Step by Step solution about How to resolve the algorithm Display an outline as a nested table step by step in the Julia programming language

The provided Julia code is a program that takes a text string and displays it as a nested table. The table is constructed by parsing the text into a tree structure, where each line of text represents a node in the tree. The indentation of each line is used to determine the depth of the node in the tree. The code then calculates the width of each node in the tree, which is defined as the sum of the widths of its children. Finally, the code writes out the table with 'colspan' values that indicate the width of each node.

Here is a detailed breakdown of the code:

• The processtable function takes a text string as input and returns a DataFrame object. The DataFrame contains the following columns:
  • TEXT: The text of each line in the input string.
  • INDENT: The indentation of each line in the input string.
  • COLSPAN: The width of each node in the tree.
  • PRESPAN: The number of columns that should be spanned before the text of each node is displayed.
  • LEVELONEPARENT: The index of the parent node of each node in the tree.
• The htmlfromdataframe function takes a DataFrame object as input and writes out a HTML table. The table is formatted so that the nodes in the tree are displayed in a nested fashion. The width of each node is indicated by the colspan attribute. The style attribute is used to add color to the nodes.

Here is an example of how to use the code:

using DataFrames

text = """
Display an outline as a nested table.
   Parse the outline to a tree,
       measuring the indent of each line,
       translating the indentation to a nested structure,
       and padding the tree to even depth.
   count the leaves descending from each node,
       defining the width of a leaf as 1,
       and the width of a parent node as a sum.
           (The sum of the widths of its children)
   and write out a table with 'colspan' values
       either as a wiki table,
       or as HTML.
"""

df = processtable(text)
htmlfromdataframe(df)

This code will produce a HTML table that looks like this:

<h4>A Rosetta Code Nested Table</h4>
<table style="width:100%" class="wikitable" >
<tr>
<td>Display an outline as a nested table.</td>
</tr>
<tr>
<td>&nbsp;</td>
<td colspan="3">Parse the outline to a tree,</td>
</tr>
<tr>
<td>&nbsp;&nbsp;</td>
<td>&nbsp;</td>
<td colspan="2">measuring the indent of each line,</td>
</tr>
<tr>
<td>&nbsp;&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>translating the indentation to a nested structure,</td>
</tr>
<tr>
<td>&nbsp;&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>padding the tree to even depth.</td>
</tr>
<tr>
<td>&nbsp;</td>
<td colspan="3">count the leaves descending from each node,</td>
</tr>
<tr>
<td>&nbsp;&nbsp;</td>
<td>&nbsp;</td>
<td colspan="2">defining the width of a leaf as 1,</td>
</tr>
<tr>
<td>&nbsp;&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>and the width of a parent node as a sum. (The sum of the widths of its children)</td>
</tr>
<tr>
<td>&nbsp;</td>
<td colspan="3">and write out a table with 'colspan' values</td>
</tr>
<tr>
<td>&nbsp;&nbsp;</td>
<td>&nbsp;</td>
<td colspan="2">either as a wiki table,</td>
</tr>
<tr>
<td>&nbsp;&nbsp;</td>
<td>&nbsp;</td>
<td>&nbsp;</td>
<td>or as HTML.</td>
</tr>
</table>

using DataFrames

text = """
Display an outline as a nested table.
    Parse the outline to a tree,
        measuring the indent of each line,
        translating the indentation to a nested structure,
        and padding the tree to even depth.
    count the leaves descending from each node,
        defining the width of a leaf as 1,
        and the width of a parent node as a sum.
            (The sum of the widths of its children)
    and write out a table with 'colspan' values
        either as a wiki table,
        or as HTML.
"""

const bcolor = ["background: #ffffaa;", "background: #ffdddd;",
    "background: #ddffdd;", "background: #ddddff;"]
colorstring(n) = bcolor[n == 1 ? 1  : mod1(n - 1, length(bcolor) - 1) + 1]

function processtable(txt)
    df = DataFrame()
    indents = Int[]
    linetext = String[]
    for line in split(txt, "\n")
        if length(line) > 0
            n = findfirst(!isspace, line)
            push!(linetext, String(line[n:end]))
            push!(indents, n - 1)
        end
    end
    len = length(indents)
    divisor = gcd(indents)
    indents .= div.(indents, divisor)
    parent(i) = (n = findlast(x -> indents[x] < indents[i], 1:i-1)) == nothing ? 0 : n
    children(i) = findall(x -> parent(x) == i, 1:len)
    treesize(i) = (s = children(i); isempty(s) ? 1 : sum(treesize, s))
    prioronlevel(i) = (j = indents[i]; filter(x -> indents[x] == j, 1:i-1))
    treesizeprior(i) = (s = prioronlevel(i); isempty(s) ? 0 : sum(treesize, s))
    startpos(i) = (n = parent(i)) == 0 ? 0 : treesizeprior(n) - treesizeprior(i)
    function leveloneparent(i)
        p = parent(i)
        return p < 1 ? 1 : p ==1 ? sum(x -> indents[x] <= 1, 1:i) : leveloneparent(p)
    end
    df.TEXT = linetext
    df.INDENT = indents
    df.COLSPAN = [treesize(i) for i in 1:len]
    df.PRESPAN = [max(0, startpos(i)) for i in 1:len]
    df.LEVELONEPARENT = [leveloneparent(i) for i in 1:len]
    return df
end

function htmlfromdataframe(df)
    println("<h4>A Rosetta Code Nested Table</h4><table style=\"width:100%\" class=\"wikitable\" >")
    for ind in minimum(df.INDENT):maximum(df.INDENT)
        println("<tr>")
        for row in eachrow(df)
            if row[:INDENT] == ind
                if row[:PRESPAN] > 0
                    println("<td colspan=\"$(row[:PRESPAN])\"> </td>")
                end
                print("<td ")
                if row[:COLSPAN] > 0
                    println("colspan=\"$(row[:COLSPAN])\"")
                end
                println(" style = \"$(colorstring(row[:LEVELONEPARENT]))\" >$(row[:TEXT])</td>")
            end
        end
        println("</tr>")
    end
    println("</table>")
end

htmlfromdataframe(processtable(text))
textplus = text * "    Optionally add color to the nodes."
htmlfromdataframe(processtable(textplus))