algorithm chooses a bin width to cover the data range and reveal the shape of the underlying distribution.

`'scott'`

Scott’s rule is optimal if the data is close to being jointly normally distributed. This rule is appropriate for most other distributions, as well. It uses a bin size of ```[3.5*std(X(:))*numel(X)^(-1/4), 3.5*std(Y(:))*numel(Y)^(-1/4)]```.

`'fd'`

The Freedman-Diaconis rule is less sensitive to outliers in the data, and might be more suitable for data with heavy-tailed distributions. It uses a bin size of ```[2*iqr(X(:))*numel(X)^(-1/4), 2*iqr(Y(:))*numel(Y)^(-1/4)]```.

`'integers'`

The integer rule is useful with integer data, as it creates bins centered on pairs of integers. It uses a bin width of 1 for each dimension and places bin edges halfway between integers.

To avoid accidentally creating too many bins, you can use this rule to create a limit of 1024 bins (210). If the data range for either dimension is greater than 1024, then the integer rule uses wider bins instead.

• `histogram2` adjusts the number of bins slightly so that the bin edges fall on "nice" numbers, rather than using these exact formulas.

• If you set the `NumBins`, `XBinEdges`, `YBinEdges`, `BinWidth`, `XBinLimits`, or `YBinLimits` properties, then `BinMethod` is set to `'manual'`.

• If you specify `BinMethod` with `BinWidth` or `NumBins`, `histogram2` only honors the last parameter.

Example: `histogram2(X,Y,'BinMethod','integers')` centers the 2-D bins on each pair of integers.

Toggle display of empty bins, specified as `'on'` or `'off'`, or as numeric or logical `1` (`true`) or `0` (`false`). A value of `'on'` is equivalent to `true`, and `'off'` is equivalent to `false`. Thus, you can use the value of this property as a logical value. The value is stored as an on/off logical value of type `matlab.lang.OnOffSwitchState`.

Example: `histogram2(X,Y,'ShowEmptyBins','on')` turns on the display of empty bins.

## Data

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Data to distribute among bins, specified as a matrix of size `m`-by-`2`. The `X` and `Y` inputs to `histogram2` correspond to the columns in `Data`, that is, `Data(:,1)` is `X(:)` and `Data(:,2)` is `Y(:)`.

`histogram2` ignores all `NaN` values. Similarly,`histogram2` ignores `Inf` and `-Inf` values, unless the bin edges explicitly specify `Inf` or `-Inf` as a bin edge. Although `NaN`, `Inf`, and `-Inf` values are typically not plotted, they are still included in normalization calculations that include the total number of data elements, such as `'probability'`.

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64` | `logical`

Bin values, returned as a numeric matrix. If `Normalization` is `'count'`, then the `(i,j)`th entry in `Values` specifies the bin count for the bin whose x edges are `[Xedges(i), Xedges(i+1)]` and whose y edges are `[Yedges(j), Yedges(j+1)]`.

Depending on the value of `Normalization`, the `Values` property instead can contain a normalized variant of the bin counts.

The binning scheme includes the leading x-dimension and y-dimension edge of each bin as well as the trailing edge for the last bins along the x-dimension and y-dimension.

For example, the `(1,1)` bin includes values that fall on the first edge in each dimension, and the last bin in the bottom right includes values that fall on any of its edges.

Type of normalization, specified as one of the values in this table. For each bin `i`:

• ${v}_{i}$ is the bin value.

• ${c}_{i}$ is the number of elements in the bin.

• ${A}_{i}={w}_{xi}\cdot {w}_{yi}$ is the area of the bin, computed using the x and y bin widths.

• $N$ is the number of elements in the input data. This value can be greater than the binned data if the data contains missing values or if some of the data lies outside the bin limits.

ValueBin ValuesNotes
`'count'` (default)

`${v}_{i}={c}_{i}$`

• Count or frequency of observations.

• Sum of bin values is at most `numel(X)` and `numel(y)`. The sum is less than this only when some of the input data is not included in the bins.

`'probability'`

`${v}_{i}=\frac{{c}_{i}}{N}$`

• Relative probability.

• The number of elements in each bin relative to the total number of elements in the input data is at most 1.

`'percentage'`

`${v}_{i}=100*\frac{{c}_{i}}{N}$`

• Relative percentage.

• The percentage of elements in each bin is at most 100.

`'countdensity'`

`${v}_{i}=\frac{{c}_{i}}{{A}_{i}}$`

• Count or frequency scaled by area of bin.

• `N(end,end)` is at most `numel(X)` and `numel(Y)`.

`'cumcount'`

`${v}_{i}=\sum _{j=1}^{i}{c}_{j}$`

• Cumulative count, or the number of observations in each bin and all previous bins in both the x and y dimensions.

• `N(end,end)` is at most `numel(X)` and `numel(Y)`.

`'pdf'`

`${v}_{i}=\frac{{c}_{i}}{N\cdot {A}_{i}}$`

• Probability density function estimate.

• The sum of the bin volumes is at most `1`.

`'cdf'`

`${v}_{i}=\sum _{j=1}^{i}\text{\hspace{0.17em}}\frac{{c}_{j}}{N}$`

• Cumulative distribution function estimate.

• `N(end,end)` is at most 1.

Example: `histogram2(X,Y,'Normalization','pdf')` bins the data using an estimate of the probability density function.

Bin counts, specified as a matrix. Use this input to pass bin counts to `histogram2` when the bin counts calculation is performed separately and you do not want `histogram2` to do any data binning.

• `counts` must be a matrix of size ```[nbinsX nbinsY]``` so that it specifies a bin count for each bin.

• The number of bins in the x-dimension is `length(XBinEdges)-1`, and the number of bins in the y-dimension is `length(YBinEdges)-1`.

• Compared to the `Values` property, `BinCounts` is not normalized. If `Normalization` is `'count'`, then `Values` and `BinCounts` are equivalent.

Example: ```histogram2('XBinEdges',-1:1,'YBinEdges',-2:2,'BinCounts',[1 2 3 4; 5 6 7 8])```

Selection mode for bin counts, specified as `'auto'` or `'manual'`. The default value is `'auto'`, so that the bin counts are automatically computed from `Data`, `XBinEdges`, and `YBinEdges`.

If you specify `BinCounts`, then `BinCountsMode` is automatically set to `'manual'`. Similarly, if you specify `Data`, then `BinCountsMode` is automatically set to `'auto'`.

## Color and Styling

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Histogram display style, specified as either `'bar3'` or `'tile'`.

• `'bar3'` — Display the histogram using 3-D bars.

• `'tile'` — Display the histogram as a rectangular array of tiles with colors indicating the bin values.

Example: `histogram2(X,Y,'DisplayStyle','tile')` plots the histogram as a rectangular array of tiles.

Histogram bar color, specified as one of these values:

• `'none'` — Bars are not filled.

• `'flat'` — Bar colors vary with height. Bars with different heights have different colors. The colors are selected from the figure or axes colormap.

• `'auto'` — Histogram bar color is chosen automatically (default).

• RGB triplet, hexadecimal color code, or color name — Bars are filled with the specified color.

RGB triplets and hexadecimal color codes are useful for specifying custom colors.

• An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range `[0,1]`; for example,``` [0.4 0.6 0.7]```.

• A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (`#`) followed by three or six hexadecimal digits, which can range from `0` to `F`. The values are not case sensitive. Thus, the color codes `"#FF8800"`, `"#ff8800"`, `"#F80"`, and `"#f80"` are equivalent.

Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

Color NameShort NameRGB TripletHexadecimal Color CodeAppearance
`"red"``"r"``[1 0 0]``"#FF0000"`

`"green"``"g"``[0 1 0]``"#00FF00"`

`"blue"``"b"``[0 0 1]``"#0000FF"`

`"cyan"` `"c"``[0 1 1]``"#00FFFF"`

`"magenta"``"m"``[1 0 1]``"#FF00FF"`

`"yellow"``"y"``[1 1 0]``"#FFFF00"`

`"black"``"k"``[0 0 0]``"#000000"`

`"white"``"w"``[1 1 1]``"#FFFFFF"`

Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB® uses in many types of plots.

`[0 0.4470 0.7410]``"#0072BD"`

`[0.8500 0.3250 0.0980]``"#D95319"`

`[0.9290 0.6940 0.1250]``"#EDB120"`

`[0.4940 0.1840 0.5560]``"#7E2F8E"`

`[0.4660 0.6740 0.1880]``"#77AC30"`

`[0.3010 0.7450 0.9330]``"#4DBEEE"`

`[0.6350 0.0780 0.1840]``"#A2142F"`

If you specify `DisplayStyle` as `'stairs'`, then `histogram2` does not use the `FaceColor` property.

Example: `histogram2(X,Y,'FaceColor','g')` creates a bivariate histogram plot with green bars.

Histogram edge color, specified as one of these values:

• `'none'` — Edges are not drawn.

• `'auto'` — Color of each edge is chosen automatically.

• RGB triplet, hexadecimal color code, or color name — Edges use the specified color.

RGB triplets and hexadecimal color codes are useful for specifying custom colors.

• An RGB triplet is a three-element row vector whose elements specify the intensities of the red, green, and blue components of the color. The intensities must be in the range `[0,1]`; for example,``` [0.4 0.6 0.7]```.

• A hexadecimal color code is a character vector or a string scalar that starts with a hash symbol (`#`) followed by three or six hexadecimal digits, which can range from `0` to `F`. The values are not case sensitive. Thus, the color codes `"#FF8800"`, `"#ff8800"`, `"#F80"`, and `"#f80"` are equivalent.

Alternatively, you can specify some common colors by name. This table lists the named color options, the equivalent RGB triplets, and hexadecimal color codes.

Color NameShort NameRGB TripletHexadecimal Color CodeAppearance
`"red"``"r"``[1 0 0]``"#FF0000"`

`"green"``"g"``[0 1 0]``"#00FF00"`

`"blue"``"b"``[0 0 1]``"#0000FF"`

`"cyan"` `"c"``[0 1 1]``"#00FFFF"`

`"magenta"``"m"``[1 0 1]``"#FF00FF"`

`"yellow"``"y"``[1 1 0]``"#FFFF00"`

`"black"``"k"``[0 0 0]``"#000000"`

`"white"``"w"``[1 1 1]``"#FFFFFF"`

Here are the RGB triplets and hexadecimal color codes for the default colors MATLAB uses in many types of plots.

`[0 0.4470 0.7410]``"#0072BD"`

`[0.8500 0.3250 0.0980]``"#D95319"`

`[0.9290 0.6940 0.1250]``"#EDB120"`

`[0.4940 0.1840 0.5560]``"#7E2F8E"`

`[0.4660 0.6740 0.1880]``"#77AC30"`

`[0.3010 0.7450 0.9330]``"#4DBEEE"`

`[0.6350 0.0780 0.1840]``"#A2142F"`

Example: `histogram2(X,Y,'EdgeColor','r')` creates a bivariate histogram plot with red bar edges.

Transparency of histogram bars, specified as a scalar value in range `[0,1]`. `histogram2` uses the same transparency for all the bars of the histogram. A value of `1` means fully opaque and `0` means completely transparent (invisible).

Example: `histogram2(X,Y,'FaceAlpha',0.5)` creates a bivariate histogram plot with semi-transparent bars.

Transparency of histogram bar edges, specified as a scalar value in the range `[0,1]`. A value of `1` means fully opaque and `0` means completely transparent (invisible).

Example: `histogram2(X,Y,'EdgeAlpha',0.5)` creates a bivariate histogram plot with semi-transparent bar edges.

Lighting effect on histogram bars, specified as one of these values.

ValueDescription
`'lit'`

Histogram bars display a pseudo-lighting effect, where the sides of the bars use darker colors relative to the tops. The bars are unaffected by other light sources in the axes.

This is the default value when `DisplayStyle` is `'bar3'`.

`'flat'`

Histogram bars are not lit automatically. In the presence of other light objects, the lighting effect is uniform across the bar faces.

`'none'`

Histogram bars are not lit automatically, and lights do not affect the histogram bars.

`FaceLighting` can only be `'none'` when `DisplayStyle` is `'tile'`.

Example: `histogram2(X,Y,'FaceLighting','none')` turns off the lighting of the histogram bars.

Line style, specified as one of the options listed in this table.

Line StyleDescriptionResulting Line
`"-"`Solid line

`"--"`Dashed line

`":"`Dotted line

`"-."`Dash-dotted line

`"none"`No lineNo line

Width of bar outlines, specified as a positive value in point units. One point equals 1/72 inch.

Example: `1.5`

Data Types: `single` | `double` | `int8` | `int16` | `int32` | `int64` | `uint8` | `uint16` | `uint32` | `uint64`

Series index, specified as a positive integer or `"none"`. This property is useful for reassigning the face colors of `Histogram2` objects so that they match the colors of other objects.

By default, the `SeriesIndex` property of a `Histogram2` object is a number that corresponds to its order of creation, starting at `1`. MATLAB uses the number to calculate indices for assigning colors when you call plotting functions. The indices refer to the rows of the arrays stored in the `ColorOrder` property of the axes. Any objects in the axes that have the same `SeriesIndex` number will have the same color.

A `SeriesIndex` value of `"none"` corresponds to a neutral color that does not participate in the indexing scheme.

#### How Manual Color Assignment Overrides `SeriesIndex` Behavior

To manually control face color, set the `FaceColor` property of the `Histogram2` object to a color value, such as a color name or an RGB triplet.

When you manually set the face color of a `Histogram2` object, MATLAB disables automatic color selection for that object and allows your color to persist, regardless of the value of the `SeriesIndex` property.

To enable automatic selection again, set the `SeriesIndex` property to positive integer, and set the `FaceColor` property to `"auto"`.

In some cases, MATLAB sets the `SeriesIndex` value to `0`, which also disables automatic color selection.

## Legend

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Text used by the legend, specified as a character vector. The text appears next to an icon of the histogram2.

Example: `'Text Description'`

For multiline text, create the character vector using `sprintf` with the new line character `\n`.

Example: `sprintf('line one\nline two')`

Alternatively, you can specify the legend text using the `legend` function.

• If you specify the text as an input argument to the `legend` function, then the legend uses the specified text and sets the `DisplayName` property to the same value.

• If you do not specify the text as an input argument to the `legend` function, then the legend uses the text in the `DisplayName` property. By default, `DisplayName` is a character vector representing the variable names of the x and y input data used to construct the histogram. If one or both of the inputs do not have variable names, then `DisplayName` is empty, `''`.

If the `DisplayName` property does not contain any text, then the legend generates a character vector. The character vector has the form `'dataN'`, where `N` is the number assigned to the histogram2 object based on its location in the list of legend entries.

If you edit interactively the character vector in an existing legend, then MATLAB updates the `DisplayName` property to the edited character vector.

Include object in the legend, specified as an `Annotation` object. Set the underlying `IconDisplayStyle` property of the `Annotation` object to one of these values:

• `"on"` — Include the object in the legend (default).

• `"off"` — Do not include the object in the legend.

For example, to exclude the `Histogram2` object called `obj` from the legend, set the `IconDisplayStyle` property to `"off"`.

```obj.Annotation.LegendInformation.IconDisplayStyle = "off"; ```

Alternatively, you can control the items in a legend using the `legend` function. Specify the first input argument as a vector of the graphics objects to include. If you do not specify an existing graphics object in the first input argument, then it does not appear in the legend. However, graphics objects added to the axes after the legend is created do appear in the legend. Consider creating the legend after creating all the plots to avoid extra items.

## Interactivity

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State of visibility, specified as `"on"` or `"off"`, or as numeric or logical `1` (`true`) or `0` (`false`). A value of `"on"` is equivalent to `true`, and `"off"` is equivalent to `false`. Thus, you can use the value of this property as a logical value. The value is stored as an on/off logical value of type `matlab.lang.OnOffSwitchState`.

• `"on"` — Display the object.

• `"off"` — Hide the object without deleting it. You still can access the properties of an invisible object.

Data tip content, specified as a `DataTipTemplate` object. You can control the content that appears in a data tip by modifying the properties of the underlying `DataTipTemplate` object. For a list of properties, see DataTipTemplate Properties.

For an example of modifying data tips, see Create Custom Data Tips.

Note

The `DataTipTemplate` object is not returned by `findobj` or `findall`, and it is not copied by `copyobj`.

Context menu, specified as a `ContextMenu` object. Use this property to display a context menu when you right-click the object. Create the context menu using the `uicontextmenu` function.

Note

If the `PickableParts` property is set to `'none'` or if the `HitTest` property is set to `'off'`, then the context menu does not appear.

Selection state, specified as `'on'` or `'off'`, or as numeric or logical `1` (`true`) or `0` (`false`). A value of `'on'` is equivalent to true, and `'off'` is equivalent to `false`. Thus, you can use the value of this property as a logical value. The value is stored as an on/off logical value of type `matlab.lang.OnOffSwitchState`.

• `'on'` — Selected. If you click the object when in plot edit mode, then MATLAB sets its `Selected` property to `'on'`. If the `SelectionHighlight` property also is set to `'on'`, then MATLAB displays selection handles around the object.

• `'off'` — Not selected.

Display of selection handles when selected, specified as `'on'` or `'off'`, or as numeric or logical `1` (`true`) or `0` (`false`). A value of `'on'` is equivalent to true, and `'off'` is equivalent to `false`. Thus, you can use the value of this property as a logical value. The value is stored as an on/off logical value of type `matlab.lang.OnOffSwitchState`.

• `'on'` — Display selection handles when the `Selected` property is set to `'on'`.

• `'off'` — Never display selection handles, even when the `Selected` property is set to `'on'`.

## Callbacks

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Mouse-click callback, specified as one of these values:

• Function handle

• Cell array containing a function handle and additional arguments

• Character vector that is a valid MATLAB command or function, which is evaluated in the base workspace (not recommended)

Use this property to execute code when you click the object. If you specify this property using a function handle, then MATLAB passes two arguments to the callback function when executing the callback:

• Clicked object — Access properties of the clicked object from within the callback function.

• Event data — Empty argument. Replace it with the tilde character (`~`) in the function definition to indicate that this argument is not used.

For more information on how to use function handles to define callback functions, see Create Callbacks for Graphics Objects.

Note

If the `PickableParts` property is set to `'none'` or if the `HitTest` property is set to `'off'`, then this callback does not execute.

Object creation function, specified as one of these values:

• Function handle.

• Cell array in which the first element is a function handle. Subsequent elements in the cell array are the arguments to pass to the callback function.

• Character vector containing a valid MATLAB expression (not recommended). MATLAB evaluates this expression in the base workspace.

For more information about specifying a callback as a function handle, cell array, or character vector, see Create Callbacks for Graphics Objects.

This property specifies a callback function to execute when MATLAB creates the object. MATLAB initializes all property values before executing the `CreateFcn` callback. If you do not specify the `CreateFcn` property, then MATLAB executes a default creation function.

Setting the `CreateFcn` property on an existing component has no effect.

If you specify this property as a function handle or cell array, you can access the object that is being created using the first argument of the callback function. Otherwise, use the `gcbo` function to access the object.

Object deletion function, specified as one of these values:

• Function handle.

• Cell array in which the first element is a function handle. Subsequent elements in the cell array are the arguments to pass to the callback function.

• Character vector containing a valid MATLAB expression (not recommended). MATLAB evaluates this expression in the base workspace.

For more information about specifying a callback as a function handle, cell array, or character vector, see Create Callbacks for Graphics Objects.

This property specifies a callback function to execute when MATLAB deletes the object. MATLAB executes the `DeleteFcn` callback before destroying the properties of the object. If you do not specify the `DeleteFcn` property, then MATLAB executes a default deletion function.

If you specify this property as a function handle or cell array, you can access the object that is being deleted using the first argument of the callback function. Otherwise, use the `gcbo` function to access the object.

## Callback Execution Control

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Callback interruption, specified as `'on'` or `'off'`, or as numeric or logical `1` (`true`) or `0` (`false`). A value of `'on'` is equivalent to `true`, and `'off'` is equivalent to `false`. Thus, you can use the value of this property as a logical value. The value is stored as an on/off logical value of type `matlab.lang.OnOffSwitchState`.

This property determines if a running callback can be interrupted. There are two callback states to consider:

• The running callback is the currently executing callback.

• The interrupting callback is a callback that tries to interrupt the running callback.

MATLAB determines callback interruption behavior whenever it executes a command that processes the callback queue. These commands include `drawnow`, `figure`, `uifigure`, `getframe`, `waitfor`, and `pause`.

If the running callback does not contain one of these commands, then no interruption occurs. MATLAB first finishes executing the running callback, and later executes the interrupting callback.

If the running callback does contain one of these commands, then the `Interruptible` property of the object that owns the running callback determines if the interruption occurs:

• If the value of `Interruptible` is `'off'`, then no interruption occurs. Instead, the `BusyAction` property of the object that owns the interrupting callback determines if the interrupting callback is discarded or added to the callback queue.

• If the value of `Interruptible` is `'on'`, then the interruption occurs. The next time MATLAB processes the callback queue, it stops the execution of the running callback and executes the interrupting callback. After the interrupting callback completes, MATLAB then resumes executing the running callback.

Note

Callback interruption and execution behave differently in these situations:

• If the interrupting callback is a `DeleteFcn`, `CloseRequestFcn`, or `SizeChangedFcn` callback, then the interruption occurs regardless of the `Interruptible` property value.

• If the running callback is currently executing the `waitfor` function, then the interruption occurs regardless of the `Interruptible` property value.

• If the interrupting callback is owned by a `Timer` object, then the callback executes according to schedule regardless of the `Interruptible` property value.

Note

When an interruption occurs, MATLAB does not save the state of properties or the display. For example, the object returned by the `gca` or `gcf` command might change when another callback executes.

Callback queuing, specified as `'queue'` or `'cancel'`. The `BusyAction` property determines how MATLAB handles the execution of interrupting callbacks. There are two callback states to consider:

• The running callback is the currently executing callback.

• The interrupting callback is a callback that tries to interrupt the running callback.

The `BusyAction` property determines callback queuing behavior only when both of these conditions are met:

Under these conditions, the `BusyAction` property of the object that owns the interrupting callback determines how MATLAB handles the interrupting callback. These are possible values of the `BusyAction` property:

• `'queue'` — Puts the interrupting callback in a queue to be processed after the running callback finishes execution.

• `'cancel'` — Does not execute the interrupting callback.

Ability to capture mouse clicks, specified as one of these values:

• `'visible'` — Capture mouse clicks only when visible. The `Visible` property must be set to `'on'`. The `HitTest` property determines if the `Histogram2` object responds to the click or if an ancestor does.

• `'none'` — Cannot capture mouse clicks. Clicking the `Histogram2` object passes the click to the object behind it in the current view of the figure window. The `HitTest` property of the `Histogram2` object has no effect.

Response to captured mouse clicks, specified as `'on'` or `'off'`, or as numeric or logical `1` (`true`) or `0` (`false`). A value of `'on'` is equivalent to true, and `'off'` is equivalent to `false`. Thus, you can use the value of this property as a logical value. The value is stored as an on/off logical value of type `matlab.lang.OnOffSwitchState`.

• `'on'` — Trigger the `ButtonDownFcn` callback of the `Histogram2` object. If you have defined the `ContextMenu` property, then invoke the context menu.

• `'off'` — Trigger the callbacks for the nearest ancestor of the `Histogram2` object that has one of these:

• `HitTest` property set to `'on'`

• `PickableParts` property set to a value that enables the ancestor to capture mouse clicks

Note

The `PickableParts` property determines if the `Histogram2` object can capture mouse clicks. If it cannot, then the `HitTest` property has no effect.

Deletion status, returned as an on/off logical value of type `matlab.lang.OnOffSwitchState`.

MATLAB sets the `BeingDeleted` property to `'on'` when the `DeleteFcn` callback begins execution. The `BeingDeleted` property remains set to `'on'` until the component object no longer exists.

Check the value of the `BeingDeleted` property to verify that the object is not about to be deleted before querying or modifying it.

## Parent/Child

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Parent, specified as an `Axes`, `Group`, or `Transform` object.

Children, returned as an empty `GraphicsPlaceholder` array or a `DataTip` object array. Use this property to view a list of data tips that are plotted on the chart.

You cannot add or remove children using the `Children` property. To add a child to this list, set the `Parent` property of the `DataTip` object to the chart object.

Visibility of the object handle in the `Children` property of the parent, specified as one of these values:

• `"on"` — Object handle is always visible.

• `"off"` — Object handle is invisible at all times. This option is useful for preventing unintended changes by another function. Set the `HandleVisibility` to `"off"` to temporarily hide the handle during the execution of that function.

• `"callback"` — Object handle is visible from within callbacks or functions invoked by callbacks, but not from within functions invoked from the command line. This option blocks access to the object at the command line, but permits callback functions to access it.

If the object is not listed in the `Children` property of the parent, then functions that obtain object handles by searching the object hierarchy or querying handle properties cannot return it. Examples of such functions include the `get`, `findobj`, `gca`, `gcf`, `gco`, `newplot`, `cla`, `clf`, and `close` functions.

Hidden object handles are still valid. Set the root `ShowHiddenHandles` property to `"on"` to list all object handles regardless of their `HandleVisibility` property setting.

## Identifiers

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Type of graphics object, returned as `'histogram2'`. Use this property to find all objects of a given type within a plotting hierarchy, such as searching for the type using `findobj`.

Object identifier, specified as a character vector or string scalar. You can specify a unique `Tag` value to serve as an identifier for an object. When you need access to the object elsewhere in your code, you can use the `findobj` function to search for the object based on the `Tag` value.

User data, specified as any MATLAB array. For example, you can specify a scalar, vector, matrix, cell array, character array, table, or structure. Use this property to store arbitrary data on an object.

If you are working in App Designer, create public or private properties in the app to share data instead of using the `UserData` property. For more information, see Share Data Within App Designer Apps.

## Version History

Introduced in R2015b

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