histogram2
Bivariate histogram plot
Description
Bivariate histograms are a type of bar plot for numeric data that group the
data into 2-D bins. After you create a Histogram2
object, you can
modify aspects of the histogram by changing its property values. This is particularly
useful for quickly modifying the properties of the bins or changing the
display.
Creation
Syntax
Description
histogram2(
creates a
bivariate histogram plot of X,Y
)X
and Y
.
The histogram2
function uses an automatic binning
algorithm that returns bins with a uniform area, chosen to cover the range
of elements in X
and Y
and reveal the
underlying shape of the distribution. histogram2
displays the bins as 3-D rectangular bars such that the height of each bar
indicates the number of elements in the bin.
histogram2(___,
specifies additional parameters using one or more name-value arguments for
any of the previous syntaxes. For example, specify
Name,Value
)Normalization
to use a different type of
normalization. For a list of properties, see Histogram2 Properties.
histogram2(
plots into the specified axes instead of into the current axes
(ax
,___)gca
). ax
can precede any of the
input argument combinations in the previous syntaxes.
returns a h
= histogram2(___)Histogram2
object. Use this to inspect and
adjust properties of the bivariate histogram. For a list of properties, see
Histogram2 Properties.
Input Arguments
X,Y
— Data to distribute among bins (as separate arguments)
vectors | matrices | multidimensional arrays
Data to distribute among bins, specified as separate arguments of
vectors, matrices, or multidimensional arrays. X
and
Y
must be the same size.
histogram2
treats matrix or multidimensional
array data as single column vectors, X(:)
and
Y(:)
, and plots a single histogram.
Corresponding elements in X
and
Y
specify the x and
y coordinates of 2-D data points,
[X(k),Y(k)]
. The data types of
X
and Y
can be different, but
histogram2
concatenates these inputs into a
single N
-by-2
matrix of the
dominant data type.
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'
.
Note
If X
or Y
contain integers
of type int64
or uint64
that
are larger than flintmax
, then it is recommended
that you explicitly specify the histogram bin
edges.histogram2
automatically bins the
input data using double precision, which lacks integer precision for
numbers greater than flintmax
.
Data Types: single
| double
| int8
| int16
| int32
| int64
| uint8
| uint16
| uint32
| uint64
| logical
nbins
— Number of bins in each dimension
positive integer scalar | two-element vector of positive integers
Number of bins in each dimension, specified as a positive integer scalar or two-element vector of positive integers.
If
nbins
is a scalar, thenhistogram2
uses that many bins in each dimension.If
nbins
is a vector, then 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 nbins
, then
histogram2
automatically
calculates how many bins to use based on the values in
X
and Y
.
If you specify nbins
with
BinMethod
or BinWidth
,
histogram2
only honors the
last parameter.
Example: histogram2(X,Y,20)
uses 20 bins in each
dimension.
Example: histogram2(X,Y,[10 20])
uses 10 bins in the
x
-dimension and 20 bins in the
y
-dimension.
Xedges
— Bin edges in x-dimension
vector
Bin edges in x-dimension, specified as a vector. The first element specifies the leading edge of the first bin in the x-dimension. The last element specifies the trailing edge of the last bin in the x-dimension. The trailing edge is only included for the last bin.
If you specify
Xedges
andYedges
withBinMethod
,BinWidth
, orNumBins
,histogram2
only honors the bin edges and the bin edges must be specified last.If you specify
Xedges
withXBinLimits
,histogram2
only honors theXedges
and theXedges
must be specified last.
Yedges
— Bin edges in y-dimension
vector
Bin edges in y-dimension, specified as a vector. The first element specifies the leading edge of the first bin in the y-dimension. The last element specifies the trailing edge of the last bin in the y-dimension. The trailing edge is only included for the last bin.
If you specify
Yedges
andXedges
withBinMethod
,BinWidth
, orNumBins
,histogram2
only honors the bin edges and the bin edges must be specified last.If you specify
Yedges
withYBinLimits
,histogram2
only honors theYedges
and theYedges
must be specified last.
counts
— Bin counts
matrix
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
[length(XBinEdges)-1 length(YBinEdges)-1]
so that
it specifies a bin count for each bin.
Example: histogram2('XBinEdges',-1:1,'YBinEdges',-2:2,'BinCounts',[1
2 3 4; 5 6 7 8])
ax
— Axes object
object
Axes object. If you do not specify an axes, then the
histogram2
function uses the current axes
(gca
).
Specify optional pairs of arguments as
Name1=Value1,...,NameN=ValueN
, where Name
is
the argument name and Value
is the corresponding value.
Name-value arguments must appear after other arguments, but the order of the
pairs does not matter.
Example: histogram2(X,Y,BinWidth=[5 10])
Before R2021a, use commas to separate each name and value, and enclose
Name
in quotes.
Example: histogram2(X,Y,'BinWidth',[5 10])
Note
The properties listed here are only a subset. For a complete list, see Histogram2 Properties.
BinWidth
— Width of bins in each dimension
two-element vector of positive values
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:
uses bins with size histogram2
(X,Y,'BinWidth',[5
10])5
in the
x
-dimension and size 10
in the
y-dimension.
XBinLimits
— Bin limits in x-dimension
two-element vector
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
.
XBinLimitsMode
— Selection mode for bin limits in x
-dimension
'auto'
(default) | 'manual'
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.
YBinLimits
— Bin limits in y-dimension
two-element
vector
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
.
YBinLimitsMode
— Selection mode for bin limits in y-dimension
'auto'
(default) | 'manual'
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.
BinMethod
— Binning algorithm
'auto'
(default) | 'scott'
| 'fd'
| 'integers'
Binning algorithm, specified as one of the values in this table.
Value | Description |
---|---|
'auto' | The default |
'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
|
'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
|
'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
, orYBinLimits
properties, thenBinMethod
is set to'manual'
.If you specify
BinMethod
withBinWidth
orNumBins
,histogram2
only honors the last parameter.
Example:
centers the 2-D bins on each
pair of integers.histogram2
(X,Y,'BinMethod','integers')
ShowEmptyBins
— Toggle display of empty bins
'off'
(default) | 'on'
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.
Normalization
— Type of normalization
'count'
(default) | 'probability'
| 'percentage'
| 'countdensity'
| 'cumcount'
| 'pdf'
| 'cdf'
Type of normalization, specified as one of the values in this table. For each
bin i
:
is the bin value.
is the number of elements in the bin.
is the area of the bin, computed using the x and y bin widths.
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.
Value | Bin Values | Notes |
---|---|---|
'count' (default) |
|
|
'probability' |
|
|
'percentage' |
|
|
'countdensity' |
|
|
'cumcount' |
|
|
'pdf' |
|
|
'cdf' |
|
|
Example:
bins the data
using an estimate of the probability density function.histogram2
(X,Y,'Normalization','pdf')
DisplayStyle
— Histogram display style
'bar3'
(default) | 'tile'
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.
FaceColor
— Histogram bar color
'auto'
(default) | 'flat'
| 'none'
| RGB triplet | hexadecimal color code | color name
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 from0
toF
. 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 Name Short Name RGB Triplet Hexadecimal Color Code Appearance "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.
RGB Triplet Hexadecimal Color Code Appearance [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 'tile'
, then the
FaceColor
property is set to 'flat'
.
Example:
creates a bivariate histogram plot with
green bars.histogram2
(X,Y,'FaceColor','g')
EdgeColor
— Histogram edge color
[0.15 0.15 0.15]
(default) | 'none'
| 'auto'
| RGB triplet | hexadecimal color code | color name
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 from0
toF
. 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 Name Short Name RGB Triplet Hexadecimal Color Code Appearance "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.
RGB Triplet Hexadecimal Color Code Appearance [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:
creates a bivariate histogram plot with
red bar edges.histogram2
(X,Y,'EdgeColor','r')
FaceAlpha
— Transparency of histogram bars
1
(default) | scalar value in range [0,1]
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.
EdgeAlpha
— Transparency of histogram bar edges
1
(default) | scalar in range [0,1]
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.
FaceLighting
— Lighting effect on histogram bars
'lit'
(default) | 'flat'
| 'none'
Lighting effect on histogram bars, specified as one of these values.
Value | Description |
---|---|
'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 |
'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.
|
Example: histogram2(X,Y,'FaceLighting','none')
turns off the
lighting of the histogram bars.
LineStyle
— Line style
"-"
(default) | "--"
| ":"
| "-."
| "none"
Line style, specified as one of the options listed in this table.
Line Style | Description | Resulting Line |
---|---|---|
"-" | Solid line |
|
"--" | Dashed line |
|
":" | Dotted line |
|
"-." | Dash-dotted line |
|
"none" | No line | No line |
LineWidth
— Width of bar outlines
0.5
(default) | positive value
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
Output Arguments
h
— Bivariate histogram
object
Bivariate histogram, returned as an object. For more information, see Histogram2 Properties.
Properties
Histogram2 Properties | Bivariate histogram appearance and behavior |
Examples
Histogram of Vectors
Generate 10,000 pairs of random numbers and create a bivariate histogram. The histogram2
function automatically chooses an appropriate number of bins to cover the range of values in x
and y
and show the shape of the underlying distribution.
x = randn(10000,1); y = randn(10000,1); h = histogram2(x,y)
h = Histogram2 with properties: Data: [10000x2 double] Values: [25x28 double] NumBins: [25 28] XBinEdges: [-3.9000 -3.6000 -3.3000 -3 -2.7000 -2.4000 -2.1000 -1.8000 -1.5000 -1.2000 -0.9000 -0.6000 -0.3000 0 0.3000 0.6000 0.9000 1.2000 1.5000 1.8000 2.1000 2.4000 2.7000 3.0000 3.3000 3.6000] YBinEdges: [-4.2000 -3.9000 -3.6000 -3.3000 -3.0000 -2.7000 -2.4000 -2.1000 -1.8000 -1.5000 -1.2000 -0.9000 -0.6000 -0.3000 0 0.3000 0.6000 0.9000 1.2000 1.5000 1.8000 2.1000 2.4000 2.7000 3 3.3000 3.6000 3.9000 4.2000] BinWidth: [0.3000 0.3000] Normalization: 'count' FaceColor: 'auto' EdgeColor: [0.1500 0.1500 0.1500] Use GET to show all properties
xlabel('x') ylabel('y')
When you specify an output argument to the histogram2
function, it returns a histogram2 object. You can use this object to inspect the properties of the histogram, such as the number of bins or the width of the bins.
Find the number of histogram bins in each dimension.
nXnY = h.NumBins
nXnY = 1×2
25 28
Specify Number of Histogram Bins
Plot a bivariate histogram of 1,000 pairs of random numbers sorted into 25 equally spaced bins, using 5 bins in each dimension.
x = randn(1000,1); y = randn(1000,1); nbins = 5; h = histogram2(x,y,nbins)
h = Histogram2 with properties: Data: [1000x2 double] Values: [5x5 double] NumBins: [5 5] XBinEdges: [-4 -2.4000 -0.8000 0.8000 2.4000 4] YBinEdges: [-4 -2.4000 -0.8000 0.8000 2.4000 4] BinWidth: [1.6000 1.6000] Normalization: 'count' FaceColor: 'auto' EdgeColor: [0.1500 0.1500 0.1500] Use GET to show all properties
Find the resulting bin counts.
counts = h.Values
counts = 5×5
0 2 3 1 0
2 40 124 47 4
1 119 341 109 10
1 32 117 33 1
0 4 8 1 0
Adjust Number of Histogram Bins
Generate 1,000 pairs of random numbers and create a bivariate histogram.
x = randn(1000,1); y = randn(1000,1); h = histogram2(x,y)
h = Histogram2 with properties: Data: [1000x2 double] Values: [15x15 double] NumBins: [15 15] XBinEdges: [-3.5000 -3 -2.5000 -2 -1.5000 -1 -0.5000 0 0.5000 1 1.5000 2 2.5000 3 3.5000 4] YBinEdges: [-3.5000 -3 -2.5000 -2 -1.5000 -1 -0.5000 0 0.5000 1 1.5000 2 2.5000 3 3.5000 4] BinWidth: [0.5000 0.5000] Normalization: 'count' FaceColor: 'auto' EdgeColor: [0.1500 0.1500 0.1500] Use GET to show all properties
Use the morebins
function to coarsely adjust the number of bins in the x dimension.
nbins = morebins(h,'x'); nbins = morebins(h,'x')
nbins = 1×2
19 15
Use the fewerbins
function to adjust the number of bins in the y dimension.
nbins = fewerbins(h,'y'); nbins = fewerbins(h,'y')
nbins = 1×2
19 11
Adjust the number of bins at a fine grain level by explicitly setting the number of bins.
h.NumBins = [20 10];
Color Histogram Bars by Height
Create a bivariate histogram using 1,000 normally distributed random numbers with 12 bins in each dimension. Specify FaceColor
as 'flat'
to color the histogram bars by height.
h = histogram2(randn(1000,1),randn(1000,1),[12 12],'FaceColor','flat'); colorbar
Tiled Histogram View
Generate random data and plot a bivariate tiled histogram. Display the empty bins by specifying ShowEmptyBins
as 'on'
.
x = 2*randn(1000,1)+2; y = 5*randn(1000,1)+3; h = histogram2(x,y,'DisplayStyle','tile','ShowEmptyBins','on');
Specify Bin Edges of Histogram
Generate 1,000 pairs of random numbers and create a bivariate histogram. Specify the bin edges using two vectors, with infinitely wide bins on the boundary of the histogram to capture all outliers that do not satisfy .
x = randn(1000,1); y = randn(1000,1); Xedges = [-Inf -2:0.4:2 Inf]; Yedges = [-Inf -2:0.4:2 Inf]; h = histogram2(x,y,Xedges,Yedges)
h = Histogram2 with properties: Data: [1000x2 double] Values: [12x12 double] NumBins: [12 12] XBinEdges: [-Inf -2 -1.6000 -1.2000 -0.8000 -0.4000 0 0.4000 0.8000 1.2000 1.6000 2 Inf] YBinEdges: [-Inf -2 -1.6000 -1.2000 -0.8000 -0.4000 0 0.4000 0.8000 1.2000 1.6000 2 Inf] BinWidth: 'nonuniform' Normalization: 'count' FaceColor: 'auto' EdgeColor: [0.1500 0.1500 0.1500] Use GET to show all properties
When the bin edges are infinite, histogram2
displays each outlier bin (along the boundary of the histogram) as being double the width of the bin next to it.
Specify the Normalization
property as 'countdensity'
to remove the bins containing the outliers. Now the volume of each bin represents the frequency of observations in that interval.
h.Normalization = 'countdensity';
Normalized Histogram
Generate 1,000 pairs of random numbers and create a bivariate histogram using the 'probability'
normalization.
x = randn(1000,1); y = randn(1000,1); h = histogram2(x,y,'Normalization','probability')
h = Histogram2 with properties: Data: [1000x2 double] Values: [15x15 double] NumBins: [15 15] XBinEdges: [-3.5000 -3 -2.5000 -2 -1.5000 -1 -0.5000 0 0.5000 1 1.5000 2 2.5000 3 3.5000 4] YBinEdges: [-3.5000 -3 -2.5000 -2 -1.5000 -1 -0.5000 0 0.5000 1 1.5000 2 2.5000 3 3.5000 4] BinWidth: [0.5000 0.5000] Normalization: 'probability' FaceColor: 'auto' EdgeColor: [0.1500 0.1500 0.1500] Use GET to show all properties
Compute the total sum of the bar heights. With this normalization, the height of each bar is equal to the probability of selecting an observation within that bin interval, and the heights of all of the bars sum to 1.
S = sum(h.Values(:))
S = 1
Histogram Using Percentages
Generate 100,000 normally distributed random vectors. Use a standard deviation of 15 and means of 100 and 85.
x = [100 85] + 15*randn(1e5,2);
Plot a histogram of the random vectors. Scale and label the z-axis as percentages.
edges = 40:15:145; histogram2(x(:,1),x(:,2),edges,edges,Normalization="percentage") ztickformat("percentage")
Adjust Histogram Properties
Generate 1,000 pairs of random numbers and create a bivariate histogram. Return the histogram object to adjust the properties of the histogram without recreating the entire plot.
x = randn(1000,1); y = randn(1000,1); h = histogram2(x,y)
h = Histogram2 with properties: Data: [1000x2 double] Values: [15x15 double] NumBins: [15 15] XBinEdges: [-3.5000 -3 -2.5000 -2 -1.5000 -1 -0.5000 0 0.5000 1 1.5000 2 2.5000 3 3.5000 4] YBinEdges: [-3.5000 -3 -2.5000 -2 -1.5000 -1 -0.5000 0 0.5000 1 1.5000 2 2.5000 3 3.5000 4] BinWidth: [0.5000 0.5000] Normalization: 'count' FaceColor: 'auto' EdgeColor: [0.1500 0.1500 0.1500] Use GET to show all properties
Color the histogram bars by height.
h.FaceColor = 'flat';
Change the number of bins in each direction.
h.NumBins = [10 25];
Display the histogram as a tile plot.
h.DisplayStyle = 'tile';
view(2)
Saving and Loading Histogram2 Objects
Use the savefig
function to save a histogram2
figure.
histogram2(randn(100,1),randn(100,1)); savefig('histogram2.fig'); close gcf
Use openfig
to load the histogram figure back into MATLAB®. openfig
also returns a handle to the figure, h
.
h = openfig('histogram2.fig');
Use the findobj
function to locate the correct object handle from the figure handle. This allows you to continue manipulating the original histogram object used to generate the figure.
y = findobj(h,'type','histogram2')
y = Histogram2 with properties: Data: [100x2 double] Values: [7x6 double] NumBins: [7 6] XBinEdges: [-3 -2 -1 0 1 2 3 4] YBinEdges: [-3 -2 -1 0 1 2 3] BinWidth: [1 1] Normalization: 'count' FaceColor: 'auto' EdgeColor: [0.1500 0.1500 0.1500] Use GET to show all properties
Tips
Histogram plots created using
histogram2
have a context menu in plot edit mode that enables interactive manipulations in the figure window. For example, you can use the context menu to interactively change the number of bins, align multiple histograms, or change the display order.
Extended Capabilities
Tall Arrays
Calculate with arrays that have more rows than fit in memory.
This function supports tall arrays with the limitations:
Some input options are not supported. The allowed options are:
'BinWidth'
'XBinLimits'
'YBinLimits'
'Normalization'
'DisplayStyle'
'BinMethod'
— The'auto'
and'scott'
bin methods are the same. The'fd'
bin method is not supported.'EdgeAlpha'
'EdgeColor'
'FaceAlpha'
'FaceColor'
'LineStyle'
'LineWidth'
'Orientation'
Additionally, there is a cap on the maximum number of bars. The default maximum is 100.
The
morebins
andfewerbins
methods are not supported.Editing properties of the histogram object that require recomputing the bins is not supported.
For more information, see Tall Arrays for Out-of-Memory Data.
Version History
Introduced in R2015bR2023b: Normalize using percentages
You can create histograms with percentages on the vertical axis by setting the
Normalization
name-value argument to
'percentage'
.
See Also
Histogram2 Properties | bar3
| discretize
| fewerbins
| morebins
| histcounts2
| histcounts
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