Output logs parsing

Most of the details about how the parsing of log output files is explained in the Outputs parsing section of the user guide. Here we will provide more information about the different objects involved and what are the constraints that should be followed to add the parsing of new quantities or of new types of outputs to the current implementation.

Let us first have a more in-depth inspection of the turbomoleio.output.parser.Parser object. The Parser takes the string of a TURBOMOLE log file as an input. It is then used to extract the relevant data information required for each Data or File object. The Parser is made of several parsing methods, each of which is in charge of parsing a specific portion of the log file. Some of the parsing methods are very specific to one type of calculation (e.g. only relevant for an escf output, or for a statpt output), while others are common to several of them (e.g. turbomoleio.output.parser.Parser.basis(), turbomoleio.output.parser.Parser.header()). Each parsing method returns a dictionary with the data parsed or None if the section to be parsed could not be found in the string. This dictionary is meant to be used by the Data and File objects during their instantiation.

The parsing methods are implemented as lazy properties, meaning that they will be generated only once, using the lazy_property context manager available in the monty package. This is advantageous since some sections contain mixed information of the same property and it may be called more than once while building a File object. The data is thus stored temporarily in the Parser instance, but this should not be a problem, because the information extracted is relatively small and the parser instance is meant to be disposed at the end of the generation of the object.

In general, when possible, the parsing methods first narrow down the section of the text that contains the information that should be extracted and then work on this to extract the exact data needed. This is usually because the outputs of TURBOMOLE do not have an organized structure and this allows to have a target specific lines of the output more easily.

The information parsed with the Parser are meant to be used by the Data objects to create an instance. In particular all the Data and File objects are subclasses of the turbomoleio.output.data.BaseData abstract class. This has a single abstract method, from_parser, that should implemented by subclasses and should be able to create a new instance of the Data or File object from a Parser instance. The BaseData class provides a from_string and a from_file method that rely on from_parser to create an instance of the subclass from the string of the output or from the file.

The Data objects are meant to contain data that can be grouped together by meaning or by type. In the from_parser method it can use the information coming from a single property of the Parser or from more than one. On the other hand the File objects in the from_parser preferably do not access directly the properties in the Parser but instead create instances of Data object and store mainly Data instances as attributes.

From a design point of view, this whole approach has been chosen since it allows to:

  • share parsing functions for different types of calculations,

  • quickly instantiate Data objects based on a single portion of the output, e.g. if the user only wants information about the energy, he can obtain it running ScfEnergiesData.from_file("ridft.log") without the waste of parsing all the rest of the file.

  • update the parsing of a specific section for new versions of TURBOMOLE without impacting the other parsing methods. This is possible since the parsing methods for the different sections are isolated.

Parsing a new quantity

If you want to start parsing a new quantity for one of the types of log files that are already handled you should proceed from the bottom up. First check if the quantity that you want to parse is part of a section that is already parsed by some method of the Parser. If that is the case you can probably modify that method, otherwise you should create a new property, mark it with the lazy_property context manager and implement the parsing inside it. A good approach is to use regular expressions both to narrow down the section that you want to focus on and to extract the exact values that you want, but for small sections analyzing the lines one by one is also acceptable.


When writing the parser always consider that the output of TURBOMOLE can change considerably depending on the different options provided (e.g. an entire section or single values might appear/disappear). Always check that your parser is working under different conditions.

Once the Parser has been modified you should either update one (or more) of the Data objects or write a new one, depending on the type of information that you extracted. In the latter case subclass the BaseData class and get your newly parsed information in the from_parser method.

Finally, if a new Data object has been created, modify the appropriate File objects that should contain it.


When adding new attributes to Data and File objects always set a None as a default in the __init__. In this way data that have been json serialized with previous versions of the code will still be deserialized correctly (only your newly added property will be missing). If instead you change or remove one of the existing attributes the old data will not be deserialized anymore. Backward compatibility changes as these should be though carefully and agreed upon by the community of users.

Parsing a new type of log

When parsing the output provided by an executable that is not supported by the current version of turbomoleio you should proceed in a similar manner as when Parsing a new quantity. In this case you will probably need to add several new methods to the Parser, addressing the different quantities and information that you want to extract. Also check which ones of the existing properties are working for your kind of output, since some of the are likely to be compatible (e.g. the header attribute will very likely be equivalent in your case as well).

After creating the parser methods, your should encapsulate that information inside Data objects and create a new File object where you will store all the extracted data. The same recommendations given in the previous sections hold here as well.

Lastly, if this is suitable, add your object to the turbomoleio.outputs.files.exec_to_out_obj dictionary. This will be used as a reference to decide which File object to use when running a specific executable. In particular it will be used in the unit tests, as explained below.


For the main discussion concerning the testing in turbomoleio you should refer to the Testing section of this developer guide. However, given the particular nature of the unit tests implemented for the log parsing we will provide some more explanations here.

The tests for the Parser object are performed running all the methods implemented on a series a TURBOMOLE output files. The generated dictionaries are then compared with references stored in the turbomoleio/testfiles/outputs folder as JSON files. A tolerance is allowed, given potentially small differences that can happen while converting strings to floats, but in general the numerical value should be exactly equivalent to those parsed. Note that for some combinations of files and methods the output will simply be None.

In case you want to add a new output file to be tested you should add it in the testfiles folder and also add it to the list in turbomoleio.output.tests.test_parser.files_list. If instead you are adding one or more new methods to the Parser, remember to add their name to the list in turbomoleio.output.tests.test_parser.parser_methods.

In case you need to generate the reference JSON file again, maybe because you have modified one of the existing Parser’s method or because you added a new one, you can use the turbomoleio.output.tests.test_parser.generate_files() helper function. This will generate a new JSON and overwrite the old one for all the files and methods that have been given in input. So you should be extremely careful when running it, since the generated files will become the new reference. If a bug is introduced in the Parser, the reference files will be generated with a bugged version and the tests will partially loose their use.

A similar approach has been chosen for the testing of the Data and Files objects. Since all the Data objects are contained in at least one File object, the tests concerning the parsing will be performed only at the level of the File objects, since repeating them for the Data as well would just be redundant.

The structure of the tests is similar to those for the Parser. The test output files are parsed using the from_file method of the corresponding File object. The object is converted to a dictionary and compared with the reference stored in the corresponding JSON file.

As before, if you want to add a new output file to be parsed, you should add it in the testfiles folder and in the list in turbomoleio.output.tests.test_files.files_list. In addition if you want to add a new type of File object you should either add it to the turbomoleio.outputs.files.exec_to_out_obj or make it available in the turbomoleio.output.tests.test_files.cls_dict_path() fixture (follow the example in the case of EscfOnlyOutput there).

A function to generate the reference JSON files similar to the one described above is available: turbomoleio.output.tests.test_files.generate_files(). The same warnings of dealing with it carefully should be kept in mind here as well.