# Histogram2 Properties

Histogram2 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, `[nX nY]`. If you do not specify `NumBins`, then `histogram2` automatically calculates how many bins to use based on the values in `X` and `Y`.

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

Example: `h.NumBins = [10 20]`

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

When 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.

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)`.

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)`.

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 vector indicates the first and last bin edges in the `x`-dimension.

`histogram2` only plots data that falls within the bin limits inclusively, ```Data(Data(:,1)>=xbmin & Data(:,1)<=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 vector indicates the first and last bin edges in the `y`-dimension.

`histogram2` only plots data that falls within the bin limits inclusively, ```Data(Data(:,2)>=ybmin & Data(:,2)<=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)]```, where `IQR` is the interquartile range.
`'integers'`The integer rule is useful with integer data, as it creates a bin for each pair of integers `X` and `Y`. 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.

### Note

If you set the `NumBins`, `XBinEdges`, `YBinEdges`, `BinWidth`, or `BinLimits` property, then the `BinMethod` property is set to `'manual'`.

Example: `histogram2(X,Y,'BinMethod','integers')` creates a bivariate histogram with the bins centered on integers.

Toggle display of empty bins, specified as either `'off'` or `'on'`. The default value is `'off'`.

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'`.

If you change the values in the `Data` property of a `histogram2` object, then the bin edges are not automatically updated. To recompute the bins, adjust a bin-related property such as `BinMethod` or `NumBins`.

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 bin inclusion scheme for the different numbered bins in `Values`, as well as their relative orientation to the x-axis and y-axis, is

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 the table.

ValueDescription
`'count'`

Default normalization scheme. The height of each bar is the number of observations in each bin. The sum of the bar heights is equal to `numel(X)` and `numel(Y)`.

`'probability'`

The height of each bar is the relative number of observations, (Number of observations in bin / Total number of observations). The sum of the bar heights is `1`.

`'countdensity'`

The height of each bar is (Number of observations in bin) / (Area of bin). The volume (Height * Area) of each bar is the number of observations in the bin. The sum of the bar volumes is equal to `numel(X)` and `numel(Y)`.

`'pdf'`

Probability density function estimate. The height of each bar is, (Number of observations in the bin) / (Total number of observations * Area of bin). The volume of each bar is the relative number of observations. The sum of the bar volumes is `1`.

`'cumcount'`

The height of each bar is the cumulative number of observations in each bin and all previous bins in both the x and y dimensions. The height of the last bar is equal to `numel(X)` and `numel(Y)`.

`'cdf'`

Cumulative density function estimate. The height of each bar is equal to the cumulative relative number of observations in each bin and all previous bins in both the x and y dimensions. The height of the last bar is `1`.

Example: `histogram2(X,Y,'Normalization','pdf')` plots an estimate of the probability density function for `X` and `Y`.

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'`. Specify `'tile'` to display the histogram as a rectangular array of tiles with colors indicating the bin values.

The default value of `'bar3'` displays the histogram using 3-D bars.

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 height have different colors. The colors are selected from the figure or axes colormap.

• `'auto'` — 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 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 histogram plot with red bar edges.

Transparency of histogram bars, specified as a scalar value between `0` and `1` inclusive. `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 between `0` and `1` inclusive. 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 the values in the table.

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`

## 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.

Control for including or excluding the object from a legend, returned as an `Annotation` object. Set the underlying `IconDisplayStyle` property 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 a graphics object, `go`, from the legend set the `IconDisplayStyle` property to `'off'`.

```go.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 one of these values:

• `'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 one of these values:

• `'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 one of these values:

• `'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 Callback Definition.

### 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 Callback Definition.

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 Callback Definition.

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'`. The `Interruptible` 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.

Whenever MATLAB invokes a callback, that callback attempts to interrupt the running callback (if one exists). The `Interruptible` property of the object owning the running callback determines if interruption is allowed. The `Interruptible` property has two possible values:

• `'on'` — Allows other callbacks to interrupt the object's callbacks. The interruption occurs at the next point where MATLAB processes the queue, such as when there is a `drawnow`, `figure`, `uifigure`, `getframe`, `waitfor`, or `pause` command.

• If the running callback contains one of those commands, then MATLAB stops the execution of the callback at that point and executes the interrupting callback. MATLAB resumes executing the running callback when the interrupting callback completes.

• If the running callback does not contain one of those commands, then MATLAB finishes executing the callback without interruption.

• `'off'` — Blocks all interruption attempts. The `BusyAction` property of the object owning the interrupting callback determines if the interrupting callback is discarded or put into a queue.

### 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.

• `Timer` objects execute according to schedule regardless of the `Interruptible` property value.

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.

Whenever MATLAB invokes a callback, that callback attempts to interrupt a running callback. The `Interruptible` property of the object owning the running callback determines if interruption is permitted. If interruption is not permitted, then the `BusyAction` property of the object owning the interrupting callback determines if it is discarded or put in the queue. 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 one of these values:

• `'on'` — Trigger the `ButtonDownFcn` callback of the `Histogram2` object. If you have defined the `UIContextMenu` 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 `'off'` or `'on'`. 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.