Grammalecte  Check-in [cdc40fbad0]

    tokens:             alpha       (beta)      gamma       (delta)     epsilon
    positive refs:                  1                       2
    negative refs:      -5          -4          -3          -2          -1

    tokens:             alpha       (beta)      ?gamma¿     (delta)     epsilon
    positive refs:                  1                       2
    negative refs:      (-5/-4)     (-4/-3)     (-3/none)   -2          -1


## CONDITIONS ##

Conditions are Python expressions, they must return a value, which will be
evaluated as boolean. You can use the usual Python syntax and libraries.

With regex rules, you can call pattern subgroups via `\1`, `\2`… `\0` is the full pattern.

Example:

    these (\w+)
        <<- \1 == "man" -1>> men        # Man is a singular noun.

You can also apply functions to subgroups like: `\1.startswith("a")` or `\3.islower()` or `re.search("pattern", \2)`.

With token rules, you can also call each token with their reference, like `\1`, `\2`... or `\-1`, `\-2`...

Example:

`value(n, values_string)`

>   Analyses the value of the nth token.
>   The <values_string> contains values separated by the sign `|`.
>   Example: `"|foo|bar|"`

`morph(n, "regex"[, "neg_regex"][, trim_left=0][, trim_right=0])`
`analyse(n, "regex"[, "neg_regex"][, trim_left=0][, trim_right=0])`

>   Same action with `morph()` and `morph0()` for regex rules.
>   Parameters <trim_left> and <trim_right> removed n characters at left or the right of the token before performing an analyse.

`space_after(n, min_space[, max_space])`

>   Returns True if the next token after token n is separated with at least <min_space> blank spaces and at most with <max_space> blank spaces.

`tag(n, tag)`

>   Returns True if <tag> exists on taken the nth token.

`tag_before(n, tag)`

>   Returns True if <tag> is found any token before the nth tag.
   
`tag_after(n, tag)`

>   Returns True if <tag> is found any token after the nth tag.

### Functions for regex and token rules

`__also__`

>   Returns True if the previous condition returned True.
>   Example: `<<- __also__ and condition2 ->>`

>   Checks if the text before the pattern matches the regex.

### Default variables

`sCountry`

>   Contains the current country locale of the checked paragraph.

    colour <<- sCountry == "US" ->> color   # Use American English spelling.


## ACTIONS ##

There are 5 kinds of actions:

1. Suggestions. The grammar checker suggests corrections.

5. Immunity. Prevent suggestions to be triggered.


### Positioning

Positioning is valid for suggestions, text processing, tagging and immunity.

By default, rules apply on the full text triggered. You can shorten the
effect of rules by specifying a back reference group of the pattern or token references.

Instead of writing `->>`, write `-n>>`  n being the number of a back reference
group. Actually,  `->>`  is similar to  `-0>>`.

Example:

    (ying) and yang <<- -1>> yin # Did you mean:

    __[s]__ (Mr.) [A-Z]\w+ <<- ~1>> Mr

    your’s
        <<- ->> yours
            # Possessive pronoun:|http://en.wikipedia.org/wiki/Possessive_pronoun

#### Expressions in suggestion or replacement

Suggestions started by an equal sign are Python string expressions extended with possible back references and named definitions:


Example:

    <<- ->> ='"' + \1.upper() + '"'      # With uppercase letters and quotation marks
    <<- ~>> =\1.upper()


### Text rewriting

Example. Replacing a string by another.

    Mr. [A-Z]\w+ <<- ~>> Mister

**WARNING**: The replacing text must be shorter than the replaced text or have the same length. Breaking this rule will misplace following error reports.

You have to ensure yourself the rules comply with this constraint, the text processor won’t do it for you.


Specific commands for text rewriting:

`~>> *`

>   Replace by whitespaces

    Mister <<- ~>> Mr
    (Mrs?)[.] <<- ~>> \1


### Disambiguation


When the grammar checker analyses a token with `morph()`, before requesting the POS tags to the dictionary, it checks if there is a stored marker for the position of the token. If a marker is found, it uses the stored data and don’t make request to the dictionary.





There are 4 commands for disambiguation.

`select(n, pattern)`

>   At reference n, select morphologies that match the pattern.

`exclude(n, pattern)`

>   At reference n, exclude morphologies that match the pattern.

`define(n, [morph_list])`

>   At reference n, set the listed morphologies (a list of strings).

`add_morph(n, [morph_list])`

>   At reference n, add the listed morphologies (a list of strings).

Examples:

    =>> select(\1, "po:noun is:pl")
    =>> exclude(\1, "po:verb")

    =>> exclude(\1, "po:verb") and define(\2, ["po:adv"]) and select(\3, "po:adv")

Note: All these functions ALWAYS return True.

If `select()` and `exclude()` generate an empty list, nothing change.

With `define()` and `add_morph()`, you must set a list of POS tags. Example:

    =>> define(\1, ["po:nom is:plur", "po:adj is:sing", "po:adv"])
    =>> add_morph(\1, ["po:adv"])

### Tagging

**Only for token rules**

### Immunity

**Only for token rules**

A immunity rule set a flag on token(s) who are not supposed to be considered as an error. If any other rules find an error, it will be ignored. If an error has already been found, it will be removed.

Example: `!2>>` means no error can be set of the second token.
Example: `!>>` means all tokens will be considered as correct.

The immunity rules are useful to create simple antipattern that will simplify writing of other rules.


## OTHER COMMANDS ##

### Comments

Lines beginning with `#` are comments.