Read Image to Grayscale and Display It in a Histogram Matlab

histogram

Clarification

Histograms are a type of bar plot for numeric data that group the data into bins. After y'all create a Histogram object, yous can modify aspects of the histogram by changing its property values. This is specially useful for quickly modifying the properties of the bins or changing the brandish.

Creation

Syntax

Description

case

histogram(X) creates a histogram plot of Ten. The histogram function uses an automatic binning algorithm that returns bins with a uniform width, chosen to cover the range of elements in X and reveal the underlying shape of the distribution. histogram displays the bins as rectangles such that the summit of each rectangle indicates the number of elements in the bin.

case

histogram(10,nbins) uses a number of bins specified by the scalar, nbins.

instance

histogram(X,edges) sorts 10 into bins with the bin edges specified past the vector, edges. Each bin includes the left edge, but does not include the right edge, except for the last bin which includes both edges.

histogram('BinEdges',edges,'BinCounts',counts) manually specifies bin edges and associated bin counts. histogram plots the specified bin counts and does non do whatsoever data binning.

example

histogram(C) , where C is a categorical array, plots a histogram with a bar for each category in C.

histogram(C,Categories) plots but the subset of categories specified past Categories.

histogram('Categories',Categories,'BinCounts',counts) manually specifies categories and associated bin counts. histogram plots the specified bin counts and does not do any data binning.

example

histogram(___,Proper noun,Value) specifies additional options with ane or more Name,Value pair arguments using any of the previous syntaxes. For example, yous can specify 'BinWidth' and a scalar to arrange the width of the bins, or 'Normalization' with a valid option ('count', 'probability', 'countdensity', 'pdf', 'cumcount', or 'cdf') to use a unlike blazon of normalization. For a list of properties, come across Histogram Backdrop.

histogram(ax,___) plots into the axes specified past ax instead of into the current axes (gca). The option ax can precede any of the input argument combinations in the previous syntaxes.

example

h = histogram(___) returns a Histogram object. Apply this to inspect and adjust the backdrop of the histogram. For a list of properties, see Histogram Properties.

Input Arguments

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X — Data to distribute amidst bins
vector | matrix | multidimensional assortment

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

histogram ignores all NaN and NaT 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, and -Inf values are typically not plotted, they are still included in normalization calculations that include the full number of data elements, such as 'probability'.

Annotation

If X contains integers of blazon int64 or uint64 that are larger than flintmax, and so it is recommended that you explicitly specify the histogram bin edges. histogram automatically bins the input data using double precision, which lacks integer precision for numbers greater than flintmax.

Information Types: single | double | int8 | int16 | int32 | int64 | uint8 | uint16 | uint32 | uint64 | logical | datetime | elapsing

C — Categorical information
categorical array

Categorical data, specified as a categorical array. histogram does non plot undefined chiselled values. However, undefined categorical values are still included in normalization calculations that include the total number of data elements, such as 'probability'.

Information Types: categorical

nbins — Number of bins
positive integer

Number of bins, specified as a positive integer. If yous do not specify nbins, then histogram automatically calculates how many bins to use based on the values in Ten.

Example: histogram(X,15) creates a histogram with fifteen bins.

edges — Bin edges
vector

Bin edges, specified as a vector. edges(1) is the left edge of the first bin, and edges(end) is the right edge of the last bin.

The value Ten(i) is in the kth bin if edges(k) ≤ 10(i) < edges(k+1). The concluding bin also includes the right bin edge, so that it contains X(i) if edges(end-1) ≤ X(i) ≤ edges(terminate).

For datetime and duration data, edges must be a datetime or duration vector in monotonically increasing order.

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

Notation

This option only applies to chiselled histograms.

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

• If you lot specify an input chiselled array C, and so 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, so Categories specifies the associated category names for the histogram.

Example: h = histogram(C,{'Large','Small'}) plots just 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

counts — Bin counts
vector

Bin counts, specified every bit 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 exercise any data binning.

The length of counts must exist 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.

Example: histogram('BinEdges',-2:two,'BinCounts',[5 8 fifteen nine])

Instance: histogram('Categories',{'Yes','No','Maybe'},'BinCounts',[22 eighteen iii])

ax — Target axes
Axes object | PolarAxes object

Target axes, specified as an Axes object or a PolarAxes object. If you do non specify the axes and if the current axes are Cartesian axes, then the histogram function uses the current axes (gca). To plot into polar axes, specify the PolarAxes object every bit the start input argument or employ the polarhistogram function.

Name-Value Pair Arguments

Specify optional comma-separated pairs of Name,Value arguments. Name is the argument proper noun and Value is the corresponding value. Name must announced within quotes. Y'all can specify several name and value pair arguments in whatsoever order equally Name1,Value1,...,NameN,ValueN.

Instance: histogram(X,'BinWidth',v)

The histogram backdrop listed hither are only a subset. For a complete list, encounter Histogram Backdrop.

Note

This option merely applies to histograms of categorical data.

Relative width of categorical bars, specified equally a scalar value in the range [0,ane]. Utilize this property to control the separation of categorical bars within the histogram. The default value is 0.9, which ways that the bar width is ninety% 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, so adjacent bars touch.

Case: 0.5

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

Bin limits, specified as a two-element vector, [bmin,bmax]. This selection plots a histogram using the values in the input array, X, that fall betwixt bmin and bmax inclusive. That is, 10(Ten>=bmin & X<=bmax).

This selection does not utilise to histograms of chiselled data.

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

Selection way for bin limits, specified as 'auto' or 'manual'. The default value is 'auto', and so that the bin limits automatically accommodate to the data.

If yous 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 information.

This selection does not apply to histograms of categorical data.

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

Value	Clarification
'auto'	The default 'auto' algorithm chooses a bin width to cover the data range and reveal the shape of the underlying distribution.
'scott'	Scott'southward rule is optimal if the data is close to being commonly distributed. This dominion is appropriate for most other distributions, as well. Information technology uses a bin width of 3.v*std(X(:))*numel(10)^(-one/3).
'fd'	The Freedman-Diaconis dominion 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 ii*IQR(X(:))*numel(X)^(-i/3), where IQR is the interquartile range of X.
'integers'	The integer rule is useful with integer data, equally it creates a bin for each integer. It uses a bin width of ane 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 (two16 ). If the data range is greater than 65536, then the integer rule uses wider bins instead. Note 'integers' does non back up datetime or duration information.
'sturges'	Sturges' rule is pop due to its simplicity. It chooses the number of bins to be ceil(ane + log2(numel(10))).
'sqrt'	The Foursquare Root rule is widely used in other software packages. It chooses the number of bins to be ceil(sqrt(numel(10))).

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

For datetime information, the bin method can exist one of these units of fourth dimension:

'2d'	'month'
'minute'	'quarter'
'hour'	'yr'
'day'	'decade'
'week'	'century'

For elapsing data, the bin method can exist one of these units of time:

'second'	'day'
'minute'	'year'
'hr'

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

This option does not employ to histograms of categorical information.

Note

If you set the BinLimits, NumBins, BinEdges, or BinWidth property, then the BinMethod property is gear up to 'transmission'.

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

Width of bins, specified as a scalar. When you specify BinWidth, and then histogram tin apply a maximum of 65,536 bins (or 2¹⁶ ). If instead the specified bin width requires more than bins, then histogram uses a larger bin width respective to the maximum number of bins.

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

This option does non apply to histograms of categorical data.

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

Category display order, specified as 'arise', '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.

Histogram brandish style, specified equally 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.

EdgeAlpha — Transparency of histogram bar edges
ane (default) | scalar value between 0 and 1 inclusive

Transparency of histogram bar edges, specified as a scalar value between 0 and one inclusive. A value of i means fully opaque and 0 means completely transparent (invisible).

Example: histogram(10,'EdgeAlpha',0.5) creates a histogram plot with semi-transparent bar edges.

EdgeColor — Histogram edge color
[0 0 0] or black (default) | 'none' | 'auto' | RGB triplet | hexadecimal color code | color name

Histogram edge colour, specified as one of these values:

• 'none' — Edges are not fatigued.

• 'auto' — Colour of each edge is called automatically.

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

  RGB triplets and hexadecimal colour 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 cord scalar that starts with a hash symbol (#) followed by 3 or six hexadecimal digits, which can range from 0 to F. The values are non case sensitive. Thus, the color codes '#FF8800', '#ff8800', '#F80', and '#f80' are equivalent.

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

  Colour Name	Short Name	RGB Triplet	Hexadecimal Color Code	Appearance
  'cherry-red'	'r'	[1 0 0]	'#FF0000'
  'greenish'	'g'	[0 i 0]	'#00FF00'
  'blue'	'b'	[0 0 ane]	'#0000FF'
  'cyan'	'c'	[0 one ane]	'#00FFFF'
  'magenta'	'm'	[1 0 ane]	'#FF00FF'
  'xanthous'	'y'	[one ane 0]	'#FFFF00'
  'black'	'yard'	[0 0 0]	'#000000'
  'white'	'west'	[i ane 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 Colour Lawmaking	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: histogram(X,'EdgeColor','r') creates a histogram plot with red bar edges.

FaceAlpha — Transparency of histogram bars
0.6 (default) | scalar value between 0 and 1 inclusive

Transparency of histogram bars, specified as a scalar value betwixt 0 and 1 inclusive. histogram uses the same transparency for all the bars of the histogram. A value of one ways fully opaque and 0 means completely transparent (invisible).

Example: histogram(Ten,'FaceAlpha',1) creates a histogram plot with fully opaque confined.

FaceColor — Histogram bar colour
'machine' (default) | 'none' | RGB triplet | hexadecimal color code | colour proper noun

Histogram bar colour, specified every bit one of these values:

• 'none' — Confined are not filled.

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

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

  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, light-green, and blue components of the color. The intensities must be in the range [0,one]; for instance, [0.4 0.6 0.7].

  • A hexadecimal colour code is a character vector or a string scalar that starts with a hash symbol (#) followed past three or half-dozen 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, y'all can specify some mutual colors by name. This tabular array lists the named colour options, the equivalent RGB triplets, and hexadecimal color codes.

  Color Proper name	Short Name	RGB Triplet	Hexadecimal Color Lawmaking	Appearance
  'crimson'	'r'	[1 0 0]	'#FF0000'
  'dark-green'	'thousand'	[0 1 0]	'#00FF00'
  'blue'	'b'	[0 0 1]	'#0000FF'
  'cyan'	'c'	[0 i 1]	'#00FFFF'
  'magenta'	'm'	[1 0 1]	'#FF00FF'
  'yellow'	'y'	[1 i 0]	'#FFFF00'
  'black'	'grand'	[0 0 0]	'#000000'
  'white'	'w'	[1 1 1]	'#FFFFFF'

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

  RGB Triplet	Hexadecimal Colour Code	Advent
  [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', so histogram does not use the FaceColor property.

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

Line manner, 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

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

Example: 1.5

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

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.

• $$\mathrm{North}$$ is the number of elements in the input information. This value can exist greater than the binned information if the data contains NaN, NaT, or <undefined> values, or if some of the information lies outside the bin limits.

Value	Bin Values	Notes
'count' (default)	$$v_i=c_i$$	Count or frequency of observations. Sum of bin values is less than or equal to numel(10). The sum is less than numel(X) but 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'	$${\mathrm{five}}_i=\frac{c_i}{{\mathrm{west}}_i}$$	Count or frequency scaled by width of bin. The surface 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'	$$5_i={\displaystyle \sum _{j=\mathrm{i}}^i{c}_j}$$	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(10). For chiselled histograms, the superlative of the final bar is less than or equal to numel(X) or sum(ismember(10(:),Categories)).
'probability'	$$v_i=\frac{c_i}{N}$$	Relative probability. The sum of the bar heights is less than or equal to 1.
'pdf'	$${\mathrm{five}}_i=\frac{c_i}{N\cdot w_i}$$	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 ane. For categorical histograms, this is the same as 'probability'. Note 'pdf' does not support datetime or duration data.
'cdf'	$$5_i={\displaystyle \sum _{j=1}^i\frac{c_j}{N}}$$	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 information, the pinnacle 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 approximate of the probability density office for Ten.

Number of categories to display, specified every bit a scalar. Y'all tin modify the ordering of categories displayed in the histogram using the 'DisplayOrder' pick.

This option just works with categorical information.

Orientation of bars, specified every bit 'vertical' or 'horizontal'.

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

Toggle summary display of data belonging to undisplayed categories, specified as 'on' or 'off', or as numeric or logical one (true) or 0 (false). A value of 'on' is equivalent to true, and 'off' is equivalent to faux. Thus, you can apply the value of this property as a logical value. The value is stored equally an on/off logical value of type matlab.lang.OnOffSwitchState.

Prepare this option to 'on' to display an additional bar in the histogram with the proper name 'Others'. This extra bar counts all elements that do non belong to categories displayed in the histogram.

Y'all can modify the number of categories displayed in the histogram, too as their club, using the 'NumDisplayBins' and 'DisplayOrder' options.

This pick only works with categorical information.

Backdrop

Object Functions

Examples

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Histogram of Vector

Generate 10,000 random numbers and create a histogram. The histogram function automatically chooses an advisable number of bins to cover the range of values in 10 and show the shape of the underlying distribution.

10 = randn(10000,1); h = histogram(x)

h =    Histogram with properties:               Data: [10000x1 double]            Values: [two 2 1 half-dozen 7 17 29 57 86 133 193 271 331 421 540 613 ... ]           NumBins: 37          BinEdges: [-3.8000 -iii.6000 -three.4000 -three.2000 -3 -2.8000 -2.6000 ... ]          BinWidth: 0.2000         BinLimits: [-3.8000 3.6000]     Normalization: 'count'         FaceColor: 'automobile'         EdgeColor: [0 0 0]    Show all properties                  

When y'all specify an output argument to the histogram function, information technology returns a histogram object. You can use this object to audit the backdrop of the histogram, such as the number of bins or the width of the bins.

Find the number of histogram bins.

Specify Number of Histogram Bins

Plot a histogram of 1,000 random numbers sorted into 25 equally spaced bins.

x = randn(thou,one); nbins = 25; h = histogram(x,nbins)

h =    Histogram with backdrop:               Data: [1000x1 double]            Values: [1 iii 0 6 fourteen 19 31 54 74 80 92 122 104 115 88 lxxx 38 32 ... ]           NumBins: 25          BinEdges: [-3.4000 -3.1200 -2.8400 -two.5600 -2.2800 -2 -1.7200 ... ]          BinWidth: 0.2800         BinLimits: [-3.4000 3.6000]     Normalization: 'count'         FaceColor: 'motorcar'         EdgeColor: [0 0 0]    Prove all properties                  

Discover the bin counts.

counts =                                          1×25                                        1     3     0     half-dozen    14    xix    31    54    74    80    92   122   104   115    88    80    38    32    21     nine     5     5     5     0     two                  

Change Number of Histogram Bins

Generate 1,000 random numbers and create a histogram.

X = randn(1000,1); h = histogram(Ten)

h =    Histogram with backdrop:               Data: [1000x1 double]            Values: [3 ane 2 xv 17 27 53 79 85 101 127 110 124 95 67 32 27 ... ]           NumBins: 23          BinEdges: [-three.3000 -3.0000 -2.7000 -2.4000 -two.yard -i.8000 ... ]          BinWidth: 0.3000         BinLimits: [-3.3000 3.6000]     Normalization: 'count'         FaceColor: 'automobile'         EdgeColor: [0 0 0]    Evidence all backdrop                  

Utilize the morebins function to coarsely adjust the number of bins.

Nbins = morebins(h); Nbins = morebins(h)

Adjust the bins at a fine grain level by explicitly setting the number of bins.

Specify Bin Edges of Histogram

Generate 1,000 random numbers and create a histogram. Specify the bin edges as a vector with wide bins on the edges of the histogram to capture the outliers that do not satisfy $$|x|<\mathrm{ii}$$ . The kickoff vector element is the left edge of the first bin, and the last vector chemical element is the right border of the last bin.

ten = randn(thousand,one); edges = [-10 -2:0.25:ii 10]; h = histogram(ten,edges);

Specify the Normalization holding as 'countdensity' to flatten out the bins containing the outliers. At present, the surface area of each bin (rather than the height) represents the frequency of observations in that interval.

h.Normalization =                    'countdensity';

Plot Categorical Histogram

Create a categorical vector that represents votes. The categories in the vector are 'yes', 'no', or 'undecided'.

A = [0 0 1 1 ane 0 0 0 0 NaN NaN i 0 0 0 1 0 1 0 1 0 0 0 ane 1 one i]; C = categorical(A,[1 0 NaN],{'yes','no','undecided'})

C =                                          1x27 categorical                                        Columns 1 through 9       no      no      aye      yes      yes      no      no      no      no     Columns 10 through 16       undecided      undecided      aye      no      no      no      yes     Columns 17 through 25       no      yes      no      yes      no      no      no      yes      yes     Columns 26 through 27       yes      yes                  

Plot a categorical histogram of the votes, using a relative bar width of 0.5.

h = histogram(C,'BarWidth',0.5)

h =    Histogram with properties:                Information: [no    no    yes    yep    yes    no    no    ...    ]             Values: [xi 14 2]     NumDisplayBins: 3         Categories: {'yes'  'no'  'undecided'}       DisplayOrder: 'data'      Normalization: 'count'       DisplayStyle: 'bar'          FaceColor: 'auto'          EdgeColor: [0 0 0]    Bear witness all properties                  

Histogram with Specified Normalization

Generate 1,000 random numbers and create a histogram using the 'probability' normalization.

x = randn(1000,i); h = histogram(x,'Normalization','probability')

h =    Histogram with backdrop:               Data: [1000x1 double]            Values: [0.0030 one.0000e-03 0.0020 0.0150 0.0170 0.0270 0.0530 ... ]           NumBins: 23          BinEdges: [-3.3000 -iii.0000 -2.7000 -2.4000 -2.1000 -one.8000 ... ]          BinWidth: 0.3000         BinLimits: [-3.3000 3.6000]     Normalization: 'probability'         FaceColor: 'car'         EdgeColor: [0 0 0]    Show all properties                  

Compute the sum of the bar heights. With this normalization, the summit of each bar is equal to the probability of selecting an ascertainment within that bin interval, and the height of all of the bars sums to 1.

Plot Multiple Histograms

Generate two vectors of random numbers and plot a histogram for each vector in the same effigy.

ten = randn(2000,1); y = 1 + randn(5000,1); h1 = histogram(x); hold                    on                    h2 = histogram(y);

Since the sample size and bin width of the histograms are different, it is difficult to compare them. Normalize the histograms so that all of the bar heights add to 1, and use a uniform bin width.

h1.Normalization =                    'probability'; h1.BinWidth = 0.25; h2.Normalization =                    'probability'; h2.BinWidth = 0.25;

Adjust Histogram Properties

Generate 1,000 random numbers and create a histogram. Render the histogram object to adjust the properties of the histogram without recreating the entire plot.

x = randn(1000,i); h = histogram(10)

h =    Histogram with backdrop:               Data: [1000x1 double]            Values: [3 1 ii xv 17 27 53 79 85 101 127 110 124 95 67 32 27 ... ]           NumBins: 23          BinEdges: [-3.3000 -3.0000 -2.7000 -2.4000 -2.1000 -1.8000 ... ]          BinWidth: 0.3000         BinLimits: [-3.3000 iii.6000]     Normalization: 'count'         FaceColor: 'auto'         EdgeColor: [0 0 0]    Show all properties                  

Specify exactly how many bins to use.

Specify the edges of the bins with a vector. The kickoff value in the vector is the left border of the first bin. The last value is the right edge of the last bin.

Alter the color of the histogram confined.

h.FaceColor = [0 0.5 0.v]; h.EdgeColor =                    'r';

Determine Underlying Probability Distribution

Generate 5,000 unremarkably distributed random numbers with a hateful of five and a standard deviation of 2. Plot a histogram with Normalization fix to 'pdf' to produce an estimation of the probability density role.

x = 2*randn(5000,one) + 5; histogram(10,'Normalization','pdf')

In this example, the underlying distribution for the normally distributed data is known. Yous can, even so, employ the 'pdf' histogram plot to make up one's mind the underlying probability distribution of the data past comparing it against a known probability density part.

The probability density function for a normal distribution with mean $$\mu$$ , standard deviation $$\sigma$$ , and variance $${\sigma }^2$$ is

$$f(x,\mu ,\sigma )=\frac{1}{\sigma \sqrt{2\pi }}\text{exp}[-\frac{(\mathrm{ten}-\mu {)}^2}{2{\sigma }^2}].$$

Overlay a plot of the probability density part for a normal distribution with a mean of v and a standard difference of two.

agree                    on                    y = -5:0.1:15; mu = v; sigma = 2; f = exp(-(y-mu).^2./(two*sigma^2))./(sigma*sqrt(ii*pi)); plot(y,f,'LineWidth',1.v)

Saving and Loading Histogram Objects

Utilize the savefig function to salvage a histogram figure.

histogram(randn(10)); savefig('histogram.fig'); close                    gcf                  

Utilize openfig to load the histogram figure dorsum into MATLAB. openfig too returns a handle to the effigy, h.

h = openfig('histogram.fig');

Use the findobj function to locate the right object handle from the effigy handle. This allows yous to continue manipulating the original histogram object used to generate the figure.

y = findobj(h,'type','histogram')

y =    Histogram with properties:               Data: [10x10 double]            Values: [2 17 28 32 sixteen 3 2]           NumBins: seven          BinEdges: [-three -2 -1 0 i 2 3 4]          BinWidth: ane         BinLimits: [-3 four]     Normalization: 'count'         FaceColor: 'auto'         EdgeColor: [0 0 0]    Testify all properties                  

Tips

• Histogram plots created using histogram have a context menu in plot edit mode that enables interactive manipulations in the figure window. For example, you lot tin can apply the context carte du jour to interactively modify the number of bins, align multiple histograms, or change the display order.

• When you add information tips to a histogram plot, they display the bin edges and bin count.

Extended Capabilities

Tall Arrays
Calculate with arrays that accept more rows than fit in memory.

This part supports tall arrays with the limitations:

• Some input options are not supported. The immune options are:

  • 'BinWidth'

  • 'BinLimits'

  • '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, in that location is a cap on the maximum number of bars. The default maximum is 100.

• The morebins and fewerbins methods are not supported.

• Editing backdrop of the histogram object that crave recomputing the bins is not supported.

For more information, see Tall Arrays for Out-of-Memory Information.

GPU Arrays
Accelerate code by running on a graphics processing unit (GPU) using Parallel Computing Toolbox™.

Usage notes and limitations:

• This function accepts GPU arrays, but does not run on a GPU.

For more information, see Run MATLAB Functions on a GPU (Parallel Computing Toolbox).

Distributed Arrays
Partitioning big arrays beyond the combined memory of your cluster using Parallel Computing Toolbox™.

Usage notes and limitations:

• This role operates on distributed arrays, just executes in the client MATLAB.

For more than data, see Run MATLAB Functions with Distributed Arrays (Parallel Computing Toolbox).

Introduced in R2014b

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Source: https://ww2.mathworks.cn/help/matlab/ref/matlab.graphics.chart.primitive.histogram.html?lang=en

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