# Topic `06-data.md`

## Data

### Reading Data Files

The first thing to consider is this: do you actually need to write a custom file reader? And if the answer is yes, the next question is: can you write the reader in as clear a way as possible? Correctness, Robustness, and Speed; pick the first two and the third can be sorted out later, _if necessary_.

A common sort of data file is the configuration file format commonly used on Unix systems. This format is often called a _property_ file in the Java world.

# Read timeout in seconds read.timeout=10

# Write timeout in seconds write.timeout=10

Here is a simple Lua implementation:

-- property file parsing with Lua string patterns props = [] for line in io.lines() do if line:find('#,1,true) ~= 1 and not line:find('^%s*$') then local var,value = line:match('([^=]+)=(.*)') props[var] = value end end

Very compact, but it suffers from a similar disease in equivalent Perl programs; it uses odd string patterns which are 'lexically noisy'. Noisy code like this slows the casual reader down. (For an even more direct way of doing this, see the next section, 'Reading Configuration Files')

Another implementation, using the Penlight libraries:

-- property file parsing with extended string functions require 'pl' stringx.import() props = [] for line in io.lines() do if not line:startswith('#') and not line:isspace() then local var,value = line:splitv('=') props[var] = value end end

This is more self-documenting; it is generally better to make the code express the _intention_, rather than having to scatter comments everywhere - comments are necessary, of course, but mostly to give the higher view of your intention that cannot be expressed in code. It is slightly slower, true, but in practice the speed of this script is determined by I/O, so further optimization is unnecessary.

### Reading Unstructured Text Data

Text data is sometimes unstructured, for example a file containing words. The `pl.input` module has a number of functions which makes processing such files easier. For example, a script to count the number of words in standard input using `import.words`:

-- countwords.lua require 'pl' local k = 1 for w in input.words(io.stdin) do k = k + 1 end print('count',k)

Or this script to calculate the average of a set of numbers using `input.numbers`:

-- average.lua require 'pl' local k = 1 local sum = 0 for n in input.numbers(io.stdin) do sum = sum + n k = k + 1 end print('average',sum/k)

These scripts can be improved further by _eliminating loops_ In the last case, there is a perfectly good function `seq.sum` which can already take a sequence of numbers and calculate these numbers for us:

-- average2.lua require 'pl' local total,n = seq.sum(input.numbers()) print('average',total/n)

A further simplification here is that if `numbers` or `words` are not passed an argument, they will grab their input from standard input. The first script can be rewritten:

-- countwords2.lua require 'pl' print('count',seq.count(input.words()))

A useful feature of a sequence generator like `numbers` is that it can read from a string source. Here is a script to calculate the sums of the numbers on each line in a file:

-- sums.lua for line in io.lines() do print(seq.sum(input.numbers(line)) end

### Reading Columnar Data

It is very common to find data in columnar form, either space or comma-separated, perhaps with an initial set of column headers. Here is a typical example:

EventID Magnitude LocationX LocationY LocationZ 981124001 2.0 18988.4 10047.1 4149.7 981125001 0.8 19104.0 9970.4 5088.7 981127003 0.5 19012.5 9946.9 3831.2 ...

`input.fields` is designed to extract several columns, given some delimiter (default to whitespace). Here is a script to calculate the average X location of all the events:

-- avg-x.lua require 'pl' io.read() -- skip the header line local sum,count = seq.sum(input.fields {3}) print(sum/count)

`input.fields` is passed either a field count, or a list of column indices, starting at one as usual. So in this case we're only interested in column 3. If you pass it a field count, then you get every field up to that count:

for id,mag,locX,locY,locZ in input.fields (5) do .... end

`input.fields` by default tries to convert each field to a number. It will skip lines which clearly don't match the pattern, but will abort the script if there are any fields which cannot be converted to numbers.

The second parameter is a delimiter, by default spaces. ' ' is understood to mean 'any number of spaces', i.e. '%s+'. Any Lua string pattern can be used.

The third parameter is a _data source_, by default standard input (defined by `input.create_getter`.) It assumes that the data source has a `read` method which brings in the next line, i.e. it is a 'file-like' object. As a special case, a string will be split into its lines:

> for x,y in input.fields(2,' ','10 20\n30 40\n') do print(x,y) end 10 20 30 40

Note the default behaviour for bad fields, which is to show the offending line number:

> for x,y in input.fields(2,' ','10 20\n30 40x\n') do print(x,y) end 10 20 line 2: cannot convert '40x' to number

This behaviour of `input.fields` is appropriate for a script which you want to fail immediately with an appropriate _user_ error message if conversion fails. The fourth optional parameter is an options table: `{no_fail=true}` means that conversion is attempted but if it fails it just returns the string, rather as AWK would operate. You are then responsible for checking the type of the returned field. `{no_convert=true}` switches off conversion altogether and all fields are returned as strings.

@lookup pl.data

Sometimes it is useful to bring a whole dataset into memory, for operations such as extracting columns. Penlight provides a flexible reader specifically for reading this kind of data, using the `data` module. Given a file looking like this:

x,y 10,20 2,5 40,50

Then `data.read` will create a table like this, with each row represented by a sublist:

> t = data.read 'test.txt' > pretty.dump(t) {{10,20},{2,5},{40,50},fieldnames={'x','y'},delim=','}

You can now analyze this returned table using the supplied methods. For instance, the method `column_by_name` returns a table of all the values of that column.

-- testdata.lua require 'pl' d = data.read('fev.txt') for _,name in ipairs(d.fieldnames) do local col = d:column_by_name(name) if type(col[1]) == 'number' then local total,n = seq.sum(col) utils.printf("Average for %s is %f\n",name,total/n) end end

`data.read` tries to be clever when given data; by default it expects a first line of column names, unless any of them are numbers. It tries to deduce the column delimiter by looking at the first line. Sometimes it guesses wrong; these things can be specified explicitly. The second optional parameter is an options table: can override `delim` (a string pattern), `fieldnames` (a list or comma-separated string), specify `no_convert` (default is to convert), numfields (indices of columns known to be numbers, as a list) and `thousands_dot` (when the thousands separator in Excel CSV is '.')

A very powerful feature is a way to execute SQL-like queries on such data:

-- queries on tabular data require 'pl' local d = data.read('xyz.txt') local q = d:select('x,y,z where x > 3 and z < 2 sort by y') for x,y,z in q do print(x,y,z) end

Please note that the format of queries is restricted to the following syntax:

FIELDLIST [ 'where' CONDITION ] [ 'sort by' FIELD [asc|desc]]

Any valid Lua code can appear in `CONDITION`; remember it is _not_ SQL and you have to use `==` (this warning comes from experience.)

For this to work, _field names must be Lua identifiers_. So `read` will massage fieldnames so that all non-alphanumeric chars are replaced with underscores. However, the `original_fieldnames` field always contains the original un-massaged fieldnames.

`read` can handle standard CSV files fine, although doesn't try to be a full-blown CSV parser. With the `csv=true` option, it's possible to have double-quoted fields, which may contain commas; then trailing commas become significant as well.

Spreadsheet programs are not always the best tool to process such data, strange as this might seem to some people. This is a toy CSV file; to appreciate the problem, imagine thousands of rows and dozens of columns like this:

Department Name,Employee ID,Project,Hours Booked sales,1231,overhead,4 sales,1255,overhead,3 engineering,1501,development,5 engineering,1501,maintenance,3 engineering,1433,maintenance,10

The task is to reduce the dataset to a relevant set of rows and columns, perhaps do some processing on row data, and write the result out to a new CSV file. The `write_row` method uses the delimiter to write the row to a file; `Data.select_row` is like `Data.select`, except it iterates over _rows_, not fields; this is necessary if we are dealing with a lot of columns!

names = {[1501]='don',[1433]='dilbert'} keepcols = {'Employee_ID','Hours_Booked'} t:write_row (outf,{'Employee','Hours_Booked'}) q = t:select_row { fields=keepcols, where=function(row) return row[1]=='engineering' end } for row in q do row[1] = names[row[1]] t:write_row(outf,row) end

`Data.select_row` and `Data.select` can be passed a table specifying the query; a list of field names, a function defining the condition and an optional parameter `sort_by`. It isn't really necessary here, but if we had a more complicated row condition (such as belonging to a specified set) then it is not generally possible to express such a condition as a query string, without resorting to hackery such as global variables.

With 1.0.3, you can specify explicit conversion functions for selected columns. For instance, this is a log file with a Unix date stamp:

Time Message 1266840760 +# EE7C0600006F0D00C00F06010302054000000308010A00002B00407B00 1266840760 closure data 0.000000 1972 1972 0 1266840760 ++ 1266840760 EE 1 1266840760 +# EE7C0600006F0D00C00F06010302054000000408020A00002B00407B00 1266840764 closure data 0.000000 1972 1972 0

We would like the first column as an actual date object, so the `convert` field sets an explicit conversion for column 1. (Note that we have to explicitly convert the string to a number first.)

Date = require 'pl.Date'

function date_convert (ds) return Date(tonumber(ds)) end

d = data.read(f,{convert={[1]=date_convert},last_field_collect=true})

This gives us a two-column dataset, where the first column contains `Date` objects and the second column contains the rest of the line. Queries can then easily pick out events on a day of the week:

q = d:select "Time,Message where Time:weekday_name()=='Sun'"

Data does not have to come from files, nor does it necessarily come from the lab or the accounts department. On Linux, `ps aux` gives you a full listing of all processes running on your machine. It is straightforward to feed the output of this command into `data.read` and perform useful queries on it. Notice that non-identifier characters like '%' get converted into underscores:

require 'pl' f = io.popen 'ps aux' s = data.read (f,{last_field_collect=true}) f:close() print(s.fieldnames) print(s:column_by_name 'USER') qs = 'COMMAND,_MEM where _MEM > 5 and USER=="steve"' for name,mem in s:select(qs) do print(mem,name) end

I've always been an admirer of the AWK programming language; with `filter` you can get Lua programs which are just as compact:

-- printxy.lua require 'pl' data.filter 'x,y where x > 3'

It is common enough to have data files without headers of field names. `data.read` makes a special exception for such files if all fields are numeric. Since there are no column names to use in query expressions, you can use AWK-like column indexes, e.g. '$1,$2 where $1 > 3'. I have a little executable script on my system called `lf` which looks like this:

#!/usr/bin/env lua require 'pl.data'.filter(arg[1])

And it can be used generally as a filter command to extract columns from data. (The column specifications may be expressions or even constants.)

$ lf '$1,$5/10' < test.dat

(As with AWK, please note the single-quotes used in this command; this prevents the shell trying to expand the column indexes. If you are on Windows, then you must quote the expression in double-quotes so it is passed as one argument to your batch file.)

As a tutorial resource, have a look at `test-data.lua` in the PL tests directory for other examples of use, plus comments.

The data returned by `read` or constructed by `Data.copy_select` from a query is basically just an array of rows: `{{1,2},{3,4}}`. So you may use `read` to pull in any array-like dataset, and process with any function that expects such a implementation. In particular, the functions in `array2d` will work fine with this data. In fact, these functions are available as methods; e.g. `array2d.flatten` can be called directly like so to give us a one-dimensional list:

v = data.read('dat.txt'):flatten()

The data is also in exactly the right shape to be treated as matrices by [LuaMatrix](http://lua-users.org/wiki/LuaMatrix):

> matrix = require 'matrix' > m = matrix(data.read 'mat.txt') > = m 1 0.2 0.3 0.2 1 0.1 0.1 0.2 1 > = m^2 -- same as m*m 1.07 0.46 0.62 0.41 1.06 0.26 0.24 0.42 1.05

`write` will write matrices back to files for you.

Finally, for the curious, the global variable `_DEBUG` can be used to print out the actual iterator function which a query generates and dynamically compiles. By using code generation, we can get pretty much optimal performance out of arbitrary queries.

> lua -lpl -e "_DEBUG=true" -e "data.filter 'x,y where x > 4 sort by x'" < test.txt return function (t) local i = 0 local v local ls = {} for i,v in ipairs(t) do if v[1] > 4 then ls[#ls+1] = v end end table.sort(ls,function(v1,v2) return v1[1] < v2[1] end) local n = #ls return function() i = i + 1 v = ls[i] if i > n then return end return v[1],v[2] end end

10,20 40,50

### Reading Configuration Files

The `config` module provides a simple way to convert several kinds of configuration files into a Lua table. Consider the simple example:

# test.config # Read timeout in seconds read.timeout=10

# Write timeout in seconds write.timeout=5

#acceptable ports ports = 1002,1003,1004

This can be easily brought in using `config.read` and the result shown using `pretty.write`:

-- readconfig.lua local config = require 'pl.config' local pretty= require 'pl.pretty'

local t = config.read(arg[1]) print(pretty.write(t))

and the output of `lua readconfig.lua test.config` is:

{ ports = { 1002, 1003, 1004 }, write_timeout = 5, read_timeout = 10 }

That is, `config.read` will bring in all key/value pairs, ignore # comments, and ensure that the key names are proper Lua identifiers by replacing non-identifier characters with '_'. If the values are numbers, then they will be converted. (So the value of `t.write_timeout` is the number 5). In addition, any values which are separated by commas will be converted likewise into an array.

Any line can be continued with a backslash. So this will all be considered one line:

names=one,two,three, \ four,five,six,seven, \ eight,nine,ten

Windows-style INI files are also supported. The section structure of INI files translates naturally to nested tables in Lua:

; test.ini [timeouts] read=10 ; Read timeout in seconds write=5 ; Write timeout in seconds [portinfo] ports = 1002,1003,1004

The output is:

{ portinfo = { ports = { 1002, 1003, 1004 } }, timeouts = { write = 5, read = 10 } }

You can now refer to the write timeout as `t.timeouts.write`.

As a final example of the flexibility of `config.read`, if passed this simple comma-delimited file

one,two,three 10,20,30 40,50,60 1,2,3

it will produce the following table:

{ { "one", "two", "three" }, { 10, 20, 30 }, { 40, 50, 60 }, { 1, 2, 3 } }

`config.read` isn't designed to read all CSV files in general, but intended to support some Unix configuration files not structured as key-value pairs, such as '/etc/passwd'.

This function is intended to be a Swiss Army Knife of configuration readers, but it does have to make assumptions, and you may not like them. So there is an optional extra parameter which allows some control, which is table that may have the following fields:

{ variablilize = true, convert_numbers = tonumber, trim_space = true, list_delim = ',', trim_quotes = true, ignore_assign = false, keysep = '=', smart = false, }

`variablilize` is the option that converted `write.timeout` in the first example to the valid Lua identifier `write_timeout`. If `convert_numbers` is true, then an attempt is made to convert any string that starts like a number. You can specify your own function (say one that will convert a string like '5224 kb' into a number.)

`trim_space` ensures that there is no starting or trailing whitespace with values, and `list_delim` is the character that will be used to decide whether to split a value up into a list (it may be a Lua string pattern such as '%s+'.)

For instance, the password file in Unix is colon-delimited:

t = config.read('/etc/passwd',{list_delim=':'})

This produces the following output on my system (only last two lines shown):

{ ... { "user", "x", "1000", "1000", "user,,,", "/home/user", "/bin/bash" }, { "sdonovan", "x", "1001", "1001", "steve donovan,28,,", "/home/sdonovan", "/bin/bash" } }

You can get this into a more sensible format, where the usernames are the keys, with this (the `tablex.pairmap` function must return value, key!)

t = tablex.pairmap(function(k,v) return v,v[1] end,t)

and you get:

{ ... sdonovan = { "sdonovan", "x", "1001", "1001", "steve donovan,28,,", "/home/sdonovan", "/bin/bash" } ... }

Many common Unix configuration files can be read by tweaking these parameters. For `/etc/fstab`, the options `{list_delim='%s+',ignore_assign=true}` will correctly separate the columns. It's common to find 'KEY VALUE' assignments in files such as `/etc/ssh/ssh_config`; the options `{keysep=' '}` make `config.read` return a table where each KEY has a value VALUE.

Files in the Linux `procfs` usually use ':` as the field delimiter:

> t = config.read('/proc/meminfo',{keysep=':'}) > = t.MemFree 220140 kB

That result is a string, since `tonumber` doesn't like it, but defining the `convert_numbers` option as `function(s) return tonumber((s:gsub(' kB$',''))) end` will get the memory figures as actual numbers in the result. (The extra parentheses are necessary so that `tonumber` only gets the first result from `gsub`). From `tests/test-config.lua':

testconfig([[ MemTotal: 1024748 kB MemFree: 220292 kB ]], { MemTotal = 1024748, MemFree = 220292 }, { keysep = ':', convert_numbers = function(s) s = s:gsub(' kB$','') return tonumber(s) end } )

The `smart` option lets `config.read` make a reasonable guess for you; there are examples in `tests/test-config.lua`, but basically these common file formats (and those following the same pattern) can be processed directly in smart mode: 'etc/fstab', '/proc/XXXX/status', 'ssh_config' and 'pdatedb.conf'.

Please note that `config.read` can be passed a _file-like object_; if it's not a string and supports the `read` method, then that will be used. For instance, to read a configuration from a string, use `stringio.open`.

### Lexical Scanning

Although Lua's string pattern matching is very powerful, there are times when something more powerful is needed. `pl.lexer.scan` provides lexical scanners which _tokenizes_ a string, classifying tokens into numbers, strings, etc.

> lua -lpl Lua 5.1.4 Copyright (C) 1994-2008 Lua.org, PUC-Rio > tok = lexer.scan 'alpha = sin(1.5)' > = tok() iden alpha > = tok() = = > = tok() iden sin > = tok() ( ( > = tok() number 1.5 > = tok() ) ) > = tok() (nil)

The scanner is a function, which is repeatedly called and returns the _type_ and _value_ of the token. Recognized basic types are 'iden','string','number', and 'space'. and everything else is represented by itself. Note that by default the scanner will skip any 'space' tokens.

'comment' and 'keyword' aren't applicable to the plain scanner, which is not language-specific, but a scanner which understands Lua is available. It recognizes the Lua keywords, and understands both short and long comments and strings.

> for t,v in lexer.lua 'for i=1,n do' do print(t,v) end keyword for iden i = = number 1 , , iden n keyword do

A lexical scanner is useful where you have highly-structured data which is not nicely delimited by newlines. For example, here is a snippet of a in-house file format which it was my task to maintain:

points (818344.1,-20389.7,-0.1),(818337.9,-20389.3,-0.1),(818332.5,-20387.8,-0.1) ,(818327.4,-20388,-0.1),(818322,-20387.7,-0.1),(818316.3,-20388.6,-0.1) ,(818309.7,-20389.4,-0.1),(818303.5,-20390.6,-0.1),(818295.8,-20388.3,-0.1) ,(818290.5,-20386.9,-0.1),(818285.2,-20386.1,-0.1),(818279.3,-20383.6,-0.1) ,(818274,-20381.2,-0.1),(818274,-20380.7,-0.1);

Here is code to extract the points using `pl.lexer`:

-- assume 's' contains the text above... local lexer = require 'pl.lexer' local expecting = lexer.expecting local append = table.insert

local tok = lexer.scan(s)

local points = {} local t,v = tok() -- should be 'iden','points'

while t ~= ';' do c = {} expecting(tok,'(') c.x = expecting(tok,'number') expecting(tok,',') c.y = expecting(tok,'number') expecting(tok,',') c.z = expecting(tok,'number') expecting(tok,')') t,v = tok() -- either ',' or ';' append(points,c) end

The `expecting` function grabs the next token and if the type doesn't match, it throws an error. (`pl.lexer`, unlike other PL libraries, raises errors if something goes wrong, so you should wrap your code in `pcall` to catch the error gracefully.)

The scanners all have a second optional argument, which is a table which controls whether you want to exclude spaces and/or comments. The default for `lexer.lua` is `{space=true,comments=true}`. There is a third optional argument which determines how string and number tokens are to be processsed.

The ultimate highly-structured data is of course, program source. Here is a snippet from 'text-lexer.lua':

require 'pl'

lines = [[ for k,v in pairs(t) do if type(k) == 'number' then print(v) -- array-like case else print(k,v) end end ]]

ls = List() for tp,val in lexer.lua(lines,{space=true,comments=true}) do assert(tp ~= 'space' and tp ~= 'comment') if tp == 'keyword' then ls:append(val) end end test.asserteq(ls,List{'for','in','do','if','then','else','end','end'})

Here is a useful little utility that identifies all common global variables found in a lua module (ignoring those declared locally for the moment):

-- testglobal.lua require 'pl'

local txt,err = utils.readfile(arg[1]) if not txt then return print(err) end

local globals = List() for t,v in lexer.lua(txt) do if t == 'iden' and _G[v] then globals:append(v) end end pretty.dump(seq.count_map(globals))

Rather then dumping the whole list, with its duplicates, we pass it through `seq.count_map` which turns the list into a table where the keys are the values, and the associated values are the number of times those values occur in the sequence. Typical output looks like this:

{ type = 2, pairs = 2, table = 2, print = 3, tostring = 2, require = 1, ipairs = 4 }

You could further pass this through `tablex.keys` to get a unique list of symbols. This can be useful when writing 'strict' Lua modules, where all global symbols must be defined as locals at the top of the file.

For a more detailed use of `lexer.scan`, please look at `testxml.lua` in the examples directory.

### XML

New in the 0.9.7 release is some support for XML. This is a large topic, and Penlight does not provide a full XML stack, which is properly the task of a more specialized library.

#### Parsing and Pretty-Printing

The semi-standard XML parser in the Lua universe is [lua-expat](http://matthewwild.co.uk/projects/luaexpat/). In particular, it has a function called `lxp.lom.parse` which will parse XML into the Lua Object Model (LOM) format. However, it does not provide a way to convert this data back into XML text. `xml.parse` will use this function, _if_ `lua-expat` is available, and otherwise switches back to a pure Lua parser originally written by Roberto Ierusalimschy.

The resulting document object knows how to render itself as a string, which is useful for debugging:

> d = xml.parse "<nodes>alice</nodes>" > = d <nodes>alice</nodes> > pretty.dump (d) { { "alice", attr = { "id", id = "1" }, tag = "node" }, attr = { }, tag = "nodes" }

Looking at the actual shape of the data reveals the structure of LOM:

* every element has a `tag` field with its name * plus a `attr` field which is a table containing the attributes as fields, and also as an array. It is always present. * the children of the element are the array part of the element, so `d[1]` is the first child of `d`, etc.

It could be argued that having attributes also as the array part of `attr` is not essential (you cannot depend on attribute order in XML) but that's how it goes with this standard.

`lua-expat` is another _soft dependency_ of Penlight; generally, the fallback parser is good enough for straightforward XML as is commonly found in configuration files, etc. `doc.basic_parse` is not intended to be a proper conforming parser (it's only sixty lines) but it handles simple kinds of documents that do not have comments or DTD directives. It is intelligent enough to ignore the `You can get pretty-printing by explicitly calling `xml.tostring` and passing it the initial indent and the per-element indent:

> = xml.tostring(d,'',' ')

<nodes> alice </nodes>

There is a fourth argument which is the _attribute indent_:

> a = xml.parse "" > = xml.tostring(a,'',' ',' ')

#### Parsing and Working with Configuration Files

It's common to find configurations expressed with XML these days. It's straightforward to 'walk' the [LOM](http://matthewwild.co.uk/projects/luaexpat/lom.html) data and extract the data in the form you want:

require 'pl'

local config = [[ <config> <alpha>1.3</alpha> <beta>10</beta> <name>bozo</name> </config> ]] local d,err = xml.parse(config)

local t = {} for item in d:childtags() do t[item.tag] = item[1] end

pretty.dump(t) ---> { beta = "10", alpha = "1.3", name = "bozo" }

The only gotcha is that here we must use the `Doc:childtags` method, which will skip over any text elements.

A more involved example is this excerpt from `serviceproviders.xml`, which is usually found at `/usr/share/mobile-broadband-provider-info/serviceproviders.xml` on Debian/Ubuntu Linux systems.

d = xml.parse [[ ... <provider> <name>Cell-c</name> <gsm> <username>Cellcis</username> <dns>196.7.0.138</dns> <dns>196.7.142.132</dns> </gsm> </provider> <provider> <name>MTN</name> <gsm> <dns>196.11.240.241</dns> <dns>209.212.97.1</dns> </gsm> </provider> <provider> <name>Vodacom</name> <gsm> <dns>196.207.40.165</dns> <dns>196.43.46.190</dns> <name>Unrestricted</name> <dns>196.207.32.69</dns> <dns>196.43.45.190</dns> </gsm> </provider> <provider> <name>Virgin Mobile</name> <gsm> <dns>196.7.0.138</dns> <dns>196.7.142.132</dns> </gsm> </provider> .... ]]

Getting the names of the providers per-country is straightforward:

local t = {} for country in d:childtags() do local providers = {} t[country.attr.code] = providers for provider in country:childtags() do table.insert(providers,provider:child_with_name('name'):get_text()) end end

pretty.dump(t) --> { za = { "Cell-c", "MTN", "Vodacom", "Virgin Mobile" } .... }

#### Generating XML with 'xmlification'

This feature is inspired by the `htmlify` function used by [Orbit](http://keplerproject.github.com/orbit/) to simplify HTML generation, except that no function environment magic is used; the `tags` function returns a set of _constructors_ for elements of the given tag names.

> nodes, node = xml.tags 'nodes, node' > = node 'alice' <node>alice > = nodes { node {id='1','alice'}} <nodes>alice</nodes>

The flexibility of Lua tables is very useful here, since both the attributes and the children of an element can be encoded naturally. The argument to these tag constructors is either a single value (like a string) or a table where the attributes are the named keys and the children are the array values.

#### Generating XML using Templates

A template is a little XML document which contains dollar-variables. The `subst` method on a document is fed an array of tables containing values for these variables. Note how the parent tag name is specified:

> templ = xml.parse "$name" > = templ:subst {tag='nodes', {id=1,name='alice'},{id=2,name='john'}} <nodes>alice john</nodes>

Substitution is very related to _filtering_ documents. One of the annoying things about XML is that it is a document markup language first, and a data language second. Standard parsers will assume you really care about all those extra text elements. Consider this fragment, which has been changed by a five-year old:

T = [[ <weather> boops! <current_conditions> <bo>whoops!</bo> </current_conditions> </weather> ]]

Conformant parsers will give you text elements with the line feed after `<current_conditions>` although it makes handling the data more irritating.

local function parse (str) return xml.parse(str,false,true) end

Second argument means 'string, not file' and third argument means use the built-in Lua parser (instead of LuaExpat if available) which _by default_ is not interested in keeping such strings.

How to remove the string `boops!`? `clone` (also called `filter` when called as a method) copies a LOM document. It can be passed a filter function, which is applied to each string found. The powerful thing about this is that this function receives structural information - the parent node, and whether this was a tag name, a text element or a attribute name:

d = parse (T) c = d:filter(function(s,kind,parent) print(stringx.strip(s),kind,parent and parent.tag or '?') if kind == '*TEXT' and #parent > 1 then return nil end return s end) ---> weather *TAG ? boops! *TEXT weather current_conditions *TAG weather condition *TAG current_conditions $condition data condition temp_c *TAG current_conditions $temp data temp_c bo *TAG current_conditions whoops! *TEXT bo

We can pull out 'boops' and not 'whoops' by discarding text elements which are not the single child of an element.

#### Extracting Data using Templates

Matching goes in the opposite direction. We have a document, and would like to extract values from it using a pattern.

A common use of this is parsing the XML result of API queries. The [(undocumented and subsequently discontinued) Google Weather API](http://blog.programmableweb.com/2010/02/08/googles-secret-weather-api/) is a good example. Grabbing the result of `http://www.google.com/ig/api?weather=Johannesburg,ZA" we get something like this, after pretty-printing:

<forecast_information> </forecast_information> <current_conditions> </current_conditions> <forecast_conditions> </forecast_conditions> ....

Assume that the above XML has been read into `google`. The idea is to write a pattern looking like a template, and use it to extract some values of interest:

t = [[ <weather> <current_conditions> </current_conditions> ]]

local res, ret = google:match(t) pretty.dump(res)

And the output is:

{ condition = "Clear", temp = "24" }

The `match` method can be passed a LOM document or some text, which will be parsed first.

But what if we need to extract values from repeated elements? Match templates may contain 'array matches' which are enclosed in '{{..}}':

<weather> {{<forecast_conditions> </forecast_conditions>}}

And the match result is:

{ { low = "60", high = "89", day = "Sat", condition = "Clear", }, { low = "53", high = "86", day = "Sun", condition = "Clear", }, { low = "57", high = "87", day = "Mon", condition = "Clear", }, { low = "60", high = "84", day = "Tue", condition = "Clear", } }

With this array of tables, you can use `tablex` or `List` to reshape into the desired form, if you choose. Just as with reading a Unix password file with `config`, you can make the array into a map of days to conditions using:

`tablex.pairmap`('|k,v| v,v.day',conditions)

(Here using the alternative string lambda option)

However, xml matches can shape the structure of the output. By replacing the `day_of_week` line of the template with `` we get the same effect; `$_` is a special symbol that means that this captured value (or simply _capture_) becomes the key.

Note that `$NUMBER` means a numerical index, so that `$1` is the first element of the resulting array, and so forth. You can mix numbered and named captures, but it's strongly advised to make the numbered captures form a proper array sequence (everything from `1` to `n` inclusive). `$0` has a special meaning; if it is the only capture (`{[0]='foo'}`) then the table is collapsed into 'foo'.

<weather> {{<forecast_conditions> </forecast_conditions>}}

Now the result is:

{ Tue = { "60", "84", "Clear" }, Sun = { "53", "86", "Clear" }, Sat = { "60", "89", "Clear" }, Mon = { "57", "87", "Clear" } }

Applying matches to this config file poses another problem, because the actual tags matched are themselves meaningful.

<config> <alpha>1.3</alpha> <beta>10</beta> <name>bozo</name> </config>

So there are tag 'wildcards' which are element names ending with a hyphen.

<config> {{$value}} </config>

You will then get `{{alpha='1.3'},...}`. The most convenient format would be returned by this (note that `_-` behaves just like `$_`):

<config> {{<_->$0}} </config>

which would return `{alpha='1.3',beta='10',name='bozo'}`.

We could play this game endlessly, and encode ways of converting captures, but the scheme is complex enough, and it's easy to do the conversion later

local numbers = {alpha=true,beta=true} for k,v in pairs(res) do if numbers[v] then res[k] = tonumber(v) end end

#### HTML Parsing

HTML is an unusually degenerate form of XML, and Dennis Schridde has contributed a feature which makes parsing it easier. For instance, from the tests:

doc = xml.parsehtml [[
Hello dolly
HTML is slack
]]

asserteq(xml.tostring(doc),[[
Hello dolly
HTML is slack
]])

That is, all tags are converted to lowercase, and empty HTML elements like `br` are properly closed; attributes do not need to be quoted.

Also, DOCTYPE directives and comments are skipped. For truly badly formed HTML, this is not the tool for you!

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