Problem 42859. Convert nested struct-array to numeric array

Created by J-G van der Toorn

Solve Later

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For fitting the data, the relevant elements of the struct-array need to be converted to a simple, multi-dimensional numeric array.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>That will be your assignment.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>Create a function struct2mat that takes a struct-array as an input, and returns an array, where dimensions of the output data correspond to the struc-array levels in incremental order. The first output level enters dimension 1 of the output, the 2nd dimension 2, etc.</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"code\"/></w:pPr><w:r><w:t><![CDATA[datastruct(1).level2(1).level3(1).data = 1:4;\n...\ndatastruct(4).level2(3).level3(2).data = 4321:4324;\n\ndataarray = struct2mat(datastruct)]]></w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>should result in</w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"code\"/></w:pPr><w:r><w:t><![CDATA[dataarray(1,1,1,1) = 1;\n...\ndataarray(1,1,1,4) = 4;\n...\ndataarray(4,3,2,3) = 4323;]]></w:t></w:r></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:customXml w:element=\"image\"><w:customXmlPr><w:attr w:name=\"height\" w:val=\"-1\"/><w:attr w:name=\"width\" w:val=\"-1\"/><w:attr w:name=\"relationshipId\" w:val=\"rId1\"/></w:customXmlPr></w:customXml></w:p><w:p><w:pPr><w:pStyle w:val=\"text\"/></w:pPr><w:r><w:t>I encourage you to write a function that does not use loops to access the different struct-array levels.</w:t></w:r></w:p></w:body></w:document>"},{"partUri":"/matlab/output.xml","contentType":"text/xml","content":"<?xml version=\"1.0\" encoding=\"UTF-8\" standalone=\"no\" ?><embeddedOutputs><metaData><evaluationState>manual</evaluationState><layoutState>code</layoutState><outputStatus>ready</outputStatus></metaData><outputArray type=\"array\"/><regionArray 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"}]}

Sometimes data happens to be nicely structured in a struct-array, distributed over various levels of the struct, according to the hierarchy in the logic of the data acquisition.
For fitting the data, the relevant elements of the struct-array need to be converted to a simple, multi-dimensional numeric array.That will be your assignment.Create a function struct2mat that takes a struct-array as an input, and returns an array, where dimensions of the output data correspond to the struc-array levels in incremental order. The first output level enters dimension 1 of the output, the 2nd dimension 2, etc.<pre class="language-matlab">datastruct(1).level2(1).level3(1).data = 1:4;
...
datastruct(4).level2(3).level3(2).data = 4321:4324;
</pre><pre class="language-matlab">dataarray = struct2mat(datastruct)
</pre>should result in<pre class="language-matlab">dataarray(1,1,1,1) = 1;
...
dataarray(1,1,1,4) = 4;
...
dataarray(4,3,2,3) = 4323;
</pre><img src = "http://i64.tinypic.com/zkrbm1.jpg">I encourage you to write a function that does not use loops to access the different struct-array levels.

Sometimes data happens to be nicely structured in a struct-array, distributed over various levels of the struct, according to the hierarchy in the logic of the data acquisition.
For fitting the data, the relevant elements of the struct-array need to be converted to a simple, multi-dimensional numeric array.

That will be your assignment.

Create a function struct2mat that takes a struct-array as an input, and returns an array, where dimensions of the output data correspond to the struc-array levels in incremental order. The first output level enters dimension 1 of the output, the 2nd dimension 2, etc.

datastruct(1).level2(1).level3(1).data = 1:4;
  ...
  datastruct(4).level2(3).level3(2).data = 4321:4324;

dataarray = struct2mat(datastruct)

should result in

dataarray(1,1,1,1) = 1;
  ...
  dataarray(1,1,1,4) = 4;
  ...
  dataarray(4,3,2,3) = 4323;

<<http://i64.tinypic.com/zkrbm1.jpg>>

I encourage you to write a function that does not use loops to access the different struct-array levels.

Solve

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Problem 42859. Convert nested struct-array to numeric array

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