Convert a Gettext PO Template file to a PO file and merge in existing translations if they are present. A translation memory (compendium) can also be used for fuzzy matching. This corresponds to a large extent with the program “msgmerge” from the gettext package.
pot2po [options] <pot> <po>
|<pot>||is a PO Template (POT) file or directory of POT files|
|<po>||is a PO file or a directory of PO files|
|--version||show program’s version number and exit|
|-h, --help||show this help message and exit|
|--manpage||output a manpage based on the help|
|show progress as: dots, none, bar, names, verbose|
|show errorlevel as: none, message, exception, traceback|
|read from INPUT in pot format|
|exclude names matching EXCLUDE from input paths|
|write to OUTPUT in po, pot formats|
|read from TEMPLATE in po, pot formats (old translations)|
|-P, --pot||output PO Templates (.pot) rather than PO files (.po)|
|--tm=TM||The file to use as translation memory when fuzzy matching|
|The minimum similarity for inclusion (default: 75%)|
|Disable all fuzzy matching|
pot2po -t zu-1.0.1 pot-2.0.2 zu-2.0.2
Here we are initialising the PO files in zu-2.0.2 based on the POT files in pot-2.0.2. We are using the old translations in zu-1.0.1 as templates so that we can reuse our existing translations in the new files.
If the POT files have undergone major reshuffling then you may want to use pomigrate2 which can now use pot2po as its merging backend. pomigrate2 will do its best to migrate your files to the correct locations before merging. It will also make make use of a compendium if requested.:
pot2po --tm=compendium.po --similarity=60 -t xh-old pot xh-new
With this update we are using compendium.po as a translations memory (you can make use of other files such as TMX, etc). We will accept any match that scores above 60%.
It helps to understand when and how pot2po will merge. The default is to follow msgmerge’s behaviour but we add some extra features with fuzzy matching:
Fuzzy matches are usually of good quality. Installation of the python-Levenshtein package will speed up fuzzy matching. Without this a Python based matcher is used which is considerably slower.