# Histogram Properties

Histogram appearance and behavior

Histogram 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 = histogram(randn(10,1)); c = h.BinWidth; h.BinWidth = 2;```

## Bins

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Number of bins, specified as a positive integer. If you do not specify `NumBins`, then `histogram` automatically calculates how many bins to use based on the values in `Data`.

This option does not apply to histograms of categorical data.

Width of bins, specified as a scalar. When you specify `BinWidth`, then `histogram` can use a maximum of 65,536 bins (or 216). If instead the specified bin width requires more bins, then `histogram` uses a larger bin width corresponding to the maximum number of bins.

For datetime and duration data, the value of `'BinWidth'` can be a scalar duration or calendar duration.

This option does not apply to histograms of categorical data.

Example: `histogram(X,'BinWidth',5)` uses bins with a width of 5.

Edges of bins, specified as a numeric vector. The first vector element specifies the left edge of the first bin. The last element specifies the right edge of the last bin. If you do not specify the bin edges, then `histogram` automatically determines the location of the bin edges.

This option does not apply to histograms of categorical data.

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

Bin limits, specified as a two-element vector, `[bmin,bmax]`. This option plots a histogram using the values in the input array, `X`, that fall between `bmin` and `bmax` inclusive. That is, `X(X>=bmin & X<=bmax)`.

This option does not apply to histograms of categorical data.

Example: `histogram(X,'BinLimits',[1,10])` plots a histogram using only the values in `X` that are between `1` and `10` inclusive.

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

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

This option does not apply to histograms of categorical data.

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

Value

Description

`'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 normally distributed. This rule is appropriate for most other distributions, as well. It uses a bin width of `3.5*std(X(:))*numel(X)^(-1/3)`.

`'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 width of `2*IQR(X(:))*numel(X)^(-1/3)`, where `IQR` is the interquartile range of `X`.

`'integers'`

The integer rule is useful with integer data, as it creates a bin for each integer. It uses a bin width of 1 and places bin edges halfway between integers. To avoid accidentally creating too many bins, you can use this rule to create a limit of 65536 bins (216). If the data range is greater than 65536, then the integer rule uses wider bins instead.

Note

`'integers'` does not support datetime or duration data.

`'sturges'`

Sturges’ rule is popular due to its simplicity. It chooses the number of bins to be ```ceil(1 + log2(numel(X)))```.

`'sqrt'`

The Square Root rule is widely used in other software packages. It chooses the number of bins to be `ceil(sqrt(numel(X)))`.

`histogram` does not always choose the number of bins using these exact formulas. Sometimes the number of bins is adjusted slightly so that the bin edges fall on "nice" numbers.

For datetime data, the bin method can be one of these units of time:

 `'second'` `'month'` `'minute'` `'quarter'` `'hour'` `'year'` `'day'` `'decade'` `'week'` `'century'`

For duration data, the bin method can be one of these units of time:

 `'second'` `'day'` `'minute'` `'year'` `'hour'`

If you specify `BinMethod` with datetime or duration data, then `histogram` can use a maximum of 65,536 bins (or 216). If the specified bin duration requires more bins, then `histogram` uses a larger bin width corresponding to the maximum number of bins.

This option does not apply to histograms of categorical data.

Note

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

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

## Categories

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Note

This option only applies to categorical histograms.

Categories included in histogram, specified as a cell array of character vectors, categorical array, or string array.

• If you specify an input categorical array `C`, then by default, `histogram` plots a bar for each category in `C`. In that case, use `Categories` to specify a unique subset of the categories instead.

• If you specify bin counts, then `Categories` specifies the associated category names for the histogram.

Example: `h = histogram(C,{'Large','Small'})` plots only the categorical data in the categories `'Large'` and `'Small'`.

Example: ```histogram('Categories',{'Yes','No','Maybe'},'BinCounts',[22 18 3])``` plots a histogram that has three categories with the associated bin counts.

Example: `h.Categories` queries the categories that are in histogram object `h`.

Data Types: `cell` | `categorical` | `string`

Category display order, specified as `'ascend'`, `'descend'`, or `'data'`. With `'ascend'` or `'descend'`, the histogram displays with increasing or decreasing bar heights. The default `'data'` value uses the category order in the input data, `C`.

This option only works with categorical data.

Number of categories to display, specified as a scalar. You can change the ordering of categories displayed in the histogram using the `'DisplayOrder'` option.

This option only works with categorical data.

Toggle summary display of data belonging to undisplayed categories, 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`.

Set this option to `'on'` to display an additional bar in the histogram with the name `'Others'`. This extra bar counts all elements that do not belong to categories displayed in the histogram.

You can change the number of categories displayed in the histogram, as well as their order, using the `'NumDisplayBins'` and `'DisplayOrder'` options.

This option only works with categorical data.

## Data

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Data to distribute among bins, specified as a vector, matrix, multidimensional array, or categorical array. If `Data` is not a vector, then `histogram` treats it as a single column vector, `Data(:)`, and plots a single histogram.

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

You can only specify categorical values for `Data` if the histogram object was originally created using categoricals.

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

Bin values, returned as a numeric vector. If `Normalization` is `'count'` (the default), then the `k`th element in `Values` specifies how many elements of `Data` fall in the kth bin interval (bin counts). The last bin includes values that are on either bin edge, but all other bins only include values that fall on the left edge.

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

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.

• ${w}_{i}$ is the width of the bin.

• $N$ is the number of elements in the input data. This value can be greater than the binned data if the data contains `NaN`, `NaT`, or `<undefined>` 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 less than or equal to `numel(X)`. The sum is less than `numel(X)` only when some of the input data is not included in the bins.

• For categorical data, sum of bin values is less than or equal to either `numel(X)` or `sum(ismember(X(:),Categories))`.

`'countdensity'`

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

• Count or frequency scaled by width of bin.

• The area (height * width) of each bar is the number of observations in the bin. The sum of the bar areas is less than or equal to `numel(X)`.

• For categorical histograms, this is the same as `'count'`.

Note

`'countdensity'` does not support datetime or duration data.

`'cumcount'`

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

• Cumulative count. Each bin value is the cumulative number of observations in that bin and all previous bins.

• The height of the last bar is less than or equal to `numel(X)`.

• For categorical histograms, the height of the last bar is less than or equal to `numel(X)` or `sum(ismember(X(:),Categories))`.

`'probability'`

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

• Relative probability.

• The sum of the bar heights is less than or equal to `1`.

`'pdf'`

`${v}_{i}=\frac{{c}_{i}}{N\text{\hspace{0.17em}}\text{\hspace{0.17em}}\cdot \text{\hspace{0.17em}}\text{\hspace{0.17em}}\text{\hspace{0.17em}}{w}_{i}}$`

• Probability density function estimate.

• The area of each bar is the relative number of observations. The sum of the bar areas is less than or equal to `1`.

• For categorical histograms, this is the same as `'probability'`.

Note

`'pdf'` does not support datetime or duration data.

`'cdf'`

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

• Cumulative density function estimate.

• The height of each bar is equal to the cumulative relative number of observations in the bin and all previous bins. The height of the last bar is less than or equal to `1`.

• For categorical data, the height of each bar is equal to the cumulative relative number of observations in each category and all previous categories.

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

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

The length of `counts` must be equal to the number of bins.

• For numeric histograms, the number of bins is `length(edges)-1`.

• For categorical histograms, the number of bins is equal to the number of categories.

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

Example: ```histogram('BinEdges',-2:2,'BinCounts',[5 8 15 9])```

Example: ```histogram('Categories',{'Yes','No','Maybe'},'BinCounts',[22 18 3])```

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` and `BinEdges`.

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 `'bar'` or `'stairs'`. Specify `'stairs'` to display a stairstep plot, which displays the outline of the histogram without filling the interior.

The default value of `'bar'` displays a histogram bar plot.

Example: `histogram(X,'DisplayStyle','stairs')` plots the outline of the histogram.

Orientation of bars, specified as `'vertical'` or `'horizontal'`.

Example: `histogram(X,'Orientation','horizontal')` creates a histogram plot with horizontal bars.

Note

This option only applies to histograms of categorical data.

Relative width of categorical bars, specified as a scalar value in the range `[0,1]`. Use this property to control the separation of categorical bars within the histogram. The default value is `0.9`, which means that the bar width is 90% of the space from the previous bar to the next bar, with 5% of that space on each side.

If you set this property to `1`, then adjacent bars touch.

Example: `0.5`

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

Histogram bar color, specified as one of these values:

• `'none'` — Bars are not filled.

• `'auto'` — The 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 `histogram` does not utilize the `FaceColor` property.

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

Histogram edge color, specified as one of these values:

• `'none'` — Edges are not drawn.

• `'auto'` — The 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: `histogram(X,'EdgeColor','r')` creates a histogram plot with red bar edges.

Transparency of histogram bars, specified as a scalar value between `0` and `1` inclusive. `histogram` 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: `histogram(X,'FaceAlpha',1)` creates a histogram plot with fully opaque 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: `histogram(X,'EdgeAlpha',0.5)` creates a histogram plot with semi-transparent bar edges.

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 whole number greater than or equal to `0`. This property is useful for reassigning the face colors of several `Histogram` objects so that they match each other. By default, the `SeriesIndex` property of a `Histogram` 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.

MATLAB automatically updates the face color of the `Histogram` object when you change its `SeriesIndex`, or when you change `ColorOrder` property on the axes. However, the following conditions must be true for the changes to have any effect:

• The `FaceColor` property on the `Histogram` object is set to `'auto'`.

• The `SeriesIndex` property on the `Histogram` object is greater than `0`.

• The `NextSeriesIndex` property on the axes object is greater than `0`.

## Legend

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

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. The default value of `DisplayName` is one of these values.

• For numeric inputs, `DisplayName` is a character vector representing the variable name of the input data used to construct the histogram. If the input data does not have a variable name, then `DisplayName` is empty, `''`.

• For categorical array inputs, `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 histogram 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 `'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 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'`, 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 `Histogram` object responds to the click or if an ancestor does.

• `'none'` — Cannot capture mouse clicks. Clicking the `Histogram` object passes the click to the object behind it in the current view of the figure window. The `HitTest` property of the `Histogram` 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 `Histogram` object. If you have defined the `ContextMenu` property, then invoke the context menu.

• `'off'` — Trigger the callbacks for the nearest ancestor of the `Histogram` 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 `Histogram` 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`, `PolarAxes`, `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 either `'histogram'` or `'categoricalhistogram'`. 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 R2014b

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Not recommended starting in R2020a