________________________________________________________________________

This file is part of Logtalk <https://logtalk.org/>  
SPDX-FileCopyrightText: 2021-2022 Paulo Moura <pmoura@logtalk.org>
SPDX-FileCopyrightText: 2021 Jacinto Dávila Quintero <jdavila@optimusprime.ai>  
SPDX-License-Identifier: Apache-2.0

Licensed under the Apache License, Version 2.0 (the "License");
you may not use this file except in compliance with the License.
You may obtain a copy of the License at

    http://www.apache.org/licenses/LICENSE-2.0

Unless required by applicable law or agreed to in writing, software
distributed under the License is distributed on an "AS IS" BASIS,
WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied.
See the License for the specific language governing permissions and
limitations under the License.
________________________________________________________________________


`csv`
=====

The `csv` library provides predicates for reading and writing CSV files
and streams:

https://www.rfc-editor.org/rfc/rfc4180.txt

The main object, `csv/4`, is a parametric object allowing passing options
for the handling of the header of the file, the fields separator, the
handling of double-quoted fields, and comments handling. When comments
handling is `true`, lines starting with the `#` character are skipped when
reading files and streams.

The `csv/3` parametric object is kept for backward compatibility and extends
`csv/4` by setting the comments option to `false`. The `csv` object extends
the `csv/4` parametric object using default option values.

The library also includes predicates to guess the separator and guess the
number of columns in a given CSV file.

Files and streams can be read into a list of rows (with each row being
represented by a list of fields) or asserted using a user-defined dynamic
predicate. Reading can be done by first loading the whole file (using the
`read_file/2-3` predicates) into memory or line by line (using the
`read_file_by_line/2-3` predicates). Reading line by line is usually the
best option for parsing large CSV files. 

Data can be saved to a CSV file or stream by providing the object and
predicate for accessing the data plus the name of the destination file
or the stream handle or alias.


API documentation
-----------------

Open the [../../apis/library_index.html#csv](../../apis/library_index.html#csv)
link in a web browser.


Loading
-------

To load all entities in this library, load the `loader.lgt` file:

	| ?- logtalk_load(csv(loader)).


Testing
-------

To test this library predicates, load the `tester.lgt` file:

	| ?- logtalk_load(csv(tester)).


Usage
-----

The `csv(Header, Separator, IgnoreQuotes, Comments)` parametric object allows
passing the following options:

1. `Header`: possible values are `missing`, `skip`, and `keep`.
2. `Separator`: possible values are `comma`, `tab`, `semicolon`, and `colon`.
3. `IgnoreQuotes`: possible values are `true` to ignore double quotes
surrounding field data and `false` to preserve the double quotes.
4. `Comments`: possible values are `false` (default) and `true` to skip
lines starting with `#`.

The `csv` object uses the default values `keep`, `comma`, `false`, and `false`.

When writing CSV files or streams, set the quoted fields option to `false`
to write all non-numeric fields double-quoted (i.e., escaped).

The library objects can also be used to guess the separator used in a CSV
file if necessary. For example:

	| ?- csv::guess_separator('test_files/crlf_ending.csv', Separator).
	Is this the proper reading of a line of this file (y/n)? [aaa,bb,ccc]
	|> y.

	Separator = comma ?

This information can then be used to read the CSV file returning a list
of rows:

	| ?- csv(keep, comma, true)::read_file('test_files/crlf_ending.csv', Rows).

	Rows = [[aaa,bbb,ccc],[zzz,yyy,xxx]] ?

	| ?- csv(keep, comma, true, true)::read_file('test_files/comments.csv', Rows).

	Rows = [[aaa,bbb,ccc],[zzz,yyy,xxx]] ?

Alternatively, the CSV data can be saved using a public and dynamic object
predicate (that must be previously declared). For example:

	| ?- assertz(p(_,_,_)), retractall(p(_,_,_)).
	yes

	| ?- csv(keep, comma, true)::read_file('test_files/crlf_ending.csv', user, p/3).
	yes

	| ?-  p(A,B,C).

	A = aaa
	B = bbb
	C = ccc ? ;

	A = zzz
	B = yyy
	C = xxx

Given a predicate representing a table, the predicate data can be written to
a file or stream. For example:

	| ?- csv(keep, comma, true)::write_file('output.csv', user, p/3).
	yes

leaving the content just as the original file thanks to the use of `true` for the
`IgnoreQuotes` option:

	aaa,bbb,ccc
	zzz,yyy,xxx

Otherwise:

	| ?- csv(keep, comma, false)::write_file('output.csv', user, p/3).
	yes

results in the following file content:

	"aaa","bbb","ccc"
	"zzz","yyy","xxx"

The `guess_arity/2` method, to identify the arity, i. e. the number of fields
or columns per record in a given CSV file, for example:

	| ?- csv(keep, comma, false)::guess_arity('test_files/crlf_ending.csv', Arity).
	Is this the proper reading of a line of this file (y/n)? [aaa,bbb,ccc]
	|> y.

	Arity = 3
