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.
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:
> 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 tokenize 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.
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><node id='1'>alice</node></nodes>" > = d <nodes><node id='1'>alice</node></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
tagfield with its name - plus a
attrfield 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 ofd, 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 <?xml directive and that is about it.
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> <node id='1'>alice</node> </nodes>
There is a fourth argument which is the attribute indent:
> a = xml.parse "<frodo name='baggins' age='50' type='hobbit'/>" > = xml.tostring(a,'',' ',' ') <frodo type='hobbit' name='baggins' age='50' />
Parsing and Working with Configuration Files
It’s common to find configurations expressed with XML these days. It’s straightforward to ‘walk’ the LOM 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 [[
<serviceproviders format="2.0">
...
<country code="za">
<provider>
<name>Cell-c</name>
<gsm>
<network-id mcc="655" mnc="07"/>
<apn value="internet">
<username>Cellcis</username>
<dns>196.7.0.138</dns>
<dns>196.7.142.132</dns>
</apn>
</gsm>
</provider>
<provider>
<name>MTN</name>
<gsm>
<network-id mcc="655" mnc="10"/>
<apn value="internet">
<dns>196.11.240.241</dns>
<dns>209.212.97.1</dns>
</apn>
</gsm>
</provider>
<provider>
<name>Vodacom</name>
<gsm>
<network-id mcc="655" mnc="01"/>
<apn value="internet">
<dns>196.207.40.165</dns>
<dns>196.43.46.190</dns>
</apn>
<apn value="unrestricted">
<name>Unrestricted</name>
<dns>196.207.32.69</dns>
<dns>196.43.45.190</dns>
</apn>
</gsm>
</provider>
<provider>
<name>Virgin Mobile</name>
<gsm>
<apn value="vdata">
<dns>196.7.0.138</dns>
<dns>196.7.142.132</dns>
</apn>
</gsm>
</provider>
</country>
....
</serviceproviders>
]]
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 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</node> > = nodes { node {id='1','alice'}} <nodes><node id='1'>alice</node></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 "<node id='$id'>$name</node>" > = templ:subst {tag='nodes', {id=1,name='alice'},{id=2,name='john'}} <nodes><node id='1'>alice</node><node id='2'>john</node></nodes>
Substitution is very related to filtering documents. One of the annoying things about XML is that it is a document markup language first, and a data language second. Standard parsers will assume you really care about all those extra text elements. Consider this fragment, which has been changed by a five-year old:
T = [[
<weather>
boops!
<current_conditions>
<condition data='$condition'/>
<temp_c data='$temp'/>
<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 $temp data temp_c bo *TAG current_conditions whoops! *TEXT bo
We can pull out ‘boops’ and not ‘whoops’ by discarding text elements which are not the single child of an element.
Extracting Data using Templates
Matching goes in the opposite direction. We have a document, and would like to extract values from it using a pattern.
A common use of this is parsing the XML result of API queries. The (undocumented and subsequently discontinued) Google Weather API is a good example. Grabbing the result of `http://www.google.com/ig/api?weather=Johannesburg,ZA" we get something like this, after pretty-printing:
<xml_api_reply version='1'> <weather module_id='0' tab_id='0' mobile_zipped='1' section='0' row='0'
mobile_row=‘0’>
<forecast_information> <city data='Johannesburg, Gauteng'/> <postal_code data='Johannesburg,ZA'/> <latitude_e6 data=''/> <longitude_e6 data=''/> <forecast_date data='2010-10-02'/> <current_date_time data='2010-10-02 18:30:00 +0000'/> <unit_system data='US'/> </forecast_information> <current_conditions> <condition data='Clear'/> <temp_f data='75'/> <temp_c data='24'/> <humidity data='Humidity: 19%'/> <icon data='/ig/images/weather/sunny.gif'/> <wind_condition data='Wind: NW at 7 mph'/> </current_conditions> <forecast_conditions> <day_of_week data='Sat'/> <low data='60'/> <high data='89'/> <icon data='/ig/images/weather/sunny.gif'/> <condition data='Clear'/> </forecast_conditions> .... /weather> l_api_reply>
Assume that the above XML has been read into google. The idea is to write a
pattern looking like a template, and use it to extract some values of interest:
t = [[ <weather> <current_conditions> <condition data='$condition'/> <temp_c data='$temp'/> </current_conditions> </weather> ]] local res, ret = google:match(t) pretty.dump(res)
And the output is:
{
condition = "Clear",
temp = "24"
}
The match method can be passed a LOM document or some text, which will be
parsed first.
But what if we need to extract values from repeated elements? Match templates may contain ‘array matches’ which are enclosed in ‘{{..}}’:
<weather>
{{<forecast_conditions>
<day_of_week data='$day'/>
<low data='$low'/>
<high data='$high'/>
<condition data='$condition'/>
</forecast_conditions>}}
</weather>
And the match result is:
{
{
low = "60",
high = "89",
day = "Sat",
condition = "Clear",
},
{
low = "53",
high = "86",
day = "Sun",
condition = "Clear",
},
{
low = "57",
high = "87",
day = "Mon",
condition = "Clear",
},
{
low = "60",
high = "84",
day = "Tue",
condition = "Clear",
}
}
With this array of tables, you can use tablex or List to reshape into the desired form, if you choose. Just as with reading a Unix password file with config, you can make the array into a map of days to conditions using:
tablex.pairmap('|k,v| v,v.day',conditions)
(Here using the alternative string lambda option)
However, xml matches can shape the structure of the output. By replacing the day_of_week
line of the template with <day_of_week data='$_'/> 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). $0 has a
special meaning; if it is the only capture ({[0]='foo'}) then the table is
collapsed into ‘foo’.
<weather>
{{<forecast_conditions>
<day_of_week data='$_'/>
<low data='$1'/>
<high data='$2'/>
<condition data='$3'/>
</forecast_conditions>}}
</weather>
Now the result is:
{
Tue = {
"60",
"84",
"Clear"
},
Sun = {
"53",
"86",
"Clear"
},
Sat = {
"60",
"89",
"Clear"
},
Mon = {
"57",
"87",
"Clear"
}
}
Applying matches to this config file poses another problem, because the actual tags matched are themselves meaningful.
<config>
<alpha>1.3</alpha>
<beta>10</beta>
<name>bozo</name>
</config>
So there are tag ‘wildcards’ which are element names ending with a hyphen.
<config>
{{<key->$value</key->}}
</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 [[ <BODY> Hello dolly<br> HTML is <b>slack</b><br> </BODY> ]] asserteq(xml.tostring(doc),[[ <body> Hello dolly<br/> HTML is <b>slack</b><br/></body>]])
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!