Scatter plot of bins for tall arrays

`binScatterPlot(X,Y)`

`binScatterPlot(X,Y,nbins)`

`binScatterPlot(X,Y,Xedges,Yedges)`

`binScatterPlot(X,Y,Name,Value)`

`h = binScatterPlot(___)`

`binScatterPlot(`

creates
a binned scatter plot of the data in `X,Y`

)`X`

and `Y`

.
The `binScatterPlot`

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.

`binScatterPlot(`

specifies
additional options with one or more name-value pair arguments using
any of the previous syntaxes. For example, you can specify `X,Y`

,`Name,Value`

)`'Color'`

and
a valid color option to change the color theme of the plot, or `'Gamma'`

with
a positive scalar to adjust the level of detail.

returns
a `h`

= binScatterPlot(___)`Histogram2`

object. Use this object to inspect
properties of the plot.

Create two tall vectors of random data. Create a binned scatter plot for the data.

X = tall(randn(1e5,1));

Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 12).

Y = tall(randn(1e5,1)); binScatterPlot(X,Y)

Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 1.5 sec Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.38 sec

The resulting figure contains a slider to adjust the level of detail in the image.

Specify a scalar value as the third input argument to use the same number of bins in each dimension, or a two-element vector to use a different number of bins in each dimension.

Plot a binned scatter plot of random data sorted into 100 bins in each dimension.

X = tall(randn(1e5,1));

Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 12).

Y = tall(randn(1e5,1)); binScatterPlot(X,Y,100)

Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.87 sec Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.31 sec

Use 20 bins in the *x*-dimension and continue to use 100 bins in the *y*-dimension.

binScatterPlot(X,Y,[20 100])

Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.22 sec Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.14 sec

Plot a binned scatter plot of random data with specific bin edges. Use bin edges of `Inf`

and `-Inf`

to capture outliers.

Create a binned scatter plot with 100 bin edges between `[-2 2]`

in each dimension. The data outside the specified bin edges is not included in the plot.

X = tall(randn(1e5,1));

Starting parallel pool (parpool) using the 'local' profile ... Connected to the parallel pool (number of workers: 12).

Y = tall(randn(1e5,1)); Xedges = linspace(-2,2); Yedges = linspace(-2,2); binScatterPlot(X,Y,Xedges,Yedges)

Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 1.1 sec

Use coarse bins extending to infinity on the edges of the plot to capture outliers.

Xedges = [-Inf linspace(-2,2) Inf]; Yedges = [-Inf linspace(-2,2) Inf]; binScatterPlot(X,Y,Xedges,Yedges)

Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.29 sec

Plot a binned scatter plot of random data, specifying `'Color'`

as `'c'`

.

X = tall(randn(1e5,1));

Y = tall(randn(1e5,1)); binScatterPlot(X,Y,'Color','c')

Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 1.5 sec Evaluating tall expression using the Parallel Pool 'local': Evaluation completed in 0.28 sec

`X,Y`

— Data to distribute among bins (as separate arguments)tall vectors | tall matrices | tall multidimensional arrays

Data to distribute among bins, specified as separate arguments
of tall vectors, matrices, or multidimensional arrays. `X`

and `Y`

must
be the same size. If `X`

and `Y`

are
not vectors, then `binScatterPlot`

treats them
as single column vectors, `X(:)`

and `Y(:)`

.

Corresponding elements in `X`

and `Y`

specify
the *x* and *y* coordinates of 2-D
data points, `[X(k),Y(k)]`

. The underlying data types
of `X`

and `Y`

can be different,
but `binScatterPlot`

concatenates these inputs
into a single `N`

-by-`2`

tall matrix
of the dominant underlying data type.

`binScatterPlot`

ignores all `NaN`

values.
Similarly, `binScatterPlot`

ignores `Inf`

and `-Inf`

values,
unless the bin edges explicitly specify `Inf`

or `-Inf`

as
a bin edge.

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 bin edges.`binScatterPlot`

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 dimensionscalar | vector

Number of bins in each dimension, specified as a positive scalar
integer or two-element vector of positive integers. If you do not
specify `nbins`

, then `binScatterPlot`

automatically
calculates how many bins to use based on the values in `X`

and `Y`

.

If

`nbins`

is a scalar, then`binScatterPlot`

uses that many bins in each dimension.If

`nbins`

is a vector, then`nbins(1)`

specifies the number of bins in the*x*-dimension and`nbins(2)`

specifies the number of bins in the*y*-dimension.

**Example: **`binScatterPlot(X,Y,20)`

uses 20 bins
in each dimension.

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

uses 10 bins in the `x`

-dimension
and 20 bins in the `y`

-dimension.

`Xedges`

— Bin edges in vector

Bin edges in *x*-dimension, specified as a
vector. `Xedges(1)`

is the first edge of the first
bin in the *x*-dimension, and `Xedges(end)`

is
the outer edge of the last bin.

The value `[X(k),Y(k)]`

is in the `(i,j)`

th
bin if `Xedges(i)`

≤ `X(k)`

< `Xedges(i+1)`

**and** `Yedges(j)`

≤ `Y(k)`

< `Yedges(j+1)`

.
The last bins in each dimension also include the last (outer) edge.
For example, `[X(k),Y(k)]`

falls into the `i`

th
bin in the last row if `Xedges(end-1)`

≤ `X(k)`

≤ `Xedges(end)`

**and** `Yedges(i)`

≤ `Y(k)`

< `Yedges(i+1)`

.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

`Yedges`

— Bin edges in vector

Bin edges in *y*-dimension, specified as a
vector. `Yedges(1)`

is the first edge of the first
bin in the *y*-dimension, and `Yedges(end)`

is
the outer edge of the last bin.

The value `[X(k),Y(k)]`

is in the `(i,j)`

th
bin if `Xedges(i)`

≤ `X(k)`

< `Xedges(i+1)`

**and** `Yedges(j)`

≤ `Y(k)`

< `Yedges(j+1)`

.
The last bins in each dimension also include the last (outer) edge.
For example, `[X(k),Y(k)]`

falls into the `i`

th
bin in the last row if `Xedges(end-1)`

≤ `X(k)`

≤ `Xedges(end)`

**and** `Yedges(i)`

≤ `Y(k)`

< `Yedges(i+1)`

.

**Data Types: **`single`

| `double`

| `int8`

| `int16`

| `int32`

| `int64`

| `uint8`

| `uint16`

| `uint32`

| `uint64`

| `logical`

Specify optional
comma-separated pairs of `Name,Value`

arguments. `Name`

is
the argument name and `Value`

is the corresponding value.
`Name`

must appear inside quotes. You can specify several name and value
pair arguments in any order as
`Name1,Value1,...,NameN,ValueN`

.

`binScatterPlot(X,Y,'BinWidth',[5 10])`

`'BinMethod'`

— Binning algorithm`'auto'`

(default) | `'scott'`

| `'integers'`

Binning algorithm, specified as the comma-separated pair consisting
of `'BinMethod'`

and one of these values.

Value | Description |
---|---|

`'auto'` | The default `'auto'` algorithm uses a maximum
of 100 bins and chooses a bin width to cover the data range and reveal
the shape of the underlying distribution. |

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

`'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 (2^{16}).
If the data range is greater than 65536, then the integer rule uses
wider bins instead. |

The `BinMethod`

property of the resulting `Histogram2`

object
always has a value of `'manual'`

.

`'BinWidth'`

— Width of bins in each dimensionscalar | vector

Width of bins in each dimension, specified as the comma-separated
pair consisting of `'BinWidth'`

and a scalar or two-element
vector of positive integers, `[xWidth yWidth]`

. A
scalar value indicates the same bin width for each dimension.

If you specify `BinWidth`

, then `binScatterPlot`

can
use a maximum of 1024 bins (2^{10})
along each dimension. If instead the specified bin width requires
more bins, then `binScatterPlot`

uses a larger
bin width corresponding to the maximum number of bins.

**Example: **`binScatterPlot(X,Y,'BinWidth',[5 10])`

uses
bins with size `5`

in the `x`

-dimension
and size `10`

in the `y`

-dimension.

`'Color'`

— Plot color theme`'b'`

(default) | `'y'`

| `'m'`

| `'c'`

| `'r'`

| `'g'`

| `'k'`

Plot color theme, specified as the comma-separated pair consisting
of `'Color'`

and one of these options.

Option | Description |
---|---|

`'b'` | Blue |

`'m'` | Magenta |

`'c'` | Cyan |

`'r'` | Red |

`'g'` | Green |

`'y'` | Yellow |

`'k'` | Black |

`'Gamma'`

— Gamma correction`1`

(default) | positive scalarGamma correction, specified as the comma-separated pair consisting
of `'Gamma'`

and a positive scalar. Use this option
to adjust the brightness and color intensity to affect the amount
of detail in the image.

`gamma < 1`

— As gamma decreases, the shading of bins with smaller bin counts becomes progressively darker, including more detail in the image.`gamma > 1`

— As gamma increases, the shading of bins with smaller bin counts becomes progressively lighter, removing detail from the image.The default value of 1 does not apply any correction to the display.

`'XBinLimits'`

— Bin limits in vector

Bin limits in *x*-dimension, specified as the
comma-separated pair consisting of `'XBinLimits'`

and
a two-element vector, `[xbmin,xbmax]`

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

`binScatterPlot`

only plots data that falls
within the bin limits inclusively, ```
Data(Data(:,1)>=xbmin
& Data(:,1)<=xbmax)
```

.

`'YBinLimits'`

— Bin limits in vector

Bin limits in *y*-dimension, specified as the
comma-separated pair consisting of `'YBinLimits'`

and
a two-element vector, `[ybmin,ybmax]`

. The vector
indicates the first and last bin edges in the *y*-dimension.

`binScatterPlot`

only plots data that falls
within the bin limits inclusively, ```
Data(Data(:,2)>=ybmin
& Data(:,2)<=ybmax)
```

.

`h`

— Binned scatter plot`Histogram2`

objectBinned scatter plot, returned as a `Histogram2`

object.
For more information, see Histogram2 Properties.

Calculate with arrays that have more rows than fit in memory.

This function fully supports tall arrays. For more information, see Tall Arrays (MATLAB).

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