Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from http://reference.wolfram.com/language/ref/ArrayResample.html below:

ArrayResample—Wolfram Language Documentation

We deliver solutions for the AI era—combining symbolic computation, data-driven insights and deep technology expertise.

• Data & Computational Intelligence
• Model-Based Design
• Algorithm Development
• Wolfram|Alpha for Business
• Blockchain Technology
• Education Technology
• Quantum Computation

WolframConsulting.com

• See Also
  • ListInterpolation
  • Interpolation
  • ImageResize
  • Upsample
  • Downsample
  • Dimensions
  • ArrayPad
  • ArrayFilter
• Related Guides
  • Rearranging & Restructuring Lists
  • Creating & Importing Signals
  • Structure Matrices & Convolution Kernels
• Tech Notes
  • Image Processing

ArrayResample[array,{n₁,n₂,…}]

resamples array to have dimensions {n₁,n₂,…}.

ArrayResample[array,dspec]

resamples array according to the dimension specification dspec.

ArrayResample[array,dspec,scheme]

specifies resampling scheme, either point or bin based.

ArrayResample[array,dspec,scheme,{{x_min,x_max},…}]

resamples only the data in the specified subrange {{x_min,x_max},…}.

• ArrayResample can be used for resampling data arrays based on a large selection of interpolation and approximation models.
• ArrayResample works with data arrays of any depth.
• The dimension specification dspec can be of the form:
• For a multidimensional array, the notation n is taken to be equivalent to {n,Automatic,…} and {n} equivalent to {n,All,…}.
• The dimension ratios for an array of dimensions is taken to be .
• The scheme determines the location of sample and resample positions and can be of the form:
• "Point" point sampling (default) "Bin" bin sampling {"Bin",alignment} bin sampling with specified alignment
• For input data of length n the "Point" resampling scheme assumes a data range from 1 to n and the "Bin" scheme assumes a data range from 0 to n with the alignment indicating the sample location within each bin.
• Bin alignment alignment can be Left, Center, Right or any number between (Left) and 1 (Right).
• The data range can be modified using the DataRange option.
• By default, the data is resampled on the entire data domain, ranging from 1 to for the "Point" scheme and from 0 to for the "Bin" scheme. Use the DataRange option to modify the coordinates of the data domain.
• With a subrange {{x_min,x_max},…} specified with respect to the DataRange, only the data values in the given interval are resampled. »
• The following options can be given:
• For possible settings for Padding, see the reference page for ArrayPad.

Subsample an array:

Downsample an array:

Resize a 2D array of data:

Exact computation:

Precision of the input is preserved:

Resizing a symbolic array:

Resample a subdomain of the input signal:

With size specified as a scalar, output dimension is selected such that dimension ratio is preserved:

By default, the "Point" sampling scheme is used:

Use the "Bin" scheme, which uses center alignment by default:

Specify the alignment of the bins:

Generate a "Point" resampling with three times the input resolution:

Compute the sampling positions:

When downsampling, by default no antialiasing is happening:

With antialiasing, all samples that fall in between new samples are averaged:

DataRange specifies the domain of resampling. Subrange specification is defined with respect to this domain:

Resample the whole data:

Resample the first half using default DataRange->{1,n}, where n is the length of data:

Resample the first half of the data using a {0,1} data range:

The default padding value is "Fixed":

Specify a different padding:

By default, the same padding is used for all dimensions:

Use different paddings for different dimensions:

By default, "Linear" resampling is used:

Use a different resampling scheme:

"Nearest" resampling averages the samples if the sampling position is halfway between samples:

Use "NearestLeft" or "NearestRight" for a bias to left or right for half-integer sampling positions:

Reduce the size of a dataset for faster visualization:

Compare array resampling for a few different kernels:

Downsample can be used to downsample by an integer factor:

Exact computations are performed with integer data:

Apply N to integer data for faster computation:

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4