11/15/2023 0 Comments Oddmar hltb![]() ![]() If you found a bug report it as soon as you can creating an issue, the code may not be perfect. It also include the full JSON of values (already converted to Python dict) received from HLTB. Reading an entryĪn entry is made of few values, you can check them in the Entry class file. Remember that, when searching by ID, the similarity value and the case-sensitive bool are ignored. search ( "Awesome Game", similarity_case_sensitive = False ) Putting 0.0 (or just 0) will return all the found games, otherwise you can write another ( float) number between 0.1 to set a new filter, such as 0.7.Īlso remember that by default the similarity check is case-sensitive between the name given and the name found, if you want to ignore the case you can use: results = HowLongToBeat ( 0.0 ). If you want all the results, or you want to change this value, you can put a parameter in the constructor: results = HowLongToBeat ( 0.0 ). To ignore games with a very different name, the standard search automatically filter results with a game name that has a similarity with the given name > than 0.4, not adding the others to the result list. This optional parameter allow you to specify in the search if you want the default search (with DLCs), to HIDE DLCs and only show games, or to ISOLATE DLCs (show only DLCs). ![]() DLC searchĪn enum has been added to have a filter in the search: SearchModifiers. This call will return an unique HowLongToBeatEntry or None in case of errors. Or, if you prefer using async: result = await HowLongToBeat (). Here's the example: result = HowLongToBeat (). Remember that it could be a bit slower, but you avoid searching the game in the array by similarity. To avoid a new parser, the search by ID use a first request to get the game title, and then use the standard search with that title, filtering the results and returning the unique game with that ID. If you prefer, you can get a game by ID, this can be useful if you already have the game's howlongtobeat-id (the ID is the number in the URL, for example in the ID is 7231). Once done, "best_element" will contain the best game found in the research.Įvery entry in the list (if not None in case of errors) is an object of type: HowLongToBeatEntry. async_search ( "Awesome Game" ) if results_list is not None and len ( results_list ) > 0 : best_element = max ( results_list, key = lambda element : element. If the list is not None you should choose the best entry checking the Similarity value with the original name, example: results_list = await HowLongToBeat (). The return of that function is a list of possible games, or None in case you passed an invalid "game name" as parameter or if there was an error in the request. Or, if you prefer using async: results = await HowLongToBeat (). The API main functions are: results = HowLongToBeat (). Usage in code Start including it in your file from howlongtobeatpy import HowLongToBeat Now call search() Usage Installation Installing the package downloading the last release pip install howlongtobeatpy Installing the package from the source codeĭownload the repo, enter the folder with 'setup.py' and run the command pip install. ![]()
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