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KEYMACRO provides a flexible toolkit for the analysis and comparison of multidimensional data. It uses a simple and flexible data format that can handle any type of multidimensional data. The package can be used for data analysis, visualization, and clustering.
The main component of the package is a matrix that represents multidimensional data. This matrix is a simple, convenient, and flexible data format. It is based on a n-tuple matrix, where n is the number of dimensions. It can be thought of as a generalization of the data matrix in MATLAB®. The package has a simple and flexible workflow that uses 2D projections to reduce the number of dimensions of the matrix. This enables both clustering and visual analysis of the data. The package was implemented in Python to enable the use of Python libraries in various fields of study. The package includes an I/O functionality that can be used by users to read and write various file types. The package also includes a Python framework, which can be used to automate repetitive data analysis. The package was designed for Python and can easily be ported to other languages. It uses a simple and flexible data format that can handle general purpose data as well as seismic data.
Source code:
The source code of the package is available under the GNU General Public License v2 or later. The code includes all the scripts used to generate the package.
Key Features:
The package includes an input-output functionality that can read and write files of various formats. This functionality can be used for reading seismic data or data generated by any geophysical instrument. It can also be used to read image files or array-like files. It can also write matrices in a compact binary format.
KEYMACRO allows multidimensional data to be represented as simple matrix operations. It can handle any number of dimensions. It is also multithread-safe and can be modified in-place.
KeyMACRO includes a clustering module that can be used to divide or combine similar clusters of data. It can use any distance metric to perform clustering. This can be used to automate classification of data based on features extracted from the data.
The package includes a simple and flexible visualization module. This module can be used to display the data in a variety of formats. The module can also be used to find clusters and regions of similar data.
Data 384a16bd22

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– Data that will be transformed, in the form of a MATLAB Variable, matrix, file or a combination of these.
– Keywords that will be translated into the included keywords.
– The option to set the name of the output file.
– The option to set the name of the output file.
– The option to choose a delimiter of the ASCii code.
– A.ASCii file that contains the transformed data.
– A Mat2ASCii file for Matlab.
– A workspace file containing the name of the ASCii file and the translated keywords.

Data is either in a vector (1D array) or a matrix (2D array). It is possible to include several files (or variables) into a single output file.
Data is always in the same dimensions, e.g. vector: with size N (e.g. 101) or matrix: with size MxN (e.g. 201×101). To transform the dimensions use the Mat2ASCii option.
The code of the output file will have a form of the following:

1.1 ===> var1,var2,var3,…varN

The matrix of keymapping will be:

1.2 ===> var1,key1,var2,key2,var3,…varN,keyN


1.3 ===> x,y,z,g,w,k,h,o,m,l,a,f,v,d,c,q,p,n,s,x,z,f,g,m,a,w,e,h,o,c,y,u,j,k,l,n,r,v,i,t,p,q,s,d,a,x,w,k,v,o,n,z,f,c,e,g,u,i,l,d,y,m,a,p,q,n,s,r,v,t,l,h,k,j,f,a,g,c,i,d,v,s,n,m,z,f,k,u,q,r,h,e,i,t,s,a,g,v,p,n,l,t,j,mзўж-г№гѓeгѓєгѓјгѓџгѓіг/

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